14 ms·

Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning

312 points by monsieurpng 9y ago

zby 9y ago
If it is shallow than maybe it should not be called deep? :)
- jxub 9y ago
  Still, this observation might be not deep enough for the hn folks ;p
- giardini 9y ago
  In any case Hinton's use of the word "deep" was an excellent one from a marketing perspective: " In 2006, a publication by Geoff Hinton, Osindero and Teh[26][27] showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation.[28] The paper referred to learning for deep belief nets." Perhaps this usage ("deep belief networks") was too effective, as so many marketing terms prove to be. Hinton is likely well-aware of the importance of terminology that captures the imagination of the public (and the marketplace for research).
polotics 9y ago
I love the way the articles trots out the line that Google translate is almost as good as a human translator, in view of Hofsdadter's recent article.
- seandhi 9y ago
  If good is defined by more factors than quality of translation, maybe. But translations between the two languages I speak, English and Spanish, are never good enough to actually use without a lot of follow up work by a human.
  - rerx 9y ago
    Have you tried the translator by DeepL? It often gets incredibly close on my experience.
    - seandhi 9y ago
      No, I haven’t. I have only used Google Translate and the one Microsoft has. Interestingly, I feel like, without any actual measurement, that Google Translate seems to fail on simple translations more frequently than before, but I also use it far less frequently now than in the past. Do you have a link for the one by DeepL? Edit: https://www.deepl.com/translator https://www.deepl.com/translator
  - mannykannot 9y ago
    > If good is defined by more factors than quality of translation, maybe. That translates to 'is not good' in my language. Your low-key sarcasm is much appreciated.
    - seandhi 9y ago
      Haha, it was only kind of sarcastic. But thank you. I meant, if you evaluate based on things like speed, volume, and other measures, and quality of translation is secondary to “good enough” it may be better than humans. However, if you base purely on accuracy, no way. :-)
    - JumpCrisscross 9y ago
      > That translates to 'is not good' in my language The original line is more precise. In most cases, I need the gist of a block of text in an unfamiliar language. Google Translate is faster and cheaper than a human translator. That makes it better, for this purpose, than a human, despite its lower accuracy. When I need legal documents translated, most human translators are not good enough. A specialist, and only a specialist, will do.
henrik_w 9y ago
Another good article in a similar vein: "Asking the Right Questions About AI" https://medium.com/@yonatanzunger/asking-the-right-questions-about-ai-7ed2d9820c48 https://medium.com/@yonatanzunger/asking-the-right-questions... HN discussion: https://news.ycombinator.com/item?id=16286676 https://news.ycombinator.com/item?id=16286676
marmaduke 9y ago
Those four words convey potential problems in any model or method, which an engineer would want to address.
ssivark 9y ago
This article is a little too glib in my opinion, preferring citations and statements to substance and explanations. For a more cutting and insightful critique, watch Ali Rahimi's short talk at NIPS 2017 (where he was presenting for a paper that won the "Test of time" award, for standing out in value a decade after publication). The standing ovation he received at the end indicate that his comments resonated with a significant fraction of the attendees. https://www.youtube.com/watch?v=Qi1Yry33TQE&feature=youtu.be&t=660 https://www.youtube.com/watch?v=Qi1Yry33TQE&feature=youtu.be... Here's a teaser from the talk: "How many of you have devised a deep neural net from scratch, architecture and all, and trained it from the ground up, and when it didn't work, felt bad about yourself, like you did something wrong? This happens to me about every three months, and let me tell you, I don't think it's you [...] I think it's gradient descent's fault. I'll illustrate..." [Addendum] Ben Recht and Ali Rahimi published an adendum to the talk, elaborating on the direction they envision -- http://www.argmin.net/2017/12/11/alchemy-addendum/ http://www.argmin.net/2017/12/11/alchemy-addendum/ Ali also has a post taking a stab at organizing some puzzling basic observations about deep learning, and motivating that with analogous historical progress in optics -- http://www.argmin.net/2018/01/25/optics/ http://www.argmin.net/2018/01/25/optics/ ----- PS: The first 11 minutes, on the idea of using random features (the main idea in the research he presented) are also interesting.
- mkagenius 9y ago
  I think deep nets solve isolated problems really well, problems such classification based on given training sets is very good. However we sometimes compare it to humans which is I think an error on our part, humans process lot more training data than the deep nets, the vast amount of data we process over the years is incomparable. Don't get me wrong, the architecture of deep nets are fine, it can learn anything given the data and the loss function -- but its difficult to formulate every human decision in some kind of loss function. Its really hard.
- skybrian 9y ago
  In case you'd rather read text than watch a video, I believe this is the same? http://www.argmin.net/2017/12/05/kitchen-sinks/ http://www.argmin.net/2017/12/05/kitchen-sinks/
Animats 9y ago
Brittle and opaque are real problems. The brittleness seems to be associated with systems which put decision surfaces too close to points in some dimension. That's what makes strange classifier errors possible.[1] (This is also why using raw machine learning for automatic driving is a terrible idea. It will do well, until it does something totally wrong for no clear reason.) Opacity comes from what you get after training - a big matrix of weights. Now what? "Deep Dream" was an attempt to visualize what a neural net used for image classification was doing, by generating high-scoring images from the net. That helped some. Not enough. The ceiling for machine learning may be in sight, though. Each generation of AI goes through this. There's a new big idea, it works on some problems, enthusiasts are saying "strong AI real soon now", and then it hits a ceiling. We've been through that with search, the General Problem Solver, perceptrons, hill-climbing, and expert systems. Each hit a ceiling after a few years. (I went through Stanford just as the expert system boom hit its rather low ceiling. Not a happy time there.) The difference this time is that machine learning works well enough to power major industries. So AI now has products, people, and money. The previous generations of AI never got beyond a few small research groups in and around major universities. With more people working on it, the time to the next big idea should be shorter. [1] https://blog.openai.com/adversarial-example-research/ https://blog.openai.com/adversarial-example-research/
- TulliusCicero 9y ago
  I haven't seen anyone saying that the current trend of deep learning using neural networks will result in strong AI soon.
  - ForrestN 9y ago
    As a person outside the field, my subjective experience is that every day comes headlines with new superlatives regarding "The Coming AI Revolution." My expectations are being set for radical transformation across most areas of technology in the next decade or two on the basis of current developments in this area.
    - currymj 9y ago
