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It could be like Turing Equivalency in that deep nets of deep nets may be able to do the job, but are not the most efficient and/or easiest AI for humans to tun
by tabtab 8y ago
It could be like Turing Equivalency in that deep nets of deep nets may be able to do the job, but are not the most efficient and/or easiest AI for humans to tune and tame.
I'm not saying gluing bunches of deep nets together is bad R&D, only that it may not be the only or best AI game in town. I suppose if we got a wad of deep nets to give General Intelligence (including "common sense"), but it took a big room of servers, that may be a good enough springboard, but I'm not sure putting all our eggs in the deep net basket is the best route to fuller AI just because it's been the most promising up to this point.
It's roughly comparable to spending all R&D perfecting vacuum tubes in the 1940's because they were the best known route back then to general digital electronics. The best starters are not always the best finishers.
- cr0sh 8y ago> It's roughly comparable to spending all R&D perfecting vacuum tubes in the 1940's Vacuum tubes continued to be improved all the way up into the early 1960s, long after transistors had taken hold: https://en.wikipedia.org/wiki/Compactron https://en.wikipedia.org/wiki/Compactron One in particular was used mainly in television sets, all the way up into the early 1970s: https://en.wikipedia.org/wiki/Nuvistor https://en.wikipedia.org/wiki/Nuvistor ...it competed with transistors, and was almost as small as some of its contemporary transistor cousins. Even today, you can't rule out the vacuum tube - for some things, there isn't any substitutes: http://www.nutsvolts.com/magazine/article/vacuum-tubes-for-the-21st-century http://www.nutsvolts.com/magazine/article/vacuum-tubes-for-t... ...and new ideas for the old technology are being investigated: http://www.sciencemag.org/news/2012/05/return-vacuum-tube http://www.sciencemag.org/news/2012/05/return-vacuum-tube
- tabtab 8y agoI didn't mean to imply that vacuum tubes stopped having uses or stopped improving. My main point is that they stopped being the primary source of electronics and digital processing innovation and progress. Even if by chance neural/deep-nets stop being the primary source of AI innovation, I'm sure people will find and make improvements in them regardless.