YHK - yet another hacker news clone

Any statement from Nostradamus yet?

Dario Amodei is in pure snake oil territory now.

It would be a sign of some kind of mental derangement… so likely it’s cynical hyping beyond all realistic prospects.

AI has made good progress in text, code, math, digital images, and digital music. Basically troves of data that can be automated and trained on, validated, and with a purely digital interface. Outside of that, just gathering the data will be difficult, regulated, and sparse (that is if anyone will even give it to you now). While we can write a test to make sure we could generate drugs of the past, verifying we have generated any new working correct drugs is an RL loop with one iteration costing years and millions of dollars.

If he's selling (figuratively) Anthropic IPO, I want to buy it.

Few persons were publicly as wrong about LLMs as he was.

The most interesting difference between then and now is that we didn’t have tech enthusiasts getting quite so invested in the conversation. Possibly because we were all familiar with IBM’s decades-long history of shipping overhyped and underwhelming products.

Unfortunately we seem to have yet to realize that that’s a habit that’s endemic to the tech industry. Instead we still see it as thousands of isolated cases that, only by sheer dumb coincidence, happen to comprise the bulk of the tech industry.

I wonder if AI will also cure psychopaths.

Knowing how little Claude has “cured” software engineering, this is just garbage self-aggrandizement.

Amodei seems to be yet another one of these founders who don’t know what they don’t know, but speak very loudly and proudly of their ignorance.

One might even say he seems manic and unwell.

"a video tied to the IPO launch in which he made a bunch of overpromise about medicine that would have made Elizabeth Holmes blush."

On the plus side, it's unlikely that Claude wrote this because it would have written the correct plural form of "overpromise," but that's just sloppy editing.

Some people legit think there is no need to go to school, work, learn or care about anything because Musk, Amodei, Altman &co promise them "universal high income" and eternal life in 10 years...

He's completely and utterly high on his own supply. That is what happens when you get told all day everyday how great your ideas are.

https://garymarcus.substack.com/p/math-is-hard-if-you-are-an...

I'm sure he'll be right this time around, just like, well, nevermind

History doesn’t always repeat, but it often rhymes.

Ironically the Eli Lilly guy sounds exactly like Claude Opus 5

Guy sounds like he has some major skeletons in the closet. Seems like many rich people go through this odd manic phase before the breakdown too.

Just do not feed that vague reward signal in their AGI training run because combined with Amodei & bros being firmly in the war hawk paranoid anti-China camp they can make us and our diseases all relics of the past just to prevent offline local inference with chinese open weight models.

[1] https://en.wikipedia.org/wiki/AI-induced_psychosis

When I hear Amodei et al talk about biology, all I can think of is that we're really bad at teaching biology to people. There's an intuition and understanding gap here that's leading to billions of dollars of trouble.

There's a famous study in synbio that led to standardization called, Reproducibility of Fluorescent Expression from Engineered Biological Constructs in E. coli

https://journals.plos.org/plosone/article?id=10.1371/journal...

88 labs measured "fluorescence from three engineered constitutive constructs in E. coli." You should eyeball the results yourself first,

https://journals.plos.org/plosone/article/figure/image?size=...

That's the result these machines spit out for the same set of samples being measured across 88 labs. This was termed a "remarkable degree of precision" (for biology) of 1.54x sd(!).

Teams couldn't converge on instrument-to-instrument variation within the SAME lab, https://journals.plos.org/plosone/article/figure/image?size=...

So the reading is different depending on when you measure it. For some reason, people in silicon valley think that biology is like classical logic and computing. You write some genetic code and you get expression out. It's not.

That's the pop-sci view of a real process. In reality, viruses and bacteria are extremely fickle, and hard to grow. A lot of the synthetic biology results aren't easily reproducible even if you know the protocol.

So you're telling me that you can fix that with "just" AI and "robots?"

Well... I would hate to break it to folks. But applying neural networks and ML to biology have been a thing since the dawn of AI. It was one of the very first applicaitons people thought about.

It used to be called computer-aided drug design, then they went all in on expert systems, then there was a time when quantitative structure–activity relationship AIs were a thing, then there was teh deep learning phase... D.E. Shaw actually did a ton of work on it, https://www.deshawresearch.com/technology.html he used his money to buy a supercomputer and used that to train neural networks...

There have been blockbuster drugs that have come out of this. Tirzepatide / mounjaro was discovered by algorithmically iterating through sister-compound families of the peptide they'd found in Gila monster venom until they found just the right one.

The issue that Anthropic and Open AI face here is that there's no low hanging fruit. There's no free lunch. It's very unlikely that Claude or GPT can do something that hasn't already been tried at some point in every AI/ML research era.

So they won't be able to "just" discover a new drug. If that was possible, then it would have already been iterated into by sheer accident based on the number of projects that have tried some variation of this with some sister architecture over the many decades preceding us.

Now where these machines do help is that they help people get started, and they also help working scientists. It's boring stuff, but think stuff like, "hey my machine isn't working, it's made by such and such. Can you write code to help me save my data?" or, "Can you proof read this grant?"

It will even accelerate the already tried approaches as it allows a smaller team to try more AI/ML algorithms than they would necessarily have had the expertise to execute before.

But will Claude magically solve medicine? No. I'm sorry, but it just doesn't work that way. And I'm not sure why they believe this, but I keep repeating this over and over again... A lot of this AI and biology stuff is nonsense. Standard undergrad and post-grad stuff whipped into fervor and fluff.

You've gone down a bad path if you're making the pharma companies seem like the good guys.

https://futurism.com/six-months-anthropic-coding

While these people are obviously professionals in their field, anyone who touched pharma/bio IT with a ten foot pole understands how much there is to improve there. And that's just the beginning - imagine how much there is to improve in understanding of the body overall?

It's not about "AI will discover a cure for X in 10 years".

But it's about having the applied intellect of a an engineer, chemist, biologist, and all mixtures of that, available on demand, in a second, at scale, enabling you to do everything around it faster, better and cheaper.

Every step of drug discovery, synthesis and testing can be improved.

Over 90% drugs tested in humans fail. And maybe 0.1% of overall discovered ones. While yes, testing on humans is the bottleneck, we can fix problems around the bottleneck easily.

If you had a data processor and 90% of data that goes into it is corrupt, the processor isn't the bottleneck yet - the data quality is.

If we can create better biological models to test these drugs, better interaction models, improve pharmacokinetics, improve yield, we can go from 1000 discovered, 1 works, to 1000 discovered, 100 works. We can predict some candidates will fail due to better models and not test these. We can improve how they are delivered to the body and make them more effective. We can catch potential side-effects early on and avoid going to trials at all.

This is what AI can do even nowadays, let alone 5 or 10 years from now.

And that is a huge improvement. It revamps the industry.

It seems to be thought that AI will help invent these novel treatments, and then Anthropic will profit from that.

When in reality, who will actually produce, distribute and administer these treatments? Not Anthropic - or any of the big AI companies.

Self driving cars roll past my office every couple minutes, even though I was told AI would never be able to drive on public streets.

AI images and videos are indistinguishable from real videos, even though I was told they'd never be able to generate hands correctly.

But I'm sure the naysayers are right this time, AI defintely won't ever be effective at medical research.

I have to assume that they're probably doing the same with medicine.