2+. A child I know only ever saw plush/Lego/picture elephants, yet on the first trip to the zoo, pointed at a live one and exclaimed triumphantly: "'phant!!!"

Right, and those exposures were probably good enough for several hundred thousand iterations of "training."

Plus as the child walks around the elephant in the zoo there's a ton of information. Just one of the angles that the child sees has to match the 3D object that they've been picking up and moving around in their hands.

Correct. It's spatial intelligence.

This thread now contains a cycle.

https://news.ycombinator.com/item?id=15610149

Most kids 3-5 can identify parts of animals they have never seen as long as there is an analog in their experience.

Not just an "analog" - specific exposure to a variety of different animals.

My 5 year old son was a monster at animal puzzles when he was 3, but we had thousands of hours with animal picture books, animal toys etc... from 4 months on.

In what way would a precise definition advance this discussion? Especially as it has gone several steps without such a definition being an issue.

Ironically, the problem that Dr. Hinton is attempting to solve could be characterized as being that ordinary CNNs have trouble learning what's most relevant in an image.

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?

My 2 year old daughter could.

Without ever having seen one? Or with having seen pictures of one?

It's true though that we can generalise from descriptions and recognise the real thing from those. If you describe an elephant as a big grey animal with big ears and a trunk they can use to grab stuff, then an adult (not a 2 year old I suspect) seeing one for the first time, will recognise it from that description.

When we see a cartoonish drawing of one, we can still distill the defining characteristics from it and use it to create a description or recognise the real thing. We can recognise a very crude childish drawing of one by looking for these characteristics. We have a lot of additional knowledge that influences our image recognition, and having a big toolbox of general recognition of tons of different objects, we don't really need to train to recognise new objects anymore, because we will distill its identifying characteristics the first time we see it.

Computers clearly don't look like that.

Some pattern recognition seems to be innate, for example chicks just hours old can recognize the shadow of a flying bird as either harmless (long neck short tail) or a predator (short neck long tail).

So, while this pattern in particular doesn't apply to humans (it really doesn't?), many animals have ready-to-use pattern recognition when they are just hours or days old.

Those are instincts bred by evolutionary because they're vital for survival. Recognising an elephant is not the same thing for us. Instead, we get a generic toolbox for quickly learning to recognise very different objects, from animals to machines to abstract shapes. We're not born being able to recognise them, but we can pick them up very quickly from even rough descriptions.

True, but if we are into exploring the possibilities of what can be done with artificial neural networks, I see no reason to limit our models to human brains only.