While I am skeptical about yesterdays award in physics, these are totally deserved and spot on. There are few approaches that will accelerate the field of drug development and chemistry as a whole in a way that the works of these three people will. Congratulations!
I was just wondering when they were going to award the alphafold2 guys the nobel after after seeing Hinton win the physics one. 100% agree, all three of them totally deserve this one. Baker's lab is pretty much keeping Deepmind in check at this point and ensuring open source research is keeping up. Hats off
Baker has been in the protein folding game for a long time and was the leader before Alphafold came in... His generative paper came out what last year (2023)?
They were not equal contributors to the seminal paper that got the prize.
From another post in this thread:
"These authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis"
> There are few approaches that will accelerate the field of drug development and chemistry as a whole in a way that the works of these three people will.
As the author of one such approach, I'm skeptical.
AlphaFold 2 just predicts protein structures. The thing about proteins is that they are often related to each other. If you are trying to predict the structure of a naturally occurring protein, chances are that there are related ones in the dataset of known 3D structures. This makes it much easier for ML. You are (roughly speaking) training on the test set.
However, for drug design, which is what AlphaFold 3 targets, you need to do well on actually novel inputs. It's a completely different use case.
Protein structures are similar to each other because of evolution (protein families exist because of shared ancestry of protein coding genes). It's not a weird coincidence that helps ML; it's inherent in the problem. Same with drug design -- very, very, few drugs are "novel" as opposed to being analogues of something naturally in the body.
They're referring to the structure of the protein when a drug is bound, that's what's novel. Novel as in, you can't think of it as "just" interpolation between known structures of evolutionarily related proteins.
That said I'm not sure that's entirely fair, since Alphafold does, as far as I know, work for predicting structures that are far away from structures that have previously been measured.
You're quite wrong about small molecule drug structures. Historically that has been the case but these days many lead structures are made by combinatorial chemistry and are not derived from natural products.
But even drugs made by combinatorial chemistry still generally end up being analogues of natural products even if they aren't derived from them. As Leslie Orgel said "Evolution is cleverer than you are"; chemists are unlikely to discover a mechanism of action that millions of years of evolution hasn't already found.
I... Don't think that's right? Although I would appreciate being corrected with some good sources on this. It's a fast moving field and combinatorial chemistry is still new enough that many recently published structures wouldn't have used it.
I'm well aware of the impact of natural products and particularly plant secondary metabolites in drug discovery. I'm also aware of combinatorial synthesis occasionally hitting structures that are close to natural products.
But from first principles, why would you need to limit yourself to that subset of molecular space?
Obviously, your structure will need to look vaguely biochemical to be compatible with the bodies chemical environment, but natural products are limited to biochemically feasible syntheses, and are therefore dominated by structures derived from natural amino acids and similar basic biochemical building blocks.
For a concrete example off the top of my head, I'm not aware of any natural diazepines - the structure looks "organic" but biochemistry doesn't often make 7-rings, and those were made long before combinatorial chemistry. Might be wrong on this one, since there's so much out there, but I think it holds.
Perhaps we are using "structure" in different senses. Yes, it is possible to generate a molecule with a chemical structure unlike any biological molecule and have it bind to a protein, but it can only do so if its 3D structure is analogous to what naturally binds there. Natural products are a source of drugs because evolution has already done this work for us.
Yes, the chemical structures can look very different when drawn in the 2D manner, but that's why 2D structures aren't very useful for understanding binding, much as primary sequences of proteins aren't that useful. Morphine and fentanyl bind to μ-opioid receptors, just like what naturally binds there (endorphins and enkephalin). But if they are binding to the same receptor, they have to have similar structures in the biologically meaningful 3D sense (at least where they bind).
It appears that you were merely saying that ligands must adopt a 3D conformation that's complementary to the receptor. Sure. That's the entire premise of molecular docking software.
But there can be very dissimilar ligands (like morphine and fentanyl) binding the same receptors. A major goal of drug discovery is to find such novel binders, not to regurgitate known ones.
> It's not a weird coincidence that helps ML; it's inherent in the problem.
This depends on the application. If you are trying to design new proteins for something, unconstrained by evolution, you may want a method that does well on novel inputs.
> Same with drug design
Not by a long shot. There are maybe on the order of 10,000 known 3D protein-ligand structures. Meanwhile, when doing drug discovery, people scan drug libraries with millions to billions of molecules (using my software, oftentimes). These molecules will be very poorly represented in the training data.
As someone in the drug discovery business I’m skeptical as I’ve seen many such “advances” flop.
I remember when computer aided drug design first came out (and several “quantum jumps” along the way). While useful they failed often at the most important cases.
New drugs tend to be developed in spaces we know very little about. Thus there is nothing useful for AI to be trained on.
Nothing quite like hearing from the computational scientist “if you make this one change it will improve binding by 1000x”. Then spending 3 weeks making it to find out it actually binds worse.
It is well known around here that Baker does very, very little of the work. He is extremely good at putting his name on his students' work though (this is par for the course in academia)... and removing theirs (this is the bad part). At least he bribes them with lots of happy hours!
Yes eventually GPT itself may be capable of winning a Nobel off its own writing but before then..the authors of the Transformer might win one ? Certainly seems a lot more plausible now.
So the Nobel commite was wrong to decide this because you think otherwise?
Interesting. Any more indepth analys about this?
Btw. you don't just build AlphaFold by doing only 'computers'. Take a look at any good docmentary about it and you will see that they do discuss chemistry on a deep level
Deepmind isn't a chemistry company. Demiss Hassabis isn't a chemist.
A tool they developed in their area may turn out to be useful in Chemistry. They may spend some relatively short time and effort to apply their tool to Chemistry. They can do the same thing in many areas in every few years and collect all the Nobel's in many areas. That effort is worth for a prize but the context is different.
It is possible that some committee members might have raised this same concern in their discussions.
relatively short : in comparison to real chemists, whose work is the basis for this development.
This is my first interaction in Hackernews, and I was expecting a more polite discussion. I just expressed my idea. You could ask for my explanations.
You didn't start a discussion with a good argument from begin with though.
And i personally really think if people from a different field, jump into a new field and revolutionize it, a nobel price is not a bad thing to appreciate this effort.
