pfisherman

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.

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.

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.

Do US companies have free access to Chinese markets?

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.

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.

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?

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?

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.

In some ways domain experience can be a hindrance, with ingrained pathways and practices shaped by constraints that no longer apply. My personal opinion is that you probably want a mix of domain experts who are enthusiastic about AI and some kids who are free of preexisting dogma, and are willing challenge assumptions and try out things that the old heads might chafe at.

An example from software engineering is that all production code should undergo meticulous human review. Saying “no” to this sounds crazy to an experienced SWE, but might not actually be that crazy.

I don’t know about this. After some time sitting with it, I think that mid level and senior ICs - especially those slow to adapt - are going to be at risk of getting replaced by entry level “AI native” kids. Net on net it probably washes out to “normal” patterns of turnover and hiring once things settle.

Think “Smithers, we need to hire some of these kids who know computers!” Only fast forward about 30 years and str.replace(“computers”,”agents”).