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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.
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
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.
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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 b…
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.
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.
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) .
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.
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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.
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_...