AlphaFold is also a high impact discovery, while Hopfield networks have very little to do with modern AI and they are only a very interesting toy model right now.
The AlphaFold paper has countless authors, many researchers and company resources underlying it. Hassabis’ contribution is management of resources and entrepreneurship, not the actual science. There are hundreds of thousands of scientists out there doing deep technical work, and they aren’t recognized.
I think we might be the end of it, as the emphasis shifts to commercialization and product development.
These AI demonstrations require so many GPUs, specialized hardware and data that nobody has but the biggest players. Moreover, they are engineering work, not really scientistic (putting together a lot of hacks and tweaks). Meanwhile, the person who led the transformer paper (a key ingredient in LLMs) hasn’t been recognized.
This will incentivize scientists to focus on management of other researchers who will manage other researchers who will produce the technical inventions. The same issue arises with citations and indices, and the general reward structure in academia.
The signal these AI events convey to me: You better focus on practical stuff, and you better move on in the management ladder.
The Nobel prize is aimed at the general public. It has a kind of late 19th century progressive humanistic ethos. It's science outreach. This way, at least once a year, the everyday layperson hears about scientific discoveries.
The Nobel isn't a vehicle to recognize hundreds of thousands of deeply technical scientific researchers. How could it be? They have to pick a symbolic figurehead to represent a breakthrough.
They could also simply give it to "DeepMind" similar to how they give the peace prize to orgs sometimes, or how the Time Person of the Year is sometimes something abstract as well (like the cutesy "You" of 2006). But it would be silly. Just deal with it, we can't "recognize" hundreds of thousands, and we want to see a personal face, not a logo of a company getting the award. That's how we are, better learn to deal with it.
> The Nobel prize is aimed at the general public...
Which is okay. The Nobel prize is okay.
> This way, at least once a year, the everyday layperson hears about scientific discoveries.
Spot on.
The problem we have is that the everyday layperson hears very little about scientific discoveries. The scientists themselves, one in a million of them, can get a Nobel prize. The rest, if they are lucky, get a somewhat okay salary. Sometimes better than that of a software engineer. Almost always worse working hours.
But I suppose it's all for the best. Imagine a world where a good scientist, one that knows everything about biology and protein folding, gets to avoid cancer and even aging, while the everyday layperson can only go to the doctor...
The Nobel prize isn't awarded for a paper. Even if (and that's a large if) all of these contributed equally to the results in the paper, some obviously did more than others to prepare the ground for that study.
The Nobel prize cannot go to a team so they have to pick individuals. This is true for many (most?) nobel prize awards. Consider for example the discovery of gravity waves - the team that built and operated LIGO was huge, but they have to pick. This has commonly been the case since the inception of the prizes - the professor gets the prize, the PhD students and postdocs don't usually. Not saying this is right, but it's the way it is.
For gravitational waves discovery, Nobel prize went to the designers of the LIGO which was done long before we actually built it. The example that will fit more your idea would be Carlo Rubbia who got the award in 1984 for leading the CERN team who discovered the W and Z bosons. He did not have any contributions than leading the experiment that did it [1]. It is not like he designed or proposed the way we used to detect them. And the Nobel prize for higgs discovery went to theorists who proposed and predict it not the experimental physicists (thousands) who discovered it in 2012.
I did raise an eyebrow at it too, but I doubt his contribution was entirely “management of resources”.
I think one must also give him the credit for the vision, risk taking and drive to apply the resources at his disposal, and RL, to these particular problems.
Without that push this research would never have been done, but there may have been many fungible people willing to iron out the details (and, to be fair, contribute some important ideas along the way).
I’m not a proponent of the “great man” theory of history, but based on the above I can see that this could be fair (although I have no way of telling if internally this is actually how it played out).
Agree. Hassabis is more than a manager. He did start DeepMind with just a few people and was a big part of the brains behind it.
Now that it has grown he might be doing more management. But the groundwork that went into AlphaFold was built on all the earlier Alphaxxx things they have built, and he contributed.
It isn't like other big tech managers that just got some new thing dumped in their lap. He did start off building this.
So can we expect that Sam Altman will be honored with Nobel prize 2025? After all physics prize went to AI researchers this year, and chemistry prize went to an organizational head.
The Nobel prize's prestige comes from its history, not from the size of the monetary award.
For an example, the Millennium Technology Prize is awarded every two years and the prize money is slightly higher than the Nobel prize (1M EUR vs 0.94M EUR). The achievements it's been awarded for tend to be much more practical, immediate and understandable than the Nobel prize achievements. The next one should be awarded in a couple of weeks.
And when that happens, it'll get 1/10th the publicity a Nobel prize gets, because the Nobel prize is older than any living human and has been accumulating prestige all that time, while the Millennium prize is only 20 years old.
You're really conflating things. Altman is no Hassabis.
Just because there is a ton of hype from OpenAI doesn't detract from what DeepMind has done. AlphaGo anybody?
Are we really already forgetting what a monumental problem protein folding was, decades of research, and AlphaFold came in and revolutionized it overnight.
We are pretty jaded these days when miracles are happening all the time and people are like "yeah, but he's just a manager 'now', what have they done for me in the last few days".
I am missing context here and would love to know more.
Say I know about ATP Synthase and how the proteins/molecules involved there interact to make a sort of motor.
How does AlphaFold help us understand that or more complicated systems?
Are proteins quite often dispersed and unique, finding each other to interact with? Or like ATP Synthase are they more of a specific blueprint which tends to arrange in the same way but in different forms?
In other words:
Situation 1) Are there many ATP synthase type situations we find too complex to understand - regular patterns and regular co-occurences of proteins but we don't understand them?
Situation 2) Or is most of the use of Protein situational and one-off? We see proteins only once or twice, very complicated ones, yet they do useful things?
I struggle to situate the problem of Unknown proteins without knowing which of the above two is true (and why?)
> These AI demonstrations require so many GPUs, specialized hardware and data that nobody has but the biggest players. Moreover, they are engineering work, not really scientistic
> The AlphaFold paper has countless authors, many researchers and company resources underlying it. Hassabis’ contribution is management of resources and entrepreneurship, not the actual science.
