101–110 of 166 posts
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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.
I've got news for you, everybody with a modern cpu uses this, which use a perceptron for branch prediction.
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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 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...
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
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Could they.... have someone else do it for them?
Glib, but it wont be cost effective at that small scale
Who says CERN needs to be cost effective?
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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_...
I didn't know what a Jujube was, but I got the idea.
Why did we stop calling this stuff machine learning again? this isn't even an llm, which has become the common bar for 'ai'
Some tried to hold out and keep calling it "ML" or just "neural networks" but eventually their colleagues start asking them why they aren't doing any AI research like the other people they read about. For a while some would say "I just say AI for the grant proposals", but it's hard to avoid buzzwords when you're writing it 3 times a day I guess.
Although note that the paper doesn't say "AI". The buzzword there is "anomaly detection" which is even weirder: somehow in collider physics it's now the preferred word for "autoencoder", even though the experiments have always thrown out 99.998% of their data with "classical" algorithms.
I've got news for you, everybody with a modern cpu uses this, which use a perceptron for branch prediction.
I didn't know that! Do you have any references that go into more depth here? I'd be curious how the architect and train it.
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LLMs also make the cynicism go up among the HN crowd.
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.
It’s impressive, honestly.
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/