14 ms·

Artificial Intelligence and What Computers Still Don't Understand

185 points by phreeza 13y ago

eschaton 13y ago
Interesting that there seems to be no mention of Cyc, which is explicitly intended to be good at problems like those mentioned.
- teddyh 13y ago
  But is it any good at those problems?
  - seiji 13y ago
    It's best to just let Cyc people continue to think they matter and let them stay busy expanding their knowledge base in the church of first order logic. It'll never go anywhere, but it's better to keep them all contained in one place rather than polluting other fields of study.
    - pnathan 13y ago
      Okay, that was way too nasty and out of line.
    - ScottBurson 13y ago
      See, this is what I mean (see my reply to eschaton). I take it then, seiji, that you also completely disagree with Levesque's program as expressed in section 4.4 of his paper?
      - seiji 13y ago
        Absolutely. 4.4 is a full endorsement of Cyc (and don't get me started on "Cyc" vs "OpenCyc"). Second 4.4 looks a little like "graphplan reborn" or "generic AI approaches from the 80s." Discrete codified knowledge is not the stuff the universe is made of. The problem Cyc will never solve is the "a picture is worth a thousand words" problem. Describing everything in a relational, hierarchal, predicate calculus arrogantly ignores the unrelated multidimensionality of, well, everything.
    - giardini 13y ago
      A merciful end to Cyc funding might be better. Monies saved could be spent elsewhere and termination would free all involved without the expectation that they produce something of value. Although it might cause some grudges ("If we'd only been allowed to add the last 2000 rules, I am certain it would have achieved consciousness!") everybody needs a new start now and then. Had Lenat published working versions or even complete explanations of his previous software (AM, EURISKO) I might be a little more sympathetic. Cyc, the project that never ends, appears to be in a state of perpetual partial implementation, it's most notable product being a constant stream of money into the project. But look on the bright side: perhaps I'm completely wrong, Cyc has actually achieved AI, and the NSA is using it right now!
  - ScottBurson 13y ago
    I think we can infer, from Levesque's failure to even mention it, that the answer has to be no. I admit I am curious exactly what Cyc can and can't do, and why. I haven't looked at it closely, though.
    - eschaton 13y ago
      What I've heard is that their system has gotten progressively better over time while their publishing had gotten worse. People in academia can get ResearchCyc and see just what its capabilities are these days; it's basically the same as full Cyc. OpenCyc is a shadow of the real thing, supposedly.
- ScottBurson 13y ago
  I agree, that is very curious. I clicked through to Levesque's paper, and there is no mention of Cyc there either. I gather Cyc has long been considered a failure by many in the AI community, and perhaps even an embarrassment by some. Still it is hard not to read Levesque's paper as a vigorous endorsement of the goals of the Cyc project, if perhaps not of its exact methods -- see sec. 4.4. I think it is incumbent on Levesque to explain what Cyc has done wrong, if he has a good theory about that -- and if he doesn't, he needs to admit it, because Cyc's ostensible failure makes it seem unlikely that any symbolic, reasoning-oriented approach to AGI could ever succeed.
  - rjsw 13y ago
    If Cyc has failed then why do there seem to be so many people trying to build XML schemas that could be used to reimplement it.
    - seiji 13y ago
      Cyc-type approaches are useful for narrow, well-specified domains, just not AGI or actual "intelligence."
      - mark_l_watson 13y ago
        It has been a few years since I went through Ben Goertzel's team's AGI papers, but I find it difficult to believe that Cyc would not be useful for background information for real world knowledge. I would use Cyc, Wikipedia info boxes,etc.
valtron 13y ago
Aside: The Loebner prize is a terrible implementation of a Turing test because the judges have no idea what they're doing. You can't just try to have a normal conversation and see which one sounds more human. You _have_ to try tricky things like the article mentions; beside syntactically ambiguous sentences, you might also try things that would be obvious to a human but that a computer would easily get confused by unless it was programmed to check for it. Likenotusingspaces. O.r. .i.n.t.e.r.l.e.a.v.i.n.g. .l.e.t.t.e.r.s. .w.i.t.h. .s.o.m.e. .r.a.n.d.o.m. .c.h.a.r.a.c.t.e.r. Also, both the participants being tested (AI and human) should be trying to convince the judge that they're the human and the other is the AI. Given that chatterbots that insert "one-liners" and other non-sequitors do well, I doubt that's happening, so again, the Loebner test is useless.
- seiji 13y ago
  "Is it an AI" tests based only on text are pretty silly anyway. There are probably a few billion people alive who can't form complete written sentences. If you have a job, you probably encounter dozens of people a year who can't write coherent emails. The Turing Test concept appeared when everybody thought intelligence was mostly words, and we could probably easily make computers spit out meaningful sequences of words. Nobody believes intelligence is how well you can write and read anymore (except fad-based media reports "OMG SIRI IZ INTELLUGENT AI SKYNET!"). The Turing Test concept persists because it's simpler than dirt to explain, even though it has never been a valid method of evaluating anything.
  - feral 13y ago
    I don't think you are being fair on the Turing test, or on the intelligence of its creator. In describing the setting of the test, Turing first describes an imitation game where a judge must tell apart a man and a woman. He writes "In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary." So the text format is out of the practicalities of defining a workable test, not because Turing thought 'intelligence is how well you can write and read'. Your other objection is that there are humans who can't form written sentences. Well, they could use the intermediary, if they cant write. But, even if you mean that they can't put a verbal sentence together, the test is framed such that a machine that can pass it can be considered to think, not so that any machine (or human) that can think is supposed to be able to pass the test. Again, from Turing's paper: "May not machines carry out something which ought to be described as thinking but which is very different from what a man does? This objection is a very strong one, but at least we can say that if, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection." In other words, passing the test - succeeding at the imitation game - is designed to be a sufficient test to demonstrate intelligence, but not a necessary one.
iUnderstand 13y ago
I have thought about this long and hard. There was a time in my life when I thought I could create something that had intelligence. Here are some of my thoughts. Intelligence isn't something that is instant, it is something that is acquired. To acquire intelligence, the being must have an environment in which to interact and learn. My idea was to mimic the way that animals and plants become adaptive to their environment. They all can die, and only the strong survive. So, each being, or thread, would have the ability to die. Well, what happens if they all die? Then the experiment would be over and that wouldn't be fun would it. So, threads would also have to have the ability to reproduce. And, what good would it be if all the threads were the same? They must be able to mutate. What about intelligence? Forget intelligence, it just represents the ability to survive. All the threads will die off that can't survive anyways, so what we are left with is a pool of survivors. Uh oh. The environment exists in a virtual realm. So my threads wont be very beneficial in the real world realm that you and I live in. We need eyes and ears into the real world. OK, no problem, we will purchase cameras to capture light, and microphones for sound. Now we have a money issue. I may be able to afford a couple hundred micro controllers, microphones, and cameras. Not bad for a little expirement, but wait "Scientists estimate that there are one quadrillion (1,000,000,000,000,000) ants living on the earth at any given time." according to hypertextbook.com http://hypertextbook.com/facts/2003/AlisonOngvorapong.shtml http://hypertextbook.com/facts/2003/AlisonOngvorapong.shtml. How can my intelligence be smarter than an ant, if there are this many ants, who are trying to survive, and have the ability to reproduce and mutate? I don't think I can. I began thinking about this more and more. We would have to speed up the evolution of our intelligent being. What are different things we could do to speed it up? Perhaps we could create more mutations, but not too many, we don't wont our bot to become extinct. Oh crap, how is it going to reproduce? I totally forgot about that. Each of our species would have to have an electronics factory built inside it. I know the solution, don't try to create your own intelligence. You can create as much ARTIFICIAL intelligence like search engines all you want, but this is not intelligence in any way shape of form. It looks like intelligence, but it is not. To achieve truly something more intelligent than a human, you will need to take what nature has already created and mutated, and then provide environment enhancements(schools?). There I said it. Schools and all theses GNC drugs that are supposed to make your brain function at an increasing rate. I am afraid we are already trying to become more intelligent every single day in the world. And yes, it is working, but we can only work as fast as nature allows us. On a closing note, Computers will never be able to think like a human, because they cannot reproduce. Nature is much more efficient.