inthewoods 9y ago
There are groups and companies exploring probabilistic programming as an alternative to CNN and other deep learning techniques. Gamalon (www.gamalon.com) combines human and machine learning to provide more accurate results while requiring much, much less training data. The models it generates are also auditable and human readable/editable - solving the "opaque" issue with deep learning techniques. Uber is exploring some of the same techniques with their Pyro framework. Having said all of this, we're not arguing that CNN have no place - in fact you can view CNNs as just a different type of program as part of an overall probabilistic programming framework. What we're seeing is that the requirement of large labeled training sets becomes a huge barriers as complexity scales - making understanding complex, multi-intent language challenging. Disclosure: I work for Gamalon
- yters 9y ago
  By "human learning" do you mean there is a human in the loop, or do you mean the system mimics how we think humans learn? Looking at their website the latter appears to be the case.
  - inthewoods 9y ago
    A human can provide guidance to the system and edit models. As to whether it “thinks like a human” I think that’s always a dangerous and loaded statement - but what we believe is that a Bayesian probabilistic approach is closer to the way humans learn and make decisions.
- joe_the_user 9y ago
  Are there scholarly papers describing the Gamalon Idea Learning system? My cursory search shows only tech press, TEDx talks and pdfs of slides. Edit: and patents, lots of patents. Edit2: maybe this: https://github.com/gamalon/chimple2 https://github.com/gamalon/chimple2
- currymj 9y ago
  agree with the other posters, could you guys please publish something on what you're doing?
sixtypoundhound 9y ago
Meta Thought: Isn't this basically describing the difference between human children and human adults? How effectively they can bridge known and unknown context?
gaius 9y ago
Calling it "deep learning" was the first mistake. It makes it sound a lot more profound than it really is. "Machine intuition" is the term I prefer.
- aoeusnth1 9y ago
  I like "machine perception". We're not much past the point of sensory-motor development.
  - enraged_camel 9y ago
    I have heard it called computer vision.
    - mkl 9y ago
      That's different. Some deep learning models are used for computer vision, and some computer vision involves deep learning. Neither is an essential part of the other.
- Quarrelsome 9y ago
  I prefer "just throwing cycles at the problem".
  - floatboth 9y ago
    You can throw cycles in various ways (like with regular algorithmic code that implements various heuristics or something). So, "just throwing cycles at statistics"
  - gaius 9y ago
    See also “p-value fishing”
- salty_biscuits 9y ago
  How about "long skinny networks" over "short and fat networks". Deep maps to profound in my mind rather than just talking about lots of layers.
visarga 9y ago
Humans, as opposed to deep learning, have embodiment. We can move about, push and prod, formulate ideas and test them in the world. A deep net can't do any of that in the supervised learning setting. The only way to do that is inside an RL agent. The problem is that any of our RL agents so far need to run inside a simulated environment, which is orders of magnitude less complex than reality. So they can't learn because they can't explore like us. The solution would be to improve embodiment for neural nets and to equip RL agents with internal world simulators (a world model) they could use to plan ahead. So we need simulation both outside and inside agents. Neural nets by themselves are not even the complete answer. But what is missing is not necessarily a new algorithm or data representation, it's the whole world-agent complex. Not to mention that a human alone is not much use - we need society and culture to unlock our potential. Before we knew the cause, we believed disease was caused by gods, and it took many deaths to unlock the mystery. We're not perfect either, we just sit on top of the previous generations. Another advantage we have - we have a builtin reward system that guides learning, which was created by evolution. We have to create this reward system for RL agents from scratch. In some special cases like board games, the board is a perfect simulation in itself (happens to be trivial, just observe the rules, play against a replica of yourself). In that case RL agents can reach superhuman intelligence, but that is mostly on account of having a perfect playground to test ideas in. In the future simulation and RL will form the next step in AI. The current limiting factor block is not the net, but the simulator. I think everyone here has noticed the blooming of many game environments used for training RL agents from DeepMind, OpenAI, Atari, StarCraft, Dota2, GTA, MuJoCo and others. It's a race to build the playground for the future intelligences. Latest paper from DeepMind? > IMPALA: Scalable Distributed DeepRL in DMLab-30. DMLab-30 is a collection of new levels designed using our open source RL environment DeepMind Lab. These environments enable any DeepRL researcher to test systems on a large spectrum of interesting tasks either individually or in a multi-task setting. Before we build an AI, we need to build a world for that AI to be in.
- bitL 9y ago
  > run inside a simulated environment It's pretty common to use DRL in real-world robotics e.g. in self-driving cars or robotic motion planing, so you don't need to simulate the real thing. The obvious issue with RL is that it needs even more iterations/episodes/epochs than a typical DL, making real-world development either impractical (time) or too costly.
geobourazanas 9y ago
Hey all, we are a team working toward democratising genetic testing. We are trying to gauge how much people know about genetic testing and what people feel about it. I have a super short form (4 questions only). Would really really appreciate some input from the good folks on this channel! https://joannavass.typeform.com/to/Vy18OL https://joannavass.typeform.com/to/Vy18OL
YeGoblynQueenne 9y ago
>> Google Translate is often almost as accurate as a human translator. This is the kind of overhyped reporting of results highlighted by Douglas Hofstadter in his recent article about Google Translate: I’ve recently seen bar graphs made by technophiles that claim to represent the “quality” of translations done by humans and by computers, and these graphs depict the latest translation engines as being within striking distance of human-level translation. To me, however, such quantification of the unquantifiable reeks of pseudoscience, or, if you prefer, of nerds trying to mathematize things whose intangible, subtle, artistic nature eludes them. To my mind, Google Translate’s output today ranges all the way from excellent to grotesque, but I can’t quantify my feelings about it. https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s... It's funny how the article above is claiming to speak of "the downsides" to deep learning, yet it spends a few paragraphs repeating the marketing pitch of Google, Amazon and Facebook, that their AI is now as good as humans in some tasks (limited as they may be) and all thanks to deep learning. To me that goes exactly counter to the article's main point and makes me wonder, what the hell is the author trying to say- and do they even know what they're talking about?
- cgmg 9y ago
  Where is the contradiction?
- StillBored 9y ago
