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.

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.

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.

That is where something like Matryoshka embeddings has appeal. You trade a little bit of performance for a guarantee of training + validation set coverage.

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?

> 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.

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.

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.

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.

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

0. https://pubmed.ncbi.nlm.nih.gov/35318324/

1. https://www.nature.com/articles/s41586-023-06127-z

I saw something similar in ML when neural nets came around. The whole “stack moar layerz” thing is a meme, but it was a real sentiment about newer entrants into the field not learning anything about ML theory or best practices. As it turns out, neural nets “won” and using them effectively required development and acquisition of some new domain knowledge and best practices. And the kids are ok. The people who scoffed at neural nets and never got up to speed not so much.

Edit: as an aside, I have learned plenty from reviewing coding agent generated implementations of various algorithms or methods.