SneerClub

So one point I have to disagree with.

More to the point, we know that thought is possible with far less processing power than a Microsoft Azure datacenter by dint of the fact that people can do it. Exact estimates on the storage capacity of a human brain vary, and aren’t the most useful measurement anyway, but they’re certainly not on the level of sheer computational firepower that venture capitalist money can throw at trying to nuke a problem from space. The problem simply doesn’t appear to be one of raw power, but rather one of basic capability.

There are a lot of ways to try to quantify the human brain's computational power, including storage (as this article focuses on, but I think its the wrong measure, operations, numbers of neural weights, etc.). Obviously it isn't literally a computer and neuroscience still has a long way to go, so the estimates you can get are spread over like 5 orders of magnitude (I've seen arguments from 10^13 flops and to 10^18 or even higher, and flops is of course the wrong way to look at the brain). Datacenter computational power have caught up to the lowers ones, yes, but not the higher ones. The bigger supercomputing clusters, like El Capitan for example, is in the 10^18th range. My own guess would be at the higher end, like 10^18, with the caveat/clarification that evolution has optimized the brain for what it does really really well, so that the compute is being used really really efficiently. Like one talk I went to in grad school that stuck with me... the eyeball's microsaccades are basically acting as a frequency filter on visual input. So literally before the visual signal has even got to the brain the information has already been processed in a clever and efficient way that isn't captured in any naive flop estimate! AI boosters picked estimates on human brain power that would put it in range of just one more scaling as part of their marketing. Likewise for number of neurons/synapses. The human brain has 80 billion neurons with an estimated 100 trillion synapses. GPT 4.5, which is believed to have peaked on number of weights (i.e. they gave up on straight scaling up because it is too pricey), is estimated (because of course they keep it secret) like 10 trillion parameters. Parameters are vaguely analogs to synapses, but synapses are so much more complicated and nuanced. But even accepting that premise, the biggest model was still like 1/10th the size to match a human brain (and they may have lacked the data to even train it right).

So yeah, minor factual issue, overall points are good, I just thought I would point it out, because this factual issue is one distorted by the AI boosters to make it look like they are getting close to human.