Scientific machine learning. The ability to mix scientific knowledge with machine learning in order to allow it to extrapolate accurately from small amounts of

Scientific machine learning. The ability to mix scientific knowledge with machine learning in order to allow it to extrapolate accurately from small amounts of data. As machine learning tries to move towards areas where large-scale data gathering is prohibitively expensive or even unethical (like in clinical trials: you can't have a million failures to train a neural network!), mixing scientific models with machine learning will be required to bring AI into these domains in a way that's beyond hype. Physics-informed neural networks [1], differentiable programming [2], universal differential equations [3], SInDy [4], etc. are all ideas directions I think is the next direction for machine learning. [1] https://www.sciencedirect.com/science/article/pii/S0021999118307125 [2] https://arxiv.org/abs/1907.07587 [3] https://arxiv.org/abs/2001.04385 [4] https://www.pnas.org/content/113/15/3932