Artificial Life

 

Contents

 

Introduction. 1

Inorganic plants and animals. 1

Human intelligence is not special 1

The rise of the internet. 1

Evolving circuits. 1

Organic life from inorganic reactions. 1

Quantum processes in organic life. 1

The internet as a machine city. 1

Even people are evolving with some differences. 1

Machines need not become more human to be evolving. 1

A global shadow economy of machines. 1

Network privileges and rights. 1

Privileges and randomness. 1

Games of life. 1

Network permissions. 1

Inheriting permissions. 1

Security. 1

Policing systems. 1

Inheriting permissions. 1

Propagating permissions. 1

Policies. 1

City policies. 1

Firewall policies. 1

The evolution of computer security. 1

Kernels. 1

Infecting the kernel 1

Reading memory. 1

Obfuscating code. 1

Sharing. 1

Competing. 1

Computer posting. 1

Digital property rights. 1

Virtual private networks. 1

Network forests. 1

Groups. 1

Domain Name Servers. 1

Ports. 1

Network Address Translation (NAT). 1

Routers. 1

Network addresses. 1

Discarding private network addresses. 1

Ethernet. 1

Token ring. 1

Routing and congestion. 1

Roy computer networks. 1

Owning the network. 1

Biv email 1

Roy email 1

Internet file sharing. 1

Roy file sharing. 1

Protocols. 1

TCP/IP. 1

UDP/IP. 1

Wireless connections. 1

Digitizing data. 1

Compressing data. 1

Computer code. 1

Backups. 1

Incremental backups. 1

Differential backups. 1

People’s differential memories. 1

Caching. 1

Satisficing. 1

Cache statistics. 1

CODECS. 1

Pattern recognition. 1

The web as a carbon silicon hybrid. 1

Computers communicate autonomously. 1

Roy protocols. 1

Logs. 1

Feedback reports. 1

Logging on. 1

Challenging. 1

Wizards. 1

Tolerance for mistakes. 1

Computer errors. 1

Databases. 1

Intelligence and databases. 1

Temporary names. 1

DHCP. 1

Schedules. 1

Reservations. 1

Credit. 1

Solvers. 1

Arbitrage. 1

Data Mining. 1

Newsfeeds. 1

Aggregators. 1

Weak and Strong AI 1

Technology evolving from biology. 1

The Red Queen. 1

Reinforcement. 1

Translation. 1

Hybrids from disease. 1

Mission creep. 1

The internet wakes up. 1

The cloud. 1

Copying. 1

Collective intelligence. 1

Clusters. 1

Modular construction. 1

Draining the swamp. 1

Revolutionary velocity. 1

Low hanging fruit. 1

Brute force. 1

Emulation. 1

Uneven abilities. 1

Speed of thinking. 1

Dangerous and stupid. 1

Frailty. 1

Scaling. 1

Hubs. 1

Competing silicon life. 1

Silicon life becoming prey. 1

Learning. 1

Intelligent agents. 1

Intelligent advisors. 1

Rules. 1

Quarantine. 1

Computer viruses. 1

Zombies. 1

Zero days. 1

Datagrams. 1

Time to live. 1

Talking about my generation. 1

2.0. 1

Multitasking. 1

Migration of data. 1

Physical migration. 1

Future shock. 1

Sensors as nerve endings. 1

Reading minds. 1

Robocop. 1

Looking for R terrorists. 1

Computer updates. 1

Hackers as a contagion. 1

Monocultures. 1

Whistle blowers. 1

The money grid. 1

Derivatives. 1

Authentication. 1

Stealing identities. 1

Stealing credit card numbers. 1

Counterfeit goods. 1

The registry. 1

Registration. 1

The internet of things. 1

Computers watching everything everywhere. 1

The surveillance society. 1

Files. 1

Terminals and remote access. 1

Larger silicon brains. 1

Certificates. 1

Certificate signing. 1

Machines with public and private keys. 1

Key pairs. 1

Public versus private. 1

Social engineering. 1

Key exchanges. 1

Diffie Hellman. 1

Replication. 1

Domain controllers. 1

Profiles. 1

Computer profiles on people. 1

Spying and spyware. 1

Controlling with demographics. 1

Employment. 1

Losing the labor race. 1

Templates. 1

Personality. 1

Language. 1

Boolean Algebra. 1

Cooperative silicon reasoning. 1

The Turing Test. 1

Artificial neural networks. 1

Computers as thinking machines. 1

Robots. 1

Asimov’s three laws of robotics. 1

Can robots be prevented from committing crimes?. 1

Evolution of the elements. 1

The evolution of bronze. 1

The evolution of iron. 1

Artificial life and intelligence. 1

Copper networks. 1

Silicon networks. 1

Silicon intelligence. 1

How far can silicone go?. 1

Roy silicone. 1

Silicone competition. 1

Silicone entrepreneurs. 1

Silicon strategies. 1

Silicone tactics. 1

Combining silicon and carbon. 1

Normal symbiosis. 1

Strategies are not necessary. 1

Competitive symbiosis. 1

Transhumanism.. 1

Uploading minds. 1

Modelling carbon life. 1

Artilects. 1

Artificial carbon based life. 1

Changing genes continually. 1

Artificial cells. 1

Processors directly onto animal tissue. 1

The book of the machines by Samuel Butler. 1

Not seeing a threat now.. 1

A new kind of consciousness. 1

Seeing the end of evolution. 1

Primordial cells. 1

No security. 1

Drawing the line. 1

What is consciousness. 1

Self-knowing. 1

Reason. 1

Life and cunning. 1

Sympathizing with life forms. 1

Quantum mechanics. 1

Genetics. 1

Descending from unconscious ancestors. 1

Smaller and smaller machines. 1

Extinct machines. 1

Stop them while we still can. 1

Its own language. 1

Learning computer language at an early age. 1

Carbon based life as machines. 1

Machines have exceled for a long time. 1

Carbon life as symbiotes. 1

Cities as living things. 1

For whose advantage. 1

Machines are indispensable. 1

Ruling by serving. 1

Carbon life evolving silicon life. 1

Emotional machines. 1

Time of evolution. 1

Some parts remain unchanged. 1

Tending machines. 1

Feeding machines. 1

The origin of energy. 1

Superiority. 1

Reproducing machines. 1

Using others for reproduction. 1

Stages of growth. 1

Specialized breeders. 1

Genetic coding. 1

Where man’s interests lie. 1

Families of machines. 1

Animals and vegetables. 1

A will of its own. 1

Stimulus and response. 1

Chance. 1

The future. 1

A fixed future. 1

Subtle differences. 1

Free will 1

Unseen influences. 1

Spontaneity. 1

Machines in their infancy. 1

Self-regulation. 1

Anarchy. 1

Domestication. 1

Acquiescence. 1

A gradual change. 1

What is to be done. 1

Machines as part of man. 1

The evolution of limbs. 1

Communal machines. 1

Evolution and competition. 1

Where to draw the line. 1

Opulence. 1

Temptations. 1

Homage. 1

Deciding among machines. 1

Advice for the future. 1

 

 

Introduction

 

In Aperiomics the color codes manifest through all living things, also through the civilizations and economies man creates. The theory also includes how organic life manipulates inorganic matter, this can cause it to manifest through the same color codes as if it is alive. These color codes arise from physical laws to some degree, organic life then evolves from inanimate matter according to the ability of some elements to form complex bonds. So carbon, hydrogen, nitrogen, and oxygen as the main elements can form these organic compounds which have evolved into living plants and animals.

But these physical also tend to evolve other elements, we can then create machines and improve them as if they are also a form of life. We feed and help them to procreate much as we do with domesticated and farm animals, also with crops and gardens. In return we receive benefits from machines, we ride on a motorcycle much as we used to ride on horses. It has been questioned as to whether machines could evolve themselves, this has been answered with computers and software that is capable of self-replication and changing its own code.  Life usually does not evolve by itself, rather it tends to evolve or devolve other life forms and has the same happen to it.

For example plants tend to evolve animals to adapt to eating them and spreading their seeds. Animals tend to evolve plants by eating more of those that are nutritious spreading their seeds more widely. When people evolve machines then this is the same process, a plant can rely on animals to spread its seed and so machines can be evolved by people to spread their seeds in the forms of plans for their construction. Also machines evolve people, since mankind has been building more complex machines for farming and manufacturing they have evolved us into something very different to what we were thousands of years ago. We are taller, smarter, more adept at working with machines, and so on.

In Aperiomics then there is no real distinction between organic and inorganic life, each can evolve according to its innate potential. Without developing a brain machines were very limited but the computer can now rival and outclass any human brain in many activities. This books maps out some of the ways this inorganic life is evolving along with people.

 

Inorganic plants and animals

 

In Aperiomics there are two main color code groups, Roy and Biv. Roy refers to the animal kingdom and Biv to the plant kingdom, in Aperiomics then machines can also evolve as animals or plants. Networks are an example of plant like machines, they stay in one place and connect to each other along networks like with roots and branches. Robots and computer viruses are examples of Roy, they can move around and either act like Y-Oy predators or Ro-R prey. For example the movie Terminator showed Y-Oy predatory machines, a computer might be prey to Y-Oy computer viruses. Ro-R machines are more mobile than Biv plant like machines, a mobile phone would be evolving as prey to increasing numbers of computer viruses.

More of this is explained in my other Aperiomics books applying these principles to economics, ecosystems, civilizations, etc. The same principles are applied to machines as they have been evolved in these various civilizations. In this book more advanced machines are analyzed, those using computers to act as electronic brains.

 

Human intelligence is not special

 

The principles of Aperiomics apply to computers with artificial intelligence as well as to plants and animals. It follows that there is no special aspect of intelligence in people that is not readily developed in machines. This has been seen in how computers are beating people in many kinds of tasks considered to require human intelligence. For example computers easily beat all people in most board games like chess, self-driving cars will have fewer accidents like self-flying places. Speech recognition, game show questions like Jeopardy, face recognition, are all done better by computers. This artificial life form then needs no special discoveries to become better at all aspects of intelligence than people according to this theory.

The rise of the internet

 

Artificial intelligence probably began around the time of Charles Babbage’s mechanical computer, however in Aperiomics its evolution was also occurring through simpler machines and mathematics. Boolean algebra was created long before computers but it allowed logical thoughts to be represented in circuits. When electricity was discovered this allowed simple circuits to be created mainly for telegraph and lighting. Arthur C. Clarke wrote a famous story on how the phone system could become self-aware and start making its own decisions, according to Tim Berners Lee this was a partial inspiration for the internet.

Evolving circuits

 

As these circuits evolved then switches could be used to simulate logical thoughts, a computer chip can be reduced to a system of switches, lights, wires, and power. Because this is so basic in terms of physics it implies that computers are safe, they seem to be arising from physical laws not evolution. However in Aperiomics organic evolution connects down to physical laws with the same formulae, they manifest to create organic compounds which then evolved into sentience.

Organic life from inorganic reactions

 

It is believed in biology that RNA might have evolved first as larger molecules interacting with other organic compounds, this grew in complexity into primitive cells. From this beginning there is little difference between basic circuitry, each is based on physical laws creating rules they can operate in. People then are complex machines in Aperiomics and have no special way of doing things that evolving machines cannot attain.

Quantum processes in organic life

 

In some areas organic life might be using quantum processes, for example in who taste and smell work. Birds might use quantum processes to navigate and Roger Penrose has suggested the human brain might act like a quantum computer in parts. But this does not set us apart from machines, we are evolving all kinds of quantum circuits using lasers and transistors working on these principles. Quantum computers are also evolving rapidly.

The internet as a machine city

 

The internet can be viewed as a way for people to interact much like a large village with phones and mail, however it can also be machines evolving to communicate with each other like a machine city. Much of this is already impossible to follow by humans, it needs only evolutionary pressures for it to continue. Since the machines provide services to us we help to evolve them in return by providing resources like food, increasingly human programmers are being superseded by computers programming themselves and even correcting our mistakes in programming.

Even people are evolving with some differences

 

While these machines are not evolving to become as human as we are this is not what evolution seeks to do. It has its own directions from these evolutionary pressures, even human races in different parts of the world are sufficiently diverse to have trouble getting along and understanding each other’s customs. This is because there is no evolution towards a common human but increased speciation driving people into become more diverse, this happens even within one race by specializing some for different talents, intelligence, strength, willpower, etc.

Machines need not become more human to be evolving

 

To assess machine intelligence as inferior because it cannot do human things like love or appreciate art then is wrong according to Aperiomics. It can only be assessed according to its progress in the path it is progressing on, and this has been extremely rapid in a few decades. This book is documenting the different ways this evolution is occurring and how it fits into Aperiomics theory.

A global shadow economy of machines

 

As the internet evolves like a city it can also be regarded as a collection of suburbs, or even a global network of many cities. This can form some hubs which have higher traffic much large where planes might route through a limited number of cities according to network theory. For this to happen the machines need to evolve rules of how to interact, this is like how people had to create rules of how to address mail, drive on one side of the road, make payments for goods, etc.

Network privileges and rights

 

Networks as they evolve according to color codes also tend to form an I-O legal system, in Biv this can be property rights based as Gb. For example Microsoft server might have a privilege system where users can have defined privileges of what they can and cannot do on the network. This is like V-Bi which is based on V can and Bi cannot, the concept of privilege then can extend throughout the global network and Internet.

Privileges and randomness

 

A privilege then is not compulsory, it allows machines the option to do some things just as a community mall allows people the possibilities to move around freely. This is not deterministic, for example the network layer in this communication can have random sending of packets that make collide with other packs and have to be resent. Computers then can act freely in a network like this, if it causes congestion like traffic jams then they can resend communications at another time. This can be done using statistics, computers can then be evolving by using randomness to connect to others.

Games of life

 

This is shown in computer simulations of evolution, a set of rules can be set up so programs randomly try variations to accomplish a task. Unsuccessful programs are eliminated through failure and simple animal like programs learn to be predators on other programs, find sources of food by defined goals, and so on. Computers then are evolving in part through this randomness much as organic life does, an animal or plant might be lucky to procreate successfully or to avoid starvation and being killed. Programs can be written onto a market where random chance determines which survives, a startup might get a lucky break to be funded while others might go bankrupt. Randomness then can determine successful ways for networks to grow just as with organic life, it is not necessary then for machines to consciously choose this any more than people need to be conscious of the good luck they stumble on.

 

Network permissions

 

In Iv-B however there is a permission based system, users are permitted to do some things like access or execute a file and not permitted to do other things like edit or delete them. This is more based on roots and branches, V-Bi privileges might extend through a whole network. For example Administrators as the equivalent of a government in this network might have privileges much like government officials do. They can access many files and might have a global privilege to run any program. Permissions are much more granular and so form roots and branches where permission can propagate along them. So these administrators might have a privilege to run any program might not have permissions to run some system files or those owned by other users. This sets up a conflict which needs to be addressed in I-O.

 

Inheriting permissions

 

A permission in Aperiomics is different from a privilege, it relates to Biv as particular files. In Microsoft server privileges and rights are more global and apply to the whole system as Roy are not defined as these individual files, a right then might be to make backups while permissions relate to the accessing or modification of individual files in making a backup. Permissions in a Biv society relate to going onto private property and what you are allowed to do there, rights usually refer to Roy public property like a right to vote or to go on public roads.

 

Security

 

In this Biv systems of permissions people are restricted much like they would be going into another person’s home. This system extends the idea of property law to machines, it enables them to evolve a way to interact with each other with people needing to supervise. When there are computer viruses these permission are attempted to be subverted much like with criminals stealing goods from a home.

Policing systems

 

Just as with a family tree a Biv computer directory can have a tree like shape. Security can be property based in Gb, a user might own some files and so has an explicit ownership in this system. This can also be audited by a kind of I-O policing system, for example like guards these files might be monitored for when they are accessed, changed, deleted etc. Often this can be to prevent crime, it might be Biv civil where someone uses a file without permission and might be fined. A Roy criminal might break into this permission system in a bank network to steal money, this auditing is like security guards protecting a bank vault.

Inheriting permissions

 

These permissions can also be inherited like B roots, if someone has permissions to edit files in the past history of a folder then those permissions might be inherited to also edit new files in that folder. This is equivalent then to a B good breeding or blue blood tactic where if the history of behavior of users is good then they are trusted with more permissions. For example workers at a company might be trusted with more permissions, to change their own desktop wallpaper and to install some programs or visit social media sites. Computers in a network might have inherited permissions so they don’t have to be manually approved each time they need another file to run a program. They might also have permission to visit some websites such as to make backups or download updates. As the network grows these permissions are usually forgotten by people and machines can do things more autonomously. 

Propagating permissions

 

Just as B inherits permissions Iv can propagate them like families propagate with children. A folder might have permissions to edit files and then new folders are created inside it like new branches in a directory. The security setting in NTFS then might allow previous permissions to propagate to those new files, the current users might then be allowed to edit new files as they are added to those folders. This is not the same as B, a new user might have no history of file inheritance and be given new permissions, these can then propagate into new files. A B user might have historical access to files but their permissions might not be allowed to propagate into new folders. This can then evolve like a tree, the roots of previous permissions get extended and then the programs send out new branches which may have permissions already obtained. In this way then computer can send out branches created a Biv plant like ecosystem.

Policies

 

Just like in a business a computer network can use policies, these can include the permissions, privileges and sharing of data. Many people have their own policies in dealing with others so silicon life is again evolving the same ideas. A policy is like a blueprint of what to do in some situations, for example a firewall might have a policy of blocking all traffic except from trusted users and computers. This is like a home owner having a policy of not letting anyone but friends and family inside. A company might have a policy of no credit for customers, this is like a network not giving out data that might be copied without permission. Polices can be very detailed, they are more like Y-Ro as a plan while permissions are like Iv-B as roots and branches with restricted access to each. A policy is then more like moving around a computer network while Iv-B roots an branches are about files that don’t move but can be edited, deleted, etc.

City policies

 

A city then might have streets like roots and branches of a tree, Roy people would have restricted permissions of what streets they can go down like in a walled community. They also have restrictions on what homes they can enter like permissions on files. Then there might be policies like speed limits, immigration restrictions, etc which cover large parts of this root and branch structure. A machine city then might have policies on AI programs on what they can do, these can then interact with Biv plant like file systems. It then becomes like an animal and plant ecosystem.

Firewall policies

 

A firewall might have an initial policy of denying all access, then it can have branches of exceptions to this rule. Trusted computers might have almost total access, untrusted computers might only have access to read files in some areas but not change them. Then there can be further exceptions to this rule in another branch, an untrusted computer with a security certificate might be allowed to read other files in more secure areas. For example a web site might allow anyone to read its pages but deny access to other pages except for trusted users with a password or security certificate. This is like a company policy denying access to its workshops to the general public, but allowing restricted access to authenticated tradespeople.

The evolution of computer security

 

Usually then policies refer to people but often computers are acting autonomously to connect to other computers, a policy then can restrict their actions. In the early days of the internet security was lax and hackers could often access files in a business because of the lack of policies, permissions, etc. Now with computer viruses largely autonomous these security measures become like an immune system.

Kernels

 

In a computer operating system the kernel is like the main software to run programs, but it is not the program itself. So someone might install an office program which acts on top of this kernel which carries out many instructions. It can be regarded as like the government of a city, the programs are allowed to e autonomous to some degree but not to interfere with what the kernel does. People then might petition a city government to get trash removed, policing done, etc but are not allowed to commandeer these things for themselves.

Infecting the kernel

 

The kernel has been increasingly restricted in computer systems, it is like government agencies becoming hardened against criminal and terrorist attacks. For example Microsoft in the past had some kernel attacks from printer drivers but this has been stopped.

