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been thought of today. Many applications will not become apparent until robots are so prevalent in society that the application is discovered by a mixture of availability, imagination, and need.
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Robotic research and development is moving faster than anyone can follow. The Internet is an excellent tool for finding information.
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Artificial life and artificial intelligence
THE EVOLUTION OF ROBOTICS LEADS TO TWO FAR-REACHing topics, the creation of artificial intelligence and artificial life.
Artificial intelligence
People dream of creating a machine with artificial intelligence (AI) that rivals or surpasses human intelligence. I feel neural networks are the best technology for developing and generating AI in computer systems. This is in contrast to other computerists who see expert systems and task-specific rule-based systems (programs) as potentially more viable. It is an undeniable fact that rule-based computer operating systems (DOS, Windows, Linux, etc.) and rule-based software are valuable and do most (close to all) of the computer labor today. Even so, the pattern matching and learning capabilities of neural networks are the most promising approach to realizing the AI dream. Recently it had been forecasted that large-scale parallel processors using a combination of neural networks and fuzzy logic could simulate the human brain within 10 years. While this forecast may be optimistic, progress is being made toward achieving that goal. Second-generation neural chips are on the market. Recently two companies (Intel Corp., Santa Clara, CA, and Nestor Inc., Providence, RI), through joint effort, created a new neural chip called the Ni1000. The Ni1000 chip, released in 1993, contains 1024 artificial neurons. This integrated circuit
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Artificial life and artificial intelligence
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has 3 million transistors and performs 20 billion integer operations per second.
Evolution of consciousness in artificial intelligence
Consciousness is a manifestation of the brain s internal processes. The generation of consciousness in Homo sapiens coincides with the evolution and development of neural structures (the brain) in the biological system. A billion years ago the highest form of life on Earth was a worm. Let s consider the ancestral worm for a moment. Does its rudimentary (neural structure) intelligence create a form of rudimentary consciousness If so, then it s akin to an intelligence and consciousness that can be created by artificial neural networks running in today s supercomputers (see Fig. 2.1). In reality, while the processing power of supercomputers approaches that of a worm, this has not yet been accomplished. The reason is that it is too difficult to program a neural network in a supercomputer that would use all the computer s processing power. The worm is unquestionably alive, but is it self-aware Is it simply a cohesive jumble of neurons replaying an ancestral record imprinted within its primordial neural structure, making it no more than a functional biological automaton
2.1 Graph showing supercomputer capabilities
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Is consciousness life
This raises a few questions: Is intelligence conscious Is consciousness life It seems safe to say that intelligence has to reach a certain level or critical mass before consciousness is achieved. In any case, artificial neural networks can and will develop consciousness. Whether the time span is 10 years or a 1000 years from now makes no difference; 1000 years is less than a blink of the eye in the evolutionary time line. (Of course, I am hoping for a 10-year cycle so I can see a competent AI machine in my lifetime.) At the point where an artificial neural network becomes conscious and self-aware, should we then consider it to be alive
Artificial life
Artificial life (AL) splinters into three ongoing research themes: self-powered neural robots, nanorobotics (may be self-replicating), and programs (software). The most evolved types of artificial life on Earth today are programs. No one has created a self-replicating robot, and nanobots are still years away from implementation. Therefore let s discuss AL programs for the time being. In AL programs, life exists only as electric impulses that make up the running program inside the computer s memory. Computer scientists have created diverse groups of AL programs that mimic many biological functions (survival, birth, death, growth, movement, feeding, sex) of life. Some programs are called cellular automations; others are called genetic algorithms. Cellular automation (CA) programs have been used to accurately model biological organisms and study the spread of communicable diseases like AIDs in the human population. These programs have also been used to study evolution, ant colonies, bee colonies, and a host of other chaos-driven statistics. Chaos algorithms are added into the programs to generate randomness. One interesting application of CA programs is to optimize neural networks running in host computers. It is hoped that these CA programs will one day create and wire large neural network systems in supercomputers. Genetic algorithms (GAs) evolve in a Darwinian fashion survival of the fittest. Two compatible GA programs can meet in the computer s running memory, mate, and mix their binary code to produce offspring. If the offspring GA program is as healthy or has greater health than its parents, it will likely survive.
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