As I read this post, I found myself puzzled by the omission of the potential of AI-research-acceleration by SotA AI models, as Daniel mentions in his comment. I think it’s worth pointing out that this has been explicitly discussed by leading individuals in the big AI labs. For instance, Sam Altman saying that scaling is no longer the primary path forward in their work, that instead algorithmic advances are.
Think about your intuitions of what a smart and motivated human is capable of. The computations that that human brain is running represent an algorithm. From neuroscience we have a lot of information about the invariant organization of human brains, the architectural constraints and priors established during fetal development under guidance from the genome. The human brain’s macroscale connectome is complicated and hacky, with a lot of weirdly specific and not fully understood aspects. The architectures currently used in ML are comparatively simple and have more uniform repeating structures. Some of the hacky specific brain connectome details are apparently very helpful though, considering how quickly humans do, in practice, learn. Figuring out what aspects of the brain would be useful to incorporate into ML models is exactly the sort of constrained engineering problem that ML excels at. Take in neuroscience data, design a large number of automated experiments, run the experiments in parallel, analyze and summarize the results, reward model for successes, repeat. The better our models get, and the more compute we have with which to do this automated search, the more likely we are to find something. The more advances we find, the faster and cheaper the process becomes and the more investment will be put into pursuing it. The combination of all of these factors implies a strong accelerating trend beyond a certain threshold. This trend, unlike scaling compute or data, is not expected to sigmoid-out before exceeding human intelligence. That’s what makes this truely a big deal. Without this potential meta-growth, ML would be just a big deal instead of absolutely pivotal. Trying to project ML development without taking this into account is like watching a fuse burn towards a bomb, and trying to model the bomb like a somewhat unusually vigorous campfire.
As I read this post, I found myself puzzled by the omission of the potential of AI-research-acceleration by SotA AI models, as Daniel mentions in his comment. I think it’s worth pointing out that this has been explicitly discussed by leading individuals in the big AI labs. For instance, Sam Altman saying that scaling is no longer the primary path forward in their work, that instead algorithmic advances are.
Think about your intuitions of what a smart and motivated human is capable of. The computations that that human brain is running represent an algorithm. From neuroscience we have a lot of information about the invariant organization of human brains, the architectural constraints and priors established during fetal development under guidance from the genome. The human brain’s macroscale connectome is complicated and hacky, with a lot of weirdly specific and not fully understood aspects. The architectures currently used in ML are comparatively simple and have more uniform repeating structures. Some of the hacky specific brain connectome details are apparently very helpful though, considering how quickly humans do, in practice, learn. Figuring out what aspects of the brain would be useful to incorporate into ML models is exactly the sort of constrained engineering problem that ML excels at. Take in neuroscience data, design a large number of automated experiments, run the experiments in parallel, analyze and summarize the results, reward model for successes, repeat. The better our models get, and the more compute we have with which to do this automated search, the more likely we are to find something. The more advances we find, the faster and cheaper the process becomes and the more investment will be put into pursuing it. The combination of all of these factors implies a strong accelerating trend beyond a certain threshold. This trend, unlike scaling compute or data, is not expected to sigmoid-out before exceeding human intelligence. That’s what makes this truely a big deal. Without this potential meta-growth, ML would be just a big deal instead of absolutely pivotal. Trying to project ML development without taking this into account is like watching a fuse burn towards a bomb, and trying to model the bomb like a somewhat unusually vigorous campfire.