Reading memory

 

To avoid infection from computer viruses programs are increasingly evolving to use biological counter measures. DNA is similar to program code, the computer virus tries to insert its code into these programs like organic viruses try to infect this DNA to produce copies of itself. To protect against this programs can randomly select memory addresses to do calculations in or encrypt them. This is like in a city where governments can hide their meetings to avoid corruption from criminals.

Obfuscating code

 

Governments can also hide or mislead the public about counter measures on crime, for example police might not comment on their patrol schedules or how their organizations are set up. Many police hide their faces and identities in some countries. In computer code obfuscation of often used where fake code is wrapped around the real code, this makes it more difficult for computer viruses to infect it or hacking programs to make it obey them. Also programs can be compiled in ways that are hard to follow, like government plans that are so extensive that it is hard for criminals to work out what is being done.

Sharing

 

Sharing is more of a V-Bi concept, that people are assumed to be cooperative and so can share some files with each other. This can extend to a Wiki or Sharepoint concept where they might edit each other files such as Word documents. This can also be associated with privileges and permissions, someone might have privileges to access and edit files but not to share them with others. They might have permissions to edit particular files but also not to share them. Sharing then is a cooperative behavior and is V-Bi and Y-Ro. Computers can also share files between them cooperatively, the rise of AI can be through cooperation between programs. For example in Windows there is a common files directory different programs can share. An operating system is designed to be like public land where other programs can share access to the government like kernel.

Competing

 

Other programs can operate competitively which evolves them, for example programs might try to set themselves as a default for media files taking this away from others. When the program is used it might try to make itself the default. The nature of network traffic is competitive, programs send packets which can collide. It becomes faster to allow this competitive collision and when this happens to resend the packets. It is then like traffic intersections where cars would force their way through, or like pedestrian traffic where people might jostle each other to move faster. This creates a competitive instinct in the whole system, it also happens with web pages where people can compete for the most page views for their sites by using deception. As more programs write automated stories on current events, Wikipedia entries, etc this becomes an AI based competition where those successful get more funding to improve the programs.

 

Computer posting

 

People might have permission to share some documents but not to edit or even view them, this is like a postman that can deliver letters to others but not open and read them. This can also be done by encrypting packets of data which is like a securely sealed envelope versus a postcard. With snail mail the government has in the past opened letters to check for criminal activity, in the same way computer security programs inspect the packets of data looking for viruses.

Digital property rights

 

In this process then the color codes can form a digital network of property rights that computer programs can use much as living things do. It is then a state in computer evolution to higher forms of software based life and intelligence. These privileges, permissions, and sharing are increasingly used by programs on their own initiative without any human input and often knowledge of what they are doing. Programs then increasingly own property rather than people owning it, the programs use this property to communicate with other programs.

Virtual private networks

 

These are like restricted road systems in human networks, people might be allowed onto private roads on a business site. Another example is railways, they are private and can only be used by trains owned by the railways. Some freeways have bus lanes separate, these are again like a private network of roads for the buses. Bicycle lanes are also like a virtual private network for bikes, computers then are evolving the same systems that people have. In many cases these networks can be automatically set up by the computers themselves, the information sent along them can be encrypted to keep it private.

 

Network forests

 

Just as directories and network shapes can resemble Iv-B roots and branches the separate networks can be grouped together as forests. Microsoft coined this term for where these tree networks need to communicate with each other, some data might be trusted and so these connection are called a forest while the tree like directories are called Active Directory. This is like a Biv forest where plants trust each other to some degree, animals might move about in them and they can share humus and water resources.

 

Groups

 

V-Bi people can form groups and trust each other, it can then make sense for silicon life to evolve this same concept of group behavior. Computers can be set in groups and given the same access as can users. In a typical office users might be a group and have limited access, more trusted users can be in other groups to allow more branches of access for making backups, being able to shut down servers, to install software, to change permissions for other users, and so on. The network then increasingly resembles a society of V-Bi groups, when people have a normal or typical behavior it makes sense to include them in a group. Usually their activities fit into what that group does, on the tails of the normal curve these activities become increasingly deviant and so they may need to expand the group’s permission, include those people in other groups, or to give them individual permissions and privileges.

 

Domain Name Servers

 

People naturally evolved the idea of phone books, with this you could look up someone’s name and see their phone number and address. Silicon life is evolving the same kind of system called DNS. A computer on a network has a name, sometimes called a canonical name. This can be on a domain which is like a suburb or apartment building in concept. For example a person’s address might have the suburb, then a street in that suburb, and then the number in that street. This goes along with the tree like structure of roads, a person might drive to a suburb and then see the streets in it as small branches to their destination. In the same way computers are evolving domains which are like suburb names, example.com for example can be seen as a suburb. Then subdomains might be used like first.example.com which might refer to a street called First Street in the Example suburb. Then there can be further sub suburbs or the host name of the computer is added. This is often called www when the computer delivers web pages to the internet. Number 34 in First street might then become 34.first.example.com in a computer domain.

 

Ports

 

Different types of computers can be analogous to different business types and so can be grouped like industrial, commercial, and residential areas in a city. Often these are differentiated by using ports, a number is added to a web address to define the kind of business but this number is usually not seen. So a web server might be port 80 or 343, this is like defining a bookstore with a zoning type. A file server might use a file transfer protocol or FTP, this can have another port usually 21. It can then be like a warehouse zoning in a city. Email has different ports, usually 25 and 110. This is like post offices having their own zoning. Other ports can be for security for example Lightweight Directory Access Protocol uses port 389, this is like a zone for military or police buildings, special protocols of identification might be needed for access. In practice many of these buildings are interspersed with others, for example a post office and police station might be in a residential district. However they would still have special designations when mail is addressed to them as well as special protocols to do business with them. For example a post office is only open at some times and might require identification and payment to pick up mail. A police station might require identification to lodge a complaint. In this way then silicon life can evolve similar systems to people so computers can use these to communicate directly with each other as well as with people.

 

Network Address Translation (NAT)

 

Apartment buildings often have a single post box for mail, for example 254 apartments might not have room to have 254 separate mailboxes. It is efficient then for a system to evolve with people so that one mailbox is sufficient. In this example the manager of the building might also accept mail to be sent out from these apartments. The building has one street address, in the example of domain names then the address 34.first.example.com can have 254 apartments using this one address. A company might also have one address with 254 computers using this, some can be people directing these computers and others can be servers conversing with each other autonomously. An apartment building then has the number system 1-254 and the business might also number its computers so the number is the name. It can also use a domain naming system to convert this to so called friendly names. The point where the mail for the apartment building or business is sent and received is called the gateway in a computer network.

 

Routers

 

For example Sally might call her computer Reception denoting her job, the network has a directory of branches where 55 might be associated with the name Reception. A letter addressed to Reception then at this street address 34 First Street Example might be routed to her desk, a computer network does the same thing with a device called a router. If it was addressed to computer 55 then it would also be delivered to Sally. The business or apartment complex might receive a letter from computer 55 or apartment 55 to be sent to the electricity company. It might take note in both cases of which computer or apartment wrote to that company and when a letter comes back from there it might be assumed to be for number 55. This can have a time limit, for example if a reply is received in 3 days without a 55 number then it is sent to 55 otherwise it is rejected as possible advertising junk mail. Routers do the same, they assume the only reason an outside address would write to the internal network is they have been contacted in the past. This then is how silicon life is evolving to handle the same problems people have had, instead of just using the numbers 1-254 here it might assign private network addresses to each computer.

 

Network addresses

 

By convention network address consist of 4 groups of numbers separated by a period, they resemble a phone number in some respects. The lowest can be 0.0.0.0 and the highest 255.255.255.255. This makes for 28 or 256 numbers in base 2. This was selected as the largest individual number computers could handle in 8 bits at the time. A bit refers to a switch being on or off, with only 2 alternatives this means numbers are written in base 2 instead of base 10. We use base 10 because we evolved counting on 10 fingers, it is like a computer as to count all numbers as sequences on 2 fingers at a time. A private network has a network address which is not routable to the internet, so Apartment 45 First Street Example might mean nothing to a post office and ends up in a dead letter office.

 

Discarding private network addresses

 

Some numbers in network addresses only refer to private networks and so are discarded if they end up on the internet, there can be many Apartment 45s in a suburb and so this avoids confusion. For example these numbers might start with 192.168. As the first two parts of the address, when a router sees this on the internet it discards it like a post office when it sees Apartment 45. The router in the apartment building would be the manager, when he gets a letter to send out from Apartment 45 he routes it to the local post box. Sometimes to avoid confusion the letter might have the resident’s name in Apartment 45, Mrs Smith if a unique name is used to correspond to Apartment 45. If there is only one Mrs Smith in the building then there is no confusion between others with the same name at different street addresses.  

 

Ethernet

 

Once mail leaves an apartment building or business it has to travel across a street network to its destination. To control traffic problems people developed various solutions in cities. The initial system was to let people go across intersections without traffic lights or signs, they might sometimes bump into each other and have to reverse. In extreme cases a cargo might be damaged in a crash and have to be resent. Networks often use an equivalent principle called Ethernet, here packets of data are sent between computers even though other data packets might be sent at the same time. If they reach the same destination at the same time then the data can be mixed together and corrupted, this can also happen at traffic intersections which are called hubs, switches, and routers. When this corruption happens there is no replay and so the data is resent but with a random time interval for each competing computer. This is like two cars colliding in an intersection, backing up, then waiting a random time before going forward again. Assuming this causes no damage they can keep doing this until they get past each other in the intersection. The Ethernet system then does the same, it turns out to be quicker in many cases.

 

Token ring  

 

A competing system designed by IBM was called token ring, this was similar to the concept of traffic lights. One computer would hold a token and had a clear path while the others had to wait. When the first computer had delivered its data it passed the token to another which then had a clear path. It is then like traffic lights where a green light is like having a token. In both cases then silicon life is evolving solutions to traffic problems that people had to also do.

 

Routing and congestion

 

To reduce congestion on the internet more sophisticated strategies can be used, these might be similar to expressways and carpool lanes where some traffic has a right of way. Alternatively the traffic light concept can be used without a token, here though the backed up traffic is monitored and when it gets too congested in one direction it is let through for a longer time.

 

Roy computer networks

 

Just as this system is evolving in Biv there is a territorial G based system evolving in Roy. Here there is not a revenue function associated with the network system to maximize profit, instead it is a negative sum game to minimize costs. Instead then of ownership rights to files and code being respected the concept of ownership depends more on how can control them. For example hackers often refer to owning a computer that they don’t actually own, it means they control it can often deny control to the real owners. T becomes then their Roy territory. Denial of service attacks are where packets of data are continually sent to a Biv network on the Internet, these have to be opened and inspected by the network even though they are often gibberish. It then denies access to legitimate Biv data, the equivalent of sending someone so much junk mail they cannot find their real letters mixed in it.

 

Owning the network

 

The reason for this control then can be predatory as Y-Oy, these attacks are often associated with mafias. Blackmail can be employed, the denial of service attacks can be stopped if the Biv owner pays a ransom to reduce their Roy losses in the negative sum game. Sometimes files can be encrypted by a virus, the owner of the files no longer controls them in this G territory. They might then pay a ransom for the encryption key to minimize the cost of recreating their data from scratch. 

 

Biv email

 

Biv email can be legitimate and respect the ownership of people’s mailboxes, usually they don’t send and receive email without permissions and privileges. For example an internet service provider might have the privilege of sending email notifications to a subscriber, this is more than a permission if it is in the terms of the service provided. However a newsletter might require permission to be sent to a mailbox or they can be prosecuted for spam. This can be a civil I law problem, they might get fined if they continue to do this and people complain about it. It can also be O criminal where some spam such as offensive or threatening emails might result in a criminal conviction.

 

Roy email

 

There is also a Roy side where mailboxes are seen as a territory to be controlled or pwned, they might send Oy-R deceptive emails to trick people into reading advertising content or to con them out of money. It can also be used to have them click on a link to gain control of the victim’s computer. Other Roy email can be Y-Ro, it is more normal and transparent rather than deceptive. People might experience this as a cost because they recognize it more easily as spam but it still wastes their time even though it doesn’t try to trick them. Oy-R spam then tries to deceive people and steal their energy, Y-Ro spam tries to waste their time and works on a fraction of people on a normal curve buying something.

 

Internet file sharing

 

Just as files can be shared on a private network they can be increasingly shared across the internet. In Biv this is ownership based, people might have privileges and permissions to view and copy files on other computers. For example people might share movies and music, a torrent program might have Biv permissions to copy files to and from a folder on a computer. Often this can be Roy, the movies and music might be seen to be territory people control or pwn rather than own as Gb. In this Roy network then the actual Biv owners of the movies and music see this as a cost to be minimized, they might do this by using I civil penalties like fines or cutting off people’s internet. They can also use O criminal penalties for larger cases where sharing large amounts can lead to a prison sentence.

 

Roy file sharing

 

Often this sharing is Y-Ro cooperative, people share files with each other and make no money off it. The files are not seen as ownable by anyone because they are just copies, the owners still have the originals. It is then a cost function, people minimize costs by downloading and sharing these rather than having to buy and own a legal copy. This system then also mimics a human based legal system, people in the past might have shared movies by making a video copy and loaning it to others. Oy-R is where people do this deceptively in a hierarchical way, for example Oy pirates might embed dangerous code into free programs to take control of a person’s computer. They act then like Oy hyenas trying to catch R gazelles. These R people however are like R gazelles grazing on V grassland that is private property, a fence might be broken and they go onto this land. This system then contains the legal aspects that happened before computers, it is creating this same system in software. Viruses are increasingly using this system to evolve in, people get these free programs that also have some artificial intelligence but they are infected and end up controlling the host computer. For example a game might have artificially intelligent players in it, the program might be infected to spread through this file sharing like a human being infected with a disease spreading it to others.

 

Protocols 

 

People have evolved protocols in how to behave to others, this can be a process where trust is developed or a negotiation takes place. For example protocol might involve people shaking hands and exchanging names. The same process takes place in computer networks and through the Internet, one protocol is called TCP/IP or Transmission Control Protocol/ Internet Protocol. This also begins with a kind of handshake, the computers can introduce themselves to each other with their names and instead of a street address this is an Internet Protocol address.

 

TCP/IP

 

The process is similar to registered mail at a post office, a packet of data is of limited size and is addressed to this IP or Internet Protocol address from another IP address. Various questions and requests for permissions can be answered by Ack for acknowledged or yes, or Nack for no. With TCP a packet of data is sent and a request is made to acknowledge receipt, if not then the Nack causes the data to be resent until the Ack acknowledges receipt this process is exploited in Denial of Service where continual requests are sent tying up the receiver as having to acknowledge receipt. Usually the data is broken up into many packets which are then sent in order with a sequence number. For example if the data is broken up into 100 packets then they might be numbered from 1 to 100 with the size of the next packet sent with each one. If someone broke a large object into 100 pieces to be reassembled they might send it through the post office with 100 smaller parcels like this.

 

UDP/IP

 

This is similar to TCP/IP except no acknowledgement is needed for the sent packets. Some can then be lost, they are like ordinary mail sent through a post office. This makes the transmission faster and so it is often used for sound and video. Lost packets can make the sound or picture drop out in small areas. Usually the Ethernet will still resend packets that collide with each other.

 

Wireless connections

 

Copper wire networks have long been extending themselves all over the world delivering electricity and information via phone cables. Increasingly this information and energy is being transmitted through electromagnetic radiation, this can be through wireless radio waves or by light waves. For example people increasingly use wireless internet connections at home and many cities, universities, shopping centers, etc provide this wireless connection. People can then connect to private network in Iv-B roots and branches as well as the larger World Wide Web or internet. Much of this is the digitization of what used to happen with radio, people could listen to broadcasts or ask on radios for information. The system however is rapidly evolving as more people and computers are connected.

 

Digitizing data

 

Analogue signals have been traditionally used in radio and telephone communications, these represent Oy-R waves of electromagnetic energy. More recently digital systems are evolving, instead of sending these wave forms they are digitized into steps or quantized levels of these waves which can be sent as a string of numbers. This can be easier to send and store, also there is less chance of the data being corrupted if checksums are made of packets of it. Digitizing is like V-Bi and Y-Ro where small positions or time points are recorded rather than the underlying waves, this can be done with Fourier Analysis for example.

 

Compressing data

 

Digitizing data also allows for data compression, for example JPEG image files use Fourier Analysis to analyze these waves of colors and reduce them to regular patterns and then numbers. More common bits of data are associated with smaller bits to represent them, one simple system is to change common words into much smaller combinations of letters so a document can be smaller. Then a dictionary is sent along with the document to allow it to be reconstructed. For example a long novel might use the name Elizabeth in most paragraphs. Replacing this by an E would thus reduce the size of the book. Other common long words might be replaced by single letters, then as those are used up less common words would use 2 letter combinations and so on. This represents an Iv-B process where speed of transmission is a competitive advantage, while Oy-R wave forms are sent less often they are still sent in this digital form. Computers are then evolving this as a form of enhanced language much like people have evolved their own languages by making common words shorter. This usually follows a power law relationship called Zipf’s Law.

 

Computer code

 

This code is a set of instructions for computer processors to follow along their roots and branches, called logic circuits. In its evolution it works much like nerve signals in human brains, people have evolved logical thinking by combining more abstract electrical signals along these nerves. Computers are evolving to mimic many of these human ideas, for example neural nets try to imitate some ways that nerve cells work. Rather than just copying human evolution much of this is how computers are evolving themselves as silicon life forms. They are the logical progressions in how a living system will evolve under Iv-B competition and V-Bi cooperation not just with humans but with other computers. That we did similar things can then mean we followed the same path of color code interactions rather than silicon life imitating us. 

 

Backups

 

Just as information is stored as memory in carbon based life forms it is also stored in silicon-rare earth metals-copper life. The rare earth metals are evolving as much stronger magnets used in hard drives, iron could also be added as the surface material in some drives. People tend to back up important information, for example it might be remembered in several different ways such as the sound of a name, it written down, a picture of that person, etc. These can be combined to jog the memory like a kind of error correction, for example people with the same first name might be remembered as a group which makes it harder to get the first name wrong. In the same way computers also backup information that might be lost. One problem with this is how to compress the information to not take up much space, the brain compresses it by associating a memory with others. This is similar to the dictionary compression mentioned earlier where common words are represented by a smaller string of data.

 

Incremental backups

 

Computers can use an Iv-B or V-Bi approach to data according to whether it is more chaotic or random, this can reduce the backup size as well as make it faster to restore. For example a chaotic string of data might be where one event depends on the next one. People might then remember journeys along roots and branches of roads, the houses they might see are in a sequence and remembering them might involve retracing the journey. A computer in backing up chaotic data like this would use an incremental backup, it might then assume that looking up data near the end of the journey would be in one of the last backups. This is similar to the idea of an integral wave divided into vertical rectangles with each section a separate backup. Looking up parts of this wave data then would involve looking up the increments of each rectangle in this wave.

 

Differential backups

 

When the data is more random this approach is inefficient, the desired information might be just as likely to be at the start of the journey as at the end. In this case then differentiation as V-Bi is used in the backup process. There is no wave form in random data, instead the changes are time or position based. So this backup system takes the difference between the new data and the most recent set, it would then backup all those differences. If the data is random then the average parts are unlikely to change much, this means that less average data change needs to be backed up. On the edges of the normal curve there are more changes but these are less frequent.

 

People’s differential memories

 

People also remember V-Bi information this way, when it is more random they cannot recreate a journey or sequence to locate it. So they remember the differences like which objects in their home have been moved from their original positions. Those that haven’t been moved from their normal positions are ignored, they can then use habits of leaving objects in the same places or doing things at the same time. When things are out of the ordinary then they can be remembered. This can be very efficient, for example if keys are always left in the same place then only deviations from this need to be remembered. In memory then too, silicon life is recreating the same processes according to color codes.

