Evidently if you fan-out data analysis work across 1946 agents instead of just one, the analysis is faster and more thorough than if you instructed a single agent to go through all of the 1946 genes sequentially. And that, in turn, is faster than if you did it yourself. Like, duh.
The linked paper is not exactly transformative, but the impression it leaves is quite a bit better than the nonsense press release.
Its not surprising to me that this press release looks half-baked.
To me, this mostly shows how much our baseline for what counts as “noteworthy” has shifted since ChatGPT came out. I’ve seen more disruptive events in the last couple of years than in the 30 before that.
What would it take to get you excited again? AI discovering that aliens exist?
In which case congratulations you’ve just been promoted to manager with no pay increase!
Joking aside Ai is doing a large chunk of my job but all it’s done is shifted what I actually spend time on.
It’s not really saved any man hours.
Think of it this way. It's as if a public company gives you a certain amount in RSUs that won't vest for a while. A year later, the company is worth 10x more. So they list the increased value of the RSUs they owe you as a loss.
In reality, the fact that this "debt" has ballooned so much is a positive sign - the valuation is high (it's a separate issue whether that valuation is realistic).
Their "real" losses are about $8B.
Also the handwritten code doesn’t remove the work. If anything, it makes the thinking more difficult (for me) because instead of thinking during the coding, I have to before and after (in iterations).
For big projects I actually go back to tradcoding some to make sure I understand the system, since I’m the one responsible.
Like a lot of tech before it I think the overriding theme is misapplication during a hype cycle.
Im convinced the people letting llms do their jobs are going to get replaced, not by agents but people who use them more reasonably.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.
That's what top-tier scientists are good at.
Personally I would forgive all of the joy being stolen by AI if they discover something fundamental and outstanding that would help all of us.
This is the weakest possible rationalization against the AI takeover. Solving real world problems isn’t “stealing joy”. Those problems shouldn’t be gatekept. It paints our scientists as rent seekers.
Well, pretty good amount of the training data was stolen. Not only from the repositories without a license, but also archives of books, and they even steal training from each other. Funny enough, they still blame each other for stealing. I don't mind if you think your joy is not stolen. But I mind if you insist when your opinion is the only right one and joy has definitely not been stolen. It's quite sensitive territory starts here, and I don't think anyone (incl.yourself) has a right to speak for everyone in that regard.
I just don't understand it. No mathematician would be upset if a total amateur solves a centuries old problem - even one they've worked their whole life on. They cheer and celebrate it.
Nor do they oppose the spread of math knowledge.
Nor do they have high salaries. Nurses make more.
Where is the "rent seeking"?
All those scientists can go pound sand as far as I am concerned. I don’t give a shit about “joy” I care about curing diseases and solving the universe’s mysteries. If AI can get us there faster then I’m 100% for it.
Exactly. Fuck these selfish pieces of shit for delaying me getting administered heroin in a cube until the heat death of the universe.
> solving the universe’s mysteries.
What do you get out of solving the universe's mysteries then? Ennui?
Clinical science is inundated with well-reasoned "potential leads". Ideas for targets, and even designing drugs to interact with those targets in the desired way, is not the bottleneck. Hasn't been for decades. The bottleneck is in clinical development i.e. human trials.
There are many many AI drug discovery companies out there, many of them very well funded, and ALL of them with no shortage of "potential leads".
These companies usually start out with the idea that their potential leads are better than the alternatives (generally because they cast a much bigger net/ considered more screening criteria than a human could, thanks to AI) and that big pharma will pay them license fees.
And yet, all of those companies, pretty much without exception, have turned into drug development companies themselves. They are forced to develop their own "potential leads" into actual approved drugs. And they are subject to the same timelines, same costs and - so far - the same probabilities of success as any other drug developer.
PerturbAI & OpenAI's "potential leads" might be better or worse than the next AI company. I would guess worse, given there is zero proprietary data involved and no clarity about why the underlying models are going to generate insightful findings. Either way, not useful.
A previous blog post from Anthropic used the mention of "CRISPR-like" to activate a mini hype cycle by association with a well-known groundbreaking discovery. Without that kind of manipulative hook these announcements are pretty thin gruel and below the bar of thousands of Pubmed papers that never made it into public view.
Seems quite inefficient to me. Unless the goal is to spend millions in API credits.
You dont need too, I know like 2 people out of 30 devs I know closely who legitmately accomplish anything behaving this way and they were superhuman before AI as well, it probably has little to do with just having access to lots of agents.
So what if DCTN1 is linked to Alzheimer's? All they will do is invent a drug to mess with the enzyme and it might only still help a small percentage of people and no one will be able to afford it.
It is well know that diet plays a large role in Alzheimer's[1] and is largely an issue of personal genetics and environment.
[1] https://news.harvard.edu/gazette/story/2024/07/alzheimers-st...
I use LLMs every day but damn... can we please market them and value them as a coding tool and not the savior/destroyer of the world. "new leads" comes off flat after the previous tone