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Flag_Red t1_j96jzng wrote

Reply to comment by thecodethinker in [R] neural cloth simulation by LegendOfHiddnTempl

> I bet stuff like this is gonna be the biggest real life use case for neural networks.

Huh? What about image/face/character/anything recognition, speech-to-text, text-to-speech, translation, natural language understanding, code autocomplete, etc?

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Wacov t1_j96nvx5 wrote

Depends how you define "biggest" but running an ML physics sim per-frame per-character in a AAA title would add up to a hell of a lot of inference.

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PacmanIncarnate t1_j9d7yaw wrote

The bigger use isn’t games, but animation or VFX. They require high quality simulations that sometimes take days to render a few seconds of simulation. Every tech that can cut that time down without a substantial loss of quality is huge.

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vman512 t1_j96s8yu wrote

maybe for people who play video games all day, this is the most real life use case

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nuclear_knucklehead t1_j9876bt wrote

Think of the zillions of FEA and CFD simulations done in the engineering world that a fast-running physics model would greatly accelerate and improve. These things are often less visible to the general audience than the high profile stuff you mention, but still have potentially billions of dollars in economic impact and productivity improvements.

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thecodethinker t1_j96u7y5 wrote

I think classification tasks (like image or face recognition) is really useful, but is more niche. We had image recognition before, NNs just do it better. They don’t open up new use cases for recognition.

Same for speech to text and text to speech.

Translation is another huge one, that’s true.

I don’t think NN code autocomplete is a “big real life use case” as we have perfectly correct autocomplete as is and for anything beyond simple programs, I have seen any model give good suggestions. Plus not everyone writes code.

Natural language “understanding” is a weird one. I’m not convinced (yet) that we have models that “understand” language, just models that are good at guessing the next word.

ChatGPTs tendency to be flat out wrong or give nonsensical answers to very niche and specific questions suggests that it isn’t doing any kind of critical thinking about a question, it’s just generating statistically probable following tokens. It just generates convincing prose as it was trained to do.

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liquiddandruff t1_j989luo wrote

the stochastic parrot argument is a weak one; we are stochastic parrots

the phenomenon of "reasoning ability" may be an emergent one that arises out of the recursive identification of structural patterns in input data--which chatgpt is shown to do.

prove that "understanding" is not and cannot ever be reducible to "statistical modelling" and only then is your null position intellectually defensible

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thecodethinker t1_j98puob wrote

Where has chat gpt been rigorously shown to have reasoning ability? I’ve heard that it passed some exams, but that could just be the model regurgitating info in its training data.

Admittedly, I haven’t looked to deeply in the reasoning abilities of LLMs, so any references would be appreciated :)

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liquiddandruff t1_j98v6ko wrote

it's an open question and lots of interesting work is happening at a frenetic pace here

A favourite discussed recently:

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synth_mania t1_j99njvy wrote

Dude the first image classification or recognition program used perceptrons, the first model of a neuron. In other words, image classification has been neural networks ever since the beginning

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thecodethinker t1_j9a4mvo wrote

Yeah, exactly my point about image classification. We’ve had it for a long time already.

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synth_mania t1_j9cug1p wrote

My point was that you said image classification has been around since before NNs. That is false. Image classification has only ever been done with NNs. Sometimes they are radically different than what is normally used today (e.g. RAMnets and WISARD), but they've always been NNs.

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