As far as I’m aware literally zero significant ML researchers have written such a thing, Not Dario, not Demis, not Sutskever, not LeCun, nor basically anyone senior in their orgs.
I just want to point out that seems like a ridiculous standard. Quintin’s recent critique is not that dissimilar to the one I would write (and I already have spent some time trying to point out the various flaws in the EY/MIRI world model), and I expect that you would get many of the same objections if you elicited a number of thoughtful DL researchers. But few if any have been motivated—what’s the point?
Here’s my critique in simplified form: the mainstream AI futurists (moravec,kurzweil,etc) predicted that AGI would be brain-like and thus close to a virtual brain emulation. Thus they were not so concerned about doom, because brain-like AGI seems like a more natural extension of humanity (moravec’s book is named ‘mind children’ for a reason), and an easier transition to manage.
In most ways that matter, Moravec/Kurzweil were correct, and EY was wrong. That really shouldn’t be even up for debate at this point. The approach that worked—DL—is essentially reverse engineering the brain. This is in part due to how the successful techniques all ended up being directly inspired by neuroscience and the now proven universal learning & scaling hypotheses[1] (deep and or recurrent ANNs in general, sparse coding, normalization, relus, etc) OR indirectly recapitulated neural circuitry (transformer ‘attention’ equivalence to fast weight memory, etc).
But in even simpler form: If you take a first already trained NN A and run it on a bunch of data and capture all its outputs, then train a second NN B on the input output dataset, the result is that B becomes a distilled copy—a distillation, of A.
This is in fact how we train large scale AI systems. They are trained on human thoughts.
The universal learning hypothesis is that the brain (and thus DL) uses simple universal learning algorithms, and all circuit content is learned automatically, which leads to the scaling hypothesis—intelligence comes from scaling up simple architectures and learning algorithms with massive compute, not continually explicitly “rewriting your source code” ala EY’s model.
I just want to point out that seems like a ridiculous standard. Quintin’s recent critique is not that dissimilar to the one I would write (and I already have spent some time trying to point out the various flaws in the EY/MIRI world model), and I expect that you would get many of the same objections if you elicited a number of thoughtful DL researchers. But few if any have been motivated—what’s the point?
Here’s my critique in simplified form: the mainstream AI futurists (moravec,kurzweil,etc) predicted that AGI would be brain-like and thus close to a virtual brain emulation. Thus they were not so concerned about doom, because brain-like AGI seems like a more natural extension of humanity (moravec’s book is named ‘mind children’ for a reason), and an easier transition to manage.
In most ways that matter, Moravec/Kurzweil were correct, and EY was wrong. That really shouldn’t be even up for debate at this point. The approach that worked—DL—is essentially reverse engineering the brain. This is in part due to how the successful techniques all ended up being directly inspired by neuroscience and the now proven universal learning & scaling hypotheses[1] (deep and or recurrent ANNs in general, sparse coding, normalization, relus, etc) OR indirectly recapitulated neural circuitry (transformer ‘attention’ equivalence to fast weight memory, etc).
But in even simpler form: If you take a first already trained NN A and run it on a bunch of data and capture all its outputs, then train a second NN B on the input output dataset, the result is that B becomes a distilled copy—a distillation, of A.
This is in fact how we train large scale AI systems. They are trained on human thoughts.
The universal learning hypothesis is that the brain (and thus DL) uses simple universal learning algorithms, and all circuit content is learned automatically, which leads to the scaling hypothesis—intelligence comes from scaling up simple architectures and learning algorithms with massive compute, not continually explicitly “rewriting your source code” ala EY’s model.