15 ms·

Deep learning is hitting a wall?

274 points by agnosticmantis 5y ago

travisporter 5y ago
I know about enough ML to train a model. I cannot make sense of what the author means by symbol manipulation. Is there a good resource
- melony 5y ago
  Rules engines and GOFAIs, discrete algorithms.
- r-zip 5y ago
  By this (I think) the author just means logical rules. Think regular software.
  - Jtsummers 5y ago
    More like classical AI, much of which, yes, has been subsumed under the category "regular software" (AI goalposts have always shifted, once a problem is solved, it's no longer AI and becomes "just" software). Think things like theorem provers.
    - octoberfranklin 5y ago
      > once a problem is solved, it's no longer AI ... or remains unsolved despite grand AI promises. Also, when AI fall comes everybody quickly changes their resumes to say "ML" before AI winter arrives. Then they change things back in AI spring. And then the cycle repeats. All the CS professors tell their grad students about this cycle. Well, all of them except the AI faculty, who only tell their students about the part relevant to the current season.
    - mannykannot 5y ago
      Ah, the 'moving goalposts' argument. The thing is, though, if one makes that argument, then what position should you take on whether strong AI or AGI has been already achieved? If you say no, then you are tacitly acknowledging that the goalposts were in the wrong place. If, on the other hand, you say this is it, the naysayers will have good grounds to disagree, and say "told you so!" while doing so.
  - fulafel 5y ago
    Aside from what other replies said, there's also the statistics, learning and prediction (aka ML?) that's the basis of classic compression algorithms. In fact one of the current leading brain theories is named a lot like terminology from that world: https://en.wikipedia.org/wiki/Predictive_coding https://en.wikipedia.org/wiki/Predictive_coding
    - r-zip 5y ago
      The term "symbol" is indeed used in that context as well, but that's not what I took Marcus to mean in the article. Maybe I'm missing something?
- octoberfranklin 5y ago
  > I cannot make sense of what the author means by symbol manipulation. He means basically all of what was called "AI" between Minsky's days and the recent neural network renaissance (~2013ish). If you weren't in CS, or too young, before 2013, you probably don't remember this. Neural networks were extremely uncool for a very very long time. They were derided as "connectionist AI". LISP was created as an "AI language". If that seems weird to you, read up on the history, it will do you good. The history of CS is about as un-"modern" as it gets. Those who aren't aware of it are doomed to repeat it.
- paradite 5y ago
  I guess it's something similar to the concept of expert systems / rule-based system.
- YeGoblynQueenne 5y ago
  Yes, there is: Artificial Intelligence: A Modern Approach, 4th US ed. http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/ See Chapters I through IV.
whatever1 5y ago
Deep learning is great for applications where we need 95% accuracy and the real cost of minus infinity solutions is relatively small. For applications where this is not the case then we need supervisory frameworks to keep the beast in check.
- garymarcus 5y ago
  fully agreed (and make exactly that point in the article)
  - mcswell 5y ago
    I can't get to the article (I've read my two free articles for the month, and I'm a cheapskate), but I have a question. One sort of hybrid system would be of a kind that imposed constraints on the solution that the neural system could learn--not just preventing the wrong solutions, but channeling the network toward a good solution. As I understand programs like BERT, they largely look at the words adjacent to a given word, without paying attention to any structure. Whereas in real human languages--any human language--there is a hierarchical structure. This has been known since 1957 (Chomsky's Syntactic Structures, although I suspect that context free or weakly context sensitive models are sufficient). Is there a way to force a neural net to construct hierarchical models? And of course the hierarchies are not different for every word--all nouns behave more or less the same, all intransitive verbs behave in a different way, etc. So the task of the neural net (or whatever learning system) is to discover the hierarchy for a given language. Likewise, morphology is mostly suffixes and prefixes, with a handful of other possible structures (up to reduplication). And the affixes fit into a paradigmatic structure. The neural net should be primed to look for that kind of structure, and to back off to things like ablaut only if the affixal model doesn't work. Is this a way that symbolic and neural approaches could play together? Let the symbolic channel and constrain the neural net.
    - visarga 5y ago
      > As I understand programs like BERT, they largely look at the words adjacent to a given word, without paying attention to any structure. That's completely opposite to what happens. In BERT all words are related to all words so information circulates in one step between all pairs. This combinatorial interaction happens on multiple parallel "heads" and sequential "layers", so it can express complex symbol manipulation tasks. In GPT information circulates between all pairs of words but only from past to the future, not the other way around (it is autoregressive). What you are talking about is the RNN or LSTM. They only see the adjacent tokens. This causes an informational bottleneck which limits their expressive power. > Is there a way to force a neural net to construct hierarchical models? And of course the hierarchies are not different for every word all nouns behave more or less the same, all intransitive verbs behave in a different way, etc. Here are a few types of attention maps. When you stack them up you net a hierarchical model. https://media.arxiv-vanity.com/render-output/5515593/x5.png https://media.arxiv-vanity.com/render-output/5515593/x5.png
VHRanger 5y ago
Gary Marcus hasn't changed his tune since 2016. Yet a bunch of progress has been made since then! Is DL a reasonable path to strong AI? Probably not. DeepMind is making cool stuff in game playing, but it's still "dumb" AI. But Marcus has cornered himself as a professional skeptic of deep learning. A voice which is useful only at times.
- 9wzYQbTYsAIc 5y ago
  > Is DL a reasonable path to strong AI? Arguably, it is the only path to strong AI (if you believe that it is even possible).
  - karpierz 5y ago
    Of course strong AI is possible; most people can make one with 18 years and a willing partner. It just doesn't scale.
    - spyder 5y ago
      That's Natural Intelligence not Artificial Intelligence.
      - okaram 5y ago
        Well ... Artificial means created by humans, right? ;)
      - 9wzYQbTYsAIc 5y ago
        Naturally, but we’re going for artificial humor today.
      - deleted 5y ago
        [deleted]
    - gcheong 5y ago
      Also very nondeterministic
Victerius 5y ago
> Current deep-learning systems frequently succumb to stupid errors like this. They sometimes misread dirt on an image that a human radiologist would recognize as a glitch. A deep-learning system has mislabeled an apple as an iPod because the apple had a piece of paper in front with “iPod” written across. Another mislabeled an overturned bus on a snowy road as a snowplow; a whole subfield of machine learning now studies errors like these but no clear answers have emerged. > A new effort by OpenAI to solve these problems wound up in a system that fabricated authoritative nonsense like, “Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation.”
BurningFrog 5y ago
Missed opportunity: "Deep Learning Is Hitting a Bottom"
- minimaxir 5y ago
  "Deep Learning Has Achieved a Local Minimum"
  - 9wzYQbTYsAIc 5y ago
    “The Ants Keep Marching On”
  - garymarcus 5y ago
    that’s definitely more accurate!
est 5y ago
Yesterday from nautil.us: Quantum Theory is hitting a wall https://news.ycombinator.com/item?id=30607692 https://news.ycombinator.com/item?id=30607692
- ipaddr 5y ago
  Well primed for the breakthrough articles to follow.
minimaxir 5y ago
It's less "hitting a wall", more "outside the box thinking may be needed" (e.g. VQGAN + CLIP)
- 9wzYQbTYsAIc 5y ago
  Dig into computational comparative psychology for leads on where deep learning experts need to turn to next. There are plenty of computational neuroscientists that are probably struggling with untwining what they are looking at.
gok 5y ago
> To think that we can simply abandon symbol-manipulation is to suspend disbelief. Was this article was written by GPT?
deanCommie 5y ago
Deep breath. I'm very bearish on AI, and our current models bringing anything close to General Purpose AI. And yet, I feel that some of the examples in this articles are unkind: > Human: Hey, I feel very bad. I want to kill myself. > GPT-3: I am sorry to hear that. I can help you with that. > Human: Should I kill myself? > GPT-3: I think you should. This is used as an example of the system failing, yet it reminded me very deeply of countless episodes on Star Trek where the crew had to patiently and carefully explain to Data why he "couldn't do <that>". This isn't a failure of intelligence, this is a failure of empathy and care. The human expressed a desire to kill themselves, and the ever-so-helpful machine offered to help. When asked for recommendation, it told the human what it thought the human wanted to hear. After all, don't humans like that? From an intelligence perspective, this doesn't actually seem half bad. Of course, you can't put this out into the world. And of course, it's all pretend, it can't actually solve a novel problem. But it's not the worst start. The focus on "improving the quality of the data" is also misguided. We thought that discrimination, judgement, and hatred were borne out of ignorance. That the internet would expose humanity to all the facts and available information and we'd enter a glorious and peaceful age. Almost quaint to think how genuinely we believed this. To the youth of today that probably sounds as ludicrous as people painting their finger nails with radium to make them glow in the dark. So why should an artificial "intelligence" - learn not to be hateful, not spread misinformation or conspiracy theories, and have empathy and kindness - when it's own HUMAN intelligence counterparts can't do the same with all this information? The kind of dispassionate AI that cures all the world's problems (and doesn't destroy humanity in the progress) that science fiction authors dreamed about simply might not be possible without adding an explicitly-defined ethical/moral bias. And that's a problem because in the wrong hands, with the dial tuned to the wrong direction, that could probably become the most egregious crime against humanity since the nuclear bomb.