ilaksh 13y ago
This guy should look into research labeled as artificial general intelligence (AGI) or deep learning. If he really understood for example what Watson can do or the leading research using things like autoencoders or hierarchical temporary memory for natural language understanding then he would have a better informed and less pessimistic attitude.
- mturmon 13y ago
  The piece actually seems like a backhanded critique of the optimism shown by the deep learning community (which of course Winograd is aware of). It also seems like an appreciative backward glance at topics that interested Winograd earlier in his career, but are less fashionable now.
eboyjr 13y ago
I think we could help improve the field by making CAPTCHAs with these kinds of questions.
- fragsworth 13y ago
  This is brilliant. First you have to come up with a way to automatically generate these questions though. If we have to make them manually, the machines can just remember all the answers. Creating the questions seems much harder than answering them.
- saltvedt 13y ago
  Won't work. Assuming that the right answer is a word in the question, the computer can just brute force the answer in a couple of guesses.
  - Houshalter 13y ago
    If you fail 50% of guesses, that should flag you as a spammer. If you ask a few questions with multiple possible answers it's pretty unlikely it would get them all correct through random guesses anyways.
    - nawitus 13y ago
      The bot can use only 1 guess per website.
  - anko 13y ago
    could you just change the number of questions to scale with the number of attempts? ie. if there are more than 3 attempts at answering a question, then ask 2 questions.
RyanMcGreal 13y ago
Essentially, the article accuses AI researchers of searching for their lost keys under streetlamps. Fair enough, but I wonder if the best use of AI research is to get computers to solve problems that a person could easily solve. I thought the point of, e.g. a search engine is to solve problems a person can't easily solve.
- dnautics 13y ago
  you should read "fluid concepts and creative analogies". There was some good headway made in this direction that got lost.
- fragsworth 13y ago
  > I wonder if the best use of AI research is to get computers to solve problems that a person could easily solve That's pretty short-sighted. These "easy" problems are stepping stones to much more difficult problems. For instance, you cannot tell a machine something like "build my website" without it being able to solve the easy stuff first.
  - cr4zy 13y ago
    And once a machine has learned to do something "easy" in the same way we do, that is via sensory perception, it will be extremely exciting to see how that knowledge can be forked and evolved via the excellent copyability inherent in computers.
    - deleted 13y ago
      [deleted]
  - bad_user 13y ago
    Oh come on, sure you can tell a computer "build my website". You can use language, or you can click a button. Oh, but the result is not what you want? Even when communicating with a human, you need to provide some specs, right? But then, natural language is way to imprecise and open to interpretation, coupled with the fact that people don't really know for sure what they want, so even when speaking with a developer, you're going to go through several iterations. So for the ultimate flexibility and automation, the user interface of such a smart appliance will end up being essentially a programming language (being the same reason for why lawyers and mathematicians have their own language that's anything but natural). Truth of the matter is, computers answering to commands aren't really that interesting, as they already do this. What we really want are computers capable of ideas. Also, natural language processing is hard because it's based on an incredible amount of implicit context that life taught us ever since we were born and when communicating to our fellow humans, it's never about just the actual words being spoken. When saying something like "build my website" your spouse probably knows you're talking about a website meant for marketing yourself and she probably knows your tastes and values too; she observed your fears and dreams while living your mundane life, even if you've never talked with her explicitly about such issues (speaking of women, they are incredibly good at reading between the lines). Solving natural language processing is basically akin to creating sentient beings.
dnautics 13y ago
I wanted to arrage for watson to go on "are you smarter than a 5th grader" and give it all sorts of questions like these (but even easier). For example: "Think of a giant flightless bird. Now, name a color that starts with the same first letter."
- foobarbazqux 13y ago
  Eggplant. Or blue.
dnautics 13y ago
any thoughts here on the parallel terraced scan, as implemented by Hofstadter in "fluid concepts and creative analogies"?
bsaul 13y ago
isnt that criticism thr exact point chomsky was trying to make in his proxy discussion with norvig from google ? i found the exemple given in the article excellent though. it's another reason why computers won't be able to solve grammar mistakes 100% correctly (at least in latin languages).
- anaphor 13y ago
  Chomsky doesn't think we should be trying to observe how people use language on a mass scale, so screw him imo. See: http://languagelog.ldc.upenn.edu/nll/?p=3180 http://languagelog.ldc.upenn.edu/nll/?p=3180 "The other argument has to do with the methods of science: Chomsky argues for "very intricate experiments that are radically abstracted from natural conditions". "
SeanLuke 13y ago
This whole article, and [I think] Levesque's IJCAI article, seems to think that artificial intelligence is just about language.
- hackinthebochs 13y ago
  Its not that its just about language, but I do think that a system that could pass the turing test would necessarily be AGI. To put it another way, to fully solve the NLP problem one would have to have created AGI. To pass the turing test a system would need to simulate or acquire the knowledge of an entire life of experiences of a person and be able to convincingly converse about those experiences with an actual person. This would undoubtedly be AGI.
  - SeanLuke 13y ago
    1. I think "AGI" is a ridiculous recent rebranding of "Hard AI" and doesn't represent any meaningful scientific or engineering pursuit. 2. Even if one accepted that NLP is AI-complete: this is a sufficient condition and not a necessary condition. But the claim being made in the articles is that it is a necessary condition: that is, that other AI disciplines are distractions because they do not lead to the AI grail that NLP supposedly leads to. This is classic GOFAI hogwash.
    - SeanLuke 13y ago
      Hard AI -> Strong AI. Brain not firing on all four cylinders.
blauwbilgorgel 13y ago
Can an alligator run the hundred-metre hurdles? http://www.youtube.com/watch?v=pjslsKZYXQ8 http://www.youtube.com/watch?v=pjslsKZYXQ8 (Can alligators jump and climb? Yes, they can!) http://www.animalquestions.org/reptiles/alligators/can-alligators-climb-fences/ http://www.animalquestions.org/reptiles/alligators/can-allig... (many have thought was a myth, it is true that alligators are in fact able to climb fences.) These anaphora questions are better solved through natural language parsing, for example turning them into predicate logic. The article is right when it says that big data algo's have a problem with these sentences. But that is not to say they can't help. In the article: The town councillors refused to give the angry demonstrators a permit because they feared violence. Who feared violence? You can use big data to calculate the semantic closeness between "town council" and "fear violence". If that is closer than between "demonstrators" and "fear violence" you can make a good guess. From the Wikipedia article on anaphora: We gave the bananas to the monkeys because they were hungry. We gave the bananas to the monkeys because they were ripe. We gave the bananas to the monkeys because they were here. A quick Google search for "bananas were ripe", "monkeys were ripe", "monkeys were hungry" and "bananas were hungry" and counting the results will solve this. Using semantic closeness can also work against you in the case of ambiguity: The robber sits on the bank. Is "bank" a furniture here? A money bank? A river bank? Semantic closeness might picture the robber sitting on a money bank. A second or third pass is necessary: calculate the chance that a person will sit on a building vs. the chance that a person will sit on furniture. This is a decades-old hard problem that is close to philosophy. Is the robot inside Searle's Chinese room intelligent? Is it really understanding what it is saying? Or is it "faking" it?
- lisper 13y ago
  > You can calculate semantic closeness between "town council" and "fear violence". If that is closer than between "demonstrators" and "fear violence" you can make a good guess. Only for that particular example. It's easy to come up with examples where that heuristic fails: "The town council denied the protestors a permit because they were in a bad mood." "The factory failed to produce the car on time because it was undergoing maintenance."
  - 13y ago
dvt 13y ago
