"The widespread misimpression that data + neural networks is a universal formula has real consequences: [...] in what students choose to study, and in what universities choose to teach."
I think this is a problem that deserves more attention. A large portion of CS students nowadays choose to focus on Machine Learning, many brilliant CS students decide to get a PhD in ML in hope of contributing to a field that will continue to develop at a pace similar to the progress we have seen in the last 7 years. This view, I think, is not justified at all. The challenges that AI faces such as incorporating a world model into the techniques that work well will be significantly harder than what most researchers openly admit, probably to the detriment of other CS research areas.
I think this is a problem that deserves more attention. A large portion of CS students nowadays choose to focus on Machine Learning, many brilliant CS students decide to get a PhD in ML in hope of contributing to a field that will continue to develop at a pace similar to the progress we have seen in the last 7 years. This view, I think, is not justified at all. The challenges that AI faces such as incorporating a world model into the techniques that work well will be significantly harder than what most researchers openly admit, probably to the detriment of other CS research areas.