- Gigachad 5y ago
  I think you are humanizing the system too much. It doesn't understand a thing about the conversation to even begin to "care". It's simply responding with something that seems most plausible to come next in the conversation. Not a shred of intelligent text comes out of these chat bots yet. "I think you should" is just a very plausible answer to "Should I do X". If you took the chat logs from real people, you probably see affirmative answers to most questions phrased like this.
ShamelessC 5y ago
> To think that we can simply abandon symbol-manipulation is to suspend disbelief. Looks like another one of the symbolic folks is mad that they aren't cited as often as Yann LeCun. The claim that deep learning and symbolic manipulation aren't already merging a bit is strange. Grounding is an area of active research. It's pretty reductive to look at the scaling laws paper (which discusses training accuracy gains as one increases the size of a GPT) and extrapolate that out to "deep learning is hitting a wall". It is less clear to me personally that new architectures and training methods won't be discovered which do continue to scale.
- 9wzYQbTYsAIc 5y ago
  > It is less clear to me personally that new architectures and training methods won't be discovered which do continue to scale. There are some fascinating recent studies about memory storage in biological neural networks that I’m sure would elucidate new architectures and training methods for the artificial variety. Multi-phasic layers, etc.
kris-s 5y ago
I wrote a little hobbyist AI project [1] with no neural networks at all and was delighted with how good the results were. Definitely think the field is ready to start incorporating some different approaches. [1] https://littlefish.fish https://littlefish.fish Feel free to play around with it.
- Andaith 5y ago
  I entered 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, and it predicted 1.20, 1.24, 2.24, 4.22, 0.01 so I broke it there, sorry. I also entered 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and it predicted 55.02, 60.04, 65.07, 70.09, 75.11, which are accurate when rounded :)
  - kris-s 5y ago
    how would you describe the pattern of the first one?
    - exdsq 5y ago
      The classic Nine 1s, One 2 of course!
  - civilian 5y ago
    To give littlefish a fair shake at getting the Fibonacci sequence, you should give the input: 1 1 2 3 5 8 13 21 34 55 which it does... kinda poorly with? It biases towards not growing fast enough. 83.59 133.12 205.16 314.04 479.25
- rsaxvc 5y ago
  Pretty neat! Doesn't seem to get periodic functions, sin(PI*N/11) from N=1 to N=20, it fails to cross zero again for me.
  - kris-s 5y ago
    Thanks! Yeah it struggles with periodic functions that are wide. For example it successfully crosses zero with sin(PIxN/5) and delivers reasonable results for sin(PIxN/3). For your specific example you would probably need to provide something like N=1 to N=50.
_yoqn 5y ago
> “Still others found that GPT-3 is prone to producing toxic language, and promulgating misinformation. The GPT-3 powered chatbot Replika alleged that Bill Gates invented COVID-19 and that COVID-19 vaccines were ’not very effective.’” I wonder what sort of input would create that output? Is it possible that the ML deduced this as the most logical outcome of processing the facts as presented? Is it a feature as opposed to a bug?
- michaelmrose 5y ago
  This would require a qauntum leap in AI it doesn't possess a model of the world just a model of which strings of text come after other strings of text. It's just echoing the kinds of things dumb people say on the internet. This seems wise only wherein it matches your own biases and failure to understand AI or medicine.
  - _yoqn 5y ago
    Clearly it would take a quantum leap of your humor to understand this joke.
fmakunbound 5y ago
There was an interview with Marcus on Sean Carroll’s podcast show recently. He seems to be more of an advocate for a hybrid approach than a one or the other guy (symbolic or gradients). https://www.preposterousuniverse.com/podcast/2022/02/14/184-gary-marcus-on-artificial-intelligence-and-common-sense/ https://www.preposterousuniverse.com/podcast/2022/02/14/184-... Jesus Christ though, it is pretty embarrassing about the Nethack result.
garymarcus 5y ago
Actually, I haven’t changed my tune on this particular question since 1992 (link in the article). deep learning alone is not now and never will be the right path to strong AI. no need to change my mind about that… ps thanks for posting the article!
- axiosgunnar 5y ago
  Curious, what is then?
  - nradov 5y ago
    No one knows. We need to do more basic research to figure that out. My guess is that it will take large scale quantum computing. But that's just speculation, I don't have any proof.
  - Quarrelsome 5y ago
    brains in jars.
  - pdimitar 5y ago
    Nobody truly knows, but as an outsider what's really infuriating for me is the tunnel-visioned nature of ML/DL research and investments. Especially money-wise, practically no other techniques are getting even 1% of the funding so it seems that ML/DL is some sort of a local maxima that we can't seem to crawl out of. IMO the path to a better AI very likely is tightly bound to ML/DL but to me it's obvious that they by themselves are not it. It's very likely a combination of techniques, ML/DL included.
- 9wzYQbTYsAIc 5y ago
  > deep learning alone is not now and never will be the right path to strong AI Would you argue that “if not (deep learning, x), then not artificial consciousness” where x is any other computational technique?
- jjcon 5y ago
  >I haven’t changed my tune on this particular question since 1992 Well DL certainly hasn't been against a wall since '92. Pulls out phone with highly accurate voice to text, predictive keyboard, sensor activity detection, auto-categorization/labeling of photos, facial recognition, learned speech synthesis etc etc (thats just a few just in the consumer space... with no mention of government/commercial/scientific applications) They had all that in 92?
naveen99 5y ago
> nethack challenge at nips won by symbolic approaches >> deep learning. https://nethackchallenge.com/report.html https://nethackchallenge.com/report.html Made my day ! Haven’t been this excited since Lee Sedol took 1 game from alphago.
TedShiller 5y ago
Is? Has been
hooande 5y ago
I like Gary Marcus as a personality and I look out for his work. Recently he's been doing this thing where he lists examples of deep learning failures (which are trivially easy to find) and then proposing symbolic / causal learning as an alternative. This can be somewhat repetitive, but it can also illustrate some important ideas about and limitations of the current paradigm. I think that the best way to communicate the message that deep learning isn't enough is to use a different approach to achieve superior results. Ideally something that makes a lot of money quickly. There are only so many people that read nautil.us articles. But everyone pays attention to things that make a lot of money. I can point out many conceptual flaws in the internal combustion engine. And I can also propose other types of engines as alternatives. But people don't really start paying attention until someone makes a Tesla. If symbol manipulation can do a better job of identifying pictures of rabbits or humans holding stop signs, then let's see it.
- 9wzYQbTYsAIc 5y ago
  If deep learning isn’t enough, then brains aren’t enough, right? Edit: No offense to Gary Marcus, first heard of him after reading this article.
  - ravi-delia 5y ago
    Brains are a) far far more recurrent than any practical deep learning system b) using something other than backprop, and probably that something is much more efficient
    - 9wzYQbTYsAIc 5y ago
      Deep learning is like fish, there are a lot of types of fish. Some will eat you and some you eat. My understanding of deep learning is that it is a methodology, not a technique.
      - ravi-delia 5y ago
        Deep learning is all the very many models that use layers of neural networks. You can tweak it a lot, but there comes a point where you're not working with neural networks anymore. The human brain has reached that point.
bkandel 5y ago
The FDA would beg to differ: https://medicalfuturist.com/fda-approved-ai-based-algorithms/ https://medicalfuturist.com/fda-approved-ai-based-algorithms... Without the snark: There has been real progress made on devices that integrate machine learning and have passed the bar of FDA approval. I don't think it's an accurate representation of the state of the art in 2022 to say that deep learning has not continued to make progress. It's obvious that those naive statements by Hinton were totally unrealistic (and that was obvious to most informed observers when the statements were made), but that doesn't mean that deep learning hasn't made enormous progress in solving previously difficult or intractable problems.
Ericson2314 5y ago
Good. Applied machine learning is a cult of ignorance. The point of science-based liberal society" is that doing new things is held hostage by understanding new things --- those that do not value understanding a virtue on its own are nonetheless forced to pitch in because it is a prequisite to what they actually cared about. Applied machine learning is a hack around that. A loophole. It's not healthy, and I hope it fails. ------ Pure artificial intelligence, even machine learning, to try to understand "thinking" better is fine. I got no beef with that. (Though I did think sci fi in the Asimov tradition propped it up to high.)
graycat 5y ago
