> ‘There will never be enough computing power to create AGI using machine learning that can do the same, because we’d run out of natural resources long before we'd even get close,’
I would say the counter-example that proves this statement false is in the author's skull, but perhaps that's overly presumptuous.
Tautologically. Machine learning is a blanket term for a wide range of approaches that allow machines to mimic the brain's capability to learn, at least in function if not form.
The brain may employ wildly different machine learning algorithms from those currently in vogue, but whatever algorithms the brain is using must be machine learning algorithms. Unless of course you define machine in this case to be something which isn't the brain, in which case they're just learning algorithms. Regardless, an architecture exists to match the brain's capabilities with not only finite but surprisingly limited hardware.
In the very vaguest sense of the word, absolutely. Not that there is a literal transformer algorithm, but there is some evolved learning algorithm in the neurons of the brain that is at least somewhat a distant cousin of what we’re doing today.
I would say the counter-example that proves this statement false is in the author's skull, but perhaps that's overly presumptuous.