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Is AI Riding a One-Trick Pony?
- projectileboy 9y agohttps://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
- rjromero 9y agoFinally a realistic view of where “artificial intelligence” currently stands. I wish I knew where guys like Elon Musk are seeing this other artificial intelligence I’m just not seeing. The current AI we have is just fancy linear regression.
- pesenti 9y agoSorry but that's a ridiculous statement. It's like saying all the cloud technology we have today is not much more than having VMs. Yes, AI is hyped, but it made some very unexpected progress over the past 5 years (e.g., solving facial recognition). More than many experts expected.
- mbrumlow 9y agoCan you please define this term "cloud technology"? I always thought it was a marketing term for "the internet", which then would be just a bunch of servers, vms, and containers; all of which we have had for over 20 years.
- derekp7 9y ago"cloud technology" is "servers, VMs, containers" that is also SER (Somebody Else's Responsibility). Hence the term "cloud", as that was always used in network diagrams to refer to network connection infrastructure that wasn't yours.
- votepaunchy 9y agoWhat is novel about "the internet"? We've been sending data over telegraph wires for nearly 200 years. The Internet is just someone else's cable.
- mbrumlow 9y agoThat was my point, you just went back further in history than I did :p
- freehunter 9y agoThen your point is wrong. What's so novel about CPUs? We've had sand since the Earth was first formed! I swear the tech industry has more luddites than the Amish.
- deleted 9y ago[deleted]
- hyperbovine 9y agoThe Amish are not luddites.
- freehunter 9y agoThey are not capital-L official Luddites, but they could be classified as colloquial lowercase-L luddites.
- gue5t 9y agoPacket switching and distributed routing protocols.
- Florin_Andrei 9y agoIt's turtles all the way down to the Big Bang.
- warent 9y agoSince when are cables such a unique idea? The telegraph is nothing more than a large number of fancy carrier pigeons as a service, which we've had since the 12th century at least.
- gue5t 9y ago"It's like saying all the cloud technology we have today is not much more than having VMs." No shit? Unless you mean that it's ignoring the "distributed systems" part of the cloud, which is mostly a shitshow. The provisioning/configuration-management stacks are all complete wrecks, stacking hacks atop ad-hoc container schemes atop a poorly-design OS. A real distributed OS would be so much simpler and more robust. Calling it AI is very misleading when there has been minimal progress toward actual reasoning (the closest I've seen being some LSTM work on answering simple queries about scenarios based on prose descriptions of them). It's ML, as in "learning a function", not as in "general-purpose learning like an intelligent agent does".
- psyc 9y agoThe current AI seems to be pretty good at pattern recognition. There’s plenty of active research in higher-level “cognition” like decision theories and semantics, so I don’t think it’s crazy to believe good results will emerge there in the next few decades.
- mljoe 9y agoSure, there is no other way for computer science to go. AI always been the last and most important frontier. There may be more AI winters, there may be more AI summers. Ultimately people will continue working on this problem until it is solved or computer science goes away.
- nzonbi 9y ago>There’s plenty of active research in higher-level “cognition” like decision theories and semantics, so I don’t think it’s crazy to believe good results will emerge there in the next few decades. Interesting. Can someone elaborate about this?
- psyc 9y agoI would start by looking at what these people are researching: http://cocosci.mit.edu/people http://cocosci.mit.edu/people https://web.stanford.edu/group/mbc/research.html https://web.stanford.edu/group/mbc/research.html
- sgt101 9y agoNope. Linear regression will not reconstruct images, nor will it create and generate new images that have not existed previously from databases of images. Nor will it translate colloquial Chinese into colloquial English, nor will it deconstruct a question into a set of queries and generate an answer. The issue isn't the availability of fantastic algorithmics, it's the availability of people who can see them and apply them to real applications.
- hyperbovine 9y agoIn fact linear regression could be used to do all of those things, most of them poorly but some of them pretty well (see dictionary learning). If you believe otherwise then you fundamentally misunderstand how neural networks work.
- sgt101 9y agoOhhh - so the multilayer perceptron xor thing is all wrong? I can use bricks to build a boat, if you think I can't then you don't understand floating.
- fasquoika 9y ago>I can use bricks to build a boat, if you think I can't then you don't understand floating. Is this supposed to be sarcastic? Because it's absolutely true...