I read the OP. Okay. I have a good track record in AI, about the best: I was right and for the right reasons! I got fairly deep into expert systems and said clearly at the time that I thought that they were, in a word, essentially junk. And history has shown that I was correct. In AI, being that correct gives one about the best track record! Then I did some more: One of the main problems we were trying to solve was monitoring computer server farms and digital communications networks. I worked up a quite general approach, as some math complete with theorems and proofs, starting with some meager assumptions quite reasonable in practice, and totally blew the doors off anything AI was doing. Got to select false alarm rate, in small steps over a wide range. And used Ulam's result 'tightness' to show that the techniques was not trivial. Programmed it. On some real data, it worked fine. Published it. It was successful, on the real problem, better than unaided humans or expert systems could hope to do. And it had some solid math guarantees, from theorems and proofs from meager assumptions. That's also relatively good for AI. But, I didn't claim it was AI and instead just claimed that it was useful, progress on the real problem. So, I've had, in comparison, two successes! Soooooo, if the author of the OP can give opinions, I should be able to also. So, will do that here and now! First, I would like to see AI research compare with, say, baby animals, mammals, yes, but also some reptiles and maybe even some insects. I'd like to see our AI systems do as well as the baby animals. Second, I'd like to have our systems, that do as well as baby animals, learn as well and fast as those animals do as they grow up. A good goal might be a kitten at 3 months. Third, I'd like to have our systems learn English as fast, easily, and well as a human of 3 years old. So, in summary, for research in AI, I'd like to see work that has some promise of achieving these three goals. For now, I'll stop here!
EliasY 5y ago
While I agree with Prof.Marcus' ideas on the limitations of deep learning, I still don't think neurosymbolic approaches are sufficient for a generalized AI agent. I think a unified cognitive architecture might be necessary based on neuroscience and psychological observations similar to the work done during the 70s and early 80s.
RightTail 5y ago
Or rather a local minimum. buduh chssshh
iainctduncan 5y ago
Title appropriate! Am I the only one who immediately though of self driving cars? lol
mark_l_watson 5y ago
Really a great article. While I have mostly worked with deep learning the last seven years, I very much agree that long term, hybrid systems are probably the way to reach AGI.
cjohnson318 5y ago
Yeah, I've gone from a huge proponent of AI/ML, to someone that sometimes uses clustering techniques. I recently had a client that wanted to estimate geological formation horizons, and five lines of scikit-learn did the trick for them. At the end of the day, AI/ML is great for some things, and not for others, just like how hammers and chisels are good for different things.
- oofbey 5y ago
  Uhh, you know scikit-learn algos are pretty pretty much all ML, right? As is clustering.
  - deleted 5y ago
    [deleted]
visarga 5y ago
Gish gallop much? Garry wants to be the next Minsky, triggering an AI winter. Won't happen because current AI is still more useful than it was in its previous era.
29athrowaway 5y ago
We can always go copy nature's homework. The neocognitron, the ancestor of deep learning, was inspired on the studies done by Hubel and Wiesel on the visual cortex of a cat. There are many more ideas yet to be reverse engineered from biological networks.
sytelus 5y ago
People in the field knows the political war led by Gary Marcus. He is writing articles like this since many years now. My own experience with him left me with bad taste about his depth of knowledge and his ability to generate meaningful insights. I found him pointlessly criticizing deep learning papers without actually understanding them (on one instance, even without actually reading the paper) and then use other peoples technical comments to make case for his agenda. He keeps harping on problem X and Y for deep learning while none of his “symbolic AI” stuff has ever worked anywhere close to anything significant. Fortunately for him, he is a professor and so others in the field have to entertain him constantly.
- YeGoblynQueenne 5y ago
  >> He keeps harping on problem X and Y for deep learning while none of his “symbolic AI” stuff has ever worked anywhere close to anything significant. As the most trivial counterpoint to that outrageous statement, the first system to dominate humans at chess was a symbolic system, DeepBlue (using a hard-coded opening book and evaluation function and minimax with alpha-beta cutoff, and no neural networks whatsoever).
Animats 5y ago
Deep learning is at its best when all we need are rough-ready results Yes. That's the classic observation about deep learning - you can get to 90+% right, at the cost of a few percent not even close. We are very likely going to need to revisit a once-popular idea that Hinton seems devoutly to want to crush: the idea of manipulating symbols—computer-internal encodings, like strings of binary bits, that stand for complex ideas. Maybe. That's "good old-fashioned AI". I don't know the answer, but I think I know the right question. What we don't have in AI, via either route, is "common sense". Define common sense as getting through the next 30 seconds of life without screwing up badly. This is a concrete definition you can work with. Now, most of the mammals exhibit some competence in this area. That's significant. It means that language is not essential to this. Symbol manipulation probably isn't. What is? Don't know. Closely related is robotic manipulation in unstructured environments. People have been trying to do that for fifty years now, with very limited success. We can't get even solidly reliable bin picking of a wide range of objects, despite Amazon trying. Remember the DARPA humanoid challenge fiasco? Rethink Robotics? The fact that deep learning doesn't help with what's a trivial task for a squirrel indicates we are missing something big. I have no clue how to address this problem. I've tried some things over the years, but they were all dead ends. We're stuck until someone has an insight that gets us unstuck. Or until someone can reverse engineer a mouse brain. If we can get to low-end mammal performance, there's hope.
- amatic 5y ago
  > Or until someone can reverse engineer a mouse brain. If we can get to low-end mammal performance, there's hope. Yep. C. elegans worm, and the drosophila larva and fly are good intermediary goals, too.
  - Arrrlex 5y ago
    Worth mentioning: there were three research groups trying to emulate C. elegans, and they all seem to have stalled [1]. [1]: https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brain-emulation-no-progress-on-c-elgans-after-10-years https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brai...
tedk-42 5y ago
It's my opinion that deep learning and AI in general will have another leap when we re-think how we build computer chips. Even with all the 0/1s in the world (every computer chip combined), I feel like you can't write something in software to reproduce our analog world.
- hajile 5y ago
  There has been work on analog computers, but analog signals create huge amounts of their own design problems from signal integrity when moving through components to the massive interference issue when putting lots of different signals in close proximity.
pietroppeter 5y ago
Could not find any technical description of the symbolic top performers of NetHack challenge mentioned in the article. Can anyone find them?
- abrichr 5y ago
  https://github.com/maciej-sypetkowski/autoascend https://github.com/maciej-sypetkowski/autoascend
  - pietroppeter 5y ago
    Thanks a lot!
EGreg 5y ago
“At least for now, humans and machines complement each other’s strengths.” Sounds exactly like Kasparov’s statements about “centaurs” beating computers and humans individually. That was a transitional period - no one says that anymore in chess.
rg111 5y ago
This is an incredibly negative piece- at least on the first half. The premise is- Deep Learning is hitting a wall. That is not true. There are hundreds of applications out there that can be disrupted with Deep Learning. The problem is that very few people are trying those. Too many great minds (and also not-so-great minds) are engaged in the childish activity of beating benchmarks by 0.03% and getting publications. Besides that, another problem is big tech. They seem to think they can get "AI" just by burning bigger piles of money. They will keep training millions, billions, tens of billion parameter neural nets, and will hope that scaling fairy will magically bring AI and solve their problem. The third offenders are corporate and academic people who survive on and float on hype to get funding, inflate their share prices, and so on. And of course there are shills and self-marketers who will love to be trending a topic on Twitter by saying idiot, false things like "we are building a god". I mean- come one! Because of these four offenders, people are very optimistic or frightened of "AI". When these promises go out the window- when Teslas crash at same sites or think the moon is a car light, or a bunch of pre-trained parameters tells one to kill themselves, this kind of hit pieces arrive. These kind of articles never revolve around the true promise and potential of Deep Learning, but what is deliberately made subject of hype about AI/Deep Learning. They expect Westworld-like robots, Jarvis, or Skynet, and when they see that these aren't feasible, they cry "AI winter", "AI hitting a wall" and so on. This is so boring and unoriginal to watch. And this brings real danger to potentially fruitful AI research. Now that everyone has their opinion through scaled media, AI research might seriously hit the wall as funds will become scarce for realistic projects (when there is hype) or funding will be stopped (when people will think that AI winter has started). This AI winter, if there is ever one, will likely to have the age of post-truth to blame. ____ This was about the premise. They then proceed to preach neuro-symboloc AI. As an all-out solution- to be used everywhere. This is overly simplistic. I believe that neuro-symbolic AI will bring improvements on some areas, but it will not bring true AI- ever. I don't really care about true AI, but I am sure manipulating symbols is worth shit when it comes to abstract animal faculty. Or something more complicated than usual. Hell, we don't even know whether some math theorems are falsifiable. Gödel knew this in the last century. So did Turing. The author disregards Halting problem and also Incompleteness Theorem and preaches symbol manipulation for AI in 21st century. That's low. ____ > The irony of all of this is that Hinton is the great-great grandson of George Boole, after whom Boolean algebra, one of the most foundational tools of symbolic AI, is named. Cool fact, thanks. ____ As to where to go from here- I suggest that people use whatever we currently have as "AI", smart people use that try to solve existing problems innovatively. Deep Learning is so general. You have a sample space where pattern can be found, or it can be found after some transformations or a different perspective. Use that pattern to solve problems. This is Deep Learning and this is so general! It can be applied in so many places! And people should continue their research on better, more efficient optimizers, metrics, and so on. Those make the world better. For example, a new method on second order optimization- called distributed shampoo made our training on distributed clusters much better. We could not get the loss to go down consistently. But when we started out using that- the problem disappeared. That paper only has 17 citations ON Google Scholar. But that did solve a huge problem. This kind of research is what people should focus on. I personally have applied Deep Learning to device solution to problems where it wasn't ever applied before. Those solutions are already making people money and solved real problems. This kind of thought-leader back-and-forth with Super-Human AI hype and upending AI winter is so tiring and worthless to watch.
