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Human Brain v/s Computer Processor

Human Brain v/s Computer Processor

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In May 1997, an updated version of Deep Blue defeated Kasparov 3½–2½ in a highly publicized six-game match. Each computer can work on a different location at the same time. The match was even after five games but Kasparov was crushed in Game 6. The final game of the match was televised on ESPN2 and was watched by an estimated 200–300 million people. What do you call a fly with no wings and no legs? A raisin. Parallel computers combine many fast computers to work on large problems such as computing tomorrow's weather. According to John Rennie, Watson can process 500 gigabytes, the equivalent of a million books, per second. After reaching a decent position Kasparov offered a draw, which was soon accepted by the Deep Junior team. M & P did not consider three-layer networks because nobody had considered them at that time, and because they were, after all, talking about perceptrons.

I suspect you may be saying – if your idea was so good why haven’t I heard of it. A group of algorithms that work together to help us do something (like buy stocks or find a date online) is called an ‘application’ – what most people now call an ‘app’.

“Are you trying to tell me that the amount of RAM available will affect how we traverse a neural network lookup table?” –Steve G I sure don’t remember telling you that…I was responding to architectural differences and the non-issue of how RAM works. Other than that I’m not quite clear on what you were saying.` –Jonathan I’m arguing that the way the RAM works is important, because one doesn’t need to have the same limitations in RAM behaviour that one has the brain. You *can* model the limitations on a sufficiently powerful computer. The largest conceivable parallel computer can't do anything useful in one hundred steps, no matter how large or how fast.

An important feature of my system was a “working area” which I called “The Facts” and which I considered to be equivalent to human short term memory. If it is the case that a discrete state machine is not hindered by this, then the brain’s architecture with all of the intricacies of neuronal activity can be implemented to the fullest extent with no other problem (although we’d of course want to abstract away as much complexity as possible). To predict the weather you have to compute the physical conditions at many points on the planet. Kasparov won the first, lost the second, and drew the next three. But like I said before, you can model a raisin with a housefly.* The key words there are sufficiently powerful. The rules computers follow for moving, copying and operating on these arrays of data are also stored inside the computer. But even though there may be hundreds or even thousands of computers working in parallel, the individual computers still need to perform billions or trillions of steps to accomplish their task. I would however like to defend Minsky & Papert’s work on perceptrons. Asked why he offered the draw, Kasparov said he feared making a blunder. When that was written (about 30 years ago), perceptrons were being promoted as a realistic paradigm for neural modelling, and in fact were the only neural-modelling paradigm that had been fully worked out. The engine evaluated three million positions per second.

There are so many topics to be sorted out here, time is too short. The Facts were, of course, context addressed.

Computers, quite literally, move these patterns from place to place in different physical storage areas etched into electronic components. According to Rennie, all content was stored in Watson's RAM for the Jeopardy game because data stored on hard drives would be too slow to be competitive with human Jeopardy champions.

But if I have many millions of neurons working together, isn't that like a parallel computer? Not really. But I don’t think we currently have enough evidence yet to make the case for either. A documentary film was made about this famous match-up entitled Game Over: Kasparov and the Machine. IBM's master inventor and senior consultant, Tony Pearson, estimated Watson's hardware cost at about three million dollars. While there were some practical implementation features the difference between the Facts and any other information in the knowledge base was that the Facts were the active focus of attention. However, if digital computers cannot simulate continuous structures with sufficient robustness, then I think AI would have to start putting more research into analog circuits. Sometimes they also copy the patterns, and sometimes they transform them in various ways – say, when we are correcting errors in a manuscript or when we are touching up a photograph. Its Linpack performance stands at 80 TeraFLOPs, which is about half as fast as the cut-off line for the Top 500 Supercomputers list. In that film Kasparov casually says, "I have to tell you that, you know, game two was not just a single loss of a game. Originally planned as an annual event, the match was not repeated.

I think the only real problem here is whether or not digital computers can simulate the continuous nature of the brain. After one win each and three draws, it was all up to the final game. Why would you want to handicap your amazingly powerful computer? *What do you call a fly with no wings? A walk. Machine" World Championship, against Deep Junior. This was the first time a computer had ever defeated a world champion in match play. It was a loss of the match, because I couldn't recover."

In January 2003, Kasparov engaged in a six-game classical time control match with a $1 million prize fund which was billed as the FIDE "Man vs. Because unless you were recreating the wetware, an emulation of the brain on any other architecture is going to be sub-optimal. Together, a set of rules is called a ‘program’ or an ‘algorithm’. Brains operate in parallel and parallel computers operate in parallel, but that's the only thing they have in common. M & P did a superb and devastating job of analysing them better than anyone before them and showing conclusively that they were inadequate; and this was, and remains, a model of intellectual clarity and original thought.

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