Levesque saves his most damning criticism for the end of his paper. It’s not just that contemporary A.I. hasn’t solved these kinds of problems yet; it’s that contemporary A.I. has largely forgotten about them. In Levesque’s view, the field of artificial intelligence has fallen into a trap of “serial silver bulletism,” always looking to the next big thing, whether it’s expert systems or Big Data, but never painstakingly analyzing all of the subtle and deep knowledge that ordinary human beings possess. This is very true. But I think that recent AI experts (as opposed to those doing this work in the 70s) have realized that trying to tackle linguistic analysis is very very (very) hard. The problem with language (or more correctly, discourse) analysis is that even outside the realm of computing, it still hasn't been fully explicated. A couple of months ago I took a graduate philosophy of language seminar (taught by the brilliant Sam Cumming at UCLA) in which we looked at various theories of discourse. It would be an understatement to say that these theories vary wildly. We have the classical RST (Rhetorical Structure Theory) by Mann and Thompson[0] (renowned linguists at USC & UCSB), Jan Van Kuppevelt's erotetic model[1], Andrew Kehler's Theory of Grammar[2] and a half-dozen or so more that I don't even remember. So let's forget about computers for a second. We don't even know how humans process discourse. My term paper was about the parallel relation which is a very talked-about topic (almost as much as anaphora; see The New Yorker article) in the academic community; not only are such linguistic phenomena difficult to theoretically model, they are nigh impossible to practically implement (say, in some sort of AI schema). So I'm not even surprised most AI folks just started doing work on SVM's or ANN's, or Markov Chains, or what-have-you. It seems more practical to do work on stuff that could actually benefit from machine learning, as opposed to trying to solve incredibly difficult (and mostly theoretical) problems like discourse analysis. The bottom line is that we're still a ways off from having computers like those in Star Trek - computers that understand anaphora, parallelism, ellipses, etc, etc. [0] http://www.sfu.ca/rst/ http://www.sfu.ca/rst/ [1] http://www.jstor.org/stable/4176301 http://www.jstor.org/stable/4176301 [2] http://www.amazon.com/Coherence-Reference-Theory-Grammar-Andrew/dp/1575862158 http://www.amazon.com/Coherence-Reference-Theory-Grammar-And...
- sgt101 13y ago
  Thanks for your post - very specific and informative. I believe that it will not be necessary to cope with the corner cases of discourse for a machine to be regarded as intelligent, and I believe that machines that can cope with those corner cases (if realisable ever) may not be regarded as intelligent (I mean, doing this is not sufficient). I think that in this sense discourse analysis is rather like computer chess - a goal that if attained may seem rather irrelevant to the grander challenge of creating machines that are capable of significant contributions to our culture. I think that we risk attacking the problems that appear to us to be interesting.
SCdF 13y ago
I'm coming around to the idea that getting computers to act like people is like building planes that flap their wings because that's what birds do. You're working with a fundamentally different medium, you should be playing to its strengths, not trying emulate the strengths of other mediums.
- devcpp 13y ago
  But the reward is so great that we can't dismiss the possibility of finding the special wings that are appropriate for that priceless plane. Think about it: we dont' just get very cheap labor working 24/7 without moral issues or personal needs. We can also make the robots take our jobs very efficiently, and technological progress will be unstoppable (particularly if they can improve themselves). It's the essence of singularity.
  - Houshalter 13y ago
    It's also really, really dangerous. Imagine an amoral being without that is hundreds or thousands of times more intelligent than humans. Do you think you could control it? That's not unrealistic. Computers already have clock cycles many thousands of times faster than the human brain (our brains get away with it by being massively parallel), have instant access to the entire world's knowledge base, can be scaled up indefinitely (just add another processor), and most importantly, can modify their code at will. If they see an improvement they can make to their own intelligence, they instantly become more intelligent, and get better at finding even more improvements, or designing even better AIs.
    - EwanG 13y ago
      So essentially what happens if your AI turns out to be like Dexter? I suspect that in early forms that might be almost MORE likely since the character (and the theory he's based on) is essentially human with a "bug" in dealing with morality. Which leads to another point, you determine your self aware AI is likely to commit murder based on a debug session. Are you allowed to wipe it without having a trial first?
techaddict009 13y ago
So who is going to make out real AI Algo which is Artificial by nature and Intelligent by working ?
deleted 13y ago
[deleted]
abecedarius 13y ago
Levesque works in knowledge representation, and all the sample questions hinge on some shallow inference from default knowledge. It's understandable to think your own field of research is central -- and it was, in the 80s. What makes it especially productive to focus on now? That's what I'd like to have seen covered here. The Turing test seems a red herring, since afaik it's not a big part of research evaluation currently.
iandanforth 13y ago
I've thought about this a lot, here's my best answer as of today. Part 1 - Existence proofs We think we can build 'intelligent' machines because human intelligence exists. We don't have a perfect idea of what it means to be intelligent, but we know, whatever we have exists so it's not impossible to create. Some disagree with this stance on philosophical or religious grounds, and others resort to pointing out even if it's possible it's really really hard. Fair enough. Part 2 - Methods Usually we start by picking the thing we think best typifies intelligence and we work to solve that. Chess, Written Language Comprehension, Visual Pattern Recognition, Spoken Language, etc. These are all called AI. All well and good, but so far we get exceptional single purpose systems (the narrower the domain the better) and then look around and say 'but that's not really intelligent.' Lately people have started to think more about the process by which things become intelligent rather than just the end behavior. Somehow the machine should naturally transition to being intelligent as opposed to being explicitly programed with intelligence. Machine learning is so-hot-right-now because of this. But, again, in the end you get a well trained system that does whatever it used as it's error metric. Like differentiate cats and sailboats. "But that's not really intelligent because it can't [play mozart/read poetry/paint a picture]." There are infinite criticisms, but they can be summed to a lack of 'generalness.' Part 3 - Representation What people searching for 'intelligence' are looking for is a system that can process data from at least as many sources in as least as many contexts as a human. The hard part there, and the one thing the brain does really really well, is being able to relate sight to sound, touch to taste, past to present, and present to future. In us there is a shared language of representation that encodes experience. In AI so far it's an unusual system that tries to relate many senses, keep a life long memory, and work in a noisy and incomplete environment and constantly make predictions about what will happen next. Part 4 - Data It is an unusual AI researcher who has all the data they want. As computer people we are impatient, and so waiting 30 years for a robot to collect the 1.4 PB of visual information a human does by that age, or the 1.8 TB of audio information just isn't done. We use existing datasets that are computationally tractable (meaning you can run them in minutes, hours, or days). And yet we do not have an existence proof that intelligence of the general human-like kind can exist without years of exposure to the world. It's reasonable to expect that it's possible, we just don't have pre-existing knowledge of that fact. Part 5 - The Future So how will we get there from here? We're probably going to have to do it the hard way. Create something that can sense the world in the ways that we can comprehend, and painstakingly rear it, collecting data, automating and hard-coding what we can, until we have a set of error metrics, motivations, data, and environment in which we begin to see the thousand different skills called 'intelligence' that humans take for granted. In short, we'll probably end up thinking a bit more like parents, and a bit less like computer scientists.
jlhawn 13y ago
In the first lecture of the Artificial Intelligence course at UC Berkeley, one slide says: "A better title for this course would be: 'Computational Rationality'"
MichaelMoser123 13y ago
