> Not really sure what this obsession with calling things you don't like AI generated is but it's poor form

Admonishing someone for correctly identifying AI-written or AI-edited blog posts is poor form, friend.

It is without a doubt written by an LLM. All of the telltale signs are there. I work with these tools 8-20 hours a day and after a while the verbiage and grammatical structures stick out like a sore thumb.

Get off the high horse. I too think this is a very interesting read. I was fascinated with the subject, but the presentation was nauseatingly distracting and immediately sets off yellow flags about how Percepta operates, and what kind of quality they're willing to settle with. It tells me they are more interested in appearances and superficiality.

The numbers that are there categorically cannot be trusted, because hallucinating those details is quite common for models. There is simply no indication that a human adequately proof-read this and therefore any of its claims must be taken with a grain of salt. Don't forget the recent Cloudflare+Matrix debacle: https://news.ycombinator.com/item?id=46781516

I share the same concerns as OP; this post lacks metrics and feels like someone did something cool and raced to get an AI to post about it, instead of giving it a proper treatment.

I don't care how sure you are. Honestly, it's irrelevant. 99% of the time, it's a more pleasant and productive conversation for everyone involved if you just focus on issues you had with the text itself than any nebulous AI involvement.

From my point of view, all you've done is said a lot of nonsense and fabricated a convoluted explanation for why you think the text is bad. I'm fine on my horse thanks.

The post is the perfect example of the kind of writing about AI that dupes people that don't really understand how things like LLMs actually work and are actually trained. Anyone who properly understands these things finds the complete and total lack of detail about training and the loss function (and of course real metrics / benchmarks) to be a monstrous red flag here.

Especially egregious to me is the claim "Because the execution trace is part of the forward pass, the whole process remains differentiable: we can even propagate gradients through the computation itself". This is total weasel-language: e.g. we can propagate any weights through any transformer architecture and all sorts of other much more insane architectural designs, but that is irrelevant if you don't have a continuous and differentiable loss function that can properly weight partially-correct solutions or the likelihood / plausibility of arbitrary model outputs. You also need a clearer source of training data (or way to generate synthetic data).

So for e.g. AlphaFold, we needed to figure out a loss function that continuously approximated the energy configuration of various molecular configurations, and this is what really allowed it to actually do something. Otherwise, you are stuck with slow and expensive reinforcement-based systems.

The other tells are garbage analogies ("Humans cannot fly. Building airplanes does not change that; it only means we built a machine that flies for us"). Such analogies add nothing to understanding, and indeed distract from serious/real understanding. Only dupes and fools think you can gain any meaningful understanding of mathematics and computer science through simplistic linguistic analogies and metaphors without learning the proper actual (visuspatial, logical, etc) models and understanding. Thus, people with real and serious mathematical understanding despise such trite metaphors.

But then, since understanding something like this properly requires serious mathematical understanding, copy like that is a huge tell that the authors / company / platform puts bullshitting and sales above truth and correctness. I.e., yes, a huge yellow flag.