Lessons from DeepMind’s breakthrough in protein-folding A.I.
Learn A.I. dos and don'ts from the way DeepMind cracked one science's most vexing challenges.
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The biggest news in A.I. this week is DeepMind’s breakthrough on protein-folding.
A question that had confounded scientists for more than 50 years—how to use a protein’s genetic sequence to predict the exact three-dimensional shape that a protein will take—has effectively been answered by DeepMind’s A.I. system, which can now predict the structure of a protein to within an atom’s width of accuracy in many cases.
I got exclusive access to DeepMind’s protein-folding team in the run up to Monday’s announcement. You can read my in-depth feature on exactly how the London-based A.I. company accomplished this goal here. You can also read about how its A.I. system, called AlphaFold 2, has already contributed to the fight against the COVID-19 pandemic here.
Today, I’ll highlight some lessons that emerged from DeepMind’s work on AlphaFold 2 that could apply to any company building an A.I. system.
• “Off-the-shelf” A.I. will only get you so far. Two years ago, DeepMind created a different A.I. system to predict protein structures. That original AlphaFold—AlphaFold 1.0 if you will—was pretty good, but not good enough to be very useful for biologists and medical researchers.
John Jumper, the senior researcher who leads DeepMind’s protein folding team, tells me that the original AlphaFold used “relatively off the shelf neural network technology,” in this case a standard type of neural network architecture originally used to classify objects in images. When it came time to try to improve the system, he says, “What we found is we’ve hit a real wall in what we were able to do with these types of techniques.”
To get better performance, DeepMind had to go back to the drawing board and design a neural network that was much more bespoke to the problem it was trying to solve. It began with a first principles question, Jumper says: “What should the solution look like? And how do we put that into our neural network instead of around it?”
That’s an important lesson for companies to remember, particularly if they are considering using outside vendors and pre-built A.I. components.
•End-to-end systems are better than assemblages of components… The 2018 AlphaFold was a collection of parts: one neural network predicted the distance between amino acid pairs in a protein, another tried to determine the most likely angles between them, and a third piece refined the overall structure. By contrast, AlphaFold 2 is what’s known as an “end-to-end system”—it takes the genetic information as an input and directly outputs a three-dimensional structure. It’s a good reminder that end-to-end systems generally achieve better performance.
•…but don’t ignore the “trust” factor. But a big problem with neural networks that perform a task end-to-end is that they can be highly inscrutable. And that opacity can make it difficult for humans using the software to trust it.
In fact, this is why, in 2018, when DeepMind built a different A.I. system to diagnose 50 different sight-threatening eye diseases from a particular kind of eye scan, it used a system consisting of two different neural networks: One took in the raw data from the scanner and turned that into disease features; one then made diagnoses. This allowed human doctors to have more insight into why the diagnostic system was making its decisions.
In the case of AlphaFold 2, what DeepMind has done instead is build in a confidence gauge, which asks AlphaFold 2 to say how confident it is in its own predictions for each part of the protein structure. That confidence doesn’t really explain why AlphaFold 2 is predicting the structure, but it will give biologists and medical researchers some sense of when they should trust the predictions and when to treat them with more skepticism
•Domain expertise matters. DeepMind trounced academic molecular biology labs that had been working on the protein-folding problem for a lot longer. Part of the reason is that while these academic labs are full of people who deeply understand protein structure, they are not computer scientists. DeepMind has a level of machine learning expertise and engineering resources that these academic labs lack. But, that being said, the team required input from protein structure experts. “We are always collaborative with domain experts,” Demis Hassabis, DeepMind’s co-founder and chief executive officer says. Eventually DeepMind even hired some of these experts, like Jumper.
•But having a diverse team matters too. DeepMind also had people on the team from a range of different science backgrounds. That diversity is helpful, Pushmeet Kohli, the head of DeepMind’s A.I. for science division, tells me, because sometimes people coming from outside the field will have an insight that people from within the field can miss.
The key to making a diverse team work? “Respect,” Kohli says. “Being respectful of all different ways that people contribute and all the different insights that all these different people have.”
But, Kohli tells me, each person on the team should never lose sight of the fact that the goal is to solve the problem—not to prove that a particular approach to solving it is the right one. “The problem is the most important thing and everyone is contributing towards it in their own different way,” he says.
•Try more than one “mode” of working. Researchers who worked on AlphaFold 2 told me that they got stuck many times and couldn’t figure out how they were ever going to make more progress. In such moments, Hassabis says, it is worth switching between two different modes of working: One, which he calls “strike mode,” involves pushing the team to ring as much performance as possible out of the existing approach. But, when this stops working, he says, it is critical to switch to a “creative mode.” In this work style, Hassabis no longer presses the team on performance—in fact, he tolerates and even expects some temporary declines—and instead encourages the team to experiment widely. “You want to encourage as many crazy ideas as possible, brainstorming,” he says.
While some people can work equally well in both modes, others are more comfortable with one work style. Hassabis says it is important to recognize this—and even be prepared to change up the team’s composition and bring in fresh people with new ideas or people better suited to a particular work mode.
Now, here’s the rest of this week’s A.I. news.
Jeremy Kahn
@jeremyakahn
jeremy.kahn@fortune.com