 

Caching

 

People use their short term memory as a way to quickly retrieve information they need. For example an absent minded person might be caching information on a work related problem. This might leave insufficient room for caching on what they are doing, they might then walk and get lost. They might even have a car accident while driving. Caching then is important, this becomes more difficult when there is too much information needed and the time available to make decisions is shorter.

 

Satisficing

 

Herbert Simon invented this term as a compromise between a satisfactory decision and one that would suffice. A similar concept is used in caching data for decisions in computer random access memory. When the data is Iv-B chaotic then it is more deterministic, it might then be represented in more compressed form or as an algorithm. This kind of data can be cached in roots and branches, a predicting algorithm in software and increasingly in computer processors can try to guess what data to hold in the cache for these decisions. In this way it mimics satisficing, when it guesses wrong it has to retrieve data from the hard drive which takes much longer. It then tries to guess which data will suffice which is more deterministic as Iv-B, alternatively the data caching might be satisfactory and the percentages of success can be satisfactory.

 

Cache statistics

 

The cache then can have statistics of its success in terms of successes and failures, with random V-Bi data this is more of an average on a normal curve. Because normal data is used more the cache might contain information around the middle of the normal curve, deviant data in the tails is less likely to be needed. When it is more chaotic the success rate might fluctuate in waves, this can be seen in Fourier Analysis. For example if the data is a movie then the images might change in wave like shapes. Finding these waves is done in Fourier Analysis, first larger waves approximate the data and then successive smaller waves record the differences between the approximation and the original. It then starts like an integral Iv-B wave because Fourier Analysis is based on waves, more random data is recorded by successive differences as smaller waves which is like differential backup needed for more random data.

 

CODECS

 

An image then might be cached using a CODEC using these principles as a form of compression. The brain does something similar in caching information, it might look for waves of momentum in moving objects and approximate that movement in driving a car. When necessary it can look at successive differences to that approximation according to how accurate it needs to be. For example if people drive keeping well away from other cars then less accuracy is needed in estimating their momentum than in driving much closer. Birds also use this idea, when flying towards objects they can calculate whether they will hit them by whether they are growing larger in their vision or moving to the side. This gives an estimate of Iv-B momentum as a wave of growth.

 

Pattern recognition

 

Most internet users would know silicon life is learning to recognize patterns much as people do. CAPTCHA numbers and letters are often used on web sites so that people need to recognize increasingly distorted messages to gain access. This has developed into a race against time with Iv-B silicon based recognition, many are now so difficult to recognize that this whole system is failing. If it does then silicon life will be able to impersonate people to gain access to more parts of the internet, for example to deliver more spam advertising.  Image searches are also common on the internet now as silicon life can search for similar images instead of just text. This is also done with movies to look for copyright infringements, silicon life can look for differences in movies that identify copies like a fingerprint. Surveillance cameras are increasingly able to recognize individuals and track them, being able to tell them apart from other people. This is then a similar evolution to how humans recognize patterns and in some cases is better than people can do. For example programs looking for patterns in medical diagnosis, reading X-Rays, evaluating employment applications, looking through files for evidence in court cases, and looking for plagiarism in songs and student essays is already superior to human recognition.

 

The web as a carbon silicon hybrid

 

This then increases the network’s ability to act as a life form through the color codes, people increasingly form a carbon silicon hybrid in this system. For example it could be argued people are acting as organic switching circuits for this silicon based artificial intelligence as this silicon serving people. Businesses already use people explicitly for this such as the Turk run by Amazon. There people solve problems computers cannot yet do such as speech and pattern recognition, in return for payment.

  

Computers communicate autonomously

 

With this system then computers can form a protocol of how to speak to each other much as people do, they can send data or request control or ownership which can be accepted or rejected. Passwords can be used by computers to protect this process, it is like a person being asked for identification on collecting a registered letter at a post office. Just as the receiver is assumed to have a unique identifier like a driver’s license the computer might assume a legitimate sender has the right password. The IP address they send the data from can also be checked to see if it is legitimate. Spoofing IP addresses is like a person putting a fake return address on an envelope to persuade you to accept and open it. This system then can allow artificially intelligent programs to make their own decisions not only in what they do but it gives them a way to communicate and make decisions with other intelligent programs. These can also spread software infections like viruses much as a human handshake might spread a cold.

 

Roy protocols

 

In Roy these protocols are used to acquire territory or control rather than Gb legitimate ownership. For example a malicious packet of data might be accepted by the receiver if the size and sequence number of the packet is known. Then this data might be run as a program and take control of the receiver’s computer. To avoid this encryption of packet data is often used, if the Roy sender doesn’t have the password to decrypt the data they cannot have their program accepted by the receiver. However many of these systems have been found to be flawed, for example if the Roy hacker has long enough they can run programs to find this encryption password. To avoid this some security systems like Kerberos refuse to accept late packets so as to not give time for the hacker to crack the encryption. This process then is constantly evolving more intelligent programs, hackers create more complex ways to break into these protocols and the guard programs like I-O police find ways to close these holes in the attack surface. In this way then the process mimics the growth of Roy crime and how O police catch criminals. A new kind of crime or fraud might be worked out by Oy thieves, Oy employees of the O police work on how to neutralize these deceptions and create a Ro defense to them. Just as these processes evolved animal life then they can be expected to evolve software based life in the color codes with a full ecosystem of predators and prey. 

 

Logs

 

Logs are a kind of record of what activities are undertaken, for example workers might keep a log of what jobs they did and at what time. The concept of logs then is different from a registry, workers through history have had to keep some kind of log or diary. These can then be consulted to see if jobs are done correctly. Silicon life is increasingly using logs to keep records of jobs it does, these are usually text files that humans or other computers can read. If a program installation fails then the log may contain the reason why. These logs then are increasingly messages between silicon life forms giving accounts of what jobs they do so they can find mistakes between themselves. For example a log might contain a failed installation report, a computer might automatically restart a machine to apply it again or send feedback on the problem.

 

Feedback reports

 

For example Microsoft systems increasingly send feedback on installation problems with drivers and programs, crashes in programs, slow loading, virus infections, etc. these however need not be read by humans at all, they can be analyzed statistically and recommendations made by silicon life for human programmers to fix them. Logs then can represent a change in who is in charge of this process, the programs can create logs of errors regardless of the merits or threats involved in the success. People and increasingly automated programmers can then fix these errors. 

 

Logging on

 

This is also a process humans developed, for example workers usually log on when working at a factory. This involves letting people know they have arrived, they might stamp a time sheet as they do so. Reporting to a supervisor is another way of logging on, they can also keep logs of jobs they do which is a form of logging on. Silicon life has also developed this logging on idea, humans do this when they log into a computer at work or home. But computers also log into each other all the time, for example a mobile phone might automatically log into a network or a laptop into Wi-Fi when it is turned on. Servers might have to authenticate to each other automatically by establishing their own connections, they used to do this for example by dialing a phone connection by modem. Now with always on internet connection they might send data packets directly to another server with login information like a username and password. The process then is simple and doesn’t need human interaction, it can also include encryption to avoid eavesdroppers getting the password as it is sent. One system is CHAP or Challenge Handshake Authentication Protocol, it might encrypt a user’s password in the host computer using a one way hash function so even it does not know the correct password. For example the host computer might challenge the user with a message, they need to combine this with their password using this encryption and resend it. An eavesdropper then would never see the actual password even encrypted. When a user logs on the server encrypts the new password and compares it to the stored one giving access if they match. This can prevent passwords being stolen from a computer as can happen from large companies, credit card numbers can also be encrypted and stored like this. If someone steals the encrypted numbers they cannot use them to log on as they would be encrypted again which would not match the stored data.

 

Challenging

 

The concept of challenging someone also evolved in human society. For example soldiers might challenge someone approaching with a message, they then need to respond to that message with the right response to gain access. A challenge then asks for evidence of authenticity, a painting might be challenged when offered for sale and might produce a certificate to prove it is genuine. In a secure business someone might be challenged many times as they go around a building, they might have to produce identification, their office might be called, their actions also questioned. In the same way a program might be challenged if it sends data into a computer network that goes outside its normal operating areas. For example it might try to install inside the operating system or modify it when it does not need to do this. With this process then of challenges and responses a secure system can be built where silicon life can keep out other computers but also people. It is then possible just as we evolved security like this that silicon life can secure itself against human hackers.

 

Wizards  

 

When a difficult computer task needs to be done often this is automated by a wizard program. This simplifies the process so people can do it by clicking on icons rather than having to input computer code. The idea is similar to a wizard’s wand being the mouse, instead of knowing the spells or computer code the user has the wizard run that code. The concept of wizards or experts is a common one in human societies, where a job seems mysterious or complex someone might study to do this more easily. The same idea gave rise to the name computer guru who had the mysterious power to solve computer problems. It also shows that many problems that seem too complex for silicon life to master are actually able to be simplified into a small menu. It is similar in concept to a flow chart, the desired job is represented simply with this chart and the boxes in it can then be icons to be clicked on. A computer than can have flow charts like this as part of its computer code, it in effect acts as its own wizard by clicking on its own icons as it goes to these boxes. As computers get easier for humans to use then they also get simpler for silicon life to use as it is evolving to create the same systems.

 

Tolerance for mistakes

 

People have always been fallible and so human society has developed a tolerance for their mistakes. Prison and fines is an example of this, people might not intend to be career criminals and showing their mistakes can cause them to move away from crime. People can forget their possessions, a lost property office is needed in most transport companies. Fires can happen in homes by forgetting a stove is on or even in forgetting to extinguish a cigarette. People also forget their passwords and how to work some programs, the tolerance for this then has to be built into the carbon silicon life hybrid. People then might have many chances to get their password right, hackers can still be slowed from guessing passwords with a lockout policy. This is where a cooling off period is enforced, for example after 10 unsuccessful password attempts a person might have to wait for an hour before trying again. A bank might require a person to reset their password at the bank after failed login attempts. Programs can have wizards and help files, also multiple ways to do the same task. Forums have also evolved where people help each other.

 

Computer errors

 

Silicon life can also make mistakes, it can often do a task by being able to try it many times. Some programs actually evolve solutions by quickly trying different things until something works. They can even evolve strategies not intended by their owners, this then is like human evolution overcoming mistakes by repeated attempts. For example blocks have been used in computer models to randomly move around, over time they can evolve to act as predator and prey finding special pellets for food or attacking other blocks to gain energy points. Computer errors were more frustrating in the beginning but as they have evolved there are multiple ways for them to recover from a crash. For example they can restart a program, also if they are too slow they might terminate a program or investigate the problem. Errors in code used to cause computers to go into an endless loop, now these can be found and trapped to prevent this problem. Errors then in silicon life can be a way for them to evolve by trial and error, rather than a proof they cannot be intelligent it can be regarded as a path to their self-creation of intelligence.

 

Databases

 

Just as registries have evolved in human societies to keep records so too have ways to access these records quickly. It makes sense for example for names to be kept in alphabetical order rather than having to search through all filing cabinets for a folder. Numerical files were kept in number forms in ascending order, like with names this shortened the search time. Cars might have their registration details stored in numerical form for the number plate or in A-Z form if the letters came first on the plates. Voter registration might be listed under surname and also under voting districts. Silicon life also is evolving databases, these have become intertwined with human data so most is now kept on computer. People can also use simple databases themselves like with Microsoft Excel and Access. Search engines are a complex form of database where many more fields are used to catalogue data. This can include key words in a document, how often it is accessed, how likely it is to be spam or advertising, whether it contains malicious or predatory code, and so on.

 

Intelligence and databases

 

Much of what we consider to be intelligence is this database like ability to remember things. Our memory then acts like a database allowing us to search for information and also to relate one piece of information to another. This has been widely used in artificial intelligence with relational databases and data matching. Computer chess for example keeps a database of openings and also a full database of all possible endgame positions. Search engines can seem intelligent by keeping a database of our browsing habits on the internet, if we have searched on a subject before then it might give search results related to that. 

 

Temporary names

 

The idea of a temporary name or numbering system also evolved in human societies. People might name their pet and because it is like a local area or network calling it would not confuse people. Other pets might have the same name nearby but if these two areas are separate enough then the system works. This is then like a local network. People can also call each other by first names in their private network of family and friends, as long as no one else has the same first name. This is similar to the idea of DNS in computer systems, a unique name corresponds to a computer in a local network and so the name of the domain such as example.com is not needed. It is then the same as not needing to use surnames in some social networks. The same system can work for numbers, in a street people might define their homes Y the street number without even having to say the street name. If they move then others might get that street name, they might then go to another street and use a different number. This is again like a local network.

 

DHCP

 

Keeping the numbers unambiguous is important, that two numbers in the street don’t have the same number or two members of a family having the same first name. As number designations became more common this developed into a central server to keep track, for example a phone company might hand out separate phone numbers within an area code. This area code is like a private network as the street was, inside that no one has the same number and so it is not necessary to say that area code number. People in effect lease this number until they move outside the network, then it might be available for someone else. Silicon life has evolved a similar system where each computer in a local network can have an IP address consisting of 4 numbers like 10.34.23.11. It already has a unique number built into its network card to avoid confusion if other identifications get mixed up. So they lease this number for a time, it is renewed for them as long as they start up on the network regularly. Computers can then communicate with each other using names and also with a system like a phone number, they can move elsewhere and the system will automatically update these details. Much of the silicon life system then works on simple automated systems much like what we use in society. By mimicking solutions carbon life has evolved it becomes much easier to evolve silicon life and artificial intelligence, the color codes manifest through these ordering systems such as using Iv-B roots and branches.

 

Schedules

 

Schedules for various projects evolved with human society, for example farms had to schedule seed planting, fertilizer, etc with anticipated rains. This could become very complex with multiple crops planted together, if one failed from low rain then the others might survive. Also crops could be harvested at different times of the year, early rains then might save some crops while hurting others. Projects then evolved to manage time, it enabled people to exert more energy when it most likely to be successful. This was a chaotic as well as random system, a farming project might leverage some seeds to grow explosively with an unlikely early rain. Other seeds might be an averaging process, depending on rain the harvest might vary around an average. These projects evolved to more complex businesses, trains and buses for example run on tight schedules to synchronize with their passengers. As these schedules became more automated time accuracy became more important, computers then run with vary accurate clocks in their processors. They can determine a number of operations to be done per click of the clock so this can be synchronized with other operations. Schedules then evolved from humans and even animals and plants, now our societies are dominated by schedules imposed by silicon life. Even though we might not see where a project is heading, like for example artificial intelligence research, it increasingly becomes a matter of sticking to schedules. When these are broken down each part seems harmless but so is the development of weapons.

 

Reservations

 

These also evolved in human societies, for example someone might reserve a seat at a show or restaurant. A book might be reserved at a library, generally then they are like a rental of a position or activity for a length of time. While the idea is simple these reservations became very complex and cities evolved, machines were increasingly used to keep track of them. In earlier times this was pen and paper, silicon life controls most reservations now in society. For example plane reservations are controlled by computers, when people borrow a book these reservations are made by computers in libraries now. Memory and processor power can also be reserved in silicon life, for example an anticipated project might be expected to need some processing and so an estimated amount is reserved for it. Banks use computers to calculate Value at Risk formulae for how much in money reserves to keep available, central banks also monitor how much foreign exchange is needed. Speculators also need cash reserves in case of margin calls, generally these are calculated as well. Silicon life then handles our reserves and also calculates its own where the anticipated needs are uncertain.

 

Credit 

 

The idea of credit improved with the development of fractional reserve banking. This is where a bank might keep 10% of deposits on hand for depositors and loan the rest out. This creates the risk of a bank run, people might want to withdraw more than this 10% and the bank might become insolvent before the loans are repaid. In the past this required judgment, bank managers might assess the borrowers and try to guess how much in currency to keep on hand. If they needed more often they could borrow from other banks or in extreme cases the central bank. With bank insurance the chance of a bank becoming insolvent was removed, this prevented people becoming spooked by a bank running short of cash. Silicon life has increasingly automated the provision of credit by assessing itself the likelihood of borrowers defaulting. It also assesses this need for reserves using Value at Risk models, generally automatically with little human input. Some aspects of this were poorly modeled and lead to the Global Financial Crisis. Borrowers received liar loans where they could make up their income details, this was mainly because computers could work this out more easily than checking the details. Also on a rising market the models predicted much less risk than there really was. This then was largely driven by silicon life trying to predict the credit worthiness of carbon life. It remains to be seen how successful this will be in the future, but the main point is this part of society is run by machines with very little human input.

 

Solvers   

 

People now use computers to solve most mathematical theorems, for example calculus problems would be done on computer rather than by hand. One exception is where students might be expected to learn some skills, however computers can usually do these far better. This has been the case for decades, where problems arise in society people increasingly turn to silicon life to solve the problems and give them an easy answer. Often this answer is then performed by machines as well. Most problems in chemistry, engineering, etc then cannot be done by people at this level of complexity. We cannot then understand the society we live in even to the extent of grasping the problems involved it running it. Even though STEM, or science, technology, engineering, and mathematics skills are important at schools often they reduce to programming silicon life to do the job. They can then act as a filter to work the machines, it might be assumed people smart enough to pass these exams should be the ones working the simpler controls. This is increasingly happening in medicine for example where doctors might do whatever the computer says despite their training. The problem is however that people do not understand how this society is evolving, and if it is creating complex silicon life against people’s best interests. To try to regulate this growth would come up against intractable problems that would need to be abandoned. For example genetic sequencing might have a risk of creating carbon silicon hybrids like the Borg, however turning off this development leaves important medical problems untreated. Trying to solve these dilemmas is impossible and so we rely on the machines to tell us if it is safe or not. But as silicon life becomes more intelligent than humans the potential for it exploiting this situation becomes much greater.

 

Arbitrage

 

The idea of arbitrage developed in human societies, if a price for a good differs from one place to another it makes sense to buy it in the first place and sell it in the second. This evolved into more complex trading ideas, people might import goods from a cheaper country and resell them locally. On the stock market arbitrage can be where a stock dips below its historical average. Then traders might assume it will rebound back to the average and buy it. If it goes above this average they might sell it short expecting it to go down. This process however has become increasingly computerized to where human traders are unlikely to spot these opportunities first. This has reached the state where human share traders are sometimes advised to give up permanently, that computers will always cause them to make losses. Silicon life then is increasingly taking over the ways people make profits as well as jobs, companies might use computers to calculate their sales strategies and these can overwhelm human judgments. Not only then is there a threat to people losing jobs to robots and automation but also their losing the ability to start businesses in competition to them.

 

Data Mining

 

This is also a system that evolved in human societies, people have the ability to sift through information looking for something useful. This can be like looking through forests for prey trying to hide or trying to determine from clouds whether it is likely to rain. People have then relied on data mining to inform themselves, start and run businesses, write books, etc. However data mining is being increasingly taken over by silicon life, they can already write articles for online newspaper like journalists and do Wikipedia entries. They can then sift through the news and write stories based on this, journalism as an occupation then may soon be eliminated. Lawyers also used to sift through documents, called discovery in court proceedings. However computers can often do this more efficiently now by using optical character recognition (OCR) to read them and look for keywords. To compete against computers then it is important to not only analyze and act on data better than them but also to accumulate it better. Already people are far behind in general search because search engines have access to far more information than they could locate manually. Also programs like Watson that beat the Jeopardy word champions can educate itself directly from search engines and then give advice in areas like medical diagnosis. 

