Sure but my point is that the energy costs are in the same magnitude.
If you want to be pedantic then only 6% of the human brain is the visual cortex. But AlexNet is also an inefficient model so something like an optimized ResNet is 100x as efficient to train. So now you're at 10.5kwh and 1.5kwh for the baby and model respectively.
You can argue details further but I'd say the energy cost of both is fairly close.
You’re missing my original point which is about continued, ongoing robustness that works in the low data regime and allows pilots/astronauts to make reasonable decisions in _completely novel_ situations (as just one example).
The networks we have are trained once and work efficiently for their training dataset. They are even robust to outliers in the distribution of that dataset. But they aren’t robust to surprises, unmentioned changes in assumptions/rules/patterns about the data.
Even reinforcement learning is still struggling with this as self-play effectively requires being able to run your dataset/simulation quickly enough to find new policies that work. Humans don’t even have time for that, much less access to a full running simulation of their environments. Although we do generate world models and there’s some work towards that I believe.
If you want to be pedantic then only 6% of the human brain is the visual cortex. But AlexNet is also an inefficient model so something like an optimized ResNet is 100x as efficient to train. So now you're at 10.5kwh and 1.5kwh for the baby and model respectively.
You can argue details further but I'd say the energy cost of both is fairly close.