ordersofmag

I think you confused "can't" with "shouldn't".

How to say you've never met a physicist....

If you're confused about who to contact to follow up on the ideas of a particular paper then write to the first author, or whichever author is listed as the contact person. This is not hard and is an orthogonal consideration to the length of the author list.

Which itself not necessarily a sign of fraud: https://www.science.org/content/article/physics-paper-sets-r...

These are all silly non-problems (speaking as someone who works with lots of professors around how to put AI systems to effective use)

-the thing that motivates you doesn't have to be the same thing that teaches you (e.g. a book can be a useful learning tool even if it isn't human)

-you can absolutely choose to interact with an LLM in ways that generate productive struggle. If you naively ask it for answers it won't be much use in this regard. But that's just a poor choice of how to use the tool. Just like hitting yourself on the head with a book doesn't mean the problem is with the book as learning tool.

-You don't need structured content 'on long scales' to learn. You learn a little bit at a time. Long textbooks are a side effect of the economics of moving paper around. Not a prerequisite for learning. An LLM will happily mimic the table of contents of a textbook or an outline for course. And you can use future prompts to generate the bits you need as you need them. One-shoting a textbook is a silly use of the tool. Plus, we're not short on textbooks. We're short on ways students can get customized help: answers to the specific ill-posed questions they have in their heads. LLM's are quite good at this. You can have textbooks AND LLM's (and professors)

-LLM's are happy to disagree with you. Just ask them to and they oblige. Ask them to respond as if they were a tutor and they will mimic the assuredness if appropriate.

Spoken as someone who has never seen a weather forecast. Computers have long produced results that were, if interpreted naively, incorrect. These systems require humans to take into account the conditional, probabilistic nature of the output and the inherent limitations of the model the computer was executing, when they interpret the output. LLM's are no different. They give you next token prediction probabilities. That is all. There is no particular reason to think that comes (or even could come) with a promise that those tokens will express truths about the world and only truths. The fact that greed-driven hype, and our human inclination to anthropomorphize the token generator inclines us to imagine things should be different doesn't make it so. No model output is truth. Never has been.

I'm genuinely unclear on what you mean here. Do you mean: 1. Most mathematicians are currently largely paid as part of their academic job (as a faculty member) and will continue to do that work (including the research part) even if extra-mural grant funding goes away. That seems entirely plausible to me.

Or do you mean? 2. Real mathematicians will continue to devote a significant part of their life/energy to mathematics research as a hobby, even if no one is paying them (in any way) to do it? That seems more of a reach and makes me wonder if many currently folks current employed as mathematicians aren't 'Real mathematicians' from your point of view.

If what you're trying to build is a perfect match for the full set of abstractions React provides then it's gonna be cleaner to use React than have AI re-invent it. But most cases I run into don't fit that description.

The LLM does not determine the next token. It generate odds for all of the tokens it knows as to their likelihood of being 'next'. It's up to the harness running the LLM (and in most cases the a temperature setting) to actually decide on a particular next token. I think it's more accurate to call the thing the LLM actually generates (an ensemble of probabilities) a 'prediction'. It might be accurate to say the harness decides on the next token based on the prediction from the LLM. The role of the LLM is much more akin to predicting your opponents move than deciding your own.

Sounds like a very narrow view on what constitutes science. There are many fields of science where there is existing data against which new ideas can be tested without additional 'real-world' measurements. Newton's theory of gravitation relied entirely on pre-existing astronomical data for which there was no existing unifying theory. He made progress by putting forward a theory which explained that data. Now you can argue that it's not really science unless you include the original data collection and subsequent real-world measurement validation steps. But I'd be comfortable saying that Newton was indeed a scientists and did make progress in science despite only doing what some might say is the 'middle' part of the process. There are plenty of modern analogs where work like this sits out there waiting to be done using existing data.

Yes. But you don't need to monitor glucose levels to do this. Because it's well known you can productively use as much as you could manage to consume (without vomiting). So all the device will ever tell you (if you're in the middle of significant endurance event) is that you need to raise your blood sugar. It'd be no more actionable than a writing 'your blood sugar is low' on your hand with a sharpie.

If 'democratic values' are everyone having an equal voice in how the systems of a country are arranged. And if those people, through a democratic process, decide that the systems of their country shouldn't support the exploitative elements of capitalism, then it would definitely be democracy in action to make that come true. Anything counter to that would be, by definition, anti-democratic.

When you say "socialism conflicts directly with democratic values" I genuinely have no idea what "democratic values" you're talking about.

So is a zip file of source code. There is no 'compilation process'. Model training isn't a fixed end point. You start with random weights. You train. The weights get better. You do this iteratively. At some point you say 'good enough' and release. People doing further training would start with those weights and further iterate. Demanding the original training data and training process would be equivalent to demanding a completely replay-able sequence of editing steps starting with a blank text file and allowing you to replay ever edit that led the original author to the released source code.

Now there are reasons you might want to know about the training data when you wouldn't care about the authoring process used by a traditional open-source process. And these get at the reason LLM's are different than traditional software and so maybe our existing definitions of what 'open-source' means aren't a good match for LLM's. Of course there is software associated with LLM's (beyond the weight) -- defining the structure of the particular neural net those weights fit into. In every open-weights model that I'm aware of that software is open source (though trivial).

99% of AI users these days aren't using a 'raw' LLM. They are interacting with a harness that includes tools to do search of the live (or recently crawled) web. And so the output users actually see could absolutely include correct citation of sources. So the 'architecturally impossible' bit may be technically correct but it is not practically relevant. Now whether those harnesses do a good job of orchestrating LLM output to get accurate citations (and whether they are transparent about their process) is another thing entirely. But if you're contemplating 'what LLM's can do' and aren't taking into account the harness and tooling ecosystem they are embedded in then you're missing the point.

