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Well, what we do have in our heads is a human brain, which I believe is not more powerful than a Turing machine, and is a working proof-of-concept created by a

Well, what we do have in our heads is a human brain, which I believe is not more powerful than a Turing machine, and is a working proof-of-concept created by a random greedy trial-and-error incremental process in a not-astronomical number of generations out of a population of less than one million primates. That tells me that we're probably not a warp-drive distance away from finding a working software implementation of its critical elements. And each time a software problem goes from "unsolvable by a computer, yet trivial for the human brain" to "trivial for both", it seems to me that we lose more than just another CAPTCHA. We're losing grounds to believe that anything the brain does is fundamentally all that difficult for computers to do, once we just stop being confused about how to do it. This has happened very frequently over my lifespan and even more rapidly in the past 12 months, so it no longer feels surprising when it happens. I think we've basically distilled the core elements of planning, intuition, perception, imagination, and language; we're clearly not there yet with reasoning, reflection, creativity, or abstraction, but I don't see why another 10 or 20 years of frantic effort won't get us there. GPT, SD, and Segment Anything are not even extensions or scaling-up of AlphaGo, so there are clearly multiple seams being mined here, and very little hesitation to explore more widely while cross-pollinating ideas, techniques, and tooling.

toss1 4y ago
Interesting approach, especially to the questions raised >>not more powerful than a Turing machine In many ways less powerful, but also has some orthogonal capabilities? >>working proof-of-concept For sure! >>probably not a warp-drive distance away from finding a working software implementation of its critical elements >>I don't see why another 10 or 20 years of frantic effort won't get us there Agree. My sense is that an AGI is on a similar time and frantic effort scale, although with not quite the same reasoning. I think it is not just airplane-to-rocket tech, but closer than warp-drive tech. It also depends if we're talking about a general-ish tech or a runaway AGI singularity. >>created by a random greedy trial-and-error incremental process in a not-astronomical number of generations out of a population of less than one million primates. True, although setting the baseline at primates is very high. Even lower mammals and birds (avian dinosaur descendants) have significant abstraction and reasoning capabilities. The "mere" birds-nest problem, of making a new thing out of random available materials, is very nontrivial. So, we first need to create that level of ability to abstract. This would include having the "AI" "understand" physical constructs such as objects, hiding, the relationship between feet, knees, hips, torso and head (and that in humans, the feet and knees point in the same direction as the face...), the physical interactions between objects... probably the entire set of inferences now embedded in CYC, and more. THEN, we need to abstract again to get from the primate to the runaway symbolic and tool wielding processing of humans and beyond. It seems that the first problem set will be more difficult. Looking again to the biological evolution, how much longer did it take for biology to develop the ability to abstract 3D shapes and relations (first hunting predators?). It was a heck of a lot more time an iterations than the million primates for a few million generations. So, this might be similar. >>to explore more widely while cross-pollinating ideas, techniques, and tooling. Yup, key there. Another key is being more biomimetic, both in the simulation of neuron functioning and in deeply integrating sensor suites to the computing system. The idea that we are just brains in jars seems an abstraction (distraction?) too far. I have a hard time seeing how our brains are more than a big node in our whole nervous and indeed biological system, and the input from the entire body is essential to growing the brain. I expect we might find something similar about AI. OTOH, in airplanes, our method of propulsion and control are quite different vs the biological solutions from birds (although the lift principles are the same), and we're still integrating a lot of bird "tech" into flying. Wheels vs legs might be a better example, although the hottest thing is legged robotics, since they don't need roads... It seems that we are similarly developing clunky, limited, and very-artificial intelligence systems, before we get to building the flexible systems seen in biology... BTW, thx for the discussion - great thoughts!

toss1 4y ago

Interesting approach, especially to the questions raised >>not more powerful than a Turing machine In many ways less powerful, but also has some orthogonal capabilities? >>working proof-of-concept For sure! >>probably not a warp-drive distance away from finding a working software implementation of its critical elements >>I don't see why another 10 or 20 years of frantic effort won't get us there Agree. My sense is that an AGI is on a similar time and frantic effort scale, although with not quite the same reasoning. I think it is not just airplane-to-rocket tech, but closer than warp-drive tech. It also depends if we're talking about a general-ish tech or a runaway AGI singularity. >>created by a random greedy trial-and-error incremental process in a not-astronomical number of generations out of a population of less than one million primates. True, although setting the baseline at primates is very high. Even lower mammals and birds (avian dinosaur descendants) have significant abstraction and reasoning capabilities. The "mere" birds-nest problem, of making a new thing out of random available materials, is very nontrivial. So, we first need to create that level of ability to abstract. This would include having the "AI" "understand" physical constructs such as objects, hiding, the relationship between feet, knees, hips, torso and head (and that in humans, the feet and knees point in the same direction as the face...), the physical interactions between objects... probably the entire set of inferences now embedded in CYC, and more. THEN, we need to abstract again to get from the primate to the runaway symbolic and tool wielding processing of humans and beyond. It seems that the first problem set will be more difficult. Looking again to the biological evolution, how much longer did it take for biology to develop the ability to abstract 3D shapes and relations (first hunting predators?). It was a heck of a lot more time an iterations than the million primates for a few million generations. So, this might be similar. >>to explore more widely while cross-pollinating ideas, techniques, and tooling. Yup, key there. Another key is being more biomimetic, both in the simulation of neuron functioning and in deeply integrating sensor suites to the computing system. The idea that we are just brains in jars seems an abstraction (distraction?) too far. I have a hard time seeing how our brains are more than a big node in our whole nervous and indeed biological system, and the input from the entire body is essential to growing the brain. I expect we might find something similar about AI. OTOH, in airplanes, our method of propulsion and control are quite different vs the biological solutions from birds (although the lift principles are the same), and we're still integrating a lot of bird "tech" into flying. Wheels vs legs might be a better example, although the hottest thing is legged robotics, since they don't need roads... It seems that we are similarly developing clunky, limited, and very-artificial intelligence systems, before we get to building the flexible systems seen in biology... BTW, thx for the discussion - great thoughts!