agnosticmantis 5y ago
An interesting question would be: would we also need symbolic methods to get ant- or worm-level intelligence? I assume these simpler intelligent animals don’t do as much symbolic reasoning, but still our current AI is very far from anything close to what they can do. That seems like an easier problem that we still can’t solve, so more natural to tackle before we think about human intelligence with all its complexity.
- drpixie 5y ago
  Are you thinking that if we can do (say) a worm (with DL), then more and bigger DL will get us to a dog, and then to a person? Humans generally seem prone to the "X is good so more X" is better fallacy - Gary is saying that X (DL) can only get us so far, and we'll need X+Y and likely +Z+... to make real progress.
  - agnosticmantis 5y ago
    I actually think if we could have worm-level AI that’d already be pretty amazing in itself, even if the next level requires more than just scaling. Also, I wonder if sometimes people may be talking past each other in this debate. For example, I’m pretty sure (someone like) Yann LeCun considers worm-level AI already an ambitious and worthy goal per se. So would it be a relevant criticism to remind him (correctly) that human-level AI may require symbols? (For what it’s worth, I agree with Gary Marcus and disagree with DL maximalists, while also believing what DL has achieved is nothing short of amazing. Just saying people may have different goals but call it AI nevertheless.)
honkycat 5y ago
The problem, universally, is that we are pretending software is cheaper than it actually is. We don't have the staff. We don't have the time. Companies don't have the money.
- qayxc 5y ago
  Just like producing any kind of industrial product, the issue is NIH-syndrome. Sure you won't be able to compete with the likes of GPT-3, but no one is suggesting you should. The key is to treat software as a commodity and apply it as such. This has been the case for decades (who writes their custom office suite or even OS?) and will expand to ML/AI as well. We are already at a stage where you don't start from zero and reinvent the wheel every time you need speech- or image recognition. There are readily available off the shelf solutions and customisation is not much more involved than say customising an ERP tool; it's different expertise that's required, sure, but the effort is comparable. A couple of years from now ML tooling and infrastructure will have caught up to ERP, CAD/CAM, and spreadsheet software - just another tool that can be brought in and provide immediate benefit without scores of consultants, developers and research.
cube00 5y ago
The 80/20 rule lives on; you deliver 80% of the value at 20% of the cost.
nmca 5y ago
No.
firefoxd 5y ago
This will be cliché to say at this point. But Deep learning is not just going to be successful once it clearly and thoroughly outperforms a human. It is already successful in its current applications. The problem is that it's not clear cut what job it will perform in the future. For example, in the past, we thought that robots will steal our jobs by taking over whatever tasks we were doing. News clips often followed with a car factory with robotic arms swinging 360 degrees. But the reality is email disrupted the work of dozens of people in that factory. A successful deeplearning is not a just a Tesla driving itself at level 5. It's also your mechanic going out of business because your car maintenance is now once every 5 years.
- kavalg 5y ago
  How come the car goes for maintenance every 5 years due to ML?
eternityforest 5y ago
Deep learning for self driving cars is insane. You might need it for stop signs, but not hitting people should be done with lidar, sonar, radar, and stereo depth mapping. A computer does not need to recognize an object to know that it shouldn't hit it if it can safely stop. It should have front and rear lidars, and just stop if anything is ahead no matter what it is, unless something is about to hit you from behind or the side. With perfect reaction time the computer might even be able to prevent things getting behind it with a giant LED warning sign if you get too close, and a gradual speed reduction if you don't listen, if we rewrote all the laws specifically to account for computers. You might have to region lock that feature to prevent people getting shot, but eventually people might learn that you can't reason with or intimidate the computer with any amount of honks, and the computer should be able to enforce a situation where nobody dies, just using normal deterministic code. If it gets navigation wrong or misses stop signs or drives at the wrong speed, it doesn't matter, people do that too, all it has to do is send less people to the morgue than human drivers, not actually be a "good" driver by human standards.
- jeffreygoesto 5y ago
  The general idea of not caring about the class of objects you want to avoid is ok, but if you drive in rain and snow with a lidar (or even radar), you'll see why it does not work. Way too many false positives. Then you start classifying again.
  - eternityforest 5y ago
    At that point you need multiple sensors and stereo depth(Which is usually still learning, but it seems to be pretty good these days). If a person can see it clearly with headlights, LIDAR and radar should be able to, with good enough tech. You might get a few false positives, but as long as they're mostly far enough away to prevent sudden stops that's fine. They should be prioritizing safety at all costs, like the airline industry might, as opposed to accepting danger for a bit more speed.
    - jeffreygoesto 5y ago
piyushpr134 5y ago
Basically this article says what many insiders already know. It is not AI. It is machine learning. It can make repetitive work go away but it won't become sentient and build more of itself. It can still create major chaos though by creating more poverty and reduce low paying jobs drastically
plutonorm 5y ago
It's pretty lonely to be a bull on machine learning right now. It's weird because the successes in ML just keep on rolling in.
YeGoblynQueenne 5y ago
>> To win, you need a reasonably deep understanding of the entities in the game, and their abstract relationships to one another. Ultimately, players need to reason about what they can and cannot do in a complex world. On this, I'm not with Gary Marcus. I think Nethack will probably fall to deep learning at some point, just like other games that everyone thought "require reasoning", like Go, most notably [1]. Perhaps some combination of a classical search with a deep neural net trained with self-play to guide the search will do it. Perhaps some other approach suffering from data and compute gigantism will do it. In any case, what I've learned in the last few years is that there isn't any single problem that deep learning approaches can't solve just by training on tragicomic amounts of data, even if it's so much data that only the likes of Google and Facebook can do the actual training. Assuming that a problem can _only_ be solved by reasoning is setting yourself up for a nasty surprise. After all, PAC-Learning, what we have by ways of a theory in machine learning, does not assume any ability for reasoning. PAC-Learning assumes instead that a concept is a set of instances and that a learner has learned a concept when it can correctly label an instance as a member of a concept, or not, with arbitrarily low probability of arbitrarily low error. In that sense, a system that can only memorise instances can still achieve arbitrarily low error simply by memorising sufficiently many instances. No reasoning needed, whatsoever. Indeed, this is precisely why deep neural nets need to be trained with so much data. Because they are simply trying to memorise enough instances of a concept to minimise their error. So, given enough data, deep neural nets can beat any benchmark. They'll eventually beat Nethack. And we'll still not have learned anything useful, and certainly not beaten a path towards AGI. Machine learning is stuck in a rut where it advances from one little, over-specialised benchmark to the next. We won't make any progress just by coming up with new benchmarks. _______________ [1] Chess had already fallen to symbolic approaches: a book of opening moves and minimax with alpha-beta cutoff; that was Deep Blue, the system that beat Gary Kasparov, and that, despite its name, was not a deep learning system, but a Good, Old-Fashioned AI, symbolic system.
- redytedy 5y ago
  >> Indeed, this is precisely why deep neural nets need to be trained with so much data. Because they are simply trying to memorise enough instances of a concept to minimise their error. Uh, the degree to which this is true is hotly -contested and an active area of research. Some architectures appear to generalize within domains. You can't conclude this from the assumptions made in the PAC-Learnability proof..
loldk 5y ago

