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.
One mechanism by which the US encourages free market capitalism is requiring reciprocity. China is not evil - they are just self interested - and it is the responsibility of the US government to negotiate a trade agreement that benefits its citizens.
Many of the comments here in this thread are absolutely vile. Very few people are undeserving of compassion.
I think some of the people in this thread should look themselves in the mirror and ask why feel different in this case than similar cases like Dan Ariely, Amy Cuddy, or Marc Tessier Levigne.
I don’t know. Seems like a distorted sense of justice to me. I am not so sure that academic fraud is in the same league as committing felonies (like E Holmes). Also don’t know that anything beyond professional sanction is deserved for professional misconduct (that is not breaking any laws or endangering people).
Perhaps their real crimes were not having better PR, or enough money to hire a crisis management firm. Whatever it was, it really seems to have brought out the worst (in) people.
Academic fraud is a crime. It gets used on grant applications to justify funding, meaning it's stealing money from the government and thus the public. It can be prosecuted as fraud in the courts. See the case of the Dana Farber Cancer Center being taken to court on behalf of the US Government by Sholto David (who won a large settlement).
> Perhaps their real crimes were not having better PR, or enough money to hire a crisis management firm.
He hired Carter Ruck, a professional law firm, who then bullied The Times with legal threats that themselves contained by lies by Arday. And he had been given nothing less than glowing PR by every "serious" institution in Britain, which all repeated his lies without question up until a few days ago.
Good PR and funds for crisis management were not an issue for him.
I don't agree. In one case a person is raising financial capital, in others they're raising social or political capital. All such frauds upon the public are bad and I think there's something wrong with society only treating it as a crime when a currency symbol is involved. It's not just the investors (financial or professional or...) who lose out; people who were correctly skeptical in advance of the public revelation are often attacked or sidelined for expressing doubt about a fabricator's grandiose claims.
Nice! Thank you for doing this. I am not surprised PCA holds up. It comes with some pretty strong theoretical guarantees. The problem with it has always been scalability. The O(n^3) complexity makes it infeasible to use for massive datasets.
That is where something like Matryoshka embeddings has appeal. You trade a little bit of performance for a guarantee of training + validation set coverage.
There are other reasons. One is for career development of your researchers. Another is to be good citizens, in good standing, in the community of scientific researchers. Another is to flex on people and buy class and respectability - to comport oneself as the “old money” does.
I think land value tax is misguided. Issue is what happens to retirees on a fixed income when value of their property increases? Are they forced from their home to lower priced area? Regardless of how one might feel about the morality of that it is also worth asking whether this might create perverse incentives to become landlords?
> Issue is what happens to retirees on a fixed income when value of their property increases?
That’s not specific to a land value tax, the same thing can happen with traditional property taxes when the property is re-assessed.
California tried to “solve” exactly that scenario with Prop 13 limiting how much the assessed value of property can increase without a change in ownership and it’s been a disaster (and a contributing factor to the housing crisis, albeit far from the only one).
My understanding is that an ideally priced IPO should not move much from the opening price in the near term. If it pops it means they left money on the table. If it drops, then I am not sure what the implication is exactly?
Now I think SpaceX is massively overhyped, but is the share price returning to IPO opening not just a sign that the banks accurately estimated something?
Your bank will get a ton of orders from institutional investors of how many shares they want at a given price. You will have a preference as to which investors you want on your cap table. Almost all of those investors value your stock less than the "pop price" (which includes the investors you want on your cap table). So you'll need to target the IPO below the "pop price" so you get them on your cap table.
You're probably picking investors based on how likely they'll let you stay on the board / CEO and if you think they're just going to dump the stock during the IPO (which would be bad for it's price).
So (unlike the SpaceX IPO) you're going to sell relatively little shares to retail who will buy at any price which during the opening days will cause it to spike as the demand (in nominal dollars) per share is beyond the IPO price target.
> but is the share price returning to IPO opening not just a sign that the banks accurately estimated something?
Sure they estimated something. But there's a ton of different things that can be estimated.
