Google DeepMind Scientists Win Nobel Chemistry Prize for Work on Proteins (npr.org) 15
Three scientists won the 2024 Nobel Prize in Chemistry on Wednesday for their groundbreaking work in predicting and designing protein structures, the Royal Swedish Academy of Sciences announced in Stockholm. David Baker of the University of Washington shares the prize with Demis Hassabis and John Jumper of Google DeepMind. Baker pioneered the creation of novel proteins, while Hassabis and Jumper developed AlphaFold, an AI model that predicts protein structures from amino acid sequences.
The laureates will split the 11 million Swedish kronor ($1 million) award for their contributions to computational protein design and structure prediction. Baker's team has produced proteins with applications in medicine and materials science since his initial breakthrough in 2003. Hassabis and Jumper's AlphaFold, announced in 2020, has predicted structures for nearly all 200 million known proteins. "We glimpsed at the beginning that it might be possible to create a whole new world of proteins that address a lot of the problems faced by humans in the 21st century," Baker said at a press briefing.
"Now it's becoming possible," Heiner Linke, chair of the Nobel chemistry committee, called the discoveries "spectacular," noting they fulfilled a 50-year-old dream of predicting protein structures from amino acid sequences. The breakthroughs have wide-ranging implications, from understanding antibiotic resistance to developing enzymes that decompose plastic. Over 2 million researchers worldwide have already utilized AlphaFold in various applications.
The laureates will split the 11 million Swedish kronor ($1 million) award for their contributions to computational protein design and structure prediction. Baker's team has produced proteins with applications in medicine and materials science since his initial breakthrough in 2003. Hassabis and Jumper's AlphaFold, announced in 2020, has predicted structures for nearly all 200 million known proteins. "We glimpsed at the beginning that it might be possible to create a whole new world of proteins that address a lot of the problems faced by humans in the 21st century," Baker said at a press briefing.
"Now it's becoming possible," Heiner Linke, chair of the Nobel chemistry committee, called the discoveries "spectacular," noting they fulfilled a 50-year-old dream of predicting protein structures from amino acid sequences. The breakthroughs have wide-ranging implications, from understanding antibiotic resistance to developing enzymes that decompose plastic. Over 2 million researchers worldwide have already utilized AlphaFold in various applications.
Next step (Score:5, Interesting)
Re:Next step (Score:5, Interesting)
To put this into some perspective. To research 1 protein takes about half a year and costs about 200 000 dollars (there are larger estimates than this also).
So multiply this with the 200 000 000 and you see that they donated to humanity:
- 100 000 000 years or research work.
- 40 000 000 000 000 dollars of research money.
And this was just one step in their plan. They have already created an AI that can be used for drug research and they estimate that first AI invented drugs will come within 2 years. Automate drug research and you have invented something close to immortality.
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Now we just need to use AI to figure out more issues (like solar cell efficiency, greener building materials, etc.) before we use it to create better ways to kill ourselves/our environment.
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This beautifully illustrates the true power of AI: To quickly iterate though a given set of parameters to find solutions
You have described a 'for' loop.
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Your statement makes me feel that you have no idea how programming works.
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If iterating through a near-infinite set of permutations were easy, all of computer science would be easy. However, it's not, and comparing a technology like AI (or somewhat equivalent technologies like genetic algorithms) that quickly whittle down the search space into one that computers can actually explore within a reasonable timeframe is tremendously valuable. However, you don't seem to realize any of those points.
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If iterating through a near-infinite set of permutations were easy, all of computer science would be easy.
There's no such thing as near infinite. There's infinite, and there's finite. Huge difference. Maybe you meant "intractably large."
comparing a technology like AI (or somewhat equivalent technologies like genetic algorithms) that quickly whittle down the search space into one that computers can actually explore within a reasonable timeframe is tremendously valuable.
So you're saying it's not the iterating that's the hard part, it's the pruning?
Demis Hassabis has an interesting past (Score:1)
Chess master at 13, he finished his A-levels at 16 (2 years early), so Cambridge University asked him to take a gap year. He went off to Bullfrog Games, and at 17 was lead designer and programmer of Theme Park working with Peter Molyneux. Bloody show-off :)
https://en.wikipedia.org/wiki/... [wikipedia.org]
Re:Demis Hassabis has an interesting past (Score:4, Informative)
I think it is even more interesting that he first completed the Computer Science Tripos and graduated in 1997, but after that he wanted to know how brains work in order to make better AI, so he went for PhD in cognitive neuroscience in 2009.
He has pretty much designed his entire life just for the purpose of mastering the art of AI.
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blivit (Score:2)
I suspect (former Slashdotter) blivit was involved with the programming.
Guess I need to make a call...