The Coming Wave: AI, Power, and Our Future

by Mustafa Suleyman

Researchers meanwhile see more and more evidence for “the scaling hypothesis,” which predicts that the main driver of performance is, quite simply, to go big and keep going bigger. Keep growing these models with more data, more parameters, more computation, and they’ll keep improving—potentially all the way to human-level intelligence and beyond. No one can say for sure whether this hypothesis will hold, but so far at least it has. I think that looks set to continue for the foreseeable future. Our brains are terrible at making sense of the rapid scaling of an exponential, and so in a field like AI it’s not always easy to grasp what is actually happening. It’s inevitable that in the next years and decades many orders of magnitude more compute will be used to train the largest AI models, and so, if the scaling hypothesis is at least partially true, there is an inevitability about what this means. Sometimes people seem to suggest that in aiming to replicate human-level intelligence, AI chases a moving target or that there is always some ineffable component forever out of reach. That’s just not the case. The human brain is said to contain around 100 billion neurons with 100 trillion connections between them—it is often said to be the most complex known object in the universe. It’s true that we are, more widely, complex emotional and social beings. But humans’ ability to complete given tasks—human intelligence itself—is very much a fixed target, as large and multifaceted as it ...

Exponentials vs. the Brain: Scaling Past Human Level