For reference, the Gary Marcus tweet in question is:
“I’m not saying I want to forget deep learning… But we need to be able to extend it to do things like reasoning, learning causality, and exploring the world .”—Yoshua Bengio, not unlike what I have been saying since 2012 in The New Yorker.
I think Zack Lipton objected to this tweet because it appears to be trying to claim priority. (You might have thought it’s ambiguous whether he’s claiming priority, but he clarifies in the thread: “But I did say this stuff first, in 2001, 2012 etc?”) The tweet and his writings more generally imply that people in the field have recently changed their view to agree with him, but many people in the field object strongly to this characterization.
The tweet is mostly just saying “I told you so.” That seems like a fine time for people to criticize him about making a land grab rather than engaging on the object level, since the tweet doesn’t have much object-level content. For example:
“Saying it louder ≠ saying it first. You can’t claim credit for differentiating between reasoning and pattern recognition.” [...] is essentially a claim that everybody knows that deep learning can’t do reasoning. But, this is essentially admitting that Marcus is correct, while still criticizing him for saying it.
Hopefully Zack’s argument makes more sense if you view it as a response to Gary Marcus claiming priority. Which is what Gary Marcus was doing and clearly what Zack is responding to. This is not a substitute for engagement on the object level. Saying “someone else, and in fact many people in the relevant scientific field, already understood this point” is an excellent response to someone who’s trying to claim credit for the point.
There are reasonable points to make about social epistemology here, but I think you’re overclaiming about the treatment of critics, and that this thread in particular is a bad example to point to. It also seems like you may be mistaken about some of the context. (Zack Lipton has no love for short-timelines-pushers and isn’t shy about it. He’s annoyed at Gary Marcus for making bad arguments and claiming unwarranted credit, which really is independent of whether some related claims are true.)
I also read OP as claiming that Yann LeCun is defending the field against critiques that AGI isn’t near. My current from-a-distance impression is indeed that LeCun wants to protect the field from aggressive/negative speculation in the news / on Twitter, but that he definitely cannot be accused of scamming people about how near AGI is. Quote:
I keep repeating this whenever I talk to the public: we’re very far from building truly intelligent machines. All you’re seeing now — all these feats of AI like self-driving cars, interpreting medical images, beating the world champion at Go and so on — these are very narrow intelligences, and they’re really trained for a particular purpose. They’re situations where we can collect a lot of data.
So for example, and I don’t want to minimize at all the engineering and research work done on AlphaGo by our friends at DeepMind, but when [people interpret the development of AlphaGo] as significant process towards general intelligence, it’s wrong. It just isn’t. it’s not because there’s a machine that can beat people at Go, there’ll be intelligent robots running round the streets. It doesn’t even help with that problem, it’s completely separate. Others may claim otherwise, but that’s my personal opinion.
We’re very far from having machines that can learn the most basic things about the world in the way humans and animals can do. Like, yes, in particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat. This makes a lot of questions people are asking themselves premature. That’s not to say we shouldn’t think about them, but there’s no danger in the immediate or even medium term. There are real dangers in the department of AI, real risks, but they’re not Terminator scenarios.
There is a conversation to be had about how scientific fields interface with public discussions of them in the news / on Twitter, and indeed I think it is on net very defensive. I don’t think this is especially self-serving behaviour though. My guess as to the experience of most scientists reading about their work exploding on Twitter is “I feel like massive numbers of people are quickly coordinating language to attack/use my work in ways that are inaccurate and I feel threatened and need to run damage control” and that their understanding of what is happening is indeed true. I still think public discourse should be optimised for truth over damage control, but I don’t model such folks as especially self-serving or pulling any sort of scam.
I also read OP as claiming that Yann LeCun is defending the field against critiques that AGI isn’t near.
Same. In particular, I read the “How does the AI field treat its critics” section as saying that “the AI field used to criticize Dreyfus for saying that AGI isn’t near, just as it now seems to criticize Marcus for saying that AGI isn’t near”. But in the Dreyfus case, he was the subject of criticism because the AI field thought that he was wrong and AGI was close. Whereas Marcus seems to be the subject of criticism because the AI field thinks he’s being dishonest in claiming that anyone seriously thinks AGI to be close.
Whereas Marcus seems to be the subject of criticism because the AI field thinks he’s being dishonest in claiming that anyone seriously thinks AGI to be close.
Note, this looks like a dishonest “everybody knows” flip, from saying or implying X to saying “everybody knows not-X”, in order to (either way) say it’s bad to say not-X. (Clearly, it isn’t the case that nobody believes AGI to be close!)
(See Marcus’s medium article for more details on how he’s been criticized, and what narratives about deep learning he takes issue with)
Gary Marcus: @Ylecun Now that you have joined the symbol-manipulating club, I challenge you to read my arxiv article Deep Learning: Critical Appraisal carefully and tell me what I actually say there that you disagree with. It might be a lot less than you think.
Yann LeCun: Now that you have joined the gradient-based (deep) learning camp, I challenge you to stop making a career of criticizing it without proposing practical alternatives.
Yann LeCun: Obviously, the ability to criticize is not contingent on proposing alternatives. However, the ability to get credit for a solution to a problem is contingent on proposing a solution to the problem.
