Comments on: I, AI (On the Unlikelyhood of Achievement of Human-Equivalent Cognition in Computational Neuroscientific Explorations) http://warren.igsig.org/2007/04/18/i-ai/ a sprawling compendium of short-stacked musings Wed, 07 Jan 2009 12:22:12 +0000 http://wordpress.org/?v=2.1.2 By: Dan W http://warren.igsig.org/2007/04/18/i-ai/#comment-2 Dan W Fri, 20 Apr 2007 05:53:30 +0000 http://warren.igsig.org/2007/04/18/i-ai/#comment-2 Many views of the brain portray it as an intrinsically complex system which resists reductionistic approaches. The failure of science at comprehending its workings allows broad, popular books like Hofstadter's and Minsky's to be successful without having solid facts or understanding to back up long-winded musings. Brain "science" is still in the domain of philosophers. You don't have to believe that it will be hard to duplicate human cognition -- it *is* hard! But then again, what is this "cognition" you speak of? For that matter, how can we precisely define consciousness? Intelligence? We cannot even precisely define what we are aiming to achieve! Our brain contains 100 billion neurons with 100 trillion connections, yet very little is known about how networks work. In fact, we currently only know about individual, isolated cells due to the limitations of recording technology. Hence, limitations in simulating the brain are not technological. Indeed, projects such as Blue Brain use supercomputers to attempt to simulate cortical columns using single-cell models. Yet, we do not know what portions of the models are important for "neural computation", how information is encoded, or even what "neural computation" exactly entails. Few (if any) fundamental brain principles are known. I disagree that top-down approaches will bear much fruit until models are falsifiable. To date, many models of neural function have been proposed (e.g. Self-Organizing Maps), but these models cannot be adequately tested using current technologies. Hence, theory has had few successes. The field of AI is inundated with top-down models of how we interpret the world, but none of them are even close at being as general like the brain. Hence, I believe it is unlikely that this approach will be successful (except for in very specific domains) without more understanding from the biologists reverse-engineering the real deal. Your third argument against our ability to emulate cognition regarding the richness of our environment is not strong. We do not know what world we inhabit if not for our perception of that world. Perception is purely a product of the brain. We have millions of receptors throughout our body which only record *contrast* in the outside world. For instance, the mechanoreceptors on our skin only record changes in pressure rather than absolute pressure, and from that our perception records a rich, seemingly absolute tactile sense. And as for binary symbol shunting, isn't it true that *any* discrete symbol can be encoded as ones and zeros (and indeed, almost anything can be considered discrete!)? PS. The blog is great so far -- your writing is always top-notch. Keep up the good work! Many views of the brain portray it as an intrinsically complex system which resists reductionistic approaches. The failure of science at comprehending its workings allows broad, popular books like Hofstadter’s and Minsky’s to be successful without having solid facts or understanding to back up long-winded musings. Brain “science” is still in the domain of philosophers.

You don’t have to believe that it will be hard to duplicate human cognition — it *is* hard! But then again, what is this “cognition” you speak of? For that matter, how can we precisely define consciousness? Intelligence? We cannot even precisely define what we are aiming to achieve!

Our brain contains 100 billion neurons with 100 trillion connections, yet very little is known about how networks work. In fact, we currently only know about individual, isolated cells due to the limitations of recording technology. Hence, limitations in simulating the brain are not technological. Indeed, projects such as Blue Brain use supercomputers to attempt to simulate cortical columns using single-cell models. Yet, we do not know what portions of the models are important for “neural computation”, how information is encoded, or even what “neural computation” exactly entails. Few (if any) fundamental brain principles are known.

I disagree that top-down approaches will bear much fruit until models are falsifiable. To date, many models of neural function have been proposed (e.g. Self-Organizing Maps), but these models cannot be adequately tested using current technologies. Hence, theory has had few successes. The field of AI is inundated with top-down models of how we interpret the world, but none of them are even close at being as general like the brain. Hence, I believe it is unlikely that this approach will be successful (except for in very specific domains) without more understanding from the biologists reverse-engineering the real deal.

Your third argument against our ability to emulate cognition regarding the richness of our environment is not strong. We do not know what world we inhabit if not for our perception of that world. Perception is purely a product of the brain. We have millions of receptors throughout our body which only record *contrast* in the outside world. For instance, the mechanoreceptors on our skin only record changes in pressure rather than absolute pressure, and from that our perception records a rich, seemingly absolute tactile sense. And as for binary symbol shunting, isn’t it true that *any* discrete symbol can be encoded as ones and zeros (and indeed, almost anything can be considered discrete!)?

PS. The blog is great so far — your writing is always top-notch. Keep up the good work!

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