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They used a custom neural net with autoencoders, which contain convolutional layers. They trained it on previous experiment data. https://arxiv.org/html/2411.1
by intoXbox 6mo ago
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 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
- dcanelhas 6mo agoI'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.
- phire 6mo agoI'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.
- LiamPowell 6mo agoHistorically 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 https://doi.org/10.1109/TIT.1963.1057864
- zingar 6mo agoThat definition moves the goalposts almost by definition, people only stopped thinking that chess demonstrated intelligence when computers started doing it.
- Eufrat 6mo agoThe 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.
- coherentpony 6mo agoI 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.
- whattheheckheck 6mo agoI am sure they are aware...
- bonoboTP 6mo agoThere 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.
- ninjagoo 6mo ago> 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
- idiotsecant 6mo agoSome 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.
- tasuki 6mo agoTbh, often enough, linear regression is exactly what is needed.
- idiotsecant 6mo agoYes, 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
- mpierini 6mo agoYou do unsupervised learning without labels with a linear regression. Interesting. What would you regress in this case? The problem is the following: you have a point cloud of data (electronic signal from arrays arranged into an irregular pattern). You know the physics that was discovered. You are looking for rare events (one in a billion or less) and you don’t know what they look like.
- mpierini 6mo agoAnd you think we did not try linear regressions? This is what we used to do 20 years ago. Then we gained two orders of magnitude in signal-to-background discrimination. And since our data are not even images, off-shelf solutions mostly don’t apply. Try to process40 MHz of incoming collisions (1 MB each) within 100 nsec with a linear regression of point-cloud data. When you are done trying, try to think that maybe (maybe…) life is not as easy as bread&butter. If you succeed, come and knock at CERN’s door. Maybe we will let you in…
- blitzar 6mo agoI'm half expecting to see "AI model" appearing as stand-in for "if > 0" at this point in the cycle.
- Foobar8568 6mo agoThis is why I am programming now in Ocaml, files themselves are AI ( ml ).
- srean 6mo agoI am sure you did not forget that pattern matching.
- Vetch 6mo agoThis 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 let v0 = 0 let v1 = 0.40978399*(0.616*u + 0.291*v) let v2 = if 0 > v1 then 0 else v1 let v3 = 0 let v4 = 0.377928*(0.261*u + 0.468*v) let v5 = if 0 > v4 then 0 else v4...
- samrus 6mo agoThats 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
- greenavocado 6mo agoMultiply-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 // inputs: u, v // --- hidden layer 1 (3 neurons) --- let v0 = 0.616*u + 0.291*v - 0.135 let v1 = if 0 > v0 then 0 else v0 let v2 = -0.482*u + 0.735*v + 0.044 let v3 = if 0 > v2 then 0 else v2 let v4 = 0.261*u - 0.553*v + 0.310 let v5 = if 0 > v4 then 0 else v4 // --- hidden layer 2 (2 neurons) --- let v6 = 0.410*v1 - 0.378*v3 + 0.528*v5 + 0.091 let v7 = if 0 > v6 then 0 else v6 let v8 = -0.194*v1 + 0.617*v3 - 0.291*v5 - 0.058 let v9 = if 0 > v8 then 0 else v8 // --- output layer (binary classification) --- let v10 = 0.739*v7 - 0.415*v9 + 0.022 // sigmoid squashing v10 into the range (0, 1) let out = 1 / (1 + exp(-v10))
- yread 6mo agoAnd why not, when linear regression works, it works so well it's basically magic, better than intelligence, artificial or otherwise
- plasino 6mo agoHaving work with people who do that, I can guarantee that’s not the case. See https://ssummers.web.cern.ch/conifer/ https://ssummers.web.cern.ch/conifer/ and HSL4ML, these run BDT and CNN
- Staross 6mo agoThat works well to get around patents btw :)
- thesz 6mo agoThere 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 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.
- dguest 6mo agoThe 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 https://opendata-qa.cern.ch/record/93940 if you can beat it with linear regression we'd be happy to know.
- thesz 6mo agoThanks. 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. [1] https://arxiv.org/pdf/2505.19689 [2] https://proceedings.mlr.press/v48/taylor16.pdf 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?
- dguest 6mo agoFor better or worse the people working on this don't really use perplexity or accuracy to evaluate models. The target is whatever you'd get for those metrics if you used the discriminants that were provided in the dataset (i.e. the GN2v01 values). As for why accuracy and perplexity aren't reported: the experiments generally choose a threshold to consider something a "b-hadron" (basically picking a point along the ROC curve) and quantify the TPR and FPR at that point. There are reasons for this, mostly that picking a standard point lets them verify that the simulation actually reflects data. See, for example, the FPR [1] and TPR [2] "calibrations". It's a good point, though, the physicists should probably try harder to report standard metrics that the rest of the ML community uses. [1]: https://arxiv.org/pdf/2301.06319 https://arxiv.org/pdf/2301.06319 [2]: https://arxiv.org/abs/1907.05120 https://arxiv.org/abs/1907.05120
- vultour 6mo agoBecause if it’s not an LLM it’s not good for the current hype cycle. Calling everything AI makes the line go up.
- danielbln 6mo agoLLMs also make the cynicism go up among the HN crowd.
- okamiueru 6mo agoHm. Is HN starting to become more skeptical of LLMs? For the past couple of years, HN has seemed worryingly enthusiastic about LLMs.
- andersonpico 6mo agoHow 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.
- irishcoffee 6mo agoFucking 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.
- etrautmann 6mo agoIt seems like most of the implementation is FPGA, which I wouldn’t call “physically burned into silicon.” That’s quite a stretch of language
- jgalt212 6mo agoBecause it does not align with LLM Uber Alles.
- fnord77 6mo agoThanks for tracking this down. I too am annoyed when so-called technical articles omit the actual techniques.
- moffkalast 6mo agoAh anomaly detection, that makes a lot more sense.