"The large ball crashed right through the table because it was made of Styrofoam. What was made of Styrofoam? (The alternative formulation replaces Stryrofoam with steel.) a) The large ball b) The table" And a very large styrofoam ball can crash a comparatively small table made of wood, all depends on relative definition of 'large' right? I think the problem is that everybody can pick his favorite feature and define it as the key of 'general intelligence'. Some say Anaphora, some say machine learning; I like Hofstaedter he says that it is all about analogy. http://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 http://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinkin... Also: The problem is that these statements can't be proven; it is all about opinions and dogmas; I think the argument is s question of power: the one who wins the argument has the power over huge DARPA funds, or whoever else gives out grants for this type of research. The run after defining problems (expert systems, big data) in AI might have something to do with the funding problem. The 'Society of Mind' argument says that there are many agents that together somehow miraculously create intelligence. http://en.wikipedia.org/wiki/Society_of_Mind http://en.wikipedia.org/wiki/Society_of_Mind This argument sounds good, but it makes it hard to search for general patterns/universal explanations of intelligence. On the one hand they have to focus on some real solvable problem, on the other hand that makes it very hard to ask and find answers to general questions; I don't know if there will be some solution to this dilemma.
- MichaelMoser123 13y ago
  Maybe western civilization is not very good at answering this type of general big questions, maybe Indian civilization has a better chance, after all they invented structural linguistics some 2500 years ago (so that was not Chomsky at all ;-) http://en.wikipedia.org/wiki/P%C4%81%E1%B9%87ini http://en.wikipedia.org/wiki/P%C4%81%E1%B9%87ini Maybe the problem needs and idle class of Brahmi who can ask questions and ponder about them without end, without having to worry about questions of funding ?
eli_gottlieb 13y ago
What, no actual experts are going to comment in here? I was looking forward to the inevitable flamewars.
ahk 13y ago
Didn't Google recently roll out what they called conversational search which did the pronoun resolution thing? The example I recall they were able to answer correctly was 'Who's the president of the USA?' And then 'How tall is he?'
timedoctor 13y ago
I think the ultimate test (and application) for artificial intelligence will be to tell the computer "Make me money" and then it figures out thousands of ways to do it and starts executing on these strategies. Potentially very dangerous outcomes however without morality. Or another initial application of artificial intelligence I think is in trading financial markets, and distilling every point of data to create models of predicting markets and making obscene amounts of money.
- kowdermeister 13y ago
  And an intelligent answer would be "go find a job" :) Or give you a motivational quote from Tumblr :)
scotty79 13y ago
My thoughts about [artificial] intelligence. Senses do not provide the data for intelligence. Their sole purpose is to provide syncing between reality and internal representation. Intelligence perceives and operates on this internal representation only. Language is just another sense that helps with syncing of internal representation with some sort reality (might be physical reality, might be reality of social relationships, might even be internal representation of another intelligent being). I've seen logs of conversations between AI and human, and human and human. What struck me how much more verbose was the AI and human talking to it. Humans between themselves used such shorthands that made the conversation almost unintelligible to me just because I'm not american or even native english speaker. What useful AI systems up to date lack is the right internal representation. Current interaction between real world and AI systems resemble interaction between an application components and unit testing mockups. Interface is sort of correct but mockups lack the actual meat inside. AI research should IMHO go towards simulating reality, physical and social in flexible enough way and finding ways of syncing this simulation to reality using narrow data channels such as language, heavily filtered out (moving) images and sounds.
- hackinthebochs 13y ago
  I disagree that an intelligence operates on a distinct internal representations of things. I believe that our minds mostly operate on our memories of sensory data. When I remember a song in my head, I'm not iterating through an internal representation, I'm remembering my sensory experience of it. There is an important component of syncing internal representation when communicating, but I think that internal representation is mostly just recordings of sensory information; its not fundamentally different than what we sensed at some point in the past.
  - scotty79 13y ago
    Memories don't work like that. Research on eye witness accounts indicate that people remember very little. They fill in the gaps with information that they acquired after the event, and what's more important for my point, with the information reasoned from the things they actually remember. And they are not aware that they filled anything in. They remember things they made up exactly same way as the actual memories of the event. Each time you remember something it's being reconstructed, re-imagined by your brain. Method of treating people with stressful memories is based on that. You allow them to remember the things that bother them in safe, positive environment and after few such recalls, large part of stress associated with this memory disappears because reading memory is sort of "destructive read" and brain to keep that memory has to write it again after read but it's not exactly the same as it was read. When you look at what's in front of you, you don't see the whole image even though you feel that way. You see only the thing you are focused on (inattentional blindness). The rest is filled in for you. You might argue that this comes directly from previous sensory input. I think you could design experiment where you show a person a misleading image, while influencing brain with TMS to interpret wrong way, focus attention of a person on some part other then the misleading one and then stop influencing the brain so that it could analyze the previously recorded sensory input properly. I'd bet that person would still see the rest of the image as he was seeing when his brain was influenced until you allowed him to focus attention on the misleading parts. Only then he'd be able to readjust. Perception of illusions also indicate existence of internal representation for me. With http://en.wikipedia.org/wiki/Spinning_Dancer http://en.wikipedia.org/wiki/Spinning_Dancer illusion you can feel the moment when your brain switches between two completely different representations of ambiguous image. When you hear sounds, what you hear and remember is not just sensory input. What you hear depends on the context. Awesome demonstration of that effect: http://www.youtube.com/watch?v=8T_jwq9ph8k&feature=player_detailpage&t=565 http://www.youtube.com/watch?v=8T_jwq9ph8k&feature=player_de... When you hear the song backward for the first time you hear nothing. But when you see the text you were supposed to hear you can actually hear those words. I think that just presenting you with the text and telling you to remember if you heard this text the first time wouldn't change what you remember hearing. Try to pause when the text for reverse version shows up and see if your memory of reversed song playing matches any of that text. I think this effect happens because senses are very thin, and sensory input is aggressively used to construct internal representation (with much of the representation created from experience) and then discarded.
graycat 13y ago
The article concentrates on the fact that current artificial intelligence (AI) software doesn't really understand the meaning in natural language. Right. So, then, maybe one approach to AI is via natural language -- program a computer to understand natural language, just typed input should be sufficient initially. How to do that? My two kitty cats understand some natural language, and I have to suspect that I roughly understand how they did that and how I could program some of it. Human babies learn natural language, and we have to suspect that the effort is a bootstrap where learn some really simple things -- e.g., "Ma Ma" and "Da Da" -- and then build on those. "Nice". "Bad". "Ma Ma nice." "Da Da bad". "Food". "Hungry". "Hungry want food.". "When hungry and want food, go to master, reach up with front paw and use claws to pull on shirt but don't pull on skin." -- my kitty cats both already figured this out either independently or learned from each other. "I can see." "Can he see me?". Kitty cats know that very well, and if they want to scratch or bite (one cat long ago, just rescued), know to wait until the target can't see the claw or mouth about to bite. So, to come in from the back porch, wait until there is noise indicating that I'm at the kitchen sink and take a position on the porch so that can be seen -- then I will let them back in. Then build on such simple things. That's what I thought long ago. Once I asked DARPA about it, and they had no response. The author of the OP has another article on how birds and babies learn to understand language. So, maybe more than one person is thinking along those lines. Doing it first with just text input should show the core problems and be sufficient. One problem: Kitty cats have great internal 3-D geometry. E.g., if the mouse runs clockwise around a packing box, then the cat can be smart enough to run counterclockwise. So, the cat understands the 3-D box and paths in space. They're not stupid you know! How to program that? Hmm ...!
- hackinthebochs 13y ago
  How did you "ask DARPA"?
  - graycat 13y ago
    Found an appropriate DARPA problem sponsor and sent him e-mail.
segmondy 13y ago
Anyone interested enough in building an AGI (Artificial General Intelligence) system? I've always been curious about this field, but never done anything about it or have any experience. Nevertheless, if there are any hackers who are into AI, curious or want to build such systems. Contact me, let's explore the possibilities.

