> Simply based on a single learning instance.
Did not get this part. I have limited sample of two kids, but I would say it takes at least a year before humans understand "cupness"
Did not get this part. I have limited sample of two kids, but I would say it takes at least a year before humans understand "cupness"
But, in general, deep learning requires far more examples to train image recognition and even then it's relatively fragile. (Not that humans can't be fooled but having models of the world in our brains help a lot. No, that's probably not a flying pig even though it looks like one.)
Than kids? Who have have video input 10h/day for years (~1B images) and can also choose their examples actively?
There are many ways in which deep approaches differ from kids (understatement of the year?), but to say that kids don’t see a lot of data seems not quite right. They’ve got a huge “world model” to draw on by the time they are good at one- or few-shot learning.
How many different cups are there in that dataset?
How many hours have I spent looking at cups? Anecdotally, my 2-year-old spends a huge amount of time playing with cups.
If I pickup a cup and move it around, pour water into it, turn it over so the fluid falls out, do it again, try some other object, compare them... how many data samples did I just receive in my 1 minute endeavor? The answer is: a lot.
Depending on how continuous (or not) space/time is and how fast your brain processes things... you're potentially being exposed to some extremely large discrete sampling of an infinite set.
How many actual discrete samples? Who knows, can probably come up with some bounding estimates with some work but that assumes we understand the brain better than we do. In addition, there's a reasonable amount of information that you likely inherit genetically so think about those massive discrete samplings over the time of your ancestors' lives and summarization and compression you get through evolution, over many thousands of years. The fact I have fingers, thumbs, and walk upright is a solution set found across a massive problem space and I get that out-of-the-womb (tm).
I don't believe modern ANN models are quite right and I suspect there's quite a bit we don't understand about our own intelligence that isn't or may never be captured here (hey, I want to be irreplaceable) but we have to consider the comparison more holistically.
One of the fundamental issues I believe DNN fail at is the fact they train against on high level problems and typically don't couple together for learning processes (it's not quite practical yet). As a human, when I learn how to do something I incorporate and connect it with future learning processes to help me learn faster or gain novel insights. Computationally this would be connecting stupid amounts of DNNs together in all sorts of ways (GANs are one line of thought similar here but I believe this would be a rich field to explore).
Personally, I'm completely against endeavors to search for a real AGI. I find it hard to believe once such a goal was obtained, it could possibly be good for my long term survival but hey, I'm a bit greedy in that respect.
Monads might be a good example of a thing for which humans don't have a massive headstart on ML due to "years of experience manipulating monad-like things" (as we do for most physical and social concepts). And I don't think anyone one-shot learns a monad.