Moravac (in the linked paper): "In both cases, the evidence for an intelligent mind lies in the machine's performance, not its makeup." Do you agree? I'm much less keen to ascribe "intelligence" to large, pretrained language models given that I know how primitive their training regime is compared to a scenario where I might have been "blended" by their ability to "chat" (double quote here since I know ChatGPT and the…
Reducing the capability of the human brain to performance alone is too simplistic, especially when looking at LLM's. Even if we would assign some intelligence to LLM's, they need a 400w GPU at inference time, and several orders of magnitude more of those at training time. The human brain runs constanly at ~20w. I highly doubt you'd be able to get even close to that kind of performance with current manufacturing proce…
Most likely because any brains that required more energy died off at evolutionary time scales. And while there are some problems with burning massive amounts of energy to achieve a task (see: global warming) this is not likely a significant short falling that large scale AI models have to worry about. Seemingly there are plenty of humans willing to hook them up to power sources at this time.
Also you might want to consider the 0-16 year training stages for human which have become more like 0-21 year training stages with at minimum 8 hours of downtime per day. This does adjust the power dynamics pretty considerably in that the time actually thinking daily drops to around 1/3rd the day boosting effective power use to 60w (as in you've wasted 2/3s the power eating, sleeping, and pooping). In addition that model you've spend a lot of power training is able to be duplicated across thousands/millions of instances in short order, where as you're praying that human you've trained doesn't step out in front of a bus.
So yes, reducing the capability of a human brain/body to performance alone is far too simplistic.