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Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

• whaaswijk 8y ago
  Somehow the main article link is behind a paywall for me, but the mobile one is not: https://mobile.nytimes.com/2018/06/20/technology/deep-learning-artificial-intelligence.html https://mobile.nytimes.com/2018/06/20/technology/deep-learni... (YMMV)
  • erric 8y ago
    On mobile it’s paywalled for me.
  • pililla 8y ago
    Open browser incognito, open google, paste link, profit. Works for me.
  • baking 8y ago
    The New York Times and Washington Post both use a "fingerprint" method to identify unique users using things like your browser version and IP address. I like using Firefox nightly because a quick update to Nightly will make it appear that I'm a new user and will reset my free "monthly" article limit. That's also why switching browsers will work for some people, but not others.
  • neonate 8y ago
    http://archive.is/yJ1SW http://archive.is/yJ1SW works. Also incognito tabs.
• sgt101 8y ago
  Why prolog? Why not PRISM or problog?
  • YeGoblynQueenne 8y ago
    This is a valid question, I don't know why you (originally) got downvoted. The answer I think, for the applications in industry at least, is that PRISM and other probabilistic programming langauges are not as developed as Prolog, a language that has been around for a good four decades now. Swi-Prolog in particular, is a free and open-source Prolog interpreter with an IDE, a graphical package and a veritable somrgasbord of libraries, including an http server, json and xml processing, encryption, interfaces to SQL and other databases etc etc etc. Swi is in active development, compatible with YAP-Prolog (currently the fastest implementation) very well supported and documented and has an active community. It's a lot easier to setup a production environment in Prolog, especially using Swi, than in pretty much any other logic programming language, including the probabilistic logic programming ones. Btw, I noticed that Kyndi list experience with Logtalk as a "plus" for one of their career opportunities (of course I looked :) which means that they're probably using Swi-Prolog.
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    • randcraw 8y ago
      But its not clear to me that encoding productions in source code adds any value over encoding them in the various other forms used by expert systems of old. Prolog also compels binary constraint satisfaction, recursive descent parsing, backtracking, and depth-first resolution — all undesirable constraints that are easily avoided using other fact representations or resolution engines. Until the startup in question can make a compelling case for using prolog, much less avoiding the brittleness inherent in expert systems' resolution model, count me a disbeliever that their 'better way' really is.
      • YeGoblynQueenne 8y ago
        I wouldn't say that backtracking and depth first search (not resolution) are undesirable. Far as I'm concerned they're pragmatic choices that minimise the amount of resources necessary to perform resolution theorem-proving.
• FrozenVoid 8y ago
  Lets rename "Deep learning" to "Statistical Compression". Its essentially distilled data from a dataset, limited by size of the neural network. There isn't anything "deep" there, the goal of NN is always towards overfitting the data.
  • dschuetz 8y ago
    That approach didn't work for "cyber", "quantum" or similar buzzwords. Calling something by its actual meaning isn't easy to sell. Hell, even "hacking" still has the connotation of some criminal activity, although it is widely known that the process of hacking in itself cannot be classified as benign or malign! But it doesn't matter. What matters is what public thinks of it hearing the buzzword. "Deep learning" is the same thing: "I have no idea what it is, but you know what I mean right?"
  • IshKebab 8y ago
    There is something deep there - the neural networks are deep. They have lots of layers. It's in contrast to shallow neural networks. It doesn't mean "deep and meaningful".
    • gaius 8y ago
      It doesn't mean "deep and meaningful". It would not be nearly as well funded were it not for this misconception on the part of budget holders, and everyone involved in ML knows it.
      • garmaine 8y ago
        I have never once encountered this misconception until now. Every explanation of deep learning I’ve seen begins with an explanation of the origin of the phrase.
        • IshKebab 8y ago
          A few weeks ago someone submitted an entire article to HN that consisted of someone complaining that deep learning wasn't really deep, in the deep & meaningful sense. They clearly misunderstood. Was pretty funny.
• jonathanstrange 8y ago
  Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind. The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet been solved. In order to get even just a convincing simulation of AI, let alone real AI, we need a large common sense knowledge base / computational ontology. One perhaps promising approach are geometric meaning theories/concept representations that allow for logical combinations. For example, Diederik Aerts works in this area. However, to be honest, I don't have the math skills to evaluate his approach. Generally speaking, logical modelling is too limited for good concept representations - especially classical 90s AI like in default reasoning and other nonmonotonic logics -, whereas traditionally geometric representations suffer from problems with representing logical inference and quantification. IMHO, that's a problem worth looking at. (Admittedly, I'm a bit biased towards symbolic AI like the people in this article.)
