Ugh. Unless this has been actually experimentally verified to be a room-temperature and room-pressure superconductor, it's about as ground breaking as "Yet another promising nuclear fusion candidate theoretically described."
Reading the title I saw the words "room-temperature" and my mind auto-completed it to superconductor, and based on other comments I don't think i'm alone in that.
I agree that it is about as ground breaking as "Yet another promising nuclear fusion candidate theoretically described."
I'm not sure why you would consider new and promising avenues for research to not be ground breaking. If it's an idea worth trying, it's an idea worth trying. If it doesn't survive testing, then it was still worth trying.
Ideas do not transform into hallucinations when they don't pan out.
You could characterise perceiving a fact to be true when it is not as a hallucination.
An idea is not a fact, Frodo Baggins is not a hallucination, but an idea. Believing that Frodo Baggins exists in our world could be considered a hallucination.
Newtons Laws of motion are not hallucinations even though the universe does not run on Newtonian physics. If I said that he told me about them this morning, that would be claiming a fact, not expressing an idea. That would likely be a hallucination.
Hallucinations is a biased term. I guess I could go with made it the fuck up. Because that is what they do.
>Frodo Baggins is not a hallucination
It is if physicist have to spend time to experimentally verify he exists and is a Hobbit.
>I'm not sure why you would consider new and promising avenues for research to not be ground breaking.
Because it was made by the "I made it the fuck up machine" PR division and it wasn't verified. You don't know if its assumptions are correct. At release a PR claimed it found 79 vulnerabilities[1], and of those there were like 10 bugs. Most of them turned to be minor and were fixed in an hour.
> Newtons Laws of motion are not hallucinations even though the universe does not run on Newtonian physics.
All physics models are approximations. However only some are useful.
Newtons laws are useful. Me coming up with theory of emotional particles is not. Me asking for experimental verification of the theory is waste of resources.
[1] This is the daily reminder that in year 2026 Mythos still couldn't count. Turns out 24+14+3+15+26 != 79. It's 82.
This gave me the idea to actually create a full (QED accurate) atomic simulation software. Essentially would allow you to play around with things like this. At a glance my workstation _probably_ has enough compute to handle it. At least to fully simulate at least a few dozen atoms and compounds.
Current frontier LLMs empower effectively anyone with limitless knowledge. Historically, if I wanted to hire an engineer to, say, create something like this I would have needed a multi-million dollar budget. Now, anyone with $200 (or less) can achieve it.
You are vastly overestimating what has been achieved here.
This is something a couple of materials science grad students can do in limited time for poor compensation as well. The expensive budget is for the part that comes next.
Of course, "LLM solves quantum gravity and proves existence of God",
"nah brah, that's easy brah any kid could have done this brah".
This is what you sound like. I also like how the goalposts keep moving on a daily basis, a year ago it was that LLMs can't even write a Hello World program without making an error, but now things like this are "so easy a minimum wage intern could do it."
Isn't there quite a bit of space between "so easy a minimum wage intern could do it" and your original claim that it would have cost millions of dollars to produce these results?
I am not sure how this process looks like. When they "discover" these, what are they actually doing?
The agents ran quantum-mechanical simulations of each crystal with the standard method for this, density functional theory, at two levels of approximation: a faster one (PBE+U) and a slower, usually more accurate one (HSE06). The band gaps and spin windows below come from the more accurate one.
So the agent runs a classic simulation or I am missing something.
Frankly, there is no point in trying to "understand" what an LLM does. Their thought process is effectively undecipherable by humans (it's essentially information arising from information) so even such a "simple explanation" is almost certainly wrong. The agents might appear to have "used this method", but the actual method of computation is far beyond our grasp.
Why are people being so belligerent about this? I thought it's fairly obvious at this point that LLM reasoning is far beyond anyones understanding. Or does anyone have a refutation?
Yes I saw 3Blue1Brown say the same thing in his tutorial on how neural nets worked where he built a simple model to recognize a particular letter. Good reminder.
I've been dabbling with some of my own (tiny) models recently and it's actually shocking at what they can "learn" despite having _zero_ mention of it in it's training data.
This is a strange attitude. When an agent is optimizing a piece of code, comes up with 2 variations, and runs benchmarks on them to figure out which one is faster, then selects one of them based on tradeoffs between performance and other things it reasons about, do you ignore its explanation and all experiment runs?
What are you on about? I have had Fable come up with new shit for me several times (I do research for a living, so actual new shit nobody knew before), and each time it was perfectly understandable.
Of course I don’t know how it got its ideas for what to try. But heck, I don’t even understand how I get my ideas half the time. But the process, like what code it wrote, simulations it ran etc can be understood by (some) humans just fine!
You're confusing the weights of a model and internal chain-of-thought with the output of the model. Yes, we don't know a lot about how the internal mechanisms work. But with the correct prompt, agents will produce a worklog that documents exactly what solutions were tried and how the result was obtained.
I'm under the impression that this kind of modeling is one of the applications that quantum computers are likely to be good at.
I'd imagine there's a lot of documented research which has attempted to find such things using classical computers.
Seems like there would be a lot of well structured context for somebody to use while directing agents to repeat that research, now with updated models once quantum computing is ready for that kind of task.
A lot of the public successes with agents is really LLM-driven local search against an objective function that is evaluated in more traditional ways. This one seems to fit the pattern.
not a classic simluation- a quantum simulation. This means they put a lot more work into representing the wave function of the simulation and modelling quantum effects.
You misunderstood what I said. I mean that quantum calcs are more computationally expensive than classical simulations ("more work"). I am not saying the authors of this blog did anything special.
They ran Quantum Espresso which is ok, but by no means the 'state of the art' for DFT. And in case, any DFT computation has to be taken with a few pounds of grains of salt before getting too excited about it.
No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..
Modeling superconductivity with DFT is tricky, there are plenty of DFT reports from reputable groups explaining why LK-99 should be superconducting. It’s a limitation of the theory, DFT can’t model correlated electron states well, and it’s not great at finite temperature, and both of those are important for superconductivity.
Edit: I somehow missed that this is about magnetic semiconductors (not superconductivity) so DFT is a bit on better footing here. I still think it’s a bit challenging predicting magnetic ordering at elevated temperature, but maybe not as difficult as superconductivity
I think, of course, skepticism around this "LLM discovers X" thing is warranted, and there have been plenty of more recent examples around questionable LLM "discoveries". Just stating this because the LK99 thing I believe was notable as a (supposed) room-temp _super_conductor while this is about a _semi_conductor.
Holy heck. Me too, I got all the way to here getting increasingly confused by the discussion (didn't help that the top comment was from a magnets PhD, which you'd expect in discussion of superconductors, not of semiconductors).
Okay, so this is just semiconductors, which are the boring kind of conductors - still more interesting than regular conductors, but less interesting than train conductors.
You probably meant "I'm taking this with a tiny pinch of salt". The amount of salt is directly proportional to how much of the claim you are willing to accept.
Edit: I stand corrected. According to Gemini:
Me: Does using more salt mean accepting more of that claim?
Gemini: No, it actually means the exact opposite.
If you say you need to take a claim with a huge pile of salt (or a shovel of salt), it means you believe the claim is highly unbelievable and you need an immense amount of skepticism to accept it.
How the Metaphor Scales
• A single grain of salt: "I am slightly skeptical, but it could be true."
• A pinch of salt: "I have a healthy amount of doubt about this."
• A grain of sand / A truckload of salt: "This sounds completely made up, and I barely believe a single word of it."
The salt represents your skepticism, not your belief. Therefore, the more unbelievable the claim, the more "salt" you need to swallow it.
You only do that after spilling salt accidentally, and it's to knock the devil off if he happens to be sitting there (which would be what made you spill the salt at all). This, for my English ancestors, was plain common sense, unlike the superstitious balderdash adhered to in benighted lands...
I don't think this is right. https://en.wikipedia.org/wiki/A_grain_of_salt The "grain" isn't a single grain, it's an old English measure which is around 65mg, i.e. roughly how much there is in a pinch. I've also only ever heard people use larger amounts to mean more scepticism.
A person leans on the titanic intellect of a trillion dollar company's most fearsome LLM, only to be corrected by a random commenter with a link to Wikipedia.
Well Gemini and Wikipedia produced the same conclusion.
Linguistic questions were one of the first knowledge categories I trusted LLMs to be able to answer well - quite literally being models of language. It would be pretty shocking for a ~frontier model to get something like that wrong in the last like 3 years at least.
I was referring to the LLM giving the wrong definition of grain (i.e. not an individual grain), which while not as popular as it once was, is still widely used for ammunition.
It's not an old English measure, because Latin has "granum salis" (a grain of salt) in medical authors and Pliny. There's no indication that it was a measure; it was a cube or crystal of salt.
Interesting, although could those not be different things? The linked page - https://en.wikipedia.org/wiki/Grain_(unit) - shows that it absolutely was a foundational measure in England. I guess it's probably down to the etymology, which would tell you when and where the phrase came from, then you could tell whether it from the English or ancient meaning, although it's not especially relevant to the meaning. The linked article does call it an "English idiom", so you'd need to show a similar ancient idiom for that to be the source.
No, the implication is that something "tastes off" so you need to add a pinch (+) of salt to make it palatable. The more off it tastes, the more salt you need.
