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WSJ Overstates the Case Of the Testy A.I.
mbeckman writes: According to a WSJ article titled "Artificial Intelligence machine gets testy with programmer," a Google computer program using a database of movie scripts supposedly "lashed out" at a human researcher who was repeatedly asking it to explain morality. After several apparent attempts to politely fend off the researcher, the AI ends the conversation with "I'm not in the mood for a philosophical debate." This, says the WSJ, illustrates how Google scientists are "teaching computers to mimic some of the ways a human brain works."
As any AI researcher can tell you, this is utter nonsense. Humans have no idea how the human, or any other brain, works, so we can hardly teach a machine how brains work. At best, Google is programming (not teaching) a computer to mimic the conversation of humans under highly constrained circumstances. And the methods used have nothing to do with true cognition.
AI hype to the public has gotten progressively more strident in recent years, misleading lay people into believing researchers are much further along than they really are — by orders of magnitude. I'd love to see legitimate A.I. researchers condemn this kind of hucksterism.
As any AI researcher can tell you, this is utter nonsense. Humans have no idea how the human, or any other brain, works, so we can hardly teach a machine how brains work. At best, Google is programming (not teaching) a computer to mimic the conversation of humans under highly constrained circumstances. And the methods used have nothing to do with true cognition.
AI hype to the public has gotten progressively more strident in recent years, misleading lay people into believing researchers are much further along than they really are — by orders of magnitude. I'd love to see legitimate A.I. researchers condemn this kind of hucksterism.
http://arxiv.org/pdf/1506.0586...
The actual paper isn't about AI much at all as it is about making neural conversational models, basically, having the computer chat-back at you in a prompt and natural way. The conversations are less about the computer responding cognitively and more about responding human-like based on the speech patterns fed into it.
The researchers tested two types of datasets, an IT Help Chat Scenario fed with data from what I'm guessing are chat databases, and the second set was fed with conversations from movies as found from OpenSubtitles dataset (not sure if this is a relation to open subtitles.org).
The machine took this vocabulary and then pumped out conversations, and the researchers just looked to see how the new sorting method worked.
I don't understand the linguistic terminology nor the modeling at all, but it seems to me that this is less about AI research and more about just getting bot to sound a lot more natural when they generate responses. I guess this eventually has AI implications, but the research paper itself never even mentions AI, nor does it seem like that's their focus. They're just working on speech, and the statements the machine regurgitated were tested not for cognizance or sentience but coherence. The machine spitting out something relatively snappy isn't the machine getting an attitude, it's the machine finding something relevant to the input that the reader takes as snappy. Such an event has no more significance than when people trained Cleverbot to respond to questions about Hitler with "Hitler did nothing wrong". This bot is no more snappy than Cleverbot is a neo-nazi.
The WSJ article links a paper from some researchers at Google:
http://arxiv.org/pdf/1506.0586...
The WSJ article isn't particularly good either; they misunderstand what's actually going on in the research, which seems to be about conversational modeling (a "weak AI" type of research, the "understanding" being very shallow). They point out a few applications of this kind of work though, and that seems pretty solid/useful. (It doesn't approach the goals of "strong AI", those being actually modeling semantics and deeper reasoning)
For every problem, there is at least one solution that is simple, neat, and wrong.
Just waiting until someone at WSJ googles for funny stuff Siri says. They will be SHOCKED at how rude she can be.
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(I work in this area of research.) You are right, the paper is about just a sequence-to-sequence transformation model that learns good replies for inputs but is not actually "understanding" what is going on.
At the same time, we *are* making some headways in the "understanding" part as well, just not in this particular paper. Basically, we have ways to convert individual words to many-dimensional numerical vectors whose mathematical relations closely correspond to semantics of the words, and we are now working on building neural networks that build up such vectors even for larger pieces of text and use them for more advanced things. If anyone is interested, look up word2vec, "distributed representations" or "word embeddings" (or "compositional embeddings").
If you already know what word2vec is, take a look at http://emnlp2014.org/tutorials...
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I use the term "cargo cult" because it's accurate. I'm reasonably well read in neurobiology and biochemistry, and participated in a fair amount of early neural network implementation. But the burden isn't on me to "know what I'm talking about". The burden is on anyone, including as you, claiming science knows anything about how the brain works. You're making the assertion, so you must provide the proof. I'm happy to consider any examples you have of how the cognitive function of your choice operates.
Yes, of course. What else did you think I meant? It's an idea. It's not a certainty. I'm not sure what your point is. Care to elaborate?
You might have meant that, but writing "no idea" didn't (and still doesn't) actually say that. The statement was made that we have no ideas. We do, in fact, have ideas.That was the assertion, and that is my answer.
Human brains are not what are at issue here, but even so, that statement is incorrect. We have made progress at the small scale (see Numenta's work) and there are multiple ideas out there that presently have significant merit. Personally, as someone working in the field and conversant with a lot of what's going on in the technical sense, I have a fairly high level of confidence that we're much closer than the popular narrative would have us believe. Am I right? We will see. :)
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