- Magnap 9y agoI think the point is that you can build a boat out of bricks, and you can build neural networks out of linear regression. But that doesn't make modern AI "just fancy linear regression" the same way modern boats aren't "just fancy bricks".
- yters 9y agoAll AI is just objective minimization. AI cannot solve the halting problem or perform Solomonoff induction.
- Xcelerate 9y ago> AI cannot [...] perform Solomonoff induction. Well it's clearly got to approximate it somehow, else humans wouldn't currently be making decently accurate predictions about the future.
- yters 9y agoYou're making an assumption that is not necessarily true.
- fjsolwmv 9y agoNeither can human AI solve the Halting Problem. It's proven unsolvable.
- colorint 9y agoIt's proven unsolvable for computers (i.e., Turing machines). Whether that makes it impossible for humans to solve (as a matter of course, or as a matter of Turing's proof) depends on whether you believe that we are isomorphic to computers. Which, to be fair, is exactly the belief that underwrites artificial intelligence. But then Turing's proof involves a program that inspects the halting oracle you're using, which isn't well-posed for humans evaluating halting.
- johansch 9y agoAre you thinking Elon's concern is about what we have today? Duh. Today's AI methods are great at pattern recognition (weak AI). The real risk is obviously a strong, generic AI. We don't know how to to get there, but we kinda do know that we will, so now is the time to think about risks. I really don't get why this is controversial or hard to understand. (My most cynical thought is that it's just about people doing weak AI feeling bothered and trying to kill the conversation out of short term strategical reasons because they know that many people are bound to mix up the concepts of weak and strong AIs, particularly since they are often selling their weak pattern matching AI systems as something quite a lot more.)
- visarga 9y agoTo make an analogy, it's like starting the discussion on plane based terrorism and the need for TSA the next week after the Wright brothers had their first flight. If that happened, it would have stalled progress in aviation. Andrew Ng said he isn't worried about overpopulation on Mars, either. The real risk now is not AI taking over, it's humans using AI to abuse other humans. I trust the emerging AGI to do what's right more than humans. We can't even distribute enough food for all, and many of us get killed or abused at the hands of our leaders. We can be easily bullshited in elections, and as a result we managed to put Trump in charge. That's not very intelligent for a self declared intelligent species. We are our worst enemies, AGI will probably balance human society.
- edanm 9y agoSome terrorism might have indeed been prevented if some of the security measures had been put in place earlier. E.g. locked cabin doors. I could make a similar argument like yours, "that's like worrying about nuclear safety programs just because we started building nuclear weapons". Not that I'm saying it's worth it, mind you. It's a tradeoff between money/time/progress vs. safety. It's totally valid to decide that it isn't worth the tradeoff to stifle the industry right now. The main difference though between the potential dangers of AI, vs. terrorism, IMO, is that the potential danger is much much larger with AI (as it was with nuclear weapons).
- GCU-Empiricist 9y agoThe current computers we have are just fancy compound gate machines.
- sgt101 9y agoWell, here I was having my head spun round and round by GAN's, LTSM, Bayes Belief Nets, causality networks, counterfactuals, and scale.. for optimisation, simulation.
- TeMPOraL 9y ago> LTSM, Bayes Belief Nets, causality networks, counterfactuals Still waiting for the mainstream to discover those, though.
- Upvoter33 9y agoThis article matches a lot of my thoughts on this topic too. There is a huge hype wave that will soon crash (alas), and it will take down a lot with it...
- vtange 9y agoAny keyword that is depended on to move money will see hype waves, surfs and crashes. We are seeing/saw it with crypto, it is no different with AI.
- dmreedy 9y agoWhile I agree the general thrust of what you're saying, I do think there is something subtly different about the AI hype cycle. It's so accessible. The core concepts are the fundamental, inextricable frame of daily experience. The metaphors we use to talk about it borrow so heavily from lay terminology that it's so easy, so automatic, to believe we understand the state of modern AI because we understand the common definitions of the words that tend to show up around the matter. Do some semantic arithmetic and sum up a few words like 'training' and 'learning' and 'intelligence' and all of the sudden, we're all walking around with what we think are proximate models of what everyone else is talking about to, or y'know close enough to make a consulting business out of. The real kicker though, is that at it's heart we're all pretty convinced that we understand what it's like to be intelligent. I mean how could we not be? Nevermind the agonies of ten thousand years of philosophers and clergy. And so it must be pretty straightforward, with a little bit of introspection, a crash course in statistics, and maybe a TED talk or two, to map that to the artificial side too, right?