That's usually how you get a Nobel prize in science. You become an accomplished scientist, and eventually you lead a big lab/department/project and with a massive massiv you work on projects where there are big discoveries. These discoveries aren't possible to attribute to individuals. If you look back through history and try to find how many "Boss professor leading massive team/project" vs. how many "Einstein type making big discovery in their own head" I think you'll find that the former is a lot more common.
> This will incentivize scientists to focus on management of other researchers who will manage other researchers who will produce the technical inventions.
I don't think the Nobel prize is a large driver of science. It's a celebration and a way to put a spotlight on something and someone. But I doubt many people choose careers or projects based on "this might get us the prize..."
> You become an accomplished scientist, and eventually you lead a big lab/department/project and with a massive massiv you work on projects where there are big discoveries.
That's a very recent thing. Up to the 90s, the Nobel committee refused to even recognize it. They just started to award those prizes at the 21 century, and on most fields they never became the majority.
"These authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis"
Not all of the work. For example, it doesn't account for the fact that Demis Hassabis, as head of DeepMind, undoubtedly recruited many of the co-authors to participate in this effort, which is worth something when it comes to the final output.
Guess how many of them were included in the prize. It's a shame that the Nobel committee shies away from awarding it to institutions, but the AlphaFold prize doesn't even make the top 10 in a list of most controversial omissions from a Nobel prize. It's a simple case of lab director gets the most credit.
Can they? I mean, in the sense that you can yolo anything, sure, but the prizes were designed in a time when it was (more) reasonable to award them to individuals, and they are defined in a will. There may not be a mechanism for updating the standards.
Yes, they can. In 1901, science was not nearly as collaborative as it is today. Especially considering the need for a Nobel Prize to be experimental and the fact that most major labs today _need_ dozens of people.
Well, it seems like amendments have been made before. [0] > Before the board ... votes on a proposal to amend the statutes ... with the first paragraph, the prize-awarding bodies shall examine the proposal.
He asked _can_ not _should_: what is the legal mechanism for doing so? Personally I don't doubt there is one but I don't think you know it off the top of your head, so I don't see it as fair to disparage OP for not knowing either.
Well, it seems like amendments have been made before. [0]
> Before the board ... votes on a proposal to amend the statutes ... with the first paragraph, the prize-awarding bodies shall examine the proposal.
J.J. and D.H. led the research. J.J., R.E., A. Pritzel, M.F., O.R., R.B., A. Potapenko, S.A.A.K., B.R.-P., J.A., M.P., T. Berghammer and O.V. developed the neural network architecture and training. T.G., A.Ž., K.T., R.B., A.B., R.E., A.J.B., A.C., S.N., R.J., D.R., M.Z. and S.B. developed the data, analytics and inference systems. D.H., K.K., P.K., C.M. and E.C. managed the research. T.G. led the technical platform. P.K., A.W.S., K.K., O.V., D.S., S.P. and T. Back contributed technical advice and ideas. M.S. created the BFD genomics database and provided technical assistance on HHBlits. D.H., R.E., A.W.S. and K.K. conceived the AlphaFold project. J.J., R.E. and A.W.S. conceived the end-to-end approach. J.J., A. Pritzel, O.R., A. Potapenko, R.E., M.F., T.G., K.T., C.M. and D.H. wrote the paper.”
The nature of scientific work has changed significantly since 1895, when the Nobel Prizes were established. 100 years ago, lots of scientific work really were driven forward largely by a singular person. That's rarely true today for groundbreaking research. I don't know if this means the Nobel needs to change or we need a another prize that reflects the collaborative work of modern science.
> The nature of scientific work has changed significantly since 1895, when the Nobel Prizes were established. 100 years ago, lots of scientific work really were driven forward largely by a singular person. That's rarely true today for groundbreaking research.
The question is: is this a necessity for doing good science today, or rather an artifact of how important research is organized today (i.e. an artifact of the bureacratic and organizational structure that you have to "accept"/"tolerate" if you want to do want to have a career in science)?
So I did a mini research project on Claude to answer your question. From 1900-1930, 87% of Nobel Prizes in Physics, Chemistry and Physiology/Medicine were awarded to individual contributions, 13% were awarded to collaborative contributions.
This ratio has flipped in the past 30 years, from 1994-2023, where 17% prizes were individual, 83% collaborative.
So I'd say yes, collaborative work is increasingly a requirement to do groundbreaking research today. The organizational structures and funding are a part of the reason as you mention. But it's also that modern scientific problems are more complex. I used to have a professor that used to say about biology "the easy problems have been solved". While I think that's dismissive to some of the ingenious experiments done in the past, there's some truth to it.
This begs the question. If all science is now structured as big research teams, we'd expect the breakthroughs to come from such teams. That doesn’t necessarily imply that teams are needed.
And, at the heart of AlphaFold2 is the language model, the tip of the spear in AI today. 'Language' can come in many forms e.g. a protein or amino acid sequence.
AlphaFold 2 wasn't Q-learning based. It was supervised SGD and the "evoformer" they introduced is very close to a transformer. So it's not exactly an LLM, but it's a pretty close equivalent for protein data.
First Obama got the Peace Prize Nobel, now Demis Hassabis gets the Chemistry Nobel. I expect at a minimum the Nobel Prize in Literature to be Donald E. Knuth.
Combine this with the Physics prize, I now have hope to receive a Nobel prize in any area.
Seriously, from now on, I won't mention Nobel prizes anywhere anymore.
Nobel peace prize has countless times been awarded to a group of people or institution. It is differently controlled but the idea is not unprecedented.
Demis Hasabis has a really interesting and unusual CV for a nobel laureate [1], he started his career in AI game programming (he worked e.g. on Popoulous II, Syndicate, Theme Park for Bullfrog, and later for Lionhead Studios on Black & White) before doing a PhD in neuroscience, becoming an entrepreneur and starting DeepMind. I would say this is a refreshing and highly uncommon pick for a nobel prize, really cool to see that you don't have to be a university professor anymore to do this kind of impactful research.
The mental exercise is to compare two identical Jeff Bezos' (identify his attributes), one has the background/funds they did, one doesn't.