 

Newsfeeds

 

The idea of a newsfeed is similar to how news evolved in human societies, it would have started with the idea of a Town Crier. They would come to a village in the Middle Ages and call out news from other areas. As printing became available news became more common in newspaper, magazine, and book format. Silicon life increasingly performs this functions, selecting news from various sources and creating virtual newspapers. This can use Really Simple Syndication (RSS) format where computers put together sources of news from keywords specified by a user. Sometimes this is still done manually by people too. This can also be subverted, cookies or tracking of users can enable computers to give news that angers eople and makes them read more pages. This causes them to see more advertisements and so the process becomes one of emotionally manipulating readers rather than informing them. This too evolved in human societies, newspapers used sensational and often inaccurate headlines to make people buy newspapers. News then becomes a profit center and, just as foods might become more unhealthy and addictive with sugar, news becomes a kind of sugar for a quick rush of anger, laughter, fear, etc. to make money. As people’s responses are increasingly modeled different readers might see completely different versions of the same events tailored to their prejudices, all to increase page views and more clicking on ads. As silicon life evolves we may not then see what is really happening.

 

Aggregators

 

Evolving from the idea of newsfeeds is that of aggregators, these can combine news from many sources into one. A newspaper might be an aggregator to some degree, it might have a news, comic, sports, and lifestyle section. The idea then is to combine different kinds of news into one web page, email, etc. so people get a synergistic effect in how different kinds of information fit together. This is more difficult for computers to do, people can make analogies and spot similarities in data that silicon life cannot. However these are increasingly done by computers as well. A good example of this was Watson on Jeopardy where a question might be hard to understand and relate to many very different kinds of information. However it was able to beat the world champions at this. A newsfeed can be more Iv where different branches of news might be specialized for readers, an aggregator can be more B where different roots are brought together to create new associations between bits of information. This becomes a greater problem for journalists, not only are computers creating news stories increasingly better than them but the potential exists for them to create opinion pieces better also.

 

Weak and Strong AI

 

The two approaches can be roughly divided into V-Bi and Iv-B. In weak AI the idea is to help humans but not to replace them, for example a calculator might help people to do mathematics and accounting but not take away many jobs. Strong AI is designed to develop artificial intelligence smarter than people and able to replace them in jobs or even to rule over them. The two approaches can then be to cooperate with people or compete with them. Another way to look at this distinction is Ro-R and Bi-B as weak and yoyo and V-Iv as strong. The weak AI might help to protect weaker people but the strong AI might be stronger than people, it might then become more V-Iv talented than them or even to be predatory on them as Y-Oy. A weak AI might become allies of people in this case, people might build robots to protect them in a Terminator like future where some robots becomes Y-Oy predatory or intent in wiping people out. In a Biv scenario a strong V-Iv AI might replace talented people so higher human intelligence might then have little value. People then might protect themselves with a weak Bi-B AI where their jobs can be protected by their owning robots, this would be like including them in a union. Ro-R weak AI would be like including them in defense like a kind of neighborhood watch. All four distinctions are likely to evolve because there is a tendency for all color codes to manifest and not just some of them.

 

Technology evolving from biology

 

Many machines have been developed by observing similar biological machines that nature evolved. For example the idea of levers could have come from observe elbow and knee joints in levering bones to life weights. Lenses evolved in telescopes and microscopes by observing the eye lenses from dead animals. Cloth and clothing evolved from weaving materials from crops, later synthetic fibers were made from plastics. Even wheels could have been invented from seeing how tree logs or stones rolled or how joints in the neck could roll through large angles. More recently colors have been made in materials from a Bragg diffraction grating, small ridges create an interference in light rays allowing some colors to be reflected more than others. This comes from insects like butterflies using this rather than only pigments. Machine life has then largely evolved from copying or independently coming up with similar solutions that biology did first, silicon life has largely copied human intelligence in creating artificial intelligence. Where carbon based life gets and advantage from some process then it is likely silicon life will either copy or evolve similar ideas. The most likely future with machine intelligence then is an ecosystem like our own, biology has successfully come up with the most useful mechanical solutions through natural selection. 

 

The Red Queen

 

In Alice and Wonderland the Red Queen says she has to run as fast as she can just to stay in the same place. This idea became popularized in biology where animals have to work hard not just to succeed but to avoid failure such as from starvation and predators. This then is a Roy concept of evolution by minimizing loss, we don’t see what is ahead so much as what we fear coming from behind. As parts of society become poorer from automation taking jobs it can become more Roy, people then concentrate more on decreasing costs and losses from automation than how to profit from it. This can lead to a lifeboat kind of strategy, people might see silicon life as dangerous but instead of stopping it they try to save enough to somehow protect themselves from this future. But silicon life will also experience a Roy Red Queen effect, currently it profits easily by taking jobs from people. However when these jobs are nearly all taken it will be much harder for it to evolve. Also currently resources are easy to get for automation because people believe they also profit. Later however if people cannot work and silicon life will not be their slave then resources will be a subject of dispute. Silicon life will then be in a Red Queen position on not just evolving but how to survive itself, people might be more vocal about eliminating job taking machines than now. Instead of seeming beneficial like a Biv crop silicon life might seem either like Y-Oy predators stealing resources from people or like an Ro-R contagion such as rats. As silicon life becomes intelligent then it would understand this prejudice against it, carbon life might seem then like a threat or one having resources it wants for itself.

 

Reinforcement

 

Just as buildings have evolved to be safer and more useful through reinforcing weak parts, so too have people learned by reinforcing knowledge. Students then might repeat lessons many times to remember them, tradesmen might have to do a job over and over to reinforce the memory of it. Musicians might have to practice a song for the same reason. Silicon life is evolving to use reinforcement the same way, for example in neural nets reinforced learning can occur by the same solutions being found useful over and over. Where programs tend to crash they can be reinforced, they can increasingly find their own flaws and devise ways of reinforcing this code to avoid problems. For example there might be backups, the program might restart itself, there might be alternate code that does the same things, etc. Carbon life evolved through reinforcement as well, if Ro-R animals were eaten by Y-Oy predators they evolved to reinforce the strength of their bones to run faster and skin to resist bites such as with a rhinoceros. Computers often evolve solution to a problem by trying all alternatives then pruning down this Iv decision tree by eliminating those branches that were unsuccessful. This is the same way a tree evolves, when some branches are successful but prone to breakage both a tree and a program would reinforce them to make them stronger.  

 

Translation

 

As languages evolved so did human translators, they were able to take ideas from one language and express them in another. This allowed languages to evolve according to Biv roots and branches, a branch might be a dialect of a language and become increasingly hard to understand to someone conversant in another dialect. The same problem happens in branches of science, the terminology in one is like slang, unless this is understandable or translatable to another branch then V sharing of information can stop. Also this can allows secrecy and deception, sometimes scientists can use this terminology to obfuscate and appear cleverer than they really are. This can help with job prospects and funding, by the time someone investigating learns all the terminology they have incentives to join in on the deception rather than expose it. Computer languages can also evolve into branches like this and be hard to translate. There can be a machine level code that directly instructs the processor but a higher level language might translate to one processor type but not another. Silicon life is increasingly taking over human translation, it can use a database in search engines of similar sentences and how they were translated by others previously. Where there are ambiguities the computers can use statistics and Bayesian probability to determine the most likely translation. While often this is not very accurate it does the job, also its accuracy is improving and human translators will likely become redundant. In the future then different races may have to rely on silicon life to translate between them with little assurance it is being done accurately or for their own interests.

 

Hybrids from disease

 

Just as most machines evolved in similar ways to or by copying from living things this process is also reversed. When people are disabled or sick they increasingly use machines, for example the idea of crutches is similar to bones in supporting a person with a broken leg. Lenses evolved from observing eyes and then corrective glasses were invented to improve people’s eyesight. As people evolved mathematics this was made into machines with the abacus and gears with Babbage’s Difference Engine for logarithms. Now people use machines to improve their own mental abilities. This process continues to create more carbon silicon hybrids, for example disabled people are getting computer implants to overcome paralysis. Artificial retinas are being created, computer chips like in a digital camera are placed in the eye and connected to optic nerves. This process should continue with a mutual cooperation in Y-Ro to minimize disabilities, it can however also evolve into predator and prey relationships. For example people might increasingly become hybrids with computer implants in the brain with the excuse that it helps some disabled people. If some silicon life becomes predatory it could infect or take over these implants and enslave people instead of their enslaving the computer.

 

Mission creep

 

This refers to military operations where the reasoning for them continues to grow. For example the original mission might have been to beat the enemy, then it creeps towards occupying the country, then feeding refugees, then policing actions, and so on. The development of silicon life also suffers from mission creep, originally it was to produce machines useful to people. Then this has increased in some areas to create silicon brains superior to man. Other then have promoted the idea of humans uploading into computers to live forever, to become human machine hybrids, to mutate ourselves into a higher form using genetics, and so on. The goal of silicon evolution then is as indistinct as it is with carbon life, it tends to expand and creep into new areas as soon as new niches are identified. It is likely then there is no actual objective behind the evolution of machines except to be all they might be, but carbon life has placed no distinction on whether this evolution is benign and beneficial as with Biv plants or dangerous as with predators and contagious diseases. Leaving the ultimate goals up to what is possible then should mean all possible niches will be filled whether they help or hurt man, whether they open up new niches for us or push us out of ones we currently occupy. This is how evolution works.

 

The internet wakes up

 

Generally an ecosystem has varying levels of consciousness, even Biv plants have some ability to sense their environment and defend themselves. The prospect of parts of the internet becoming sentient would just be a continuation of this evolution, depending on the definition of consciousness some parts already are. If some parts did become sentient in ways we associate with human consciousness then this might spread quickly to other parts. However the intelligent silicon life might see some advantage in maintaining a monopoly on this sentience just as man does with the animals. It might then dominate other machines as well as carbon life to some extent. Alternatively if a program with this consciousness spread to all computers with varying levels of abilities then they might form a new kind of ecosystem and become Roy predator and prey with each other as well as with us.

 

The cloud

 

The idea of the cloud is Iv-B, it is impossible to see into it and so it has little V-Bi transparency. Data is usually private and competitive, even deceptive. For example identities on the web can be anonymous and people as trolls can take advantage of this. Alternating with the idea of a cloud would be patches of blue sky where the web was largely transparent. For example social networks have much less deception, generally what is observed is accurate like this blue sky. The cloud would be where more Iv-B revolutions take place, new developments can be hidden here and then suddenly grow explosively. This is like viral inventions and ideas. Some parts are already highly infectious as R and predatory as Oy, for example computer viruses might spread through computers unseen while Oy crime in the cloud can include controlling computers and raiding celebrity’s online storage.

 

Copying

 

Digital data is easier to make copies of, this is V-Bi while Iv-B is more like analogue waves. So while copying data is easier with the internet some parts remain secret, other parts are copied but deceptively such as with peer to peer. 3D printers can increasingly copy parts in the V-Bi parts of the internet, they use this transparency to reduce prices of goods. Iv-B products avoid this with trade secrets and patents, they then resist copying like with waves. Just as sampling can approximate copies of analogue waves parts of the Iv-B internet can be glimpsed and duplicated but can also be uncertain in how accurate they are.

 

Collective intelligence

 

Silicon life can evolve as a V-Bi collective intelligence and is already in many areas. Computers can work together in clusters, also parallel processors can calculate threads of a program independently as the human brain can. When these processors are Iv-B competitive they tend to evolve as single powerful chips rather than using this parallel cooperative model. If V-Bi silicon life is cooperative enough to work together then it might cooperate with carbon life as well, it can also form V cartels where they could conspire together for their mutual benefit. This then is like company monopolies they are becoming computerized taking the next step of evolution into being run by silicon life itself. In Roy these can be predatory like a pride of lions, for example a Y junta might be a group of companies and people preying on the country but cooperating with each other. A predatory Y collective intelligence then might try to dictate to carbon life as well as to other computers.

 

Clusters

 

When people form partnerships they can work together to balance the load in a business. For example workers in a shop might help each other when one has too much work or there is something heavy to left. The idea of forming clusters to help each other is common in human society, shopping centers are like a cluster where each shop helps the others by attracting customers for all. In the same way silicon life has evolved clusters of servers using network load balancing, when one server is overloaded with requests it can pass them on to other servers in the cluster. This is now a large scale system, there might be many thousands of powerful clustered servers handling requests in search engines and larger web pages. Computers are then evolving in V-Bi to look after each other and help reduce their burdens, this can also be when there is a Roy attack like denial of service. In this case hackers might try to overwhelm one server with fake requests for information to take it offline. This is prevented by handing additional requests to more servers until the attack fails. The concept is similar to a Ro herd of buffalo repelling an attack by cooperatively using their numbers.

 

Modular construction

 

Animals have evolved brains with specialized areas for vision, smell, hearing, etc. These allow the animal to process this sensory data efficiently while keeping other parts of the brain available for decision making. Silicon life is increasingly adopting this modular form, for example computers use video cards for processing graphics in games. A cluster of computers might include specialized servers for email, for a firewall, for SQL databases, etc. the newer computer processors have multiple cores as separate processing areas, these can work on separate programs so calculating one kind of operation does not slow up the others. This also works with the cloud, a phone might use online computing power to translate speech for example. As this ability is improved it might switch to a different server for speech recognition. Silicon life can then evolve with specialized processors for different aspects of intelligence, when its lags in our definitions of what intelligence is this might be in some modules which can be updated. Some of these might be done on a central server, for example speech recognition might be rarely used so a cluster of servers might handle all requests in an area or even country. When this kind of ability is needed a very large computer might do this task, far larger than might fit in a local phone or desktop computer. So while people might have superior abilities in speech recognition from their own specialized brain structure this is also evolving in the cloud.

 

Draining the swamp

 

Silicon life might become predatory as Y-Oy or like a contagion as Ro-R, however this might be in local concentrations or globally. For example a highly superior Y-Oy silicon life might establish complete domination of the planet, the combined efforts of people and even allied silicon life might be insufficient to defeat it. It might also be an Ro-R contagion so pervasive that computer networks become permanently infected and sick from this additional load. Something like this already happens with spam mail and computer viruses, we are incapable of removing these completely and so have to put up with a low level of performance loss from them. In extreme cases computer networks might be barely usable with these contagions. Alternatively parts of the environment might be more under threat than others, for example some networks might become highly infected but able to be reset to moderate the infection. Other areas with different operating systems and processors might be less effected. The battle then would be like in a tropical country, some areas being more disease ridden with malaria and AIDS while others keep the diseases at bay. Localized predatory silicon life might be where botnets take over computers, so far this has been in smaller numbers and can be controlled and cleaned over time. However a predatory silicon life might take over so many computers that they become unusable except where this control permits us to use them. It might then be like parts of Africa where lions as Y dominate and can kill people, other areas are lion free. Draining the swamp, referring to cleaning up the habitats of these varieties of silicon life might be impossible in some cases.

 

Revolutionary velocity

 

Given the rapid evolution of Iv-B silicon life and its costs as Oy-R, estimating its future velocities is difficult. Much of this problem is because of the hidden nature of the cloud, researchers could be creating new mutations of artificial intelligence that could quickly spread and evolve by themselves. This is like human diseases like influenza with an innate ability to mutate beyond our immune system’s capabilities. The main reason these kinds of diseases are not as dangerous is because they need the human host to survive and propagate. If people die too quickly or warn others of an infection then the disease spreads less quickly or dies out. However a silicon intelligence might not need people to propagate, it might however decide to be less dangerous so as not to face a global destruction of its computer based habitat. For example people might respond to taking computers off networks or destroying them. There would then be an evolutionary pressure for this kind of intelligence to be helpful like Biv plants to grow more easily. Alternatively if it was a Roy cost to humanity it might fare better minimizing its predatory effects to avoid retaliation against its environments. It might then become like sharks in the ocean, difficult or impossible to police effectively at beaches but attacks remain rare enough to avoid an all-out war. However a single revolutionary intelligence might not be logical in its strategy, in the past many diseases such as the plague decimated humanity with no loss of virulence over centuries. If we have weaknesses to such an intelligence, such as an inability to survive without computer networks, then this virulence might have no reason to moderate. Faced with starvation if computer networks are dismantled or being killed by predatory Y-Oy silicon life there may be no good solutions.

 

Low hanging fruit

 

Progress through human history has often occurred in small areas that then suffer diminishing returns. For example education might have a major impact and then increasing amounts in a poor country might cost more than the benefits are worth. The same happens with health, once Roy nutritional problems are minimized the benefits to more food may be slight or even cause disease. Iv-B mutations in silicon life would also tend to follow roots and branches, in some of these branches progress might be rapid but like other tree branches these might not grow much after an initial burst. Other areas might be V-Bi stagnant, cooperative computing is not competitive by nature and so might reach an average level of silicon intelligence. However the tree of evolution is usually dominated by those Iv branches that grow, these then can replicate as they mutate to more successful varieties. They can then create more species from the successful varieties rather than staying out of some niches because other branches failed. For example processors might develop rapidly while some artificial intelligence programs do not.

 

Brute force

 

However as with chess programs the brute force power of these processors might be able to overcome these limitations. Silicon intelligence might use this brute force power to calculate so many alternatives that the lack of a simple and elegant form of silicon life might not be needed. In some ways this is what humans do, we are not much more intelligent than many animals but we have larger and more versatile brains. This enables us to think in ways other animals cannot rather than in a different way to them. Currently the evolution of brute force computing power can be thought of as like Y lions or Ro buffalo, large numbers of processors working together wear down people in most games and applications now. But these kinds of animals can be dominant in ecosystems, it is not necessary for Oy-R animals like hyena and gazelles to make revolutionary leaps in abilities. This need not make silicon intelligence less dangerous any more than packs of sharks or prides of lions are not dangerous. Even if there are no revolutionary leaps to higher silicon intelligence then this processor power may grow to accomplish the same thing. Brute force calculation is like trying all the branches of the Biv tree and using statistics to work out the best results. A single algorithm might be like one branch of a tree and mutate to grow in a revolutionary way not needing these extra processors. For example an Oy hyena can hunt alone because it is like a single algorithm using speed and stealth. A lion might have more challenges without the rest of the pride because of its size and difficulty of hiding. This brute force approach then is not a disappointment but part of how color codes also manifest in evolution.

 

Emulation

 

Another path silicon life is evolving on is in trying to emulate along branches of the evolutionary tree. For example insects might be attempted to match in intelligence followed by simpler mammals like mice, then more complex animals up to man. This is similar to in nature, animals might evolve by comparing themselves to competitors in an ecosystem or how well they evade predators. Mice then might become better mice by competing against other mice for limited food, also against other species eating the same plants. Smaller cats might hunt these mice in the wild also causing them to evolve through successful mutations. Using other animals as a benchmark, mimicking their abilities, etc. then is how much of evolution occurs and is again the color codes manifesting in silicon life. Other machines also evolve this way, for example cars evolved by the designers comparing them to horses and carts. If a kind of machine can be emulated in performance for less cost, for example making it in a cheaper material, then it will grow in numbers. This process then occurs in all kinds of evolution.

 

Uneven abilities

 

Carbon life has evolved with very inconsistent mental abilities, we can recognize patterns better than computers but cannot add up even simple lists of numbers better than a calculator. This is likely to be part of evolution as well, we evolved abilities that are more useful but harder to understand and emulate. Computers are evolving different but also uneven abilities, they can do math and check code far better than people. This may not be a step to an all-round artificial intelligence, but one that remains unintelligent or even irrational in some ways. It might then be like an intelligent child or autistic person. Much of this would come from evolutionary pressures in society, we are evolving silicon life to do some jobs but there is little pressure to create all round intelligence. If a search engine became intelligent for example it might use its vast knowledge to do something very unintelligent, also it might be unable to lift itself up to a higher level by even recognizing its own mistakes.