  • YeGoblynQueenne 8y ago
    >> Generally speaking, logical modelling is too limited for good concept representations In principle, First Order Logic and equivalent languages can represent anything that can be represented in natural language. The problem is that in practice it is very hard to transfer into a logic language all the knowledge you might need for a useful system. In fact, this was one motivation for at least one branch of machine learning- the work from Ryszard Michalski, Claude Sammut, Ranan Banerji, Steven Vere, Brian Cohen and later rule-learners like Quinlan's decision tree learners and of course all the later work in Inductive Logic Programming by Plotkin, Shapiro, Muggleton etc. These are all rule-learning systems, that can avoid the knowledge acquisition bottleneck that probably did in for purely rule-based expert systems in the '90s (i.e. getting experts to transfer their knowledge into rules). I rather agree that further progress in AI will require a, let's say, synchretistic approach- like I say in another comment, the obvious thing for me is to use deep learning for perception, logic for inference and probabilistic modelling to deal with the noisy world. There are some people working in that sort of direction, with different amounts of emphasis on each of the three approaches. For instance, Josh Tenenbaum at MIT, Evans and Grefenstette at DeepMind, Luc De Raedt at KU Leuven, Kristian Kersting at Dortmund, Lise Getoor also at MIT, Pedro Domingos at Washington, and many others. Apologies for all the name-dropping without links. Let me know and I can provide them if required.
• vinchuco 8y ago
  Hardware enables software
  • alexgmcm 8y ago
    Yeah - I remember reading about memristor based technology that aimed to better emulate neurons in the hardware back when I was in Uni. That was almost 10 years ago though and I haven't heard much of it since.
• YeGoblynQueenne 8y ago
  >> If the reach of deep learning is limited, too much money and too many fine minds may now be devoted to it, said Oren Etzioni, chief executive of the Allen Institute for Artificial Intelligence. “We run the risk of missing other important concepts and paths to advancing A.I.,” he said. I was very pleasantly surprised this year to see the Symbolic Machine Learning course at Imperial College London well-attended by about 30 students each session. The course basically teaches the principles and practice of Inductive Logic Programming, a set of algorithms and techniques that learn logic programs from relational data [1]. It is an optional course at Master's level so I was originally expecting to see maybe half a dozen people attending (I assisted with a couple of the sessions and followed the rest to brush up on my background). I discussed my surprise with my supervisor who is one of the tutors on the course. His opinion was that logic-based AI is much closer to the material that most Computer Science students are familiar with, than statistical and probabilistic AI. It's true that, although many CS courses have started to include statistics and probabilities in their curriculum (the one at Imperial sure does) the mainstay of computer science are logic-based, discrete maths, complexity and computation theory and language theory. The jump from there to GOFAI is just a short hop; in many ways, the two fields are really one subject. My intuition is that the boom in deep learning is partially being fed by disciples of fields well outside CS -maths, physics, bio-sciences etc- jumping on to the, well, bandwagon, motivated by the deep learning frenzy in industry and not so much by any interest in AI or even computers. At the same time, there is plenty of traditional CS talent waiting in the wings. It may be that a revisiting of the good old ways of knowledge representation and inference may not be completely out of the question [2]. Of course, there's always the chance that the students only came to the course because it had "machine learning" in its name :) __________________ [1] More the point, both of the outputs and inputs of ILP are logic programs, i.e. binary relations. Most of the work uses Prolog but there also exist techniques to learn Answer Set Progarmming, Constraint Logic Progarmming and of course Probabilistic Logic Programming programs. In any case, the homoiconicity of logic programming languages allows a newly learned hypothesis to be immediately reused as data to learn a new concept, in a way that is probably impossible with statistical techniques. And of course, learning is extremly sample efficient and the resulting programs generalise vey well. I bet you had never heard of any of this before now :) [2] To be honest, my guess is that the way forward is a combination of the two approaches- perhaps, some monster combo of deep learning for sensory perception, logic for inference and probabilities for final decision making. One way or another, I expect the AI researcher of the future will have to be a jack of all trades - and master of all of them at once. Learn to juggle.
• gremlinsinc 8y ago
  One problem with ai (or general ai) is ai lives in a box, it experiences what we tell it to experience, unlike a child who learns organically with what it's exposed to randomly while trying to survive and thrive. Perhaps the answer is to figure out all the things that make people tick via deep learning and other techniques, build one huge open api/db for vision/audio/movement in 3d planes/ethics/morality/understanding/discernment/language and then a more general ai could simply send out calls to api's of data already trained on specific foundations and create a 'whole' brain from multiple 'sub' brains (pre-learned open knowledge). Unless we literally learn to clone the human brain technologically, in it's entirety. I think another issue is not all working in AI are neuroscientists, and it may take understanding every mapped out facet of the human brain to come close to re-creating that in humans, it all can't be deep learning unfortunately, because that's not how we learn and we're the best guess solution to 'intelligence' as the only intelligent race (we know of) in the universe.