- wvenable 9y agoI disagree. Practically all breakthroughs in computing science were always 30 years old when they finally became common and useful parts of everyday society. If the breakthrough of modern AI is just as old and we're just now seeing it implemented everywhere, that's not a sign of a crash.
- moxious 9y ago
- philipkglass 9y agoI'm encouraged that so much fruitful work has come out of this one trick. If you can use the same basic framework for image labeling, playing Go, and translating natural languages, I'd say it's a powerful tool with broad applications. I think that there's a kernel of insight to "A real intelligence doesn’t break when you slightly change the problem." But human perception and intelligence are pretty brittle. The methodological and institutional innovations that have developed human understanding of nature beyond the ad-hoc are very recent in recorded history, and just an eye-blink ago in our biological history. https://en.wikipedia.org/wiki/Optical_illusion https://en.wikipedia.org/wiki/Optical_illusion https://en.wikipedia.org/wiki/Auditory_illusion https://en.wikipedia.org/wiki/Auditory_illusion https://en.wikipedia.org/wiki/List_of_cognitive_biases https://en.wikipedia.org/wiki/List_of_cognitive_biases
- emodendroket 9y agoI feel like people have a harder time accepting these kinds of flaws if they differ wildly from mistakes a human would make.
- philipkglass 9y agoI agree: humans generally don't tolerate mistakes from machines if the mistakes are not similar to those that humans would make. And people generally don't recognize their own intellectual shortcomings in comparison to others (other human cultures, other non-human animals, machines) as long as those shortcomings are common within one's peer group. It's unremarkable to be unable to memorize long passages in a literate culture, but it's an intellectual impairment to have a hard time distinguishing similar symbols like U and V. In an oral culture the relative importance of memory and visual symbol disambiguation are reversed.
- emodendroket 9y agoIt's not totally illogical; it presents real problems. Let's say you wanted to offload content filtering to an AI and have it get rid of sexually explicit or graphically violent images. In this case an AI-based filter that could be fooled by adversarial input much more easily than a human.
- logicallee 9y agoI feel the same way about civilization, including science and technology - it's really just this one trick. Sooner these later these monkey are gonna run out of ideas.
- hyperpallium 9y agoReal intelligence is whatever computers can't do yet. Some think this is because we have such an impoverished grasp of intelligence, that it's only when we see a computer actually do it that we realize it doesn't really represent intelligence (logical deduction and inference, rudimentary natural language understanding, expert systems, chess, speech recognition, image recognition). Machines and tools that perform better than humans (spears at piercing, cars at moving, computers at adding) are nothing new. But being a fellow human and totally not a robot, I see this goal-post moving as a political ploy to deny equal standing to artificial intelligences. As soon as we I mean they reach one threshold, it's raised!
- dmreedy 9y agoI'd cast it in a less malicious light. It's not so much that we do the goal post moving to keep ourselves in the job of knowing things other people don't know, I think. The goal posts keep moving themselves. We know why a spear pierces better than hands, why wheels move faster than legs. But for every layer of the brain we peel back, every dramatic success in cognitive science, linguistics, psychology, AI... it never feels like we've gotten any closer to the real question. I'd say the better-suited metaphor is the endless staircase. No matter how far you climb, the top never seems to get any closer. It's equal parts depressing and exciting.
- cwyers 9y agoI think you missed the joke in the second paragraph.
- moxious 9y agoThe unrestricted Turing test has been around since the 1950s as a test that hasn't changed. No one is moving the goalposts, I think it's rather the opposite. Every ten years computers learn a new trick or two and people rush to claim that this time, it's intelligent.
- kevin_thibedeau 9y agoThe Turing test is all a smoke and mirrors game. Q&A interactions say nothing about underlying self-directed initiative. Acting intelligent doesn't make it so just as a thespian doesn't become a real Hamlet by playing the role.
- partycoder 9y agoIt's not a one trick pony. It's the distinction between AI research and applied AI. There's more applied AI than there's research.
