91–100 of 166 posts
Earlier quoted context omitted.
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.
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.
Earlier quoted context omitted.
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.
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.
A bit of hype in the AI wording here. This could be called a chip with hardcoded logic obtained with machine learning
Earlier quoted context omitted.
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
// 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))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.
[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.
I've got news for you, everybody with a modern cpu uses this, which use a perceptron for branch prediction.
At this point AI basically means "we didn't know how to solve the problem so we just threw a black box at it".
Earlier quoted context omitted.
I'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.
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.
https://opendata-qa.cern.ch/record/93940
if you can beat it with linear regression we'd be happy to know.
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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.