--- Pei Wang <[EMAIL PROTECTED]> wrote: > To me, traditional computer science (CS) studies "what is the best > solution to a problem if the system has SUFFICIENT knowledge and > resources", and AI is about "what is the best solution to a problem if > the system has INSUFFICIENT knowledge and resources". I also believe > that traditional AI failed largely because it conceptually stayed too > closely to CS.
I think for resources it's the other way around. CS is concerned with the space and time complexity of algorithms. I believe the failure of AI is due to lack of these resources. The brain has about 10^15 bits of memory (counting synapses and using common neural models) and computes 10^16 operations per second (assuming 10 bits/second information rate, higher if individual pulses are significant). We observe that many problems that humans solve, like arithmetic or chess or logical inference, don't require lots of computing power. So we guess that this might be true for everything else. But I don't think that is true. Most of your resting metabolism is used to power your brain. Animals with smaller brains can survive on less food. With this evolutionary pressure, why did we evolve such large brains if the same computation could be done on smaller ones? Most software engineers will tell you to get your program working first, then optimize later. But in AI, what choice do you have? So we put all our effort into abstract knowledge representation and ignore the hard parts like language and vision. Then where will that knowledge come from? What if you had sufficient computing power. Then how would you solve AGI? -- Matt Mahoney, [EMAIL PROTECTED] ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
