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
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.