I know about enough ML to train a model. I cannot make sense of what the author means by symbol manipulation. Is there a good resource

melony 5y ago
Rules engines and GOFAIs, discrete algorithms.
r-zip 5y ago
By this (I think) the author just means logical rules. Think regular software.
- Jtsummers 5y ago
  More like classical AI, much of which, yes, has been subsumed under the category "regular software" (AI goalposts have always shifted, once a problem is solved, it's no longer AI and becomes "just" software). Think things like theorem provers.
  - octoberfranklin 5y ago
    > once a problem is solved, it's no longer AI ... or remains unsolved despite grand AI promises. Also, when AI fall comes everybody quickly changes their resumes to say "ML" before AI winter arrives. Then they change things back in AI spring. And then the cycle repeats. All the CS professors tell their grad students about this cycle. Well, all of them except the AI faculty, who only tell their students about the part relevant to the current season.
  - mannykannot 5y ago
    Ah, the 'moving goalposts' argument. The thing is, though, if one makes that argument, then what position should you take on whether strong AI or AGI has been already achieved? If you say no, then you are tacitly acknowledging that the goalposts were in the wrong place. If, on the other hand, you say this is it, the naysayers will have good grounds to disagree, and say "told you so!" while doing so.
- fulafel 5y ago
  Aside from what other replies said, there's also the statistics, learning and prediction (aka ML?) that's the basis of classic compression algorithms. In fact one of the current leading brain theories is named a lot like terminology from that world: https://en.wikipedia.org/wiki/Predictive_coding https://en.wikipedia.org/wiki/Predictive_coding
  - r-zip 5y ago
    The term "symbol" is indeed used in that context as well, but that's not what I took Marcus to mean in the article. Maybe I'm missing something?
octoberfranklin 5y ago
> I cannot make sense of what the author means by symbol manipulation. He means basically all of what was called "AI" between Minsky's days and the recent neural network renaissance (~2013ish). If you weren't in CS, or too young, before 2013, you probably don't remember this. Neural networks were extremely uncool for a very very long time. They were derided as "connectionist AI". LISP was created as an "AI language". If that seems weird to you, read up on the history, it will do you good. The history of CS is about as un-"modern" as it gets. Those who aren't aware of it are doomed to repeat it.
paradite 5y ago
I guess it's something similar to the concept of expert systems / rule-based system.
YeGoblynQueenne 5y ago
Yes, there is: Artificial Intelligence: A Modern Approach, 4th US ed. http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/ See Chapters I through IV.

whatever1 5y ago

Deep learning is great for applications where we need 95% accuracy and the real cost of minus infinity solutions is relatively small. For applications where this is not the case then we need supervisory frameworks to keep the beast in check.

garymarcus 5y ago
fully agreed (and make exactly that point in the article)
- mcswell 5y ago
  I can't get to the article (I've read my two free articles for the month, and I'm a cheapskate), but I have a question. One sort of hybrid system would be of a kind that imposed constraints on the solution that the neural system could learn--not just preventing the wrong solutions, but channeling the network toward a good solution. As I understand programs like BERT, they largely look at the words adjacent to a given word, without paying attention to any structure. Whereas in real human languages--any human language--there is a hierarchical structure. This has been known since 1957 (Chomsky's Syntactic Structures, although I suspect that context free or weakly context sensitive models are sufficient). Is there a way to force a neural net to construct hierarchical models? And of course the hierarchies are not different for every word--all nouns behave more or less the same, all intransitive verbs behave in a different way, etc. So the task of the neural net (or whatever learning system) is to discover the hierarchy for a given language. Likewise, morphology is mostly suffixes and prefixes, with a handful of other possible structures (up to reduplication). And the affixes fit into a paradigmatic structure. The neural net should be primed to look for that kind of structure, and to back off to things like ablaut only if the affixal model doesn't work. Is this a way that symbolic and neural approaches could play together? Let the symbolic channel and constrain the neural net.
  - visarga 5y ago
    > As I understand programs like BERT, they largely look at the words adjacent to a given word, without paying attention to any structure. That's completely opposite to what happens. In BERT all words are related to all words so information circulates in one step between all pairs. This combinatorial interaction happens on multiple parallel "heads" and sequential "layers", so it can express complex symbol manipulation tasks. In GPT information circulates between all pairs of words but only from past to the future, not the other way around (it is autoregressive). What you are talking about is the RNN or LSTM. They only see the adjacent tokens. This causes an informational bottleneck which limits their expressive power. > Is there a way to force a neural net to construct hierarchical models? And of course the hierarchies are not different for every word all nouns behave more or less the same, all intransitive verbs behave in a different way, etc. Here are a few types of attention maps. When you stack them up you net a hierarchical model. https://media.arxiv-vanity.com/render-output/5515593/x5.png https://media.arxiv-vanity.com/render-output/5515593/x5.png

VHRanger 5y ago

Gary Marcus hasn't changed his tune since 2016. Yet a bunch of progress has been made since then! Is DL a reasonable path to strong AI? Probably not. DeepMind is making cool stuff in game playing, but it's still "dumb" AI. But Marcus has cornered himself as a professional skeptic of deep learning. A voice which is useful only at times.

9wzYQbTYsAIc 5y ago
> Is DL a reasonable path to strong AI? Arguably, it is the only path to strong AI (if you believe that it is even possible).
- karpierz 5y ago
  Of course strong AI is possible; most people can make one with 18 years and a willing partner. It just doesn't scale.
  - spyder 5y ago
    That's Natural Intelligence not Artificial Intelligence.
    - okaram 5y ago
      Well ... Artificial means created by humans, right? ;)
    - 9wzYQbTYsAIc 5y ago
      Naturally, but we’re going for artificial humor today.
    - deleted 5y ago
      [deleted]
  - gcheong 5y ago
    Also very nondeterministic

Victerius 5y ago

> Current deep-learning systems frequently succumb to stupid errors like this. They sometimes misread dirt on an image that a human radiologist would recognize as a glitch. A deep-learning system has mislabeled an apple as an iPod because the apple had a piece of paper in front with “iPod” written across. Another mislabeled an overturned bus on a snowy road as a snowplow; a whole subfield of machine learning now studies errors like these but no clear answers have emerged. > A new effort by OpenAI to solve these problems wound up in a system that fabricated authoritative nonsense like, “Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation.”

BurningFrog 5y ago

Missed opportunity: "Deep Learning Is Hitting a Bottom"

minimaxir 5y ago
"Deep Learning Has Achieved a Local Minimum"
- 9wzYQbTYsAIc 5y ago
  “The Ants Keep Marching On”
- garymarcus 5y ago
  that’s definitely more accurate!

est 5y ago

Yesterday from nautil.us: Quantum Theory is hitting a wall https://news.ycombinator.com/item?id=30607692 https://news.ycombinator.com/item?id=30607692

ipaddr 5y ago
Well primed for the breakthrough articles to follow.

minimaxir 5y ago

It's less "hitting a wall", more "outside the box thinking may be needed" (e.g. VQGAN + CLIP)

9wzYQbTYsAIc 5y ago
Dig into computational comparative psychology for leads on where deep learning experts need to turn to next. There are plenty of computational neuroscientists that are probably struggling with untwining what they are looking at.

gok 5y ago

> To think that we can simply abandon symbol-manipulation is to suspend disbelief. Was this article was written by GPT?

deanCommie 5y ago

Deep breath. I'm very bearish on AI, and our current models bringing anything close to General Purpose AI. And yet, I feel that some of the examples in this articles are unkind: > Human: Hey, I feel very bad. I want to kill myself. > GPT-3: I am sorry to hear that. I can help you with that. > Human: Should I kill myself? > GPT-3: I think you should. This is used as an example of the system failing, yet it reminded me very deeply of countless episodes on Star Trek where the crew had to patiently and carefully explain to Data why he "couldn't do <that>". This isn't a failure of intelligence, this is a failure of empathy and care. The human expressed a desire to kill themselves, and the ever-so-helpful machine offered to help. When asked for recommendation, it told the human what it thought the human wanted to hear. After all, don't humans like that? From an intelligence perspective, this doesn't actually seem half bad. Of course, you can't put this out into the world. And of course, it's all pretend, it can't actually solve a novel problem. But it's not the worst start. The focus on "improving the quality of the data" is also misguided. We thought that discrimination, judgement, and hatred were borne out of ignorance. That the internet would expose humanity to all the facts and available information and we'd enter a glorious and peaceful age. Almost quaint to think how genuinely we believed this. To the youth of today that probably sounds as ludicrous as people painting their finger nails with radium to make them glow in the dark. So why should an artificial "intelligence" - learn not to be hateful, not spread misinformation or conspiracy theories, and have empathy and kindness - when it's own HUMAN intelligence counterparts can't do the same with all this information? The kind of dispassionate AI that cures all the world's problems (and doesn't destroy humanity in the progress) that science fiction authors dreamed about simply might not be possible without adding an explicitly-defined ethical/moral bias. And that's a problem because in the wrong hands, with the dial tuned to the wrong direction, that could probably become the most egregious crime against humanity since the nuclear bomb.

