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For example, SAT Solvers, logic programming and theorem provers, classical planning, classical search, adversarial search (MCTS) etc are alll AI techniques that

For example, SAT Solvers, logic programming and theorem provers, classical planning, classical search, adversarial search (MCTS) etc are alll AI techniques that have nothing to do with "curve fitting". None of those techniques are new, nor are they fueling the AI hype cycle. I purposely conflated AI with "deep learning" because it is the source of the hype. And in reality, what most AI startups claim to be using.

YeGoblynQueenne 7y ago
Deep learning is not new either. LSTMs are from the 1990s. CNNs are from the 1980's (and note well that most recent successes of deep learning are basically tweaks of these two architectures). What is new is the hype around deep learning that took off after 2012, and because Google and Facebook decided to champion it. In any case, as far as I can tell "AI today" is anything that is "AI" and that exists "today". How do you mean "AI today"? >> I purposely conflated AI with "deep learning" because it is the source of the hype. I don't understand why you would do that. You are aware that there is hype and that it is increased by misuse of the term AI. And you purposefully misuse the term AI in a way that increases the hype? Why?
- codesushi42 7y ago
  Where did I ever claim that deep learning is new? I am fully aware of its history. Before deep learning, research on neural networks dates all the way back to the 1950s. You are completely missing the point: "deep learning" and AI are mostly synonymous in the current hype cycle, and it began with breakthroughs in deep learning. How do you mean "AI today" The AI industry that I work in.
  - dang 7y ago
    > You are completely missing the point Can you please edit swipes like that out of your comments here? They tend to degrade discussion. If you simply provide correct information, your comments will be stronger and their effect on the thread at large more salutary. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
    - corodra 7y ago
      That's a weird thing to tell someone. A person can't say that they're being misunderstood because that degrades a discussion? Degrades how? Are you putting yourself as supreme judge of the situation? Reading through the back and forth, I agree that OP had some of their statements made into strawmen. Does it degrade to mention that I think they may be in the right? Maybe miss worded a bit, yes. But the general point made throughout, I think is valid. Is me disagreeing with you degrading your sensibilities?
      - dang 7y ago
        Of course not, but there's a long road between "disagreeing" (which is no problem) to "saying you're being misunderstood" (may be fine, but usually is already a step down in discussion quality) to "you're completely missing the point" (outright swipe). Commenters here should stay in the former zone and avoid the latter. That's not hard to do.
        kranner 7y ago
        > That's not hard to do. As a third party, may I suggest that this statement amounts to a 'swipe'?
        dang 7y ago
        I don't see why? I say that because it sounds like the kind of thing that's hard to do, but actually isn't.
        kranner 7y ago
        Well, if you meant it in that straightforward sense, then it was not a swipe. But I've seen the same statement used in a derogatory manner in the sense of "get this done, it's not difficult [if you're not an idiot]" by a bad manager or two. To me it does carry that other sense.
    - deleted 7y ago
      [deleted]
  - tlb 7y ago
    I don't know that anyone claims that deep learning is the only technology needed for AGI. The biggest proponents will say that it's the missing piece. But a full system will involve lots of ideas including tree search.
  - YeGoblynQueenne 7y ago
    I don't understand why you pointed out that SAT solvers etc are not new, if you were not comparing them to deep learning, for the purposes of discussing what is "AI now". AI is most certainly not synonymous with deep learning. It is just people in the industry who do not know anything about AI and who recently jumped on the deep learning bandwagon, who think they are, and people in the tech press who don't have the time to do proper research. I don't see why we need to perpetuate their misconceptions. Actually, we don't.
    - YeGoblynQueenne 7y ago
      >> It is just people in the industry who do not know anything about AI and who recently jumped on the deep learning bandwagon, who think they are, Yes, I suppose this is not the best way to say what I wanted to say without getting peoples' back up. I'm leaving it as it is since it's already been read a few times from what I can tell, but here's a less rash version. What I mean is that, because of the tremendous recent success and public exposure of deep learning, many people have become interested in it who do not have a background in AI, or even in computer science, and who therefore enter the field with big gaps in their understanding of what "AI" means. That is my experience anyway. Well, it's a shame to work in a field and not understand its history, not least the history of what has already been achieved and what has failed, and how, so that one does not have to repeat history. So it's in everyone's interest to avoid making statements with great certainty when this certainty is not backed up by long-term knowledge. For the record, I'm a newcome to the field myself. But I have a background in classic AI, specifically logic programming, so I do know the long story.
