It's a good list, but "alignment" is much more targetted. I attended a workshop with researchers from OpenAI and Anthropic also present, where the objective was to hash out what the problems in alignment even are. This, it turned out, was extraordinarily difficult.
The problems with alignment are to do with how we define and search for this vague notion when it is mathematically ill-posed at present.
Biology already has numerous “Millennium Problems” and the rewards are much more than $1M. For example, reverse Alzheimer’s Disease. Or less ambitious develop high fidelity in vitro and animal models of human disease.
Those don't have the property of being easily verified, which the problems proposed here do. I think that's a smart idea because it's a way to capture quick PR seeking AI-lab dollars for quite useful outcomes.
Being easily verifiable does not elevate something to the level of being a grand challenge. It really drives home the point that biology is not math or coding.
I guess to me the grandiosity here feels fake and unearned - like they vibe slopped it together without much input in the way of deep thought or expertise.
For example, synthesize arbitrary dna sequences > 3000 nt in length with error rate < 0.001 at > 95% purity. Easily verifiable, beyond the frontier, and generally useful.
I agree with you there are other much grander challenges, but apart from that I don't think the list is horrible, because as I said it's incentive-aligned with the current moment we are in. Your suggestion would definitely be a good addition to the list for sure.
I think some of the items on the list are also way less likely to be worked on (like cryogenics) than others, because they really require some heavy infrastructure to iterate. My money for which of these gets cracked with help of an AI would be rubisco, that can be quite effectively worked on in a closed loop lab fashion with a standard lab or CRO.
Biology is a really really big field. Many problems in biology are that of math and coding. Just as a simple example look at the golden ratio in living organisms. Biology follows a lot of different algorithms because they are energy efficient and come with massive secondary benefits.
The golden ratio is one of those things you can study for years and be a amazed by.
>The golden ratio (≈ 1.618) and its related golden angle (137.5°) show up in nature because they maximize space, exposure, and flow.
The golden ratio is an algorithm, but it is also a natural law that systems of many different scales follow. Wherever we look we find examples of it. Without this algorithm the world is a much harder place to explain.
What many scientists starting to look for is these algorithms we have not identified in natural systems as an explanatory means of their workings. Also because they are algorithms they are things we can put into computer systems to increase efficiency, or may naturally emerge from evolutionary learning systems like AI in what we call emegence.
Did you even read what you linked to, or did you just read the headline?
>There is no convincing evidence that the golden ratio is linked to idealized human proportions or facial beauty. There is currently no evidence to support the use of the golden ratio in orthognathic or facial aesthetic/reconstructive surgical planning or analysis of results.
This is about peoples faces. What I listed was about things like available surface area for things like chemical reactions and mixing, you know things that are mathematically calculatable. Much in the same manner a hexagon is an optimal low energy shape, hence we see lots of them in nature too.
This is all well outside my area of expertise, but my impression is that Michael Levin's work with bioelectricity is on the cusp of #6 Somatic limb regeneration.
Everyone interested in biology and intelligence should look up Michaels work, it is amazing because he's ignored a lot of assumptions that are held by the community and come up with new and interesting tests.
Also his work on algorithmic intelligence in cells and the mathematical foundation of intelligence will change many fields, including AI, if it can be empirically tested.
Its much harder to produce a good fundamental list of biology problems because we are no where near as far along, so many basic things are not understood. There are still many unknown unknowns and these particular problems are limited to the realm of verifiable in a lab, which rules out much of what makes biology interesting and hard to study, and that is the organisms themselves and the interactions between their cells. The petri dish differs from the rat differs from the human and that is a part of what makes biology harder to progress.
None of these is likely the root to the wide array of chronic illnesses we can't treat and those seem like the next step to really put a lot of research into given how many people suffer from them and where we are today they seem achievable with the right investment.
> The petri dish differs from the rat differs from the human and that is a part of what makes biology harder to progress.
They all use DNA.
But I agree that nobody has shown in the lab how any of this leads to a genetic system that can self-reproduce reliably and assemble biomolecules/metabolites. This is a missing link. Just showing how RNA or amino acids arise, says nothing about any genetic system. They can not even answer whether RNA or DNA viruses existed first; and whether these existed before organisms/cells did.
The goal of the suggested "origins of life" problem, as written, isn't to guess at history - it's to create some similar plausible self-emergent system in the lab.
Seems a bit of an ambitious goal for a language model though! There are presumably dozens of steps before you get to an RNA-based full-blown cell, and it's only been very recently that humans have managed to make an artificial self-replicating "cell" (container) of any type in the lab.
I really really recommend looking up Michael Levins bio work on intelligence at the cellular and organ level.
His most recent work is starting to touch on some pretty far out concepts. And I should note, that he does not state they are true or not, but they are attempting to make empirical methods on testing them.
One of the concepts they are looking at is "math as an actual thing" and that some simple algorithms have new deeper behaviors that we're just finding now. The hypothesis is that over the eons life has probed mathematics at scales humanity cannot even begin to imagine and is exploiting this deep algorithmic efficiency in accomplishing any number of tasks. These algorithms could help explain the missing links.
I'd suggest starting with some of his older work to see they are a credentialed research scientist and not making up 'woo' whole cloth.
I’m curious how you found Michael’s work? Like what led you to familiarize yourself with his research?
He seems to be essentially the only biologist that hackernews knows and he always (always!) comes up in biology discussion. I’m wondering how this particular situation arose.
It's been long enough that I'm not really sure. It's every time I see some of his work the way he looks at problems always intrigued me.
Dogma in science tends to blind people, and when a field starts slowing down and running into things we cannot address then we have to start looking at what we should be questioning.
