On Mon, Dec 24, 2012 at 5:10 PM, Ben Goertzel <[email protected]> wrote:
> "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 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. 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. 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 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 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. 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 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. -- -- Matt Mahoney, [email protected] ------------------------------------------- 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
