3 ms·

I understand where you're coming from. In my college days I bought all those thick academic books on "machine learning" when the term meant Lenat more than it

I understand where you're coming from. In my college days I bought all those thick academic books on "machine learning" when the term meant Lenat more than it meant statistics. That route does seem to have failed us. Or at least not have gone much of anywhere in the years since. At the same time, I'm not sure the Google-y approach to machine learning has made real progress in the last 10 years. Sure, we've got cars that may or may not be able to handle actual road conditions, but search, and more importantly any sign whatsoever of computers knowing what we want to do has stalled out for quite some time now. I dunno. There's got to be another approach that yields more progress than either of the semantic or statistical paths.

bane 14y ago
That route does seem to have failed us. Or at least not have gone much of anywhere in the years since. I'd like to reference the recent Higgs result as a compare contrast example from a different field. The search for the Higgs is slightly younger than the search for AI - but of about the same age so it's worth comparing. It took a very long time to yield basic results -- namely "does it exist?" The search for the Higgs was pure Research. The day after the Higgs discovery nothing changed in the world except that we now know it exists. Given 20-30 more years of R&D we might get a hoverboard, or faster blenders or something, and the total time investment will have been about 70 years from "notion on a chalk board" to "hoverboard". AI researchers might use the Higgs as an example to not poo poo their field since they are still in that long long time between theoretical proposal and working discovery. Detractors might say "but all you propose is just shoving more factoids into your AI model hoping it springs to life"! The analogy with the Higgs is that researchers were for a long time simply proposing to build bigger and bigger accelerators until the Higgs fell out. I'm not a physicist but I'm hoping that there was a stronger theoretical framework surrounding the Higgs then "let's keep crashing stuff into each other harder and harder till what we want comes out of it". Likewise, I'm not a Cyc-style semantic AI researcher (or an AI researcher of any particular type), but I'm hoping that the field has more going for it than "let's keeping tossing factoids into our Semantic Graph until it springs to life". I'm willing to think that a 70 year R&D time is worth it if we end up with commercially ready 1.0 equivalent Minds at the end http://en.wikipedia.org/wiki/Mind_(The_Culture) http://en.wikipedia.org/wiki/Mind_(The_Culture). But at this point I don't think we're any closer to this then we were 20 or 30 years ago. It's a perpetual Research horizon at this point. To put it back into perspective with the Higgs, 15 years ago they were starting construction on the LHC. AI researchers will lament the lack of funding in their field, etc. But I have yet to hear a compelling research direction the field would go if it were suddenly gifted the cost of an LHC or two to advance the field.

bane 14y ago

That route does seem to have failed us. Or at least not have gone much of anywhere in the years since. I'd like to reference the recent Higgs result as a compare contrast example from a different field. The search for the Higgs is slightly younger than the search for AI - but of about the same age so it's worth comparing. It took a very long time to yield basic results -- namely "does it exist?" The search for the Higgs was pure Research. The day after the Higgs discovery nothing changed in the world except that we now know it exists. Given 20-30 more years of R&D we might get a hoverboard, or faster blenders or something, and the total time investment will have been about 70 years from "notion on a chalk board" to "hoverboard". AI researchers might use the Higgs as an example to not poo poo their field since they are still in that long long time between theoretical proposal and working discovery. Detractors might say "but all you propose is just shoving more factoids into your AI model hoping it springs to life"! The analogy with the Higgs is that researchers were for a long time simply proposing to build bigger and bigger accelerators until the Higgs fell out. I'm not a physicist but I'm hoping that there was a stronger theoretical framework surrounding the Higgs then "let's keep crashing stuff into each other harder and harder till what we want comes out of it". Likewise, I'm not a Cyc-style semantic AI researcher (or an AI researcher of any particular type), but I'm hoping that the field has more going for it than "let's keeping tossing factoids into our Semantic Graph until it springs to life". I'm willing to think that a 70 year R&D time is worth it if we end up with commercially ready 1.0 equivalent Minds at the end http://en.wikipedia.org/wiki/Mind_(The_Culture) http://en.wikipedia.org/wiki/Mind_(The_Culture). But at this point I don't think we're any closer to this then we were 20 or 30 years ago. It's a perpetual Research horizon at this point. To put it back into perspective with the Higgs, 15 years ago they were starting construction on the LHC. AI researchers will lament the lack of funding in their field, etc. But I have yet to hear a compelling research direction the field would go if it were suddenly gifted the cost of an LHC or two to advance the field.