I'm sure I've seen basic hill climbing (and other optimisation algorithms) described as AI, and then used evidence of AI solving real-world science/engineering problems.
Historically this was very much in the field of AI, which is such a massive field that saying something uses AI is about as useful as saying it uses mathematics. Since the term was first coined it's been constantly misused to refer to much more specific things.
From around when the term was first coined: "artificial intelligence research is concerned with constructing machines (usually programs for general-purpose computers) which exhibit behavior such that, if it were observed in human activity, we would deign to label the behavior 'intelligent.'" [1]
That definition moves the goalposts almost by definition, people only stopped thinking that chess demonstrated intelligence when computers started doing it.
The term artificial intelligence has always been just a buzzword designed to sell whatever it needed to. IMHO, it has no meaningful value outside of a good marketing term. John McCarthy is usually the person who is given credit for coming up with the name and he has admitted in interviews that it was just to get eyeballs for funding.
CERN has been doing HEP experiments for decades. What did it use before the current incarnation of AI? The AI label seems to be more marketing and superficial than substantial. It’s a bit sad that a place like CERN feels the need to make it public that it is on the bandwagon.
For those that have experience with ML, yes. For those that have recently become acquainted with it (more on business side) they seem to really struggle with this in my experience. '
> Is a LLM logic in weights derived from machine learning?
I was just answering this question. LLM logic in weights is fundamentally from machine learning, so yes. Wasn't really saying anything about the article.
Strictly speaking, expert systems are AI as well, as in, an expert comes up with a bunch of if/else rules. So yes technically speaking even if they didn’t acquire the weights using ML and hand-coded them, it could still be called AI.
Thanks for the thoughtful comments and links really appreciated the high-signal feedback.
We've updated the article to better reflect the actual VAE-based AXOL1TL architecture (variational autoencoder for anomaly detection). Added the arXiv paper and Thea Aarrestad's talks to the Primary Sources.
So they aren't "burned into silicon" then? The article mentions FPGAs and ASICs but it's a bit vague. I would be surprised if ASICs actually made sense here.
They make sense when you consider that 'on detector' electronics has all sorts of constraints that FPGAs cant compete on: Power, Density, Radiation hardness, Material budget.
Not on the same extreme level, but I know that some coffee machines use a tiny CNN based model locally/embedded. There is a small super cheap camera integrated in the coffee machine, and the model does three things: (1) classifies the container type in order to select type of coffee, (2) image segmentation - to determine where the cup/hole is placed, (3) regression - to determine the volume and regulate how much coffee to pour.
Intuitively, I’ve always had an impression that using an analogue circuit would be feasible for neural networks (they just matrix multiplication!). These should provide instantaneous output.
Isn’t this kind of approach feasible for something so purpose-built?
> CERN is using extremely small, custom large language models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).
Huh? The first paragraph literally says they are using LLMs
> [ GENEVA, SWITZERLAND — March 28, 2026 ] — CERN is using extremely small, custom large language models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).
the site might have fixed it, to me it says "artificial intelligence" instead of LLM, still bad but not" steaming pile of poo on you bank statement" bad
Are they some ancient small-scale integration VLSI design? Do they broadcast on a low-frequency VHF band? Face it: Oxymorons like those are part of the technical world. "VLSI" was a current term back when whole CPUs were made out of fewer transistors than we use for register files now, and "VHF" is low frequency even by commercial broadcasting standards.
That's what Groq did as well: burning the Transformer right onto a chip (I have to say I was impressed by the simplicity, but afterwards less so by their controversial Kushner/Saudi investment) .
> That's what Groq did as well: burning the Transformer right onto a chip
Are you perhaps confusing Groq with the Etched approach? IIUC Etched is the company that "burned the transformer onto a chip". Groq uses LPUs that are more generalist (they can run many transformers and some other architectures) and their speed comes from using SRAM.
My guess would be never. The closest you can get is "multi project wafers" where you get bundled with a load of other projects. As I understand it they're on the order of $100k which is cheap, but if you actually want to design and verify a chip you're looking at at least several million in salaries and software costs. Probably more like $10m, especially if you're paying US salaries. And of course that would be for a low performance design.
