Breakthrough Promises Faster Computer Chips
Researchers have shown that a single sheet of graphene, which measure just a few tenths of a nanometer, or even a few sheets, can exhibit special properties. One such property is very high mobility, in which electrons can pass through it very quickly – a good characteristic for fast electronics. Another is magnetism, which would enable magnetic fields to be used to control the spin of graphene electrons, which would enable spin-based electronics, also called spintronics. Graphene’s properties also change dramatically when it touches other materials making it a good candidate material for chemical sensors.
Also: Researchers Find Better Way to Manufacture Fast Computer Chips (from Nanotechnology Now)
Intelligent Agents @WikiRank. (excerpt)
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes and acts upon an environment (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.
Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their Wiktionary:Autonomy(autonomy), and so prefer the term autonomous intelligent agents. Still others (notably ) considered goal-directed behavior as the essence of intelligent and so prefer a term borrowed from economics, “rational agent”
(diagram and excerpt from Intelligent Agents, Chapter 2, (PDF) Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, c 1995
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An agent’s behavior can be based on both its own experience and the built-in knowledge used in constructing the agent for the particular environment in which it operates. A system is autonomous4 to the extent that its behavior is determined by its own experience. It would be too stringent, though, to require complete autonomy from the word go: when the agent has had little or no experience, it would have to act randomly unless the designer gave some assistance. So, just as evolution provides animals with enough built-in reflexes so that they can survive long enough to learn for themselves, it would be reasonable to provide an artificial intelligent agent with some initial knowledge as well as an ability to learn.
Autonomy not only fits in with our intuition, but it is an example of sound engineering practices. An agent that operates on the basis of built-in assumptions will only operate successfully when those assumptions hold, and thus lacks flexibility. Consider, for example, the lowly dung beetle. After digging its nest and laying its eggs, it fetches a ball of dung from a nearby heap to plug the entrance; if the ball of dung is removed from its grasp en route, the beetle continues on and pantomimes plugging the nest with the nonexistent dung ball, never noticing that it is missing. Evolution has built an assumption into the beetle’s behavior, and when it is violated, unsuccessful behavior results. A truly autonomous intelligent agent should be able to operate successfully in a wide variety of environments, given sufficient time to adapt.
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Okay. I don’t know if you, Frank, by your response intended to imply what jumps out as a concern from the field of meta-psychology as it looks toward computational cognitive neuropsychology. It would be something along this line: if there are intentional phenomena that cannot be shown to in anyway to be a factor in behavior, then would such phenomena be worthless to a machine enabled to instantiate the exact same behaviors?
This could frame your comment, “a device that encompasses most human knowledge.” A couple of things pop up here; one, what makes knowledge useful? two, what basis of knowledge would allow a ‘deck’ to accumulate new knowledge using–at least–processes as robust as human processes? Obviously, these two are among the myriad of interesting conundrums.
Although I am not up on machine-based learning, one of the factors of learning I am focused on is how novelty is introduced in human learning environments. For example, you write something and I then go out, intelligent agent that I am, to find out a little more. Yet, this behavior is promoted by the novelty inherent in your writing about this, rather than about something else. So, from this-your ‘novelty,’ I go out and learn a little. Also, that we’re in an environment together where this behavior of mine can be aroused by your behavior is itself dependent on all sorts of prior contingencies, many of which are novel, ‘as luck would have it.’
So it would be that a machine-based intelligent agent could do this if it could choose to respond to such kinds of–what I term–local interactive novelties. Then, from this, a machine would have to be permeable to novelty.
Another way of exemplifying this would be to put an agent in an environment, say in a chair at Starbucks, and wonder to what other potential for learning in that human environment would such an agent gravitate, or search out?
Is this search activity a difficult problem for artificial intelligence? I go out and use google to learn a little. I can’t describe the heuristic or reason for stopping my search. When would a machine-based intelligent agent know when to stop?
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