Just searching it up in YouTube is a good starting place. He has visited a number of different podcasts. Then from there you can drill down into papers on the interesting things.
> Demonstrate the emergence of life from chemical precursors in a laboratory setting
Wait, isn’t this an already solved problem? I remember in the 90s school books (material written in the 80s-70s likely) it was presented as “so and so did electric discharges in an atmospheric gas mixtures and they got organic molecules” therefore the origins of life is solved. I remember thinking how cool that was. I guess the “therefore” step was sort of a lie if it’s still an open problem…
Science was presented in a just-so way in our youth. It still is ( #Scicomms ), but back then, whether reading Junior Scholastic or the margin captions of a high school science textbook handed down ex cathedra, there was no communication pathway to pose vexing questions in a publicly visible way as we do now.
We don't even know if Miller or Urey themselves would have (or did) frame their results that way. The honest framing is 'hey this is one way to form amino acids, and maybe that pathway is reflective of the origin of life, but that supposition remains conjecture on our part.'
Yeah that's the one. Just looked it up it was it the early 1950s. I guess I was either too impressionable or the textbook and/or teacher cut a few corners because I remember it being presented as "we solved the origins of life already" situation.
This is a collection of pet projects by folks who are not distinguished biologists. Interesting, maybe, but not to be placed on the same pedestal as the mathematics Millenium Prize of a similar name.
Arguably, the origin of life is a question for all time -- no way that a VC webpage is going to change whether someone takes that problem on, and it will be commercialized with big-name capitalists instead of 'FutureHouse' should it ever be developed in the lab.
This. Part of the allure of the Clay Millenium prizes is that some of the best minds in the field came up with them. Let’s hear what Eric Lander, Jennifer Doudna, Aviv Regev, Feng Zhang, David Baker, Robert Weinberg, George Church, Bert Vogelstein, etc have to say.
Oddly most of these challenges seem to seek to solve problems that would enable phenomenal business opportunities, that many biologists would find questionable.
Not an expert, but as I understand from my colleagues you can make a case for lots of really important problems related to protein folding to be included in the list. As protein folding hasn't been nearly "solved" by alphafold as has been reported in many places.
> Cryopreservation
+
Demonstrate the ability to cryopreserve and recover live wild-type mice with high viability.
Specifically, demonstrate the reversible cryopreservation of live, intact, wild-type adult mice in a whole-body frozen or vitrified state. The mice must remain frozen or vitrified for at least 24 hours, must be recovered with >99% viability, and must not suffer any permanent organ damage or bodily harm. Somatic genetic engineering is discouraged but permitted. All experiments must be conducted with ethics approval.
This has been done in the 50ies, freezing and thawing mice with microwaves. IIRC the recovery rate was about 74% with no observed side effects.
The research was given up on because in this specific case a mouse is a bad biological model. Thawing agents must scale cubically with size. The author says that at approximately the size of a cat you can’t quickly enough evaporate all the agent because the energy required will simply burn the tissue.
TLDR: it’s possible, it’s been done, it could be perfected if necessary, it does NOT scale beyond mice
Bacteria that break down plastic already exist, both naturally and synthetically. Optimizing those is one of the most popular student projects in my university. Hardly a millennium problem.
What seems ridiculous to me is putting together any such lists, even in math, and expecting/hoping the AI companies to solve them. The people working for the AI companies are not scientists or experts in anything outside of building LLMs. The best chance of making progress on genuinely tough (not just computationally challenging) tasks is to put advanced tools in the hands of actual scientists.
Actual scientists are not interested in using these tools to their full capability because of a fundamental psychological block where they have the need to feel superior to the LLM. The point is that AI labs have a thesis that AI will supersede humans in all cognitive work, and since few scientists have this view (or have the temperament to even contemplate this view), they have to do it themselves. In any case, they are attracting strong/talented biologists (Dario himself has a biology background) to work on this and provide their expertise, but presumably they filtered for scientists who are in fact "AI-pilled".
Look at what's happening in the math community- rather than excitedly embracing the power of AI's ability to generate new proofs, they are screaming for it to slow down (and quite a few want it simply stopped). Many scientists, even in ostensibly more real-world fields are broadly cut from the same cloth.
There may be a few scientists rejecting use of AI for that reason, but I would expect them to be a tiny minority, just as I would expect the number of developers who accept that AI is a powerful coding tool, but refuse to use it just because they want to feel superior, is very small!
A much more practical reason we're not yet seeing a lot of headline scientific mathematical breakthroughs is just that it is ungodly expensive! e.g. The Navier-Stokes result cost around $20M at API prices, and academics just don't have that kind of money to spend. You'll see more mathematical and scientific results from practitioners when either the cost of compute needed for these sort of brute force results is more in line with the size of academic grants, and/or the AI companies donate more compute to the scientific community.
There are different reactions from different mathematicians of course - Terrance Tao vs Cedric Villani, and no doubt a lot of shock at the speed of advance, but it seems the reasoned complaint why they don't want the AI companies themselves working on these problems is because the outcome is not the same - you get a result that in of itself may have been suspected or useless (Navier Stokes), but no write up of any new math or insights that were developed along the way, which is the real reason mathematics and people like Erdos pushed these famous problems in the first place - because they were expected to yield interesting mathematics, just as years of work on FLT had done. Imagine if instead of Wiles's work, and all that had gone before him, all we had was a $20M compute bill, hundreds of pages of impenetrable math, and a billion lines of Lean proving it was true?!
