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
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
My personal take is that the current generation of generative models peaked, for the reasons stated in the video (diminishing returns). This current gen will be useful, but progress-wise it’ll be a dead end.
In the future however I believe that models with a different architecture will cause a breakthrough, being able to perform better with less training. And probably less energy requirements, too.
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.
I don’t think that reinventing computers will do any good. The issue that I see is not hardware, but software - the current generative models are basically brute force, you throw enough data and processing power at the problem until it becomes smaller, but at the end of the day you’re still relying too much on statistical patterns behind the wrong entities.
Instead I think that the ML architecture will change. And this won’t be done by those tech bros full of money burning effigies, who have a nasty/stupid/disgraceful tendency to confuse symbolic representations with the things being represented. Instead it’ll be done by researchers in some random compsci or robotics lab, in a random place of the world. They’ll be doing some weird stuff like emulating the brain of a fruit fly, and someone will point out “hey, you see this feature? It has ML applications”. And that’ll be when they actually add some intelligence to those systems, i.e. the missing piece of the puzzle. It won’t be AGI but it’ll be better than now, at least.
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.
Not even another info transferring entity would solve it. Be it quantum computers, photonic computers, at the end of the day we’d be simply brute forcing the problem harder, due to increased processing power. But we need something else than brute force due to the diminishing returns.
Just to give you an idea. A human needs around 2400kcal/day to survive, or 100kcal/h = 116W. Only 20% of that is taken by the brain, so ~23W. (I bet that most of that is used for motor control, not reasoning.) We clearly suck as computing machines, and yet our output is considerably better than the junk yielded by LLMs and diffusion models, even if you use a really nice computer and let the model take its time producing its [babble | six fingers “art”]. Those models are clearly doing lots of unnecessary operations, while failing hard at what they’re expected to do.
Regarding research, my point is that what’s going to fix generative models is likely from outside the field of artificial intelligence. It’ll be likely something small and barely related, that happens to have some ML application.
Sam Altman gives a pretty good indication that your point is correct when he began asking for $7 trillion for new AI chip development.