If this is the way to superintelligence, it remains a bizarre one. “This is back to a million monkeys typing for a million years generating the works of Shakespeare,” Emily Bender told me. But OpenAI’s technology effectively crunches those years down to seconds. A company blog boasts that an o1 model scored better than most humans on a recent coding test that allowed participants to submit 50 possible solutions to each problem—but only when o1 was allowed 10,000 submissions instead. No human could come up with that many possibilities in a reasonable length of time, which is exactly the point. To OpenAI, unlimited time and resources are an advantage that its hardware-grounded models have over biology. Not even two weeks after the launch of the o1 preview, the start-up presented plans to build data centers that would each require the power generated by approximately five large nuclear reactors, enough for almost 3 million homes.
Imagine if the engineers for computers were just as short sighted. If they had stopped prioritizing development when computers were massive, room sized machines with limited computing power and obscenely inefficient.
Not all AI development is focused on increasing complexity. Much is focused on refinement, and increasing efficiency. And there’s been a ton of progress in this area.
This article and discussion is specifically about massively upscaling LLMs. Go follow the links and read OpenAI’s CEO literally proposing data centers which require multiple, dedicated grid-scale nuclear reactors.
I’m not sure what your definition of optimization and efficiency is, but that sure as heck does not fit mine.
Sounds like you’re only reading a certain narrative then. There’s plenty of articles about increasing efficiency, too.