  That article pretty much sums up what my sister in law (runs a professional translation business) says. To me, someone who doesn't have a second language strong enough to verify the accuracy of translation, I simply run things through google translate, to the target language and then run the output back to English. Its the translation equivalent of the game of telephone. Take "the fat cat sat on the mat"): "the big cat was sitting on the carpet" or "The cats of oil sat on the bed" and lots of other things which are just odd..
  - Balgair 9y ago
    What does this prove? Languages are different, you should expect any automatic translation program to do something like this, as it is trying to translate without prior knowledge of the 'conversation' you are having. Many languages put a lot of meaning into a single word. Look at English. "Tank" is good example. It's either a thing that holds a lot of fluid, a thing that blows up buildings on treads, or a verb that means you are taking a lot of damage in lieu of others taking damage. One word has a lot of meaning. Then you can get into conjugations and tenses, yeesh. Google translate is not meant to be a natural language processor, it's just a dumb translator. It can't figure out context as it is just a simple text box and doesn't look at the million and one things natural language processing would use. Trying to play telephone with it just proves that a dumb text box is dumb.
csours 9y ago
We had a presentation at work about AI and Deep Learning a while ago, and I asked what is the test approach or test plan for deep learning... the answer I got was a strange look. If you have a self driving car crash and the cause is "the algorithm", that's not going to be satisfying to customers, insurance agencies, or regulators [I should be clear, the team giving the presentation does not work on SDCs].
tanilama 9y ago
Nothing new to see here. Just a recycle of Gary's Marcus's recent criticism of deep learning.
skybrian 9y ago
The problem I see with this article is that it starts with problems with deep learning today and extrapolates to say that researchers won't get past them: "None of these shortcomings is likely to be solved soon." There's no evidence for this, and I think it underestimates the large and well-funded machine learning community. Maybe they'll run into a brick wall, but who's to say a bunch of smart researchers won't figure out ways around today's limitations? Without physical constraints or impossibility results, I think the only rational outside view is that this is unpredictable. For any of today's pressing problems with machine learning, maybe someone will post a new research paper tomorrow with a different approach that solves it. Or maybe not?
- randcraw 9y ago
  The 50 year history of AI research portends the difficulty in surmounting secondary obstacles unearthed by each new AI technique's innovations. The limits of deep learning are almost certainly no different from past statistically-driven methods in reaching comparable limits, especially those obstacles that show no signs of vulnerability to the slings and arrows inherent in rich semantics. The recent article by Doug Hofstadter in The Atlantic on using DNNs to do machine translation highlights this nicely. To date, no work in DNNs has shown any potential for redressing problems of the common sense knowledge needed to know when your translated sentence is semantic nonsense. Likewise, no statistic-based learning method has shown itself capable of tapping into much less creating the deep knowledgebases and wealth of relations that give bare facts the semantic meaning needed to convey or understand subtleties in messages that are more than trivial. Deft mimicry may win a Turing Test, but complex syntheses of thought (like writing a fictional story in which characters have internal thoughts and hidden motives that depend on relationships, subtexts, and dependencies) — no form of AI has yet shown any potential to solve such problems. Too much internal state and complex relations must be modeled, much less, the ability to extend and translate these to many possible worlds — as humans do every day. No, statistics-based AI techniques (like deep nets) show little promise to grow indefinitely unto full (wo)manhood.
  - skybrian 9y ago
    I'm just an outside observer, but I wouldn't say "no potential". For example, I'm reminded of a paper [1] about a technique that allows translation between language pairs that weren't in the training data. This doesn't rise to the level of common sense yet, but it seems impressive, and there is a language-independent portion of the neural net that seems to be encoding something. [1] https://research.googleblog.com/2016/11/zero-shot-translation-with-googles.html https://research.googleblog.com/2016/11/zero-shot-translatio...
vladislav 9y ago
If the hypothesis this article is aiming to strike down is that there is currently a clear path to solving AGI, then it's simply fighting a potential misconception of the masses. The reality is that recent advances in AI, while not providing the full picture, are quite strong. Object recognition ten years ago, even at the level of 2011's Imagenet results, seemed impossible. The downsides to deep learning mentioned in the article, are both clear side effects of the formulation, and simultaneously being addressed by the community in various ways. For what it's worth, I'm with LeCun and Hinton on this one. The abstract human reasoning required for even high level perceptual tasks is difficult for humans to consciously dissect, but it could easily be that it's actually relatively mundane when subjected to appropriate representations.
- bobthechef 9y ago
  > The abstract human reasoning required for even high level perceptual tasks is difficult for humans to consciously dissect, but it could easily be that it's actually relatively mundane I don't think so. Philosophers have theorized about abstract thought for thousands of years. The materialist metaphysics that is uncritically and unconsciously held by the triumphalist philistines isn't even capable IN PRINCIPLE of accounting for it. In any case, AI is useful, and will continue to be, but the fanfare is just noise distracting from the truth of what it is.
  - vladislav 9y ago
    relatively mundane when subjected to appropriate representations. You omitted the important part of my statement. And the difficulty humans encounter in reasoning about the nature of abstract thought itself is something I also pointed out. My point is that this is not necessarily an indication of inherent difficulty. The ability to model and explain the nature of a self-reflecting mechanism is not a guaranteed natural ability of that mechanism itself.
andbberger 9y ago
I've said it before and I'll say it again. Machine learning is specifically not magic. It only works to the extent that we can build our own priors into the model. A typical media story... deep learning really is great. It represents the first time we've really figured out how to do large-scale nonlinear regression. But it is certainly not a magic bullet. However moderate headlines don't get as many hits as overhyped ones so every day we get another ridiculous article spouting nonsense... Very tiresome. The truth is pretty interesting, can we talk about that instead? For instance, how and why deep learning works at ALL is very much an open question. Consider - we're taking an incredibly nonlinear, nonconvex optimization problem and optimizing in just about the dumbest way imaginable, first-order gradient descent. It is really amazing that this works as well as it does. ... Why does deeper work better than wider? It has been known for many years that a shallow net has equivalent expressivity to a deep one. So what gives? (actually some interesting work towards answering this question in recent years by Sohl-Dickenstein et. al)