Of course, that's not possible, so then you do the same with other highly intelligent and skilled tech professionals. I'd argue that without the funding and other resources, those skilled pro's won't get anywhere. But with it, some would do incredibly well. It's not common in a global sense, but we see it every single day.
Comparing Bezos to thousands/millions of randomised others is pointless.
Then you may say, oh but Amazon is unique. Yes, but then there are other factors at play. Like the luck (skill? funding?) to take advantage of a unique moment in time at the start of the web. That moment isn't available eveI mean, try to start an Amazon today ... etc
I think the question is not whether Bezos, or Gates, were helped by a reasonably wealthy family. The question is- was that wealth helpful because it allowed them to fully develop their own potential; or they have no merit at all and anybody with that wealth would have done the same?
I think those who point out the privileged start of these entrepreneurs are suggesting the second, and yet that makes no sense (millions had the same privilege and didn't get anywhere close).
Disagree. I'm not suggesting the person in question does not have potential or merit and that anyone can do it. It's just that the wealth of a family can pour rocket fuel on that person to enable them to reach their potential.
Yes, that's debatable in each case, everyone has a different story. But it's very likely.
You do get the counterfactual, the plucky upstart who came from nothing. But I'd wager big that that's much rarer and more difficult.
If you gave the $300,000 Bezos got from his father to 10,000 random Americans in 1994, none of them would have created a company the equivalent of Amazon's scale.
How many of those 10k would have the same background? We can pretend Bezos' dad raised him in a "normal" middle class background then randomly dropped 300K on him, or we can acknowledge he is the business equivalent of an Olympic athlete.
So $300K in 1994 is about $640K. That's nice but about 80th percentile of net worth. It's nice his parents believed in him. How many of your parents would do that for you? I'm sure at 1 in 5 of them have that kind of money because of the distribution here. So the difference here is He was smart, he got lucky, and your parents don't believe in you enough on this front.
But compare and contrast Bezos and Musk. Bezos's mid-life crisis is leaving his wife to run around on his yacht banging models. Musk's mid-life crisis is trying to destroy democracy so he and his mom won't have to pay US taxes. Neither one is a role model, but I don't even get the point of the latter.
Which brings us back to AlphaFold. The AlphaFold team did something amazing. But also, they had a backer that believed in them. David Baker, for better or worse, didn't achieve what they did and he'd been at it for decades. It's amazing what good backing can achieve.
Miguel worked at Exxon for 32 years as an engineer and a manager. It's not like he was the CEO or anything close to that. There would literally be hundreds of thousands of people in a similar position to him across the world.
Also worth noting that Jeff Bezos was(and I think still is) the youngest person who ever became a senior VP at DE Shaw. That is a position earned by merit alone.
I think you are agreeing with the poster you are responding to, right? Bezos is the equivalent of an Olympic athlete: a combination of innate talent as well as opportunity.
That's one metric, that only reflects Amazon's function as an income generator.
I view businesses through other metrics as well, including their impact on society in a variety of different ways. From some of those perspectives, it is not clear to me that Amazon (where I was the 2nd employee) is a net benefit.
This Amazon the company specifically or online shopping in general? E.g. if Amazon hadn't been made and some other online retailer had dominated (or even if there had been many!)
That may be true, but I don't think that's really the crux of the argument. This article talks about how Amazon was initially funded by Bezos' family members: https://luxurylaunches.com/celebrities/jeff-bezos-parents-in.... The bigger point is that relatively very few parents (like a couple percent maybe?) would be in a position to give their kid $250k to start a new venture, and it's not that surprising that the most financially successful people in the world needed both: intrinsic talent and drive, and a huge amount of support from their birth circumstances.
The way I like to put it is that both of the following are true:
1. Bezos is uniquely talented and driven, and his success depended on that
2. Bezos' success also depended on him having an uncommon level of access to capital at a young age.
The reason I like to say "both of these are true" is that so often today I see "sides" that try to argue that only one is true, e.g. libertarian-leaning folks (especially in the US) arguing that everything is a pure meritocracy, and on the other side that these phenomenally successful people just inherited their situation (e.g. "Elon Musk is only successful because his dad owned an emerald mine")
Because having some degree of runway is almost always necessary but never sufficient. Thousands of Americans receive similar amounts of money from their parents in the form of inheritance of the family home and other major assets. Only one took windfall of that size and created Amazon.
The "necessary, but not sufficient" is unintuitive to most people. Billionaires who come from working class families are almost unheard of, but probably more than half the self made (for a definition, something like multiplied familial investments by at least 100x maybe?) billionaires come from upper middle class families.
I wonder if they are actually more likely to come from upper middle class (where parents are highly paid professionals) than the proper idle rich or even CEOs and company founders...
There’s another side to this: if you accept the idea of “nature” — genes capable of carrying “talent” (in some sense) — it should be common for children of talented people to be talented.
Of course, talent doesn’t always mean prosperity. But in a society modeled on meritocracy, it often will.
hypothesis : it's not per se affluence. it's the culture of the family and social circle. A dollop of $ to have some free time and maybe buy some books would help and might be necessary.
imagine a family where youngster is encouraged to work on intellectual problems. where you aren't made fun of for touching nerdy things. or for doing puzzles. where the social circle endorses learning. these things more important than $ in a first world economy. (if third world, yes give me some money please for a book or even just food. and hopefully with time, an internet connected device then the cream will rise they can just watch feynman on YouTube...)
that said, it's "better" than it used to be. hundreds of years ago most interesting science, etc. was done by the royal class. not because they are smarter (I assume). But they had free time. And, social encouragement perhaps too.
bill gates and zuck dropped out of Harvard right? it's not per se Harvard, at least not the graduating bit? being surrounded by other smart people is helpful -- and or people who encourage intellectual endeavors.
> hundreds of years ago most interesting science, etc. was done by the royal class
Not really true. Newton, Copernicus, Kepler, Galileo, Mendel, Faraday, Tesla... Not from royals, nor from high nobility. Many great scientists were born to merchant families, of a level that wasn't even all too rare.