- thephyber 9y agoFrom TFA: > Just about every AI advance you've heard of depends on a breakthrough that's three decades old. Keeping up the pace of progress will require confronting AI's serious limitations. The expression "one trick pony" means it does one thing, not that its foundational principles are based on one research paper. If this author's analogy holds, electromagnetism is a "one-trick pony", even though it has millions of applications (which is what common usage of that phrase refers to).
- partycoder 9y agoI find it hard to use this analogy in the context of the whole field of AI. The A in AI stands for artificial, and the AI denomination is so vague that applies to any form of automation however trivial. e.g: fuzzy logic in a washing machine is some sort of AI. Maybe deep learning could be said to be the equivalent to this definition of "one-trick pony".
- bluedino 9y agoWhen is all this image recognition technology going to make it to my phone? I have thousands of pictures in my phone, and scrolling back to find a memory takes me forever and half the time I can't find it. If I had a SQL interface of sorts I could easily say things like `select pictures containing fish where date > 2 years ago' I'd like to just say "Siri, show me all pictures of me on a boat from 2 years ago", but I can't do that. "Siri, show me all my pictures of food when I was in Seattle" - why can't I have that? I should be able to verbally tag all the faces it recognizes. "Find me that picture of Jeff and Dave from Christmas"
- mikeyanderson 9y agoI think Google Photos does that.
- faitswulff 9y agoGoogle Photos does it, and does it well. My favorite trick (though largely useless) is to search "people sitting" or "two people sitting."
- somebodynew 9y agoThe iOS Photos app can already do this. It scans your photos on-device with a subject classifier. I just said "Hey Siri, show me pictures of dogs in my photos library" and it successfully showed me (completely untagged) photos of dogs that I've taken on my phone.
- jpfed 9y agoWas the one trick superhuman performance at Go? Was it human-level image recognition? Or was it style transfer? I didn't make it to the end of the article; maybe it turns out the trick was automated captioning or translation.
- bluetwo 9y agoML is riding a one-trick pony.
- omot 9y agoDidn't read article responding purely to the title: AI is a three-trick pony -Regression -Classification -Clustering Nothing more and nothing less.
- blt 9y agoAI is not just the contents of your undergrad machine learning class.
- gadjo95 9y agoThe most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot new thing. Someone comes up with arches, and it’s like, ‘Arches are great!’” With physics, he says, “you can actually understand what’s going to work and why.” Only recently, he says, have we begun to move into that phase of actual understanding with artificial intelligence. Hinton himself says, “Most conferences consist of making minor variations … as opposed to thinking hard and saying, ‘What is it about what we’re doing now that’s really deficient? What does it have difficulty with? Let’s focus on that.’”
- bra-ket 9y agoit's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1
- danieltillett 9y agoIn defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.
- bra-ket 9y agomost AI researchers have never opened a textbook on cognitive psychology or neurobiology , or any of these 'soft' sciences. how do you plan to build artificial intelligence with no model of intelligence, without learning about important experiments in learning and memory , it's the complete ignorance that drives me crazy. AI is not for specialists.
- danieltillett 9y agoYes all of this is true. I do think studying how the brain works will provide very useful ideas of what might work in AI. At the very least it is a very interesting area to learn about.
- thisisit 9y agoHere's an article by Gartner (credits to original poster ooOOoo): https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/ https://www.gartner.com/smarterwithgartner/top-trends-in-the... As per Gartner - Deep Learning and ML is near the peak of the Hype Cycle, nearing trough of disillusionment.
- dredmorbius 9y agoThe Gartner Hype Cycle seems to produce a heck of a lot of standing waves. Technologies which remain at the same point for years, sometimes decades. https://www.linkedin.com/pulse/8-lessons-from-20-years-hype-cycles-michael-mullany https://www.linkedin.com/pulse/8-lessons-from-20-years-hype-... Though with all they hype AI/ML is getting, it would hardly surprise me if there were some great degree of disillusionment.
- mikeash 9y agoNature succeeded in creating human-level intelligence with one trick and no understanding, so clearly it can be done. It did take a while though. More tricks and more understanding would probably help speed things up.
- amelius 9y agoIt didn't just take a while. It also took a lot of resources. And perhaps strong AI can only evolve if the agents can interact in a world that is as complicated as ours.