- ggm 9y ago
  Here here. Training an algorithm to mimic prior knowledge. The prior knowledge is inherently there. It's like the inflationary claims for QC which depend on demonstrated factorisation to a known product of primes: it's not magic when you seek to minimise error to a known goal but you have to know the goal.
- pps43 9y ago
  > Machine learning [..] only works to the extent that we can build our own priors into the model. AlphaZero is a good counterexample. In contrast to AlphaGo, it has no priors.
  - aoeusnth1 9y ago
    Well it does have some priors baked into it by the (convolutional) architecture of the network. See the “deep image prior” project for a feel for just how strong this convolutional prior is.
deleted 9y ago
[deleted]
John_KZ 9y ago
This article presents zero evidence or indications for their claim. One argument is persistence. Quoting: " “We are born knowing there are causal relationships in the world, that wholes can be made of parts, and that the world consists of places and objects that persist in space and time,” Which is unsubstantiated and irrelevant, because AI that understands 3D is only now being developed. Also children up to 3 years of age or so, cannot understand the perspective of 3rd parties. There might be some hard-wired rules in our brain, but that's not intelligence anyway. The article has a point about something: Conventional feed-forward, convolutional neural networks can only model a very limited space in the grand scheme of things. Backpropagation is not perfect. There are other learning methods. Hell, sometimes there isn't a global minimum anyway. But saying that Deep Learning will stall in the near future is just wrong, and in my opinion the reasons why are evident to all those who follow the latest developments.
mcguire 9y ago
I would argue that 'greedy' applies to neutral networks in a different sense: they seize on their first solution; they have no mechanism to re-evaluate a decision that is proving false.
- hadsed 9y ago
  Sure they do, it happens at training time. Each time you update parameters you look at all your predictions and figure out how well you did. That informs your next tweak to the parameters. Run online training and you are constantly doing this.
dmix 9y ago
> Marcus believes that deep learning is not “a universal solvent, but one tool among many.” And without new approaches, Marcus worries that AI is rushing toward a wall, beyond which lie all the problems that pattern recognition cannot solve. The thing that fascinates me is that we've only just scratched the surface of what today's ML tech can solve. Let's worry about that wall when we get there... In the meantime let's not lose sight of today's potential in some misguided idealistic pursuit of perfectionism or "general artificial intelligence". There are countless problems which current deep learning research combined with some well-thought out UI/UX could solve today in a myriad of industries. The 1990's software 'revolution' in industry/business was largely just formalization/automation of paper-based processes into spreadsheets and simple databases, which then evolved into glorified CRUD/CMS software interfaces on desktops, then web/SaaS, and then another massively boost with smartphones. If such a simple translation of human processes into machines can achieve trillions of dollars in value then there is no doubt machine learning can do the same for hundreds of thousands of other simple problem-sets which we haven't even considered. Plus the desktop/smartphone/internet/etc infrastructure is already in place for it to be plugged into. This can only be negatively judged in the context of all significant steps forward in technology being oversold and misunderstood. But in practical real-world utility we're very far from fully utilizing what has been researched and accomplished today in a small set of markets. And the proliferation of this tech should be encouraged, promoted, and accurately communicated to tech/business talent who can potentially use it, rather than downplayed because it fails to live up to some media hyperbole or SciFi fantasies of where we should be in 2018. The article mentions that taking AI/ML/data science courses has just become the "hottest new field" for young smart kids to join. Well that means we're just on the cusp of taking advantage of that technological evolution and it's FAR too early to look at what's been accomplished today and be pessimistic about deep learnings potential.
anonytrary 9y ago
Keep in mind, folks -- the following is also true: > Greedy, Brittle, Opaque, and Shallow: The Downsides to Evolved Human Intelligence I wonder if we can have our cake and eat it too, in the realm of machine learning and artificial intelligence.
bobthechef 9y ago
> “We are born knowing there are causal relationships in the world, that wholes can be made of parts, and that the world consists of places and objects that persist in space and time,” he says. Here's a little too overconfident in the claim that these are innate ideas or something a priori.
fellellor 9y ago
The article seems a bit outdated in that Hinton himself has argued for the need to go beyond backpropagation.
- giardini 9y ago
  Marcus' article mentions Hinton's recent misgivings about backprop: "Perhaps most notably of all, Geoff Hinton has been courageous enough to reconsider has own beliefs, revealing in an August interview with the news site Axios 16 that he is “deeply suspicious” of back-propagation, a key enabler of deep learning that he helped pioneer, because of his concern about its dependence on labeled data sets. Instead, he suggested (in Axios’ paraphrase) that “entirely new methods will probably have to be invented.” I share Hinton’s excitement in seeing what comes next.
jokoon 9y ago
I am still wondering, why is it not possible, or maybe too hard, to take a trained neural network and reduce its amount of neurons to get a simplified solution to a problem? Is there some theoretical or mathematical analysis of neural networks?
dsign 9y ago
It's the combined enthusiasm of academics and entrepreneurs the one driving the current revolution on artificial intelligence. Said enthusiasm is punctuated by high profile CEOs and investors making fiery remarks from time to time. But alas, we people are not enthusiastic forever about something, and sooner or later our collective psyche will move on. That doesn't mean that the technology revolution, and it's AI component will stop. We have had machine learning for a long time, doing its thing, as best we managed to make it work. It's impact inside our collective speech was more subdued, that for sure, but it was there. Research and development never stopped. Even if some big name university funded it less and shut up about building GAI, well there were still thousands of less shiny institutions and companies working in more tractable problems and building a foundation. And, to be clear, I wish the current collective enthusiasm lasts a little bit longer, because we have a long way to go still and research grants and investment money flow better when the media is abuzz with the subject. In particular, we need to either move out or build upon matrix crunchers like deep learning. Better forms of AI and eventually GAI will need a little bit of innovation in chip-making and in computing architectures in general.