- sgt101 7y ago
  aside: 2012 is the date because people figured out how to use GPU's to do the training, which meant that instead of 3/4 tries at getting the thing to work in a day you could do 30-40. This made things a lot more practical...
  - jtmcmc 7y ago
    yep yep yep this is very much it as well although if you recall it was also around the time when Andrew Ng at google was doing things with DL on massive amounts of computation on CPU with DL too.
- GauntletWizard 7y ago
  At this point, the battle for terminology is lost. What the casual observer is talking about when they talk about "AI" is "Deep Learning" is Curve Fitting. The war for terminology is more lost than differentiating "Hacker" from "Cracker" when referring to computer security. There's a specialist arena where the distinction is occasionally respected. This is not that forum. Artificial General Intelligence is so far off that it's not a general conversation topic, it's not even a specialized conversation topic - It's a fantasy conversation topic.
  - ScottBurson 7y ago
    > This is not that forum. I think "Hacker News" is exactly that forum.
- fromthestart 7y ago
  >What is new is the hype around deep learning that took off after 2012, and because Google and Facebook decided to champion it. Have you seen what neural nets are now capable of? Speech synthesis/transcription, voice synthesis, image synthesis/labeling/infill, style transfer, music synthesis, and a host of other classes of optimization problems which have intractable explicit programmitic solutions. The hype is justified, because ML has finally arrived, thanks primarily to hardware, and secondarily to the wealth of modern open research, heavily influenced congregations of leading researchers enabled by funding at Google, Facebook, etc. The problems being solved by "curve fitting" ML were simply unsolvable by any practical, generalizable means before recently, and the revolution is just getting started.
  - YeGoblynQueenne 7y ago
    Yes, I've seen what neural nets are now capable of- they are capable of exactly what they were always capable of, except "now" (in the last few years) we have more data and more compute to train them to actually do it. Says Geoff Hinton [1]. I have also seen what neural nets are incapable of. Specifically, generalisation and reasoning. Says François Chollet of Keras [2]. AI, i.e. the sub-field of computer science research that is called "AI" and that consists of conferences such as AAAI, IJCAI, NeurIPS, etc, and assorted journals, cannot progress on the back of a couple of neural net architectures incapable of generalisation and reasoning. We had reasoning down pat in the '80s. Eventually, the hype cycle will end, the Next Big Thing™ will come around and the hype cycle will start all over again. It's the nature of revolutions, see? So hold your horses. Deep learning is much more useful for AI researchers who want to publish a paper in one of the big AI conferences, and to the FANG companies who have huge data and compute, than it is to anyone else. Anyone else who wants to do AI will need to wait their turn and hope something else comes around that has reasonable requirements to use, and scales well. Just as the original article suggests. _________________ [1] http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-in-baby-steps-and-the-future-of-google-and-ai/ http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-... Geoffrey Hinton: I think it’s mainly because of the amount of computation and the amount of data now around but it’s also partly because there have been some technical improvements in the algorithms. Particularly in the algorithms for doing unsupervised learning where you’re not told what the right answer is but the main thing is the computation and the amount of data. [2] https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html Say, for instance, that you could assemble a dataset of hundreds of thousands—even millions—of English language descriptions of the features of a software product, as written by a product manager, as well as the corresponding source code developed by a team of engineers to meet these requirements. Even with this data, you could not train a deep learning model to simply read a product description and generate the appropriate codebase. That's just one example among many. In general, anything that requires reasoning—like programming, or applying the scientific method—long-term planning, and algorithmic-like data manipulation, is out of reach for deep learning models, no matter how much data you throw at them. Even learning a sorting algorithm with a deep neural network is tremendously difficult.
deleted 7y ago
[deleted]

