Anyways, I studied SCLC in grad school and saw lots of scans of people with tumors from their heads to their feet, and saw the enormous resources dedicated to caring for SCLC patients and to searching for a cure. It’s hard to overstate how profoundly evil the cigarette companies were and still are. They got people (children) addicted knowing what was coming for them, knowing they were killing them in horrific ways. Now we all get to pay for that in funerals and tax dollars.

So, in biology a gene's name is sometimes an acronym but it's meaning is generally forgotten

It really shines through in pieces like this that LLM's have a severely constrained worldview and underdeveloped theory of mind. They can't imagine that a line like "A 200-line POC that goes from 0/5 to 5/5 in four proposer steps" means nothing to me as a subtitle for the page. After all "proposer steps" and "5/5" are *right there* in it's context. Surely everyone has "proposer steps" in their context, right?

Comments URL: https://news.ycombinator.com/item?id=47979132

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I mean an amyloid injection is wildly unethical and it’s also not the natural progression of Alzheimer’s. Removing amyloid is a simple matter of investing billions of dollars into drug development. Also how do you tell whether that was actually “causal” if the patients improve after plaque removal.

I mean come on, you have to work the evidence and the experimental tools that we actually have. This kind of epistemic puritanism doesn’t help anyone.

Check out this paper from the Lander lab: https://elifesciences.org/articles/61026

It’s a bit jargon heavy but it’s a nice case study in how tumor growth is controlled through all the same mechanisms that normal tissue growth uses. Even cells with an outright cancerous gene mutation are basically still just doing normal growth and development.

That's the problem, they don't hate these journals, they love them. Generally speaking they're old people who became influential by publishing in these journals. Their reputation and influence was built on a pile of Science and Nature papers. Their presentations all include prominent text indicating which figures came from luxury journals. If Science and Nature lose their prestige so do they (or at least that's what they think)

This was very apparent when eLife changed their publishing model. Their was a big outpouring of rage from older scientists who had published in eLife when it was a more standard "high impact" journal. Lots of "you're ruining your reputation and therefore mine".

A certain age -> which one? Why?

your eyes determine -> How? What molecular growth signaling pathways are involved? How do they integrate with your brain's visual processing centers and how does that relate to "how well [your eyes] focus". Is there a biomechanical signal from muscle stress or eye curvature?

How would you test this? You'd have to change this process somehow to show that the effect is real, but you obviously can't do that with humans, so you'd probably have to use mice, but their eyes are different, but how so?

Without any of this information, it's a nice "just-so" story about cavemen looking at the horizon, but not much more than that.

I’ve never heard about modern people doing serious persistence hunting, except for a stunt that I read about years ago. I think it was organized by like Outside or some running publication that got pro marathoners to try and they failed because they didn’t know anything about hunting

I do find it interesting that they state that each task is done with a fine tuned model. I wonder if that’s a limitation of the current data set their foundation model is trained on (which is what I think they’re suggesting in the post) or if it reflects something more fundamental about robotics tasks. It does remind me of a few years ago in LLMs when fine tuning was more prevalent. I don’t follow LLM training methodology closely but my impression was that the bulk of recent improvements have come from better RL post training and inference time reasoning.

Obviously they’re pursuing RL and I’m not sure spending more tokens at inference would even help for fine manipulation like this, notwithstanding the latency problems with that.

So, maybe the need for fine tuning goes away with a better foundation model like they’re suggesting? I hope this doesn’t point towards more fundamental limitations on robotics learning with the current VLA foundation model architectures