Are we talking about Einstein? If I remember correctly, according to Walter Isaacson, Einstein managed to get so many good papers out not despite, but because he was not working for an university. It gave him more freedom to reject existing ideas. Also the years I can find on Wikipedia do not seem to support your claim. He started as a clerk in 1903, and had his miracle year and submitted his PhD dissertation in 1905.
From what I've read, he explicitly sought a position that would give him time to work on his physics ideas. Whether he would or not would have achieved the same working for a university is merely his opinion. In particular, it was not the case that he was working for one, and found it to be incompatible with his research vision, and left academia to become a patent clerk.
Didn't know he worked on Black & White. Black & White was really ahead of it's time for 2001, it did a much better job of having NPC simulations in groups based on how you played as a god.
I'm always interested in hearing about these people who go and get a PhD in an unrelated field to their original studies, often years after leaving university and working in an industry. Here it says Hasabis did an undergraduate degree in a computer science program, and them spent a decade working on computer games at studios, and then somehow just rocked up to a university and asked to do a PhD in neuroscience.
I feel if I tried to do that in the US- (where I got a masters degree in engineering, spent a 15 yrs as an aerospace engineer,)- tried to go back and ask to do a PhD in, say, Physics - I'd be promptly told to go fuck myself (or, fuck myself but then enroll in a new undergrad or maaaybe graduate program only after re-taking GRE's. Straight PhD? Never heard it work like that.)
I have known a several people who made the jump from Computer Science to Biology at graduate school. Usually, it's either via genomics or neuroscience (as in Hassabis' case), where there is a large need for people who can do data crunching or computational modelling.
Why do you think that? It’s not my experience. At the grad school level they’ll take anyone who can do the work and is interested. Outside experience, even in unrelated fields, is often a plus. Grad students just out of undergrad have no idea how the world works.
That may be more true in a super crowded and hot area, like AI now where reputable profs get dozens of PhD applicants per position, who all have already published relevant works in the field at top venues during their masters.
In more chill fields where the waters are relatively calm, this may be less of an issue.
But let's also consider the fact that Hassabis did his undergrad at the University of Cambridge, likely with excellent results. He wasn't just some random programmer.
likely because hassabis was a child prodigy.
Was a chess master at 13. Lead Cambridge chess team. its not surmise to assume that demis had impeccable school record
I made the jump from mostly-math undergrad to materials science PhD (close to chemistry/physics, if you don't know the field). I was welcomed with open arms.
If you've got any math-heavy STEM graduate degree, you can likely jump into a physics PhD. You might need to take some senior-level undergraduate courses to catch up, but the transition is quite doable. At some point, your overall intelligence, enthusiasm, and work ethic matter more than your specific background.
There is a lot of variation in how PhD studies work. In some places, you are just a faceless candidate applying to a department, which discourages you from contacting the faculty before you are admitted. In other places, you must convince a professor to supervise and fund you before you are even allowed to apply. Some universities require you (or your supervisor) pay tuition fees for a number of years before you can graduate, while others don't care what you do, as long as you can produce a thesis that meets their standards.
You can jump from social sciences to STEM. Your formal admission can wait for a year or two after you actually started. Or you can move to another university and get a PhD in a few months, because the administrative requirements in the original one were too unreasonable. These things happen, because universities are independent organizations that like doing things their own way.
PhD is a thankless, low paid position with insane hours and zero guaranteed return. Outside of a few elite programs and universities getting into PhD program is fairly easy - they take anyone qualified.
What? Physics programs don't work like that, at least not T1-2 ones I know about. A physics PhD is not pay to play and anyone thinking it is would fail out on their qual at every university I'm familiar with.
Yep. I distinctly remember reading an interview in the German GameStar magazine in '99 or something with him where he talks about his early work with Bullfrog. Over the years I read his name from time to time as he moved towards research. Pretty amazing career.
Great achievement, although I think it's interesting that this Nobel prize was awarded so early, with "the greatest benefit on mankind" still outstanding. Are there already any clinically approved drugs based on AI out there I might have missed?
In comparison, the one for lithium batteries was awarded in 2019, over 30 years after the original research, when probably more than half of the world's population already used them on a daily basis.
Arguably awarding early is more in line with the intention expressed in Nobel's will: "to those who, during the preceding year, have conferred the greatest benefit to humankind". It seems to have drifted into "who did something decades ago that we're now confident enough in the global significance of to award a prize". I suspect that if the work the prize recognized reslly had to have been carried out in the preceding year the recipients would be rather different.
Given that drugs take around 10 years to get to market, and that some time is needed for industrial adoption as well, it's not very reasonable to expect clinically approved drugs before a few years.
This is really sad. A new recipe for feeding honeybees to make tastier honey could get to market in perhaps a month or two. All the chemical reactions happening in the bees gut and all the chemicals in the resulting honey are unknown, yet within a matter of weeks its being eaten.
Yet if we find a new way of combining chemicals to cure cancer, it takes a decade before most can benefit.
I feel like we don't balance our risks vs rewards well.
I think the idea is that we're, as a species, much more comfortable with the idea that 15 years down the line that 50% of treated colonies collapse in a way directly attributable to the treatment than we are with the idea that 15 years down the line 50% of treated humans die in a way directly attributable to the treatment.
Now if the human alternative to treatment is to die anyway than i think that balance shifts. I do think we should be somewhat liberal with experimental treatments for patients in dire need, but you have to also understand that experimental treatments can just be really expensive which limits either the people who can afford it, or if it's given for free, the amount the researcher can make/perform/provide.
10 years is a very long time. I've had close family members die of cancer and any opportunity for treatment (read: hope) is good in my opinion. But i wouldn't say there's no reason that it takes so long
Yesterday's physics win was rather odd but this I have no problem with!
Lol does this mean there's a chance the Transformer Authors win a Nobel in literature sometime? Certainly seems a lot more plausible than before yesterday.
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[ 5.8 ms ] story [ 238 ms ] threadI mean this is a fast award cycle.
[1]: https://www.science.org/doi/10.1126/science.abj8754
[2]: https://cen.acs.org/analytical-chemistry/structural-biology/...
"These authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis"
Also I really hope the Nobel Prize of Economics goes to Bill Gates! He facilitated sooo much advances by releasing Excel that this must be recognized!
