Predicting, controlling, and understanding are three distinct things, and current machine learning models like AlphaFold give us prediction and some control, but human-communicable understanding still has to be derived separately by people.
Jumper draws a three-way distinction between predicting an outcome, controlling it, and understanding it in a human-communicable way, arguing AlphaFold gives us prediction (and some control) but understanding remains a separate human task. ✦ AI generated
John Jumper · Machine Learning Street Talk · 2026-06-22 · original ↗
starts at this moment · 36:46
So predict means that you say I'm going to do a thing, what am I going to what will be the value of my machine, what will appear on my computer screen in the future? That is predict. Control is I want to measure this thing in the future and I want it to come out 17. Right? That's control. Understand is a lot like predict except there's a human in the loop.
verbatim transcript · starts at 36:46
36:46point learning how to refine and optimize the structure. >> Okay, so we I think we should distinguish three things. Predict, control, understand first. So predict means that you say I'm going to do a thing, what am I going to what will be the value of my machine, what will appear on my computer screen in the future? That is predict. Control is I want to measure this thing in the
37:08future and I want it to come out 17. Right? That's control. Understand is a lot like predict except there's a human in the loop. Understand means that I have such a small collection of facts that you will predict and you will do it with facts that I can communicate to another human. Um in kind of this compact fits fits on an index card. That's almost understand. And so I think these machines let us
37:35predict. They let us control. We have to derive our own understanding at this moment, right? We can experiment now on the artifact. We can look at the 200 million predicted structures, not just the 200,000 experimental structures in order to help us understand, but it doesn't do the act of understanding for us. It does the act of predict and maybe control. Now though, then there's maybe one other
38:01thing. There is the algorithm and it's really important I think concept to machine learning. There's the algorithm you program and the algorithm you get or you know, machine learning is code meets data produces weights. And so one of the one of the kind of lasting debates in machine learning, how much work is done by the code, how much work is done by the data that ends up in the weights. And so,
38:25what I think you what we see in AlphaFold in a certain sense is a very beautifully intuitive algorithm, an algorithm we can in some sense understand, right? That it does successive geometric refinement. I communicated that to you in a few words. You probably almost saw it in your head even though I don't think you've seen the these videos. I mean, they're in the supplement of our Nature paper, but but