185 points by phreeza 13y ago

Interesting that there seems to be no mention of Cyc, which is explicitly intended to be good at problems like those mentioned.

teddyh 13y ago
But is it any good at those problems?
- seiji 13y ago
  It's best to just let Cyc people continue to think they matter and let them stay busy expanding their knowledge base in the church of first order logic. It'll never go anywhere, but it's better to keep them all contained in one place rather than polluting other fields of study.
  - pnathan 13y ago
    Okay, that was way too nasty and out of line.
  - ScottBurson 13y ago
    See, this is what I mean (see my reply to eschaton). I take it then, seiji, that you also completely disagree with Levesque's program as expressed in section 4.4 of his paper?
    - seiji 13y ago
      Absolutely. 4.4 is a full endorsement of Cyc (and don't get me started on "Cyc" vs "OpenCyc"). Second 4.4 looks a little like "graphplan reborn" or "generic AI approaches from the 80s." Discrete codified knowledge is not the stuff the universe is made of. The problem Cyc will never solve is the "a picture is worth a thousand words" problem. Describing everything in a relational, hierarchal, predicate calculus arrogantly ignores the unrelated multidimensionality of, well, everything.
  - giardini 13y ago
    A merciful end to Cyc funding might be better. Monies saved could be spent elsewhere and termination would free all involved without the expectation that they produce something of value. Although it might cause some grudges ("If we'd only been allowed to add the last 2000 rules, I am certain it would have achieved consciousness!") everybody needs a new start now and then. Had Lenat published working versions or even complete explanations of his previous software (AM, EURISKO) I might be a little more sympathetic. Cyc, the project that never ends, appears to be in a state of perpetual partial implementation, it's most notable product being a constant stream of money into the project. But look on the bright side: perhaps I'm completely wrong, Cyc has actually achieved AI, and the NSA is using it right now!
- ScottBurson 13y ago
  I think we can infer, from Levesque's failure to even mention it, that the answer has to be no. I admit I am curious exactly what Cyc can and can't do, and why. I haven't looked at it closely, though.
  - eschaton 13y ago
    What I've heard is that their system has gotten progressively better over time while their publishing had gotten worse. People in academia can get ResearchCyc and see just what its capabilities are these days; it's basically the same as full Cyc. OpenCyc is a shadow of the real thing, supposedly.
ScottBurson 13y ago
I agree, that is very curious. I clicked through to Levesque's paper, and there is no mention of Cyc there either. I gather Cyc has long been considered a failure by many in the AI community, and perhaps even an embarrassment by some. Still it is hard not to read Levesque's paper as a vigorous endorsement of the goals of the Cyc project, if perhaps not of its exact methods -- see sec. 4.4. I think it is incumbent on Levesque to explain what Cyc has done wrong, if he has a good theory about that -- and if he doesn't, he needs to admit it, because Cyc's ostensible failure makes it seem unlikely that any symbolic, reasoning-oriented approach to AGI could ever succeed.
- rjsw 13y ago
  If Cyc has failed then why do there seem to be so many people trying to build XML schemas that could be used to reimplement it.
  - seiji 13y ago
    Cyc-type approaches are useful for narrow, well-specified domains, just not AGI or actual "intelligence."
    - mark_l_watson 13y ago
      It has been a few years since I went through Ben Goertzel's team's AGI papers, but I find it difficult to believe that Cyc would not be useful for background information for real world knowledge. I would use Cyc, Wikipedia info boxes,etc.

valtron 13y ago

Aside: The Loebner prize is a terrible implementation of a Turing test because the judges have no idea what they're doing. You can't just try to have a normal conversation and see which one sounds more human. You _have_ to try tricky things like the article mentions; beside syntactically ambiguous sentences, you might also try things that would be obvious to a human but that a computer would easily get confused by unless it was programmed to check for it. Likenotusingspaces. O.r. .i.n.t.e.r.l.e.a.v.i.n.g. .l.e.t.t.e.r.s. .w.i.t.h. .s.o.m.e. .r.a.n.d.o.m. .c.h.a.r.a.c.t.e.r. Also, both the participants being tested (AI and human) should be trying to convince the judge that they're the human and the other is the AI. Given that chatterbots that insert "one-liners" and other non-sequitors do well, I doubt that's happening, so again, the Loebner test is useless.

seiji 13y ago
"Is it an AI" tests based only on text are pretty silly anyway. There are probably a few billion people alive who can't form complete written sentences. If you have a job, you probably encounter dozens of people a year who can't write coherent emails. The Turing Test concept appeared when everybody thought intelligence was mostly words, and we could probably easily make computers spit out meaningful sequences of words. Nobody believes intelligence is how well you can write and read anymore (except fad-based media reports "OMG SIRI IZ INTELLUGENT AI SKYNET!"). The Turing Test concept persists because it's simpler than dirt to explain, even though it has never been a valid method of evaluating anything.
- feral 13y ago
  I don't think you are being fair on the Turing test, or on the intelligence of its creator. In describing the setting of the test, Turing first describes an imitation game where a judge must tell apart a man and a woman. He writes "In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary." So the text format is out of the practicalities of defining a workable test, not because Turing thought 'intelligence is how well you can write and read'. Your other objection is that there are humans who can't form written sentences. Well, they could use the intermediary, if they cant write. But, even if you mean that they can't put a verbal sentence together, the test is framed such that a machine that can pass it can be considered to think, not so that any machine (or human) that can think is supposed to be able to pass the test. Again, from Turing's paper: "May not machines carry out something which ought to be described as thinking but which is very different from what a man does? This objection is a very strong one, but at least we can say that if, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection." In other words, passing the test - succeeding at the imitation game - is designed to be a sufficient test to demonstrate intelligence, but not a necessary one.

iUnderstand 13y ago

I have thought about this long and hard. There was a time in my life when I thought I could create something that had intelligence. Here are some of my thoughts. Intelligence isn't something that is instant, it is something that is acquired. To acquire intelligence, the being must have an environment in which to interact and learn. My idea was to mimic the way that animals and plants become adaptive to their environment. They all can die, and only the strong survive. So, each being, or thread, would have the ability to die. Well, what happens if they all die? Then the experiment would be over and that wouldn't be fun would it. So, threads would also have to have the ability to reproduce. And, what good would it be if all the threads were the same? They must be able to mutate. What about intelligence? Forget intelligence, it just represents the ability to survive. All the threads will die off that can't survive anyways, so what we are left with is a pool of survivors. Uh oh. The environment exists in a virtual realm. So my threads wont be very beneficial in the real world realm that you and I live in. We need eyes and ears into the real world. OK, no problem, we will purchase cameras to capture light, and microphones for sound. Now we have a money issue. I may be able to afford a couple hundred micro controllers, microphones, and cameras. Not bad for a little expirement, but wait "Scientists estimate that there are one quadrillion (1,000,000,000,000,000) ants living on the earth at any given time." according to hypertextbook.com http://hypertextbook.com/facts/2003/AlisonOngvorapong.shtml http://hypertextbook.com/facts/2003/AlisonOngvorapong.shtml. How can my intelligence be smarter than an ant, if there are this many ants, who are trying to survive, and have the ability to reproduce and mutate? I don't think I can. I began thinking about this more and more. We would have to speed up the evolution of our intelligent being. What are different things we could do to speed it up? Perhaps we could create more mutations, but not too many, we don't wont our bot to become extinct. Oh crap, how is it going to reproduce? I totally forgot about that. Each of our species would have to have an electronics factory built inside it. I know the solution, don't try to create your own intelligence. You can create as much ARTIFICIAL intelligence like search engines all you want, but this is not intelligence in any way shape of form. It looks like intelligence, but it is not. To achieve truly something more intelligent than a human, you will need to take what nature has already created and mutated, and then provide environment enhancements(schools?). There I said it. Schools and all theses GNC drugs that are supposed to make your brain function at an increasing rate. I am afraid we are already trying to become more intelligent every single day in the world. And yes, it is working, but we can only work as fast as nature allows us. On a closing note, Computers will never be able to think like a human, because they cannot reproduce. Nature is much more efficient.

