Melanie Mitchell‘s piece, Artificial Intelligence Hits the Barrier of Meaning (NY Times behind limited paywall), is spot-on regarding the hype surrounding the current A.I. boom. It’s soon to come out in book length from FSG, so I suspect I’ll hear about it again in the New Yorker.
Like Professor Mitchell, I started my Ph.D. at the tail end of the first A.I. revolution. Remember, the one based on rule-based expert systems? I went to Edinburgh to study linguistics and natural language processing because it was strong in A.I., computer science theory, linguistics, and cognitive science.
On which natural language tasks can computers outperform or match humans? Search is good, because computers are fast and it’s a task at which humans aren’t so hot. That includes things like speech-based call routing in heterogeneous call centers (something I worked on at Bell Labs).
Then there’s spell checking. That’s fantastic. It leverages simple statistics about word frequency and typos/brainos and is way better than most humans at spelling. It’s the same algorithms that are used for speech recognition and RNA-seq alignment to the genome. These all sprung out of Claude Shannon’s 1948 paper, “A Mathematical Theory of Communication”, which has over 100K citations. It introduced, among other things, n-gram language models at the character and word level (still used for speech recognition and classification today with different estimators). As far as I know that paper contained the first posterior predictive checks—generating examples from the trained language models and comparing them to real language. David McKay’s info theory book (the only ML book I actually like) is a great introduction to this material and even BDA3 added a spell-checking example. But it’s hardly A.I. in the big “I” sense of “A.I.”.
Speech recognition has made tremendous strides (I worked on it at Bell Labs in the late 90s then at SpeechWorks in the early 00s), but its performance is still so far short of human levels as to make the difference more qualitative than quantitative, a point Mitchell makes in her essay. It would no more fool you into thinking it was human than an animatronic Disney character bolted to the floor. Unlike games like chess or go, it’s going to be hard to do better than people at language, but it would certainly be possible. But it would be hard to do that the same way they built, say Deep Blue, the IBM chess-playing hardware that evaluated so many gazillions of board positions per turn with very clever heuristics to prune search. That didn’t play chess like a human. If the better language was like that, humans wouldn’t understand it. IBM Watson (natural language Jeopardy playing computer) was closer to behaving like humans with its chain of associative reasoning—to me, that’s the closest we’ve gotten to something I’d call “A.I.”. It’s a shame IBM’s oversold it since then.
Human-level general purpose A.I. is going to be an incredibly tough nut to crack. I don’t see any reason it’s an unsurmounable goal. It’s not going to happen in a decade without a major breakthrough. Better classifiers just aren’t enough. People are very clever, insanely good at subtle chains of associative reasoning (though not so great at logic) and learning from limited examples (Andrew’s sister Susan Gelman, a professor at Michigan, studies concept learning by example). We’re also very contextually aware and focused, which allows us to go deep, but can cause us to miss the forest for the trees.
The post Melanie Mitchell says, “As someone who has worked in A.I. for decades, I’ve witnessed the failure of similar predictions of imminent human-level A.I., and I’m certain these latest forecasts will fall short as well. “ appeared first on Statistical Modeling, Causal Inference, and Social Science.