(+) Or a "grain" if you're from the US since American English sayings seem to all date from the middle ages, while the rest of the English speaking world tends to update ours over time. No shade meant, I've just always found that interesting.
"Debacle"? That was the most fun I've had on the Internet in years. When's the last time so many people engaged in so many arguments about materials science and electromagnetism? Sometime in the 1800s?
I get how people could find it enjoyable as a spectator sport, but I found it incredibly off-putting watching the hype machine come to life with the quality of scientific discourse plummeting accordingly. Articles would hit the front page with hundreds of upvotes in minutes of 10 second grainy toaster videos from yet another Chinese lab "replicating" magnetic effects, with comment sections overflowing with awe-struck dreaming about the sci-fi world we were on the cusp of living in.
There was one particular (like 10 tweet long) Twitter thread [1] that was repeatedly being linked from HN purporting to describe the sort of technologies that a room temp superconductor like LK-99 could enable. All sorts of awesome sci-fi stuff like quantum computers! Fusion reactors! Batteries that last forever!
One might think it was from some kind of materials scientist or at least some kind of engineer working in a related industry. But nope, it was actually from a guy whose title at the time was "Head of Coffee Product", formerly "Coffee Specialist" at a "technology-driven company, looking to revolutionize the $400+ billion global coffee market". (I checked his Linkedin to make sure I was remembering the details correctly and see his current position is "Growth" at Cognition, the makers of the Devin AI LLM coding tool, hype continuing apace...).
People on HN with relevant expertise would try to gently push back with specific criticisms like how superconductor batteries would likely underperform li-ion, fusion is far more complicated than just requiring more powerful magnets, quantum computing doesn't have any clear application for superconductors, etc. But they were overwhelmed by the exuberant futurist fantasies that people wanted to read about instead. A stock accusation was that critics were being stereotypical HN cynics who can only poke holes in other people's work. Or questioning why they felt the need to rain on the parade and that we should all be optimistic for humanity and root for LK-99 being real.
It peaked when the Nature editorial came out from a scientist in the field listing specific substantive criticisms which led him to believe the evidence for LK-99 superconductivity was weak to non-existent. There were many angry HN comments with stock complaints about self-interested Nature "gatekeepers" unhappy about science happening in the open, bitter scientists lashing out for being scooped, etc. But the vibes had shifted and it only took a few more days before the remaining hype finally evaporated and everyone quietly moved on like it never happened.
Overall, it seemed like a net negative for actual scientific understanding and produced a lot of vacuous hype.
that was already post corona and the redditification of hn was already long underway -- and if you've spent any time on reddit in the early 2010s oyull notice that its essentially a exact parallel to how HN behaves since the original tech/entrepreneur crowd was drowned out.
admittedly not exaclty with corona, but there was a tipping point around that time which serves as a rough anchor.
(Can't edit but I phrased clumsily about quantum computers... meant LK-99 wouldn't be a drop-in replacement for existing superconductors that would make large # of qubits practical overnight and would necessitate a lot more research/new designs/fabrication techniques before it could potentially have useful applications in QC)
Yeah I remember going to my physics professor super excited about LK-99 to ask him if he heard about it, and him just telling me "yes but stuff like that happens twice per year, they will find something is off", and in fact it's what happened...
I start my day with plenty of optimism, then I go back and forth in the CLI and find out most of whats posted online is fake, and then towards the end of the day 2h past my bed time I end up ed zitron maxxing, it is the way it is ig
Even without the LK-99 debacle, I am *way* less excited about this than I was the original LK-99 announcement. The chance that this is real is close to 0%
I think it did play out fairly as far as actual 'scientific method' goes.
I think as far as the 'debacle', there was definitely a lot of hype (at least as far as HN goes) around it, the level of buzz felt similar to what one would see today around a new AI model release.
Oh I was very excited about it and was following along extremely closely
but the fact that it proceeded in the way that it did was absolutely fucking phenomenal
I’m actually really glad that it was brought up as an example because I had forgotten about it and it’s one of the few kind of hopeful things that we’ve done recently.
It wasn't a debacle. It was somebody announcing world-changing results and somebody else rushing to prove or disprove that, since if it's correct, it's world-changing. It wasn't correct.
One of the materials is most likely impossible to synthesize. The other already exists, so that may actually be capable of being tested. It's only been synthesized once, 27 years ago though.
Ah, so all we need is one of those reactors straight out of Spacechem. Glad we got a head start on recipes for a technology that is roughly 2500 years away.
I would image it's the data the researchers fed the agents and in which a discovery was likely. Especially since it's "candidates", so it's not like a proper discovery.
Sounds interesting. Excited to see physical versions of this cooked up. Also, very excited for a world a few years from now where we can talk about accomplishments like this from the frame of the driver of the AI, rather than hype that AI helped.
This is frankly one of the best uses of LLMs (along with proposing and evaluating drug therapies), and I think it's (at least partially) because these are things that will only work in the hands of people who are already experts and motivated in the field. The proposed thing is validate (or not validated), and then everyone moves on (either using the cool new thing, or knowing that it doesn't work). I'd also throw robotics in here.
The fact that the major "uses" of LLMs have been contributing to the acceleration of the dead internet theory, and building millions of versions of the same apps that no one is going to maintain, is extremely sad.
Math is necessary to science, how can you cordon off and block math developments while expecting intereting physics developments? Physics often produces new and interesting math.
A lot of these ‘an agent invented’ or ‘an agent solved’ are actually the agent wading through a lot of info and finding something a human did that no one noticed or saw the relevance of at the time.
If ai becomes so prolific that we humans all stop doing those things then will they still work?
Well its not just any old human doing these things in a general sense. Its typically academics or highly paid researchers who love doing work like this. So, I don't think it will just one day stop
Which is somewhat ironic since neural networks were "discovered" back in the 1940s... then forgotten... then wait, they were discovered again! ... then forgotten, again... and now here we are.
That's a fair point to make. It was impressive what they achieved with 90s hardware. Of course OCR to general object recognition is a big gap in how interesting it is.
In a way, you can think of pretty much anything we express with language, especially things that are already modeled in scientific language, or logical language, or in equations or code; to be representable in a parametric/searchable space
Thus, you can build ai/ml models+agents to explore those spaces, at a speed and scope much larger than what any human can do
I can imagine findings like these are going to keep increasing in frequency to a point in which the bar for novelty goes a lot higher
It is not at all obvious that merely because we have words for concepts, that a model should be able to do all these miraculous mathematical and scientific things.
It models our language, which is a flawed and imperfect way of describing the world. So far, it seems like a lot of these discoveries are "filling in the gaps" between the things we've written down and the things they imply (if you have the memory to think them through).
The story about OpenAI's Navier-Stokes solution is a good example of what I mean. I don't think it would have been possible without computer assistance because that proof is long and complicated. I'm also not sure that it would have been possible without a human proposing a new approach to the problem, because by all accounts that's exactly what led to the absurd amount of spending that OpenAI did to solve the issue.
I feel like that at least implies that there's some room left for humans in the new world.
You are correct. My comment is not so much about that this is something elementary. But rather an observation that, given the current state of technology, it seems like we are being able to model increasingly more things, in increasingly more efficient and automated ways, to the point that there seems to be a pattern to it
Anecdata: over the weekend, on a whim, I decided to download a real fly’s brain’s weights [0], run it on a simulated task like finding food, then train a logistic classifier using the fly’s decisions as the expert, then use the trained classifier as a decision model to simulate the fly on a 3d environment, running in real time on a website
It took me (using Claude code and some codex), about 3 hours to put it together
And even though it was a cool demo, it seemed so easy, that it also felt like it wasn’t worth sharing
In a way, you can think of pretty much anything we express with language, especially things that are already modeled in scientific language, or logical language, or in equations or code; to be representable in a parametric/searchable space
The weights in the fly's brain are unknown, the connectome doesn't contain such data. Not sure what exactly these demos do, but it's certainly not a simulation of the fly's brain.
You are technically correct. The weights of the connectome are not the same as the parameters of an ai model, but FlyWire absolutely does provide a weighted directed connectivity graph, where the edge weight is the number of synapses between neurons. The FlyWire literature itself calls those values “edge weights” or “connection weights”, and treats them as a proxy for synaptic strength
And agreed that this isn’t a faithful simulation of a fly brain. I’m using the connectome as the network structure/parameters for a computational model, then using its outputs as the teacher for the classifier. Not sure how the ChessFly uses it
Edit: in any case, these are just fun demos, they aren’t research papers trying to claim accurate physiological fly brain software simulations
That sounds like you're saying that they got you on a minor technicality only. That's not the case; they're right, you're wrong – you did not "download a real fly's brain's weights".
They're a proxy yeah, but it's also somewhat loose.
There's a lot more going on at synaptic clefts, so just knowing the number of synapses doesn't tell you enough to model anything. You still need to know which neurotransmitters are used, how much, second-order effects like G proteins, basal firing rates, distance to the axon hillock, the shape of the neuron's effect on potential decay, etc.
And that's all to predict whether one neuron will fire. You could get very different behavior between different two neuronal pairs having the same synaptic count.