It seems that in a frantic pace to prove general usefulness of these LLMs the AI companies are also picking up the role of traditional scientific researchers without really have enough proper communication with the scientific community at large... I agree putting tools in the actual scientists will be very helpful but do they have the patience waiting or even parsing their feedback?
Sure - the AI companies want some quick trophy kills to feature in their IPO prospectus, but they are not going to themselves be cracking the genuinely tough problems.
There is a difference between what's easy/hard for a human vs computer, and LLMs haven't changed that. You might expect a computer to be good at tasks requiring prodigious memory and compute, and it turns out that some of these long-standing math problems are of that nature - not requiring new breakthroughs but rather just massive exploration of what is already known and what they were trained on.
There will no doubt be more math results like this, but presumably also ones that are "hard for a human, easy for a computer", requiring massive search (e.g. find an example/counter-example cf Navier-Stokes & Jacobian conjecture) rather than creativity.
Of course. This is just a list of someone's ideas for equivalently difficult (and transformative) unsolved problems in biology. Many of these would of course have obvious and immediately impactful practical applications, unlike the math problems.
> ... This is just a list of someone's ideas for equivalently difficult (and transformative) unsolved problems in biology. ...
I think GP was saying the proof of the pudding is in the tasting: the longer a problem has provably resisted resolution the more difficult it is considered...
bombastically decorating a problem as equivalently difficult does not make it so.
Well, if LLMs actually turn out to solve some of the Millennium problems, at least some of these biology problems are almost certainly at least as difficult. Certainly solving any of them would be Nobel-worthy if biology nobels existed (and solving some of them would likely easily be worthy of a medicine Nobel). It's not like these are new problems whose difficulty is unknown!
The Millenium Prize problems didn't only withstand decades/century of resolution, they also withstood the same test of time in ridicule of the challenge statement.
This list of "Millenium Problems for Biology" contains such brainfart level "analogies" that there the list will be ridiculed, for the question / challenge itself displays a lack of understanding of the subject in question. Science is also asking the right questions.
Consider for example:
> 10. Protein Amplification Chain Reaction:
> Demonstrate exponential amplification of arbitrary peptide substrates.
> Specifically, demonstrate input-protein-dependent synthesis of new, full-length, sequence-faithful covalent polypeptide copies from amino-acid monomers without a nucleic-acid template or preformed cognate scaffold, in a single pot reaction. For the challenge to be considered complete, at least 100 random peptide sequences of at least 50 amino acids each must be preregistered, synthesized, and pooled. It must then be shown that the abundance of these peptides in solution can be amplified at least 1000x with at least 90% sequence accuracy on a per-residue basis. Reasonable modifications may be added to the peptide sequences to facilitate post-amplification analysis if necessary, provided they are not active in the amplification. Methods that rely on explicit sequencing of the peptide are not permitted. Methods that rely on reverse translation to generate a nucleic acid intermediate are not permitted, because they are duplicative with a separate Millennium Problem.
The analogy is very clear: to amplify DNA or RNA one uses PCR, basically throw the desired product in a cauldron with monomer building blocks, then by repeated heating and cooling the lone monomers find their permitted locations on a complementary pre-existing strand, and form the new polymer strand.
So it seems natural to ask for a generalization to protein polymers, except every biologist or chemist knows its nonsense: proteins don't have a complementary strand! You can't demand chemistry or physics to magically copy without a complementary template!
You may ask "but if that were true, how can we already have PCR for RNA?"
Well pretty simple: while this is done routinely, its only possible indirectly: convert the RNA to double-strand DNA, use PCR on this DNA and then convert the amplified DNA back to RNA!
The demand to not involve sequencing or the hypothetical reverse translatase from one of the other problem statements turns this one into a non-existence theorem, but the challenge doesn't describe a winner for demonstrating its impossibility!
I assure you that any chemist or biologist being asked why we dont have PCR for protein, will understand your lack of knowledge, and explain how PCR works, so that you understand that PCR was only possible because of the complementary strand!
This list will be ridiculed for being not even wrong.
These are almost all so stupid. Written by people who clearly don't know their chemistry
Reverse translatase and protein amplification in particular.
How the fuck do you plan on selectively priming protein amplification. If you know ANY protein chemistry, you will know "the juice is not worth the squeeze" -- how would I exponentially amplify a protein? I'd do mass spec proteomics, synthesize the DNA, and express it.
Simply amazing that electrofixation is not on the list.
I was going to comment to complain about those two as well!
Do you mean electrofixation of nitrogen? What level of biological involvement are you imagining? More like biology producing (some?) of the catalysts, or more like the entire reaction happening inside of cells?
Agreeing on the model gap, the bit left out is that even human-to-human it breaks. Same drug, same dose, wildly different response depending on genotype and gut flora.
Do we really have to solve these problems though? Why would anyone want to have an inferior bio limb when they can substitute it with a superior and easily repairable/upgradable mechanical limb? Granted that the artificial limbs we have right now are pretty basic, but wouldn’t it make sense to improve it rather than trying to grow new limbs?
Also if you think humans would ever become a space faring species, would it really make sense to stick to our carbon based biology or should we invest in transforming into silicon based beings
"Your limb replacement technology is very good, but I believe humanity should transform into silicon-based beings, and so for that reason i'm out"- dragons den 2030
There are certainly non engineering problems out there... For one example, in biomedicine it's known cancer in different organs can have preference of having immune hot (in skin and lung cancer, e.g.) and cold tumors (in pancreatic and prostate cancer, e.g.), and current immunotherapy work much less well on the latter. Why this pattern exists is still an open question very important for treating patients and it's not a how did nature engineer question as it's a unintended byproduct from several tiers of complex biology involving understanding mutational burden in tumor cells, immune surveillance surrounding the tumors, and mechanical structure of the tissue, etc. The engineering payoff comes later, i.e., immunotherapy works better on a particular subset of patients, after explaining and understanding the biological phenomenon first.
