Not to be a luddite, but large language models are fundamentally not meant for tasks of this nature. And listen to this:
> Most notably, it provides confidence levels in its findings, which Cheeseman emphasizes is crucial.
These 'confidence levels' are suspect. You can ask Claude today, "What is your confidence in __" and it will, unsurprisingly, give a 'confidence interval'. I'd like to better understand the system implemented by Cheeseman. Otherwise I find the whole thing, heh, cheesy!
Can't LLMs be fed the entire corpus of literature to synthesise (if not "insight") useful intersections? Not to mention much better search than what was available when I was a lowly grad...
Call me when a disinterested third-party says so. PR announcements by the very people who have a large stake in our belief in their product are unreliable.
This company predicts software development is a dead occupation yet ships a mobile chat UI that appears to be perpetually full of bugs, and has had a number of high profile incidents.
"This company predicts software development is a dead occupation"
Citation needed?
Closest I've seen to that was Dario saying AI would write 90% of the code, but that's very different from declaring the death of software development as an occupation.
What quotes? This is an AI summary that may or may not have summarized actual quotes from the researchers, but I don't see a single quote in this article, or a source.
Why are you commenting if you can't even take a few minutes to read this ? It's quite bizarre. There's a quote and repo for Cheeseman, and a paper for Biomni.
There is only one quote in the entire article, though:
> Cheeseman finds Claude consistently catches things he missed. “Every time I go through I’m like, I didn’t notice that one! And in each case, these are discoveries that we can understand and verify,” he says.
Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
>Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
Why? What purpose would quotes serve better than a paper with numbers and code? Just seems like nitpicking here. The article could have gone without a single quote (or had several more) and it wouldn't really change anything. And that quote is not really vague in the context of the article.
Conflict of interest is a thing. The researchers could be AI hallucinations. The quotes could be too. Or the researchers could be real and intentionally saying things that are untrue. Who knows.
What is interesting is that HN seems to have reached a crescendo of AI fanboi posts. Yet if you step outside the bubble the Microsoft and Nvidia CEOs are begging people to actually like AI, Dell's come out and said that people don't want AI, and forums are littered with people complaining about negative consequences of AI. Go figure.
Anthropic puts out plenty of AI slop. I'll wait for a human who doesn't have a financial interest in propping up Anthropic to review the slop before passing judgement.
> scholarly dark matter that exists to pad CVs and satisfy bureaucratic metrics, but which no one actually reads or relies upon.
Is it cynical to believe this is already true and has been forever?
Is it naive to hope that when AI can do this work, we will all admit that much of the work was never worth doing in the first place, our academic institutions are broken, and new incentives are sorely needed?
I’m reminded of a chapter in Abundance where Ezra Klein notes how successful (NIH?) grant awardees are getting older over time, nobody will take risks on young scientists, and everyone is spending more of their time churning out bureaucratic compliance than doing science.
Have you done anything interesting with this army that you can share and you're proud of? Specifically something concrete you can link to, not just something you can imagine or describe?
> Most notably, it provides confidence levels in its findings, which Cheeseman emphasizes is crucial.
These 'confidence levels' are suspect. You can ask Claude today, "What is your confidence in __" and it will, unsurprisingly, give a 'confidence interval'. I'd like to better understand the system implemented by Cheeseman. Otherwise I find the whole thing, heh, cheesy!
There should be some research results showing their fundamental limitations then. As opposed to empirical observations. Can you point at some?
Citation needed?
Closest I've seen to that was Dario saying AI would write 90% of the code, but that's very different from declaring the death of software development as an occupation.
> Cheeseman finds Claude consistently catches things he missed. “Every time I go through I’m like, I didn’t notice that one! And in each case, these are discoveries that we can understand and verify,” he says.
Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
Why? What purpose would quotes serve better than a paper with numbers and code? Just seems like nitpicking here. The article could have gone without a single quote (or had several more) and it wouldn't really change anything. And that quote is not really vague in the context of the article.
What is interesting is that HN seems to have reached a crescendo of AI fanboi posts. Yet if you step outside the bubble the Microsoft and Nvidia CEOs are begging people to actually like AI, Dell's come out and said that people don't want AI, and forums are littered with people complaining about negative consequences of AI. Go figure.
Funny you say that.
Taking CV-filler from 80% to 95% of published academic work is yet another revolutionary breakthrough on the road to superintelligence.
Is it cynical to believe this is already true and has been forever?
Is it naive to hope that when AI can do this work, we will all admit that much of the work was never worth doing in the first place, our academic institutions are broken, and new incentives are sorely needed?
I’m reminded of a chapter in Abundance where Ezra Klein notes how successful (NIH?) grant awardees are getting older over time, nobody will take risks on young scientists, and everyone is spending more of their time churning out bureaucratic compliance than doing science.