I think a better question would be "when are FPGAs going to stop being so ridiculously overpriced". That feels more possible to me (but still unlikely).
Doesn't this vary wildly depending on the process node though? The cutting edge stuff keeps getting increasingly ridiculous meanwhile I thought you could get something like 50 nm for cheap. I also remember seeing years ago that some university had a ~micron (IIRC) process that you could order from.
Does anyone know why they are using language models instead of a more purpose-built statistical model? My intuition is that a language model would either be overfit, or its training data would have a lot of noise unrelated to the application and significantly drive up costs.
I don’t know why people feel the need for such revisionism but AI has been a field encompassing things far more basic than this for longer than most commenters have been alive.
> AI has been a field encompassing things far more basic than this for longer than most commenters have been alive.
When I was 13, having just started programming, I picked up a book from a "junk bin" at a book store on Artificial Intelligence. It must have been from the mid-80s if not older.
It had an entire chapter on syllogism[1] and how to implement a program to spit them out based on user input. As I recall it basically amounted to some string exteaction assuming user followed a template and string concatenation to generate the result. I distinctly recall not being impressed about such a trivial thing being part of a book on AI.
https://arxiv.org/html/2411.19506v1
Why is it so hard to elaborate what AI algorithm / technique they integrate? Would have made this article much better
Already the case with consulting companies, have seen it myself
At some point someone will realise that backpropagation and adjoint solves are the same thing.
From around when the term was first coined: "artificial intelligence research is concerned with constructing machines (usually programs for general-purpose computers) which exhibit behavior such that, if it were observed in human activity, we would deign to label the behavior 'intelligent.'" [1]
[1]: https://doi.org/10.1109/TIT.1963.1057864
https://www.youtube.com/watch?v=8IZwhbsjhvE (From Zettabytes to a Few Precious Events: Nanosecond AI at the Large Hadron Collider by Thea Aarrestad)
Page: https://www.scylladb.com/tech-talk/from-zettabytes-to-a-few-...
(Probably not for this here though.)
I was just answering this question. LLM logic in weights is fundamentally from machine learning, so yes. Wasn't really saying anything about the article.
Much of the early AI research was spent on developing various algorithms that could play board games.
Didn't even need computers, one early AI was MENACE [1], a set of 304 matchboxes which could learn how to play noughts and crosses.
[1] https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_...
So they aren't "burned into silicon" then? The article mentions FPGAs and ASICs but it's a bit vague. I would be surprised if ASICs actually made sense here.
Isn’t this kind of approach feasible for something so purpose-built?
> CERN is using extremely small, custom large language models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).
> This work represents a compelling real-world demonstration of “tiny AI” — highly specialised, minimal-footprint neural networks
FPGAs for Neural Networks have been s thing since before the LLM era.
> [ GENEVA, SWITZERLAND — March 28, 2026 ] — CERN is using extremely small, custom large language models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).
Like (~9K) Jumbo Frames!
Are you perhaps confusing Groq with the Etched approach? IIUC Etched is the company that "burned the transformer onto a chip". Groq uses LPUs that are more generalist (they can run many transformers and some other architectures) and their speed comes from using SRAM.
I think a better question would be "when are FPGAs going to stop being so ridiculously overpriced". That feels more possible to me (but still unlikely).
5 years ago we would've called it a Machine Learning algorithm. 5 years before that, a Big Data algorithm.
> 5 years before that, a Big Data algorithm.
The DNN part? Absolutely not.
I don’t know why people feel the need for such revisionism but AI has been a field encompassing things far more basic than this for longer than most commenters have been alive.
When I was 13, having just started programming, I picked up a book from a "junk bin" at a book store on Artificial Intelligence. It must have been from the mid-80s if not older.
It had an entire chapter on syllogism[1] and how to implement a program to spit them out based on user input. As I recall it basically amounted to some string exteaction assuming user followed a template and string concatenation to generate the result. I distinctly recall not being impressed about such a trivial thing being part of a book on AI.
[1]: https://en.wikipedia.org/wiki/Syllogism
In the 1990s I remember taking my friend's IRC chat history and running it through a Markov model to generate drivel, which was really entertaining.
> The AXOL1TL V5 architecture comprises a VICReg-trained feature extractor stacked on top of a VAE.