Gary Marcus: Folks, let’s stop pretending that the problem of object recognition is solved. Deep learning is part of the solution, but we are obviously still missing something important. Terrific new examples of how much is still be solved here: #AIisHarderThanYouThink
Critic: Nobody is pretending it is solved. However, some people are claiming that people are pretending it is solved. Name me one researcher who is pretending?
Yann LeCun: Yeah, obviously we “pretend” that image recognition is solved, which is why we have a huge team at Facebook “pretending” to work on image recognition. Also why 6500 people “pretended” to attend CVPR 2018.
The most relevant quote from the Nature paper he is criticizing (he’s right that it doesn’t discuss methods working poorly off distribution):
Unsupervised learning had a catalytic effect in reviving interest in deep learning, but has since been overshadowed by the successes of purely supervised learning. Although we have not focused on it in this Review, we expect unsupervised learning to become far more important in the longer term. Human and animal learning is largely unsupervised: we discover the structure of the world by observing it, not by being told the name of every object.
Human vision is an active process that sequentially samples the optic array in an intelligent, task-specific way using a small, high-resolution fovea with a large, low-resolution surround. We expect much of the future progress in vision to come from systems that are trained end-toend and combine ConvNets with RNNs that use reinforcement learning to decide where to look. Systems combining deep learning and reinforcement learning are in their infancy, but they already outperform passive vision systems at classification tasks and produce impressive results in learning to play many different video games.
Natural language understanding is another area in which deep learning is poised to make a large impact over the next few years. We expect systems that use RNNs to understand sentences or whole documents will become much better when they learn strategies for selectively attending to one part at a time.
Ultimately, major progress in artificial intelligence will come about through systems that combine representation learning with complex reasoning. Although deep learning and simple reasoning have been used for speech and handwriting recognition for a long time, new paradigms are needed to replace rule-based manipulation of symbolic expressions by operations on large vectors
For reference, the Gary Marcus tweet in question is:
I think Zack Lipton objected to this tweet because it appears to be trying to claim priority. (You might have thought it’s ambiguous whether he’s claiming priority, but he clarifies in the thread: “But I did say this stuff first, in 2001, 2012 etc?”) The tweet and his writings more generally imply that people in the field have recently changed their view to agree with him, but many people in the field object strongly to this characterization.
The tweet is mostly just saying “I told you so.” That seems like a fine time for people to criticize him about making a land grab rather than engaging on the object level, since the tweet doesn’t have much object-level content. For example:
Hopefully Zack’s argument makes more sense if you view it as a response to Gary Marcus claiming priority. Which is what Gary Marcus was doing and clearly what Zack is responding to. This is not a substitute for engagement on the object level. Saying “someone else, and in fact many people in the relevant scientific field, already understood this point” is an excellent response to someone who’s trying to claim credit for the point.
There are reasonable points to make about social epistemology here, but I think you’re overclaiming about the treatment of critics, and that this thread in particular is a bad example to point to. It also seems like you may be mistaken about some of the context. (Zack Lipton has no love for short-timelines-pushers and isn’t shy about it. He’s annoyed at Gary Marcus for making bad arguments and claiming unwarranted credit, which really is independent of whether some related claims are true.)
I also read OP as claiming that Yann LeCun is defending the field against critiques that AGI isn’t near. My current from-a-distance impression is indeed that LeCun wants to protect the field from aggressive/negative speculation in the news / on Twitter, but that he definitely cannot be accused of scamming people about how near AGI is. Quote:
There is a conversation to be had about how scientific fields interface with public discussions of them in the news / on Twitter, and indeed I think it is on net very defensive. I don’t think this is especially self-serving behaviour though. My guess as to the experience of most scientists reading about their work exploding on Twitter is “I feel like massive numbers of people are quickly coordinating language to attack/use my work in ways that are inaccurate and I feel threatened and need to run damage control” and that their understanding of what is happening is indeed true. I still think public discourse should be optimised for truth over damage control, but I don’t model such folks as especially self-serving or pulling any sort of scam.
Same. In particular, I read the “How does the AI field treat its critics” section as saying that “the AI field used to criticize Dreyfus for saying that AGI isn’t near, just as it now seems to criticize Marcus for saying that AGI isn’t near”. But in the Dreyfus case, he was the subject of criticism because the AI field thought that he was wrong and AGI was close. Whereas Marcus seems to be the subject of criticism because the AI field thinks he’s being dishonest in claiming that anyone seriously thinks AGI to be close.
Note, this looks like a dishonest “everybody knows” flip, from saying or implying X to saying “everybody knows not-X”, in order to (either way) say it’s bad to say not-X. (Clearly, it isn’t the case that nobody believes AGI to be close!)
(See Marcus’s medium article for more details on how he’s been criticized, and what narratives about deep learning he takes issue with)
Skimming that post it seems like he mentions two other incidents (beyond the thread you mention).
First one:
Second one:
The most relevant quote from the Nature paper he is criticizing (he’s right that it doesn’t discuss methods working poorly off distribution):
Ok, I added a note to the post to clarify this.
Hi Paul. Thanks for lucid analysis and generosity with your time to set the record straight here.