312 points by monsieurpng 9y ago

zby 9y ago

If it is shallow than maybe it should not be called deep? :)

jxub 9y ago
Still, this observation might be not deep enough for the hn folks ;p
giardini 9y ago
In any case Hinton's use of the word "deep" was an excellent one from a marketing perspective: " In 2006, a publication by Geoff Hinton, Osindero and Teh[26][27] showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation.[28] The paper referred to learning for deep belief nets." Perhaps this usage ("deep belief networks") was too effective, as so many marketing terms prove to be. Hinton is likely well-aware of the importance of terminology that captures the imagination of the public (and the marketplace for research).

polotics 9y ago

I love the way the articles trots out the line that Google translate is almost as good as a human translator, in view of Hofsdadter's recent article.

seandhi 9y ago
If good is defined by more factors than quality of translation, maybe. But translations between the two languages I speak, English and Spanish, are never good enough to actually use without a lot of follow up work by a human.
- rerx 9y ago
  Have you tried the translator by DeepL? It often gets incredibly close on my experience.
  - seandhi 9y ago
    No, I haven’t. I have only used Google Translate and the one Microsoft has. Interestingly, I feel like, without any actual measurement, that Google Translate seems to fail on simple translations more frequently than before, but I also use it far less frequently now than in the past. Do you have a link for the one by DeepL? Edit: https://www.deepl.com/translator https://www.deepl.com/translator
- mannykannot 9y ago
  > If good is defined by more factors than quality of translation, maybe. That translates to 'is not good' in my language. Your low-key sarcasm is much appreciated.
  - seandhi 9y ago
    Haha, it was only kind of sarcastic. But thank you. I meant, if you evaluate based on things like speed, volume, and other measures, and quality of translation is secondary to “good enough” it may be better than humans. However, if you base purely on accuracy, no way. :-)
  - JumpCrisscross 9y ago
    > That translates to 'is not good' in my language The original line is more precise. In most cases, I need the gist of a block of text in an unfamiliar language. Google Translate is faster and cheaper than a human translator. That makes it better, for this purpose, than a human, despite its lower accuracy. When I need legal documents translated, most human translators are not good enough. A specialist, and only a specialist, will do.

henrik_w 9y ago

Another good article in a similar vein: "Asking the Right Questions About AI" https://medium.com/@yonatanzunger/asking-the-right-questions-about-ai-7ed2d9820c48 https://medium.com/@yonatanzunger/asking-the-right-questions... HN discussion: https://news.ycombinator.com/item?id=16286676 https://news.ycombinator.com/item?id=16286676

marmaduke 9y ago

Those four words convey potential problems in any model or method, which an engineer would want to address.

ssivark 9y ago

This article is a little too glib in my opinion, preferring citations and statements to substance and explanations. For a more cutting and insightful critique, watch Ali Rahimi's short talk at NIPS 2017 (where he was presenting for a paper that won the "Test of time" award, for standing out in value a decade after publication). The standing ovation he received at the end indicate that his comments resonated with a significant fraction of the attendees. https://www.youtube.com/watch?v=Qi1Yry33TQE&feature=youtu.be&t=660 https://www.youtube.com/watch?v=Qi1Yry33TQE&feature=youtu.be... Here's a teaser from the talk: "How many of you have devised a deep neural net from scratch, architecture and all, and trained it from the ground up, and when it didn't work, felt bad about yourself, like you did something wrong? This happens to me about every three months, and let me tell you, I don't think it's you [...] I think it's gradient descent's fault. I'll illustrate..." [Addendum] Ben Recht and Ali Rahimi published an adendum to the talk, elaborating on the direction they envision -- http://www.argmin.net/2017/12/11/alchemy-addendum/ http://www.argmin.net/2017/12/11/alchemy-addendum/ Ali also has a post taking a stab at organizing some puzzling basic observations about deep learning, and motivating that with analogous historical progress in optics -- http://www.argmin.net/2018/01/25/optics/ http://www.argmin.net/2018/01/25/optics/ ----- PS: The first 11 minutes, on the idea of using random features (the main idea in the research he presented) are also interesting.

mkagenius 9y ago
I think deep nets solve isolated problems really well, problems such classification based on given training sets is very good. However we sometimes compare it to humans which is I think an error on our part, humans process lot more training data than the deep nets, the vast amount of data we process over the years is incomparable. Don't get me wrong, the architecture of deep nets are fine, it can learn anything given the data and the loss function -- but its difficult to formulate every human decision in some kind of loss function. Its really hard.
skybrian 9y ago
In case you'd rather read text than watch a video, I believe this is the same? http://www.argmin.net/2017/12/05/kitchen-sinks/ http://www.argmin.net/2017/12/05/kitchen-sinks/

Animats 9y ago

Brittle and opaque are real problems. The brittleness seems to be associated with systems which put decision surfaces too close to points in some dimension. That's what makes strange classifier errors possible.[1] (This is also why using raw machine learning for automatic driving is a terrible idea. It will do well, until it does something totally wrong for no clear reason.) Opacity comes from what you get after training - a big matrix of weights. Now what? "Deep Dream" was an attempt to visualize what a neural net used for image classification was doing, by generating high-scoring images from the net. That helped some. Not enough. The ceiling for machine learning may be in sight, though. Each generation of AI goes through this. There's a new big idea, it works on some problems, enthusiasts are saying "strong AI real soon now", and then it hits a ceiling. We've been through that with search, the General Problem Solver, perceptrons, hill-climbing, and expert systems. Each hit a ceiling after a few years. (I went through Stanford just as the expert system boom hit its rather low ceiling. Not a happy time there.) The difference this time is that machine learning works well enough to power major industries. So AI now has products, people, and money. The previous generations of AI never got beyond a few small research groups in and around major universities. With more people working on it, the time to the next big idea should be shorter. [1] https://blog.openai.com/adversarial-example-research/ https://blog.openai.com/adversarial-example-research/

