The 50 year history of AI research portends the difficulty in surmounting secondary obstacles unearthed by each new AI technique's innovations. The limits of deep learning are almost certainly no different from past statistically-driven methods in reaching comparable limits, especially those obstacles that show no signs of vulnerability to the slings and arrows inherent in rich semantics. The recent article by Doug Hofstadter in The Atlantic on using DNNs to do machine translation highlights this nicely.
To date, no work in DNNs has shown any potential for redressing problems of the common sense knowledge needed to know when your translated sentence is semantic nonsense. Likewise, no statistic-based learning method has shown itself capable of tapping into much less creating the deep knowledgebases and wealth of relations that give bare facts the semantic meaning needed to convey or understand subtleties in messages that are more than trivial.
Deft mimicry may win a Turing Test, but complex syntheses of thought (like writing a fictional story in which characters have internal thoughts and hidden motives that depend on relationships, subtexts, and dependencies) — no form of AI has yet shown any potential to solve such problems. Too much internal state and complex relations must be modeled, much less, the ability to extend and translate these to many possible worlds — as humans do every day.
No, statistics-based AI techniques (like deep nets) show little promise to grow indefinitely unto full (wo)manhood.
I'm just an outside observer, but I wouldn't say "no potential". For example, I'm reminded of a paper [1] about a technique that allows translation between language pairs that weren't in the training data.
This doesn't rise to the level of common sense yet, but it seems impressive, and there is a language-independent portion of the neural net that seems to be encoding something.
To date, no work in DNNs has shown any potential for redressing problems of the common sense knowledge needed to know when your translated sentence is semantic nonsense. Likewise, no statistic-based learning method has shown itself capable of tapping into much less creating the deep knowledgebases and wealth of relations that give bare facts the semantic meaning needed to convey or understand subtleties in messages that are more than trivial.
Deft mimicry may win a Turing Test, but complex syntheses of thought (like writing a fictional story in which characters have internal thoughts and hidden motives that depend on relationships, subtexts, and dependencies) — no form of AI has yet shown any potential to solve such problems. Too much internal state and complex relations must be modeled, much less, the ability to extend and translate these to many possible worlds — as humans do every day.
No, statistics-based AI techniques (like deep nets) show little promise to grow indefinitely unto full (wo)manhood.