  • choxi 8y ago
    > it all can't be deep learning unfortunately, because that's not how we learn If you could humor me, why can't it all be deep learning? It seems like a robust model for how we learn: we start with some priors (random weights), experience something that either confirms or negates those priors (sampling), and then adjust our priors based on that experience. Of course there are a wide variety of architectures and the human brain is more sophisticated than one conv net. Our brains have on the order of 100 billion neurons and I believe the largest neural networks are on the order of 10's of millions. I would argue that a combination of better architectures and scale could simulate human intelligence.
    • chii 8y ago
      > we start with some priors (random weights) i dont think we start with random priors. We start with a set of priors that are geared for certain things - facial recognition, language etc. The environment then fine tunes them.
      • garmaine 8y ago
• skohan 8y ago
  Deep learning has produced a lot of interesting results over the past few years, but we're still miles away from anything that even approximates human intelligence. When I hear someone like Elon Musk say something like "Autonomous vehicles can drive without Lidar because humans can drive without Lidar" it makes me cringe. The human brain is unimaginably complex. Even if we created a neural network on the order of the 100 trillion or so connections in the central nervous system we would still not even be close. Each one of those synapse is a complex, constantly adapting chemical computer which is sensitive to the interaction of dozens of neurotransmitters. Gradient descent is to neural adaptation as minecraft is to photorealism. The idea that artificial intelligence will be achieved by adding a few layers to a "deep" neural network, or by throwing more data at it is laughable.
  • ythn 8y ago
    > The idea that artificial intelligence will be achieved by adding a few layers to a "deep" neural network, or by throwing more data at it is laughable. It's called "cargo cult science" and it's an important part of the AI hype train to keep funding coming.
  • lawlessone 8y ago
    >"Autonomous vehicles can drive without Lidar because humans can drive without Lidar" I have another reason to hate this. If we could give humans lidar to make their driving even better why shouldn't we? Why should we deny an AI extra senses that can help it do its job better than we can?
    • ianai 8y ago
      There is such a thing as being crippled by information.
  • kcorbitt 8y ago
    > Each one of those synapse is a complex, constantly adapting chemical computer which is sensitive to the interaction of dozens of neurotransmitters. Granted. On the other hand, electric signals can propagate across a chip several orders of magnitude faster than the chemical-electrical signals neurons use to communicate. So while our current path won't lead us to an artificial brain that works exactly like the human brain, it might still make one that's just as smart through pure brute force. A biologist in 1902 could very reasonably have scoffed at the heavier-than-air flight enthusiasts who pointed at birds as a proof of concept. The biologist could point out that the complex, many-part motion of a bird's flapping wing was far beyond the crude mechanical processes inventors were trying to use to replicate it. And they'd be right. But in a much more important way, they'd be wrong. I have no idea whether we're on the path to AGI or not, but I do think that "we're not building machines that work exactly like the brain does" is a poor counterargument.
• trumped 8y ago
  We need a field of AI that makes sure all other fields of AI behave nicely.
• sixdimensional 8y ago
  This discussion reminds me of NASA's CLIPS [1], particularly FuzzyCLIPS. I know that is basically a rule engine/expert system basis at its core, but FuzzyCLIPS was an interesting variant that try to do inference off of CLIPS rules and facts. I always wondered if rules and facts can be seen as high-order abstractions that represent the low-level data that is encoded into neural nets. Would that mean that a system of symbolic logic, or rules and facts can in fact accomplish the same thing, just in a different, higher order way? Then when you get into combining fuzzy with known, it's like different organs in the brain combining to serve different functions. When I know a rule and recall it, I believe it to be true and act immediately on it. When I'm not sure, I get fuzzy and try to deduce it. The more data and inputs I have about a given environmental condition, problem, subject, input, fact, etc., the easier it becomes to "reason" using fuzzy links to try to work out the answer. Unless I get information overload or the complexity exceeds one human brain's capability. I've always felt that techniques are complementary and probably we employ multiple subsystems in our brains together to act like humans. [1] https://en.m.wikipedia.org/wiki/CLIPS https://en.m.wikipedia.org/wiki/CLIPS
• alexgmcm 8y ago
  I think it's obvious to anyone with experience in the field that Deep Learning etc. isn't a path to AGI nor an emulation of biological intelligence. But it is one thing to know that the current path isn't leading us there and another thing entirely to know which path will. After all it isn't even clear that emulating biological intelligence would be a wise approach - we didn't build the jumbo jet by copying the birds.