Yann is undoubtedly a pillar of the deep learning era of AI. But, I strongly disagree with what he is saying. Firstly, the Navier-stokes equations existed befo

Yann is undoubtedly a pillar of the deep learning era of AI. But, I strongly disagree with what he is saying. Firstly, the Navier-stokes equations existed before mechanized flight was discovered. It was not explicitly invented to reason about flight, in that way that some of the deep learning "theory" is being projected. It also worked perfectly (in sub-sonic speeds) for almost every situation involving flight. ML has time and again proven to not be internally consistent, with contradictions on nearly every corner. Batch norm was "theoretically" thought to be an essential part of neural networks, until suddenly it wasn't. Drop out was considered essential to avoid overfitting, until it wasn't. We still do not know if an information bottle neck is good or bad. ADAM's whole formula was incorrect and no one realized that for 6 years. > The mechanism of action of many drugs (if not most of them). > An example? How does lithium treat bipolar disorder? This example does more to disapprove his point that approve of it. Medical and nutritional sciences are among the least understood fields out there, with some of their "fundamentals" flipping on their head over the last 50 years. The only reason these "sciences" continue being used is because medicine is essential. A reasonably effective solution with side effects is still better than dead people. It is a begrudging compromise, not a example to be emulated. AI is not essential. AI imposes on your life without consent. AI will soon be ingrained into every single aspect of your life. Yann seems to be conflating explainability with causality. Explanaibility can also mean fully observed correlation. Explainbility can mean predictable and reproducible behavior of ML models given a hypothesis. Explanability can mean the ability to ascertain if the change in model performance was because of the hypothesis or a way to exploit an unintended aspect of the data/model architecture. Explainability fundamentally allows ML researchers to make strides in the field in a meaningful way, and not blindly throw different computational structures at thousands of GPUs and let luck of the draw decide what works and when. Looking back at ideas such as Transformers and ResNets, there was literally no way for the authors to guess that this new computational structure would revolutionize the field. It could easily have been an idea someone else tried or rotted in someone's TO-TRY list. Explainbility and some theoretical logic around NN development would allow for a systematic way to go about research in the ML community as whole. That's unlikely to happen, but I would rather see people strive for it than not.