YeGoblynQueenne 7y ago

Deep learning is not new either. LSTMs are from the 1990s. CNNs are from the 1980's (and note well that most recent successes of deep learning are basically tweaks of these two architectures). What is new is the hype around deep learning that took off after 2012, and because Google and Facebook decided to champion it. In any case, as far as I can tell "AI today" is anything that is "AI" and that exists "today". How do you mean "AI today"? >> I purposely conflated AI with "deep learning" because it is the source of the hype. I don't understand why you would do that. You are aware that there is hype and that it is increased by misuse of the term AI. And you purposefully misuse the term AI in a way that increases the hype? Why?

codesushi42 7y ago
Where did I ever claim that deep learning is new? I am fully aware of its history. Before deep learning, research on neural networks dates all the way back to the 1950s. You are completely missing the point: "deep learning" and AI are mostly synonymous in the current hype cycle, and it began with breakthroughs in deep learning. How do you mean "AI today" The AI industry that I work in.
- dang 7y ago
  > You are completely missing the point Can you please edit swipes like that out of your comments here? They tend to degrade discussion. If you simply provide correct information, your comments will be stronger and their effect on the thread at large more salutary. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
  - corodra 7y ago
    That's a weird thing to tell someone. A person can't say that they're being misunderstood because that degrades a discussion? Degrades how? Are you putting yourself as supreme judge of the situation? Reading through the back and forth, I agree that OP had some of their statements made into strawmen. Does it degrade to mention that I think they may be in the right? Maybe miss worded a bit, yes. But the general point made throughout, I think is valid. Is me disagreeing with you degrading your sensibilities?
    - dang 7y ago
      Of course not, but there's a long road between "disagreeing" (which is no problem) to "saying you're being misunderstood" (may be fine, but usually is already a step down in discussion quality) to "you're completely missing the point" (outright swipe). Commenters here should stay in the former zone and avoid the latter. That's not hard to do.
      - kranner 7y ago
        > That's not hard to do. As a third party, may I suggest that this statement amounts to a 'swipe'?
        dang 7y ago
        I don't see why? I say that because it sounds like the kind of thing that's hard to do, but actually isn't.
        kranner 7y ago
        Well, if you meant it in that straightforward sense, then it was not a swipe. But I've seen the same statement used in a derogatory manner in the sense of "get this done, it's not difficult [if you're not an idiot]" by a bad manager or two. To me it does carry that other sense.
  - deleted 7y ago
    [deleted]
- tlb 7y ago
  I don't know that anyone claims that deep learning is the only technology needed for AGI. The biggest proponents will say that it's the missing piece. But a full system will involve lots of ideas including tree search.
- YeGoblynQueenne 7y ago
  I don't understand why you pointed out that SAT solvers etc are not new, if you were not comparing them to deep learning, for the purposes of discussing what is "AI now". AI is most certainly not synonymous with deep learning. It is just people in the industry who do not know anything about AI and who recently jumped on the deep learning bandwagon, who think they are, and people in the tech press who don't have the time to do proper research. I don't see why we need to perpetuate their misconceptions. Actually, we don't.
  - YeGoblynQueenne 7y ago
    >> It is just people in the industry who do not know anything about AI and who recently jumped on the deep learning bandwagon, who think they are, Yes, I suppose this is not the best way to say what I wanted to say without getting peoples' back up. I'm leaving it as it is since it's already been read a few times from what I can tell, but here's a less rash version. What I mean is that, because of the tremendous recent success and public exposure of deep learning, many people have become interested in it who do not have a background in AI, or even in computer science, and who therefore enter the field with big gaps in their understanding of what "AI" means. That is my experience anyway. Well, it's a shame to work in a field and not understand its history, not least the history of what has already been achieved and what has failed, and how, so that one does not have to repeat history. So it's in everyone's interest to avoid making statements with great certainty when this certainty is not backed up by long-term knowledge. For the record, I'm a newcome to the field myself. But I have a background in classic AI, specifically logic programming, so I do know the long story.