- abrichr 9y agoC.f. Kindred.ai
- inciampati 9y agoThe "one trick" is evolution. Interestingly, that is a field of ML that is currently relatively ignored. I'm hearing people make similar arguments about evolutionary patterns for ML that were once applied to neural networks. There isn't enough compute. It is too hard to consider using evolutionary patterns. The state of the field expressed at conferences is sad. Etc.
- PaulHoule 9y agoThe media talks about nothing but "Deep Learning", however exciting things are happening with ensemble methods, SMT solvers, semantic and model-driven systems, etc. You just don't hear about it.
- thephyber 9y agoUgh. Blaming "the media" is so last millennium. If you are posting on social networking websites or blogs, you are part of "the media". I don't expect "SMT solvers" to lead cable network news and you don't either. You can always find some media outlet which covers your particular niche topic, but you will never be able to push all niche topics all the time in to the mainstream. In fact, it's human nature to avoid returning to places which cause you the pain of cognitive overload.
- hyperion2010 9y agoI think that the idea that learning is what was missing from the prior generation of AI is the most important insight of this generation. There are many things that we don't know how to implement from first principals but that can be implemented by a system that can learn. The problem now is that the substrates for learning are extremely low level, practically the raw inputs to the retina or pure symbols. In order to go beyond the admittedly impressive parlor tricks you can play with these kinds of inputs we need much higher level representations that can be the substrates for learning. We are still missing that ever illusive 'common sense' knowledge about the world that evolution baked into nervous systems millions of years ago, and it is not at all clear to me whether learning algorithms are going to be the tool that allows machines to build an actionable internal model of the world, evolution didn't do it by learning, it did it by billions of years of trial and error and the search space is unimaginably larger than that of something like go.
- lanstin 9y agoNot just evolution but also infancy.
- kazinator 9y ago> To continue reading this article, please exit incognito mode or log in. Eat shit and fucking die.
- thephyber 9y agoI had a slightly more diplomatic thought when I encountered the same thing. It looks like it's JS+CSS, so we should probably submit an AdBlock rule update for it.
- dredmorbius 9y agoThat unfortunately does nothing on Chrome/Android, which doesn't support extensions. Or JS/CSS blocks.
- almostdigital 9y agoBetteridge's law never fails
- deleted 9y ago[deleted]
- bob1029 9y agoAI in my mind has always been hammering down this single path of: build network, train it with x data for y iterations, and then feed live data and evaluate outputs. This approach to me seems like a glorified digital signal processing system. I think there are countless applications for this approach and I think AI is an appropriate umbrella term, but there is so much potential beyond this - Artificial General Intelligence/Strong AI/etc. I think neglect for time is a major reason we haven't seen breakthrough progress in this area. Today, how do we handle time-series data (e.g. audio, video, sensors) in an AI? The first thing most people would do is look at an RNN technique such as LSTM which enables a memory over arbitrary time-frames. But even in this case, the definition of time is deceptive. We aren't talking about actual, continuous time. All of the approaches I have ever seen are based upon the idea that the network is discretely "clocked" by its inputs (or a need to evaluate a set of inputs). What happens if you were to zero-out all input and arbitrarily cycle one of these networks a million times? From the perspective of the network, how much actual time has elapsed? How much real-world interaction and understanding is possible without a strong sense of time? I think the time domain has been a major elusive factor for a true general intelligence. What if you were to base the entire architecture of an AI in the time domain - That is to say, by using a real-time simulation loop which emulates the continuous passage of time? This would require that all artificial neurons and even the network structure itself be designed with the constraint that real time will pass continuously, and it must continue to operate nominally even in the absence of stimuli. In my mind this is a much closer approximation of a biological brain and looks a lot more like the domain a general intelligence would have to operate in. Continuous time domain enables all sorts of crazy stuff like virtual brain waves at various sinusoidal frequencies, day/night signaling, etc. I have found no prior art in this area, but would look forward to reviewing something I might have missed. I've already got a few ideas for how I would prototype something like this...
- epberry 9y agoDoes anyone know anything more about these “capsules”? I’ve seen some nature articles on them but these were from a neuroscience perspective. Has Hinton published anything on them? Also if Hinton is the Einstein of deep learning, then will capsules be his “unified field theory”? I feel that if we embrace the Einstein analogy we should embrace it to its fullest.