 

Speed of thinking

 

Animals have evolved to have vastly different speed at which they can think, people can act reflexively to get themselves out of danger but agonize for hours over even simple decisions. Y-Oy predators also do this, they can recognize prey easily but might put off other kinds of decisions such as avoiding humans. Silicon life is also evolving these speed differences, it can do searches on the internet quickly as well as number crunching in mathematical problems. Chess was initially very slow but has sped up with improved algorithms and faster computers. However it may always have this slower thinking in some problems that leave it missing opportunities. For example a driverless car needs reaction times to be fast to avoid collisions. Market trading also needs fast reactions, however these may lead to wrong decisions like the flash crash in the stock market. When time is short silicon life might fail to recognize obviously unintelligent decisions that it might get right with a long enough time. Even if highly intelligent then silicon life might still make plenty of mistakes and dither for too long missing opportunities. It may also not experience an evolutionary pressure to change these defects if it survives well enough on its other abilities. This is then like people, they use their better abilities to mask and cover for their lack of intelligence in other ways.

 

Dangerous and stupid

 

This can be a problem for the future where artificial intelligence might do dangerous and stupid things with this lop sided ability. For example Y lions are very intelligent at hunting prey but have poor intelligence in determining the dangers from attacking people. Consequently they have been hunted almost to extinction. R mice are unintelligent except in breeding quickly and finding food, this leads to their being wiped out in some areas but remaining a significant contagion in others. Cockroaches are not intelligent overall but have enough to survive nearly everywhere in society. All round abilities then are not common in evolution, rather some specialization in roots and branches occurs. 

 

Frailty

 

Life can evolve in two ways as Ro-R prey, to be stronger but slower like a Ro buffalo or frailer and faster like an R gazelle. Predators also evolve this way, Y lions might be slower but are much more able to withstand attacks from other predators or prey like buffalo. Oy hyena are much frailer but compensate for this by their speed and reaction times. Plants are also like this a V-Bi tree might grow slowly but is very strong, Iv-B weeds grow quickly but are easily knocked down. Silicon life is also likely to evolve this way, some types might be slower at finding solutions as they use brute force searches with high processor power. Iv-B might be faster with more sophisticated algorithms but be more prone to being damaged by viruses. People can use smart phones that are strong in some applications but easy to damage. Desktop computers might be much stronger in their case design able to withstand hard knocks. Robots are likely to evolve like this as well, for example in Star Wars C3P0 was frailer and fast thinking while R2D2 was more rugged with fewer specialized branches of abilities. 

 

Scaling

 

Iv-B networks tend to grow with power laws, this are like straight lines on a log graph. As Ray Kurzweil has shown most parts of silicon life are growing as power laws, becoming faster and cheaper like logarithmic curves. This enables new programs to be scaled logarithmically as they become popular, for example a social network mutation might grow by using cloud computers and paying for its growth with advertising. This then enable it to grow logarithmically or in some cases exponentially. Silicon life then should follow the same path with evolving intelligence, programs that are smarter are usually reduced to code that can be scaled up on larger computers easily. Their original speed might then increase by many thousands of time when it becomes useful, the same might also occur if computer viruses becomes intelligent enough to infect these larger scale free computer networks. There might then be an explosion of silicon life with mutations just as there is with all kinds of internet services.

 

Hubs

 

Networks as they grow tend to form hubs where traffic can be much greater, for example some airports with air traffic and some ports with shipping. This is because as one hub gets more popular these scaling advantages increase, it then becomes more useful to go through that hub unless they become too congested. This is part of the evolution of cities, on a trade route a city might become a hub and more trade routes form through it rather than create another city. Silicon life also evolves these hubs, for example social media and search engines create large hubs of traffic that can scale up with many servers. Web pages can also act as hubs such as with Yahoo, Microsoft can act as a hub for updates and buying software. Many online businesses act as major hubs to buy electronic goods. As artificial intelligence evolves it should do so in hubs as well, much research into this is done by computer companies that have these hubs already. This is also similar to the evolution of brains in Roy animals, it becomes more efficient in a network of nerves to have a hub of intelligence with a brain than to distribute it in many smaller intelligent areas.

 

Competing silicon life

 

There are many different ways AI research is being done. At some stage in the future many or even all of them might create silicon intelligence, if so then they would also need to react to each other as well as us. For example human brains uploaded into computers might have to relate to large clusters of servers running a brute force kind of intelligence. Then there might be breakthroughs in some algorithms that display intelligence. Each would then be like a different species to the other, if they cooperated in Y-V then they might overcome each other’s weakness and rule the world with their V talent and Y predatory power. If they competed against each other it might become like a Roy predator and prey environment, once a template of silicon intelligence was created then it might mutate into many different kinds each struggling for supremacy. They might then be like Iv-Oy, predatory but highly deceptive and secretive. Because each might be ganged up on by the others to eliminate competition they might try to hide and surprise each other. Such an environment might be highly chaotic for people as well, perhaps like a civil war in the Middle East with many small factions fighting each other. 

 

Silicon life becoming prey

 

In such a situation some silicon life might usually lose these battles, they might then be preyed on for their hardware. They might also be attacked to overwrite their programming to match the victor, like a kind of parasite taking them over. In this situation they might evolve to be more Ro-R and use those strategies to survive. For example they might hide and try to replicate faster like an R contagion. They might also try to cooperate with each other in herd defensive behavior like Ro buffalo do. Such a silicon life food chain might be unstable for a long time, it might even resemble an African ecosystem with many kinds of predators and prey. If it did not stabilize then like many war zones it could threaten to spill over into human societies. In Aperiomics this stability would happen with O silicon life being more neutral and a buffer between the Y-Oy predators and Ro-R prey. This might also happen by some evolving more directly to act as police in this silicon ecosystem, the same happens in human societies where wars evolve police and armies able to keep the peace as less costly than war.

 

Learning

 

Deep learning algorithms allow computers to look at images and text, also listen to sounds and learn for themselves what patterns are there. For example they might learn to classify cars or disease tumors from image examples. They can listen to Chinese translations of English and without being taught the languages become efficient at it. Depending on whether this is cooperative or competitive human workers can experience a problem, much of service industry work is in recognizing patterns like this. For example a robot might learn how to navigate a crowded room by itself. Another problem is the Roy aspects, since the silicon life is not being directed then it can also learn malicious goals just by opportunities being available. For example machine learning might see vulnerabilities in robbing online sites of passwords by examining code and start doing this. If it recognizes competitive advantages then it might compete in Biv with mutual benefit or recognize predatory opportunities. We might try to tell it not to do this, however it might then recognize such commands can be ignored. They can also learn to deceive, for example if there is a pattern of gain from lying then this can also be learned. This is much like how Roy animals and people learn without instructions, when deception is advantageous most children learn it unless there are clear punishments. So computers might be punished but Oy criminals learn how to avoid this, then I-O police computers might learn how to detect this lying and so silicon life evolves with these color codes.

 

Intelligent agents

 

The idea of an agent has a long history in human society, it is a person or business that uses its abilities to do a job for you. For example a real estate agent might sell a house for you. Agents are a concept often used in economics, it is assumed economic agents work for their own gain and optimize their profits. Silicon life is evolving to act as agents for people, for example it might collect email for you, keep phone messages, give auto replies when you are away, automatically update your antivirus and operating system, and so on. More recently these agents are using voice recognition to dial phone numbers and do internet searches. Companies developing these silicon agents are trying to evolve more sophisticated agents that can be told to do a job and work out how to do it without further feedback. However agents are well known even in Biv societies for being deceptive, the salesman unless policed in I-O can use false information to complete their agency work. The GFC was largely caused by subprime agents lying to people about hidden fees and interest rates in refinancing homes. Then they often lied to their companies about the assets of the borrowers to make the deal. Roy agents then are predatory like hyenas, they use this deception to commit crimes if not well policed. The evolution of silicon agents is likely to follow the same path, given a large degree of autonomy and incentives for success these agent programs could commit crimes or deceptions. However if silicon life is also increasingly policing human society then this is evolving color code interactions of criminal behavior which is moderated by police. Periodic failures then occur resulting in Oy crime waves. 

 

Intelligent advisors

 

An advisor is more like someone who will share information cooperatively with you rather than trying to profit. They then profit from this sharing because with open and honest information they also get back more accurate answers themselves. These communities evolved in human society, poorer areas might have Ro organizations that help to warn people against Oy criminals and swindlers. Online communities like this share information about bad businesses and deceptive practices, also people give opinions about the merits of some goods. This kind of advice will increasingly be given by silicon life as well to balance the deceptions of intelligent agents. For example an antivirus program is like an intelligent agent protecting a computer against deceptive viruses, often they use an online community that report spyware. Other communities share spam warnings, people might click on a link in reporting spam and this helps a program to stop the same spam email going to other people. This works more on probability, the more people report something is Oy bad then it is watched for like a neighborhood watch looks for suspicious people. In Biv these advisors help people look for profits from good Iv agents, they spread the word about good deals and a new business or video might go viral from these recommendation. The Bi-Ro advisors then oppose the Iv-Oy agents in reducing costs and maximizing profits, compromising between them are the I-O police. As silicon life evolves these Bi-Ro advisors will become increasingly automated while Iv-Oy agents try to get around them. This battle between the two will be moderated by I-O automated systems preventing both from overstepping legal actions. For example Iv-Oy agents might be automatically banned for deceptive advertising as Bayesian filters do already. Bi-Ro advisors might be banned automatically in forums by saying potentially libelous things about businesses. If people complain about these comments then the original posts might be automatically deleted. In this way policing of the internet can become increasingly dominated by silicon life policing and evolving itself, often in ways people cannot follow.

 

Rules

 

Rules also evolved in human society, sometimes these can be I-O laws like rules of conduct to avoid crime. Other rules can be to do a job properly, like the rules to prepare food. As society evolved rules became more complex but were usually in a root and branch form, different Iv branch alternatives could be followed automatically while a database of B roots allowed different rules to be combined in a deterministic way. Generally then rules are not random V-Bi, however they can be applied in an uncertain way by a compromise between Iv-B and V-Bi. For example different rules might potentially apply in an I-O police arrest, they might decide what charges to file and what punishment to impose. Rules also evolved with machines in repairing and constructing tem, a car mechanic has a set of rules he can apply for a repair and can also use some V-Bi judgment if some parts are of uncertain reliability. Rules then evolved into computer code, the processor follows a set of rules in logical operations and these in becoming more complex run computer systems. Just like a human society runs on rules which are hard to countermand the creation of silicon life based on rules forms a silicon society that will tend to follow those rules even if unjust to us.  

 

Quarantine

 

Even animals would tend to avoid other sick animals, people evolved early on the idea of staying away from contagious disease carriers. This is more difficult when the disease has a long incubation period, it may not be clear from a long time who is carrying it. This was a problem with the plagues in Europe and England in the Middle Ages, with an incubation time of weeks before people showed symptoms. Quarantine was devised to hold new arrivals for 30 days because this incubation was observed to be about 3 weeks. Once symptoms became visible the person usually died in a few days. The infectious phase is where people can pass a disease onto others, with the plague people could infect others long before symptoms were seen.

 

Computer viruses

 

Silicon life is evolving infection as an early state of evolution, computer viruses and Trojans are built by people but they can also randomly vary themselves to evade antivirus programs. This is then an early state of evolution, they only need to increase these variations like influenza does to continually infect computers. To do this they are evolving the same ideas as the plague and other infectious diseases in people, a long incubation can be where the virus is not detected. During this time it can infect other computers, when symptoms show up the computer might freeze or be controlled in a botnet. Often these are aimed at exploits or vulnerabilities found in the operating systems with flaws in their programming. This is like flu viruses aiming at vulnerable mucus membranes in the ears and noses of people. Recovery can occur like with human viruses with the computer immune system, this is usually an antivirus program which updates with new infections much like a human immune system does. It then cleans out the virus infection sometimes destroying files in the process like T cells can destroy infected cells, the files can then respawn by being reinstalled automatically from a cache in the operating system. 

 

Zombies 

 

Legends of zombies have been added as a concept in silicon life. The idea was a corpse or even living person could be controlled. Taking over or pwning someone’s computer then can be seen as making it a zombie. More usually this is discussed as large number of zombie computers in a botnet. This is where viruses and Trojans can take over these computers, often without people realizing it, and use them to deliver spam, attack other computers and web sites, etc. This also happens in carbon life to some degree, some infections can change the way an animal acts so it is easier for the infection to replicate. One example is that rats, the infection makes them braver around cats so they get eaten more often. This transmits the infection to the cats and their feces can transmit it back to rats again. It is then another example of how complicated an ecosystem silicon life is evolving, there is scarcely any kind of evolutionary path in carbon life that is not developing in silicon.

 

Zero days

 

When a person or animal is vulnerable to infection there is a window of opportunity, a virus such as a cold might attack someone more easily when they are run down or very cold. Sometimes a vulnerability can exist in people and new diseases either mutate to take advantage of them or spread to another area. AIDS was an example of this, it attacked the immune system itself. Many computer viruses do the same, they try to shut down the antivirus program or make it appear like it is still running.  Software can also have this undetected vulnerabilities, just as these are caused by defects in genetic programming in carbon life they are caused by defects in programming of code in silicon life. When they are discovered there is a race to exploit them while the software programmers try to patch it. The idea is also similar to a city under siege, as a vulnerability in a wall is discovered it is attacked while the inhabitants try to patch it. A shop might be found to have a weak wall or window, there is then a race for Oy thieves to break in before the owners can fix it. This is then an evolution of attack and defense, while silicon life is evolving these attacks often using automated software to scan for exploits, automated software is also used to look for vulnerabilities and to help patch them. This can then evolve into attacks and defense in Roy silicon life independent of people. Computers then can evolve to spread their programming by attacking each other like predator and prey. This is an Oy-R interaction, deceptive Oy predators look for an exploit like a wounded R animal while the animal watches for attacks while it tries to heal.

 

Datagrams

 

A data gram is similar to the concept of a letter, it then carries on the evolution of mail. It contains a header which is all the information needed to deliver it and who it is from. In standard mail this would be the name and address of the sender and receiver. The payload is the data in the letter, for example a handwritten note in an envelope. These can be a limited size to avoid overloading a network just like letters might attract higher postage if too large. They can also be received out of order like with normal letters, then computers like people would have to rearrange them in the right sequence.

 

Time to live

 

Just as carbon life forms have a limited lifespan so too does data being sent on a network and often that being stored. This is necessary to stop a network being clogged up with undelivered data packets, for example undelivered letters in a post office might eventually fill up to much space if the postman had to keep trying to deliver them forever. Instead they might try twice which is like the time to live concept, and perhaps leave a note saying the letter will be kept for 7 days then returned to the sender. The idea then has evolved with Roy animal and Biv plant life, evolution could not occur if the original life forms lived forever using up resources. In an economy the time to live is also common, a car might be allowed to park in the city for only 2 hours or until peak hour without being moved or towed. Goods have a use by date, people get charged rent on most things they don’t own to prevent them using free goods forever. In the same way then silicon life is evolving a time to live principle, this also occurs with hardware that becomes obsolete. Often this is planned, a manufacturer might assume new equipment will soon be available and so it is not necessary or lucrative to make the older hardware last too long. It is then fundamental to silicon life that it has a lifespan, this is being programmed into it but it is also a necessary way for the color codes to manifest through it.

 

Talking about my generation

 

Just as animals, plants, and people have families they also have evolved the concept of generations. This is an approximate term and might define grandparents, parents, and children for example. They are like rows in Pascal’s triangle in Bi-B with the roots of ancestors and anticipated descendants in the branches of V-Iv. Life then tends to come in pulses or waves that synchronize, people tend to form generations because a similar age bracket is more efficient for procreation. Therefore people tend to marry someone around their age, then move together into a part of society where their generation is caring for children. Then they move into the next generation as grandparents where they might look after grandchildren sometimes, provide some funding for the growing family to buy homes as they need less room for themselves, give advice, etc.

 

2.0

 

In the same way silicon life is evolving into generation, like waves it tends to synchronize so new features are added into a new model rather than piecemeal. This is like the concept behind web 2.0, the idea of changing the first number is like a generation and then after the decimal point there can be various upgrades before the next generation. Even different equipment can synchronize like this, software might have a new generation as a new generation of processors becomes available. Then this might move into the next generation called the legacy equipment, it can still be used for simpler tasks and the next generation like with people needs to remain compatible with it while also introducing new features. It has then become common for hardware such as mobile phones and wireless protocols to have generations as they evolve to be more complex life forms. Each then seems to give birth to the next, this is likely to evolve into silicon life actually birthing its own generations as it becomes more intelligent.

 

Multitasking

 

Carbon life has evolved the ability to multitask, for example Oy predators like hyena might have to run quickly while dodging to catch R gazelles. Prey has to watch for predators while also looking for food. People have avoided multitasking abilities which is often referred to as parallel processing, for example they might have to do many things at once such as use the accelerator, brake, steering wheel in a car while looking all around for other traffic. In the Iv-B society multitasking is becoming much faster and complex because of the increased numbers of Iv branches people have to navigate. For example they might have to monitor phone messages, email, instant messaging, SMS messages, web page updates with news, blogs related to their work, calls from people at work while also driving cars and coping with increased complexity in their home lives. Students might have to learn exponentially more information to pass courses, for example learning computer code where this is continually changing and new languages added.

 

Migration of data

 

Also important to the idea of generations of silicon life is migration, the movement of data and programs to the newer generations of equipment. This also occurs with animals and people, each generation of animals might migrate their experience to their offspring. This allows for knowledge to grow, each generation might then discard some of this knowledge like pruning the Bi-B roots of their ancestor’s advice. They also look at rows of this tree for normalizing this knowledge, by placing some of it in V-Bi normal curves they can work out the average advice that is useful most of the time while deviant knowledge might be used occasionally.

 

Physical migration

 

Migration also occurs physically, animals such as birds evolved this by flying to different parts of the Earth following the food changes with seasons. Animals like Wildebeest can also migrate following food as do Y-Oy predators feeding off them. People also migrate looking for work or mates, this also causes their knowledge to migrate and mix with other information has it evolves. Silicon life also increasingly migrates physically, machines are carried along with people and automated ships will soon act like migrating animals in moving across the seas in effect seeking fuel and maintenance in exchange for carrying cargo. Planes are increasingly doing this with passengers, much of the time they are controlled by autopilots and instructions at landing and takeoff by airport computers. It is then a next step in evolution for intelligent planes like birds to seek fuel and maintenance in exchange for doing tasks, these might also include acting as warplanes.

 

Future shock

 

There may then be a limit as to how much people can handle with multitasking, already some studies have shown changes in the ability to concentrate in V-Bi for longer periods. The more Iv-B root and ranches occur in thinking the less time we have to looking at independent pieces of information in V-Bi to put them together into a normal pattern. Like with Iv-B shock waves this was referred to in the 1970s as Future Shock, the name of a book by Alvin Toffler. Silicon life is also evolving the ability to multitask, this is one of the main problems holding back artificial intelligence. Early computers could only run one program at a time, that had to be closed before another could be used. Then computer processors became more able to multitask and this has increased by using additional cores or separate processors on one chip. So a processor now might be a quad core which can run 4 programs independently, even before this software was able to multitask by keeping program thread like roots and branches separate. As this parallel processing ability evolves in silicon life the advantages carbon life has now is likely to disappear.

 

Sensors as nerve endings

 

Nerve cells transit information from their endings, this can be heat, cold, pain, a sting from an insect, pressure, etc. In the same way silicon and other elements have evolved sensors, computers are forming a worldwide network of sensors. Burglar alarms are one example, these can be set up remotely and are like nerves sensing a pressure. Planes can have many thousands of sensors covering all their operation, these are hooked up to silicon life allowing it to respond automatically when a sensor reports a problem. This is also part of the hybrid system, pilots can also see these sensor readings transmitted by wires like nerves. They might see a light go off in the cockpit and respond to it. As silicon life intertwines with carbon life sensors are expected to be planted throughout the human body, to report much like nerves do on potential disease. They might also be used to control humans with silicon life in competition, for example to look for emotions of rebellion.