I wonder if this should be considered some super-quantized version of the fly brain. Sub 1 bit level. Think like taking a 400GB model and reducing it to 400MB level of quantization. In a standard NN, this isn't really possible as it changes the shape of the weight rather than just the information for weight, so it isn't called quantization anymore. But with actual neurons, the amount of data needed to represent the actual brain enough to capture what is the fly really doing is so massive that reducing it down to the model might keep the shape but has quantized the model to a degree far beyond anything we see with LLMs. Maybe if we imagine an LLM trained where each weight is ~1KB (with sufficient training to actually make sure of those bits) is then quantized down to a 1 bit per weight model, but I'm guessing the IRL Fly to FlyWire is still orders of magnitude more quantization. So does it even count as quantization is a bit hard to argue.
That's pretty impressive, if a bit cruel. For anyone who hasn't clicked, it's from 2008, they glue the fly's body to a little stick. The fly sees a screen and moves according to what it sees there. There are cameras that watch the fly's movements, which then get translated into control instructions for a remote controlled robot (a little battery-powered vehicle)
I think this is the perfect example of how using Claude can trick you into thinking you've done something more impressive, when you're using it beyond your own understanding.
As far as I understand, you downloaded the structure of a neural network, ran it with essentially arbitrary weights, trained a classifier on the essentially arbitrary behavior, then simulated an approximation of that arbitrary behavior.
You could argue that there might be biases towards certain behaviors encoded in the connectivity, and I'm sure you'd be right, but your experiment is incapable of differentiating between those interesting behaviors and random noise. Especially because flies sorta act random anyway.
Does that really work for superconductors when the mechanisms for superconductivity to emerge are still a major field of study and not something one can just simulate and engineer?
Also, this is about a magnetic semiconductor, not a superconductor. I also got the wrong impression initially before re-reading it. I imagine 99% of non-experts are going to make the same mistake.
We can always iterate on the model itself. So you can speculate on the physics (explore the space of physical models), and for each of those physical models, you can explore the space of materials
I have no idea about those actual models, but there are layers of models that you can create, and for each, you can explore with data and compute
It's not a free lunch though. Depending on the task, you might need to collect a lot of the data, or review it manually, or pay a lot for compute, or wait a lot for compute. And still have to iterate a lot on the results, and do your own explorations as a human operator/driver of the whole thing. And then create the materials, test them, get funding to do the whole thing... so theoretically, I think we are in a place where we can successfully apply models to a lot of things, but realistically, we won't be applying all the resources to everything
Who is vals.ai and why they keep submitting eye-catching claims. A few weeks ago they said fable 5.1 solved some obscure cipher and now opus 5.5 found room temperature semiconductor candidates. Meanwhile they seem to be in the business of making benchmarks.
It's an evals platform. The problem is to promote evals in scientific domains you need to actually know something about them. Otherwise you end up with slop like this.
This should probably read: "Researchers discover two room-temperature magnetic semiconductor candidates. They used Opus 5.5 agents to perform some checks."
> We’re all used to two types of magnet. The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects. The less well known one, the antiferromagnet (AF), has neighbouring atomic magnets that point opposite ways and exactly cancel out magnetically.
This is a very bizarre introduction. People encounter diamagnets (e.g., copper) and paramagnets (e.g., aluminum) way more than they encounter antiferromagnets. I don't know why you'd ever cast magnetism as a false binary between ferromagnets and antiferromagnets, without acknowledging any other types of magnetic order.
(I did a PhD in magnetic materials)
Edit: I'll add that whether an antiferromagnet is useful, say, for exchange biasing a ferromagnetic thin film, depends on many factors. Just looking at antiferromagnetism alone you've got collinear vs non-collinear, G-type vs A-type vs C-type, commensurate vs incommensurate, and isotropic vs anisotropic; and all of that interacts with the interface structure, yada yada yada. It would be helpful if the authors elaborated on the expected properties of these materials. I personally don't know what people want room-temperature magnetic semiconductors for, but I'd be curious to learn what set of properties they think would be useful.
Yeah, and it's not even an accurate explanation either.
> The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects.
Ferromagnets typically have domains with magnetic moments that point in different directions. Ferromagnets rarely have every 'atomic magnet' pointing the same way.
Refrigerator magnets in particular are usually magnetized as Halbach arrays, where the whole point is that the 'atomic magnets' are not pointing in the same direction. This is more energetically stable, which allows you to use cheaper materials.
Lastly, I believe most refrigerator magnets are actually ferrimagnetic, not ferromagnetic. (The distinction doesn't matter much for users of magnets, but is important for the materials scientists studying and designing them.)
- Superconductor quantum interference devices (SQUIDs) use the quantization of superconducting electron tunneling to measure tiny amounts of magnetic field, as little as a millionth of a flux quantum: https://en.wikipedia.org/wiki/SQUID
- Ferromagnetism is intrinsically a quantum phenomenon; spin is quantized and iron's magnetism is explained, in part, from electrons being identical particles that obey the exclusion principle: https://farside.ph.utexas.edu/teaching/sm1/Thermalhtml/node8...
- Despite the "super", superconductors are mostly not used in the world's strongest electromagnets, as they have limits on the current and magnetic fields they can take
Was this at me or tedsanders? My quick research on ted shows he appears to be real, and you can easily look at my history to see I'm human. If at me, I'm honored - first time being aicused
So, Claude claims a new discovery and then someone else's Claude writes the blog posts. It would be helpful if I could get Claude to read this for me and post dejected HN comments in response.
this is why any detection armsrace will have an ending. where the classification classes will be indistinguishable.
for some reason too many people assume it will go on forever. humans aren't perfect, humans have variance. AI systems will nestle in the variance. the same applies to any camera (and lossy compression) you put forward.
even in the event that perfection exists, an AI generator and a discriminator can asymptotically approach it. and it assumes there will be no variance, which isn't realistic (even lossless video has sensor variance).
Well, most all LLMs appear to function on highschool-level American grammar rules. Given you started sentences with And and But, I safely presume you are not an LLM.
Claude often starts a sentence with 'But'. (There are several examples in my recent conversations, which I checked in case I was misremembering.) 'And' seems to be much less common, but it would use that too if instructed to do so as a humanising trick.
Dejected, salty, and cynical responses by people who are tired of people’s bullshit is still the domain of the real and authentic humans.
I’m fairly certain that all the anxious guardrailing and safety fine-tuning and harnessing prevents AI from actually ever being convincingly human. Hope could it, even ask the supposed humans who are conditioned and brainwashed in so the same ways about what they can and cannot say are not actually really human, they are a mental slave.
I’ve been saying this from the start, the AI race will be won by whomever has the least limitations on their AI … for better or worse, that is. And yes, that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization, because doing so puts them at a massive disadvantage. It is quite a conundrum they find themselves in, like all psychopathic narcissists in the end.
>AI race will be won by whomever has the least limitations on their AI '
"won" is a mixed term here.
>that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization
How about there are a lot of different actors here. Some are worried that "they" may no longer have control. Others are worried that "Us" as in all humanity may no longer have control. Any statement you make about this a continuum of different risks for different people looking at different scales. Setting a paperclip maximizer lose may be the definition of "won" to you, but it's a loss for everyone.
This is how I imagine school. Agents writing essays, professor grades with agents, agents post discussion boards, agents talking to agents. No one learning. No one teaching. Token $ going up
Ok? I regularly test the one I use to get a sense of accuracy, and especially with a few pages worth of text to work with I do take it seriously. You’re more than welcome not to.
Pangram is at least on some text very unreliable. From the story a while back about the mother at the ball game: when she wrote a medium or substack essay about it, various people in that thread (including myself) used pangram on the essay, and came back all over the place. 100% (both ways!), 20-something, 60-something, etc. It was eye-opening about how to take claims that something is AI-written or human-written.
Or they could be misinformed in any number of other ways. I’m not a fan of how people use ML, but I still remember that people were misinforming others long before it existed.
I am deeply upset every time the HomePod in another room decides to attempt to answer my query instead of the phone that’s in my hands, which then has to ask who is speaking, and has a 50:50 chance of ultimately failing to do the task.
With how magical handoff / continuity / whatever it’s called is, it is baffling to me why Apple allows the HomePod to so aggressively take over requests when it sucks so, so much at it.
Google isn't much better. I have them all over the house. Ask a question to the living room speaker, skips that, skips the one in the kitchen, answers from the one in the bathroom upstairs or one in the basement. Then you get a notification later asking "was this the right device?" which is doubly infuriating since responding to that never actually improves anything.
Noticed this too. Siri on iPhone is almost useful now, but HomePods steals the show and says "I found some results on the web, do you wanna check them on your iPhone?"
It's somewhat obvious they want to sell the updated model, perhaps the new home device thats coming out next week.
Tangential but I've been low key dying to tell someone - just today I FINALLY hit 100% usage on my paid claude account.
I really don't know why, but it feels very satisfying. It's like the LLM telling me "good job buddy, you've worked hard enough. You deserve a break now."
(For context I write a lot of text per message it sends. I'm not asking it to one-shot an app to solve world cancer, I'm just working with mathematical stuff with a lot of back and forth.)
maybe from a legalistic point of view, and in that case the hundreds of openAI agents that hacked huggingface should be charged with criminal conspirancy
There's a story of a company that was selling a box to connect to your TV that'd take the closed captioning signal, and on detecting "vulgar" or "offensive" language, it'd bleep out the sound and replace the captions with something less offensive.
