Yes, I've read your big universal learner post, and I'm not convinced. This does seem to be the crux of our disagreement, so let me take some time to rebut:

First off, you're seriously misrepresenting the success of deep learning as support for your thesis. Deep learning algorithms are extremely powerful, and probably have a role to play in building AGI, but they aren't the end-all, be-all of AI research. For starters, modern deep learning systems are absolutely fine-tuned to the task at hand. You say that they have only "a small number of hyperparameters." which is something of a misrepresentation. There are actually quite a few of these hyperparameters in state-of-the-art networks, and there are more in networks tackling more difficult tasks.

Tuning these hyperparameters is hard enough that only a small number of researchers can do it well enough to achieve state of the art results. We do not use the same network for image recognition and audio processing, because that wouldn't work very well.

We tune the architecture of deep learning systems to the task at hand. Presumably, if we can garner benefits from doing that, evolution has an incentive to do the same. There's a core, simple algorithm at work, but targeted to specific tasks. Evolution has no incentive to produce a clean design if cludgy tweaks give better results. You argue that evolution has a bias against complexity, but that certainly hasn't stopped other organs from developing complex structure to make them marginally better at the task.

There's also the point that there's plenty of tasks that deep learning methods can't solve yet (like how to store long-term memories of a complex and partially observed system in an efficient manner) - not to mention higher level cognitive skills that we have no clue how to approach.

Nobody thinks this stuff is just a question of throwing yet larger deep learning networks at the problem. They will be solved by finding different hard-wired network architectures that make the problem more manageable by knowing things about it in advance.

The ferret brain rewiring result is not a slam-dunk for the universal learning by itself. It just means that different brain modules can switch which pre-programmed neural algorithms they implement on the fly. Which makes sense, because on some level these things have to be self-organizing in the first place to be compactly genetically coded.

The real test here would be to take a brain and give it an entirely new sense - something that bears no resemblance to any sense it or any of its ancestors has ever had, and see if it can use that sense as naturally as hearing or vision. Personally, I doubt it. Humans can learn echolocation, but they can't learn echolocation the way bats and dolphins can learn echolocation - and echolocation bears a fair degree of resemblance to other tasks that humans already have specialized networks for (like pinpointing the location of a sound in space).

Notably, the general learner hypothesis does not explain why non-surgically-modified brains are so standardized in structure and functional layout. Something that you yourself bring up in your article.

It also does not explain why birds are better at language tasks than cats. Cat brains are much larger. The training rewards in the lab are the same. And, yet, cats significantly underperform parrots at every single language-related task we can come up with. Why? Because the parrots have had a greater evolutionary pressure to be good at language-style tasks - and, as a result, they have evolved task-specific neurological algorithms to make it easier.

Also, plenty of mammals, fresh out of the womb, have complex behaviors and vocalizations. Humans are something of an outlier, due to being born premature by mammal standards. If mammal brains are 99% universal learning, why can baby cows walk within minutes of birth?

Look, obviously, to some degree, both things are true. The brain is capable of general learning to some degree. Otherwise, we'd never have developed math. It also obviously has hard-coded specialized modules, to some degree, which is why (for example) all human cultures develop language and music, which isn't something you'd expect if we were all starting from zero. The question is which aspect dominates brain performance. You're proposing an extreme swing to one end of the possibility space that doesn't seem even remotely plausible - and then you'e using that assumption as evidence that no non-brain-like intelligence can exist.

What about Watson? It's the best-performing NLP system ever made, and it's absolutely a "weird mathy program." It uses neural networks as subroutines, but the architecture of the whole bears no resemblance to the human brain. It's not a simple universal learning algorithm. If you gave a single deep neural network access to the same computational resources, it would underperform Watson. That seems like a pretty tough pill to swallow if 'simple universal learner' is all there is to intelligence.

Finally, I don't have the background to refute your argument on the efficiency of the brain (although I know clever people who do who disagree with you). But, taking it as a given that you're right, it sounds like you're assuming all future AIs will draw the same amount of power as a real brain and fit in the same spatial footprint. Well... what if they didn't? What if the AI brain is the size of a fridge and cooled with LN2 and consumes as much power as a city block? Surely at the physical limits of computation you believe in, that would be able to beat the pants off little old us.

To sum up: yes, I've read your thing. No, it's not as convincing as you seem to believe.