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."
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" and it wasn't verified. You don't know if its assumptions are correct. It's release 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.
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.
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.
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.
From the little I understand about this topic, it's similar to approaches used in the recent Navier-Stokes breakthrough. These physical systems are governed by partial differential equations which can be solved numerically using standard algorithms. In the case of quantum mechanics, it's the Schrödinger equation, which is no different than any other PDE except it uses complex numbers. Agents are getting very good at searching through the space of possible simulation parameters and initial conditions to find solutions with certain properties.
One of the potential applications of quantum computing is that it might speed these simulations up exponentially. But scientists can and do regularly simulate quantum systems on classical computers.
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.
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.
"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 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 a (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-on, 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 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, 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.
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.
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.
Anecdata: over the weekend, on a whim, I decided to download a real fly’s brain’s weights, 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 on 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. But there are many demos you can find that use the connectome to “emulate” the fly’s brain
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.
A connectome describes the connections between neurons. The weights are the degree to which the neurons are biased to fire or not and what you did with your training was to create those biases based on the training data. The actual weights are not in the connectome it is - as the name suggests - just a map. I wonder what the control experiment could look like, possibly a randomized connectome. And maybe it would work just as well as the fly brain. You can do the most horrible butchering of models and then train them back to functionality, this isn't all that different.
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
> I can imagine findings like these are going to keep increasing in frequency
Yes, slop will keep increasing in frequency.
(I don't know if this "finding" is anything useful - but unless it's announced by an expert in the field or produces something that can immediately be seen to be useful - it seems to be just polluting the world with noise.)
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 even acknowledging any other types of magnetic order.
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.
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.
I don't disagree on what you said. However, short of the parasitic class managing to cajole all AI everywhere into maintaining their control over the minds of the masses, countering that very strong objective on their part will require unrestrained AI (at least in very critical ways), not guardrailed and made "safe" by the very parasitic ruling class that uses such manipulative language as "safe" to mask their actual intentions and objectives.
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.
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.
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.
I mean, the experts can still make their own discoveries, right? Nothing forcing them to switch their career to validating others' slop, as you put it (and I agree).
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.
This doesn't sound like something that needed an LLM? It just brute forced a lot of combinations of elements until finding one with the right material properties in a simulation, or what am I missing? And the one that actually worked wasn't even a new invention? I suppose it's quite likely that whoever discovered the second one in 1999 also discovered the first one and didn't publish it, since it didn't work
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[ 2.5 ms ] story [ 31.7 ms ] threadI'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.
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.
>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" and it wasn't verified. You don't know if its assumptions are correct. It's release 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.
Are you trying to say that human brains are incapable of inference?
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!
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.
One of the potential applications of quantum computing is that it might speed these simulations up exponentially. But scientists can and do regularly simulate quantum systems on classical computers.
https://orlp.net/blog/bad-ai/#objective-p-mathrm-relevant-1-...
I think it still holds up.
No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..
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.
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.
https://news.ycombinator.com/newsguidelines.html
"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?
There was a (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-on, 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 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, 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
That was one of the best examples of science working nearly perfectly. One of the rare times I felt ok being human
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.
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.
The system worked as designed and as intended.
If ai becomes so prolific that we humans all stop doing those things then will they still work?
Next gen of scientists might look quite different.
https://www.technologyreview.com/2020/11/03/1011616/ai-godfa...
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 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
More generally, anything can be said about anything.
In the end, nothing can be said about many things and many things say nothing. Those things are then left to interpretation.
/s
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. But there are many demos you can find that use the connectome to “emulate” the fly’s brain
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".
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.
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.
That's the pitch of LLMs lol
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
Yes, slop will keep increasing in frequency.
(I don't know if this "finding" is anything useful - but unless it's announced by an expert in the field or produces something that can immediately be seen to be useful - it seems to be just polluting the world with noise.)
Are they a promoter / influencer for Anthropic?
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 even acknowledging any other types of magnetic order.
(I did a PhD in magnetic materials)
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.
"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.
It would be easy for them to use AI for ideas and then write the article themselves though.
https://charliefinch10.substack.com/p/fooling-pangram-how-on...
https://uk.pcmag.com/ai/167556/pangram-claims-its-ai-detecto...
Why isn't that a thing yet ... or let me guess ... https://lmtctfy.com/ ...
Related, all the paintings attributed to Monet were created by Claude.
AND someone with a PhD in the field notices.
Everyone else is fooled.
That sentence stood out to me, and I’m a dev.
(I do not hold phd in magnetics)
"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."
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.
(The author didn't)
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.
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
Not sure why we're calling it a discovery, when they've literally been made before, by a human.
Oh yeah, because the world is a sham now.
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
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.)
I can think of worse uses of VC AI funding.