TulliusCicero 9y ago
I haven't seen anyone saying that the current trend of deep learning using neural networks will result in strong AI soon.
- ForrestN 9y ago
  As a person outside the field, my subjective experience is that every day comes headlines with new superlatives regarding "The Coming AI Revolution." My expectations are being set for radical transformation across most areas of technology in the next decade or two on the basis of current developments in this area.
  - currymj 9y ago

inthewoods 9y ago

There are groups and companies exploring probabilistic programming as an alternative to CNN and other deep learning techniques. Gamalon (www.gamalon.com) combines human and machine learning to provide more accurate results while requiring much, much less training data. The models it generates are also auditable and human readable/editable - solving the "opaque" issue with deep learning techniques. Uber is exploring some of the same techniques with their Pyro framework. Having said all of this, we're not arguing that CNN have no place - in fact you can view CNNs as just a different type of program as part of an overall probabilistic programming framework. What we're seeing is that the requirement of large labeled training sets becomes a huge barriers as complexity scales - making understanding complex, multi-intent language challenging. Disclosure: I work for Gamalon

yters 9y ago
By "human learning" do you mean there is a human in the loop, or do you mean the system mimics how we think humans learn? Looking at their website the latter appears to be the case.
- inthewoods 9y ago
  A human can provide guidance to the system and edit models. As to whether it “thinks like a human” I think that’s always a dangerous and loaded statement - but what we believe is that a Bayesian probabilistic approach is closer to the way humans learn and make decisions.
joe_the_user 9y ago
Are there scholarly papers describing the Gamalon Idea Learning system? My cursory search shows only tech press, TEDx talks and pdfs of slides. Edit: and patents, lots of patents. Edit2: maybe this: https://github.com/gamalon/chimple2 https://github.com/gamalon/chimple2
currymj 9y ago
agree with the other posters, could you guys please publish something on what you're doing?

sixtypoundhound 9y ago

Meta Thought: Isn't this basically describing the difference between human children and human adults? How effectively they can bridge known and unknown context?

gaius 9y ago

Calling it "deep learning" was the first mistake. It makes it sound a lot more profound than it really is. "Machine intuition" is the term I prefer.

aoeusnth1 9y ago
I like "machine perception". We're not much past the point of sensory-motor development.
- enraged_camel 9y ago
  I have heard it called computer vision.
  - mkl 9y ago
    That's different. Some deep learning models are used for computer vision, and some computer vision involves deep learning. Neither is an essential part of the other.
Quarrelsome 9y ago
I prefer "just throwing cycles at the problem".
- floatboth 9y ago
  You can throw cycles in various ways (like with regular algorithmic code that implements various heuristics or something). So, "just throwing cycles at statistics"
- gaius 9y ago
  See also “p-value fishing”
salty_biscuits 9y ago
How about "long skinny networks" over "short and fat networks". Deep maps to profound in my mind rather than just talking about lots of layers.

visarga 9y ago

Humans, as opposed to deep learning, have embodiment. We can move about, push and prod, formulate ideas and test them in the world. A deep net can't do any of that in the supervised learning setting. The only way to do that is inside an RL agent. The problem is that any of our RL agents so far need to run inside a simulated environment, which is orders of magnitude less complex than reality. So they can't learn because they can't explore like us. The solution would be to improve embodiment for neural nets and to equip RL agents with internal world simulators (a world model) they could use to plan ahead. So we need simulation both outside and inside agents. Neural nets by themselves are not even the complete answer. But what is missing is not necessarily a new algorithm or data representation, it's the whole world-agent complex. Not to mention that a human alone is not much use - we need society and culture to unlock our potential. Before we knew the cause, we believed disease was caused by gods, and it took many deaths to unlock the mystery. We're not perfect either, we just sit on top of the previous generations. Another advantage we have - we have a builtin reward system that guides learning, which was created by evolution. We have to create this reward system for RL agents from scratch. In some special cases like board games, the board is a perfect simulation in itself (happens to be trivial, just observe the rules, play against a replica of yourself). In that case RL agents can reach superhuman intelligence, but that is mostly on account of having a perfect playground to test ideas in. In the future simulation and RL will form the next step in AI. The current limiting factor block is not the net, but the simulator. I think everyone here has noticed the blooming of many game environments used for training RL agents from DeepMind, OpenAI, Atari, StarCraft, Dota2, GTA, MuJoCo and others. It's a race to build the playground for the future intelligences. Latest paper from DeepMind? > IMPALA: Scalable Distributed DeepRL in DMLab-30. DMLab-30 is a collection of new levels designed using our open source RL environment DeepMind Lab. These environments enable any DeepRL researcher to test systems on a large spectrum of interesting tasks either individually or in a multi-task setting. Before we build an AI, we need to build a world for that AI to be in.

bitL 9y ago
> run inside a simulated environment It's pretty common to use DRL in real-world robotics e.g. in self-driving cars or robotic motion planing, so you don't need to simulate the real thing. The obvious issue with RL is that it needs even more iterations/episodes/epochs than a typical DL, making real-world development either impractical (time) or too costly.

geobourazanas 9y ago

Hey all, we are a team working toward democratising genetic testing. We are trying to gauge how much people know about genetic testing and what people feel about it. I have a super short form (4 questions only). Would really really appreciate some input from the good folks on this channel! https://joannavass.typeform.com/to/Vy18OL https://joannavass.typeform.com/to/Vy18OL

YeGoblynQueenne 9y ago

>> Google Translate is often almost as accurate as a human translator. This is the kind of overhyped reporting of results highlighted by Douglas Hofstadter in his recent article about Google Translate: I’ve recently seen bar graphs made by technophiles that claim to represent the “quality” of translations done by humans and by computers, and these graphs depict the latest translation engines as being within striking distance of human-level translation. To me, however, such quantification of the unquantifiable reeks of pseudoscience, or, if you prefer, of nerds trying to mathematize things whose intangible, subtle, artistic nature eludes them. To my mind, Google Translate’s output today ranges all the way from excellent to grotesque, but I can’t quantify my feelings about it. https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s... It's funny how the article above is claiming to speak of "the downsides" to deep learning, yet it spends a few paragraphs repeating the marketing pitch of Google, Amazon and Facebook, that their AI is now as good as humans in some tasks (limited as they may be) and all thanks to deep learning. To me that goes exactly counter to the article's main point and makes me wonder, what the hell is the author trying to say- and do they even know what they're talking about?