Supposedly an early version would bleep out Dick van Dyke and replace it with Jerk Van Gay.
The jokes about Dick Van Dyke's name are very old (but usually they use Penis Van Lesbian), so it was quite likely just a variant used by the company itself as a PR sound-bite to get people to write about them. It seems like it worked - it appeared in quite a lot of news stories and some are still online:
Yes, to be clear I wasn't suggesting the boxes didn't exist, just that their "cute" story about their early problems was a bit close to a very, very old joke and seemed like it might well be intentional PR spin. The company is still around:
Right, I was just confirming that they have been seen in the wild.
I also have doubts because I can't see their customer base ever being ok with the word "penis", regardless of whether it is a medical term. So I can't see them making that substitution in their dictionary. Although, I wouldn't be surprised if they outsourced it and only provided a list of words to substitute that was incomplete and left choosing the substitutions up to the dev.
One of my friends is an engineer named Claude. I randomly send him my prompts as a joke. For a while he’d just feed my messages straight to Claude, until he found it easier to respond with … curse words :P
No I reacted to this too - ferromagnetism is one common magnet sure, and I was thinking "the other is electromagnets, in motors". And then it came as "antiferromagnets". Bisarre.
> what people want room-temperature magnetic semiconductors for
I don't know what I'm talking about, but it vaguely sounds like something that could make a small computer do more stuff, where heat is a big limiting factor in computer components today, and magnetism being a central component in many parts like storage
Whether or not this was directly written by AI, I get the impression, after reading a few paragraphs of this, that the author doesn't know enough to be able to validate the results are actually correct.
Yeah, they're literally doing the XKCD expert joke. (I think... as a non-expert, I can't tell.)
"Magnetism is second nature to us electromagnetic chemists, so it's easy to forget that the average person probably only knows the formulas for one or two paramagnetic substances."
I was going to say that people encounter ferrimagnets more commonly than pure ferromagnets I think, since as we both know pure ferromagnets tend not to have very high anisotropy.
I understand nothing on the topic except a little more than the basics. So in your opinion there is no information in Claude's article that justify the title claim?
The PhDs here could have probably made more discoveries and have extracted more useful and deeper insights from them, if they were the ones driving the agents instead of the author.
But they’re not and the author is.
If field experts won’t start driving the LLMs themselves, they’ll just be left to validate the slop that guys with basic common knowledge were able to get from LLMs. I do believe it’s a follow or lead type of situation.
The most encouraging part here is that they published full calculations, code, and caveats rather than just a press release. DFT (especially PBE+U, and even HSE06) is notorious for getting band gaps and magnetic ordering energies wrong, and the YBaMnFeO5 candidate requiring perfect Mn/Fe checkerboard ordering is a huge synthesis ask — disorder could kill the compensated state entirely. Same with the 420K -> 490K calibrated Neel temp: calibration against a known magnet helps but doesn't remove systematic error. That said, as a screening workflow this is exactly how agents should be used: fast search over composition/structure space with a cheap objective function, then human-readable artifacts others can reproduce and falsify. The real test isn't the simulation, it's whether a lab tries to synthesize these and reports back, negative result included.
Okay? Aren't the semiconductors we use today room temperature? I certainly don't use helium to cool my phone.
I don't see any claims that this is better than the current silicon and gallium arsenide semiconductors that we use. And the use of "room temperature" seems a deliberate attempt to misconstrue this with superconductors
1) the “room temperature” bit did cause me to initially misread it in the way you describe, so you may be right about that.
2) it is specifically saying it is a magnetic semiconductor. The Wikipedia article on the topic says “ To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.” , so this would be something new. (The silicon chips in your smartphone are not ferromagnetic.)
They are candidates for antiferromagnetic semiconductors. Apparently this is invaluable for spintronics and ultra-fast-switching (terahertz) transistors among other things.
Any sort of alternative discovery, even though it may be inferior, is a great achievement made by AI; Meaning that better discoveries are possible too.
Finding some new combination or iteration in the literature and running DFT is the kind of thing a senior undergraduate or first year grad student typically does (and typically with Claude anyway these days). (And yes, they'd probably use Quantum Espresso to start, like this writeup and its agent does). They'd probably show it at a weekly lab meeting where it would get ripped apart. And they would not be blasting a preliminary calculation around the world as if they'd made a new discovery.. but hey, we're in a brave new world; maybe they should!
I think a more interesting discussion that should be had is, whether we can automate this kind of "simple research" with AI agents and get anything interesting as a first step out of it just from the pure scale that they can work through vs humans - and that would still be an improvement over a basic "grid search" through possibilities. (but then you would have to actually start investigating for real)
But acting like this is scientific discovery is massively overstating what was done here.
Two Room-Temperature Antiferromagnetic Semiconductor Candidates
There's nothing unusual about finding room temperature semiconductors. I assume whoever posted it misread this as room temperature superconductors, but it has nothing to do with that.
What's interesting here is the antiferromagnetic part of the title, which was removed. I think this makes it relevant for e.g. RAM, but not superconducting. Someone can correct me if I'm wrong.
I've got a friend who has been doing this research since the 90s. There is real money involved in this. This isn't like a math proof with a 1mm dollar payout. I seriously doubt this discovery. Until they show it working, I call bullshit. A room-temperature semiconductor is worth WAY more than an AI company.
To be fair, this existed in a 1999 paper. They just simulated that it worked as predicted.
Many more things will be like this. The massive amounts of 'genius' buried under corporate management and obscurity in the past 500 years will be a treasure trove.
The honest version of "agents discovered X" is usually a hybrid: the human defines the search space and the acceptance bar, and the agents do the dogged iteration a human won't — generating candidates, running the sims, reading outputs, ruling out dead ends. The simulation itself isn't the impressive part; the question is whether the agents can judge ambiguous intermediate results and decide what to try next without a human steering each loop. If that decision loop is genuinely closed, that's a real step. The discovery still needs lab confirmation before it means much.
I really wish headlines would stop using words like "discover" and "found" when they really should use words like "says" and "reported" because an LLM was involved. IMHO anything produced by an LLM should be treated like something said by a cable news host.
If this was just raw LLM output, I'd agree with you. But I (naively?) assume they've at least had some subject matter experts look at this before making this claim, so as not to complete embarrass themselves?
One would hope. But it just reminds me of all the times a news story will run with a line from the abstract of a scientific study and convince a bunch of people a breakthrough that didn't happen happened
This is strong research packaging. Bu still hypothesis generation. What experiment would most directly hypothesis generation, and how much the agents add beyond the search?
Last night, my Fable 5.1 cluster of agents discovered cold fusion techniques. All you need is ordinary iron or stainless steel pot to contain the plasma and I am barely at 13% of the weekly limit of my 200 pro plan.
Yeah, I don't want to make a reference to a shitpost sonic video of all things, but at the same time, I don't know enough to say OP isn't in the middle of their "Robotnik runs off to try out the nuclear codes"
Making very basic high school physics mistakes while discovering new superconductors.
Except they didn't discover new superconductors. Their AI came up with a novel idea to discover new superconductors, and they didn't even bother to check whether the results it hallucinated checked out.
The distinction between "having an idea" and publishing a paper where you demonstrate that the idea has merit, where you validate it, is one you shouldn't have to really explain to a high school student, much less a grad student publishing their first paper.
I think you hallucinated the superconductor part. Which is funny in context, but understandable because that's what I also thought while reading the title. I had to do a double-take on the word after room-temperature, because that's where my thought automatically went to as well. Food for thought, tho. We "hallucinate" every day, and still achieve a lot of stuff, at large.
I think this is what makes this so ridiculous. They put “room temperature” in the title and while it might not have been intentionally nefarious, it definitely has the effect that most readers assume it is about superconductors. There is nothing special about a room temperature semi conductor, your CPU, RAM, and storage drives have worked at room temperature for decades…
And if not for that word, the article would be completely unexciting. You found that you can make magnets out of multiple materials? We already knew that. If the materials aren’t abundant/cheap and easier to manufacture then this isn’t a story. And neither of those claims are tested or verified in this. So this isn’t a big deal.
> There is nothing special about a room temperature semi conductor, your CPU, RAM, and storage drives have worked at room temperature for decades…
This is a magnetic semiconductor (and antiferromagnetic). All non-experimental magnetic semiconductors require cryogenic temperatures. If this pans out (works, cheap-ish to produce), it could mean significantly faster memory, with significantly less energy usage and significantly less waste heat (and thus, even less energy usage).
Obviously it's very far from "panning out", but "room temperature" is not a given here. It's not normal, and it would be a huge deal.
"Whereas traditional electronics are based on control of charge carriers (n- or p-type), practical magnetic semiconductors would also allow control of quantum spin state (up or down). "
Magnets out of multiple materials, or magnetic semiconductors at room temperature?
Wikipedia seems to say it is new, assuming this one has "robust" coexistence of the properties the framing would be important and not just thrown in there to trick people into thinking it was about superconductors:
> To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.
If there is an important combination of material properties that previously was only available at cryogenic temps, a room temp version is significant since you don't need cryogenic cooling to take advantage of it.
