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> AI is nonsense. Dijkstra was right. Define AI first. One of the first few lines on Wikipedia about AI: The scope of AI is disputed: as machines become incre
by davnn 8y ago
> AI is nonsense. Dijkstra was right.
Define AI first.
One of the first few lines on Wikipedia about AI:
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet."
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- User23 8y agoI believe that intelligence is more than quantifiable. Von Neumann machines, no matter how complex, will never achieve intelligence. Again, to be completely clear, I’m not claiming machine intelligence is impossible, just that what we see presently isn’t it in any honest sense.
- yorwba 8y agoWhat do you even mean by "Von Neumann machines, no matter how complex, will never achieve intelligence."? If you mean machines that self-replicate, then there're plenty counterexamples around already (https://www.xkcd.com/387/ https://www.xkcd.com/387/). If you mean computers using the von Neumann architecture, then that's weirdly specific, since modern computers are at best vaguely inspired by that model, and GPUs in particular work very differently.
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- crististm 8y agoHow is self-replication a requirement for intelligence?
- kyle-rb 8y agoYou're using Von Neumann machine in the sense of a self-replicating machine. I think the commenter you're replying to meant Von Neumann architecture, which is the basic layout of a computer, and has nothing to do with self-replication. See this Wikipedia disambiguation page: https://en.wikipedia.org/wiki/Von_Neumann_machine https://en.wikipedia.org/wiki/Von_Neumann_machine
- User23 8y agoThanks for clarifying. The man was such a polymath and has made so many contributions I should have been more clear myself.
- crististm 8y agoHow about this (incomplete) one: the ability to learn a subject at hand and then to apply this knowledge into another field. For example, the machine becomes a master chess player then uses this ability to become a master at backgammon.
- davnn 8y agoThat's called transfer learning in ML and we are not very good at it, yet.
- crististm 8y agoAlmost there! Have you seen Feynman talking that knowing the birds' names doesn't mean you know anything about them? Let's see how the machine learns about the concept of transfer learning in the first place :)
- faeyanpiraat 8y agoI’m not sure about the quality of my linked article but it seems like that humans becoming good at chess does not noticeably improve their other skills. They will simply be better at chess. So our expectations might bee too high against AI getting vastly better only by doing better transfer learning. http://theconversation.com/does-playing-chess-make-you-smarter-a-look-at-the-evidence-76062 http://theconversation.com/does-playing-chess-make-you-smart...
- YeGoblynQueenne 8y agoLearning chess will make you better at other board games, even if it doesn't improve, say, your maths or language skills. For instance, chess may teach you lessons about sacrificing assets to earn a reward later on, or lessons about material advantage, tempo, controlling the board and so on, that can be applied to many different games. That counts as transfer learning and it is something that statistical machine learning cannot do at all, basically (i.e. a new model has to be trained from scratch for each different game). As an aside- you might argue that there are common elements of board games' design that make it easier to reuse knowledge. However, statistical machine learning systems can still not transfer knowledge of one game to others, even given the common design elements that should make it easier.
- ekianjo 8y ago> "AI is whatever hasn't been done yet." If you can't replicate what a human do, it's not AI. The fact that we can only beat humans for very, very narrow applications/games and that we don't have a generalized model for learning is a clear failure of the AI hype.
- davnn 8y agoValid definition, why not. My point is that talking about AI without defining what you mean doesn't make sense. Honestly, that's also the problem with most articles about AI. Articles about AI are either praising the great mystical AI for recognizing cats or blaming AI for not achieving X, but humans can.
- edanm 8y agoIt's totally valid to define AI this way. But just keep in mind, when most people talk about AI, they're knowingly talking of something much more limited. So you're going to constantly have communication failures with people who are defining AI differently than you. (Many people nowadays use the term AGI [Artificial General Intelligence] to mean what you think of as AI, btw).
- ekianjo 8y agoYeah I know about AGI but I dont like that term because it implies that the classifiers we have nowadays are good enough to be called "intelligence". They are just statistical models with great number of layers, nothing else.
- davnn 8y agoMaybe you want to read: https://en.m.wikipedia.org/wiki/Model-dependent_realism https://en.m.wikipedia.org/wiki/Model-dependent_realism
- YeGoblynQueenne 8y agoYour description ("statistical models with great number of layers") tells me you're talking about neural networks. However, we have "nowadays" many classifiers that are not neural networks and therefore have no layers of any sort, like SVMs, KNN or logistic regression and are not even statistical, like decision trees/forests. I should also point out that literally all the classifiers "we have nowadays" as per your comment, have been known for at least 20 years (including deep neural networks). I'm pointing all this out because your comment suggests to me that your knowledge of AI and machine learning in particular is very recent and goes as far as perhaps the last five or six years, when deep nets popularised the field. If that is so- please consider reading up on the history of AI. It is an interesting field that goes back several decades and has had many impressive successes (and some resounding failures) that predate deep learning by many years. I recommend the classic AI textbook "AI- A modern Approach" by Stuart Russel and Peter Norvig. You'll notice there that, even in recent versions, machine learning is a tiny part of the material covered. Because there is so much more to AI than just deep neural networks, or statistical classifiers. If I'm wrong, on the other hand, and you already have a broad knowledge of the field, then I apologise for assuming too much.
- bocklund 8y agoI heard a good set of definitions. Data science: observing trends in data Machine learning: humans develop models that are fit to data to make predictions AI: Computers make modeling decisions entirely autonomously. No human input. Dump data, get predictions.
- ekianjo 8y agoSo would you consider unsupervised classifiers to be AI?
- TeMPOraL 8y agoI think the key concept here is Moravec’s Paradox. People believing in the "AI is whatever hasn't been done yet" quip are moving the goalpost in the definition. Consider speech recognition. Obviously an AI problem solved, right? Except, the AI problem and what the current solutions are solving are two different things. The AI problem is understanding and appropriately reacting to spoken words, as if a human was on the other end. Current implementations are glorified pattern matchers run over clever hashes of sound recordings. There is no understanding happening, there are no concepts forming within the machine (and the understanding is not back-fed into pattern matcher to correct the sensory input on the fly). The difficult parts, the ones that make speech an AI problem, have been entirely sidestepped with mathematical tricks. It sort of works, but its scope is nowhere near the original AI problem. Similar analysis can be made for anything that is mentioned with the "no longer AI" quip. Can a DNN recognize a hot dog? Sort of, for some definition of "hot dog", only if input images are clear and similar enough to the training set. It's a cute trick, and you can make a business out of it if you control enough of the environment around the system, but it's still nowhere near what we mean when thinking about AI recognizing objects.