sgt101 7y ago
aside: 2012 is the date because people figured out how to use GPU's to do the training, which meant that instead of 3/4 tries at getting the thing to work in a day you could do 30-40. This made things a lot more practical...
- jtmcmc 7y ago
  yep yep yep this is very much it as well although if you recall it was also around the time when Andrew Ng at google was doing things with DL on massive amounts of computation on CPU with DL too.
GauntletWizard 7y ago
At this point, the battle for terminology is lost. What the casual observer is talking about when they talk about "AI" is "Deep Learning" is Curve Fitting. The war for terminology is more lost than differentiating "Hacker" from "Cracker" when referring to computer security. There's a specialist arena where the distinction is occasionally respected. This is not that forum. Artificial General Intelligence is so far off that it's not a general conversation topic, it's not even a specialized conversation topic - It's a fantasy conversation topic.
- ScottBurson 7y ago
  > This is not that forum. I think "Hacker News" is exactly that forum.
fromthestart 7y ago
>What is new is the hype around deep learning that took off after 2012, and because Google and Facebook decided to champion it. Have you seen what neural nets are now capable of? Speech synthesis/transcription, voice synthesis, image synthesis/labeling/infill, style transfer, music synthesis, and a host of other classes of optimization problems which have intractable explicit programmitic solutions. The hype is justified, because ML has finally arrived, thanks primarily to hardware, and secondarily to the wealth of modern open research, heavily influenced congregations of leading researchers enabled by funding at Google, Facebook, etc. The problems being solved by "curve fitting" ML were simply unsolvable by any practical, generalizable means before recently, and the revolution is just getting started.
- YeGoblynQueenne 7y ago
  Yes, I've seen what neural nets are now capable of- they are capable of exactly what they were always capable of, except "now" (in the last few years) we have more data and more compute to train them to actually do it. Says Geoff Hinton [1]. I have also seen what neural nets are incapable of. Specifically, generalisation and reasoning. Says François Chollet of Keras [2]. AI, i.e. the sub-field of computer science research that is called "AI" and that consists of conferences such as AAAI, IJCAI, NeurIPS, etc, and assorted journals, cannot progress on the back of a couple of neural net architectures incapable of generalisation and reasoning. We had reasoning down pat in the '80s. Eventually, the hype cycle will end, the Next Big Thing™ will come around and the hype cycle will start all over again. It's the nature of revolutions, see? So hold your horses. Deep learning is much more useful for AI researchers who want to publish a paper in one of the big AI conferences, and to the FANG companies who have huge data and compute, than it is to anyone else. Anyone else who wants to do AI will need to wait their turn and hope something else comes around that has reasonable requirements to use, and scales well. Just as the original article suggests. _________________ [1] http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-in-baby-steps-and-the-future-of-google-and-ai/ http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-... Geoffrey Hinton: I think it’s mainly because of the amount of computation and the amount of data now around but it’s also partly because there have been some technical improvements in the algorithms. Particularly in the algorithms for doing unsupervised learning where you’re not told what the right answer is but the main thing is the computation and the amount of data. [2] https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html Say, for instance, that you could assemble a dataset of hundreds of thousands—even millions—of English language descriptions of the features of a software product, as written by a product manager, as well as the corresponding source code developed by a team of engineers to meet these requirements. Even with this data, you could not train a deep learning model to simply read a product description and generate the appropriate codebase. That's just one example among many. In general, anything that requires reasoning—like programming, or applying the scientific method—long-term planning, and algorithmic-like data manipulation, is out of reach for deep learning models, no matter how much data you throw at them. Even learning a sorting algorithm with a deep neural network is tremendously difficult.

deleted 7y ago

[deleted]