I have been thinking about this for quite a while. In particular this paper which learns robust “agents” in Lenia seems very relevant to themes in alignment research: Learning Sensorimotor Agency in Cellular Automata
Continuous cellular automata have a few properties which in my view make them a potentially interesting testbed for agency research in AI alignment:
They seem to be able to support (or make discoverable) much more robust and complex behaviours and agents than discrete CAs, which makes them seem a bit less like “toy” models.
They can be differentiable, which allows for more efficient search for interesting behaviours (as in the linked paper). This should also be amenable to being accelerated by GPUs.
I am hoping to get the time at some point to explore some of these ideas using Lenia (I am working a full time job so it would have to be more of a side project). In particular I would like to re-implement the sensorimotor agency paper then see what avenues that opens. Perhaps trying to quantitatively measure abstraction within Lenia, for example can we come up with a measure of abstraction that can automatically identify these “agents”. Or something along the lines of the information theory of individuality, to see whether optimizing globally for these measures (with gradient descent) actually produces something that we recognise as agents / individuals.
I will admit that a lot of my motivation for this is just that I find continuous cellular automata fascinating and fun, rather than considering this the most promising direction for alignment research. But I do also think it could be fruitful for alignment research.
I have been thinking about this for quite a while. In particular this paper which learns robust “agents” in Lenia seems very relevant to themes in alignment research: Learning Sensorimotor Agency in Cellular Automata
Continuous cellular automata have a few properties which in my view make them a potentially interesting testbed for agency research in AI alignment:
They seem to be able to support (or make discoverable) much more robust and complex behaviours and agents than discrete CAs, which makes them seem a bit less like “toy” models.
They can be differentiable, which allows for more efficient search for interesting behaviours (as in the linked paper). This should also be amenable to being accelerated by GPUs.
I am hoping to get the time at some point to explore some of these ideas using Lenia (I am working a full time job so it would have to be more of a side project). In particular I would like to re-implement the sensorimotor agency paper then see what avenues that opens. Perhaps trying to quantitatively measure abstraction within Lenia, for example can we come up with a measure of abstraction that can automatically identify these “agents”. Or something along the lines of the information theory of individuality, to see whether optimizing globally for these measures (with gradient descent) actually produces something that we recognise as agents / individuals.
I will admit that a lot of my motivation for this is just that I find continuous cellular automata fascinating and fun, rather than considering this the most promising direction for alignment research. But I do also think it could be fruitful for alignment research.