What do you mean by this? The person you responded to never used the words "should" or "should not", and then you basically repeated what they said using more neutral words...
What is so controversial about saying that SpaceX seems overpriced?
His point is that sellers can do whatever suits their interests best, they don't have a duty to pick a fair price. And that it's on the buyer to decide whether to accept it or reject the price.
Duh? That point was actually implied by my initial comment.
Nobody said anything about fairness or duty.
My point was that if the seller is trying to maximize its self-interest by maximizing the IPO price, leaving no room for growth after IPO, then buyers probably want to take a pass at that price.
Not if it's going to keep dropping, which I think is fair to assume as people wake up to the fundamentals after the hype is done (not financial advice)
> the share price returning to IPO opening not just a sign that the banks accurately estimated something
I mean who knows where it will end up by the end of the year. Meme-stock-hype train could continue or it could reasonably crash far further back down to earth.
You could drop the verb clause? This would make the headline accurate while keeping it punchy.
> why Americans switch to soy
And
> why developers switch to codeberg
But the cynic in me thinks that the form of the headline that drastically overstates the the phenomenon in question by implication is something that has been workshopped and is commonly used because it turns something kind of boring into a spectacle.
Same experience with Fable. Utterly useless for anything bio related.
Author of the post here wrote Salmon, which is a widely used bioinformatic tool in molecular biology. And the irony is that Anthropic has probably packaged Salmon as a tool in their Claude for Science suite with now remuneration or recognition for the original author.
There has been a lot of recent bs going on in biomed ML with companies publishing without releasing source code, restrictive licenses, etc; which have always given off a whiff of bad citizenship - Ark Institute and Deep Mind I am looking at you - but I feel like this is taking it to a new level.
Leveraging open source bioinformatics code and published methods to take in profit selling into the biotech vertical while restricting access to Fable feels downright cancerous. I think the EA crowd at Anthropic probably has good intentions, but has galaxy brained themselves into becoming bad actors that make Sam Altman and OpenAI look like a paragon of trustworthiness in comparison.
One quick piece of semantic and linguistic housekeeping for the commenters…
Heritable != Molecular / Genetic Mechanism
There is a conflation of these terms in popular discourse that does a disservice to the field of statistical genetics, imo. There are mechanisms of inheritance that operate various length / time scales other than that of biological macromolecules. For example, if you tell me what language your parents natively speak I can tell you your primary language with >90% accuracy.
So before we start getting 3 replies deep into any thead, please remember that retrospective observational data measured with unqualified instruments is notoriously confounded and that we can barely infer causal structure in controlled functional genomics experiments (much less a GWAS of phewas). So let’s all please keep an open mind and not be so certain about our beliefs.
This comment reads as if it were dropped into a generic "genetics of lifespan" thread,. The Dynomight article is already making a much more sophisticated version of some of these same points. The article's central argument is precisely that heritability is a contingent observational statistic, not a Platonic form. This particular article isn't conflating heritability with genetic mechanism at all. It's interrogating a simulation model and its assumptions. The warning about "unqualified instruments" and "retrospective observational data" feels off as this paper isn't a straightforward observational study. it's a parametric simulation fitted to twin registry data.
This comment might be very useful in a Reddit thread full of people saying "50% of lifespan is in your DNA," but it's a bit off-target as a response to this particular article.
Agreed, the Dynomight article is on-the-mark. I work in this field and was really puzzled when I read this paper. Yes, it is obvious that excluding extrinsic causes of death will increase heritability estimates. But is death from influenza genetic or extrinsic?
The typo on the first page of the Science article is on the authors, not the editors.
The accurate version of the result would be something like: “if you model lifespan as aging + i.i.d. noise and dial the noise to zero, heritability of the aging component is ~40-50% in our model.” Which is barely a finding, since by construction reducing i.i.d. noise has to increase heritability of whatever non-noise remains.