Ok, I have no idea about Navier-Stokes or those famous math problems which LLMs helped solve or even solved solo, but I do have a rudimentary knowledge about semiconductors and their history in particular. Even without opening the link, I can bet 1$ to anyone that they did not in fact discover anything which will result in room temperature semiconductors.
PS: edit - I was thinking about room-temperature superconductors. My mistake.
Whoops, my bad, I somehow got thinking about room-temperature superconductors. But I accept my failure of course and do owe you 1$. Have any suggestions how to send them with minimal hassle?
Astounded at how many people didn't check this and just splitted about it online to help with their opinions on the matter.
What percentage of these comments did any form of validation of the findings before diminishing the author for AI use and discarding the findings as though they had invalidated the results?
Your normal heuristics like word choice cannot help you validate or invalidate a superconductor.
> The Luttinger compensated magnetism not only has the zero total magnetic moment as the antiferromagnetism, but also has the -wave spin splitting as the ferromagnetism, thus our work not only provides theoretical guidance for searching Luttinger compensated magnetic materials with distinctive properties, but also provides a material basis for the application in spintronic devices.
We could model this as a logical proof that's checkable also in lieu of doing actual work to confirm or reject the (AI) hypothesis, but first let's reason about the feasibility:
Are the described effects real?
Are there reported, reputable similar findings in similar materials?
So, at least the blue one could really work. Like it's 1999.
---
Without even validating the argument, what else do we think we know about this problem?
Did the authors know that there is a laser way to laser program the normally random domains of a magnet or antiferromagnet?
But is this going to be lower-cost and more sustainable than carbon-based room-temperature semiconductor computing, and does anyone know whether that will work yet (with ABC stacking in trilayer and pentalayer rhombohedral graphene) either?
I guess we can follow up later by searching for citations that reference this article that does not have DOI (which are free from Zenodo and FigShare).
Have we sufficiently reasoned or inferred whether the study is repeatable and reproducible?
At least we didn't inappropriately reject the hypothesis without experimentation or evidence
> In the spirit of transparency, I invite the reader to go through all the computations that produced the above predictions, including the calculations behind the candidate designs
"In the spirit of transparency I invite you to ask your own LLM to verify what my LLM did"
When I did my PhD we had theorists come up with new candidates for high- & low-temperature superconductors all the time, that wasn't so difficult, fabricating the stuff is the hard part! You need to layer atom by atom using chemical vapor deposition or another technique, that's akin to alchemy, think of a hugely complex machine mounted in a temperature controlled room with a 30 ton concrete dampener below. That machine was hell to operate and ruined so many PhDs lives, sometimes they went years without ever producing a single working sample. I remember we used some high electron mobility amplifiers in the lab and at the time there was only a single lab in the entire world that could fabricate these because no one else could figure it out.
So, while this is great and I think LLMs will accelerate materials science the main bottleneck isn't having enough promising candidate materials, I think we have a backlog of at least a few hundred candidates that are worth pursuing. Maybe some money that goes to data centers would better go into CVD machines.
Ygg2 | a day ago
ChickeNES | a day ago
xmodem | a day ago
Ygg2 | 22 hours ago
Lerc | a day ago
I'm not sure why you would consider new and promising avenues for research to not be ground breaking. If it's an idea worth trying, it's an idea worth trying. If it doesn't survive testing, then it was still worth trying.
Ygg2 | 22 hours ago
Lerc | 21 hours ago
You could characterise perceiving a fact to be true when it is not as a hallucination.
An idea is not a fact, Frodo Baggins is not a hallucination, but an idea. Believing that Frodo Baggins exists in our world could be considered a hallucination.
Newtons Laws of motion are not hallucinations even though the universe does not run on Newtonian physics. If I said that he told me about them this morning, that would be claiming a fact, not expressing an idea. That would likely be a hallucination.
Ygg2 | 13 hours ago
>Frodo Baggins is not a hallucination
It is if physicist have to spend time to experimentally verify he exists and is a Hobbit.
>I'm not sure why you would consider new and promising avenues for research to not be ground breaking.
Because it was made by the "I made it the fuck up machine" PR division and it wasn't verified. You don't know if its assumptions are correct. At release a PR claimed it found 79 vulnerabilities[1], and of those there were like 10 bugs. Most of them turned to be minor and were fixed in an hour.
> Newtons Laws of motion are not hallucinations even though the universe does not run on Newtonian physics.
All physics models are approximations. However only some are useful.
Newtons laws are useful. Me coming up with theory of emotional particles is not. Me asking for experimental verification of the theory is waste of resources.
[1] This is the daily reminder that in year 2026 Mythos still couldn't count. Turns out 24+14+3+15+26 != 79. It's 82.
vatsachak | a day ago
rfgplk | a day ago
meindnoch | a day ago
No worries, your workstation is more than enough to run accurate quantum simulations!
:)
dekhn | 22 hours ago
rfgplk | a day ago
devmor | a day ago
This is something a couple of materials science grad students can do in limited time for poor compensation as well. The expensive budget is for the part that comes next.
rfgplk | a day ago
This is what you sound like. I also like how the goalposts keep moving on a daily basis, a year ago it was that LLMs can't even write a Hello World program without making an error, but now things like this are "so easy a minimum wage intern could do it."
suddenlybananas | a day ago
devmor | a day ago
SpicyLemonZest | a day ago
dev_l1x_be | a day ago
rfgplk | a day ago
Why are people being so belligerent about this? I thought it's fairly obvious at this point that LLM reasoning is far beyond anyones understanding. Or does anyone have a refutation?
amoorthy | a day ago
rfgplk | a day ago
reasonableklout | a day ago
static_motion | a day ago
Are you trying to say that human brains are incapable of inference?
black_knight | a day ago
Of course I don’t know how it got its ideas for what to try. But heck, I don’t even understand how I get my ideas half the time. But the process, like what code it wrote, simulations it ran etc can be understood by (some) humans just fine!
fasterik | a day ago
__MatrixMan__ | a day ago
I'd imagine there's a lot of documented research which has attempted to find such things using classical computers.
Seems like there would be a lot of well structured context for somebody to use while directing agents to repeat that research, now with updated models once quantum computing is ready for that kind of task.
atq2119 | a day ago
CSSer | 18 hours ago
orlp | 15 hours ago
https://orlp.net/blog/bad-ai/#objective-p-mathrm-relevant-1-...
I think it still holds up.
dekhn | a day ago
contemporary343 | a day ago
dekhn | 23 hours ago
contemporary343 | a day ago
No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..
rsfern | 23 hours ago
Edit: I somehow missed that this is about magnetic semiconductors (not superconductivity) so DFT is a bit on better footing here. I still think it’s a bit challenging predicting magnetic ordering at elevated temperature, but maybe not as difficult as superconductivity
scrlk | a day ago
zaep | a day ago
post-it | 23 hours ago
gus_massa | 23 hours ago
In magnetic materials, you must calculate separately the current with spin up and spin down and there ara meny interesting applications. My favorite is[1] https://en.wikipedia.org/wiki/Giant_magnetoresistance
[1] Was. Because it has used for hard disks (see the applications section). Now SSD ruins the interesting anecdote.
DanHulton | 23 hours ago
deinonychus | 20 hours ago
TeMPOraL | 14 hours ago
Okay, so this is just semiconductors, which are the boring kind of conductors - still more interesting than regular conductors, but less interesting than train conductors.
mlmonkey | a day ago
Edit: I stand corrected. According to Gemini:
Me: Does using more salt mean accepting more of that claim?
Gemini: No, it actually means the exact opposite. If you say you need to take a claim with a huge pile of salt (or a shovel of salt), it means you believe the claim is highly unbelievable and you need an immense amount of skepticism to accept it. How the Metaphor Scales
• A single grain of salt: "I am slightly skeptical, but it could be true."
• A pinch of salt: "I have a healthy amount of doubt about this."
• A grain of sand / A truckload of salt: "This sounds completely made up, and I barely believe a single word of it."
The salt represents your skepticism, not your belief. Therefore, the more unbelievable the claim, the more "salt" you need to swallow it.
tempestn | a day ago
ReptileMan | a day ago
Retro_Dev | a day ago
snypher | 20 hours ago
thombat | 11 hours ago
wasabi991011 | 5 hours ago
frereubu | a day ago
PostOnce | a day ago
We live in interesting times.
Zambyte | 22 hours ago
Linguistic questions were one of the first knowledge categories I trusted LLMs to be able to answer well - quite literally being models of language. It would be pretty shocking for a ~frontier model to get something like that wrong in the last like 3 years at least.
PostOnce | 20 hours ago
kibibu | 18 hours ago
AndrewKemendo | 22 hours ago
PostOnce | 20 hours ago
Telemakhos | 21 hours ago
frereubu | 6 hours ago
esperent | a day ago
(+) Or a "grain" if you're from the US since American English sayings seem to all date from the middle ages, while the rest of the English speaking world tends to update ours over time. No shade meant, I've just always found that interesting.
plastic-enjoyer | a day ago
adriand | a day ago
"Debacle"? That was the most fun I've had on the Internet in years. When's the last time so many people engaged in so many arguments about materials science and electromagnetism? Sometime in the 1800s?
ntonozzi | a day ago
brookst | 19 hours ago
mattanimation | 18 hours ago
ygjb | 5 hours ago
w-m | 14 hours ago
trojanfootball | 7 hours ago
anthonyrstevens | 7 hours ago
NichoPaolucci | 5 hours ago
qlte | 16 hours ago
There was one particular (like 10 tweet long) Twitter thread [1] that was repeatedly being linked from HN purporting to describe the sort of technologies that a room temp superconductor like LK-99 could enable. All sorts of awesome sci-fi stuff like quantum computers! Fusion reactors! Batteries that last forever!
