I tend to see “generalisation” as the be-all and end-all of proper learning.
Learning skills or categories, for instance, is generalisation—we are given some examples and talked through some attempts and failures, and from that we have to develop a general skill or category that (hopefully) is the one that the teacher intended.
Current ML requires vast amounts of data to differentiate between categories that humans differentiate, and still get it wrong. So their learning failures and their inflated data requirements are both failures of generalisation.
Another example: wire-heading. An AI is given data on what it should do, via a human-designed reward channel for instance. Then it seizes control of the reward channel and gives itself maximum reward. The AI has failed at learning and at generalisation, because it learnt the wrong lesson from the data we gave it.
Arguably that’s more a failure of human teaching than of AI learning. But humans can teach other humans. So what we can say is that current AIs cannot learn or generalise in a human way from any of the teaching (labelled data is an example of teaching) that humans can do.
So we cannot transmit our current categories to AIs, and we can’t make AIs learn or generalise in the future in ways that humans would.
I tend to see “generalisation” as the be-all and end-all of proper learning.
Learning skills or categories, for instance, is generalisation—we are given some examples and talked through some attempts and failures, and from that we have to develop a general skill or category that (hopefully) is the one that the teacher intended.
Current ML requires vast amounts of data to differentiate between categories that humans differentiate, and still get it wrong. So their learning failures and their inflated data requirements are both failures of generalisation.
Another example: wire-heading. An AI is given data on what it should do, via a human-designed reward channel for instance. Then it seizes control of the reward channel and gives itself maximum reward. The AI has failed at learning and at generalisation, because it learnt the wrong lesson from the data we gave it.
Arguably that’s more a failure of human teaching than of AI learning. But humans can teach other humans. So what we can say is that current AIs cannot learn or generalise in a human way from any of the teaching (labelled data is an example of teaching) that humans can do.
So we cannot transmit our current categories to AIs, and we can’t make AIs learn or generalise in the future in ways that humans would.