This would require an accurate definition of ageing. What is ageing? How is it related to life span? Because in theory, there can be definitions of ageing that are not tied to life span. For instance, do bacteria age? Does this affect life span? What is the life span of a bacterium anyway? Does hydra age? (For those who don't know much about biology: literally everything ages, if you define ageing as functional decline over time. Even viruses would age, if you define it as functional infections possible plotted over time. Does DNA and RNA age? The definitions become blurry; almost no molecule is immune to changes and modifications, so just about anything would age. So it really depends on the definition, and we need to read the definition before we can accept assumptions based on it. Thus: what is ageing and how does it relate to lifespan, as definition?)
> For example, if you tell me what language your parents natively speak I can tell you your primary language with >90% accuracy.
According to the link above, the heritability of the primary language is zero, whereas the heritability of what language(s) a person speaks in general (whether primary or secondary) is not necessarily zero and varies by language.
I believe that your example of "what language your parents natively speak" is incorrect.
Some ways of measuring heritability would have trouble detecting this as environmental, but that is considered a deficiency in those measures, not part of the definition of heritability. Any serious study into heritability of language would quickly find it is largely due to the common environment.
We have many bits and pieces of causal structure for some human traits courtesy of GWAS and PheWAS but you are right that lifespan genetics of humans is seriously compromised by rapid changes in life styles and environments.
Heritability has a very specific meaning in quantitative genetics [1], which in many ways is not what your intuition would suggest [2]. It is this usage that the article talks about that.
That said, there are plenty of critiques of this definition of heritability, and not just because it is different from what a layperson would expect it to mean.
For example, the way it is used also usually has a big problem in that the standard formula assumes that Cov(G, E) = 0 (or at least is negligible), whereas in practice that is not actually true [3, 4].
This definition of heritability is also mathematically flawed in that it assumes (without evidence) that P = G + E, or at least can be reasonably approximated this way. Given that human development is the result of a feedback loop involving genetic and environmental factors, one would expect a model closer to something like a Markov chain. Proposed justifications of a simple additive model as an approximation (e.g. via the central limit theorem for highly polygenic traits) have to my knowledge never been tested.
More recent genome-wide association studies [5] have actually shown a considerable gap between heritability estimates from genotype data and heritability estimates from twin studies, known as the "missing heritability problem".
OP has another post on the definition of heritability, which I really liked: https://dynomight.net/heritable/ . I'm a layman, though, so since you seem knowledgeable, I would love to hear your thoughts on that article!
For instance, OP's definition H = Var[G] / Var[P] seems to bypass the issues you mentioned:
> For example, the way it is used also usually has a big problem in that the standard formula assumes that Cov(G, E) = 0 (or at least is negligible), whereas in practice that is not actually true [3, 4].
> This definition of heritability is also mathematically flawed in that it assumes (without evidence) that P = G + E, or at least can be reasonably approximated this way.
> For instance, OP's definition H = Var[G] / Var[P] seems to bypass the issues you mentioned:
No, this is exactly the definition I am talking about. The problem is that while theoretically you could work with Var(G)/Var(P) even if Cov(G, E) ≠ 0, studies are not designed to capture that.
In fact, the standard ACE model [1] used in twin studies explicitly assumes among other things that there is no gene-environment correlation. This means that it gets silently added to one or more of the ACE components; not because of any ill intentions, but simply because if you included covariance, the resulting system of equations would be underdetermined and could not be solved [2].
But to make matters worse, gene-environment correlation/interaction itself is disproportionately absorbed by the A and C components rather than E. All this can lead to inflated heritability estimates.
And to clarify, I am not making any pronunciations about how much relevance or magnitude that effect has; for all I know, this could in the end be a minor effect. My point here is that there is a lot of mathematical handwaving going on with very limited testability of the modeling.
[2] If you want to be precise, you need to actually distinguish between gene-environment correlation and interaction and use P = G + E + (G x E), but that makes the system even more underdetermined, because now we have both Cov(G, E) and Var(G x E) to worry about.
> Heritability has a very specific meaning in quantitative genetics [1]
Literally the first paragraph of that page is
> Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"
That matches what I assumed it meant, and it seems like OP and the post are arguing that that is some kind of surprising interpretation.
