I see this stance of yours parroted over and over but a 3 year old can tell a dog from a cat doesn't need to be trained on millions of images. Also uses way less energy for that.
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.
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.
Again happy to be corrected.
If you want to be pedantic then 6% of the human brain is the visual cortex but then you also have to argue that AlexNet is horribly inefficient to train. So you cut the brain cost to 6% and the model cost to 1%. They're still within an order of magnitude (favoring the model) which I'd say is pretty close in terms of energy usage.
A 3 year old has 3 years of multimodal training data and RLHF + a few billions of years of evolution that have primed and biased our visual and cognitive systems.
That requires a lot more data than machine models that literally zero inherent bias. Assuming you want a true apples to apples comparison.
Humans are very bad at this task; it takes a massive effort to learn this many birds. In fact it's a great counterexample to human few shot learning ability...