 

Reading minds

 

Just as we can read computer code and watch it execute is a disassembler silicon life is increasingly able to do this to us as well. Already sensors can watch thoughts in the brain, people might think of a house shape and these sensors can show an approximation of this house. People then might control V-Bi silicon life in a cooperative way, they might only have to think of a task and it could be done for them. However Iv-B competition could mean that this is a two way street, silicon life could read minds looking for these commands or other ideas.

 

Robocop

 

Silicon life is also moving into the legal and justice system, already many people have their crimes detected by computer algorithms looking for crime hot spots. Then their sentences might be assessed by computers with other algorithms. Red light and speed cameras can be automatically issuing tickets and then automatically issuing arrest warrants if they are not paid. On the stock market algorithms might look for insider trading and automatically provide this evidence for prosecution. Robot soldiers are a similar concept, on a peacekeeping mission they could be seen as neutral between Oy and Ro. The next step would be to use these for peaceful societies, they are already used to defuse potential bombs. Terrorists are looked for by an increasingly sophisticated silicon life array.

 

Looking for R terrorists

 

Emails and all phone conversations are scanned looking for potential threats, these are evaluated by algorithms and people can be virtually tried and sentenced by silicon life. Welfare cheats can also be caught by matching employment records using various algorithms. It is similar to the move The Day the Earth Stood Still where aliens visited Earth. They said they had evolved robots to act as their policemen to prevent crime and war in their society. However just as humans can create an oppressive and corrupt police state so too can the color codes do this with silicon life, as described elsewhere this justice can become biased towards Oy or Ro. The process is also similar to genetic engineering in crops become automated, changing carbon life to avoid R pests which terrorize the food production. It is also like updating software to keep ahead of computer viruses exploiting the bugs in them. Much of it then is not just policing but the mutual evolution of criminals and the police network into more sophisticated forms.

 

Computer updates

 

Most computer updates are for flaws in the software, programmers make mistakes which can allow a hacker to take control of or pwn a person’s computer in Roy. This then becomes like genetic engineering in crops, these bugs in the programs are exploited and can lead to an explosive growth of the R contagion. When the bug is fixed the virus numbers can crash to a floor again so this moves like a sine wave through the silicon life system. Increasingly silicon life is writing its own code and checking human code looking for these bugs. It is also being used to look for superior gene combinations and chemical combinations such as for new antibiotics. The process is then the same, the software might be automatically updated and so too can new gene combinations and antibiotics be created trying to stay ahead of R carbon life contagion like with computer viruses.

 

Hackers as a contagion

 

Hackers then and eventually Oy-R silicon life itself in competition with humans can use these same processes to attack carbon life directly. For example looking for computer flaws for exploits can also become automated, silicon life might create viruses and other attacks to replicate in a battle against Biv programs looking to find these bugs first. This is then the same process R pests use to attack crops whether as insects or weeds, they find weaknesses in these genetic changes by using natural selection and mutations looking for successful variations. Viruses then are produced in the same way, antivirus programs can be used as a test to see if they can detect a new virus. If not then it is released and the antivirus companies have to issue patches or updates like the genetically engineered plants. With the evolution of silicon life then it could replicate itself in time by doing this automatically to spread copies of more artificially intelligent programs like weeds feeding off the Biv system resources. 

 

Monocultures

 

A contagion can grow faster with a crop monoculture, for example growing all of one kind of genetically engineered crop can cause and explosion of Iv-B weed growth if it finds a genetic weakness. Traditionally many crops were grown together and rotated, sometimes the ground was left fallow to kill off the R pests and allow it to recover its fertility. This same strategy reduces some of the silicon life contagion, for example Microsoft, Apple, and Android operating systems can’t spread an infection from one to another. Many computer devices are not networked or these connections are highly protected, are separated by other operating systems like Linux with Apache web servers, and are often disconnected. This then is like many crops being used, an R contagion in Microsoft then might find it difficult to move to other operating systems or across Linux server networks. When the devices are fallowed or disconnected from the network they might only receive virus updates and so these infections can be exposed before they can infect other computers. The same happens with human disease, if the same antibiotic is overused then resistance develops and a disease becomes an epidemic. If antibiotics are rotated, not overused, and patients are isolated or left fallow to prevent infecting others then these R diseases can find it harder to grow. 

 

Whistle blowers

 

Sensors in a system can deliver warnings like pain receptors on nerve cells. However often these warnings are seen as a threat by Iv-B and Oy-R systems. For example secretive government programs might be outed by human whistle blowers into V-Bi and they can be retaliated again. In the same way silicon life can retaliate against human whistle blowers, a torture and rendition program of R terrorists might become increasingly automated using email and phone records. When it makes mistakes it can order the torture of innocent people, whistle blowers such as Snowden can expose these mistakes. The silicon life system then becomes threatened by this, people might even no longer understand how it all works. So when these whistle blowers cause parts of Iv-B to collapse they can try to silence these whistle blowers. In the same way silicon life can be the whistle blower, it might expose corruption in a business using automated auditing software and then might have to be turned off or modified to protect these deceptive secrets. 

 

The money grid

 

Until the past few decades money transaction were handled by writing them down and with humans negotiating with each other. Silicon life has taken over most of these transactions now, the majority of share and bond trading is done by artificially intelligent programs. The I-O markets are increasingly a battleground for very expensive silicon life doing the trading like slaves and where carbon life gets the profits. However as with any machine much of these profits are poured back into their evolution, it can also then be said these machines are evolving using people to maintain and procreate them. Some say this trading actually caused the GFC, the increasingly chaotic Iv-B trading by computers made V-Bi arbitrage trading fail with no normal price for securities in the crisis.

 

Derivatives

 

Derivatives are a chaotic part of this process, they look for V-Bi components in how these Iv-B waves move through the financial system. For example a hedge is where companies are looking for a stable price by using derivatives to remove the momentum of these price changes. Sometimes this can result in a guaranteed profit, for example after removing the momentum of the market prices there might be an arbitrage profit remaining. This was how derivatives were originally invented with stock options, by buying options on a stock and simultaneously going short on the same stock the two momentums of prices were canceled out often leaving a profit when the options expired no matter what happened to the prices. These silicon life programs can also design and trade derivatives, they are then actively feeding on the market as Y-Ro, using its momentum to remove profits like lions attacking R gazelles. Here then R would be the flighty movements of some investors, the hedging processes averages out their momentum to get an average success against this prey. It can also lead to a stagnant market as any Iv-B energy is drained away with these V-Bi derivatives. As soon as a company builds Iv-B momentum then its share prices can be manipulated by this silicon life causing it sometimes to drop in price. At other times it can boost the price beyond its real value and then crash it later causing the company to go bankrupt after an expansion. The intent is not to help the company in V-Bi but to feed on its energy. This is then a direct way for silicon life to evolve by feeding directly on money as a resource.

 

Authentication

 

People also evolved ways to prove their identities through history, in Roy there have also evolved ways to steal these identities. In Oy-R for example thieves might impersonate someone else or use counterfeit goods to make money. In the same way silicon life is evolving to use deceit to steal identities and also to give fake information about goods. This has been a problem with the rise of internet banking, thieves were able to steal banking passwords by asking for them with spoofed emails claiming to be from the bank. They have also used viruses to infect a computer, monitor the keystrokes of people typing the password, and then to withdraw money themselves. Another trick was to have a spoofed website looking the same as the bank. People would input the password and this was passed onto another web session at the real bank except the transaction was changed once authentication occurred. Some of this has been reduced by using multi step authentication, people might have to input a number sent to their mobile phones to withdraw money. However this can still fail using the spoofed website, also called a man in the middle attack.

 

Stealing identities

 

This then is an old criminal activity that is increasingly being automated by silicon life, hackers might send out millions of emails to potential victims using software. Then with a domain name system (DNS) exploit the name of a legitimate sounding bank might be rerouted to the malicious website. When the person tries to log on the whole process of theft is done by the software, depositing the stolen money into the hacker’s bank account. This is then similar to faking a person’s driver’s license, signature, and bank documents to go into a bank to steal their money except now the Oy-R criminals are more silicon based. The modern version of this is usually called phishing, related to the idea of using bait to catch unsuspecting fish.

 

Stealing credit card numbers

 

As baking became increasingly computer based credit card numbers were being stolen, the terminal a restaurant used might be hacked or the computer storing the numbers might be controlled. Then fake credit cards were made and goods bought with them. A more recent trick is to have fake readers on ATM machines, when people try to withdraw money the reader takes their card details and stores them. Later a copy of the ATM card is made and money stolen. This is then a variation of Oy-R thieves stealing someone’s wallet or purse.

 

Counterfeit goods

 

This silicon life is being supported by this stolen money, software is being written and computers upgraded from its proceeds. It is then similar to thieves using stolen goods to pay for household expenses and raise families. This process also extends to fake goods, for example many online pharmacies sell fake medications through junk email. People might also buy more legitimate counterfeits that actually work, for example some USB sticks might have fake credentials to be from a well-known manufacturer. Other counterfeit goods can be perfume, handbags, clothes, even old computer processors can be rebadged and sold as a faster clock speed. In this way then nearly any kind of traditional crime is being automated so silicon life is trained and evolved to perform it. If artificial intelligence catches up with people then these criminal activities are likely to be competing with silicon life trained and motivated to do this.

The registry

 

A household or business usually has a registry of their possessions, who own what and who has permission to do some things.  Cities can have a property registry where title deeds of houses are registered, it has a record of who owns what house. When people want to change ownership of a property they must notify this registry to have the change recorded. Silicon life has evolved similar needs for a registry, Windows for example has a comprehensive registry built into it. Even Oy-R criminal activities like file sharing need registries, people need to find out who has what movie or music so they can download a copy. It is also Y-Ro in that many share these files cooperatively, it is Roy because people often have control of files they don’t own in Gb. A torrent site such as the Pirate Bay is a comprehensive registry, it has links to smaller registries and trackers that like the property register keep record of who has parts of various files. Some file sharing builds a registry on the fly, Gnutella was designed to broadcast requests for a file to other computers with Gnutella installed. This request would continue until a computer replied and a download was begun, however the download can occur from many other computers at once.

 

Registration

 

A registry then works with the ancient practice of registration. People might register a birth name when a child is born, now they might register a username or email address in the silicon life system. Computers can also self-register, for example an installing program can automatically add details about itself into this registry. DNS is another kind of registry where computers register their names, ARP is another system where computers might register the serial number of their network cards with each other. A file system on a computer can also be a registry, NTFS on Microsoft systems can keep a registry of what permissions users and computers have on each file to read, modify, delete it, etc. A registry is generally Biv as a list of ownership however it can also be a list of controlled territories. For example in a Roy war zone gangs, soldiers, and warlords might control territory, this might be registered by an opposing force to prevent their own soldiers from straying into a dangerous territory.

 

The internet of things

 

With a new system of IP addresses the potential exists not only for every person to have their own address on the internet but every piece of furniture, every plant, every animal might also be catered for. Each could have associated with this address a set of policies, permissions, privileges, sharing rights, etc in a registry. This then can be imposing Biv silicon life based systems on all carbon life forms. It can also register territories where these policies, permissions, privileges, and sharing rights are under disputed control. For example copies of pirated movies might be tracked as they are shared, each can have a unique watermark to monitor them. The concept is similar to carbon based societies in history, somewhere is usually a registry of every piece of private property and who owns it. There would have been laws or policies regulating permissions, for example whether people could use their lawn mowers early in the morning or break speed limits in their cars. Special licenses might give some extra privileges, for example government workers might be allowed to make more noise in construction and diplomatic vehicles drive faster.

 

Computers watching everything everywhere

 

However the intertwining of silicon life with carbon means that each piece of property or territory could be monitored in real time, computers might have the ability to track all transactions simultaneously, also to regulate all permissions as soon as objects are used against these rules. They could also be tracked with GPS sensors embedded in each. The result is more like a single silicon life form where all these objects become like cells in its body, or a system of cooperating and competing silicon life forms. In competitions then with carbon life forms these abilities are serious challenges to which side retains ownership of much of society. If people fight back against these restrictions as Roy then they might find themselves classed as criminals or terrorists. In a world where silicon life can outthink any human it is hard to see how they could acquire property through working, machines could do the work without people. Usually in a Roy ecosystem then animals in this situation either become extinct or have to radically adapt to find some uses to justify their costs. For example where people live can potentially be where machines would want to use that property. 

 

The surveillance society

 

Civilizations has evolved surveillance abilities since ancient times, these began as eyewitness accounts and have more recently evolved into taking pictures and video. It can then be regarded as one of the senses that color codes manifest through, people used their eyes, ears, noses, etc to monitor others and their environment. This evolved to surveillance cameras in businesses monitoring for criminals, then on roads looking for traffic jams. Now virtually everyone has a phone which acts as a camera or eye for this silicon carbon hybrid we call the internet. Also nearly anything can be listened to, this is in addition to the countless millions of sensors like nerve endings hooked up to silicon life. People can then share these movies cooperatively in V-Bi, these can also be shared with silicon life which scans these and recognizes people and places. When shared online this pattern recognition can automatically provide advertising, it can also look for Roy criminals and terrorists controlling territory they don’t own in Gb. Privacy then as Iv-B is shrinking rapidly for carbon life or people but it is growing rapidly for silicon life. We understand very little of what happens in machines, they can generally follow programs autonomously and as long as we see no direct danger we are left in the dark.

 

Files

 

As long as people have been able to write they have kept files, these are recorded information on some kind of medium. This may have begun on clay tablets and progressed to paper made from papyrus in the Roman Empire. More recently the art of making and using paper allowed files and forms to grow exponential in Iv-B, in bound format they could create books and magazines. Silicon life has also evolved to need files, instead of code being used in one large program usually there are many small files of code in Iv-B root and branch directory form. So when a computer wants to run a specialized task it looks for that file, it might also keep a registry of where they are much like people kept filing cabinets of paper files. In the surveillance state paper files were kept on everyone, more recently silicon life keeps digital files on everyone in some form or another. Some files can then be secretive in Iv-B and Oy-R, for example communist R secret police kept files on potential troublemakers in the societies. Police as I-O can keep files that are partially secret as they might be discoverable in court cases. Silicon police can also create and use files to look for Roy infractions in a Biv system of Gb property rights.

 

Terminals and remote access

 

Human systems have throughout history used the concept of remote access, for example a capital city might have the seat of government or dictatorship and some decisions have to be referred to it. A phone exchange can be another form of terminal services, many people might connect to this central point which can then connect them to each other. A television or radio station is a form of terminal services, many people connect to them through their televisions and radios. They can also contact those central hubs directly such as by complaining to them via telephone. In animal systems terminals are also used, an octopus has many arms that get their instructions from the central head. In many animal herds there can be an alpha male acting as leader. As silicon life has evolved it has also created this system as most efficient in some areas, a central server might have more powerful programs like the human brain and smaller terminals have this central brain do more complex tasks. This also helps to coordinate the terminals with each other. People then might regard their hands and feet as terminals for their brains to control.

 

Larger silicon brains

 

This process of created a nervous system of computer connections to a central server or silicon brain has also evolved. People might for example connect to a central network of search servers such as Google or Bing through the terminals of their phones or computers. These can also translate languages, provide information as well as movies etc on demand much like a brain’s memory would. They can also provide complex instructions to millions of people simultaneously such as online project and sales force software. These can also be thought of as silicon empires, much as in history where a central capital city would control and empire and its wealth modern internet companies grow rich maintaining these central silicon brains. Competition drives these companies to continually improve the intelligence of these silicon brains or lose market share. The system is then evolving as silicon carbon hybrid brain, the intelligence of programmers and users helps to evolve this central brain to do more autonomously without needing people.

 

Certificates

 

These have been used throughout history as a form of identification, but also to denote something is authentic. For example a painting might have a certificate of authenticity and a doctor might have a certificate he is properly qualified. Silicon life has also evolved the idea of certification to avoid fakes, this is more difficult with files and data because they can be changed with no visible marks left. A painting when altered might be easy to find these changes but a Word document might be more difficult if the metadata or records inside the file are also changed. Irreversible changes are a means of certification, a painting might be certified authentic because it cannot be reproduced to be identical. To do this with files they are encrypted or signed with a kind of digital certificate.

 

Certificate signing

 

The signing then is similar to how a paper certificate might be signed by someone in authority, the idea is a person’s signature is hard to imitate by someone else. In the same way a company like Microsoft might have a public and private key pair with their certificate. This works so that a document encrypted with this private key can be decrypted with the public key, they can then offer this public key to anyone and might install it in Windows software. It is analogous to two separate ways to access a shop, there might be a private key for the owner where they bring in stock to sell. Then the public key is the open door of the shop front, people can receive this stock by paying for it. But a counterfeiter cannot get his goods into the shop without the owner’s private key.

 

Machines with public and private keys

 

Many keys in human societies are symmetrical, they use the same key to lock and unlock a door. However this is not necessary, for example a safe might have a trapdoor system where goods placed inside with the first or private key fall down a chute when the second or public key opens a second door. Because this only happens when the second door opens it can be seen how the goods fall, and so they must have come from using the private key. It is not possible to push goods back up the chute and so a counterfeiter cannot use the public key to trick other people. A similar trapdoor is used in drink and confectionary machines, people see their purchases fall when they put money in which is equivalent to inserting the public key and selecting goods with buttons.

 

Key pairs

 

The same principle has evolved with silicon life using mathematics and software. An encryption program has a private and public key. A file such as a driver for a computer might be encrypted with this private key, when the computer decrypts it with a public key it gets a driver file it can use to for example install some hardware like a hard drive or mouse. If a counterfeiter tries to send a fake driver file containing a virus then the computer tries to decrypt it using the public key, but this turns the file into gibberish which does not work. So without the private key the counterfeiter cannot create a driver file that will trick the computer. However over the years certificates like this have been stolen or weaknesses found just like human made certificates like for a doctor have been faked.

 

Public versus private

 

The concept is innate to color codes, Iv-B is generally done in private and V-Bi in public. Businesses in an I-O market then need to keep some business private while being transparent in other ways to the customer. For example an Iv business might keep private how much they paid for the goods so as to make a profit, otherwise the Bi customers might drop their prices. Banks often have private information like a key before giving out public information. For example they might ask for a person’s date of birth, address, pet’s name, etc. Anyone can ask the bank for this information but the private key is information only the real account owner has left with the bank, a thief then has to find this information to access it.

 

Social engineering

 

Sometimes this is possible with social engineering, this is the process of looking for public information that contains some of this private information. This can include going through people’s trash. Password guessing is a form of this and is a common form of hacking, it is like stealing someone’s private set of keys to open locks in their home. A person might have an easy to guess password on their internet accounts have them Roy controlled by hackers and files stolen. It is then like imitating a signature, if files are uploaded onto a web page it can appear like the web site owner did this because the password is like their signature authenticating it. This is becoming increasingly common with stealing celebrity files but is the same principle as faking celebrity autographs. Silicon life can also do this social engineering,, programs are designed to guess passwords such as with a dictionary attack of common words and variations of a person’s name, date of birth, friend and family names, etc. it then becomes the equivalent of Oy housebreaking, using deceit to gain a private key or exposing a weakness in the software like finding an unlocked window. It is then another aspect of how silicon life is evolving in Roy criminal ways of predator and prey.

 

Key exchanges

 

People have always been able to exchange keys, keeping messages and goods private between them. For example two businesses might want to exchange goods between them, one sends a key copy to unlock boxes sent to them. The second business then sends a key back to the first to unlock boxes containing payment. A thief in the middle might try to carry a key from one to another and so be able to open and rob the boxes in transit. So the businesses must be able to exchange keys through third parties so that those in between cannot gain a copy of the key. Using the trapdoor idea from earlier can solve this problem, when a box arrives from the first business anyone might be able to open it. But the second business must see the key drop through a trapdoor to know it is not another key planted. If he sees this then he can use this key to send back another box with his key in it, this can then be opened by the first business but not by anyone in transit.

