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Comment by lccerina | Hacker News Reader

Utter disrespect for using the term "biology" relating to LLM. No one would call the analysis of a mechanical engine "car biology". It's an artificial system, call it system analysis.

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The analogy stems from the notion that neural nets are "grown" rather than "engineered". Chris Olah has an old, but good post with some specific examples: https://colah.github.io/notes/bio-analogies/

It makes sense if you define "biology" as "incredibly complicated system not designed by humans that we kind of poke at to try to understand it."

"not designed by humans"? Since when? Unless you count cortical organoids /wetware (grown in some instrumented petri dish) every artificial neural network, doesn't matter how complicated, it is designed by humans. With equations and rules designed by humans. Backpropagation, optimization algorithms, genetic selections etc... all designed by humans.

There is no biology here, and there are so many other words that describe perfectly what they are doing here, without twisting the meaning of another word.

By not designed I'm talking about the synaptic weights

Still designed by humans. The loss function, backpropagation and all other mechanisms didn't just appear magically in the neural network. Someone decided which loss function to use, which architecture or which optimization techniques. Only because it takes a big GPU a lot of number crunching to assign those weights, it doesn't mean it's biological.

In the same way, a weather forecast model using a lot of complicated differential equations is not biological. A finite element model analyzing some complicated electromagnetic field, or the aerodynamics of a car is not biological. Just because someone around 70-75 years ago called them 'perceptrons' or 'neurons' instead of thingamajigs does not make them biology.

"Still designed by humans." No they are not. They are learned via backpropagation. This is the entire reason why neural networks work so well and why we have no idea how they work when they get big.

And who designed backpropagation? It is not a magical property of artificial neurons or some law of nature or god's miracle. A bunch of mathematicians banged their head on the problem of backpropagation, tossed it to a computer, and voilà , neural networks made sense. Neural networks work so well because someone chooses the right loss function for the right problem. Wrong loss function -> wrong results. It's not magic. Nor it's biology.

Sure, but it makes no sense at all if you define biology as “the smell of a freshly opened can of tennis balls.” The original comment is probably better understood using a standard definition of the words it used, rather than either of our definitions.

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