In my previous life I did similar computational physics work with similar environments. I ran into a similar set of problems and found that with a little work one can cobble together tools to automate all the steps that are a hassle. And then the hassle goes away. And this was pre-Claude Code. And pre-Google. And actually pre-ssh. Good old telnet for me. Some shell script or a little AppleScript. Granted no 2FA in those day. But the same learned helplessness with people sitting in front of a device who's entire purpose is to automate things and when presented with a task that would be better automated they fail to use the tools at hand to solve the problem. Kids these days (and back in those days too for that matter).

Attempts have been made starting back in 2005 https://www.congress.gov/bill/109th-congress/senate-bill/786...

Nope. The main cost of the old climate.gov was the salaries of the folks writing the articles and pulling together the resources. They were not getting paid exorbitantly and are quite interested in still getting paid. Source: I am one of those people.

The site wasn't (isn't) about the data. It's about articles that contextualize the data. The money raised has allowed them to stand up a new site with all the old articles (which truth be told were all still nominally accessible via the internet archive) and will help fund them to create new ones. So it will stay relevant by paying the people who in the past worked for NOAA to creation the content, to now create the content paid for by donation.

I think the point is that there has been a push to move away from this data continuing to be available from these sources.

The grass is there to keep the dirt in place.

If you want to get pedantic we can't simulate anything with complete accuracy in the absence of a theory that encompasses all the known forces. Which we don't have. (Damn you gravity. Can't you just get along with the others)

To a useful level of accuracy we can certainly simulate water. And we can do the same for a single proton for some definitions of useful (but not other definitions).

It could be that once we truly understand math in a complete way it would lead inexorably to the definition of one and only one possible universe with only one possible set of rules and c and G would simply fall out naturally. I'd agree it seems unlikely given our current understanding of math and physics (and their relationship to each other). But given both are incomplete it remains a possibility. The one theme that seems to hold true as we dig deeper and deeper into how the world works is that the fundamental rules seem to get more and more unified.

But the metaphorical goal is to cover distance not get fit or to make the best use of what you trained for. A trained runner on a bike is faster than a trained runner.

At least if the metaphor is about coding as a means to creating usefully functional code as efficiently as possible. Careful coding by hand may eventually be a hobby activity.

Personally, while I do get some satisfaction in coding by hand it was always the production of something useful that I found most rewarding. I was never someone who wrote code for a hobby. With LLM's I'm more productive. And I find that very satisfying.

I had a box set up as NAT (running amazon linux) when we moved from a local datacenter to AWS in 2012. Shut it down last year. It had not been rebooted. Should have grabbed a screen capture of the uptime. Part of me wanted to leave it to reach 5000 days....

Perhaps LLM's do replicate the important features of "neurons and synapses and learning" and perhaps a "statistical modelling of our words" is pretty much what our brains are doing? Most of the counter-arguments I've seen boil down to "but humans are special" which I'm not sure I find compelling.

Not saying LLM's are conscious. Just that much of our amazement about their behavior seems to say more about us realizing the things we can do are not magic, rather than them being so amazing.

Tell me about the techniques you use to ensure all the code you use is 'correct'. and then explain why those techniques can't also be used by an AI.

It's not that you're prompting it wrong. It's that you're judging the output against a standard (human intelligence) that just isn't relevant--no matter how much we want it to be and no matter how much the fluency of the output tricks us into thinking there's a human-like mind behind it.

Now granted, if the boat salesmen were pushing hard on the idea that the boat would fly and even put little wings on the side and I bought the boat I might get really angry when I found out that it didn't fly. And I might angrily storm into the salesroom yelling about how the design is defective. But if someone pointed out 'hey, it's a boat perhaps you should stick to sailing around in it and stop getting your undies in a bundle about it not flying' the correct response is probably to take a closer look, ignore the salesmen, and cruise around the lake. LLM's are quite handy at some things and have some weird limits. Learn the limits, enjoy your time at sea.

You have misunderstood what structural racism is. It is not about the majority of people being racist. Is about the systems being constructed in ways that lead to racist outcomes. You can have a society with zero racist individuals and if they continue to enact the racist systems (perhaps created by racist folks long dead) you'll have structural racism. I don't disagree with the idea that the mis-understanding you have is widespread though, and would certainly be a cause for folks not being comfortable with the idea (as they have mis-understood it).

It's like you're making the best you can of the current situation you find yourself in as an individual while also working toward changing the overall situation.

Or you point your Claude code at a different LLM provider. It's not complicated and there are lots of vendors (and in the open-weights space multiple vendors serving the same models competing on price). Sure DeepSeek 4 isn't quite Opus at the moment. But it's plenty good to do the work. We've got different competing front-end tools and different competing back-end providers. No one 'owns' your company. Maybe that will change as the market evolves and one of the frontier tools become so much better than one vendor will own the market. But that's not where we are now.

I just put the terribly generic query "what tools would you recommend to integrate fraud prevention or account takeover protection into my product" into both Claude (Sonnet) and Gemini (3.1 Pro) via the standard web interface and both took the first step of searching the web. That's consistent with my past experience -- the usual harnesses typically will search the web in cases where I might expect/want them to. Now whether you product has good web visibility or not in those searches and how the LLM's weigh the relative merits of open-source tools versus commercial offerings in deciding what to highlight in their responses is a different issue. As is the change in what constitutes effective SEO in an era where bots, rather then human eyes are the proximal important target. But I don't think the core issue with folks finding your products is the move away from user-driven search toward using models with out-of-date training cutoffs.

FWIW while neither model included your product in it's initial response, when I followed up with "what about open-source" both did another search and Claude's response included your tool....