The Limitations of Deep Learning
As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn&#x27;t special; deep models tend to be more accurate than other methods, but fundamentally they aren&#x27;t much closer to working like the human brain than e.g. gradient boosting models.<p>I think a lot of the issue stems from layman explanations of neural networks. Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains; these descriptions frequently extend to DL tutorials as well. It&#x27;s important for that idea to be dispelled when people actually start applying deep models. The model&#x27;s intuition doesn&#x27;t work like a human&#x27;s, and that can often lead to unsatisfying conclusions (e.g. the panda --&gt; gibbon example that Francois presents).<p>Unrelatedly, if people were more cautious about anthropomorphization, we&#x27;d probably have to deal a lot less with the irresponsible AI fearmongering that seems to dominate public opinion of the field. (I&#x27;m not trying to undermine the danger of AI models here, I just take issue with how most of the populace views the field.)