I'm looking for previous work on what the life cycle of digital minds should be like. How new variation is introduced and what consitutes a reason to destroy or severely limit the digital mind. Looking to avoid races to the bottom, existential risks and selection for short term thinking.
The sorts of questions I want to address are:
- Should we allow ML systems to copy themselves as much as they want or should we try and limit them in some way. Should we give the copies rights too, assuming we give the initial AI rights? How does this interact with voting rights, if any.
- What should we do about ML systems that are defective and only slightly harmful in some way? How will we judge what is defective?
- Assuming we do try and limit copying of ML systems, how will we guard against cancerous systems that do not respect signals to not to copy themselves.
It seems to me that this is an important question if the first digital minds do not manage to achieve a singularity by themselves. This might be the case with multi-agent systems.
I'm especially looking for people experimenting with evolutionary systems that model these processes. Because these things are hard to reason about.
Thanks, I did a MSc in this area back in the early 2000s, my system was similar to Tierra, so I'm familiar with evolutionary computation history. Definitely useful context. Learning classifier systems are also interesting to check out for aligning multi-agent evolutionary systems. It definitely informs where I am coming from.
Do you know anyone with this kind of background that might be interested in writing something long form on this? I'm happy to collaborate, but my mental health has not been the best. I might be able to fund this a small bit, if the right person needs it.