I find it interesting that we have a single word for creativity in the context of novel work.
These systems are incredible at generating novel art or poetry. But when it comes to problem solving, they struggle adapting to a novel problem.
With most humans, the ability to adapt to novelty seems to correlate with the ability to generate it.
I have two theories on this.
1. Perhaps these are two very different things. Novel art is closer to having a high performing random number function. Adapting to novel situations is closer to mathematical or strategic thinking.
2. Perhaps we are deeply fooling ourselves by building the greatest Chinese room machine imaginable - and in reality this machine does not build general understanding the same way we do. Douglas Hofstadter would be very disappointed with us if this is the case.
I still don't know which of these two seems more correct. Of course it might be both of them. I would like to see some more dedicated testing on the second point, as it does seem to be experimentally verifiable. For example, if this system truly has the ability to reason, it should be able to reason equally well in contexts where significant training data exists as well as contacts without any training data. We should be able to present the system with isomorphic problems across two different contexts and compare its performance to see if it has flat reasoning abilities.
These systems are incredible at generating novel art or poetry. But when it comes to problem solving, they struggle adapting to a novel problem.
With most humans, the ability to adapt to novelty seems to correlate with the ability to generate it.
I have two theories on this.
1. Perhaps these are two very different things. Novel art is closer to having a high performing random number function. Adapting to novel situations is closer to mathematical or strategic thinking.
2. Perhaps we are deeply fooling ourselves by building the greatest Chinese room machine imaginable - and in reality this machine does not build general understanding the same way we do. Douglas Hofstadter would be very disappointed with us if this is the case.
I still don't know which of these two seems more correct. Of course it might be both of them. I would like to see some more dedicated testing on the second point, as it does seem to be experimentally verifiable. For example, if this system truly has the ability to reason, it should be able to reason equally well in contexts where significant training data exists as well as contacts without any training data. We should be able to present the system with isomorphic problems across two different contexts and compare its performance to see if it has flat reasoning abilities.