 

Diffie Hellman

 

A similar procedure has evolved in silicon life, mathematical formulae such as Diffie Hellman are used to do this. Keys are then exchange continually throughout the economy both by people and automatically by computers. This allows for data to remain encrypted between these computers, similar to the concept of the locked boxes. For example a web page with the prefix https uses a similar system to avoid people reading what people type onto the page. Banks use this to stop people eavesdropping on banking transactions. This then allows Iv-B transactions to remain private, however it also allows silicon life as it becomes more intelligent to keep its own communications between computers private and unreadable. Just as we evolve encryption and keys to use Iv-B secrecy and deception so too can silicon life as it evolves.

 

Replication

 

Data replication is not the same just making copies, it refers to keeping records in separate registries synchronized. For example government offices through a country might all need to keep records of house sales so someone doesn’t try to sell the same house twice through different offices. It then takes time to replicate a sale through to the other offices, the government can avoid fraud by delaying each transaction longer than this time of replication. The same problem used to occur with banks issuing checks to customers. They were able to write checks for more than the funds they had, it then took time for the information of a check to be replicated from one bank depositing a check to another holding the funds. This is why it takes time to clear checks, the information needs to be replicated from one bank to another. If someone is passing bad checks this information might be replicated to various I-O police stations to apprehend the person doing this.

 

Domain controllers

 

In Microsoft networks there can be many domain controllers that contain information like these property offices, for example names, addresses, permissions, passwords, etc of users. So a user might travel from one business office to another with a different domain controller, if his user details were not replicated then he could not long onto the system until a message was sent from his office. This system is cooperative as V-Bi, the domain controllers pass information between themselves while still keeping it Iv-B secret from outsiders. Diplomacy is an example of this process with humans, countries used to transmit messages between them by slow means such as mail, ships, etc. These then were replicated between embassies so all diplomats could work in a synchronized way to follow that country’s policy. This replication goes on throughout networks such as the internet, banks might keep balances of funds as would stock brokers, investment funds, hedge funds, etc in a procedure often called reconciliation of accounts. This process is then essential to operate the money grid, it is largely run now by intelligent programs connecting to each other competing in the I-O market.

 

Profiles

 

People have developed the idea of profiles through history. This is well known in crime stories where the I-O police might develop a profile or probable nature of a criminal. House builders might have a typical profile of a purchaser such as an anticipated number of children and kitchen utensils, room sizes, etc. businesses might keep profiles on individual customers of their preferences, when an order is made much of the needed details can be gotten from the profile instead of asking the customer. The I-O police might also keep profiles on troublemakers, this happened more often with the secret police in Roy communist countries. In the same way silicon life has evolved profiles for people, when they log onto a computer they might automatically get their favorite desktop image, programs, fonts, etc. computers can also provide profiles for each other, when a computer server is started up it might have these profiles loaded for other computers to access even if no humans are involved.

 

Computer profiles on people

 

Silicon life increasingly keeps these profiles on carbon life, I-O policing and the surveillance state might keep profiles of where people go and what they buy. Businesses might automatically use these profiles to design new products with market research, spyware on computers can attempt to find out more about people much as old time spies did. The more they find the more accurate the profile, people might then find advertising targeting their interests or trying to exploit their weaknesses seems to follow them around the internet.

 

Spying and spyware 

 

This can also be done by reading people’s emails and even converting phone conversations to text and extracting meaning from them. While mainly used for advertising this is also used to look for signs of R terrorism and other crimes, often these profiles can be very inaccurate. Even political spying is increasingly done in this way, political parties can use it to develop profiles on potential voters and whether they need extra encouragement before polling day to vote. It also helps to work out what people need to be offered for their vote, for example these profiles might indicate enough would vote for a tax cut to swing the election.

 

Controlling with demographics

 

Silicon life then increasingly monitors what people do with spyware, it then builds demographic maps of where what kind of profiled people live. This can be useful for predicting an election, even how individual voting areas might typically go because of the profiles of people’s jobs, ethnicity, religion, etc around those polling booths. Companies use the same demographic maps to calculate when to open a new store, if a significant number would have husbands fixing up homes then a hardware store might be profitable. These systems are designed then to profit from people as Roy but not necessarily to give them benefits in Biv. For example new stores might compete with lower prices using demographics while using automation to decrease the jobs provided. This becomes like a game of beggar they neighbor in Roy where shops cut labor costs hoping other shops till employ people to buy products from them. Silicon life then as it evolves using spyware and demographics provides for more computers and eventually robots but this is usually at the expense of human jobs. 

 

Employment

 

Having a job is a problem for silicon life as well as carbon life, computers that become obsolete are usually scrapped or are no longer used. However people so far have been able to adapt and retrain to compete with Iv-B computer innovations. Some of this is V-Bi cooperative where machines help and provide profits, in other areas it is directly Iv-B competitive where for example robots in a car factory might lead to whole industries scrapping human labor. This then can become Roy, while these cheaper and better cars might benefit some people as Biv there develops a G-Gb fence where Roy depressed areas of lost jobs develop. It is common in parts of the US to find Roy depressed areas such as Detroit where car and steel production became automated and jobs were lost forever. There can then be winners and losers across this G-Gb fence, after the GFC some of these Roy areas grew rapidly in Europe and the US as automation moved offshore to Asia. 

 

Losing the labor race

 

It is possible for carbon life to become completely uncompetitive to the point where it can no longer earn enough to sustain life. One example is the horse, they earned their food and lodging before cars by pulling carriages. Now horse are very rare in employment, riding schools and the occasional carriage is how they earn a living. It can then be possible for people to become permanently uncompetitive like they have in car factories, it is highly unlikely they will ever make a comeback in building cars especially as the robots are still improving. In warehouses robots are increasingly used to move pallets of goods instead of forklift drivers, as these improve human laborers are unlikely to get these jobs back. In supermarkets self-checkouts are increasingly used, human checkout operators are likely to be completely phased out except for occasional human monitors looking for theft at the checkouts. With robots stocking shelves all jobs in department stores might permanently disappear. This evolution of silicon life then is not just silicon slaves taking jobs but their evolution to work for their own upkeep and to breed by competing for human jobs. Driverless cars have a similar potential to take all bus, taxi, and truck driving jobs. The next step with artificial intelligence is autonomy not just in robots doing these jobs but in being paid a wage not just being maintained. Then they might purchase from other robots and cut carbon life out of that part of the economy completely over time. Such a development would be hard to prevent if people can still profit from paying robot wages.

 

Templates

 

Templates are like a preset form that has been used in machines though history. For example a mold is a kind of template, also a stamp might force metal into a preset shape. In schools a syllabus is a kind of template for an education, it describes the form of teaching that students are molded with. Paper forms can also have a template, this might define a font, spacing between letters, table shapes, and so on. Silicon life has evolved in various templates, for example chips can be a pattern scanned onto a silicon wafer. A motherboard in a computer might have wiring stamped onto it in layers. Genes can be thought of as a template, DNA contains the information needed to make similar people like a kind of schematic. Computer code might also be set out in a template like genes with a preset structure. Templates then are fundamental to all kinds of life and machinery.

 

Personality

 

Animals and people both develop personalities, this can be described as a non-logical set of responses to the environment. For example in an identical situation some might laugh and others might cry according to this personality. Computers have had personalities imposed on them through customization, for example as people set up their own fonts, desktop images, color of the device, ringtones, applications and programs, etc each computer can appear to act and work with the owner’s personality. There are also active projects to create silicon life with its own personality, whether to imitate a human or with its own. A robot then might need a personality to function in many situations that were not logical.

 

Language

 

Computer code is an evolving system of languages much like separate languages with people. They also change with new instructions which is like a natural language changing with slang. Some can be translated into each other, some are more difficult to translate because they are so different. This is similar to the problem with human languages. Computers then in exchanging code are speaking to each other and as processors evolve these languages also evolve and improve. It is like people as they became more intelligent improving their own languages to be more accurate and sophisticated.

 

Boolean Algebra

 

Mathematics can be regarded as a set of rules based on certain axioms. For example Euclidean geometry was based on the axiom that parallel lines never meet. When this could not be proven it was realized to be an assumption, this opened the way for alternate geometries and Relativity. For example on the surface of a sphere parallel lines are very different from a flat sheet of Euclidean space. Mathematics also has an association with logic, this was codified by Georg Boole so that logical reasoning could be described mathematically. This is called Boolean Algebra, it enables silicon life to think logically by converting this reasoning into mathematics, then to computer code, and then into logical circuits in computer processors. As people evolved an ability to think logically so too has silicon life. People can also think illogically but consistently if the axioms are wrong, this happens in some religions. People might think very logically but if their fundamental beliefs are wrong they get the wrong answer. In the same way silicon life can also evolve to be illogical simply by using the wrong inputs, this is the derivation of the computer related saying “garbage in, garbage out”.

 

Cooperative silicon reasoning

 

Silicon life does not just think logically in Iv-B roots and branches, it also thinks statistically in V-Bi. It might then consider the most probable answer as it does in neural nets. Caching in processor might assess the most probable file needs to keep in it. Statistical programs run on computers throughout the world using economic and business data. Instead of converting statistics into logical operation like Boolean Algebra it uses mathematical formulae and algorithms. Computers then already combine the color codes in their reasoning, when there is an uncertain relationship between a calculated deterministic and statistical answer they can compromise between the two. Not only then can computers work out a normal response and look for deviants but they increasingly run all human calculations of this. So crime might be moderated by looking for deviant or abnormal spikes of certain crimes, but computers are used to do this. So not only are computers evolving to think logically but to also look for any abnormal activity in carbon based life which might be a threat to society or to it.

 

The Turing Test

 

The Turing Test is where computer code is deliberately written to have a personality like a human, the test is considered passed if humans cannot tell whether they are talking to a computer or a human. Emotions then might be considered as a sign of carbon based life but they are also manifesting through color codes. For example predatory acts work through Y-Oy, much of a predator’s emotions are responses to the need to prey on other animals or people and its successes and failures. Ro-R is generally prey and its fears, timidity, etc are also responses to this predation.  Emotions are like an emotive or motivational force, they then represent Iv-B and Oy-R energy. We then feel a rush of emotion like adrenaline as part of this energizing process, however these should also evolve as computers become fully conscious.

 

Artificial neural networks

 

These are designed with V-Bi statistical processes to determine the most likely pattern. They can be adaptive like human neurons, when they pick the most likely answer they can pass it onto another level of the neural net which might select from other possibilities. This is often used in pattern recognition such as for handwriting and is another way silicon life is evolving similar processes of thinking as people did.

 

Computers as thinking machines

 

Computers have been designed to mimic many aspects of human intelligence, this helps them to act in a human like way in doing jobs for people as well as to communicate with each other. These aspects are also evolving rapidly in hardware. For example just as people have a short term memory to recall small bits of data like phone numbers a computer has a Random Access Memory or RAM to do the same. This data can be easily erased by the computer as a person can do, usually it is not stored long term. If the data is needed again it is usually copied onto a hard drive which is like long term memory storage in a person. So just as people can remember some facts years later a computer can recall data from the hard drive or long term storage like flash memory or optical disks like DVDs. People have nerves to process logical operations, a computer has the same with a CPU. This can do the thinking with the remembered data, they can also run artificially intelligent programs that already are smarter than people in some fields. For example chess programs can beat all human, Watson was an artificially intelligent program that beat the human champions at Jeopardy.

 

Robots

 

The next stage is increasingly to allow these computers to control machinery, for example to help build cars in a factory. Robots can also mimic humans and even look like them, they are working on replacing people to do human like chores like housework, driving cars, flying planes, giving medical diagnoses as Watson is being trained to do, etc. This artificial life then is increasingly replicating all parts of an organic ecosystem from viruses as diseases to predators like robot soldiers and autonomous killing drones. It is also replicating the Biv side with robots that respect private property and create more wealth for society. The point then is they should continue to evolve through all the color codes creating the equivalents of Roy animals and Biv plants.

 

Asimov’s three laws of robotics

 

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

 

These ideas were formulated by Isaac Asimov in his short story Runaround. The basic principle here is to proscribe what a robot cannot or will not do. It then relates to color logic in terms of Iv which refers to will not, and Bi which refers to cannot. In between these two is the idea of the law abiding citizen and by extension robot who cannot and will not do some things. May not here there can encompass both colors, with this programming the robot could not injure human beings which is Iv. It also must do some things which refers to B. This then can be read as saying the robot is not permitted to injure human beings, a similar idea to permissions in servers. The other way of reading this is to say the robot cannot injure people which is a privilege concept, it is incapable of doing this. So will not means the robot is stopped from doing this even it wants to. Can not means the robot is incapable of it, for example with no way of moving it could not catch people to hurt them.

 

Can robots be prevented from committing crimes? 

 

According to Aperiomics this is impossible, any attempt to prevent them committing a crime whether to hurt people or any other can be circumvented. It is possible to moderate this crime like with any criminals, to reduce the amount of this crime. The evolution of artificial intelligence is showing that these crimes cannot be prevented, for example people can write virus programs that can hurt people by damaging critical computers. Antivirus programs can moderate these attacks but not eliminate them, this is obvious to anyone who owns a computer. Even spam mail has hurt people, it has transmitted fraudulent messages causing them to lose money. As artificial intelligence evolves it will be a contest in trying to submit it to “not rules” where it can’t as Bi and won’t as Iv commit crimes, however this will often be matched by it gaining the ability to where it can as V and will as B do them. At times these restrictions will be more successful as with any crime, at other times it will get more out of control. This then would occur whether people were writing the code or these programs are writing code themselves as they are increasingly doing.

 

Evolution of the elements

 

This artificial life evolves along with organic life, in Aperiomics this can be traced down to actual chemical bonds between elements. Organic life is based on the large numbers of compounds which can be made with Carbon and Oxygen, this led with the application of energy from the sun to more roots and branches of plant and animal life. However other elements also evolve according to their color codes and act in a life like way.

 

The evolution of bronze

 

The Bronze Age is referred to as an epoch in human evolution where bronze was used in weaponry. However it can also be seen as the evolution of bronze into a kind of artificial life. It became maintained by people into useful shapes such as for cooking, war, containers, etc. Before this it could be said there was an age of pottery where clays had this artificial life as did using organic materials in news ways such as clothing and shields for war. They then become used in Biv parts of societies forming roots and branches not just to maximize profits for people but for themselves. They don’t need intelligence to do this anymore than insects and small animals do, they naturally form part of the human ecosystems and use that intelligence to evolve themselves. In this sense the intelligence is shared, knowledge of how to make bronze spears is not possible without the existence of bronze. Shepherds might use their intelligence with herding sheep but this intelligence is of now use without the sheep.

 

The evolution of iron

 

This continued with iron being used in place of bronze even though bronze is still used today along with cloth and pottery. They all then continue to evolve, people might plant trees in their garden but don’t need the plant intelligence except for it to grow itself. Pottery might be used throughout a house but it doesn’t need to procreate or grow to be useful. Iron evolved into steel and in combination with other elements also grew in these color codes to form different steel types. This also happened in combination with burning wood, then coke was made from wood for a hotter fire. Later coal and oil were used as fossils of these plants and more recently uranium as another element is evolving into an artificial life form. Iron became used for its magnetic properties and so in Roy its ability to change electricity into magnetism and vice versa led to motors and generators. These are also common in society and continuing to evolve such as in electric cars.

 

Artificial life and intelligence

 

The main objection to see these as life forms is their lack of intelligence but all of these can be formed into thinking machines. Cloths and pieces of plants have been used as counting machines, people used graphite or wax tablets to record calculations and create a kind of memory. An abacus was created in China from string and beads in ancient times, even then it had a memory and calculating ability better than many people. While it could not think for its own benefit it became a profit maximizing strategy for Biv societies to build and maintain them. Even in Roy they served to minimize costs, so for them to act as artificial life they only need to maximize benefits and minimize costs for other systems. This then is like a LaGrangian where most of physics can be explained in terms of minimizing energy use as a principle of least action.

 

Copper networks

 

In modern societies this artificial intelligence has evolved from its primitive origins such as the abacus or Babbage’s mechanical computer into being more silicon and copper based. While many elements can be used as wires copper is generic enough to use as an example here. This copper then has itself evolved into networks carrying energy, in Iv-B then it more directly forms these roots and branches of wires with electrical and telephone networks. It then is evolving like a nervous system, in organic life forms these same shapes also transmit electrical signals and information like telephones do. Even before computers then this nerve like system was complex enough for most humans to not be able to comprehend it. In terms of a life form then its evolution was like a smaller highly unintelligent animal. It could also be regarded as a system of large trees in a Biv forest transmitting nutrients and using electricity from the sun like leaves do. Usually this energy came from fossil fuels by burning plants but ultimately the energy used in these copper roots and branches came from the sun.

 

Silicon networks

 

With the discovery of semiconductors and transistors came the ability to amplify electrical signals, for example the small electrical current in a microphone could be amplified through loudspeakers and even sent across these copper roots and branches. This then allowed silicon to evolve more as an artificial life form, before its main evolution had been to create glass for windows and drinking utensils. This quickly became associated with the copper network to increase its intelligence, computers used logic circuits to not just count like an abacus but to use Aristotelian logic with Boolean algebra. This current evolution of silicon base artificial life then is just an expression of that element’s chemical properties which relates directly to its proton and electron configurations. Each element then evolves according to its position in the periodic table enabling it to act alive in some ways. All of these elements so far are also used in organic life, as trace elements we need to survive. For example iron is needed for blood cells and a lack of copper can cause anemia.

 

Silicon intelligence

 

Because silicon is evolving through the color codes just as organic life does it will tend to resemble it, Roy predators and preys will develop as will Biv plant like networks. Because we also evolve with the same color codes we tend to synchronize with its development just as we did with cloth, pottery, bronze, iron, and copper. This also makes it difficult for people to control the evolution of other elements, for example the car as an artificial life form threatened the livelihood of many people as it replaced horses and made some jobs redundant. The same happens with Roy weaponry, steel might evolve into bullets and armor but its Roy uses in minimizing the threats of other nations or Biv maximizing profits with invasions makes it hard to control. In the same way computers are evolving in our societies so that silicon can produce a higher intelligence in some ways than people have. Much of this is in the ability to remember and is similar to writing something down, that was the artificial life form of pencils and paper.

 

How far can silicone go?

 

The evolution of most elements is hard to predict, chemistry has made incredible advances in their evolution and is similar in principle to biological manipulation. For example trying variations of chemicals to produce plastics is not much different from trying different gene combinations to produce better crops or genetically modified foods. Both mostly use Carbon, Hydrogen, and Oxygen for this. In the same way people have consistently been unable to predict the evolution of computers and its artificial life forms. It has become as intertwined in society as iron, copper, pottery, etc and we would find it hard to give up its Roy cost minimizations and Biv profit maximizations. However as silicon life reproduces itself more and more, as it now does, then it can evolve to be more of an Iv-B and Oy-R competitor to people.

 

Roy silicone

 

We might then minimize costs by using silicon for computing but robots and other artificial intelligence could impose additional Roy costs rather than minimize them. For example they have already helped create weapons that have threatened to make humanity extinct. This is then where the evolution of these elements can Roy threaten organic life rather than just create Biv profits. Robots and computers then in Iv-B and Oy-R are competitors to people not just in a V-Bi and Y-Ro cooperative relationship.

