[thousandautumns]

> I’m not sure if this is just an attempt to down play their results or if it’s more academic jealousy because the funding goes to the “cool stuff” like AI/ML in the CS dept. and the Stats dept. is seen as old and boring.

No one is trying to downplay the legitimately impressive results of AI/ML. Deep learning, convolutional neural networks and GANs have had incredible success in fields like computer vision, and image/speech recognition. But outside of those areas the "results" for the current fads in AI/ML learning have been grossly overstated. You have academic computer scientists like Judea Pearl decry the "backward thinking" of statistics and who are championing a "causal revolution", despite not actually doing anything revolutionary. You have modern machine learning touted ad nauseam as a panacea to any predictive problem, only for systematic reviews to show they don't actually out perform traditional statistical methods [1]. And you have industry giants like IBM and countless consulting companies promise AI solutions to every business problem that turn out to be more style than substance, and "machine learning" algorithms that are just regression.

There's a reason why AI research has gone through multiple winters, and why another is looming. Those in AI/ML seem to be more prone and/or willing to overpromise and underdeliver.

[1] https://www.sciencedirect.com/science/article/pii/S089543561...

[laichzeit0]

I would also add in that list RNNs for time-series forecasting, especially LSTMs. I would say this is a bit more than “just regression”.