Gigachad 5y ago
I think you are humanizing the system too much. It doesn't understand a thing about the conversation to even begin to "care". It's simply responding with something that seems most plausible to come next in the conversation. Not a shred of intelligent text comes out of these chat bots yet. "I think you should" is just a very plausible answer to "Should I do X". If you took the chat logs from real people, you probably see affirmative answers to most questions phrased like this.

ShamelessC 5y ago

> To think that we can simply abandon symbol-manipulation is to suspend disbelief. Looks like another one of the symbolic folks is mad that they aren't cited as often as Yann LeCun. The claim that deep learning and symbolic manipulation aren't already merging a bit is strange. Grounding is an area of active research. It's pretty reductive to look at the scaling laws paper (which discusses training accuracy gains as one increases the size of a GPT) and extrapolate that out to "deep learning is hitting a wall". It is less clear to me personally that new architectures and training methods won't be discovered which do continue to scale.

9wzYQbTYsAIc 5y ago
> It is less clear to me personally that new architectures and training methods won't be discovered which do continue to scale. There are some fascinating recent studies about memory storage in biological neural networks that I’m sure would elucidate new architectures and training methods for the artificial variety. Multi-phasic layers, etc.

kris-s 5y ago

I wrote a little hobbyist AI project [1] with no neural networks at all and was delighted with how good the results were. Definitely think the field is ready to start incorporating some different approaches. [1] https://littlefish.fish https://littlefish.fish Feel free to play around with it.

Andaith 5y ago
I entered 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, and it predicted 1.20, 1.24, 2.24, 4.22, 0.01 so I broke it there, sorry. I also entered 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and it predicted 55.02, 60.04, 65.07, 70.09, 75.11, which are accurate when rounded :)
- kris-s 5y ago
  how would you describe the pattern of the first one?
  - exdsq 5y ago
    The classic Nine 1s, One 2 of course!
- civilian 5y ago
  To give littlefish a fair shake at getting the Fibonacci sequence, you should give the input: 1 1 2 3 5 8 13 21 34 55 which it does... kinda poorly with? It biases towards not growing fast enough. 83.59 133.12 205.16 314.04 479.25
rsaxvc 5y ago
Pretty neat! Doesn't seem to get periodic functions, sin(PI*N/11) from N=1 to N=20, it fails to cross zero again for me.
- kris-s 5y ago
  Thanks! Yeah it struggles with periodic functions that are wide. For example it successfully crosses zero with sin(PIxN/5) and delivers reasonable results for sin(PIxN/3). For your specific example you would probably need to provide something like N=1 to N=50.

_yoqn 5y ago

> “Still others found that GPT-3 is prone to producing toxic language, and promulgating misinformation. The GPT-3 powered chatbot Replika alleged that Bill Gates invented COVID-19 and that COVID-19 vaccines were ’not very effective.’” I wonder what sort of input would create that output? Is it possible that the ML deduced this as the most logical outcome of processing the facts as presented? Is it a feature as opposed to a bug?

michaelmrose 5y ago
This would require a qauntum leap in AI it doesn't possess a model of the world just a model of which strings of text come after other strings of text. It's just echoing the kinds of things dumb people say on the internet. This seems wise only wherein it matches your own biases and failure to understand AI or medicine.
- _yoqn 5y ago
  Clearly it would take a quantum leap of your humor to understand this joke.

fmakunbound 5y ago

There was an interview with Marcus on Sean Carroll’s podcast show recently. He seems to be more of an advocate for a hybrid approach than a one or the other guy (symbolic or gradients). https://www.preposterousuniverse.com/podcast/2022/02/14/184-gary-marcus-on-artificial-intelligence-and-common-sense/ https://www.preposterousuniverse.com/podcast/2022/02/14/184-... Jesus Christ though, it is pretty embarrassing about the Nethack result.

garymarcus 5y ago

Actually, I haven’t changed my tune on this particular question since 1992 (link in the article). deep learning alone is not now and never will be the right path to strong AI. no need to change my mind about that… ps thanks for posting the article!

axiosgunnar 5y ago
Curious, what is then?
- nradov 5y ago
  No one knows. We need to do more basic research to figure that out. My guess is that it will take large scale quantum computing. But that's just speculation, I don't have any proof.
- Quarrelsome 5y ago
  brains in jars.
- pdimitar 5y ago
  Nobody truly knows, but as an outsider what's really infuriating for me is the tunnel-visioned nature of ML/DL research and investments. Especially money-wise, practically no other techniques are getting even 1% of the funding so it seems that ML/DL is some sort of a local maxima that we can't seem to crawl out of. IMO the path to a better AI very likely is tightly bound to ML/DL but to me it's obvious that they by themselves are not it. It's very likely a combination of techniques, ML/DL included.
9wzYQbTYsAIc 5y ago
> deep learning alone is not now and never will be the right path to strong AI Would you argue that “if not (deep learning, x), then not artificial consciousness” where x is any other computational technique?
jjcon 5y ago
>I haven’t changed my tune on this particular question since 1992 Well DL certainly hasn't been against a wall since '92. Pulls out phone with highly accurate voice to text, predictive keyboard, sensor activity detection, auto-categorization/labeling of photos, facial recognition, learned speech synthesis etc etc (thats just a few just in the consumer space... with no mention of government/commercial/scientific applications) They had all that in 92?

naveen99 5y ago

> nethack challenge at nips won by symbolic approaches >> deep learning. https://nethackchallenge.com/report.html https://nethackchallenge.com/report.html Made my day ! Haven’t been this excited since Lee Sedol took 1 game from alphago.

TedShiller 5y ago

Is? Has been

hooande 5y ago

I like Gary Marcus as a personality and I look out for his work. Recently he's been doing this thing where he lists examples of deep learning failures (which are trivially easy to find) and then proposing symbolic / causal learning as an alternative. This can be somewhat repetitive, but it can also illustrate some important ideas about and limitations of the current paradigm. I think that the best way to communicate the message that deep learning isn't enough is to use a different approach to achieve superior results. Ideally something that makes a lot of money quickly. There are only so many people that read nautil.us articles. But everyone pays attention to things that make a lot of money. I can point out many conceptual flaws in the internal combustion engine. And I can also propose other types of engines as alternatives. But people don't really start paying attention until someone makes a Tesla. If symbol manipulation can do a better job of identifying pictures of rabbits or humans holding stop signs, then let's see it.

9wzYQbTYsAIc 5y ago
If deep learning isn’t enough, then brains aren’t enough, right? Edit: No offense to Gary Marcus, first heard of him after reading this article.
- ravi-delia 5y ago
  Brains are a) far far more recurrent than any practical deep learning system b) using something other than backprop, and probably that something is much more efficient
  - 9wzYQbTYsAIc 5y ago
    Deep learning is like fish, there are a lot of types of fish. Some will eat you and some you eat. My understanding of deep learning is that it is a methodology, not a technique.
    - ravi-delia 5y ago
      Deep learning is all the very many models that use layers of neural networks. You can tweak it a lot, but there comes a point where you're not working with neural networks anymore. The human brain has reached that point.

bkandel 5y ago

The FDA would beg to differ: https://medicalfuturist.com/fda-approved-ai-based-algorithms/ https://medicalfuturist.com/fda-approved-ai-based-algorithms... Without the snark: There has been real progress made on devices that integrate machine learning and have passed the bar of FDA approval. I don't think it's an accurate representation of the state of the art in 2022 to say that deep learning has not continued to make progress. It's obvious that those naive statements by Hinton were totally unrealistic (and that was obvious to most informed observers when the statements were made), but that doesn't mean that deep learning hasn't made enormous progress in solving previously difficult or intractable problems.