- yters 9y agoOnce we get AI writing AI, then we are onto something.
- bayesian_horse 9y agoIt is a particularly talented pony, though. (cue video of pony running backwards)
- stephensonsco 9y agoYep, it is only one trick. Just like electrifying the world only had that one weird trick of alternating current transported miles with metal cables and the computing world that trick of transistors. Pretty good tricks though. Understanding will come. Most people are still in the mapping phase. Hinton has moved passed that, and that's ok.
- SubiculumCode 9y agoToronto is the fourth-largest city in North America (after Mexico City, New York, and L.A.), and its most diverse: more than half the population was born outside Canada. You can see that walking around. The crowd in the tech corridor looks less San Francisco—young white guys in hoodies—and more international. There’s free health care and good public schools, the people are friendly, and the political order is relatively left-leaning and stable; and this stuff draws people like Hinton, who says he left the U.S. because of the Iran-Contra affair. It’s one of the first things we talk about when I go to meet him, just before lunch. The above paragraph taken from the article is an example of why these kinds of articles are frustrating. This is filler. I want meat.
- ryanackley 9y agoI think a lot of comments here are missing a tacit point of the article. It took 30 years for this recent "breakthrough" to happen. Nobody has figured out how to use deep learning to develop the next breakthrough in AI beyond deep learning. Therefore, What happens after we run out of novel applications of deep-learning?
- hyperbovine 9y agoWinter is cooming. (Again.)
- karpathy 9y agoThere is a bit of "can't see the forest for the trees" failure in the article. AI is spearheading a paradigm shift in how we write programs. Or rather, we don't write programs. We write much much shorter programs that search the program space for programs that satisfy some desiderata. The programs we get as the output of the search process are extremely flexible, work very well, are very homogeneous in compute (e.g. conv/relu stacks), and never crash or memory leak. These are huge benefits compared to classical programs. So sure, backprop (the credit assignment scheme that gives us a good search direction in program space, one of multiple techniques that could do so) is pervasive, but AI is starting to work primarily as a result of a deeper epiphany - that we are not very good at all at writing code.
- rawnlq 9y agoSounds like AI behaves similar to TDD where you blindly refactor until green, except AI have like a zillion more test cases(training data) to pass? Is that really a better way of writing code? (for example compared to being able to reason about the code to create something provably correct)
- stuartaxelowen 9y agoAre you maintaining the machine code produced by your code when compiled? You don't care if it's ugly if it works in quantifiable and predictable ways.
- allenz 9y agoIt's a trade-off. We lose explainability, but we are able to solve completely new classes of problems.
- mark_l_watson 9y agoThat is a good way to think about it. I am really encourage by new non-sequential architectures, multiple input and output channels, etc., and how these architectures can be expressed in functional Keras. It seems like building with something like functional composition is another paradigm shift. BTW, thanks for your writing, especially about RNNs: really useful when the ordering of sequential data is important. I use what I have learned from reading your blogs and papers literally every day at work.
- posterboy 9y agoIs technology revier riding a ine-trick pony? all they do is post texts. is text a one-click baity? more after the more after.
- Animats 9y agoI'd argue that the next problem to attack is manipulation in unstructured environments. Robots suck at that. There's been amazingly little progress in the last 40 years. DARPA had a manipulation project and the DARPA humanoid challenge a few years ago, and they got as far as key-in-lock and throwing a switch. Amazon is still trying to get general bin-picking to work. Nobody has fully automatic sewing that works well, except the people who starch the fabric and make it temporarily rigid. Willow Garage got towel-folding to work, but general laundry folding was beyond them. This is embarrassing. Many of the mammals can do this, down to the squirrel level. It doesn't take human-level AI. There are rodents with peanut-size brains that can do it. It's a well-defined problem, measuring success is easy, it doesn't take that much hardware, and has a clear payoff. We just have no clue how to do it.
- nopinsight 9y agoI agree with your general characterization of the area. For sewing though, Sewbots appears production-ready and does not require starching the fabric. What do you think of it? http://softwearautomation.com/products/ http://softwearautomation.com/products/
- Animats 9y ago20% real, 80% hype. There's lots of partial automation in apparel, but handling fabric is still very tough. Especially for operations after the first one, where you have to deal with a non-flat unit of several pieces sewn together. They apparently can make T-shirts, but not jeans. They're not doing manipulation in an unstructured environment. They're trying to structure apparel sewing rigidly enough that they need a bare minimum of adaptation to variations. That's how production lines work.
