There is a massive difference between a 6 month old and a 10 year old and both would be considered children.

In that time period all of the brain infrastructure to do single shot/transfer learning at speed is developed. So showing a 10 year old a picture of an elephant with relevant label could probably be learned in a single shot. Not so with a 1 year old.

Sorry, but the relevance of these facts to this discussion still escapes me. You have previously said that your 5-year-old could "probably"/"maybe" (from the same sentence!) reliably do this, which is comfortably within your 10-year bound. Even if only adults were capable of this, Dr. Hinton's observation, appropriately amended to reflect that fact, would still be pertinent.

The relevance is that human inference is built from exposure to billions of "samples" of audio, visual, tactile information and that the CNN's that he invented are only capturing the slightest smallest sample of that. Not even the equivalent of an embryo level of data and processing capability.

People basically ignore that it takes humans YEARS of 24/7 training on ungodly amounts of data to be able to do anything close to reliable inference on even the most basic of tasks. That's the point.

...to which you might add half a billion years of neural architecture evolution. Who do you say are ignoring these facts? No-one in this discussion, as far as I can tell. If anyone is downplaying the difficulty, I would expect to find that among people who think AI is already a solved problem.

I do not want to attribute to you a claim that you have not explicitly made, but you seem to be suggesting that, because of the difficulty, current performance does not rule out the possibility that CNNs alone might eventually be able to perform as well as humans (in terms of things like using existing knowledge to speed the assimilation of new knowledge, including new knowledge in new categories.) Maybe so, but that very much remains to be demonstrated (and the burden of doing so would rest with anyone who wants to make the claim.) Meanwhile, Hinton cannot be faulted for attempting to improve on the process.

I think the gist of your claim is accurate.

Said more clearly, I am all but certain that the logical/mathematical process of correlating identifiable and measurable attributes through iterative search is the correct approach to reach general intelligence goals.

There are many improvements in efficiency, both in data acquisition, labeling, processing etc... that will need to happen make it tractable computationally, but fundamentally I think it's the correct approach.

Where I differ from Hinton is that he seems to think that human level processing requires less data than I believe it does. It's a subtle point actually.

So why does Hinton think otherwise?