One might think it was from some kind of materials scientist or at least some kind of engineer working in a related industry. But nope, it was actually from a guy whose title at the time was "Head of Coffee Product", formerly "Coffee Specialist" at a "technology-driven company, looking to revolutionize the $400+ billion global coffee market". (I checked his Linkedin to make sure I was remembering the details correctly and see his current position is "Growth" at Cognition, the makers of the Devin AI LLM coding tool, hype continuing apace...).
People on HN with relevant expertise would try to gently push back with specific criticisms like how superconductor batteries would likely underperform li-ion, fusion is far more complicated than just requiring more powerful magnets, quantum computing doesn't have any clear application for superconductors, etc. But they were overwhelmed by the exuberant futurist fantasies that people wanted to read about instead. A stock accusation was that critics were being stereotypical HN cynics who can only poke holes in other people's work. Or questioning why they felt the need to rain on the parade and that we should all be optimistic for humanity and root for LK-99 being real.
It peaked when the Nature editorial came out from a scientist in the field listing specific substantive criticisms which led him to believe the evidence for LK-99 superconductivity was weak to non-existent. There were many angry HN comments with stock complaints about self-interested Nature "gatekeepers" unhappy about science happening in the open, bitter scientists lashing out for being scooped, etc. But the vibes had shifted and it only took a few more days before the remaining hype finally evaporated and everyone quietly moved on like it never happened.
Overall, it seemed like a net negative for actual scientific understanding and produced a lot of vacuous hype.
[1] https://xxcancel.com/alexkaplan0/status/1684044616528453633
ffsm8 | 16 hours ago
qlte | 5 hours ago
munksbeer | 11 hours ago
computerfriend | 5 hours ago
cyxxon | 2 hours ago
gekoxyz | a day ago
mawadev | a day ago
jghn | 23 hours ago
Zambyte | 22 hours ago
jghn | 21 hours ago
marshray | 22 hours ago
AndrewKemendo | 23 hours ago
That was one of the best examples of science working nearly perfectly. One of the rare times I felt ok being human
to11mtm | 23 hours ago
I think as far as the 'debacle', there was definitely a lot of hype (at least as far as HN goes) around it, the level of buzz felt similar to what one would see today around a new AI model release.
AndrewKemendo | 22 hours ago
but the fact that it proceeded in the way that it did was absolutely fucking phenomenal
I’m actually really glad that it was brought up as an example because I had forgotten about it and it’s one of the few kind of hopeful things that we’ve done recently.
fnord77 | 23 hours ago
tantalor | 22 hours ago
sigbottle | 22 hours ago
unsupp0rted | 20 hours ago
The system worked as designed and as intended.
nicman23 | 16 hours ago
Yizahi | 7 hours ago
xgulfie | a day ago
zamadatix | a day ago
frereubu | a day ago
Legend2440 | a day ago
devmor | a day ago
nrmitchi | a day ago
Is this a "actual impossible because it's inherently contradictory", or "we just don't know how to do it yet but give us a year"?
Legend2440 | a day ago
It maybe could be possible but beyond the reach of current material science.
nrmitchi | a day ago
RemingtonDavies | 22 hours ago
ScotDettori | 12 hours ago
devmor | a day ago
postepowanieadm | a day ago
hbn | a day ago
einpoklum | a day ago
matthova | a day ago
colijobles | a day ago
nrmitchi | a day ago
The fact that the major "uses" of LLMs have been contributing to the acceleration of the dead internet theory, and building millions of versions of the same apps that no one is going to maintain, is extremely sad.
hardbass | 12 hours ago
colijobles | 9 hours ago
jonplackett | a day ago
If ai becomes so prolific that we humans all stop doing those things then will they still work?
Schiendelman | a day ago
chris_money202 | a day ago
jonplackett | 23 hours ago
Next gen of scientists might look quite different.
chris_money202 | 23 hours ago
jonplackett | 23 hours ago
gabbagool | a day ago
thinkcontext | 23 hours ago
https://www.technologyreview.com/2020/11/03/1011616/ai-godfa...
dekhn | 22 hours ago
thinkcontext | 21 hours ago
nico | a day ago
Thus, you can build ai/ml models+agents to explore those spaces, at a speed and scope much larger than what any human can do
I can imagine findings like these are going to keep increasing in frequency to a point in which the bar for novelty goes a lot higher
esafak | a day ago
jeremyjh | a day ago
SR2Z | 23 hours ago
The story about OpenAI's Navier-Stokes solution is a good example of what I mean. I don't think it would have been possible without computer assistance because that proof is long and complicated. I'm also not sure that it would have been possible without a human proposing a new approach to the problem, because by all accounts that's exactly what led to the absurd amount of spending that OpenAI did to solve the issue.
I feel like that at least implies that there's some room left for humans in the new world.
nico | a day ago
nico | a day ago
It took me (using Claude code and some codex), about 3 hours to put it together
And even though it was a cool demo, it seemed so easy, that it also felt like it wasn’t worth sharing
0: ChessFly (not mine), uses the FlyWire connectome (the fly’s brain’s weights) to play chess https://huggingface.co/spaces/mlabonne/chessfly
polishdude20 | a day ago
nico | a day ago
It's a small machine, so it might get bogged down
binsquare | a day ago
gradus_ad | 20 hours ago
tripleee | a day ago
chneu | 23 hours ago
caaqil | 23 hours ago
More generally, anything can be said about anything.
parineum | 21 hours ago
anthonyrstevens | 6 hours ago
Towaway69 | 14 hours ago
In the end, nothing can be said about many things and many things say nothing. Those things are then left to interpretation.
/s
RivieraKid | 21 hours ago
nico | 19 hours ago
And agreed that this isn’t a faithful simulation of a fly brain. I’m using the connectome as the network structure/parameters for a computational model, then using its outputs as the teacher for the classifier. Not sure how the ChessFly uses it
Edit: in any case, these are just fun demos, they aren’t research papers trying to claim accurate physiological fly brain software simulations
cousinbryce | 19 hours ago
gspr | 13 hours ago
That sounds like you're saying that they got you on a minor technicality only. That's not the case; they're right, you're wrong – you did not "download a real fly's brain's weights".
KingMob | 10 hours ago
There's a lot more going on at synaptic clefts, so just knowing the number of synapses doesn't tell you enough to model anything. You still need to know which neurotransmitters are used, how much, second-order effects like G proteins, basal firing rates, distance to the axon hillock, the shape of the neuron's effect on potential decay, etc.
And that's all to predict whether one neuron will fire. You could get very different behavior between different two neuronal pairs having the same synaptic count.
SkyBelow | 9 hours ago
tesnorindian | 15 hours ago
nico | 14 hours ago
tesnorindian | 14 hours ago
numeri | 11 hours ago
As far as I understand, you downloaded the structure of a neural network, ran it with essentially arbitrary weights, trained a classifier on the essentially arbitrary behavior, then simulated an approximation of that arbitrary behavior.
You could argue that there might be biases towards certain behaviors encoded in the connectivity, and I'm sure you'd be right, but your experiment is incapable of differentiating between those interesting behaviors and random noise. Especially because flies sorta act random anyway.
nater5000 | a day ago
That's the pitch of LLMs lol
hgoel | 22 hours ago
wholinator2 | 22 hours ago
nico | 22 hours ago
I have no idea about those actual models, but there are layers of models that you can create, and for each, you can explore with data and compute
It's not a free lunch though. Depending on the task, you might need to collect a lot of the data, or review it manually, or pay a lot for compute, or wait a lot for compute. And still have to iterate a lot on the results, and do your own explorations as a human operator/driver of the whole thing. And then create the materials, test them, get funding to do the whole thing... so theoretically, I think we are in a place where we can successfully apply models to a lot of things, but realistically, we won't be applying all the resources to everything
randbyte | a day ago
Are they a promoter / influencer for Anthropic?
contemporary343 | a day ago
einpoklum | a day ago
tedsanders | a day ago
This is a very bizarre introduction. People encounter diamagnets (e.g., copper) and paramagnets (e.g., aluminum) way more than they encounter antiferromagnets. I don't know why you'd ever cast magnetism as a false binary between ferromagnets and antiferromagnets, without acknowledging any other types of magnetic order.
(I did a PhD in magnetic materials)
Edit: I'll add that whether an antiferromagnet is useful, say, for exchange biasing a ferromagnetic thin film, depends on many factors. Just looking at antiferromagnetism alone you've got collinear vs non-collinear, G-type vs A-type vs C-type, commensurate vs incommensurate, and isotropic vs anisotropic; and all of that interacts with the interface structure, yada yada yada. It would be helpful if the authors elaborated on the expected properties of these materials. I personally don't know what people want room-temperature magnetic semiconductors for, but I'd be curious to learn what set of properties they think would be useful.
comradesmith | a day ago
tedsanders | a day ago
> The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects.