It seems that physics -> chemistry -> biology is a layering of abstractions and/or emergent phenomena, and any explanations of mechanics at these higher levels is more along the lines of "how was this built from these lego blocks" rather than being some more fundamental discovery.
You keep on making assertions - perhaps you could back it up with some explanation of why you regard this as a more fundamental discovery (or however you would like to characterize it)?
In a sense all experimental science has an element of engineering. For instance the first problem, "the origins of life" is framed to require an experiment to show that any proposed mechanism actually works.
Edit: A true expert would never define a problem in such a sloppy way:
"Specifically, the protein must convert N₂ to ammonia at rates that are at least of a similar order of magnitude to the rates of naturally occurring proteins, and must fall well below the sequence- and structure-similarity thresholds relative to all known nitrogenase and nitrogenase-like proteins. The protein may be designed de novo, discovered in nature, or engineered or evolved from naturally occurring starting points."
The Millennium Problems list in math was created in 2000, hence the name. There is a $1 million prize for solving any of the problems. Creating this list is just someone's idea of listing problems of equal importance. The name doesn't fit and it's just a list.
I’m going to be honest, I don’t know if improving Rubsico is possible. Nature has had every reason to and billions of years. It’s optimized as far as it can go and you either lose speed or specificity, I don’t think having both is possible.
Your statement piked out my interest and I went searching for what Rubisco was. My prediction is that this particular one will be cracked out soon, and at around 2045, creating disgustingly efficient variants of RuBisCO along with other photosynthesis mechanisms will be AGIs’ hot hobby.
That there are no more efficient variations of it in nature just tells us that the local minimum is really deep and that natural evolution, as it is, can’t produce anything better, not even with a billion years and 10^30 organisms serving as a “brute force lab”. It’s also the kind of problem an AGI system would tackle for purely ideological reasons, i.e. to prove that it is superior to nature.
> Demonstrate the emergence of life from chemical precursors in a laboratory setting.
> Specifically, demonstrate the unassisted emergence of self replicating RNA- and protein-based cells from a plausible primordial soup with a plausible energy source. A “cell” may be any compartment with a defined boundary.
So, right now, nobody can explain how life originated. Proving that aminoacids, DNA or RNA form, does NOT mean that this is equal to life. This is a problem that the whole field has - it still can not explain how life originated. Showing that individual components can arise spontaneously, is not the same as showing e. g. how a cell formed and so forth. Where does the encoding problem fit into any of that, for instance? You need to prove how you can assemble systems. Just having a working ribosome does not connect it to DNA as a genetic backup system; and RNA tends to be unstable. Even when you have assumptions how this works together, you need to prove that this is how things originate(d). Nobody has done so since decades. It is an unsolved problem.
is it yet another vibecoded website? akin to the poster discussion, all vibecoded websites do not need to look like the same, please put some care or taste in your prompts.
Someone should make the millennium problems for AI alignment so the frontier labs will actually try to solve that problem.
> Someone should make the millennium problems for AI alignment so the frontier labs will actually try to solve that problem.
Rather: Someone should make the millennium problems for human alignment so the top researchers will actually try to solve that problem. :-)
A group already (sort-of) has for the broader theory: https://arxiv.org/abs/2604.21691
It's a good list, but "alignment" is much more targetted. I attended a workshop with researchers from OpenAI and Anthropic also present, where the objective was to hash out what the problems in alignment even are. This, it turned out, was extraordinarily difficult.
The problems with alignment are to do with how we define and search for this vague notion when it is mathematically ill-posed at present.
The only thing the frontier labs are interested in is their trillion dollar IPOs.
You'd think that believing they are going to destroy humanity would motivate them.
If you ignore what people say and instead observe what they do, the world makes a lot more sense.
They're for-profit corporations above all.
They are human institutions above all. Saying 'we're a corporation' doesn't at all absolve them from human traits, consequences, and responsibilities.
Corporations are really the embodiment of Moloch.
Biology already has numerous “Millennium Problems” and the rewards are much more than $1M. For example, reverse Alzheimer’s Disease. Or less ambitious develop high fidelity in vitro and animal models of human disease.
Those don't have the property of being easily verified, which the problems proposed here do. I think that's a smart idea because it's a way to capture quick PR seeking AI-lab dollars for quite useful outcomes.
Being easily verifiable does not elevate something to the level of being a grand challenge. It really drives home the point that biology is not math or coding.
I guess to me the grandiosity here feels fake and unearned - like they vibe slopped it together without much input in the way of deep thought or expertise.
For example, synthesize arbitrary dna sequences > 3000 nt in length with error rate < 0.001 at > 95% purity. Easily verifiable, beyond the frontier, and generally useful.
I agree with you there are other much grander challenges, but apart from that I don't think the list is horrible, because as I said it's incentive-aligned with the current moment we are in. Your suggestion would definitely be a good addition to the list for sure.
I think some of the items on the list are also way less likely to be worked on (like cryogenics) than others, because they really require some heavy infrastructure to iterate. My money for which of these gets cracked with help of an AI would be rubisco, that can be quite effectively worked on in a closed loop lab fashion with a standard lab or CRO.
>that biology is not math or coding
Biology is a really really big field. Many problems in biology are that of math and coding. Just as a simple example look at the golden ratio in living organisms. Biology follows a lot of different algorithms because they are energy efficient and come with massive secondary benefits.
> Just as a simple example look at the golden ratio in living organisms
What is this example meant to illustrate?
