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

Implement it...from scratch? Its literally least squares regression. Its a few lines of code. What are you trying to say here?

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

    // 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))

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.

For 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

[2]: https://arxiv.org/abs/1907.05120