The flash you get out of a joke is build up on a huge framework of human interaction that takes literally years to acquire for the "dedicated" BI of a brain. The fact the AI is only starting to reach the power of facial recognition which is for us a seemingly "low level automatic" function tells us the amount of learning left for an AI to come close to our understanding of the world. Not only in terms of computational power but in length and diversity of the learning process.
Assuming sufficiently powerful Neural Networks, they will probably go through years of learning our world through interaction, just like a kid does, before it "gets" the joke. Doesn't mean it's impossible, and that's quit scary (in a good and bad sense I guess).
Years _worth_ of learning. They could learn by watching videos, or playback of sensory data of early AIbots and by playing video games created for the specific purpose of training them which will be much more efficient.
Good point. The thought actually actually popped into my mind after I wrote the post : "Wall E" and many more films/books have suggested this accelerated learning path.
However this is information only, not interaction, I suspect this will have a serious distorsion effect on how the AI "perceives". It's a wild guess, but I believe interaction is at the root of understanding.
Edit : yes you also mention interaction through video games, which I skipped when I scanned your comment. But then again video games might be still far from the depth of real world interaction, more of a learning enforcer than the source of it - like books are for us....
Interaction in a sense acts as a method of preventing over-fitting. If you can't interact and are given a fixed batch to learn from you can find trivial overfitted good predictive models (e.g. the identity model).
One way of reproducing this aspect of interactions is simply using standard ML techniques to prevent overfitting, such as cross validation.
There's another aspect that's more difficult to reproduce that is the "online learning" aspect of interactions. If you can interact, you can form hypothesis in real time, test and modify them. This can greatly enhance learning efficiency I suppose -- you may directly explore fails in your models and improve in an optimal way.
This aspect also might be reproduced I believe simply through a large enough dataset. The learner could be given some capability to explore this dataset in a non-sequential way and look for informative results in it.
But there is already so much content on the internet, that accelerated learning could still happen by way of proxied interactions visible in "old" content. Does your AI have to interact with people to learn how to interact? Or is watching interaction good enough?
In a nutshell I don't see how any kind of adaptive intelligence can bypass the reinforcement process of trial and error through interaction. Then again you could have simulated interaction, but that may be the equivalent of the machine dreaming :-)
Assuming sufficiently powerful Neural Networks, they will probably go through years of learning our world through interaction, just like a kid does, before it "gets" the joke. Doesn't mean it's impossible, and that's quit scary (in a good and bad sense I guess).