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
I'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.
> 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
Some career do-nothing-but-make-noise in my organization hired a firm to 'Do AI' on some shitty data and the outcome was basically linear regression. It turns out that you can impressive executives with linear regression if you deliver it enthusiastically enough.
Tbh, often enough, linear regression is exactly what is needed.
Yes, and we do it every day and call it 'linear regression' and don't need a data center full of expensive toys to do it
Not everyone knows everything so knowledge is the new oil.
I do know about linear regression even had quite some of it at university.
But I still wouldn’t be able to just implement it on some data without good couple days to weeks of figuring things out and which tools to use so I don’t implement it from scratch.
I'm half expecting to see "AI model" appearing as stand-in for "if > 0" at this point in the cycle.
This is why I am programming now in Ocaml, files themselves are AI ( ml ).
I am sure you did not forget that pattern matching.
This is essentially what any relu based neural network approximately looks like (smoother variants have replaced the original ramp function). AI, even LLMs, essentially reduce to a bunch of code like
Thats a bit far. Relu does check x>0 but thats just one non-linearity in the linear/non-linear sandwich that makes up universal function approximator theorem. Its more conplex than just x>0
The relu/if-then-else is in fact centrally important as it enables computations with complex control flow (or more exactly, conditional signal flow or gating) schemes (particularly as you add more layers).
Multiply-accumulate, then clamp negative values to zero. Every even-numbered variable is a weighted sum plus a bias (an affine transformation), and every odd-numbered variable is the ReLU gate (max(0, x)). Layer 2 feeds on the ReLU outputs of layer 1, and the final output is a plain linear combination of the last ReLU outputs
i let v0 = 0.616u + 0.291v - 0.135 let v1 = if 0 > v0 then 0 else v0
is there something 'less good' about:
Am I the only one who stutter-parses "0 > value" vs my counterexample?Is Yoda condition somehow better?
Shouldn't we write: Let v1 = max 0 v0
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
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.
I am somewhat cynically waiting for the AI community to rediscover the last half a century of linear algebra and optimisation techniques.
At some point someone will realise that backpropagation and adjoint solves are the same thing.
There are plenty of smart people in the "AI community" already who know it. Smugly commenting does not replace actual work. If you have real insight and can make something perform better, I guarantee you that many people will listen (I don't mean twitter influencers but the actual field). If you don't know any serious researcher in AI, I have my doubts that you have any insight to offer.
I am sure they are aware...
There is an HIGGS dataset [1]. As name suggest, it is designed to apply machine learning to recognize Higgs bozon.
[1] https://archive.ics.uci.edu/ml/datasets/HIGGS
In my experiments, linear regression with extended (addition of squared values) attributes is very much competitive in accuracy terms with reported MLP accuracy.
The LHC has moved on a bit since then. Here's an open dataset that one collaboration used to train a transformer:
https://opendata-qa.cern.ch/record/93940
if you can beat it with linear regression we'd be happy to know.
Thanks.
The paper [1] referenced in your link follows the lagacy of the paper on the HIGGS dataset, and does not operate with quantities like accuracy and/or perplexity. HIGGS dataset paper provided area under ROC, from which one had to approximate accuracy. I used accuracy from the ADMM paper [2] to compare my results with. As I checked later, area under ROC in [1] mostly agrees with [2] SGD training results on HIGGS.
I think that perplexity measure is appropriate there in [1] because we need to discern between three outcomes. This calls for softmax and for perplexity as a standard measure.So, my questions are: 1) what perplexity should I target when dealing with "mc-flavtag-ttbar-small" dataset? And 2) what is the split of train/validate/test ratio there?
And why not, when linear regression works, it works so well it's basically magic, better than intelligence, artificial or otherwise
Having work with people who do that, I can guarantee that’s not the case. See https://ssummers.web.cern.ch/conifer/ and HSL4ML, these run BDT and CNN
That works well to get around patents btw :)
It seems like most of the implementation is FPGA, which I wouldn’t call “physically burned into silicon.” That’s quite a stretch of language
Because if it’s not an LLM it’s not good for the current hype cycle. Calling everything AI makes the line go up.
LLMs also make the cynicism go up among the HN crowd.
Hm. Is HN starting to become more skeptical of LLMs? For the past couple of years, HN has seemed worryingly enthusiastic about LLMs.
How so? Half the people here have LLM delusion in every thread posted here; more than half of the things going to the frontpage are AI. Just look at hours where Americans are awake.
Fucking Americans. Only 4% of the world population, with the magic of disproportionately afflicting the global news headlines which make their way here.
It’s impressive, honestly.
Thanks for tracking this down. I too am annoyed when so-called technical articles omit the actual techniques.
Because it does not align with LLM Uber Alles.