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]
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.
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. '
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.
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?
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.
> 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.
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.
They used a custom neural net with autoencoders, which contain convolutional layers. They trained it on previous experiment data.
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
Because if it’s not an LLM it’s not good for the current hype cycle. Calling everything AI makes the line go up.
I'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.
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]
[1]: https://doi.org/10.1109/TIT.1963.1057864
> I'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.
Already the case with consulting companies, have seen it myself
Very important! This is not a LLM like the ones so often called AI these days. Its a neural network in a FPGA.
> FPGA
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.
Might be related: https://www.youtube.com/watch?v=T8HT_XBGQUI (Big Data and AI at the CERN LHC by Dr. Thea Klaeboe Aarrestad)
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-...
When is the price of fabbing silicon coming down, so every SMB can do it?
A bit of hype in the AI wording here. This could be called a chip with hardcoded logic obtained with machine learning
AI is not a new thing, and machine learned logic definitely counts as AI.
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. '
Yeah, and don’t forget Eliza!
Is a LLM logic in weights derived from machine learning?
Good one… but Is a DB query filter AI? I forgot to say though is sounds like a really cool thing to do
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.
It is 100% valid to label an algorithm that plays tic-tac-toe as "AI"
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_...
Yup this is exactly my point, in the 80s there were plenty of “AI” companies and “fuzzy logic” was the buzzword of the day.
Well, yes. That's literally what it is.
What what is? The article has nothing to do with LLMs. It even explicitly says they don’t use LLMs.
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?
You might wanna look at https://taalas.com/
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.
I think chips having a single LLM directly on them will be very common once LLMs have matured/reached a ceiling.
Do they actually have ASICs or just FPGAs? The article seems a bit unclear.
Hey Siri, show me an example of an oxymoron!
> 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).
There's no mention of SLMs or LLMs, though.
> 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.
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
Yes, they changed it: https://theopenreader.org/index.php?title=Journalism%3ACERN_...
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.
haha, yea they are part of it for sure, and I'm not dunking on the use of them, but I rather smile a bit when I stumble upon them.
Like (~9K) Jumbo Frames!
the fact that 99% of LHC data is just gone forever is insane
cern has been using neural networks for decades
Does string theory finally make sense when we ad AI hallucinations?
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.
It's not an LLM, it is a purpose built model. https://arxiv.org/html/2411.19506v1
5 years ago we would've called it a Machine Learning algorithm. 5 years before that, a Big Data algorithm.
We’ve been calling neural nets AI for decades.
> 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.
> 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
Eliza was 1960s.
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.
i hate that we're in this linguistic soup when it comes to algorithmic intelligence now.
This might be some journalistic confusion. If you go to the CERN documentation at https://twiki.cern.ch/twiki/bin/view/CMSPublic/AXOL1TL2025 it states
> The AXOL1TL V5 architecture comprises a VICReg-trained feature extractor stacked on top of a VAE.
… they’re not? Who said they are? The article even explicitly says they’re not?