Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!iuvax!rutgers!cs.utexas.edu!sun-barr!newstop!sun!amdahl!kp From: kp@uts.amdahl.com (Ken Presting) Newsgroups: comp.ai Subject: Re: Can Machines Think? Summary: Algorithms don't reproduce causality Keywords: causality, finiteness Message-ID: <013Y02gH77ra01@amdahl.uts.amdahl.com> Date: 22 Dec 89 00:35:12 GMT References: <31821@iuvax.cs.indiana.edu> <4689@itivax.iti.org> Reply-To: kp@amdahl.uts.amdahl.com (Ken Presting) Followup-To: comp.ai Organization: Amdahl Corporation, Sunnyvale CA Lines: 49 In article <4689@itivax.iti.org> dhw@itivax.UUCP (David H. West) writes: >Real intelligent systems (e.g. humans) function quite successfully >at a finite temperature despite the influence of thermal >fluctuations (Brownian motion), which cause finite random perturbations >of everything. A finite system embedded in a thermal environment >cannot encode an infinite amount of information. A finite *digital* system is limited in the density of its states by thermal (and other) fluctuations. You are right to point out that the brain is limited in its computational power by this fact. I think that the most productive and interesting approach to cognitive science (if not artificial intelligence) must take advantage of the brain's limitations; CS wants to learn about how the brain computes and the algorithms it follows. But the brain viewed as a computational system is different from the brain viewed as a causal system. The same distinction applies to electronic computers - there are plenty of hardware failure modes to complicate the causal description of the system which are irrelevant to the computational description. If we want to claim that we've accurately modeled the computational power of the brain by demonstrating that our model is faithful to the causal interactions in the brain, we're stuck with modeling the details (including thermal motion) down to whatever level a critic might demand. The word "encode" might be ill-chosen for causal systems. If position & velocity (et al) encode anything, the code would have infinitely many "symbols," one for each state of the system. David continues: >This would seem to indicate that your argument has no bearing on >what may be necessary for intelligence, (...) This is true. Certainly there is no reason to believe that infinite processing power (or infinite anything else) is necessary for intelligence. What I think we can conclude is that a familiar "safety net" argument for AI doesn't quite work (different from the original): (1) The brain is a causal system that thinks (2) We can model causal systems with numerical methods (3) Therefore, we can make a model that thinks . Searle (I believe) would object that we would get a simulation of thinking, not the real thing. I'm objecting that it is physically impossible to make an adequate model. If we could let the model crank as long as it needed before responding (ie relax the real-time constraint) then my argument would not hold. The model would eventually converge close enough to the real system to be indistinguishable. But that won't pass the Turing test.