The golden ratio is one of those things you can study for years and be a amazed by.
>The golden ratio (≈ 1.618) and its related golden angle (137.5°) show up in nature because they maximize space, exposure, and flow.
The golden ratio is an algorithm, but it is also a natural law that systems of many different scales follow. Wherever we look we find examples of it. Without this algorithm the world is a much harder place to explain.
What many scientists starting to look for is these algorithms we have not identified in natural systems as an explanatory means of their workings. Also because they are algorithms they are things we can put into computer systems to increase efficiency, or may naturally emerge from evolutionary learning systems like AI in what we call emegence.
The golden ratio in nature is numerology. Biology is beautiful enough in itself without having the golden ratio shoehorned into it. https://pmc.ncbi.nlm.nih.gov/articles/PMC10792139/
Did you even read what you linked to, or did you just read the headline?
>There is no convincing evidence that the golden ratio is linked to idealized human proportions or facial beauty. There is currently no evidence to support the use of the golden ratio in orthognathic or facial aesthetic/reconstructive surgical planning or analysis of results.
This is about peoples faces. What I listed was about things like available surface area for things like chemical reactions and mixing, you know things that are mathematically calculatable. Much in the same manner a hexagon is an optimal low energy shape, hence we see lots of them in nature too.
I think it illustrates that there are cool things to find beneath the lamp post of math and coding.
Not just cool things. But real things that effect physics.
I think those are better described as problems of medicine whereas the problems here are indeed about fundamental biology
This is all well outside my area of expertise, but my impression is that Michael Levin's work with bioelectricity is on the cusp of #6 Somatic limb regeneration.
SMEs please correct the record if I'm mistaken.
Unfamiliar with Levin’s work, but:
There’s also this study [0] I read recently, along a different path of using known growth factors and proteins to kick off regeneration.
[0] https://www.nature.com/articles/s41467-026-72066-8
ECM (Extracellular Matrix) has been studied for similar properties:
https://www.biorxiv.org/content/10.64898/2026.08.05.742898v1...
"Extracellular matrix particle treatment induces digit regeneration in soft-tissue preserved amputation (SPA) model of adult mice"
Everyone interested in biology and intelligence should look up Michaels work, it is amazing because he's ignored a lot of assumptions that are held by the community and come up with new and interesting tests.
Also his work on algorithmic intelligence in cells and the mathematical foundation of intelligence will change many fields, including AI, if it can be empirically tested.
Is inner-ear cell regeneration included in #06?
Its much harder to produce a good fundamental list of biology problems because we are no where near as far along, so many basic things are not understood. There are still many unknown unknowns and these particular problems are limited to the realm of verifiable in a lab, which rules out much of what makes biology interesting and hard to study, and that is the organisms themselves and the interactions between their cells. The petri dish differs from the rat differs from the human and that is a part of what makes biology harder to progress.
None of these is likely the root to the wide array of chronic illnesses we can't treat and those seem like the next step to really put a lot of research into given how many people suffer from them and where we are today they seem achievable with the right investment.
> The petri dish differs from the rat differs from the human and that is a part of what makes biology harder to progress.
They all use DNA.
But I agree that nobody has shown in the lab how any of this leads to a genetic system that can self-reproduce reliably and assemble biomolecules/metabolites. This is a missing link. Just showing how RNA or amino acids arise, says nothing about any genetic system. They can not even answer whether RNA or DNA viruses existed first; and whether these existed before organisms/cells did.
The goal of the suggested "origins of life" problem, as written, isn't to guess at history - it's to create some similar plausible self-emergent system in the lab.
Seems a bit of an ambitious goal for a language model though! There are presumably dozens of steps before you get to an RNA-based full-blown cell, and it's only been very recently that humans have managed to make an artificial self-replicating "cell" (container) of any type in the lab.
>They all use DNA.
I really really recommend looking up Michael Levins bio work on intelligence at the cellular and organ level.
His most recent work is starting to touch on some pretty far out concepts. And I should note, that he does not state they are true or not, but they are attempting to make empirical methods on testing them.
One of the concepts they are looking at is "math as an actual thing" and that some simple algorithms have new deeper behaviors that we're just finding now. The hypothesis is that over the eons life has probed mathematics at scales humanity cannot even begin to imagine and is exploiting this deep algorithmic efficiency in accomplishing any number of tasks. These algorithms could help explain the missing links.
I'd suggest starting with some of his older work to see they are a credentialed research scientist and not making up 'woo' whole cloth.
I’m curious how you found Michael’s work? Like what led you to familiarize yourself with his research?
He seems to be essentially the only biologist that hackernews knows and he always (always!) comes up in biology discussion. I’m wondering how this particular situation arose.
It's been long enough that I'm not really sure. It's every time I see some of his work the way he looks at problems always intrigued me.
Dogma in science tends to blind people, and when a field starts slowing down and running into things we cannot address then we have to start looking at what we should be questioning.
How do you keep up to date with his work?
Just searching it up in YouTube is a good starting place. He has visited a number of different podcasts. Then from there you can drill down into papers on the interesting things.
https://en.wikipedia.org/wiki/Lists_of_problems
https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_b...
> Demonstrate the emergence of life from chemical precursors in a laboratory setting
Wait, isn’t this an already solved problem? I remember in the 90s school books (material written in the 80s-70s likely) it was presented as “so and so did electric discharges in an atmospheric gas mixtures and they got organic molecules” therefore the origins of life is solved. I remember thinking how cool that was. I guess the “therefore” step was sort of a lie if it’s still an open problem…
Science was presented in a just-so way in our youth. It still is ( #Scicomms ), but back then, whether reading Junior Scholastic or the margin captions of a high school science textbook handed down ex cathedra, there was no communication pathway to pose vexing questions in a publicly visible way as we do now.
