A few months ago, I wrote a post about using slower computing substrates as a possibly new way to safely train and align ASI. If you haven't read that post, basically the idea is that if we consider compute speed as a factor in Total Intelligence (alongside say, quality of...
Lately, much has been discussed about PauseAI, or even stopping research completely, until further progress has been made in theory or technical approaches to alignment. After thinking about this for some time, I wondered if there was a way to formalize this reasoning in mathematical terms when I stumbled upon...
Thanks for the feedback. It would be great to learn more about your agenda and see if there are any areas where we may be able to help each other.
You're absolutely correct. I've reached out to the original framework authors to confirm. I will be creating a PR for their repo as well as for the one that I've forked. I suspect this won't change much about overall win/loss rates, but will be running a few tests here to confirm.
Thanks for the excellent feedback. I did consider action ratio at first, but it does have some slightly different considerations that made it a little challenging to do for an initial pass. The first is based on current limitations with the TextSC2 framework - there isn't a way to obtain detailed action logs for the in-game AI the same way we can for our agents, so it would require an "agent vs agent" setup instead of "agent vs in-game AI". And while TextSC2 supports this, it currently does not allow for real-time play when doing it (probably because "agent vs agent" setups require running two instances of the game at once on... (read more)
A few months ago, I wrote a post about using slower computing substrates as a possibly new way to safely train and align ASI.
If you haven't read that post, basically the idea is that if we consider compute speed as a factor in Total Intelligence (alongside say, quality of intelligence), then it should be possible to keep quality the same and lower compute speed in order to lower Total Intelligence while keeping quality the same.
An intuition pump is to imagine a scenario where we are able to slow down Einstein's brain, by slowing actual biochemical and electrical processes, so that it produces the Theory of Relativity in 40 years instead of 10.
The... (read 3321 more words →)
I do concede that in theory it should be possible to do this on standard computers, and that it might actually be a good way to test this hypothesis out and gather empirical data today.
Where I'm not so sure is whether even "slow" standard computers think slower than humans do. In other words, imagine some future AI architecture that is OOM more power and compute efficient. It may not be entirely unreasonable to suppose that we could get an algorithmic/architectural innovation that would enable GPT-4 level performance on old legacy hardware (eg, from the 80s). Indeed, at the unit level, we have calculators from the 60s that can out-multiply the fastest mental... (read more)
Lately, much has been discussed about PauseAI, or even stopping research completely, until further progress has been made in theory or technical approaches to alignment. After thinking about this for some time, I wondered if there was a way to formalize this reasoning in mathematical terms when I stumbled upon what might be an interesting, possibly novel approach to alignment:
What if we leveraged the nature of slower computing substrates to run AI at a slower pace than current digital computers?
By "slower substrate", I don't mean just diminishing CPU/GPU clock speeds, number of cores, or RAM/VRAM. I mean choosing fundamentally slower forms of computing that would be impossible to speed up past some... (read 812 more words →)
Regarding the second issue (the point that the LLM may not know how to play at different time ratios) - I wonder whether this may be true for current LLM and DRL systems where inference happens in a single step (and where intelligence is derived mainly from training), BUT potentially not true for the next crop of reasoners, where a significant portion of intelligence is derived from the reasoning step that happens at inference time and which we are hoping to target with this scheme. One can imagine that sufficiently advanced AI, even if not explicitly trained for a given task, will eventually succeed at that task, by reasoning about it and... (read more)