I don't know how people like Matt Schumer can attempt what looks like fraud and deception being chalked off as a giant oopsies (which isn't really convincing) and not face any consequences.

For rest of us, this is a privilege that we don't have. We can't deceive, defraud our investors because it has real consequences....but not for people like Matt Schumer, why is that?

Theranos everywhere? Except you can’t afford to mess up when it comes to health.

If by "multi-modal", you mean "it takes several different datatypes as input or output", then yes, it's multi-modal. See Figure 1 in the Tech Report.

Foundational maybe isn't the best label for this kind of model. My understanding of foundational models is that they are made to be a baseline which can be further fine tuned for specific downstream tasks. This seems more like an already fine tuned model, but I haven't looked carefully enough at the methodology to say.

There is a pretty noticeable improvement for antibody-antigen interactions - looks like double-digit percents. Check out figure 4 here: https://chaiassets.com/chai-1/paper/technical_report_v1.pdf

We already have some pretty horrific and documented/accessible bioweapons.

This gets into the philosophy of restricting access to knowledge. The conclusion I keep arriving at is that we’re lucky that there don’t appear to be many Timothy McVeighs walking around. I don’t think there is a practical defense from people like that.

I think you overestimate the difficulty of discovering bioweapons. There is a reason toxicology is the dead end for tons of drug molecules. It is very easy already to design molecules that will kill someone.

This is as unethical as that time JVC released VHS which allowed people to record videos but also pirate content!!1

Nobody has really been able to make a convincing argument whether these sorts of tools haven't lead to large-scale terrorism through bioweapons because the underlying problem is hard (for a sufficiently motivated adversary), or that terrorists don't have the resources/knowledges/skill, and as far as we can tell, the sufficiently motivated adversaries who have tried either failed, succeeded secretly, or were convinced to walk back from the brink due to the potential consequences.

In short there are other ways to negatively affect large numbers of people that are easier, and presumably those avenues are being explored first. But we don't know what we don't know.

If you're implying that the answer is "yes this is too dangerous", could you possibly give a few examples of technological developments that aren't "very dangerous to release publicly" by the same standard?

For instance, would any of the following technologies be acceptably "safe"?

- physical locks (makes it possible to keep work secret or inaccessible to the government)

- solar power (power is suddenly much cheaper, means bad guys can do more with less money)

- general workload computers (run arbitrary code, including bad things)

- printing press (ideology spreads much more quickly, erodes elite hold over culture)

- bosch-haber process (necessary for creating ammunition necessary to fight the world wars)

The science to restrict is molecular biology (bacteria) or virology, not applied mathematics (AI). These folks can already do some wild things with the materials they have on hand and don't need fancy AI to help them.

Structure prediction is just one small slice of all of the things you'd need to do. Choosing a vector, culturing it, splicing it into an appropriate location for regulation, making sure it's compatible with the environment, making sure your payload is conserved, study the mechanism of infection and make sure all of the steps are unimpeded, make sure it works with all of the host and vector kinetics, study the pathology, study the epidemiology. And that's just for starters.

This would require a team and an enormous amount of resources. People motivated enough to do this can already do it and don't need the AI piece.

There’s still a long way from in-silico prediction to wet-lab validation. You need a full-blown molecular biology lab to test any of these.

Then again, you can just release existing dangerous pathogens. Like, poison a water with something deadly. So you don’t need a new one if you’re a terrorist.

Not a solution, but maybe if a bad actor tried to create a bioweapon, a trusted organization could use this technology as an antidote. Unfortunately this still leaves the possibility of some kind of insidious, undetectable bioweapon.

No, it's a very small piece for what you'd need to make bioweapons.

...as difficult as discovering new medicines, you mean?

Chemistry and molecular biology are fiendishly complicated fields, far more complex and less predictable than what general (and most of the non-biochem STEM majors) imagine them to be.

How do I know? I thought of one brilliant startup idea that would solve so many of the world's problems if only we used computers to simulate biological systems.

Result: https://xkcd.com/1831/

Reference materials:

https://www.amazon.ca/Molecular-Biology-Cell-Loose-Version/d...

I strongly recommend to treat it as introductory-level text on the same level as "K&R - C Programming Language". Yes, all 1464 pages of it.

https://www.amazon.ca/Fundamentals-Systems-Biology-Synthetic...

On the same level as above text, but with more math.

https://www.amazon.com/Introduction-Computational-Chemistry-...

That or any other book on computational chemistry will give you an understanding why it is difficult to design anything of value in biological systems. ML can only help so much.

Also check out this page for entire field scope:

https://en.wikipedia.org/wiki/Omics

The saving grace of civilization is that, for the most part, terrorists are dumb.

I don't think it'd be too difficult. Train a PLM to generate proteins, validate with AF3, and send them off to a lab. You might want to read the ESM-3 paper if you're interested in stuff like this (not affiliated in any way).

Ah yes - thanks! We've changed it to the article title now.

Submitters: "Please submit the original source. If a post reports on something found on another site, submit the latter." - https://news.ycombinator.com/newsguidelines.html

(Submitted title was "Chai-1 Defeats AlphaFold 3")

Fwiw, the authors never actually claimed this. From their technical report [0]:

> Chai-1 achieves a ligand RMSD success rate of 77%, which is comparable to the 76% achieved by AlphaFold3

[0] https://chaiassets.com/chai-1/paper/technical_report_v1.pdf

I believe Manifold does this sort of thing, though I've never used it myself.

-180 it’s a wrapper around alphafold with some pre prompt.

> “-150 a new model isn’t released in the next month claiming it is currently the best at something” would let me retire years early.

An optimist and their seed funding are easily parted.

Thanks for your work and also for your comments of AF3 and Chai-1. It sounds like you are implying there are potentially gross and subtle types of data set leakages taking place between the train and test which are resulting in what seem to be inflated performance metrics? These are pretty serious issues if so. Also I would agree with previous authors that marginal Improvement over sota is proof more that they have recreated something than really made significant new progress. But this has been an issue with LLMs for sometime now. But it sounds like they have some bright engineers from good brand name companies who are coming together with some VC backing of the team to try and do something in this space. I do appreciate that the weights are open. I would like to learn more about their future direction and their training methods