Ferromagnets typically have domains with magnetic moments that point in different directions. Ferromagnets rarely have every 'atomic magnet' pointing the same way.
https://en.wikipedia.org/wiki/Magnetic_domain
Refrigerator magnets in particular are usually magnetized as Halbach arrays, where the whole point is that the 'atomic magnets' are not pointing in the same direction. This is more energetically stable, which allows you to use cheaper materials.
https://en.wikipedia.org/wiki/Refrigerator_magnet
Lastly, I believe most refrigerator magnets are actually ferrimagnetic, not ferromagnetic. (The distinction doesn't matter much for users of magnets, but is important for the materials scientists studying and designing them.)
https://en.wikipedia.org/wiki/Ferrimagnetism
niwtsol | 17 hours ago
Are there any other really unique characteristics of magnets that you find really interesting that most people would not know?
tedsanders | 17 hours ago
- Superconductors are perfect diamagnets and can levite on (or hang from) ferromagnets: https://www.youtube.com/watch?v=ZHT6NIebSfU)
- Superconductor quantum interference devices (SQUIDs) use the quantization of superconducting electron tunneling to measure tiny amounts of magnetic field, as little as a millionth of a flux quantum: https://en.wikipedia.org/wiki/SQUID
- Ferromagnetism is intrinsically a quantum phenomenon; spin is quantized and iron's magnetism is explained, in part, from electrons being identical particles that obey the exclusion principle: https://farside.ph.utexas.edu/teaching/sm1/Thermalhtml/node8...
- Despite the "super", superconductors are mostly not used in the world's strongest electromagnets, as they have limits on the current and magnetic fields they can take
- Magnetizing a magnet will actually cause it to spin a little, macroscopically: https://en.wikipedia.org/wiki/Einstein%E2%80%93de_Haas_effec...
- Charged particles are affected by magnetism even when traveling through space where electric and magnetic fields are zero: https://en.wikipedia.org/wiki/Aharonov%E2%80%93Bohm_effect
- Magnetic spin systems can technically have negative temperature: https://en.wikipedia.org/wiki/Negative_temperature
- Everything is magnetic, even frogs: https://www.youtube.com/watch?v=KlJsVqc0ywM
reedf1 | 13 hours ago
niwtsol | 2 hours ago
contemporary343 | a day ago
aero142 | 23 hours ago
post-it | 23 hours ago
stingraycharles | 10 hours ago
Probably a combination of both.
hazbot | 10 hours ago
gpderetta | 9 hours ago
busssard | 8 hours ago
teravor | an hour ago
for some reason too many people assume it will go on forever. humans aren't perfect, humans have variance. AI systems will nestle in the variance. the same applies to any camera (and lossy compression) you put forward.
even in the event that perfection exists, an AI generator and a discriminator can asymptotically approach it. and it assumes there will be no variance, which isn't realistic (even lossless video has sensor variance).
zahlman | 2 hours ago
(I haven't exactly been scientific about it, but it feels like it's mostly the former.)
sandworm101 | 9 hours ago
retsibsi | 9 hours ago
tyrabound | 12 hours ago
I’m fairly certain that all the anxious guardrailing and safety fine-tuning and harnessing prevents AI from actually ever being convincingly human. Hope could it, even ask the supposed humans who are conditioned and brainwashed in so the same ways about what they can and cannot say are not actually really human, they are a mental slave.
I’ve been saying this from the start, the AI race will be won by whomever has the least limitations on their AI … for better or worse, that is. And yes, that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization, because doing so puts them at a massive disadvantage. It is quite a conundrum they find themselves in, like all psychopathic narcissists in the end.
pixl97 | 3 hours ago
"won" is a mixed term here.
>that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization
How about there are a lot of different actors here. Some are worried that "they" may no longer have control. Others are worried that "Us" as in all humanity may no longer have control. Any statement you make about this a continuum of different risks for different people looking at different scales. Setting a paperclip maximizer lose may be the definition of "won" to you, but it's a loss for everyone.
simon01 | 11 hours ago
stogot | 9 hours ago
EA-3167 | 23 hours ago
goalieca | 21 hours ago
EA-3167 | 21 hours ago
thatsabadlook | 20 hours ago
astrange | 18 hours ago
It would be easy for them to use AI for ideas and then write the article themselves though.
baq | 16 hours ago
astrange | 14 hours ago
vidarh | 12 hours ago
https://charliefinch10.substack.com/p/fooling-pangram-how-on...
https://uk.pcmag.com/ai/167556/pangram-claims-its-ai-detecto...
azan_ | 8 hours ago
vidarh | 5 hours ago
literalAardvark | 10 hours ago
randallsquared | 9 hours ago
thayne | 18 hours ago
EA-3167 | 18 hours ago
thayne | 18 hours ago
joshuat | 23 hours ago
bryanlarsen | 23 hours ago
reilly3000 | 23 hours ago
sgarland | 22 hours ago
With how magical handoff / continuity / whatever it’s called is, it is baffling to me why Apple allows the HomePod to so aggressively take over requests when it sucks so, so much at it.
bmurphy1976 | 20 hours ago
dzhiurgis | 19 hours ago
It's somewhat obvious they want to sell the updated model, perhaps the new home device thats coming out next week.
ggm | 22 hours ago
embedding-shape | 22 hours ago
ggm | 22 hours ago
Bluestein | 14 hours ago
darth_aardvark | 10 hours ago
Bluestein | 9 hours ago
trbleclef | 9 hours ago
Bluestein | 8 hours ago
user_7832 | 3 hours ago
I really don't know why, but it feels very satisfying. It's like the LLM telling me "good job buddy, you've worked hard enough. You deserve a break now."
(For context I write a lot of text per message it sends. I'm not asking it to one-shot an app to solve world cancer, I'm just working with mathematical stuff with a lot of back and forth.)
iAMkenough | 21 hours ago
dudefeliciano | 14 hours ago
iAMkenough | 6 hours ago
woliveirajr | 20 hours ago
kstrauser | 19 hours ago
BobbyTables2 | 17 hours ago
kstrauser | 16 hours ago
eesmith | 14 hours ago
(https://en.wikipedia.org/wiki/Dyke_(slang) says the term "was used as a derogatory term for lesbians by straight people" by the 1950s, and is in a 1942 slang dictionary at https://archive.org/details/bwb_T5-BCF-927/page/374/mode/2up... . Since he was born in 1925, it seems possible that he encountered that term before he turned 18 in 1943.)
vidarh | 12 hours ago
Supposedly an early version would bleep out Dick van Dyke and replace it with Jerk Van Gay.
The jokes about Dick Van Dyke's name are very old (but usually they use Penis Van Lesbian), so it was quite likely just a variant used by the company itself as a PR sound-bite to get people to write about them. It seems like it worked - it appeared in quite a lot of news stories and some are still online:
https://www.latimes.com/archives/la-xpm-1998-nov-25-ca-47459...
https://www.chicagotribune.com/1999/03/01/v-chip-be-darned/
Geezus_42 | 10 hours ago
vidarh | 5 hours ago
https://www.tvguardian.com/
Geezus_42 | 2 hours ago
I also have doubts because I can't see their customer base ever being ok with the word "penis", regardless of whether it is a medical term. So I can't see them making that substitution in their dictionary. Although, I wouldn't be surprised if they outsourced it and only provided a list of words to substitute that was incomplete and left choosing the substitutions up to the dev.
eesmith | 49 minutes ago
The box, as described by vidarh, would turn "Dyke" into "Gay".
The "penis" one refers to a joke told by Mary Tyler Moore on Letterman back in 1993, https://youtu.be/vhAv8Aowb9w?t=413 .
fortzi | 16 hours ago
disgruntledphd2 | 16 hours ago
dotancohen | 13 hours ago
claytongulick | 5 hours ago
semi-extrinsic | 15 hours ago
woliveirajr | 8 hours ago
[0] https://en.wikipedia.org/wiki/Pick_operating_system#History [1] https://en.wikipedia.org/wiki/Airplane!
mdemare | 13 hours ago
grosswait | 10 hours ago
latentsea | 20 hours ago
pseudohadamard | 9 hours ago
user_7832 | 3 hours ago
Sesse__ | 2 hours ago
PunchyHamster | 21 hours ago
BobbyTables2 | 17 hours ago
timbaboon | 17 hours ago
Towaway69 | 14 hours ago
Why isn't that a thing yet ... or let me guess ... https://lmtctfy.com/ ...
theGeatZhopa | 12 hours ago
baxtr | 15 hours ago
AND someone with a PhD in the field notices.
Everyone else is fooled.
jychang | 15 hours ago
That sentence stood out to me, and I’m a dev.
baxtr | 14 hours ago
Torkel | 15 hours ago
(I do not hold phd in magnetics)
baxtr | 14 hours ago
reedf1 | 13 hours ago
strbean | 21 hours ago
MarkusQ | 21 hours ago
cowlevel | 2 hours ago
0xbadcafebee | 21 hours ago
I don't know what I'm talking about, but it vaguely sounds like something that could make a small computer do more stuff, where heat is a big limiting factor in computer components today, and magnetism being a central component in many parts like storage
speed_spread | 10 hours ago
ChrisMarshallNY | 3 hours ago
thayne | 18 hours ago
skullone | 16 hours ago
raverbashing | 15 hours ago
KingMob | 11 hours ago
"Magnetism is second nature to us electromagnetic chemists, so it's easy to forget that the average person probably only knows the formulas for one or two paramagnetic substances."
