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Yann was not making a statement about deep nets' ability (or lack thereof) to fly a plane in a way that matches expert design. He's making the point that the M

Yann was not making a statement about deep nets' ability (or lack thereof) to fly a plane in a way that matches expert design. He's making the point that the ML field's obsession with causal inference (and causal discovery) is overrated, precisely because our gold standards of interpretable, safety-critical systems (airplane flight) are based on Navier Stokes/CFD. Planes were made to fly based on empirical validation of these models, long before we gained a more detailed understanding of how causality (the equations and models themselves are time reversible, implying that they contain imperfect knowledge of causality)

ssivark 7y ago
> Planes were made to fly based on empirical validation of these models, long before we gained a more detailed understanding of how causality And their success is so repeatable that if they fail once we make a Really Big Deal out of it. I don’t think any ML model is close to that level of engineering rigor, let alone deep learning. Moving on to aerodynamics, we have a pretty good causal model and can simulate the system given a pattern of boundary conditions. Further, some people (who have studied aero/CFD) can even intuitively predict approximately what happens (otherwise we would have a hard time designing planes!). It just so happens that it’s not as simple as high school physics, and cannot be compressed in to perfect+simple rules (trade off between those two). Speaking in the context of time reversibility, you are being fast and loose, and using the word “causality” in a sense that is irrelevant to the rest of your comment.