ilaksh 13y ago

This guy should look into research labeled as artificial general intelligence (AGI) or deep learning. If he really understood for example what Watson can do or the leading research using things like autoencoders or hierarchical temporary memory for natural language understanding then he would have a better informed and less pessimistic attitude.

mturmon 13y ago
The piece actually seems like a backhanded critique of the optimism shown by the deep learning community (which of course Winograd is aware of). It also seems like an appreciative backward glance at topics that interested Winograd earlier in his career, but are less fashionable now.

eboyjr 13y ago

I think we could help improve the field by making CAPTCHAs with these kinds of questions.

fragsworth 13y ago
This is brilliant. First you have to come up with a way to automatically generate these questions though. If we have to make them manually, the machines can just remember all the answers. Creating the questions seems much harder than answering them.
saltvedt 13y ago
Won't work. Assuming that the right answer is a word in the question, the computer can just brute force the answer in a couple of guesses.
- Houshalter 13y ago
  If you fail 50% of guesses, that should flag you as a spammer. If you ask a few questions with multiple possible answers it's pretty unlikely it would get them all correct through random guesses anyways.
  - nawitus 13y ago
    The bot can use only 1 guess per website.
- anko 13y ago
  could you just change the number of questions to scale with the number of attempts? ie. if there are more than 3 attempts at answering a question, then ask 2 questions.

RyanMcGreal 13y ago

Essentially, the article accuses AI researchers of searching for their lost keys under streetlamps. Fair enough, but I wonder if the best use of AI research is to get computers to solve problems that a person could easily solve. I thought the point of, e.g. a search engine is to solve problems a person can't easily solve.

dnautics 13y ago
you should read "fluid concepts and creative analogies". There was some good headway made in this direction that got lost.
fragsworth 13y ago
> I wonder if the best use of AI research is to get computers to solve problems that a person could easily solve That's pretty short-sighted. These "easy" problems are stepping stones to much more difficult problems. For instance, you cannot tell a machine something like "build my website" without it being able to solve the easy stuff first.
- cr4zy 13y ago
  And once a machine has learned to do something "easy" in the same way we do, that is via sensory perception, it will be extremely exciting to see how that knowledge can be forked and evolved via the excellent copyability inherent in computers.
  - deleted 13y ago
    [deleted]
- bad_user 13y ago
  Oh come on, sure you can tell a computer "build my website". You can use language, or you can click a button. Oh, but the result is not what you want? Even when communicating with a human, you need to provide some specs, right? But then, natural language is way to imprecise and open to interpretation, coupled with the fact that people don't really know for sure what they want, so even when speaking with a developer, you're going to go through several iterations. So for the ultimate flexibility and automation, the user interface of such a smart appliance will end up being essentially a programming language (being the same reason for why lawyers and mathematicians have their own language that's anything but natural). Truth of the matter is, computers answering to commands aren't really that interesting, as they already do this. What we really want are computers capable of ideas. Also, natural language processing is hard because it's based on an incredible amount of implicit context that life taught us ever since we were born and when communicating to our fellow humans, it's never about just the actual words being spoken. When saying something like "build my website" your spouse probably knows you're talking about a website meant for marketing yourself and she probably knows your tastes and values too; she observed your fears and dreams while living your mundane life, even if you've never talked with her explicitly about such issues (speaking of women, they are incredibly good at reading between the lines). Solving natural language processing is basically akin to creating sentient beings.

dnautics 13y ago

I wanted to arrage for watson to go on "are you smarter than a 5th grader" and give it all sorts of questions like these (but even easier). For example: "Think of a giant flightless bird. Now, name a color that starts with the same first letter."

foobarbazqux 13y ago
Eggplant. Or blue.

dnautics 13y ago

any thoughts here on the parallel terraced scan, as implemented by Hofstadter in "fluid concepts and creative analogies"?

bsaul 13y ago

isnt that criticism thr exact point chomsky was trying to make in his proxy discussion with norvig from google ? i found the exemple given in the article excellent though. it's another reason why computers won't be able to solve grammar mistakes 100% correctly (at least in latin languages).

anaphor 13y ago
Chomsky doesn't think we should be trying to observe how people use language on a mass scale, so screw him imo. See: http://languagelog.ldc.upenn.edu/nll/?p=3180 http://languagelog.ldc.upenn.edu/nll/?p=3180 "The other argument has to do with the methods of science: Chomsky argues for "very intricate experiments that are radically abstracted from natural conditions". "

SeanLuke 13y ago

This whole article, and [I think] Levesque's IJCAI article, seems to think that artificial intelligence is just about language.

hackinthebochs 13y ago
Its not that its just about language, but I do think that a system that could pass the turing test would necessarily be AGI. To put it another way, to fully solve the NLP problem one would have to have created AGI. To pass the turing test a system would need to simulate or acquire the knowledge of an entire life of experiences of a person and be able to convincingly converse about those experiences with an actual person. This would undoubtedly be AGI.
- SeanLuke 13y ago
  1. I think "AGI" is a ridiculous recent rebranding of "Hard AI" and doesn't represent any meaningful scientific or engineering pursuit. 2. Even if one accepted that NLP is AI-complete: this is a sufficient condition and not a necessary condition. But the claim being made in the articles is that it is a necessary condition: that is, that other AI disciplines are distractions because they do not lead to the AI grail that NLP supposedly leads to. This is classic GOFAI hogwash.
  - SeanLuke 13y ago
    Hard AI -> Strong AI. Brain not firing on all four cylinders.

blauwbilgorgel 13y ago

Can an alligator run the hundred-metre hurdles? http://www.youtube.com/watch?v=pjslsKZYXQ8 http://www.youtube.com/watch?v=pjslsKZYXQ8 (Can alligators jump and climb? Yes, they can!) http://www.animalquestions.org/reptiles/alligators/can-alligators-climb-fences/ http://www.animalquestions.org/reptiles/alligators/can-allig... (many have thought was a myth, it is true that alligators are in fact able to climb fences.) These anaphora questions are better solved through natural language parsing, for example turning them into predicate logic. The article is right when it says that big data algo's have a problem with these sentences. But that is not to say they can't help. In the article: The town councillors refused to give the angry demonstrators a permit because they feared violence. Who feared violence? You can use big data to calculate the semantic closeness between "town council" and "fear violence". If that is closer than between "demonstrators" and "fear violence" you can make a good guess. From the Wikipedia article on anaphora: We gave the bananas to the monkeys because they were hungry. We gave the bananas to the monkeys because they were ripe. We gave the bananas to the monkeys because they were here. A quick Google search for "bananas were ripe", "monkeys were ripe", "monkeys were hungry" and "bananas were hungry" and counting the results will solve this. Using semantic closeness can also work against you in the case of ambiguity: The robber sits on the bank. Is "bank" a furniture here? A money bank? A river bank? Semantic closeness might picture the robber sitting on a money bank. A second or third pass is necessary: calculate the chance that a person will sit on a building vs. the chance that a person will sit on furniture. This is a decades-old hard problem that is close to philosophy. Is the robot inside Searle's Chinese room intelligent? Is it really understanding what it is saying? Or is it "faking" it?

