John Jumper
AlphaFold Lead, DeepMind. On 1 show, 0 appearances, plus 1 compilation re-air not counted. The Shows tab opens the full record on each.
John Jumper is a computational biologist who led the AlphaFold team at DeepMind. He received the 2024 Nobel Prize in Chemistry for solving protein folding with AlphaFold.
1shows
0appearances
15statements
0resolved
0supported
0contradicted
Everything John Jumper said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Jumper: Equivariance explains only 2-3 GDT of AlphaFold 2's 30-point gain
“The sixth row there, no IPA, invariant point attention, that removes all the equivariance in alpha fold, and it hurts a bit, but only a bit. Alpha fold itself on this GDT scale that you can see on the left graph, alpha fold two was about 30 GDT better than alp…”
Jumper: AlphaFold made structural biology 5% to 10% faster
“I like to think that our work Made the whole field of what's called structural biology, biology that deals with structures, you know, five or 10% faster. But the amount to which that matters for the world is enormous.”
Jumper: Scientific AI will eventually be driven by broad, general models
“I think we will start to see this on more general systems, be them LLMs or others, That we will find more and more scientific knowledge within them, and we'll use them for important, important purposes, and I think this is really where this is going, and I thi…”
Jumper: The true cost of ML compute is testing failed ideas
“The real cost of compute is the cost of ideas that didn't work. All the things you had to do to get there.”
Jumper: A two-day open-source hack created the best protein interaction predictor
“The tweet on the left from Yoshitaka Morawaki came out two days after our code was available. We had predicted the structure of individual proteins, but we considered, we were working on building a system that would predict how proteins came together. But, ah,…”
Jumper: AlphaFold 2's final model trained on 128 TPUv3s for two weeks
“The final model itself was a 128 TPU V three cores, roughly equivalent to a GPU per core for two weeks.”
Jumper: AlphaFold 2 architecture on 1% of data matched AlphaFold 1
“The Al Qureshi lab, did a very, ah, careful experiment, where they took AlphaFold II, the architecture, and they trained it on one percent of the available data. And they could show that alpha fold two trained on one percent of the data was as accurate or more…”
Jumper: Transformative AI systems come from many mid-scale ideas, not one
“It isn't about one idea. It's about many mid-scale ideas that add up to a transformative system, and it's very, very important when you're building these systems to think about what we would call in this context biological relevance.”
Jumper: Protein prediction leads LLMs and ML in blind assessment
“Protein structure prediction is in some ways far ahead of LLMs or the general machine learning space, and having blind assessment.”
Jumper: AlphaFold had one-third the error of any CASP competitor
“And we had about a third of the error of any other group on this assessment.”
Jumper: AlphaFold has received roughly 35,000 academic citations
“There about, I think, 35,000 citations of AlphaFold, but within that is there are tens of thousands of examples of people using our tools to do science that I couldn't do on my own.”
Jumper: About 200,000 protein structures are known, growing by 12,000 annually
“About 200,000 protein structures are known. They pretty regularly increase at about 12,000 a year.”
Jumper: Protein sequence discovery outpaces structure determination by 3,000 times
“So billions of protein sequences are being discovered. About 3000 times faster are we learning about protein sequence than protein structure.”
Jumper: Experimental protein structure determination takes 1-2 years and $100K
“The green is the experimental structure that took someone a year or two of effort. If you want to put an economic value on it, on the order of a 100,000 dollars”
Jumper: AlphaFold database scaled from 300,000 to 200 million protein predictions
“One is that we open source the code and we actually open source the code about a week before we released a database of predictions starting originally at 300,000 predictions and later going to two hundred million. Essentially every protein from an organism who…”
One line per show, most statements first. The link opens John's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 Y COMBINATORLEDGER | AlphaFold Lead, DeepMind | 0 +1 | 15 | — | full record on the Y Combinator Startup Podcast → |