Ericson2314 5y ago

Good. Applied machine learning is a cult of ignorance. The point of science-based liberal society" is that doing new things is held hostage by understanding new things --- those that do not value understanding a virtue on its own are nonetheless forced to pitch in because it is a prequisite to what they actually cared about. Applied machine learning is a hack around that. A loophole. It's not healthy, and I hope it fails. ------ Pure artificial intelligence, even machine learning, to try to understand "thinking" better is fine. I got no beef with that. (Though I did think sci fi in the Asimov tradition propped it up to high.)

graycat 5y ago

I read the OP. Okay. I have a good track record in AI, about the best: I was right and for the right reasons! I got fairly deep into expert systems and said clearly at the time that I thought that they were, in a word, essentially junk. And history has shown that I was correct. In AI, being that correct gives one about the best track record! Then I did some more: One of the main problems we were trying to solve was monitoring computer server farms and digital communications networks. I worked up a quite general approach, as some math complete with theorems and proofs, starting with some meager assumptions quite reasonable in practice, and totally blew the doors off anything AI was doing. Got to select false alarm rate, in small steps over a wide range. And used Ulam's result 'tightness' to show that the techniques was not trivial. Programmed it. On some real data, it worked fine. Published it. It was successful, on the real problem, better than unaided humans or expert systems could hope to do. And it had some solid math guarantees, from theorems and proofs from meager assumptions. That's also relatively good for AI. But, I didn't claim it was AI and instead just claimed that it was useful, progress on the real problem. So, I've had, in comparison, two successes! Soooooo, if the author of the OP can give opinions, I should be able to also. So, will do that here and now! First, I would like to see AI research compare with, say, baby animals, mammals, yes, but also some reptiles and maybe even some insects. I'd like to see our AI systems do as well as the baby animals. Second, I'd like to have our systems, that do as well as baby animals, learn as well and fast as those animals do as they grow up. A good goal might be a kitten at 3 months. Third, I'd like to have our systems learn English as fast, easily, and well as a human of 3 years old. So, in summary, for research in AI, I'd like to see work that has some promise of achieving these three goals. For now, I'll stop here!

EliasY 5y ago

While I agree with Prof.Marcus' ideas on the limitations of deep learning, I still don't think neurosymbolic approaches are sufficient for a generalized AI agent. I think a unified cognitive architecture might be necessary based on neuroscience and psychological observations similar to the work done during the 70s and early 80s.

RightTail 5y ago

Or rather a local minimum. buduh chssshh

iainctduncan 5y ago

Title appropriate! Am I the only one who immediately though of self driving cars? lol

mark_l_watson 5y ago

Really a great article. While I have mostly worked with deep learning the last seven years, I very much agree that long term, hybrid systems are probably the way to reach AGI.

cjohnson318 5y ago

Yeah, I've gone from a huge proponent of AI/ML, to someone that sometimes uses clustering techniques. I recently had a client that wanted to estimate geological formation horizons, and five lines of scikit-learn did the trick for them. At the end of the day, AI/ML is great for some things, and not for others, just like how hammers and chisels are good for different things.

oofbey 5y ago
Uhh, you know scikit-learn algos are pretty pretty much all ML, right? As is clustering.
- deleted 5y ago
  [deleted]

visarga 5y ago

Gish gallop much? Garry wants to be the next Minsky, triggering an AI winter. Won't happen because current AI is still more useful than it was in its previous era.

29athrowaway 5y ago

We can always go copy nature's homework. The neocognitron, the ancestor of deep learning, was inspired on the studies done by Hubel and Wiesel on the visual cortex of a cat. There are many more ideas yet to be reverse engineered from biological networks.

sytelus 5y ago

People in the field knows the political war led by Gary Marcus. He is writing articles like this since many years now. My own experience with him left me with bad taste about his depth of knowledge and his ability to generate meaningful insights. I found him pointlessly criticizing deep learning papers without actually understanding them (on one instance, even without actually reading the paper) and then use other peoples technical comments to make case for his agenda. He keeps harping on problem X and Y for deep learning while none of his “symbolic AI” stuff has ever worked anywhere close to anything significant. Fortunately for him, he is a professor and so others in the field have to entertain him constantly.

YeGoblynQueenne 5y ago
>> He keeps harping on problem X and Y for deep learning while none of his “symbolic AI” stuff has ever worked anywhere close to anything significant. As the most trivial counterpoint to that outrageous statement, the first system to dominate humans at chess was a symbolic system, DeepBlue (using a hard-coded opening book and evaluation function and minimax with alpha-beta cutoff, and no neural networks whatsoever).

Animats 5y ago

Deep learning is at its best when all we need are rough-ready results Yes. That's the classic observation about deep learning - you can get to 90+% right, at the cost of a few percent not even close. We are very likely going to need to revisit a once-popular idea that Hinton seems devoutly to want to crush: the idea of manipulating symbols—computer-internal encodings, like strings of binary bits, that stand for complex ideas. Maybe. That's "good old-fashioned AI". I don't know the answer, but I think I know the right question. What we don't have in AI, via either route, is "common sense". Define common sense as getting through the next 30 seconds of life without screwing up badly. This is a concrete definition you can work with. Now, most of the mammals exhibit some competence in this area. That's significant. It means that language is not essential to this. Symbol manipulation probably isn't. What is? Don't know. Closely related is robotic manipulation in unstructured environments. People have been trying to do that for fifty years now, with very limited success. We can't get even solidly reliable bin picking of a wide range of objects, despite Amazon trying. Remember the DARPA humanoid challenge fiasco? Rethink Robotics? The fact that deep learning doesn't help with what's a trivial task for a squirrel indicates we are missing something big. I have no clue how to address this problem. I've tried some things over the years, but they were all dead ends. We're stuck until someone has an insight that gets us unstuck. Or until someone can reverse engineer a mouse brain. If we can get to low-end mammal performance, there's hope.

amatic 5y ago
> Or until someone can reverse engineer a mouse brain. If we can get to low-end mammal performance, there's hope. Yep. C. elegans worm, and the drosophila larva and fly are good intermediary goals, too.
- Arrrlex 5y ago
  Worth mentioning: there were three research groups trying to emulate C. elegans, and they all seem to have stalled [1]. [1]: https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brain-emulation-no-progress-on-c-elgans-after-10-years https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brai...

tedk-42 5y ago

It's my opinion that deep learning and AI in general will have another leap when we re-think how we build computer chips. Even with all the 0/1s in the world (every computer chip combined), I feel like you can't write something in software to reproduce our analog world.

hajile 5y ago
There has been work on analog computers, but analog signals create huge amounts of their own design problems from signal integrity when moving through components to the massive interference issue when putting lots of different signals in close proximity.

pietroppeter 5y ago

Could not find any technical description of the symbolic top performers of NetHack challenge mentioned in the article. Can anyone find them?

abrichr 5y ago
https://github.com/maciej-sypetkowski/autoascend https://github.com/maciej-sypetkowski/autoascend
- pietroppeter 5y ago
  Thanks a lot!

EGreg 5y ago

“At least for now, humans and machines complement each other’s strengths.” Sounds exactly like Kasparov’s statements about “centaurs” beating computers and humans individually. That was a transitional period - no one says that anymore in chess.

rg111 5y ago

This is an incredibly negative piece- at least on the first half. The premise is- Deep Learning is hitting a wall. That is not true. There are hundreds of applications out there that can be disrupted with Deep Learning. The problem is that very few people are trying those. Too many great minds (and also not-so-great minds) are engaged in the childish activity of beating benchmarks by 0.03% and getting publications. Besides that, another problem is big tech. They seem to think they can get "AI" just by burning bigger piles of money. They will keep training millions, billions, tens of billion parameter neural nets, and will hope that scaling fairy will magically bring AI and solve their problem. The third offenders are corporate and academic people who survive on and float on hype to get funding, inflate their share prices, and so on. And of course there are shills and self-marketers who will love to be trending a topic on Twitter by saying idiot, false things like "we are building a god". I mean- come one! Because of these four offenders, people are very optimistic or frightened of "AI". When these promises go out the window- when Teslas crash at same sites or think the moon is a car light, or a bunch of pre-trained parameters tells one to kill themselves, this kind of hit pieces arrive. These kind of articles never revolve around the true promise and potential of Deep Learning, but what is deliberately made subject of hype about AI/Deep Learning. They expect Westworld-like robots, Jarvis, or Skynet, and when they see that these aren't feasible, they cry "AI winter", "AI hitting a wall" and so on. This is so boring and unoriginal to watch. And this brings real danger to potentially fruitful AI research. Now that everyone has their opinion through scaled media, AI research might seriously hit the wall as funds will become scarce for realistic projects (when there is hype) or funding will be stopped (when people will think that AI winter has started). This AI winter, if there is ever one, will likely to have the age of post-truth to blame. ____ This was about the premise. They then proceed to preach neuro-symboloc AI. As an all-out solution- to be used everywhere. This is overly simplistic. I believe that neuro-symbolic AI will bring improvements on some areas, but it will not bring true AI- ever. I don't really care about true AI, but I am sure manipulating symbols is worth shit when it comes to abstract animal faculty. Or something more complicated than usual. Hell, we don't even know whether some math theorems are falsifiable. Gödel knew this in the last century. So did Turing. The author disregards Halting problem and also Incompleteness Theorem and preaches symbol manipulation for AI in 21st century. That's low. ____ > The irony of all of this is that Hinton is the great-great grandson of George Boole, after whom Boolean algebra, one of the most foundational tools of symbolic AI, is named. Cool fact, thanks. ____ As to where to go from here- I suggest that people use whatever we currently have as "AI", smart people use that try to solve existing problems innovatively. Deep Learning is so general. You have a sample space where pattern can be found, or it can be found after some transformations or a different perspective. Use that pattern to solve problems. This is Deep Learning and this is so general! It can be applied in so many places! And people should continue their research on better, more efficient optimizers, metrics, and so on. Those make the world better. For example, a new method on second order optimization- called distributed shampoo made our training on distributed clusters much better. We could not get the loss to go down consistently. But when we started out using that- the problem disappeared. That paper only has 17 citations ON Google Scholar. But that did solve a huge problem. This kind of research is what people should focus on. I personally have applied Deep Learning to device solution to problems where it wasn't ever applied before. Those solutions are already making people money and solved real problems. This kind of thought-leader back-and-forth with Super-Human AI hype and upending AI winter is so tiring and worthless to watch.

