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
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):