Piero Scaruffi(Copyright © 2013 Piero Scaruffi | Legal restrictions )
These are excerpts and elaborations from my book "The Nature of Consciousness"
Another approach to building intelligent programs is based on “Artificial Life” (a term coined by Chris Langton in 1987). “Intelligence” (or, better, “cognition”) cannot do without life. Intelligence is a product of life, and it is an evolutionary product of the evolution of life. On the other hand, there is more and more evidence to support the mirror view: that life is very much about cognition, that all life is “cognitive” in nature.
The first computer viruses were produced at Bell Labs in 1962 (the term was coined by David Gerrold in his novel "When Harley was one"). When computer viruses became famous, they simply popularized the discipline that was attempting to build self-replicating automata at software level, a school of thought started by Von Neumann decades earlier. Self-replication (the ability to produce offspring from self-contained instructions) is the prerequisite to evolution.
It turns out that self-replicating and evolving systems can also replace expert systems.
Artificial Intelligence solves a problem by reasoning about the knowledge of the problem's domain. Artificial Life (“Alife”) lets possible solutions "evolve" in that domain until they fit the problem. Sometimes there is no perfect solution, just a "best fit". Solutions evolve in populations according to a set of "genetic" algorithms à la Holland that mimic biological evolution. Each generation of solutions, as obtained by applying those algorithms to the previous generation, is better "adapted" to the problem at hand.
In 1952 the Norwegian mathematician Nils Barricelli became the first person to actually run artificial evolution experiments on computers (basically, a one-dimensional cellular automaton).
In 1970 the US computer scientist Michael Conrad and the US physicist Howard Pattee developed one of the earliest artificial models of life, that modeled competition among individuals equipped with a genotype representation and a phenotype obtained by interpreting the genotype as instructions.
Likewise, software environments such as the "Tierra" program, developed in 1992 by an US ecologist, Thomas Ray, simulate a world and an evolving population of organisms. Tierra is populated with digital organisms that compete for space in the computer memory and for time in the computer processor. Whatever space and time they manage to get, they use it to reproduce themselves. Like with most simulations of this type, a digital organism’s phenotype is also its genotype (the genome is also the body, or viceversa).
Ray draws a distinction between two types of Alife: weak (simulation of life) and strong (synthesis/instantiation of life). The difference is that one is man-made while the other has evolved to be living from inanimate "matter". Tierra, for example, starts out with instances of a simple replicating code and is left to evolve into a living system capable of metabolizing, reproducing and evolving while it interacts with its environment. Ray focuses on "the second major event in the history of life, the origin of diversity."
As Langton points out, the key concept in Alife is “emergent behavior”.
These virtual worlds are more than simple simulations of algorithms. They may well be philosophical investigations into the very nature of the universe. For example, the Italian physicist Tommaso Toffoli speculated that the universe could be viewed as a computer. Frank Tipler points out that, at the very least, there is no way to tell a computer simulation of the real world from the real world, as long as one is inside the simulation. A simulated observer would perceive the simulated world exactly the same way that the real observer perceives the real world. Any test to reveal whether her world is the real world would succeed, by definition. Therefore, there is also no way for me to tell whether I am a simulated observer inside a simulated universe, or a real observer inside a real universe. Therefore, the distinction between reality and simulation becomes fictitious.
Most evolutionary engineering is software-based. Hardware-based simulations of natural evolution are based on the idea of a software bit string that is used to configure programmable logic devices as a genetic algorithm chromosome, so that the configuration of the circuit will evolve at electronic speed. The final goal is to build machines that evolve independently, or, more properly, are “evolvable hardware” (a discipline that was officially born in 1995). The Swiss computer scientist Daniel Mange builds electronic circuits that can grow/evolve rather than be designed. Mange's "embryological electronics" employs field programmable gate arrays that exhibit the ability to reproduce the circuit of any programmable function and to self-repair. The “Firefly Machine”, for example, is based on a variation of Von Neumann’s cellular programming techniques: parallel cellular machines evolve to solve a problem. The "Embryonics" project deals with ontogeny, or growth: just like any multicellular organism grows over its lifetime, so a multicellular automata should exhibit embryonic development driven by the same processes of cellular division and differentiation.
Another center for biologically-inspired systems is the Evolvable Systems Lab in Japan, headed by Tetsuiya Higuchi.
Basically, Artificial Life replaced the "problem solver" of Artificial Intelligence with an evolving population of problem solvers. The “intelligence” required to solve a problem is not in an individual anymore, it is in an entire population and its successive generations; it is not due to the knowledge of a solver, but to the evolutionary algorithms of nature that operate on the genetic code of a population.
It is not the solver who is smart enough to solve the problem, but the knowledge she has. It is evolution that eventually builds the solver who is smart enough to solve the problem using the knowledge that is available.
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