cgmg 9y ago
Where is the contradiction?
StillBored 9y ago
That article pretty much sums up what my sister in law (runs a professional translation business) says. To me, someone who doesn't have a second language strong enough to verify the accuracy of translation, I simply run things through google translate, to the target language and then run the output back to English. Its the translation equivalent of the game of telephone. Take "the fat cat sat on the mat"): "the big cat was sitting on the carpet" or "The cats of oil sat on the bed" and lots of other things which are just odd..
- Balgair 9y ago
  What does this prove? Languages are different, you should expect any automatic translation program to do something like this, as it is trying to translate without prior knowledge of the 'conversation' you are having. Many languages put a lot of meaning into a single word. Look at English. "Tank" is good example. It's either a thing that holds a lot of fluid, a thing that blows up buildings on treads, or a verb that means you are taking a lot of damage in lieu of others taking damage. One word has a lot of meaning. Then you can get into conjugations and tenses, yeesh. Google translate is not meant to be a natural language processor, it's just a dumb translator. It can't figure out context as it is just a simple text box and doesn't look at the million and one things natural language processing would use. Trying to play telephone with it just proves that a dumb text box is dumb.

csours 9y ago

We had a presentation at work about AI and Deep Learning a while ago, and I asked what is the test approach or test plan for deep learning... the answer I got was a strange look. If you have a self driving car crash and the cause is "the algorithm", that's not going to be satisfying to customers, insurance agencies, or regulators [I should be clear, the team giving the presentation does not work on SDCs].

tanilama 9y ago

Nothing new to see here. Just a recycle of Gary's Marcus's recent criticism of deep learning.

skybrian 9y ago

The problem I see with this article is that it starts with problems with deep learning today and extrapolates to say that researchers won't get past them: "None of these shortcomings is likely to be solved soon." There's no evidence for this, and I think it underestimates the large and well-funded machine learning community. Maybe they'll run into a brick wall, but who's to say a bunch of smart researchers won't figure out ways around today's limitations? Without physical constraints or impossibility results, I think the only rational outside view is that this is unpredictable. For any of today's pressing problems with machine learning, maybe someone will post a new research paper tomorrow with a different approach that solves it. Or maybe not?

randcraw 9y ago
The 50 year history of AI research portends the difficulty in surmounting secondary obstacles unearthed by each new AI technique's innovations. The limits of deep learning are almost certainly no different from past statistically-driven methods in reaching comparable limits, especially those obstacles that show no signs of vulnerability to the slings and arrows inherent in rich semantics. The recent article by Doug Hofstadter in The Atlantic on using DNNs to do machine translation highlights this nicely. To date, no work in DNNs has shown any potential for redressing problems of the common sense knowledge needed to know when your translated sentence is semantic nonsense. Likewise, no statistic-based learning method has shown itself capable of tapping into much less creating the deep knowledgebases and wealth of relations that give bare facts the semantic meaning needed to convey or understand subtleties in messages that are more than trivial. Deft mimicry may win a Turing Test, but complex syntheses of thought (like writing a fictional story in which characters have internal thoughts and hidden motives that depend on relationships, subtexts, and dependencies) — no form of AI has yet shown any potential to solve such problems. Too much internal state and complex relations must be modeled, much less, the ability to extend and translate these to many possible worlds — as humans do every day. No, statistics-based AI techniques (like deep nets) show little promise to grow indefinitely unto full (wo)manhood.
- skybrian 9y ago
  I'm just an outside observer, but I wouldn't say "no potential". For example, I'm reminded of a paper [1] about a technique that allows translation between language pairs that weren't in the training data. This doesn't rise to the level of common sense yet, but it seems impressive, and there is a language-independent portion of the neural net that seems to be encoding something. [1] https://research.googleblog.com/2016/11/zero-shot-translation-with-googles.html https://research.googleblog.com/2016/11/zero-shot-translatio...

vladislav 9y ago

If the hypothesis this article is aiming to strike down is that there is currently a clear path to solving AGI, then it's simply fighting a potential misconception of the masses. The reality is that recent advances in AI, while not providing the full picture, are quite strong. Object recognition ten years ago, even at the level of 2011's Imagenet results, seemed impossible. The downsides to deep learning mentioned in the article, are both clear side effects of the formulation, and simultaneously being addressed by the community in various ways. For what it's worth, I'm with LeCun and Hinton on this one. The abstract human reasoning required for even high level perceptual tasks is difficult for humans to consciously dissect, but it could easily be that it's actually relatively mundane when subjected to appropriate representations.

bobthechef 9y ago
> The abstract human reasoning required for even high level perceptual tasks is difficult for humans to consciously dissect, but it could easily be that it's actually relatively mundane I don't think so. Philosophers have theorized about abstract thought for thousands of years. The materialist metaphysics that is uncritically and unconsciously held by the triumphalist philistines isn't even capable IN PRINCIPLE of accounting for it. In any case, AI is useful, and will continue to be, but the fanfare is just noise distracting from the truth of what it is.
- vladislav 9y ago
  relatively mundane when subjected to appropriate representations. You omitted the important part of my statement. And the difficulty humans encounter in reasoning about the nature of abstract thought itself is something I also pointed out. My point is that this is not necessarily an indication of inherent difficulty. The ability to model and explain the nature of a self-reflecting mechanism is not a guaranteed natural ability of that mechanism itself.