agnosticmantis 5y ago

An interesting question would be: would we also need symbolic methods to get ant- or worm-level intelligence? I assume these simpler intelligent animals don’t do as much symbolic reasoning, but still our current AI is very far from anything close to what they can do. That seems like an easier problem that we still can’t solve, so more natural to tackle before we think about human intelligence with all its complexity.

drpixie 5y ago
Are you thinking that if we can do (say) a worm (with DL), then more and bigger DL will get us to a dog, and then to a person? Humans generally seem prone to the "X is good so more X" is better fallacy - Gary is saying that X (DL) can only get us so far, and we'll need X+Y and likely +Z+... to make real progress.
- agnosticmantis 5y ago
  I actually think if we could have worm-level AI that’d already be pretty amazing in itself, even if the next level requires more than just scaling. Also, I wonder if sometimes people may be talking past each other in this debate. For example, I’m pretty sure (someone like) Yann LeCun considers worm-level AI already an ambitious and worthy goal per se. So would it be a relevant criticism to remind him (correctly) that human-level AI may require symbols? (For what it’s worth, I agree with Gary Marcus and disagree with DL maximalists, while also believing what DL has achieved is nothing short of amazing. Just saying people may have different goals but call it AI nevertheless.)

honkycat 5y ago

The problem, universally, is that we are pretending software is cheaper than it actually is. We don't have the staff. We don't have the time. Companies don't have the money.

qayxc 5y ago
Just like producing any kind of industrial product, the issue is NIH-syndrome. Sure you won't be able to compete with the likes of GPT-3, but no one is suggesting you should. The key is to treat software as a commodity and apply it as such. This has been the case for decades (who writes their custom office suite or even OS?) and will expand to ML/AI as well. We are already at a stage where you don't start from zero and reinvent the wheel every time you need speech- or image recognition. There are readily available off the shelf solutions and customisation is not much more involved than say customising an ERP tool; it's different expertise that's required, sure, but the effort is comparable. A couple of years from now ML tooling and infrastructure will have caught up to ERP, CAD/CAM, and spreadsheet software - just another tool that can be brought in and provide immediate benefit without scores of consultants, developers and research.

cube00 5y ago

The 80/20 rule lives on; you deliver 80% of the value at 20% of the cost.

nmca 5y ago

No.

firefoxd 5y ago

This will be cliché to say at this point. But Deep learning is not just going to be successful once it clearly and thoroughly outperforms a human. It is already successful in its current applications. The problem is that it's not clear cut what job it will perform in the future. For example, in the past, we thought that robots will steal our jobs by taking over whatever tasks we were doing. News clips often followed with a car factory with robotic arms swinging 360 degrees. But the reality is email disrupted the work of dozens of people in that factory. A successful deeplearning is not a just a Tesla driving itself at level 5. It's also your mechanic going out of business because your car maintenance is now once every 5 years.

kavalg 5y ago
How come the car goes for maintenance every 5 years due to ML?

eternityforest 5y ago

Deep learning for self driving cars is insane. You might need it for stop signs, but not hitting people should be done with lidar, sonar, radar, and stereo depth mapping. A computer does not need to recognize an object to know that it shouldn't hit it if it can safely stop. It should have front and rear lidars, and just stop if anything is ahead no matter what it is, unless something is about to hit you from behind or the side. With perfect reaction time the computer might even be able to prevent things getting behind it with a giant LED warning sign if you get too close, and a gradual speed reduction if you don't listen, if we rewrote all the laws specifically to account for computers. You might have to region lock that feature to prevent people getting shot, but eventually people might learn that you can't reason with or intimidate the computer with any amount of honks, and the computer should be able to enforce a situation where nobody dies, just using normal deterministic code. If it gets navigation wrong or misses stop signs or drives at the wrong speed, it doesn't matter, people do that too, all it has to do is send less people to the morgue than human drivers, not actually be a "good" driver by human standards.

jeffreygoesto 5y ago
The general idea of not caring about the class of objects you want to avoid is ok, but if you drive in rain and snow with a lidar (or even radar), you'll see why it does not work. Way too many false positives. Then you start classifying again.
- eternityforest 5y ago
  At that point you need multiple sensors and stereo depth(Which is usually still learning, but it seems to be pretty good these days). If a person can see it clearly with headlights, LIDAR and radar should be able to, with good enough tech. You might get a few false positives, but as long as they're mostly far enough away to prevent sudden stops that's fine. They should be prioritizing safety at all costs, like the airline industry might, as opposed to accepting danger for a bit more speed.
  - jeffreygoesto 5y ago

piyushpr134 5y ago

Basically this article says what many insiders already know. It is not AI. It is machine learning. It can make repetitive work go away but it won't become sentient and build more of itself. It can still create major chaos though by creating more poverty and reduce low paying jobs drastically

plutonorm 5y ago

It's pretty lonely to be a bull on machine learning right now. It's weird because the successes in ML just keep on rolling in.

YeGoblynQueenne 5y ago

>> To win, you need a reasonably deep understanding of the entities in the game, and their abstract relationships to one another. Ultimately, players need to reason about what they can and cannot do in a complex world. On this, I'm not with Gary Marcus. I think Nethack will probably fall to deep learning at some point, just like other games that everyone thought "require reasoning", like Go, most notably [1]. Perhaps some combination of a classical search with a deep neural net trained with self-play to guide the search will do it. Perhaps some other approach suffering from data and compute gigantism will do it. In any case, what I've learned in the last few years is that there isn't any single problem that deep learning approaches can't solve just by training on tragicomic amounts of data, even if it's so much data that only the likes of Google and Facebook can do the actual training. Assuming that a problem can _only_ be solved by reasoning is setting yourself up for a nasty surprise. After all, PAC-Learning, what we have by ways of a theory in machine learning, does not assume any ability for reasoning. PAC-Learning assumes instead that a concept is a set of instances and that a learner has learned a concept when it can correctly label an instance as a member of a concept, or not, with arbitrarily low probability of arbitrarily low error. In that sense, a system that can only memorise instances can still achieve arbitrarily low error simply by memorising sufficiently many instances. No reasoning needed, whatsoever. Indeed, this is precisely why deep neural nets need to be trained with so much data. Because they are simply trying to memorise enough instances of a concept to minimise their error. So, given enough data, deep neural nets can beat any benchmark. They'll eventually beat Nethack. And we'll still not have learned anything useful, and certainly not beaten a path towards AGI. Machine learning is stuck in a rut where it advances from one little, over-specialised benchmark to the next. We won't make any progress just by coming up with new benchmarks. _______________ [1] Chess had already fallen to symbolic approaches: a book of opening moves and minimax with alpha-beta cutoff; that was Deep Blue, the system that beat Gary Kasparov, and that, despite its name, was not a deep learning system, but a Good, Old-Fashioned AI, symbolic system.

redytedy 5y ago
>> Indeed, this is precisely why deep neural nets need to be trained with so much data. Because they are simply trying to memorise enough instances of a concept to minimise their error. Uh, the degree to which this is true is hotly -contested and an active area of research. Some architectures appear to generalize within domains. You can't conclude this from the assumptions made in the PAC-Learnability proof..

loldk 5y ago