Path: utzoo!attcan!uunet!snorkelwacker!tut.cis.ohio-state.edu!cs.utexas.edu!rutgers!aramis.rutgers.edu!athos.rutgers.edu!nanotech From: Daniel.Mocsny@uc.edu (daniel mocsny) Newsgroups: sci.nanotech Subject: Re: Review of Penrose's review of Moravec Message-ID: Date: 8 Feb 90 00:48:10 GMT Sender: nanotech@athos.rutgers.edu Organization: College of Engg., Univ. of Cincinnati Lines: 55 Approved: nanotech@aramis.rutgers.edu In article alan@oz.nm.paradyne.com (Alan Lovejoy) writes: >However, artificial brains can and do match, even overmatch, many >of the capabilities of natural brains. And every generation of artificial >brains is more capable than the preceding generation. Also, research is >continuing into the inner workings of natural brains, and it is resulting >in an explosion of new knowledge which is being used to construct >ever-better artificial brains. The first and second sentences above lead me to conclude that by "artificial brain" you refer to (conventional) digital computers. The third sentence must refer to the ongoing work in connectionist AI. While I am partially aware of the work of Grossberg, Carver Mead, Hopfield, et al., where has any insight into the structure of the human brain fed back into conventional microprocessor design? My impression was that the theory of digital computation has proceeded quite independently of any knowledge of the human brain, and that most connectionism is an application of digital computers, not a method for designing better ones. I expect that to change, especially with the hardware-based work of people like Mead. A near-term application might be to interface hardware neural networks as real-world data filters and classifiers for conventional computers. For example: Most of the work people do to support digital computers appears to revolve around organizing sloppy, noisy data into the neat, orthogonal forms that digital computers can handle. For example, consider the general problem of technical publishing. A scientist can rapidly fill notepads with rough sketches, scribbled equations, etc. But translating the information in the scribbles into, say, LaTeX commands is a tedious and slow process. WYSIWYG editors can help, but they merely shift the problem rather than solve it---i.e., they still require humans to translate their natural expression into some rather stilted and artificial sequence of logical operations. Now imagine a neural network that implemented the mapping between my scribblings and syntactically valid LaTeX codes. That would put me one step closer to being able to work as fast as I can think... Well---I'm on the verge of digressing horribly, so I'll cut it here. But I'm still curious---have digital computers gotten anything from brain studies yet, or was the above passage merely a bit of literary license? Dan Mocsny dmocsny@uceng.uc.edu [I think the term "artificial brain" has to be understood as including both hardware and software. No one on either side of the question claims that a sufficiently powerful processor, new from the factory without any program at all, will be an "artificial mind". If your position is "well of course they could simply simulate the brain on a computer, but that would be cheating", you're actually well on the AI side of the debate. --JoSH]