andbberger 9y ago

I've said it before and I'll say it again. Machine learning is specifically not magic. It only works to the extent that we can build our own priors into the model. A typical media story... deep learning really is great. It represents the first time we've really figured out how to do large-scale nonlinear regression. But it is certainly not a magic bullet. However moderate headlines don't get as many hits as overhyped ones so every day we get another ridiculous article spouting nonsense... Very tiresome. The truth is pretty interesting, can we talk about that instead? For instance, how and why deep learning works at ALL is very much an open question. Consider - we're taking an incredibly nonlinear, nonconvex optimization problem and optimizing in just about the dumbest way imaginable, first-order gradient descent. It is really amazing that this works as well as it does. ... Why does deeper work better than wider? It has been known for many years that a shallow net has equivalent expressivity to a deep one. So what gives? (actually some interesting work towards answering this question in recent years by Sohl-Dickenstein et. al)

ggm 9y ago
Here here. Training an algorithm to mimic prior knowledge. The prior knowledge is inherently there. It's like the inflationary claims for QC which depend on demonstrated factorisation to a known product of primes: it's not magic when you seek to minimise error to a known goal but you have to know the goal.
pps43 9y ago
> Machine learning [..] only works to the extent that we can build our own priors into the model. AlphaZero is a good counterexample. In contrast to AlphaGo, it has no priors.
- aoeusnth1 9y ago
  Well it does have some priors baked into it by the (convolutional) architecture of the network. See the “deep image prior” project for a feel for just how strong this convolutional prior is.

deleted 9y ago

[deleted]

John_KZ 9y ago

This article presents zero evidence or indications for their claim. One argument is persistence. Quoting: " “We are born knowing there are causal relationships in the world, that wholes can be made of parts, and that the world consists of places and objects that persist in space and time,” Which is unsubstantiated and irrelevant, because AI that understands 3D is only now being developed. Also children up to 3 years of age or so, cannot understand the perspective of 3rd parties. There might be some hard-wired rules in our brain, but that's not intelligence anyway. The article has a point about something: Conventional feed-forward, convolutional neural networks can only model a very limited space in the grand scheme of things. Backpropagation is not perfect. There are other learning methods. Hell, sometimes there isn't a global minimum anyway. But saying that Deep Learning will stall in the near future is just wrong, and in my opinion the reasons why are evident to all those who follow the latest developments.

mcguire 9y ago

I would argue that 'greedy' applies to neutral networks in a different sense: they seize on their first solution; they have no mechanism to re-evaluate a decision that is proving false.

hadsed 9y ago
Sure they do, it happens at training time. Each time you update parameters you look at all your predictions and figure out how well you did. That informs your next tweak to the parameters. Run online training and you are constantly doing this.

dmix 9y ago

> Marcus believes that deep learning is not “a universal solvent, but one tool among many.” And without new approaches, Marcus worries that AI is rushing toward a wall, beyond which lie all the problems that pattern recognition cannot solve. The thing that fascinates me is that we've only just scratched the surface of what today's ML tech can solve. Let's worry about that wall when we get there... In the meantime let's not lose sight of today's potential in some misguided idealistic pursuit of perfectionism or "general artificial intelligence". There are countless problems which current deep learning research combined with some well-thought out UI/UX could solve today in a myriad of industries. The 1990's software 'revolution' in industry/business was largely just formalization/automation of paper-based processes into spreadsheets and simple databases, which then evolved into glorified CRUD/CMS software interfaces on desktops, then web/SaaS, and then another massively boost with smartphones. If such a simple translation of human processes into machines can achieve trillions of dollars in value then there is no doubt machine learning can do the same for hundreds of thousands of other simple problem-sets which we haven't even considered. Plus the desktop/smartphone/internet/etc infrastructure is already in place for it to be plugged into. This can only be negatively judged in the context of all significant steps forward in technology being oversold and misunderstood. But in practical real-world utility we're very far from fully utilizing what has been researched and accomplished today in a small set of markets. And the proliferation of this tech should be encouraged, promoted, and accurately communicated to tech/business talent who can potentially use it, rather than downplayed because it fails to live up to some media hyperbole or SciFi fantasies of where we should be in 2018. The article mentions that taking AI/ML/data science courses has just become the "hottest new field" for young smart kids to join. Well that means we're just on the cusp of taking advantage of that technological evolution and it's FAR too early to look at what's been accomplished today and be pessimistic about deep learnings potential.

anonytrary 9y ago

Keep in mind, folks -- the following is also true: > Greedy, Brittle, Opaque, and Shallow: The Downsides to Evolved Human Intelligence I wonder if we can have our cake and eat it too, in the realm of machine learning and artificial intelligence.

bobthechef 9y ago

> “We are born knowing there are causal relationships in the world, that wholes can be made of parts, and that the world consists of places and objects that persist in space and time,” he says. Here's a little too overconfident in the claim that these are innate ideas or something a priori.

fellellor 9y ago

The article seems a bit outdated in that Hinton himself has argued for the need to go beyond backpropagation.

giardini 9y ago
Marcus' article mentions Hinton's recent misgivings about backprop: "Perhaps most notably of all, Geoff Hinton has been courageous enough to reconsider has own beliefs, revealing in an August interview with the news site Axios 16 that he is “deeply suspicious” of back-propagation, a key enabler of deep learning that he helped pioneer, because of his concern about its dependence on labeled data sets. Instead, he suggested (in Axios’ paraphrase) that “entirely new methods will probably have to be invented.” I share Hinton’s excitement in seeing what comes next.

jokoon 9y ago

I am still wondering, why is it not possible, or maybe too hard, to take a trained neural network and reduce its amount of neurons to get a simplified solution to a problem? Is there some theoretical or mathematical analysis of neural networks?

dsign 9y ago

It's the combined enthusiasm of academics and entrepreneurs the one driving the current revolution on artificial intelligence. Said enthusiasm is punctuated by high profile CEOs and investors making fiery remarks from time to time. But alas, we people are not enthusiastic forever about something, and sooner or later our collective psyche will move on. That doesn't mean that the technology revolution, and it's AI component will stop. We have had machine learning for a long time, doing its thing, as best we managed to make it work. It's impact inside our collective speech was more subdued, that for sure, but it was there. Research and development never stopped. Even if some big name university funded it less and shut up about building GAI, well there were still thousands of less shiny institutions and companies working in more tractable problems and building a foundation. And, to be clear, I wish the current collective enthusiasm lasts a little bit longer, because we have a long way to go still and research grants and investment money flow better when the media is abuzz with the subject. In particular, we need to either move out or build upon matrix crunchers like deep learning. Better forms of AI and eventually GAI will need a little bit of innovation in chip-making and in computing architectures in general.