COMPUTERS WITH BRAINS

Dr. S. S. VERMA; Department of Physics, S.L.I.E.T., Longowal; Distt.-Sangrur (Punjab)-148 106

computer with brainComputers have already controlled most of our daily life activities and there are supercomputers that can decode the human genome, play chess and calculate prime numbers out to 13 million digits but still we want to build a computer that operates like the brain of a mammal. Present computers are dubbed electronic brains but with the consistent doubling of computer power due to Moore’s law, we’ expect to have computers with brains in near future that surpass the human mind. The conventional view is that intelligence involves translating experience into representation and then manipulating the objects in that representation according to a set of logical rules. By comparing computers with brains, we can easily see that adaptation and evolution are not the same. A system that adapts to changing circumstances by switching processes according to predetermined rules, as a computer program does, is not self-evolving. A self-evolving computer would have to do more than switch from one chunk of code to another. It would have to write its own software.

Scientists say that while the project will take its inspiration from the brain’s architecture and function, it isn’t possible or even desirable to recreate the entire structure of the brain down to the level of the individual synapse. A lot of the work will be to determine what kinds of neurons are crucial and which ones we can do without. It all comes down to an understanding of what is necessary for teaching an artificial brain to reason and learn from experience. Value systems or reward systems are important aspects. Learning is crucial because it needs to learn from experience just like mammals do. So a system modeled after the neurons that release neuromodulators could be important. For example, neurons in the brain stem flood the brain with a neurotransmitter during times of sudden stress, signaling the “fight-or flight” response. Scientists say that the ideal artificial brain will need to be plastic, meaning it is capable of changing as it learns from experience. The design will likely convey information using electrical impulses modeled on the spiking neurons found in mammal brains. And advances in nanotechnology should allow a small artificial brain to contain as many artificial neurons as a small mammal brain.

A more useful description of intelligence might be that it is the momentary instantiation of an evolutionary process. This definition applies to instances of biological evolution that might seem superficially unlike human intelligence. In the opposite sense, it might be also true that brains create perceptions and new ideas through a process that resembles biological evolution but that takes place in a matter of milliseconds, as some scientists have speculated. Given this view of intelligence, the earth’s ecosystem would be among the most intelligent things in the known universe because of the novelty it has created, including the human nervous system itself. Taking the notion of a homeomorphism between intelligence and evolution in both directions, we would say that a system that does not evolve is an automaton and has no intelligence whatsoever, regardless of how complex that system may be. We should use intelligence to mean more than just flexible manipulation of data.

Scientists are studying complex wiring of the brain to build the computer of the future, one that combines the brain’s abilities for sensation, perception, action, interaction and cognition and its low power consumption and compact size. Understanding the process behind these seemingly effortless feats of the human brain and creating a computational theory based on it remains one of the biggest challenges for computer scientists. University of Wisconsin-Madison research psychiatrist Giulio Tononi, who was recently selected to take part in the creation of a “cognitive computer,” says the goal of building a computer as quick and flexible as a small mammalian brain is more daunting than it sounds. But Prof. Tononi and scientists from Columbia University and IBM will work on the software for the thinking computer, while nanotechnology and supercomputing experts from Cornell, Stanford and the University of California-Merced will create the hardware for the intelligent computers. The idea is to create a computer capable of sorting through multiple streams of changing data, to look for patterns and make logical decisions. Mammal brains are good at is being flexible, learning from experience and adapting to different situations. The finished cognitive computer should be as small as the brain of a small mammal and use as little power as a 100-watt light bulb. Even the brains of the smallest mammals are quite impressive when you consider what tasks they perform with a relatively small volume and energy input. It’s a major challenge that is what our brains do every day.

Scientists believe that the era of truly intelligent machines — even computer programs that can match wits with a mammal minds — is within reach. The creation of such a machine will involve an adaptive computer system that is much more intelligent than today’s best computers.   Such computers must also be capable to learn to think paradoxically — suspending judgment of contradictory ideas — in order to better mimic human intelligence. Presently, computers aren’t good at looking at the static position — to make final decisions at a given moment — but they are good at looking at large numbers of possibilities. A more truly intelligent approach is needed if computers are to make the leap which scientists believe is possible in the coming decades. Traditional computer programming, however, limits a computer’s ability to think like a human. Future computer systems will require more emphasis on massively parallel learning systems. Much of the research in intelligent agents — from Internet search engines and computer spell-checkers to systems used in high-tech manufacturing — has focused on developing the learning systems.  Now it’s time to emphasize the massively parallel part of such systems.

A truly intelligent system is one that can adapt to changes or new information and learn from the changes. An adaptive intelligent agent is one that is capable of learning. There is a need to create logical systems that are able to embrace contradictions, as humans do. The computers need to be able to reason on the data and still be able to prevent coming up with absurdities. We’re talking about a system that could constrain contradictions. Sooner rather than later, future intelligent computers will be going to change the way we view computers.

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