Why Computers Still Don't Understand People 277
Gary Marcus writes in the New Yorker about the state of artificial intelligence, and how we take it for granted that AI involves a very particular, very narrow definition of intelligence. A computer's ability to answer questions is still largely dependent on whether the computer has seen that question before. Quoting:
"Siri and Google’s voice searches may be able to understand canned sentences like 'What movies are showing near me at seven o’clock?,' but what about questions—'Can an alligator run the hundred-metre hurdles?'—that nobody has heard before? Any ordinary adult can figure that one out. (No. Alligators can’t hurdle.) But if you type the question into Google, you get information about Florida Gators track and field. Other search engines, like Wolfram Alpha, can’t answer the question, either. Watson, the computer system that won “Jeopardy!,” likely wouldn’t do much better. In a terrific paper just presented at the premier international conference on artificial intelligence (PDF), Levesque, a University of Toronto computer scientist who studies these questions, has taken just about everyone in the field of A.I. to task. ...Levesque argues that the Turing test is almost meaningless, because it is far too easy to game. ... To try and get the field back on track, Levesque is encouraging artificial-intelligence researchers to consider a different test that is much harder to game ..."
An eskimo would have the same problem (Score:4, Insightful)
*People* can't understand people (Score:5, Insightful)
People are irrational. They ask stupid questions that make no sense. They use slang that confuses the communication. They have horrible grammar and spelling. And overseeing it all is a language fraught with multiple meanings for words depending on the context, which may well include sentences and paragraphs leading up to the sentence being analyzed.
Is it any surprise that computers can't "understand" what we mean, given the minefield of language?
Re:An eskimo would have the same problem (Score:2, Insightful)
So can some computer programs: Watson includes a confidence percentage in its answer.
Missing human "imagination" (Score:5, Insightful)
The thing missing with many of the current AI techniques is they lack human "imagination" or the ability to simulate complex situations in your mind. Understanding goes beyond mere language. Statistical models and second-order logic just can't match a quick simulation. When a person thinks about "Could a crocodile run a steeplechase?" they don't put a bunch of logical statements together. They very quickly picture a crocodile and a steeplechase in a mental simulation based on prior experience. From this picture, a person can quickly visualize what that would look like (very silly). Same with "Should baseball players be allowed to glue small wings onto their caps?". You visualize this, realize how silly it sounds, and dismiss it. People can even run the simulation in their heads as to what would happen (people would laugh, they would be fragile and fall off, etc).
Re:An eskimo would have the same problem (Score:5, Insightful)
An eskimo would have the same problem, does that mean he cannot understand people ?
In this case he wouldn't understand, but because he lacks knowledge not intelligence. Show him an alligator and a 100 meter hurdles race and he'll be able to answer but the AI will still draw a blank. Ignorance can be cured but we still haven't found a cure for stupid, despite all efforts from education systems worldwide. No wonder we're doing no better with computers.
Re:Helps to remember... (Score:5, Insightful)
And neither helps here. The fact is, you don't know if an alligator can run the hundred-metre hurdles. When you're asked to answer the question, you imagine the scenario - construct and run a simulation - and answer the question based on the results. In other words, an AI needs imagination to answer questions like these. Or to plan its actions, for that matter.
Re:Missing the point as usual (Score:5, Insightful)
I'm pretty sure that 'computer science' is either math or dishonestly labelled trade school, depending on where you get it.
Re:Missing the point as usual (Score:5, Insightful)
"The whole field of AI is built around the assumption that we can solve B without solving A."
Unless one harbors active 'intelligent design' sympathies, it becomes more or less necessary to suspect that intelligences can be produced without understanding them. Now, how well you need to understand them in order to deliver results with less than four billion years of brute force across an entire planet... That's a sticky detail.
Re:Missing the point as usual (Score:5, Insightful)
I've long been a proponent of the idea that there would be far less misunderstandings if it were renamed to "Computational Science". The discipline is the study of how to sequentially break down and solve problems. That we do so with these electronic devices we've so named "computers" is kinda tangential.
Re:Missing the point as usual (Score:5, Insightful)
Even if we restrict the definition of "science" to your definition; that is that science is purely "evidence-based, hypothesis-driven testing", computer science would still fit the bill.
Remember, that CS is as diverse a field as modern physics is. You have theoretical CS, where you tackle questions like: "What is a good, logical definition for computability?" or "How can you logically prove that a program terminates/runs in X time/consumes X resources, no matter the input". This is fully equivalent to the questions of theoretical physics, where you tackle the Grand Unified Theory -- joining gravity, the weak and strong force as well es electromagnetism.
These theoretical question can be brought up without need of evidence -- if all you're interested in is disproving something. According to your definition, this means that the theoretical aspects of both physics and CS are not "science". Okay, let's run with that.
The nice aspect of theoretical questions that can't be disproven by pure thought is, that they lead us on to try to discover concrete evidence that a given theory is true or false in real application! And this is where your rather narrow definition of science comes in, and the point where we find that both practical physics and practical CS fulfill the criteria.
For example in physics, we can test the theory of relativity by building telescopes that look at stars and black holes, to see whether the hypothesis' predictions hold true to raise the hypothesis to the state of a theory. As can be seen with the term people use for "X of relativity", this has happened for relativity.
But if you look with even more than a superficial glance at CS, you will see that the same process is at work in moving from theoretical CS to practical CS. One open question of theoretical CS is whether P = NP or not [1]. So far, we are incapable of disproving either possibility with pure thought. Thus, we turn to practical CS where people try to find evidence of either in the real world. After all, if you can create a program on a real computer that solves an NP-hard problem while never leaving the limits of P, you have conclusively shown that P = NP. So far, we've only found approximative or heuristic solutions that do that, so after 50 years of turning up with "no evidence" we are allowing ourselves to say that the hypothesis of "P != NP" should be treated (even if only cautiously) as a theory -- and we're indeed doing that, as you can see if you look at most modern encryption methods.
But you might say: That is not enough! After all, you could reduce any written computer program on a physical hardware to a sequence of logical steps in a system modeled with pure-thought. And indeed you can, as the Turing-Model of computation promises exactly that -- and so far physical evidence agrees with us. But isn't the same true for physics? After all, physicists search for such a description, too! It's what Maxwell-Clark, Einstein and lots of other physicist were and are after when they ultimately search(ed) for the Grand Unified Theory. How can you blame CS for already having found its Unified Theory?
But the last example finally puts the nail in you view: What about Quantum Computers? They are the point where physics and CS meet; both on the theoretical part (Quantum Theory / Quantum Computation) as well as the practical part (building the thing and proving that the shit actually works as advertised).
So, if we accept your definition of science; then it follows directly that if CS is not a science, Physics can't be either.
[1] - http://en.wikipedia.org/wiki/P_versus_NP_problem [wikipedia.org]