And based on this year's announcements so far I am not sure that my sarcastic comments should be taken as a joke!
As the author of one such approach, I'm skeptical.
AlphaFold 2 just predicts protein structures. The thing about proteins is that they are often related to each other. If you are trying to predict the structure of a naturally occurring protein, chances are that there are related ones in the dataset of known 3D structures. This makes it much easier for ML. You are (roughly speaking) training on the test set.
However, for drug design, which is what AlphaFold 3 targets, you need to do well on actually novel inputs. It's a completely different use case.
More here: https://olegtrott.substack.com/p/are-alphafolds-new-results-...
That said I'm not sure that's entirely fair, since Alphafold does, as far as I know, work for predicting structures that are far away from structures that have previously been measured.
You're quite wrong about small molecule drug structures. Historically that has been the case but these days many lead structures are made by combinatorial chemistry and are not derived from natural products.
I'm well aware of the impact of natural products and particularly plant secondary metabolites in drug discovery. I'm also aware of combinatorial synthesis occasionally hitting structures that are close to natural products.
But from first principles, why would you need to limit yourself to that subset of molecular space?
Obviously, your structure will need to look vaguely biochemical to be compatible with the bodies chemical environment, but natural products are limited to biochemically feasible syntheses, and are therefore dominated by structures derived from natural amino acids and similar basic biochemical building blocks.
For a concrete example off the top of my head, I'm not aware of any natural diazepines - the structure looks "organic" but biochemistry doesn't often make 7-rings, and those were made long before combinatorial chemistry. Might be wrong on this one, since there's so much out there, but I think it holds.
> very, very, few drugs are "novel" as opposed to being analogues of something naturally in the body
But "analog" means "structural analog" in this context (see https://en.wikipedia.org/wiki/Structural_analog ), which is why people disagreed with you, presumably.
It appears that you were merely saying that ligands must adopt a 3D conformation that's complementary to the receptor. Sure. That's the entire premise of molecular docking software.
But there can be very dissimilar ligands (like morphine and fentanyl) binding the same receptors. A major goal of drug discovery is to find such novel binders, not to regurgitate known ones.
It did very poorly at this last time I checked. Maybe AlphaFold3 is better?
This depends on the application. If you are trying to design new proteins for something, unconstrained by evolution, you may want a method that does well on novel inputs.
> Same with drug design
Not by a long shot. There are maybe on the order of 10,000 known 3D protein-ligand structures. Meanwhile, when doing drug discovery, people scan drug libraries with millions to billions of molecules (using my software, oftentimes). These molecules will be very poorly represented in the training data.
The theoretical chemical space of interest to drug discovery is bigger still, with on the order of 1e60 molecules in it: https://en.wikipedia.org/wiki/Chemical_space
I remember when computer aided drug design first came out (and several “quantum jumps” along the way). While useful they failed often at the most important cases.
New drugs tend to be developed in spaces we know very little about. Thus there is nothing useful for AI to be trained on.
Nothing quite like hearing from the computational scientist “if you make this one change it will improve binding by 1000x”. Then spending 3 weeks making it to find out it actually binds worse.
It needed Oriol as well doing IC work
(lol - one of the PDF attachments to that page is 'Illustration: A string of amino acids' : actually it's a bit better than the title implies :).
Actually, Figure 2 - "How does AlfaFold2 Work?" is impressive to fit that on one page. Nice.
Interesting. Any more indepth analys about this?
Btw. you don't just build AlphaFold by doing only 'computers'. Take a look at any good docmentary about it and you will see that they do discuss chemistry on a deep level
It is possible that some committee members might have raised this same concern in their discussions.
relatively short : in comparison to real chemists, whose work is the basis for this development.
This is my first interaction in Hackernews, and I was expecting a more polite discussion. I just expressed my idea. You could ask for my explanations.
And i personally really think if people from a different field, jump into a new field and revolutionize it, a nobel price is not a bad thing to appreciate this effort.
Contrast that with Phyics Nobel for advancing AI using physics.
I think we might be the end of it, as the emphasis shifts to commercialization and product development.
These AI demonstrations require so many GPUs, specialized hardware and data that nobody has but the biggest players. Moreover, they are engineering work, not really scientistic (putting together a lot of hacks and tweaks). Meanwhile, the person who led the transformer paper (a key ingredient in LLMs) hasn’t been recognized.
This will incentivize scientists to focus on management of other researchers who will manage other researchers who will produce the technical inventions. The same issue arises with citations and indices, and the general reward structure in academia.
The signal these AI events convey to me: You better focus on practical stuff, and you better move on in the management ladder.
The Nobel isn't a vehicle to recognize hundreds of thousands of deeply technical scientific researchers. How could it be? They have to pick a symbolic figurehead to represent a breakthrough.
They could also simply give it to "DeepMind" similar to how they give the peace prize to orgs sometimes, or how the Time Person of the Year is sometimes something abstract as well (like the cutesy "You" of 2006). But it would be silly. Just deal with it, we can't "recognize" hundreds of thousands, and we want to see a personal face, not a logo of a company getting the award. That's how we are, better learn to deal with it.
Which is okay. The Nobel prize is okay.
> This way, at least once a year, the everyday layperson hears about scientific discoveries.
Spot on.
The problem we have is that the everyday layperson hears very little about scientific discoveries. The scientists themselves, one in a million of them, can get a Nobel prize. The rest, if they are lucky, get a somewhat okay salary. Sometimes better than that of a software engineer. Almost always worse working hours.
But I suppose it's all for the best. Imagine a world where a good scientist, one that knows everything about biology and protein folding, gets to avoid cancer and even aging, while the everyday layperson can only go to the doctor...
Just insane.
There is a reason so many of us work as software engineers now; I earn about 5x more than I would as a university lecturer/assistant professor.
However, it appears that some the authors were more equal than others.
[1] https://home.cern/science/experiments/ua1
I think one must also give him the credit for the vision, risk taking and drive to apply the resources at his disposal, and RL, to these particular problems.
Without that push this research would never have been done, but there may have been many fungible people willing to iron out the details (and, to be fair, contribute some important ideas along the way).
I’m not a proponent of the “great man” theory of history, but based on the above I can see that this could be fair (although I have no way of telling if internally this is actually how it played out).