We don't even know if Miller or Urey themselves would have (or did) frame their results that way. The honest framing is 'hey this is one way to form amino acids, and maybe that pathway is reflective of the origin of life, but that supposition remains conjecture on our part.'
You're thinking of the Miller-Urey experiment. They got some biomolecules (amino acids, etc) but definitely not life
Yeah that's the one. Just looked it up it was it the early 1950s. I guess I was either too impressionable or the textbook and/or teacher cut a few corners because I remember it being presented as "we solved the origins of life already" situation.
biologists got even further - experiments managed to make RNA longer than a different team's self-replicating RNA
still not all the way to something alive-like
I recently watched this video on the topic https://www.youtube.com/watch?v=OsXGgrXwllc
This is a collection of pet projects by folks who are not distinguished biologists. Interesting, maybe, but not to be placed on the same pedestal as the mathematics Millenium Prize of a similar name.
Arguably, the origin of life is a question for all time -- no way that a VC webpage is going to change whether someone takes that problem on, and it will be commercialized with big-name capitalists instead of 'FutureHouse' should it ever be developed in the lab.
This. Part of the allure of the Clay Millenium prizes is that some of the best minds in the field came up with them. Let’s hear what Eric Lander, Jennifer Doudna, Aviv Regev, Feng Zhang, David Baker, Robert Weinberg, George Church, Bert Vogelstein, etc have to say.
Oddly most of these challenges seem to seek to solve problems that would enable phenomenal business opportunities, that many biologists would find questionable.
Not an expert, but as I understand from my colleagues you can make a case for lots of really important problems related to protein folding to be included in the list. As protein folding hasn't been nearly "solved" by alphafold as has been reported in many places.
Wait a minute:
> Cryopreservation + Demonstrate the ability to cryopreserve and recover live wild-type mice with high viability. Specifically, demonstrate the reversible cryopreservation of live, intact, wild-type adult mice in a whole-body frozen or vitrified state. The mice must remain frozen or vitrified for at least 24 hours, must be recovered with >99% viability, and must not suffer any permanent organ damage or bodily harm. Somatic genetic engineering is discouraged but permitted. All experiments must be conducted with ethics approval.
This has been done in the 50ies, freezing and thawing mice with microwaves. IIRC the recovery rate was about 74% with no observed side effects. The research was given up on because in this specific case a mouse is a bad biological model. Thawing agents must scale cubically with size. The author says that at approximately the size of a cat you can’t quickly enough evaporate all the agent because the energy required will simply burn the tissue.
TLDR: it’s possible, it’s been done, it could be perfected if necessary, it does NOT scale beyond mice
It's missing some important stuff? Like creating bacteria that can produce fuels or break down plastics.
Bacteria that break down plastic already exist, both naturally and synthetically. Optimizing those is one of the most popular student projects in my university. Hardly a millennium problem.
What seems ridiculous to me is putting together any such lists, even in math, and expecting/hoping the AI companies to solve them. The people working for the AI companies are not scientists or experts in anything outside of building LLMs. The best chance of making progress on genuinely tough (not just computationally challenging) tasks is to put advanced tools in the hands of actual scientists.
Actual scientists are not interested in using these tools to their full capability because of a fundamental psychological block where they have the need to feel superior to the LLM. The point is that AI labs have a thesis that AI will supersede humans in all cognitive work, and since few scientists have this view (or have the temperament to even contemplate this view), they have to do it themselves. In any case, they are attracting strong/talented biologists (Dario himself has a biology background) to work on this and provide their expertise, but presumably they filtered for scientists who are in fact "AI-pilled".
Look at what's happening in the math community- rather than excitedly embracing the power of AI's ability to generate new proofs, they are screaming for it to slow down (and quite a few want it simply stopped). Many scientists, even in ostensibly more real-world fields are broadly cut from the same cloth.
There may be a few scientists rejecting use of AI for that reason, but I would expect them to be a tiny minority, just as I would expect the number of developers who accept that AI is a powerful coding tool, but refuse to use it just because they want to feel superior, is very small!
A much more practical reason we're not yet seeing a lot of headline scientific mathematical breakthroughs is just that it is ungodly expensive! e.g. The Navier-Stokes result cost around $20M at API prices, and academics just don't have that kind of money to spend. You'll see more mathematical and scientific results from practitioners when either the cost of compute needed for these sort of brute force results is more in line with the size of academic grants, and/or the AI companies donate more compute to the scientific community.
There are different reactions from different mathematicians of course - Terrance Tao vs Cedric Villani, and no doubt a lot of shock at the speed of advance, but it seems the reasoned complaint why they don't want the AI companies themselves working on these problems is because the outcome is not the same - you get a result that in of itself may have been suspected or useless (Navier Stokes), but no write up of any new math or insights that were developed along the way, which is the real reason mathematics and people like Erdos pushed these famous problems in the first place - because they were expected to yield interesting mathematics, just as years of work on FLT had done. Imagine if instead of Wiles's work, and all that had gone before him, all we had was a $20M compute bill, hundreds of pages of impenetrable math, and a billion lines of Lean proving it was true?!
It seems that in a frantic pace to prove general usefulness of these LLMs the AI companies are also picking up the role of traditional scientific researchers without really have enough proper communication with the scientific community at large... I agree putting tools in the actual scientists will be very helpful but do they have the patience waiting or even parsing their feedback?
Sure - the AI companies want some quick trophy kills to feature in their IPO prospectus, but they are not going to themselves be cracking the genuinely tough problems.