> OK, but check this out: Say I redefine “hair color” to mean “hair color except ignoring epigenetic and embryonic stuff and pretending that no one ever goes gray or dyes their hair et cetera”. Now, hair color is 100% heritable. Amazing, right?
Uhm, no. That is exactly what I (and I think most people) would expect the answer to be.
> That matches what I assumed it meant, and it seems like OP and the post are arguing that that is some kind of surprising interpretation.
The unintuitive part is that in quantitative genetics, heritability is defined in terms of variance in traits at the population level, not as the passing of traits from parents to offspring (that would be heredity [1]). Of course, I may have misinterpreted what you said in your OP when you cited the wiktionary definition of "[g]enetically transmissible from parent to offspring", and if so, I apologize, but at the time it seemed to me that you were talking about heredity.
> Uhm, no. That is exactly what I (and I think most people) would expect the answer to be.
What the article is talking about is that if you fix Var(E) = 0, then Var(P) = Var(G) in the standard heritability model, i.e. all phenotypic variance is explained entirely by genotypic variance (because in that model, Var(P) = Var(G) + Var(E)).
Fun fact (even if only tangentially unrelated): In Western countries, wearing glasses is a highly heritable trait, because wearing glasses is a strong proxy variable for refractive error [2], such as nearsightedness, which is highly heritable. It is often brought up as another example of how the quantitative genetics definition does not match conventional use of the word.
The heritability statistic that occurs in the literature is the ratio of genetic variance to phenotypic variance.
Two corrollaries:
* When discussing heritability results from the literature, we are discussing that statistic, not your intuitive understanding of what the word should mean.
* In the scientific literature, your conception of heritability doesn't operate. In the scientific sense, the number of hands you have has low heritability, despite being genetically determined.
I think you're going to find "let's check Wiktionary" is not the decisive move in these kinds of discussions that it is elsewhere.
Another great example of the unintuitiveness of heritability is the fact that earrings are highly heritable. Earrings are highly correlated to a specific genetics (being female), so they're very "heritable", even though that correlation is an arbitrary cultural fashion.
See my sibling comment. This is misleading for the same reason, but in this case the cause of misleading is narrowing the timespan under consideration to approximately now.
> In the scientific sense, the number of hands you have has low heritability, despite being genetically determined.
This is only a surprise because unlike layman the author of this joke insists on considering heritability among humans specifically. While "heritability among humans" sounds like a reasonable comment to a layman, the author of this joke is misleading the layman, because layman (before being mislead) correctly thinks of "heritability" as "heritability among all living things with genes".
There is a genetic component to alcohol use disorder, for example. But if one is in an environment where there is no access to alcohol whatsoever, then that person, despite their genes, will not develop an alcohol use disorder. The disorder can still be passed from parent to child, but it's more complicated than just genes.
I heared the same distinction as OP, but it is the other way around, it's the degree to what a trait is inherited from you parents which cannot be explained by the enviroment or Random Chance.
What you're expecting heritability to mean is essentially "are genes responsible for expressing this trait", which is very different from "can I get this trait from my parents?" which does not impose any particular method for passing on the trait.
If the study doesn't use sequenced genes of parents and children as input into the model, it can't make the distinction between genetic or non genetic influence by parents.
That is exactly wrong. The measure of heritability used in the scientific literature is very much tied to genetics, just not in a very direct way. That is, heritability is a measure of how much of the variance in a trait is explained by genetics vs environment. In this sense, wealth will have a relatively low heritability, because it is weekly tied to genetics, even though it is very much a trait most people inherit from their parents. Skin color will have a high heritability, because the variance in skin color is almost entirely explained by genetics.
The unintuitive part is that traits with almost no genetic variance at all, such as the number of arms, have very low heritability - since, in a population study, almost the entire variance in the number of arms will be explained by environmental factors (very very few families have 1 or 3 arms as a recurring trait - and there are way more people who lose their arms during life).
"Welcome to science hell, professor. This is IshKebab, he once saw something on the internet about your field of expertise and is going to spend eternity lecturing you on it."[1]
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