- cross-posted to:
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.ml
A new paper suggests diminishing returns from larger and larger generative AI models. Dr Mike Pound discusses.
The Paper (No “Zero-Shot” Without Exponential Data): https://arxiv.org/abs/2404.04125
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Probably.
I’ve already thought that in terms of major progression AI has peaked as early as in 2022 when chatgpt and various diffusers were all hyped up. It was kinda obvious, since our silicon tech is already basically maxed out. There are lots of potential optimizations, but they are minor advancements compared to the raw compute power growth we’ve had till the near past. And in order to make the next revolution in the AI field, those moneybags will have to spend the colossal amount of money to basically reinvent either computers themselves or the ML architechture.
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under the “reinventing computers” i mean chosing another information transfering entity for our processing units. For instance, photonics is a perspective field, as photons are much smaller, thus potentially we could make even smaller logical elements also as they produce much less heat.
What’s about ML architechture, of course it won’t be the tech bros, of course it would be scientists, but don’t forget that untill someone sponsors them, the research could take literal decades before there will be discovered anything revolutional. Scientists are not some kind of gurus who live in moutains and fed by the energy of the sun. In order to make a living they have jobs besides scientific research. That’s why grants and other research funding methods do exist. And as you could’ve guessed, these are greatly dependant on guys with money and their interest in said researchi.
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there’s a lot to optimize in LLMs and i never said otherwise. Though, photonic computers if the field would be researched, could consume as much as an LED lamp making it even more effective than our brain. given the total amount of computers in the world, even the slightest power consumption optimization would save colossal amount of energy, and in case of photonics the raw numbers could possibly be unimagineable.
I bet they simply will find a way to greatly simplify the mathematical apparatus of the neuron interaction. Matrix multiplication is kinda slow and there’s lots of it
Sam Altman gives a pretty good indication that your point is correct when he began asking for $7 trillion for new AI chip development.