lisper 13y ago
> You can calculate semantic closeness between "town council" and "fear violence". If that is closer than between "demonstrators" and "fear violence" you can make a good guess. Only for that particular example. It's easy to come up with examples where that heuristic fails: "The town council denied the protestors a permit because they were in a bad mood." "The factory failed to produce the car on time because it was undergoing maintenance."
- 13y ago

dvt 13y ago

Levesque saves his most damning criticism for the end of his paper. It’s not just that contemporary A.I. hasn’t solved these kinds of problems yet; it’s that contemporary A.I. has largely forgotten about them. In Levesque’s view, the field of artificial intelligence has fallen into a trap of “serial silver bulletism,” always looking to the next big thing, whether it’s expert systems or Big Data, but never painstakingly analyzing all of the subtle and deep knowledge that ordinary human beings possess. This is very true. But I think that recent AI experts (as opposed to those doing this work in the 70s) have realized that trying to tackle linguistic analysis is very very (very) hard. The problem with language (or more correctly, discourse) analysis is that even outside the realm of computing, it still hasn't been fully explicated. A couple of months ago I took a graduate philosophy of language seminar (taught by the brilliant Sam Cumming at UCLA) in which we looked at various theories of discourse. It would be an understatement to say that these theories vary wildly. We have the classical RST (Rhetorical Structure Theory) by Mann and Thompson[0] (renowned linguists at USC & UCSB), Jan Van Kuppevelt's erotetic model[1], Andrew Kehler's Theory of Grammar[2] and a half-dozen or so more that I don't even remember. So let's forget about computers for a second. We don't even know how humans process discourse. My term paper was about the parallel relation which is a very talked-about topic (almost as much as anaphora; see The New Yorker article) in the academic community; not only are such linguistic phenomena difficult to theoretically model, they are nigh impossible to practically implement (say, in some sort of AI schema). So I'm not even surprised most AI folks just started doing work on SVM's or ANN's, or Markov Chains, or what-have-you. It seems more practical to do work on stuff that could actually benefit from machine learning, as opposed to trying to solve incredibly difficult (and mostly theoretical) problems like discourse analysis. The bottom line is that we're still a ways off from having computers like those in Star Trek - computers that understand anaphora, parallelism, ellipses, etc, etc. [0] http://www.sfu.ca/rst/ http://www.sfu.ca/rst/ [1] http://www.jstor.org/stable/4176301 http://www.jstor.org/stable/4176301 [2] http://www.amazon.com/Coherence-Reference-Theory-Grammar-Andrew/dp/1575862158 http://www.amazon.com/Coherence-Reference-Theory-Grammar-And...

sgt101 13y ago
Thanks for your post - very specific and informative. I believe that it will not be necessary to cope with the corner cases of discourse for a machine to be regarded as intelligent, and I believe that machines that can cope with those corner cases (if realisable ever) may not be regarded as intelligent (I mean, doing this is not sufficient). I think that in this sense discourse analysis is rather like computer chess - a goal that if attained may seem rather irrelevant to the grander challenge of creating machines that are capable of significant contributions to our culture. I think that we risk attacking the problems that appear to us to be interesting.

SCdF 13y ago

I'm coming around to the idea that getting computers to act like people is like building planes that flap their wings because that's what birds do. You're working with a fundamentally different medium, you should be playing to its strengths, not trying emulate the strengths of other mediums.

devcpp 13y ago
But the reward is so great that we can't dismiss the possibility of finding the special wings that are appropriate for that priceless plane. Think about it: we dont' just get very cheap labor working 24/7 without moral issues or personal needs. We can also make the robots take our jobs very efficiently, and technological progress will be unstoppable (particularly if they can improve themselves). It's the essence of singularity.
- Houshalter 13y ago
  It's also really, really dangerous. Imagine an amoral being without that is hundreds or thousands of times more intelligent than humans. Do you think you could control it? That's not unrealistic. Computers already have clock cycles many thousands of times faster than the human brain (our brains get away with it by being massively parallel), have instant access to the entire world's knowledge base, can be scaled up indefinitely (just add another processor), and most importantly, can modify their code at will. If they see an improvement they can make to their own intelligence, they instantly become more intelligent, and get better at finding even more improvements, or designing even better AIs.
  - EwanG 13y ago
    So essentially what happens if your AI turns out to be like Dexter? I suspect that in early forms that might be almost MORE likely since the character (and the theory he's based on) is essentially human with a "bug" in dealing with morality. Which leads to another point, you determine your self aware AI is likely to commit murder based on a debug session. Are you allowed to wipe it without having a trial first?

techaddict009 13y ago

So who is going to make out real AI Algo which is Artificial by nature and Intelligent by working ?

deleted 13y ago

[deleted]

abecedarius 13y ago

Levesque works in knowledge representation, and all the sample questions hinge on some shallow inference from default knowledge. It's understandable to think your own field of research is central -- and it was, in the 80s. What makes it especially productive to focus on now? That's what I'd like to have seen covered here. The Turing test seems a red herring, since afaik it's not a big part of research evaluation currently.

iandanforth 13y ago

I've thought about this a lot, here's my best answer as of today. Part 1 - Existence proofs We think we can build 'intelligent' machines because human intelligence exists. We don't have a perfect idea of what it means to be intelligent, but we know, whatever we have exists so it's not impossible to create. Some disagree with this stance on philosophical or religious grounds, and others resort to pointing out even if it's possible it's really really hard. Fair enough. Part 2 - Methods Usually we start by picking the thing we think best typifies intelligence and we work to solve that. Chess, Written Language Comprehension, Visual Pattern Recognition, Spoken Language, etc. These are all called AI. All well and good, but so far we get exceptional single purpose systems (the narrower the domain the better) and then look around and say 'but that's not really intelligent.' Lately people have started to think more about the process by which things become intelligent rather than just the end behavior. Somehow the machine should naturally transition to being intelligent as opposed to being explicitly programed with intelligence. Machine learning is so-hot-right-now because of this. But, again, in the end you get a well trained system that does whatever it used as it's error metric. Like differentiate cats and sailboats. "But that's not really intelligent because it can't [play mozart/read poetry/paint a picture]." There are infinite criticisms, but they can be summed to a lack of 'generalness.' Part 3 - Representation What people searching for 'intelligence' are looking for is a system that can process data from at least as many sources in as least as many contexts as a human. The hard part there, and the one thing the brain does really really well, is being able to relate sight to sound, touch to taste, past to present, and present to future. In us there is a shared language of representation that encodes experience. In AI so far it's an unusual system that tries to relate many senses, keep a life long memory, and work in a noisy and incomplete environment and constantly make predictions about what will happen next. Part 4 - Data It is an unusual AI researcher who has all the data they want. As computer people we are impatient, and so waiting 30 years for a robot to collect the 1.4 PB of visual information a human does by that age, or the 1.8 TB of audio information just isn't done. We use existing datasets that are computationally tractable (meaning you can run them in minutes, hours, or days). And yet we do not have an existence proof that intelligence of the general human-like kind can exist without years of exposure to the world. It's reasonable to expect that it's possible, we just don't have pre-existing knowledge of that fact. Part 5 - The Future So how will we get there from here? We're probably going to have to do it the hard way. Create something that can sense the world in the ways that we can comprehend, and painstakingly rear it, collecting data, automating and hard-coding what we can, until we have a set of error metrics, motivations, data, and environment in which we begin to see the thousand different skills called 'intelligence' that humans take for granted. In short, we'll probably end up thinking a bit more like parents, and a bit less like computer scientists.

jlhawn 13y ago

In the first lecture of the Artificial Intelligence course at UC Berkeley, one slide says: "A better title for this course would be: 'Computational Rationality'"

MichaelMoser123 13y ago

"The large ball crashed right through the table because it was made of Styrofoam. What was made of Styrofoam? (The alternative formulation replaces Stryrofoam with steel.) a) The large ball b) The table" And a very large styrofoam ball can crash a comparatively small table made of wood, all depends on relative definition of 'large' right? I think the problem is that everybody can pick his favorite feature and define it as the key of 'general intelligence'. Some say Anaphora, some say machine learning; I like Hofstaedter he says that it is all about analogy. http://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 http://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinkin... Also: The problem is that these statements can't be proven; it is all about opinions and dogmas; I think the argument is s question of power: the one who wins the argument has the power over huge DARPA funds, or whoever else gives out grants for this type of research. The run after defining problems (expert systems, big data) in AI might have something to do with the funding problem. The 'Society of Mind' argument says that there are many agents that together somehow miraculously create intelligence. http://en.wikipedia.org/wiki/Society_of_Mind http://en.wikipedia.org/wiki/Society_of_Mind This argument sounds good, but it makes it hard to search for general patterns/universal explanations of intelligence. On the one hand they have to focus on some real solvable problem, on the other hand that makes it very hard to ask and find answers to general questions; I don't know if there will be some solution to this dilemma.

