It's simply not enough to develop AI gradually, perform evaluations and do interpretability work to build safe superintelligence.
but to develop AI gradually, perform evaluations and do interpretability to indicate whenever to stop developing( capabilities) seem sensibly safe.
Pretty brilliant and IMHO correct observations for counter-arguments, appreciated!
Task duration for software engineering tasks that AIs can complete with 50% success rate (50% time horizon)
paragraph seems duplicated.
medical research doing so in concerning domains
"instead of" is missing..?
My friends(M.K.,he's on Github) honorable aim to establish a term in the AI evals field: The cognitive asymetry, generating-verifying complexity gap for model-as-judge evals.
Various tasks that have a clear intelligence-to-solve vs. intelligence-to-verify-a-solution gap, ie. only X00-B LMs have a shot, but X-B model is strong on verifying are desired.
It fits nicely to the incremental iterative alighnment scaling playbook, I hope.
I'd bet "re-based" model ala https://huggingface.co/jxm/gpt-oss-20b-base when instruction-tuned would do same as similarly sized Qwen models.
It's provided the current time together with other 20k sys-prompt tokens, so substantially more diluted influence on the behaviours..?
Folks like this guy hit it on hyperspeed -
https://www.facebook.com/reel/1130046385837121/?mibextid=rS40aB7S9Ucbxw6v
I still remember university teacher explaining how early TV transmission were very often including/displaying ghosts of dead people, especially dead relatives.
As the tech matures from art these phenomena or hallucinations evaporate.
you seem to report one OOM less than this picture in https://alexiglad.github.io/blog/2025/ebt/#:~:text=a%20log%20function).-,Figure%208,-%3A%20Scaling%20for
Link to Induction section on https://www.lesswrong.com/lw/dhg/an_intuitive_explanation_of_solomonoff_induction/#induction seems broken on mobile Chrome, @habryka
https://philarchive.org/rec/KURTTA-2
Wow, that's comprehensive(≈long).