There is a difference between what's easy/hard for a human vs computer, and LLMs haven't changed that. You might expect a computer to be good at tasks requiring prodigious memory and compute, and it turns out that some of these long-standing math problems are of that nature - not requiring new breakthroughs but rather just massive exploration of what is already known and what they were trained on.
There will no doubt be more math results like this, but presumably also ones that are "hard for a human, easy for a computer", requiring massive search (e.g. find an example/counter-example cf Navier-Stokes & Jacobian conjecture) rather than creativity.
It's inane, actually - I thought this might be something put on by an Allen Institute or some actually well-respected organization.
> 02 - Cryopreservation
Demonstrate the ability to cryopreserve and recover live wild-type mice with high viability.
> 06 - Somatic limb regeneration
Demonstrate the ability to regenerate lost limbs in adult wild-type mice.
Interesting, but looks like these problems are proposed in September 2026, unlike original 7 Millennium Prize Problems of Maths
Of course. This is just a list of someone's ideas for equivalently difficult (and transformative) unsolved problems in biology. Many of these would of course have obvious and immediately impactful practical applications, unlike the math problems.
> ... This is just a list of someone's ideas for equivalently difficult (and transformative) unsolved problems in biology. ...
I think GP was saying the proof of the pudding is in the tasting: the longer a problem has provably resisted resolution the more difficult it is considered...
bombastically decorating a problem as equivalently difficult does not make it so.
Well, if LLMs actually turn out to solve some of the Millennium problems, at least some of these biology problems are almost certainly at least as difficult. Certainly solving any of them would be Nobel-worthy if biology nobels existed (and solving some of them would likely easily be worthy of a medicine Nobel). It's not like these are new problems whose difficulty is unknown!
The Millenium Prize problems didn't only withstand decades/century of resolution, they also withstood the same test of time in ridicule of the challenge statement.
This list of "Millenium Problems for Biology" contains such brainfart level "analogies" that there the list will be ridiculed, for the question / challenge itself displays a lack of understanding of the subject in question. Science is also asking the right questions.
Consider for example:
> 10. Protein Amplification Chain Reaction:
> Demonstrate exponential amplification of arbitrary peptide substrates.
> Specifically, demonstrate input-protein-dependent synthesis of new, full-length, sequence-faithful covalent polypeptide copies from amino-acid monomers without a nucleic-acid template or preformed cognate scaffold, in a single pot reaction. For the challenge to be considered complete, at least 100 random peptide sequences of at least 50 amino acids each must be preregistered, synthesized, and pooled. It must then be shown that the abundance of these peptides in solution can be amplified at least 1000x with at least 90% sequence accuracy on a per-residue basis. Reasonable modifications may be added to the peptide sequences to facilitate post-amplification analysis if necessary, provided they are not active in the amplification. Methods that rely on explicit sequencing of the peptide are not permitted. Methods that rely on reverse translation to generate a nucleic acid intermediate are not permitted, because they are duplicative with a separate Millennium Problem.
The analogy is very clear: to amplify DNA or RNA one uses PCR, basically throw the desired product in a cauldron with monomer building blocks, then by repeated heating and cooling the lone monomers find their permitted locations on a complementary pre-existing strand, and form the new polymer strand.
So it seems natural to ask for a generalization to protein polymers, except every biologist or chemist knows its nonsense: proteins don't have a complementary strand! You can't demand chemistry or physics to magically copy without a complementary template!
You may ask "but if that were true, how can we already have PCR for RNA?"
Well pretty simple: while this is done routinely, its only possible indirectly: convert the RNA to double-strand DNA, use PCR on this DNA and then convert the amplified DNA back to RNA!
The demand to not involve sequencing or the hypothetical reverse translatase from one of the other problem statements turns this one into a non-existence theorem, but the challenge doesn't describe a winner for demonstrating its impossibility!
I assure you that any chemist or biologist being asked why we dont have PCR for protein, will understand your lack of knowledge, and explain how PCR works, so that you understand that PCR was only possible because of the complementary strand!
This list will be ridiculed for being not even wrong.
> 06 - Somatic limb regeneration
At least those things cannot be solved by just burning GPT tokens.
> Demonstrate the ability to cryopreserve and recover live wild-type mice with high viability.
I thought we already managed to successfully cryopreserve and recover small rodents like hamsters in the 50s.
Edit: See https://en.wikipedia.org/wiki/Cryopreservation#History
These are almost all so stupid. Written by people who clearly don't know their chemistry
Reverse translatase and protein amplification in particular.
How the fuck do you plan on selectively priming protein amplification. If you know ANY protein chemistry, you will know "the juice is not worth the squeeze" -- how would I exponentially amplify a protein? I'd do mass spec proteomics, synthesize the DNA, and express it.
Simply amazing that electrofixation is not on the list.
I was going to comment to complain about those two as well!
Do you mean electrofixation of nitrogen? What level of biological involvement are you imagining? More like biology producing (some?) of the catalysts, or more like the entire reaction happening inside of cells?
Which ones are not stupid?
Cryopreservation, even though I don't care much for it.
The rubisco one is sort of not dumb, but if you actually care about carbon fixation you'd just not bother using rubisco at all instead.
Programmable Proteases is fine.
Somatic regeneration is fine.
You'd think halting or reversing aging would be on this list...
Agreeing on the model gap, the bit left out is that even human-to-human it breaks. Same drug, same dose, wildly different response depending on genotype and gut flora.
It has not escaped my notice that a significant portion of these are of the form "Design a protein that..."