Now that it has grown he might be doing more management. But the groundwork that went into AlphaFold was built on all the earlier Alphaxxx things they have built, and he contributed.
It isn't like other big tech managers that just got some new thing dumped in their lap. He did start off building this.
Isn't sama on track to end up with more financial resources than Nobel had at his disposal?
Plus I think he's got enough of a philanthropic streak which can prove to be not so shabby.
There could very well be a foundation someday awarding the Altman Prize well into the 22nd century.
Whether or not his most dynamic legacy would be something as simultaneously useful/dangerous as dynamite.
For an example, the Millennium Technology Prize is awarded every two years and the prize money is slightly higher than the Nobel prize (1M EUR vs 0.94M EUR). The achievements it's been awarded for tend to be much more practical, immediate and understandable than the Nobel prize achievements. The next one should be awarded in a couple of weeks.
And when that happens, it'll get 1/10th the publicity a Nobel prize gets, because the Nobel prize is older than any living human and has been accumulating prestige all that time, while the Millennium prize is only 20 years old.
https://x.com/S_OhEigeartaigh/status/1843979139948355893
Just because there is a ton of hype from OpenAI doesn't detract from what DeepMind has done. AlphaGo anybody?
Are we really already forgetting what a monumental problem protein folding was, decades of research, and AlphaFold came in and revolutionized it overnight.
We are pretty jaded these days when miracles are happening all the time and people are like "yeah, but he's just a manager 'now', what have they done for me in the last few days".
Say I know about ATP Synthase and how the proteins/molecules involved there interact to make a sort of motor.
How does AlphaFold help us understand that or more complicated systems?
Are proteins quite often dispersed and unique, finding each other to interact with? Or like ATP Synthase are they more of a specific blueprint which tends to arrange in the same way but in different forms?
In other words:
Situation 1) Are there many ATP synthase type situations we find too complex to understand - regular patterns and regular co-occurences of proteins but we don't understand them?
Situation 2) Or is most of the use of Protein situational and one-off? We see proteins only once or twice, very complicated ones, yet they do useful things?
I struggle to situate the problem of Unknown proteins without knowing which of the above two is true (and why?)
So is the Large Hadron Collider.
That's usually how you get a Nobel prize in science. You become an accomplished scientist, and eventually you lead a big lab/department/project and with a massive massiv you work on projects where there are big discoveries. These discoveries aren't possible to attribute to individuals. If you look back through history and try to find how many "Boss professor leading massive team/project" vs. how many "Einstein type making big discovery in their own head" I think you'll find that the former is a lot more common.
> This will incentivize scientists to focus on management of other researchers who will manage other researchers who will produce the technical inventions.
I don't think the Nobel prize is a large driver of science. It's a celebration and a way to put a spotlight on something and someone. But I doubt many people choose careers or projects based on "this might get us the prize..."
That's a very recent thing. Up to the 90s, the Nobel committee refused to even recognize it. They just started to award those prizes at the 21 century, and on most fields they never became the majority.
[0] https://scholar.google.com/citations?view_op=view_citation&h...
EDIT: typos
"These authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis"
if A=B and A=C, then A=C
Technically true, but you might still want to double check your logic.
https://arxiv.org/pdf/1207.7214#page=26
Guess how many of them were included in the prize. It's a shame that the Nobel committee shies away from awarding it to institutions, but the AlphaFold prize doesn't even make the top 10 in a list of most controversial omissions from a Nobel prize. It's a simple case of lab director gets the most credit.
https://www.nobelprize.org/organization/special-regulations-...
https://www.nobelprize.org/organization/special-regulations-...
J.J. and D.H. led the research. J.J., R.E., A. Pritzel, M.F., O.R., R.B., A. Potapenko, S.A.A.K., B.R.-P., J.A., M.P., T. Berghammer and O.V. developed the neural network architecture and training. T.G., A.Ž., K.T., R.B., A.B., R.E., A.J.B., A.C., S.N., R.J., D.R., M.Z. and S.B. developed the data, analytics and inference systems. D.H., K.K., P.K., C.M. and E.C. managed the research. T.G. led the technical platform. P.K., A.W.S., K.K., O.V., D.S., S.P. and T. Back contributed technical advice and ideas. M.S. created the BFD genomics database and provided technical assistance on HHBlits. D.H., R.E., A.W.S. and K.K. conceived the AlphaFold project. J.J., R.E. and A.W.S. conceived the end-to-end approach. J.J., A. Pritzel, O.R., A. Potapenko, R.E., M.F., T.G., K.T., C.M. and D.H. wrote the paper.”
Hey, I wonder who these mysterious "J.J." and "D.H." might be?
The question is: is this a necessity for doing good science today, or rather an artifact of how important research is organized today (i.e. an artifact of the bureacratic and organizational structure that you have to "accept"/"tolerate" if you want to do want to have a career in science)?
This ratio has flipped in the past 30 years, from 1994-2023, where 17% prizes were individual, 83% collaborative.
So I'd say yes, collaborative work is increasingly a requirement to do groundbreaking research today. The organizational structures and funding are a part of the reason as you mention. But it's also that modern scientific problems are more complex. I used to have a professor that used to say about biology "the easy problems have been solved". While I think that's dismissive to some of the ingenious experiments done in the past, there's some truth to it.
it's the year of AI (ChatGPT preparing its acceptance speech)
1: https://en.wikipedia.org/wiki/Demis_Hassabis
(installing the first electric lighting for the 1885 Munich Oktoberfest)
I suspect Bezos, then Gates, then Musk, but it could be any order.
Think the Wright Brothers first aeroplane vs a rocket ship.
Of course, that's not possible, so then you do the same with other highly intelligent and skilled tech professionals. I'd argue that without the funding and other resources, those skilled pro's won't get anywhere. But with it, some would do incredibly well. It's not common in a global sense, but we see it every single day.
Comparing Bezos to thousands/millions of randomised others is pointless.
Then you may say, oh but Amazon is unique. Yes, but then there are other factors at play. Like the luck (skill? funding?) to take advantage of a unique moment in time at the start of the web. That moment isn't available eveI mean, try to start an Amazon today ... etc
I think those who point out the privileged start of these entrepreneurs are suggesting the second, and yet that makes no sense (millions had the same privilege and didn't get anywhere close).