MichaelMoser123 13y ago
Maybe western civilization is not very good at answering this type of general big questions, maybe Indian civilization has a better chance, after all they invented structural linguistics some 2500 years ago (so that was not Chomsky at all ;-) http://en.wikipedia.org/wiki/P%C4%81%E1%B9%87ini http://en.wikipedia.org/wiki/P%C4%81%E1%B9%87ini Maybe the problem needs and idle class of Brahmi who can ask questions and ponder about them without end, without having to worry about questions of funding ?

eli_gottlieb 13y ago

What, no actual experts are going to comment in here? I was looking forward to the inevitable flamewars.

ahk 13y ago

Didn't Google recently roll out what they called conversational search which did the pronoun resolution thing? The example I recall they were able to answer correctly was 'Who's the president of the USA?' And then 'How tall is he?'

timedoctor 13y ago

I think the ultimate test (and application) for artificial intelligence will be to tell the computer "Make me money" and then it figures out thousands of ways to do it and starts executing on these strategies. Potentially very dangerous outcomes however without morality. Or another initial application of artificial intelligence I think is in trading financial markets, and distilling every point of data to create models of predicting markets and making obscene amounts of money.

kowdermeister 13y ago
And an intelligent answer would be "go find a job" :) Or give you a motivational quote from Tumblr :)

scotty79 13y ago

My thoughts about [artificial] intelligence. Senses do not provide the data for intelligence. Their sole purpose is to provide syncing between reality and internal representation. Intelligence perceives and operates on this internal representation only. Language is just another sense that helps with syncing of internal representation with some sort reality (might be physical reality, might be reality of social relationships, might even be internal representation of another intelligent being). I've seen logs of conversations between AI and human, and human and human. What struck me how much more verbose was the AI and human talking to it. Humans between themselves used such shorthands that made the conversation almost unintelligible to me just because I'm not american or even native english speaker. What useful AI systems up to date lack is the right internal representation. Current interaction between real world and AI systems resemble interaction between an application components and unit testing mockups. Interface is sort of correct but mockups lack the actual meat inside. AI research should IMHO go towards simulating reality, physical and social in flexible enough way and finding ways of syncing this simulation to reality using narrow data channels such as language, heavily filtered out (moving) images and sounds.

hackinthebochs 13y ago
I disagree that an intelligence operates on a distinct internal representations of things. I believe that our minds mostly operate on our memories of sensory data. When I remember a song in my head, I'm not iterating through an internal representation, I'm remembering my sensory experience of it. There is an important component of syncing internal representation when communicating, but I think that internal representation is mostly just recordings of sensory information; its not fundamentally different than what we sensed at some point in the past.
- scotty79 13y ago
  Memories don't work like that. Research on eye witness accounts indicate that people remember very little. They fill in the gaps with information that they acquired after the event, and what's more important for my point, with the information reasoned from the things they actually remember. And they are not aware that they filled anything in. They remember things they made up exactly same way as the actual memories of the event. Each time you remember something it's being reconstructed, re-imagined by your brain. Method of treating people with stressful memories is based on that. You allow them to remember the things that bother them in safe, positive environment and after few such recalls, large part of stress associated with this memory disappears because reading memory is sort of "destructive read" and brain to keep that memory has to write it again after read but it's not exactly the same as it was read. When you look at what's in front of you, you don't see the whole image even though you feel that way. You see only the thing you are focused on (inattentional blindness). The rest is filled in for you. You might argue that this comes directly from previous sensory input. I think you could design experiment where you show a person a misleading image, while influencing brain with TMS to interpret wrong way, focus attention of a person on some part other then the misleading one and then stop influencing the brain so that it could analyze the previously recorded sensory input properly. I'd bet that person would still see the rest of the image as he was seeing when his brain was influenced until you allowed him to focus attention on the misleading parts. Only then he'd be able to readjust. Perception of illusions also indicate existence of internal representation for me. With http://en.wikipedia.org/wiki/Spinning_Dancer http://en.wikipedia.org/wiki/Spinning_Dancer illusion you can feel the moment when your brain switches between two completely different representations of ambiguous image. When you hear sounds, what you hear and remember is not just sensory input. What you hear depends on the context. Awesome demonstration of that effect: http://www.youtube.com/watch?v=8T_jwq9ph8k&feature=player_detailpage&t=565 http://www.youtube.com/watch?v=8T_jwq9ph8k&feature=player_de... When you hear the song backward for the first time you hear nothing. But when you see the text you were supposed to hear you can actually hear those words. I think that just presenting you with the text and telling you to remember if you heard this text the first time wouldn't change what you remember hearing. Try to pause when the text for reverse version shows up and see if your memory of reversed song playing matches any of that text. I think this effect happens because senses are very thin, and sensory input is aggressively used to construct internal representation (with much of the representation created from experience) and then discarded.

graycat 13y ago

The article concentrates on the fact that current artificial intelligence (AI) software doesn't really understand the meaning in natural language. Right. So, then, maybe one approach to AI is via natural language -- program a computer to understand natural language, just typed input should be sufficient initially. How to do that? My two kitty cats understand some natural language, and I have to suspect that I roughly understand how they did that and how I could program some of it. Human babies learn natural language, and we have to suspect that the effort is a bootstrap where learn some really simple things -- e.g., "Ma Ma" and "Da Da" -- and then build on those. "Nice". "Bad". "Ma Ma nice." "Da Da bad". "Food". "Hungry". "Hungry want food.". "When hungry and want food, go to master, reach up with front paw and use claws to pull on shirt but don't pull on skin." -- my kitty cats both already figured this out either independently or learned from each other. "I can see." "Can he see me?". Kitty cats know that very well, and if they want to scratch or bite (one cat long ago, just rescued), know to wait until the target can't see the claw or mouth about to bite. So, to come in from the back porch, wait until there is noise indicating that I'm at the kitchen sink and take a position on the porch so that can be seen -- then I will let them back in. Then build on such simple things. That's what I thought long ago. Once I asked DARPA about it, and they had no response. The author of the OP has another article on how birds and babies learn to understand language. So, maybe more than one person is thinking along those lines. Doing it first with just text input should show the core problems and be sufficient. One problem: Kitty cats have great internal 3-D geometry. E.g., if the mouse runs clockwise around a packing box, then the cat can be smart enough to run counterclockwise. So, the cat understands the 3-D box and paths in space. They're not stupid you know! How to program that? Hmm ...!

hackinthebochs 13y ago
How did you "ask DARPA"?
- graycat 13y ago
  Found an appropriate DARPA problem sponsor and sent him e-mail.

segmondy 13y ago

Anyone interested enough in building an AGI (Artificial General Intelligence) system? I've always been curious about this field, but never done anything about it or have any experience. Nevertheless, if there are any hackers who are into AI, curious or want to build such systems. Contact me, let's explore the possibilities.