Machine Learning: The Great Stagnation
Some good points in the article, but I disagree with the tone and the conclusion.<p>> we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research<p>There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning). In no way is machine learning unique in this. Sounds like the author is simply disappointed that, like in any other profession, day-to-day of a researcher is a slog and not a perennial intellectual festival. We've been in an exciting deep learning craze for a while, but it's silly to expect it to last forever. Back to the grind now.<p>> Machine Learning Researchers can now engage in risk-free, high-income, high-prestige work<p>Not sure what author means by "risk-free". Yes, if you're not publishing enough you're most likely not going to starve. Is that a bad thing? Is the survival instinct the only good motivator for doing good research?<p>I would argue that there's plenty of risk, in that people who don't publish good research don't get very far in their academic careers, which in my view is good enough motivation. "They must do good research or starve" is a rather cynical take, especially from someone who seems to not be doing too badly for themselves.<p>I'd rather more fields provided similar benefits. Maybe then going into science wouldn't be associated with so much sacrifice, so more smart people would choose science over investment banking or such, and we'd make more scientific progress faster.<p>> CNNs use convolutions which are a generalization of matrix multiplication.<p>A nitpick: CNNs are most definitely <i>not</i> a generalization of matrix multiplication. In fact, the opposite: you can view CNNs as a matrix multiplication with a particular matrix structure.