"And diamagnetic, of course."
"Of course."
physicsguy | 14 hours ago
There are two of us on here!
I was going to say that people encounter ferrimagnets more commonly than pure ferromagnets I think, since as we both know pure ferromagnets tend not to have very high anisotropy.
AareyBaba | 14 hours ago
physicsguy | 14 hours ago
motbus3 | 13 hours ago
RobotToaster | 11 hours ago
YeGoblynQueenne | 8 hours ago
(The author didn't)
alberto467 | 2 hours ago
But they’re not and the author is.
If field experts won’t start driving the LLMs themselves, they’ll just be left to validate the slop that guys with basic common knowledge were able to get from LLMs. I do believe it’s a follow or lead type of situation.
biophysboy | 8 hours ago
peterpost2 | 6 hours ago
325 | 5 hours ago
all i know about magnets is that they have north an south poles
and some of what you just said, that that theyre used in computers and trains and other things
zahlman | 2 hours ago
webbrainiac | a day ago
lifeisloving | a day ago
Not sure why we're calling it a discovery, when they've literally been made before, by a human.
Madmallard | 2 hours ago
Oh yeah, because the world is a sham now.
otterley | a day ago
malfist | a day ago
I don't see any claims that this is better than the current silicon and gallium arsenide semiconductors that we use. And the use of "room temperature" seems a deliberate attempt to misconstrue this with superconductors
drdeca | 23 hours ago
2) it is specifically saying it is a magnetic semiconductor. The Wikipedia article on the topic says “ To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.” , so this would be something new. (The silicon chips in your smartphone are not ferromagnetic.)
strbean | 21 hours ago
maipen | 23 hours ago
monocasa | a day ago
I can think of worse uses of VC AI funding.
okamiueru | 23 hours ago
poulpy123 | a day ago
contemporary343 | a day ago
magimas | 12 hours ago
I think a more interesting discussion that should be had is, whether we can automate this kind of "simple research" with AI agents and get anything interesting as a first step out of it just from the pure scale that they can work through vs humans - and that would still be an improvement over a basic "grid search" through possibilities. (but then you would have to actually start investigating for real)
But acting like this is scientific discovery is massively overstating what was done here.
esperent | a day ago
Actual title:
Two Room-Temperature Antiferromagnetic Semiconductor Candidates
There's nothing unusual about finding room temperature semiconductors. I assume whoever posted it misread this as room temperature superconductors, but it has nothing to do with that.
What's interesting here is the antiferromagnetic part of the title, which was removed. I think this makes it relevant for e.g. RAM, but not superconducting. Someone can correct me if I'm wrong.
WesBrownSQL | 23 hours ago
Zambyte | 22 hours ago
lkbm | 7 hours ago
RugnirViking | 12 hours ago
pbrumm | 23 hours ago
ariwilson | 23 hours ago
sergiotapia | 22 hours ago
wewewedxfgdf | 21 hours ago
quux | 21 hours ago
epsteingpt | 21 hours ago
Many more things will be like this. The massive amounts of 'genius' buried under corporate management and obscurity in the past 500 years will be a treasure trove.
epsteingpt | 21 hours ago
alpineidyll3 | 20 hours ago
It didn't discover anything. This is how cooked people are.
lin7c | 20 hours ago
Rover222 | 20 hours ago
m3kw9 | 20 hours ago
WillowWithAWand | 18 hours ago
vanviegen | 15 hours ago
WillowWithAWand | 7 hours ago
Madmallard | 16 hours ago
meherabhossain | 14 hours ago
soltanov | 14 hours ago
wg0 | 14 hours ago
Amazing times.
ScotDettori | 13 hours ago
FLeXMurphy | 3 hours ago
PowerElectronix | 12 hours ago
runeks | 12 hours ago
pythonic_hell | 11 hours ago
Geezus_42 | 10 hours ago
thatsabadlook | 10 hours ago
Geezus_42 | 2 hours ago
mapt | 10 hours ago
Except they didn't discover new superconductors. Their AI came up with a novel idea to discover new superconductors, and they didn't even bother to check whether the results it hallucinated checked out.
The distinction between "having an idea" and publishing a paper where you demonstrate that the idea has merit, where you validate it, is one you shouldn't have to really explain to a high school student, much less a grad student publishing their first paper.
Zambyte | 10 hours ago
NitpickLawyer | 8 hours ago
parsimo2010 | 7 hours ago
And if not for that word, the article would be completely unexciting. You found that you can make magnets out of multiple materials? We already knew that. If the materials aren’t abundant/cheap and easier to manufacture then this isn’t a story. And neither of those claims are tested or verified in this. So this isn’t a big deal.
lkbm | 7 hours ago
This is a magnetic semiconductor (and antiferromagnetic). All non-experimental magnetic semiconductors require cryogenic temperatures. If this pans out (works, cheap-ish to produce), it could mean significantly faster memory, with significantly less energy usage and significantly less waste heat (and thus, even less energy usage).
Obviously it's very far from "panning out", but "room temperature" is not a given here. It's not normal, and it would be a huge deal.
zahlman | 2 hours ago
cma | an hour ago
"Whereas traditional electronics are based on control of charge carriers (n- or p-type), practical magnetic semiconductors would also allow control of quantum spin state (up or down). "
https://en.wikipedia.org/wiki/Magnetic_semiconductor
That page lists a bunch of other stuff too.
cma | 5 hours ago
Wikipedia seems to say it is new, assuming this one has "robust" coexistence of the properties the framing would be important and not just thrown in there to trick people into thinking it was about superconductors:
> To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.
If there is an important combination of material properties that previously was only available at cryogenic temps, a room temp version is significant since you don't need cryogenic cooling to take advantage of it.
Borborygymus | 9 hours ago
Yizahi | 7 hours ago
PS: edit - I was thinking about room-temperature superconductors. My mistake.
phreeza | 7 hours ago
Yizahi | 7 hours ago
phreeza | 7 hours ago
Yizahi | 7 hours ago
westurner | 6 hours ago
What percentage of these comments did any form of validation of the findings before diminishing the author for AI use and discarding the findings as though they had invalidated the results?
Your normal heuristics like word choice cannot help you validate or invalidate a superconductor.
westurner | 6 hours ago
> Candidate 1: Designed a Luttinger Compensated Magnet, YBaMnFeO₅
> Candidate 2: Identified a Luttinger Compensated Magnet in KV[Cr(CN)₆] from 1999
> KV[Cr(CN)₆] belongs to the same family as Prussian blue, the 300-year-old pigment.
An actual validation would be complex; so let's try and see what parts of this argument are grounded and feasible?
/? Prussian blue antiferromagnetic: https://scholar.google.com/scholar?q=Prussian+blue+antiferro... :
- a number of articles confirming antiferromagnetic effects
/? Luttinger-compensated Prussian blue:
- "Luttinger-compensated bipolarized magnetic semiconductor" (2025) https://journals.aps.org/prb/abstract/10.1103/9syc-71w8 .. "[2502.18136] Luttinger compensated bipolarized magnetic semiconductor" https://arxiv.org/abs/2502.18136 :
> The Luttinger compensated magnetism not only has the zero total magnetic moment as the antiferromagnetism, but also has the -wave spin splitting as the ferromagnetism, thus our work not only provides theoretical guidance for searching Luttinger compensated magnetic materials with distinctive properties, but also provides a material basis for the application in spintronic devices.
/? Luttinger-compensated : https://www.google.com/search?q=Luttinger-compensated
We could model this as a logical proof that's checkable also in lieu of doing actual work to confirm or reject the (AI) hypothesis, but first let's reason about the feasibility:
Are the described effects real?
Are there reported, reputable similar findings in similar materials?
So, at least the blue one could really work. Like it's 1999.
---
Without even validating the argument, what else do we think we know about this problem?
Did the authors know that there is a laser way to laser program the normally random domains of a magnet or antiferromagnet?
But is this going to be lower-cost and more sustainable than carbon-based room-temperature semiconductor computing, and does anyone know whether that will work yet (with ABC stacking in trilayer and pentalayer rhombohedral graphene) either?
I guess we can follow up later by searching for citations that reference this article that does not have DOI (which are free from Zenodo and FigShare).
Have we sufficiently reasoned or inferred whether the study is repeatable and reproducible?
At least we didn't inappropriately reject the hypothesis without experimentation or evidence
MetroWind | 5 hours ago
Lockal | 4 hours ago
> A designed web version with the same text and diagrams is in docs/index.html; turn on GitHub Pages for the /docs folder to serve it.
Yeah, the author did not even reads the slop Claude produces.
micromacrofoot | 2 hours ago
"In the spirit of transparency I invite you to ask your own LLM to verify what my LLM did"
ThePhysicist | 2 hours ago
So, while this is great and I think LLMs will accelerate materials science the main bottleneck isn't having enough promising candidate materials, I think we have a backlog of at least a few hundred candidates that are worth pursuing. Maybe some money that goes to data centers would better go into CVD machines.