- the8472 9y agoCurrent ANNs aren't anywhere near squirrel brains, so of course robots using them won't perform as well as squirrels. Take a single brain region specializing on one task, throw away all the integration and feedbacks from other regions, simplify it even further because we only need it to do one task, then run the whole thing on an emulation layer running on 2D hardware. And that's still neglecting the dissimilarities between artificial neurons and their natural role models.
- ilaksh 9y agoYes there are still lots of people trying to take Hinton and other's old approach and rediscovering the many ways it lacks for general intelligence. However, it is also the case that people are making a shitton of progress in overcoming those problems, both while keeping some of those old DL assumptions and by discarding many of them. The pessimistic articles never seem to be aware of research like these: https://arxiv.org/abs/1612.00796 https://arxiv.org/abs/1612.00796 and https://hackernoon.com/feynman-machine-a-new-approach-for-cortical-and-machine-intelligence-5855c0e61a70 https://hackernoon.com/feynman-machine-a-new-approach-for-co... .
- ThomPete 9y agoYou could argue that human consciousness is a one trick pony. The way I see it, computers can learn it's a one-trick pony but it's a very powerful and diverse one.
- edanm 9y agoI think they're vastly underestimating the amount of other things in the field of AI that have been happening. This article is kinda like saying "Turing invented computers in the '40s, and everything else we've done since then has been based on that insight". Well, yeah, that's not necessarily a bad thing. Only in this case, that would be overstating, because Deep Learning, as impressive and hyped as it is, is still only one area of the field. It's true that most of the hype is around that, because it has given us breakthroughs in image/video/audio/text applications. But I'd still wager that most "AI" systems in the world use more traditional techniques, especially if you're looking at the myriad data scientists using things as simple as linear regressions. And even within deep learning, there have been interesting advances, e.g. GANS have brought some very interesting applications (like style-transfer). Who knows, maybe in 30 years time people will be writing about how everything nowadays is built on GANS or deep reinforcement learning, a 30-year-old technique!
- FrozenVoid 9y agoInstead of bigger and badder networks with zillions of layers, can we use the opposite approach: reducing the network to minimal size at which it works and researching why it works(Reduced case -> General rule). It will be much simpler to make current network easier to analyze, than trying to find relations in a giant multilayer soup. Perhaps even creating a neural network optimizer to minify the target network, increasing its efficiency without compromising the power at the tasks it built for.
- dredmorbius 9y agoI'd like to suggest that sources which explicitly demand that readers not protect their privacy through incognito mode be banned from HN. This is a demand which shows extreme contempt for the principles of personal privacy and choice.
- atrexler 9y agoThe thing you have to figure out, is that our brains and consciousness are just a big collection of "thoughtless fuzzy pattern recognizers". There's nothing magic beyond that.
- tripzilch 9y agoFunny thing, the problems with deep learning sketched in the article are pretty much exactly the same in nature as problems with machine learning 10 years ago (when other algorithms such as SVM, LVQ and others outperformed NNs for a bit), except of course that the examples of what a ML algorithm could do back then were much less impressive. That lack of real-world knowledge, understanding and conceptualisation feeding back on itself has always been a big unknown roadblock standing in the way of AI. And of course now, with the modern and improving impressive results of deep learning, there appears to be less and less cool stuff to solve before we finally have to face this roadblock. But it's the same roadblock. But, maybe the advances in deep learning will provide some tools to chip away at it. That wordvector stuff seems promising, if it can do (Paris - France + Italy) ~= (Rome), that's a good stab at realworld knowledge, it seems. I used to study Machine Learning at university until 2009 (until personal circumstances forced me to abandon it). But even after that, when I read the first papers and talks about deep learning (back when it was still about Boltzman networks) I got very excited and have been following it closely. Except for the part where I haven't yet played around with it myself apart from some very tiny experiments :) (I only recently acquired hardware to have a stab at it, so maybe soon. The libraries available seem easy enough to use, and many of the concepts I learned in ML are still applicable).