It turns out that proteins are responsible for a whole lot of different things in biology.
https://xkcd.com/3056
Do we really have to solve these problems though? Why would anyone want to have an inferior bio limb when they can substitute it with a superior and easily repairable/upgradable mechanical limb? Granted that the artificial limbs we have right now are pretty basic, but wouldn’t it make sense to improve it rather than trying to grow new limbs?
Also if you think humans would ever become a space faring species, would it really make sense to stick to our carbon based biology or should we invest in transforming into silicon based beings
You should really step outside more and touch some grass
"Your limb replacement technology is very good, but I believe humanity should transform into silicon-based beings, and so for that reason i'm out"- dragons den 2030
There is a curious lack of small molecules or biocatalysis in this list
Who is this by? Who verifies the result? Is there prize money?
On the page, it says
EDISON SCIENTIFIC · FUTUREHOUSE
SAM RODRIQUES · MICHAELA HINKS
https://edisonscientific.com/team
It’s strange they are all engineering problems.
What would be your idea of a biology problem that isn't an "engineering" one?
It seems that understanding biology could be characterized as trying to figure out "how did nature engineer this".
For that matter isn't all of science this way?
There are certainly non engineering problems out there... For one example, in biomedicine it's known cancer in different organs can have preference of having immune hot (in skin and lung cancer, e.g.) and cold tumors (in pancreatic and prostate cancer, e.g.), and current immunotherapy work much less well on the latter. Why this pattern exists is still an open question very important for treating patients and it's not a how did nature engineer question as it's a unintended byproduct from several tiers of complex biology involving understanding mutational burden in tumor cells, immune surveillance surrounding the tumors, and mechanical structure of the tissue, etc. The engineering payoff comes later, i.e., immunotherapy works better on a particular subset of patients, after explaining and understanding the biological phenomenon first.
Chemiosmotic effect
What aspect of that is an unsolved problem, and one that feels more fundamental rather than engineering / in need of explanation ?
Its not unsolved. It's very solved. It was not an engineering question.
It seems that physics -> chemistry -> biology is a layering of abstractions and/or emergent phenomena, and any explanations of mechanics at these higher levels is more along the lines of "how was this built from these lego blocks" rather than being some more fundamental discovery.
You keep on making assertions - perhaps you could back it up with some explanation of why you regard this as a more fundamental discovery (or however you would like to characterize it)?
In a sense all experimental science has an element of engineering. For instance the first problem, "the origins of life" is framed to require an experiment to show that any proposed mechanism actually works.
Right? Where's the substrates of consciousness, or the actual solutions for protein/rna folding, or the requirements for evolution?
Substrates of consciousness? You mean the neural correlates of consciousness which we already have?
As a neuroscientist: no. Not what I mean.
What else?
AI slop. next.
Edit: A true expert would never define a problem in such a sloppy way: "Specifically, the protein must convert N₂ to ammonia at rates that are at least of a similar order of magnitude to the rates of naturally occurring proteins, and must fall well below the sequence- and structure-similarity thresholds relative to all known nitrogenase and nitrogenase-like proteins. The protein may be designed de novo, discovered in nature, or engineered or evolved from naturally occurring starting points."
Slap an LLM on those and call it a day
Need to scrape the logs from some leading researchers first
It's missing an obvious one: morphogenesis.
That’s number 6: limb regeneration.
No, morphogenesis is a more general problem and we know very little about it.
Agreed. Morphogenesis is more general. Limb regeneration is how morphogenesis is represented in this problem set.
So anyone can create a list of problems and slap the name Millennium Problems on them?
The Millennium Problems list in math was created in 2000, hence the name. There is a $1 million prize for solving any of the problems. Creating this list is just someone's idea of listing problems of equal importance. The name doesn't fit and it's just a list.
Seemingly anyone can take an unordered list of things and slap "periodic table" on it even if it doesn't have any periodicity.
Now you can enjoy one man-week of rage across the remainder of your life as you won't be able to unsee it. You're welcome
I’m going to be honest, I don’t know if improving Rubsico is possible. Nature has had every reason to and billions of years. It’s optimized as far as it can go and you either lose speed or specificity, I don’t think having both is possible.
Your statement piked out my interest and I went searching for what Rubisco was. My prediction is that this particular one will be cracked out soon, and at around 2045, creating disgustingly efficient variants of RuBisCO along with other photosynthesis mechanisms will be AGIs’ hot hobby.
That there are no more efficient variations of it in nature just tells us that the local minimum is really deep and that natural evolution, as it is, can’t produce anything better, not even with a billion years and 10^30 organisms serving as a “brute force lab”. It’s also the kind of problem an AGI system would tackle for purely ideological reasons, i.e. to prove that it is superior to nature.
> Demonstrate the emergence of life from chemical precursors in a laboratory setting.
> Specifically, demonstrate the unassisted emergence of self replicating RNA- and protein-based cells from a plausible primordial soup with a plausible energy source. A “cell” may be any compartment with a defined boundary.
So, right now, nobody can explain how life originated. Proving that aminoacids, DNA or RNA form, does NOT mean that this is equal to life. This is a problem that the whole field has - it still can not explain how life originated. Showing that individual components can arise spontaneously, is not the same as showing e. g. how a cell formed and so forth. Where does the encoding problem fit into any of that, for instance? You need to prove how you can assemble systems. Just having a working ribosome does not connect it to DNA as a genetic backup system; and RNA tends to be unstable. Even when you have assumptions how this works together, you need to prove that this is how things originate(d). Nobody has done so since decades. It is an unsolved problem.
is it yet another vibecoded website? akin to the poster discussion, all vibecoded websites do not need to look like the same, please put some care or taste in your prompts.