> Fine. Which idea of anyone's do you believe will directly produce > general intelligence - i.e. will enable an AGI to solve problems in > new unfamiliar domains, and pass the general test I outlined? (And > everyone surely agrees, regardless of the test, that an AGI must have > "general" intelligence).
Well, as I said before, I don't know which will directly produce general intelligence and which of them will fail. I have my own theories about which approaches are more likely to succeed than others, and about which approaches are fundamentally wrong. However, most serious ideas seem to have a plausible story, and I'm not ready to completely rule out any serious idea until it is proven wrong. I'll briefly discuss some ideas below. You may not agree with my interpretation of the approaches, and you may not fully agree with my argument about why they're plausible... but I think that you surely have to agree that a plausible argument can be made for most of this research, and it is clear that the people conducting the research can see themselves as addressing the crucial questions. That is, while I'm not the right person to be arguing the details of these approaches, I'm confident that many researchers here wouldn't be devoting their time to their research if they didn't see a coherent picture for how their work fits into the grand scheme of AGI. Many apologies to other readers if I've not included your preferred approach or have misrepresented/misinterpreted your ideas. I've just taken a quick and informal sample here. The details aren't as important as the overall message. Logic ------------------- An automated theorem prover is an extremely general purpose intelligence. Consider, for example, how logics may be adapted to many different domains on the Semantic Web or the increasing strength of competitors in General Game Playing competitions (surely not long before they're better than the average human at any novel game?). As to whether logic can be applied to general purpose embodied intelligent systems remains to be seen - I think the symbol grounding problem points towards logic not being enough - but researchers looking into logics with uncertainty or logics that incorporate iconic representations are effectively exploring a possible "solution" to the symbol grounding problem. In other words, these researchers are saying "Logical deduction offers true 'general intelligence' in symbolic domains, and we're trying to adapt that intelligence to real life situations": a plausible crux idea and worth pursuing. Hybrid Systems ------------------- If we just keep doing what we're doing in "Narrow AI", but look at combining many components into a coherent architecture then it seems plausible that we'll eventually end up with a system that is indistinguishable from an ideal general intelligence. It may not be an elegant answer, but it may be an answer. This gives good reason to pursue integration. Consider for example, problems like the DARPA Grand Challenges. In current systems, obstacles may be specifically identified against a hand-coded database. In the next generations, these representations might become more generic and learnt from experience. I see a plausible progression to increasingly more powerful systems. When the system can identify and learn the behavior of any new object it encounters (and the rules that govern it), it may then be able to reason about that object and construct plans that uses the object in novel ways. At first the planning algorithms seek merely to visit way-points. Future versions, with richer goals, richer models and more powerful reasoning may autonomously deduce novel behaviors beyond their explicit programming (e.g., that truck will run into the pedestrian! my higher goal of not hurting pedestrians means that the best plan is one in which I stop in front of the truck so that it crashes into me instead of the pedestrian). Genetic Algorithms and other search algorithms ------------------- If you have a "genetic language" that is sufficiently general, and infinite computing power, then a good genetic algorithm can eventually solve any computable problem. Evolution eventually discovered human beings - given infinite computing power, then at worst you could evolve a virtual human! It seems reasonable then to consider exploring genetic or other search algorithms that have a bias towards the kinds of problems encountered by humans and AGI. Activation, Similarity, Analogizing, HTM, Confabulation and other "targeted" approaches ------------------- There seem to be a lot of groups working on specific modes of thought. You may not be convinced that they're solving enough of the problem, but it seems plausible to me that maybe general intelligence really is easy once you've managed to solve some particular problem. That is, we might have a 80/20 rule or even a 99.9/0.1 rule at play with intelligence. Maybe the brain only does learn a few techniques for problem solving, and all the hard work is done in finding analogies between the successful techniques and the given problem. This would be a reasonable argument for pursing analogizing. Maybe the deepest challenge really is in finding what concepts are associated with other concepts - i.e., that the vast majority of our brain performs nothing more than primitive association based learning, and our higher-level cognition is just the "icing on the cake" (that was easily evolved), but that unlocks all of the general powers of association forming. As possible evidence that something simple might be 99.9% of intelligence, it might be worth considering the care taken in experiments testing animal intelligence. Great care must be taken to rule out conditioning, because conditioning can be used to successfully explain so many behaviors. Many seemingly intelligent behaviors demonstrating deep cognition are later discovered to be better explained by conditioning; a system that can efficiently learn by conditioning might be prove to be so close to generally intelligent that it only takes a small effort to close that final gap to true AGI. Computational Forms of Universal Intelligence ------------------- The universal AIXI approach to intelligence is a plausible solution to AGI (under assumptions of infinite computing power and that general intelligence is a computable problem). It therefore seems reasonable to consider that computational approximations of this ideal model might lead to AGI. Neural Networks ------------------- Even though neural networks seem to have fallen out of favor compared to their early days, the human brain serves as an existence proof that with this approach (or related approaches) it is possible to achieve AGI. My point, again, is that we don't know how the first successful AGI will work - but we can see many plausible ideas that are being pursued in the hope of creating something powerful. Some of these are doomed to fail; but we don't really know which ones they are until we try them. It doesn't seem fair for you to say that nobody has offered a "crux" idea, and I'd prefer that people follow their passions rather than insist that everybody should get hung up on the centuries/millennia old question of what exactly is intelligence. -Benjamin Johnston ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=93749339-13e4f1
