We’ve come a long way from having to make the case for artificial intelligence (AI) in biology. Just a few years ago, I argued in The New York Times how fears around the “black box” of AI in medicine are often misplaced — especially given how much of a black box the doctor’s mind is — and where the limits and opportunities may be. Today, however, there’s so much evidence of how AI can revolutionize healthcare and the life sciences (not to mention other fields), even outperforming humans on a wide range of tasks once thought to be too complex to be tackled by algorithms. 

But even with this evidence now in hand, the reality, in practice, is that the potential of artificial intelligence in biology will be limited — unless it gets a lot smarter. Current methodologies rely on a naïve, blank slate as the starting point. AI can be trained (much like dogs), but not understand; it can play the game, but only with known rules; and it can’t really go beyond their training. Take for example the application of identifying small molecules that can bind to a disease-causing protein, where AI could accelerate and expand drug discovery beyond human capabilities. Today’s AI has to infer the laws of physics (e.g. how close atoms can pack), chemistry (e.g. the strength of different chemical bonds), and biology (e.g. the flexibility of the protein’s binding pocket) from the data it is trained on. And if that dataset is too limited in any direction, the basic rules of these fields will be violated, leading to pointless results.