Matt, **** > "Why is evaluating partial progress toward human-level AGI so hard?" > http://multiverseaccordingtoben.blogspot.com/2011/06/why-is-evaluating-partial-progress.html
I don't buy it. **** I don't expect you personally to "buy", or even make a serious effort to understand, anything differing from your prior views.... So I mostly posted those links for any others who were interested but hadn't seen them.... If this were just a conversation with you personally, I wouldn't bother, since I realize your own subjective beliefs and intuitions on these matters are extremely firmly set in your mind. **** I realize there is a cognitive synergy between different components like language and vision, but that is not an excuse for not testing. Synergy makes testing easier because improving any component will improve the test scores of all components. For example, a language model would improve the ability of an image recognition system to score higher in a test matching different photos of the same objects, by enabling it to recognize and understand printed words in the images. Likewise, an image recognition system would make more knowledge available to a language model. **** In principle this is true -- once the components are mature enough, and so is the framework for interconnecting them. But if you have a system whose components will display effective synergy ONLY once they are developed sufficiently, and when they are interconnected within a sufficiently sophisticated framework -- THEN you have a situation where in the early stages of development the benefits of the synergy will be difficult to see... **** I also don't buy that all the parts need to be in place before we can see progress. That is wishful thinking. In fact, we find historically that the opposite happens. You see a lot of progress initially as the easy parts of the problem are solved first. You can solve half of the language modeling problem with a simple parser and a few hundred rules. But the full problem requires a vast understanding of real world and common sense knowledge and the ability to reason, generalize, and solve problems. Natural intelligence has a lot of redundancy and fault tolerance. If one part fails, the rest still works at a reduced level. A blind or deaf person can still be intelligent. *** What we find historically is only a very partial guide to the process of building minds. The systems built in the past lack the complexity of interconnectivity of a human-like mind.... It's true that natural intelligence has a lot of redundancy and fault tolerance. However, I'm not trying to build a biological-style intelligence with OpenCog.... Largely because I think that would require a lot more computational resources... > I am not suggesting that you throw out all of the work on OpenCog and > start over with a radically new design. I am suggesting that you start > applying it to some real problems. I already have a text prediction > (compression) benchmark. Perhaps some test results might attract the > interest of investors. (That's how I got my current job). I am currently applying part of OpenCog (MOSES) to a couple practical machine learning applications (in finance and in genetics). That is my current job. I don't currently think that lossless text compression is very good proto-AGI application area, as I think there are probably more straightforward ways to get incremental improvements on current lossless compression results. However, I would find it interesting (if I had time for it, which I don't currently) to see if integrating OpenCog's PLN reasoning formalism into some probabilistic text compressor could be gotten to yield some improvement. This would require lots of extension to the current PLN code, I feel... The problem I've found with using practical applications to direct AGI development is that the timelines and resource restrictions associated with practical application development, inexorably push one toward making single-component systems that are relatively quick to tweak, improve and test..... Within the context of any particular application project, it's difficult to justify doing a bunch of foundational work on something as complex as OpenCog.... But when one customizes a component (say, MOSES or PLN) for some application, one often does so in a way that's orthogonal (or only loosely related) to what one would need to do, to get it to work in an AGI context... I have not found it so difficult to get investors for narrow vertical applications of OpenCog components. If one of our current vertical applications succeeds dramatically, then (after a few more years) this may generate sufficient revenue to fund the AGI work, which includes key aspects very different from the vertical application work. Raising $$ is always hard, but raising $$ for AGI is far harder than doing so for narrow application development, of course... > I find it > curious that a system that could potentially replace most human labor, > worth hundreds of trillions of dollars, can't even find a few million. > Are people really betting that you have less chance of success than > winning a lottery? The inconsistency of humans' judgments is well known; this is far from the only instance of the phenomenon ;p There are various issues going on here, including (according to my crude guessing) fear of the Terminator, left-over effects from the old AI Winter, and most of all peoples' general fear and skepticism of the unknown... >> "The real reasons we don't have AGI yet" >> http://www.kurzweilai.net/the-real-reasons-we-dont-have-agi-yet > > I agree that computers are not powerful enough to model a human brain > sized neural network or to run lots of experiments. Training data is > another problem. The human vision is trained on the equivalent of > decades of high resolution video. I think that language is an easier > problem. Watson shows that the problem of human-level performance is > at least feasible. Watson is a cool engineering achievement but doesn't really show what you say it does, as it's restricted to an artificial domain. >Google's cat-face neural network recognizer has a > long way to go to get to that level. (And BTW they do have a > quantitative result in their paper: 15% accuracy on ImageNet, the best > so far. IMHO ImageNet is far too small to train a vision system > anyway). I know they have a quantitative result in their paper. However, my point is: that quantitative result is not particularly strong, and is not the thing that got people excited... > I think the hardest problem will turn out to be robotics. About 80% of > our neurons and most of our synapses are in the cerebellum. It is also > the oldest part of our brain in terms of evolution, and therefore the > most complex. Other parts of the body are even older than the brain, are they therefore even more complex? Your logic seems dubious... I agree that movement, planning and other cerebellum-centric brain functions are important to consider in AGI. But, I'm unconvinced by your argument that they are the most difficult part. -- Ben ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