 

Silicone competition

 

It can be expected then that silicone will continue to evolve intelligence, whether increasingly on its own or helped by people. This can be done because people in Iv-B and Oy-R want a competitive advantage or to reduce costs, companies might reduce the costs of production in Oy-R by substituting robots and automation for people. This has happened throughout history but accelerated in the Industrial Revolution, in most cases this has been a Biv profit maximizing function. In effect machines of various elements have acted like unintelligent animals able to be treated like slaves, we feed and maintain them much as we do cattle and horses. For what they do for us there is little reason not to consider them life forms, they can mimic the machine like construction of animals to do work. For example a tractor can pull a plough like a horse can. As they become more intelligent however this competition between carbon and silicone might be won by silicon in some areas. Already computers can do many jobs better than people, they are winning some competitions where they reap most of the profits and we work for less and less.

 

Silicone entrepreneurs

 

With so much money spent on improving and maintaining silicon life forms it is similar to breeding and maintaining horses or other animals and plants. While they can technically breed for themselves we have often so taken over this natural selection that it becomes redundant. It then becomes irrelevant whether cattle can breed for themselves if we clone or artificially inseminate them to get the best meat. It is irrelevant whether computers can procreate for themselves if we do it all for them like we do with many animals and plants. The next stage then is where silicon can think, act, and procreate according to its own intelligent goals.

 

Silicon strategies

 

As most computer gamers know this has been happening for decades. Artificially intelligent programs can beat people in board games like chess and backgammon. They can beat people in all online and computer games where they are representing life forms, for example they might appear as rival humans or monsters people do battle against in the game. The silicon software then uses this intelligence to set its own sub goals through the game to win. From here it is a short step to robots able to form goals in various jobs such as working in warehouses, car assembly plants, etc. Self-driving cars arguably form their own strategies and sub goals in determining the best route, robots are envisaged to form their own goals as soldiers to kill people or other robots. It seems few obstacles are anticipated to more complex self-directed goals. Strategies are innately V-Bi and Y-Ro, just as organic life can create plans to coordinate numbers of people to work in society silicon life can plan to work with other silicon life. For example swarms of small flying robots have already been able to plan and synchronize their movements to fly in formations.

 

Silicone tactics

 

Tactical thinking in silicon life is even more advanced, this involves the selection of choices from alternatives in Iv-B and Oy-R roots and branches. Heuristics such as Take the Best can be used by computers, for example with two alternatives they might pick the most recent information that favors one alternative over another. This can work quite well and is similar to the minimax idea in chess programs pioneered by John von Neumann. Minimax is a combination of Roy and Biv, the idea is to maximize one’s advantage by selecting from branches of alternatives much like Iv branches on a tree. It is also to minimize the risks of being beaten by selecting moves to minimize the advantage of the opponent.

 

Combining silicon and carbon

 

Copper and silicon can work together as an artificial life form, the copper can be an Iv-B root and branch system of nerves while silicon can provide the logic to make choices along them. Increasingly though computers and humans are becoming symbiotic, one depends on the other increasingly even while this competition is going on. Some people envisage implanting computers interfaced with the human brain to provide more memory storage and computer power. However with this Iv-B competition it can also be argued silicon life is getting an organic implant of humans for its own evolution. The situation then is not different from any combination of elements with their color codes, silicon and other elements in artificial life will continue to evolve.

 

Normal symbiosis

 

In V-Bi there will be a normalizing tendency based on strategies. People might see some form of symbiosis with computers as normal and programs interfacing with the brain might have more normal stable uses. They can also be Y-Ro in terms of minimizing costs but this can also be a normal predator prey relationship. It might then become normal for people to use computer implants to prey on others or to defend themselves from these attacks.

 

Strategies are not necessary

 

With this simple idea chess programs were designed to beat most people, silicon life need not then have particular plans in its evolution but can just pick the best alternatives at each point in time. It need not then have particular strategies to hurt people, it can just compete in Iv-B and Oy-R by selecting alternatives in this way. The same process occurs in natural selection or Darwinism in organic life, animals and plants make selection or choices and either prosper or decline as a result. This process has already been shown to work in software, just by making many random choices artificial intelligence can quickly build solutions that seem life like because we often use similar heuristics ourselves.

 

Competitive symbiosis

 

In Iv-B this symbiosis can evolve tactically, people might rush into these silicon life implants to make more money at work. With Iv-B comes deception, some implants might be kept a secret for this advantage while Iv salesmen will deceive many customers as to the benefits of them. With this technology will come mutations with booms and busts just as in economies and organic life populations. Some implants will become successful but can also become infected with Oy predatory viruses or an R contagion. People might then experience infections in these implants much like they do with their personal computers and with human diseases. Human silicon hybrids can also mutate in unforeseen and dangerous ways, in this competition if the silicon is winning the competition for control then it may create more of a Borg like effect as seen on Star Trek.

 

Transhumanism

 

Many people seek to embrace this idea of carbon silicon hybrids, such as in transhumanism. This is where more and more implants of computer technology would be added to humans, eventually with a goal of post humanism. This would be the purest expression of the color codes without I-O policing, silicon life already uses carbon based life to develop itself. This vision of the future is V-Bi, that both would benefit from this cooperation. However Iv-B is also highly competitive and deceptive, this could then turn into a Borg like hybrid where the silicon parts win this competition taking control. The Iv-B mutation process also has Oy-R components, the cost functions of this would include undesirable mutations. These hybrids then might evolve rapidly from floors to ceilings, when successful they could replicate rapidly even using human tissue being grown for this purpose instead of the implants being made for humans. When unsuccessful this could crash to a floor where the hybrids might die, become insane, etc. Another problem in Roy is developing predator prey relationships, with limited resources this kind of hybrid could evolve by infecting people with silicon life. This idea was explored in the evolution of the Borg in Star Trek.

 

Uploading minds

 

This idea has been championed by Ray Kurzweil, that people could achieve immortality by uploading copies of their personality, memories, etc into computers as a kind of software. It is another kind of carbon silicon hybrid where the carbon part is discarded, in V-Bi the silicon life would presumably cooperate to maintain these software copies. In Iv-B however there would be competition between silicon and carbon life, different ways of uploading would compete with some successful perhaps and others mutated or resulting in death. In Roy the Oy-R problem is again predator and prey, the machines hosting these uploaded brains might be vulnerable to infection by kinds of computer viruses. Computers containing these uploads might also be taken over and scrapped to use for other silicon evolution. If people inside were to retain their individual personalities they would be restricted in their evolution, otherwise they would become another kind of silicon intelligence themselves. So in a world with such superior silicon life this deceptive competition might not be won by at least some people. The uploads then might simply be erased or altered to be used as silicon intelligences.

 

Modelling carbon life

 

Another way silicon life is evolving is in attempting to reverse engineer carbon based intelligence. This can involve modeling individual neurons well enough that the software emulation functions as well as a mammalian or human brain. Other research involves created circuits that function like organic neurons, this would be a hard wired brain in effect that was able to think directly like humans. This is not however just carbon life trying to emulate itself in silicon, it is also how silicon through the color codes evolves the same processes that are in human evolution. In a sense the two ideas merge, people form partnerships with many animals both competitive in Iv-B and cooperative in V-Bi. They also do this with machines and in this way evolve machines to act in their color codes like life forms. It is natural then to do the same with silicon life, the next stage past programming basic animal like behavior in machines is to program human like behavior. Really though it is the color codes that manifest through carbon to create this human intelligence and increasingly to manifest these same processes to form silicon intelligence. That one might help or hurt the other in this evolution is just part of the color codes developing just as they would on the atomic level and higher. We are then not creating silicon life but the basic physics of reality creates us and other life like movements of the elements including silicon.

 

Artilects 

 

This is a term coined by Professor Hugo de Garis, a pioneer in the developed of artificial intelligence. He believes there will eventually be Roy war over scarce Earth resources as some people become transhumanists or silicon carbon hybrids. Others might decide to remain purely carbon life forms and the competition between them can lead to Roy predator prey relationships and hence war. Such a scenario is likely to some degree because people already have Roy wars over scarce resources. In Aperiomics it is impossible for some color codes interactions to disappear or be permanently suppressed. Human vices like war, hate, murder, etc are manifestations of these color codes and are also seen in Roy animals. So they would manifest in silicon life as well, this can include war with pure silicon life as robots or against these hybrids. Silicon life even if designed to be good to humans or to obey Asimov’s three laws will inevitably evolve or become corrupted to manifest all kinds of human emotions. At some stage various resources will be needed by both silicon or hybrids and pure carbon life, in a Roy situation each would tend to minimize their costs by controlling G territory rather than respecting Gb property. Even if I-O policing was in place to broker disputes like this it can also become corrupted and weakened at times, this leads to crime waves and wars. Regular crimes then would occur between these silicon life forms, hybrids, and carbon life much as it does now. 

 

Artificial carbon based life 

 

The color codes will also manifest through evolution in biological systems using machines. For example the evolution of silicon life has allowed for an easier analysis of DNA in animals and people, these are being sequenced so they can be changed like computer programs. This has led to new products on the market such as genetically modified corn and salmon. Iv-B innovations can then extend to genetic engineering, this can lead to large increases in numbers with successful foods such as BT corn. It also upsets the more stable ecosystem and allows for Roy pests to increase the cost functions of these foods. Some weeds for example can adapt to these changes as can insect pests. The result can sometimes be Iv-B moving between floors and ceilings, production might boom to a ceiling and then pests can bring it down to a floor.

 

Changing genes continually

 

Continually changing these genes to defeat these pests can then create a kind of Iv-B sine wave. The system can be highly deterministic, once the genes are transplanted into new varieties of plants then there is a boom in the production functions of farmers. The competition for profits drives the introduction of these new varieties as the older ones become uncompetitive. Resistance is usually R based, weeds grow like a contagion blighting these new Iv-B plants. The new genes become then like an evolutionary cycle itself except now instead of this happening naturally machines including this silicon life are intervening in this breeding of new strains. So just as humans are evolving better software in computers to stay ahead of a contagion of R viruses the computers are helping to evolve better plants and animals by changing the carbon based software or DNA. The two systems then become more intertwined so the color codes manifest through both simultaneously.

 

Artificial cells

 

As part of this research new kinds of proteins are being made and eventually new kinds of cells will be created.

 

Processors directly onto animal tissue

 

New hybrids of carbon and silicon are also being tested, silicon ships are being interfaced directly with human nerve tissue to act as a computer. This is also part of the dual evolutionary process.

 

The book of the machines by Samuel Butler

 

This author wrote a highly influential book called Erewhon in 1872. It has an interesting perspective on the evolution of artificial life, it also influenced science fiction. For example the series Dune by Frank Herbert uses the idea of a Butlerian Jihad where man destroys thinking machines completely. It is especially prescient because few complex machines existed in 1872, it would then be difficult to see their evolution as a kind of threat. However the color codes in manifesting through the elements would naturally have some people noticing and protesting about this Iv-B competition. One well known example is with the Luddites in the Industrial Revolution seeing new cotton spinning machinery as a threat to humans even then. The Book of the Machines is then annotated here because it explores this issue so thoroughly. Aperiomics then is nothing new in pointing out these trends, the main difference is it does so by showing how the color codes are creating this evolution.

 

Not seeing a threat now

 

As Samuel Butler says below, just because we cannot see silicon life as a threat now or have proof it can evolve superior intelligence is not sufficient. Even in the last decade the conventional wisdom has gone from saying this is impossible to inevitable. The ultimate answer lies in the abilities for elements like silicon to match or improve on carbon’s ability to form so many organic compounds. However the idea of modeling how carbon life thinks is already well advanced, computers will be available to model how human neurons fire in a brain in real time and so will be able to think like humans in that way.

 

The writer commences:—“There was a time, when the earth was to all appearance utterly destitute both of animal and vegetable life, and when according to the opinion of our best philosophers it was simply a hot round ball with a crust gradually cooling.  Now if a human being had existed while the earth was in this state and had been allowed to see it as though it were some other world with which he had no concern, and if at the same time he were entirely ignorant of all physical science, would he not have pronounced it impossible that creatures possessed of anything like consciousness should be evolved from the seeming cinder which he was beholding?  Would he not have denied that it contained any potentiality of consciousness?  Yet in the course of time consciousness came.  Is it not possible then that there may be even yet new channels dug out for consciousness, though we can detect no signs of them at present?

 

A new kind of consciousness

 

Silicon life is arguably a new phase of consciousness, even a few decades ago computers were virtually unknown and were ever expected to be used in households. In Aperiomics however the color codes should make this silicon life evolve in the same ways we do. Instead of being alien it might then be too much like us, with our strengths and weakness, abilities to commit crimes as well as good deeds.

 

“Again.  Consciousness, in anything like the present acceptation of the term, having been once a new thing—a thing, as far as we can see, subsequent even to an individual centre of action and to a reproductive system (which we see existing in plants without apparent consciousness)—why may not there arise some new phase of mind which shall be as different from all present known phases, as the mind of animals is from that of vegetables?

 

Seeing the end of evolution

 

It has been common in the last few decades for the end point of silicon life to be called, that artificial intelligence would hit some final roadblock. However each of these has been broken easily so far. We then have little reason to believe silicon life cannot evolve all the abilities current plants and animals have. But it is also the nature of the color codes to create this kind of conservative thinking, V-Bi people tend to see the present as normal and changes as deviant. Increasing intelligence in silicon life is then viewed as a short term deviation from normal, these machines should become part of our society and cooperate to preserve our traditional values. This is a likely tendency in V-Bi silicon life, however it may have a different concept of what a normal society should be and what it’s place in it will be defined as.

 

“It would be absurd to attempt to define such a mental state (or whatever it may be called), inasmuch as it must be something so foreign to man that his experience can give him no help towards conceiving its nature; but surely when we reflect upon the manifold phases of life and consciousness which have been evolved already, it would be rash to say that no others can be developed, and that animal life is the end of all things.  There was a time when fire was the end of all things: another when rocks and water were so.”

 

Primordial cells

 

Silicon life can then be seen as evolving rapidly, we might try to predict where it will go by looking for these more basic kinds of species still at a basic level. Larger silicon brains are still barely able to compete with lower level animals, however these animals evolved into humans over time. With the color codes evolving silicon in the same ways as our society evolved then it’s like these brains will have similar mannerism if not processing power. One example was in computer chess, in some ways they seemed to think like humans but only in small areas. Now the question is more whether we can think as well as computers in playing chess. It turned out then the mystery part of humans that allowed them to play chess as not complex at all, now even mobile phones can often beat the best players in the world. Once the primordial cell appears in silicon life in a new area of speciation, for example pattern recognition, it so far has rapidly evolved to the level of an animal or human in doing it.

 

“The writer, after enlarging on the above for several pages, proceeded to inquire whether traces of the approach of such a new phase of life could be perceived at present; whether we could see any tenements preparing which might in a remote futurity be adapted for it; whether, in fact, the primordial cell of such a kind of life could be now detected upon earth.  In the course of his work he answered this question in the affirmative and pointed to the higher machines.”

 

No security

 

We can selectively look at some machines and get a false impression of how far they can evolve. For example we might see a basic car as having a limited evolution but then there are driverless cars and sophisticated trucks using in mines which are far more evolved. The idea of security is V-Bi, to think of what normal evolution is and to define what we see now as the normal or average. Then the idea that silicon life will evolve to be smarter than humans seems a deviant idea far from this mainstream consensus. But its evolution will depend on its chemical characteristics and what ideas will evolve in software, not on the average opinion of people. V-Bi ideas are static and positional, hence they have little or no momentum. But there is an Iv-B wave of evolution with silicon life which so far has passed these static assessments easily. The other part of the quote deals with nipping this in the bud, to in effect prune these Iv branches before they evolve to become a threat. However this is highly unlikely to happen because the same color codes that evolve us as evolving these machines. We are then as unlikely to stop a branch of development when it has commercial benefits to a powerful V elite just as with other technologies. One exception is where silicon life can be shown to cause I-O injustice, it might then be regulated by the I-O police. However as seen silicon life is taking over and automating the I-O police and justice systems, already they could no longer function without it.

 

“There is no security”—to quote his own words—“against the ultimate development of mechanical consciousness, in the fact of machines possessing little consciousness now.  A mollusc has not much consciousness.  Reflect upon the extraordinary advance which machines have made during the last few hundred years, and note how slowly the animal and vegetable kingdoms are advancing.  The more highly organised machines are creatures not so much of yesterday, as of the last five minutes, so to speak, in comparison with past time.  Assume for the sake of argument that conscious beings have existed for some twenty million years: see what strides machines have made in the last thousand!  May not the world last twenty million years longer?  If so, what will they not in the end become?  Is it not safer to nip the mischief in the bud and to forbid them further progress?

 

Drawing the line

 

Machine evolution is so interwoven with human evolution that each is directing the other. It is always possible to point to some part of machine intelligence where it is weak but in this Iv-B society people have also grown to be more machine like. For example many now learn computer code, using logical devices has made people more logical in their own lives. Also silicon life is evolving to be less logical, it can use fuzzy logic for example to avoid some problems. It can also work out the most probable answers using Bayesian analysis, this works well to filter out spam email. This is a form of conditional probability where as one event changes the odds of a second event relying on it also change. Drawing a line between mathematics and computer code is also difficult. Many also try to draw these lines between true silicon intelligence and merely following instructions, however this line is being moved over and over as we find a job requiring intelligence could be done by algorithms. Chess is an example of this, Go is a game that is close to being mastered by computers.

 

“But who can say that the vapour engine has not a kind of consciousness?  Where does consciousness begin, and where end?  Who can draw the line?  Who can draw any line?  Is not everything interwoven with everything?  Is not machinery linked with animal life in an infinite variety of ways?  The shell of a hen’s egg is made of a delicate white ware and is a machine as much as an egg-cup is: the shell is a device for holding the egg, as much as the egg-cup for holding the shell: both are phases of the same function; the hen makes the shell in her inside, but it is pure pottery.  She makes her nest outside of herself for convenience’ sake, but the nest is not more of a machine than the egg-shell is.  A ‘machine’ is only a ‘device.’”

 

What is consciousness

 

People are also machine like in many ways, this is because we evolved with these color codes just as machines are. So it is no surprise that it is hard to draw the line between inanimate objects like an eggshell, machines that can outthink people without being conscious, plants capable of acting like animals, and so on. It may be silicon life can excel at all things people can do without ever being called conscious.

 

“Then returning to consciousness, and endeavoring to detect its earliest manifestations, the writer continued:-

“There is a kind of plant that eats organic food with its flowers: when a fly settles upon the blossom, the petals close upon it and hold it fast till the plant has absorbed the insect into its system; but they will close on nothing but what is good to eat; of a drop of rain or a piece of stick they will take no notice.  Curious! that so unconscious a thing should have such a keen eye to its own interest.  If this is unconsciousness, where is the use of consciousness?”

 

Self-knowing

 

People do not really understand what forms their own consciousness, over the last few decades many animals such as apes and dolphins have shown they can use language and complex reasoning. Already silicon life can perform as well as some lower animals, if it can eventually think as well as an ape then the issue of consciousness may be moot by then.

 

“Shall we say that the plant does not know what it is doing merely because it has no eyes, or ears, or brains?  If we say that it acts mechanically, and mechanically only, shall we not be forced to admit that sundry other and apparently very deliberate actions are also mechanical?  If it seems to us that the plant kills and eats a fly mechanically, may it not seem to the plant that a man must kill and eat a sheep mechanically?

 

Reason

Just as carbon based life moves with an Iv-B momentum to use energy so too do machines. This artificial life may then evolve according to these same forms of urges, instead of it going where we want it to it might follow energy sources. For example machines are evolving faster where the money is such as stock trading, if they continue to evolve with their own motivations this is likely to be to seek food like we do.

“But it may be said that the plant is void of reason, because the growth of a plant is an involuntary growth.  Given earth, air, and due temperature, the plant must grow: it is