Yes, that's debatable in each case, everyone has a different story. But it's very likely.
You do get the counterfactual, the plucky upstart who came from nothing. But I'd wager big that that's much rarer and more difficult.
https://en.wikipedia.org/wiki/Ted_Jorgensen
So $300K in 1994 is about $640K. That's nice but about 80th percentile of net worth. It's nice his parents believed in him. How many of your parents would do that for you? I'm sure at 1 in 5 of them have that kind of money because of the distribution here. So the difference here is He was smart, he got lucky, and your parents don't believe in you enough on this front.
But compare and contrast Bezos and Musk. Bezos's mid-life crisis is leaving his wife to run around on his yacht banging models. Musk's mid-life crisis is trying to destroy democracy so he and his mom won't have to pay US taxes. Neither one is a role model, but I don't even get the point of the latter.
Which brings us back to AlphaFold. The AlphaFold team did something amazing. But also, they had a backer that believed in them. David Baker, for better or worse, didn't achieve what they did and he'd been at it for decades. It's amazing what good backing can achieve.
https://en.wikipedia.org/wiki/Miguel_Bezos
Also worth noting that Jeff Bezos was(and I think still is) the youngest person who ever became a senior VP at DE Shaw. That is a position earned by merit alone.
10k random Americans with backgrounds in software and a business idea? Not so clear.
You also seem very certain that Amazon's scale is a good thing, overall, which I remain unconvinced of.
What do you find unconvincing about roughly 30 billion in net income and free cashflow in 2023?
I view businesses through other metrics as well, including their impact on society in a variety of different ways. From some of those perspectives, it is not clear to me that Amazon (where I was the 2nd employee) is a net benefit.
The way I like to put it is that both of the following are true:
1. Bezos is uniquely talented and driven, and his success depended on that
2. Bezos' success also depended on him having an uncommon level of access to capital at a young age.
The reason I like to say "both of these are true" is that so often today I see "sides" that try to argue that only one is true, e.g. libertarian-leaning folks (especially in the US) arguing that everything is a pure meritocracy, and on the other side that these phenomenally successful people just inherited their situation (e.g. "Elon Musk is only successful because his dad owned an emerald mine")
I wonder if they are actually more likely to come from upper middle class (where parents are highly paid professionals) than the proper idle rich or even CEOs and company founders...
To my mind, Joe Rogan is the most recent embodiment of the American dream which is why I think he is so popular
Look how that turned out...
Maybe the dream is more like a nightmare.
Of course, talent doesn’t always mean prosperity. But in a society modeled on meritocracy, it often will.
imagine a family where youngster is encouraged to work on intellectual problems. where you aren't made fun of for touching nerdy things. or for doing puzzles. where the social circle endorses learning. these things more important than $ in a first world economy. (if third world, yes give me some money please for a book or even just food. and hopefully with time, an internet connected device then the cream will rise they can just watch feynman on YouTube...)
that said, it's "better" than it used to be. hundreds of years ago most interesting science, etc. was done by the royal class. not because they are smarter (I assume). But they had free time. And, social encouragement perhaps too.
bill gates and zuck dropped out of Harvard right? it's not per se Harvard, at least not the graduating bit? being surrounded by other smart people is helpful -- and or people who encourage intellectual endeavors.
Not really true. Newton, Copernicus, Kepler, Galileo, Mendel, Faraday, Tesla... Not from royals, nor from high nobility. Many great scientists were born to merchant families, of a level that wasn't even all too rare.
He began his work on the PhD prior to 1903.
I could never get it working on DosBox (some timing issue). Haven't tried in over a decade, though. Should see if I can get it working.
I feel if I tried to do that in the US- (where I got a masters degree in engineering, spent a 15 yrs as an aerospace engineer,)- tried to go back and ask to do a PhD in, say, Physics - I'd be promptly told to go fuck myself (or, fuck myself but then enroll in a new undergrad or maaaybe graduate program only after re-taking GRE's. Straight PhD? Never heard it work like that.)
In more chill fields where the waters are relatively calm, this may be less of an issue.
But let's also consider the fact that Hassabis did his undergrad at the University of Cambridge, likely with excellent results. He wasn't just some random programmer.
If you've got any math-heavy STEM graduate degree, you can likely jump into a physics PhD. You might need to take some senior-level undergraduate courses to catch up, but the transition is quite doable. At some point, your overall intelligence, enthusiasm, and work ethic matter more than your specific background.
You can jump from social sciences to STEM. Your formal admission can wait for a year or two after you actually started. Or you can move to another university and get a PhD in a few months, because the administrative requirements in the original one were too unreasonable. These things happen, because universities are independent organizations that like doing things their own way.
Only if you need money.
If you pay the tuition they'll receive you with open arms.
I've seen this for a guy is his mid to late 30s.
As everything in the states, it's completely pay to play and largely pay to win.
That I don't know but I have my doubts.
This was in a very well know university, so make of it what you wish.
I won't give any further details.
In comparison, the one for lithium batteries was awarded in 2019, over 30 years after the original research, when probably more than half of the world's population already used them on a daily basis.
This is really sad. A new recipe for feeding honeybees to make tastier honey could get to market in perhaps a month or two. All the chemical reactions happening in the bees gut and all the chemicals in the resulting honey are unknown, yet within a matter of weeks its being eaten.
Yet if we find a new way of combining chemicals to cure cancer, it takes a decade before most can benefit.
I feel like we don't balance our risks vs rewards well.
Now if the human alternative to treatment is to die anyway than i think that balance shifts. I do think we should be somewhat liberal with experimental treatments for patients in dire need, but you have to also understand that experimental treatments can just be really expensive which limits either the people who can afford it, or if it's given for free, the amount the researcher can make/perform/provide.
10 years is a very long time. I've had close family members die of cancer and any opportunity for treatment (read: hope) is good in my opinion. But i wouldn't say there's no reason that it takes so long
Lol does this mean there's a chance the Transformer Authors win a Nobel in literature sometime? Certainly seems a lot more plausible than before yesterday.