Already, there are dozens of fine-tuned Llama2 models scoring above 70 on MMLU. They are laughably far from threats. This does seem like an exceptionally low bar. GPT-4, given the right prompt crafting, and adjusting for errors in MMLU has just been shown to be capable of 89 on MMLU. It would not be surprising for Llama models to achieve >80 on MMLU in the next 6 months.
I think focusing on a benchmark like MMLU is not the right approach, and will be very quickly outmoded. If we look at the other criteria (which, as you propose it now, any and all are a tripwire for regulation) parameter counts also sticks out as a somewhat arbitrary and overly limiting metric. There are many academic models with >80B parameters which are far less performant and agentic than e.g. Llama 70B.
Of the proposed trips, cost of training seems the most salient. I would focus on that and possibly only that for the time being. >$10M model training cost seems like a reasonable metric. If your concern is that the bar will lower over time, build some scaling down of costs per annum for the threshold into the proposal.
2If you are specifically trying to just ensure that all big AI labs are under common oversight, the most direct way is via compute budget. E.g., any organization with compute budget >$100M allocated for AI research. Would capture all the big labs. (OpenAI spent >$400M on compute in 2022 alone).
No need to complicate it with anything else.
If I remember correctly, Eliezer's old site had some references to this. Was this not common invocation on SL4?
Yes! Or even further, "I am now focusing my life on risk reduction and have significantly reduced akrasia in all facets of my life."
Worth noting, it's givewell.net. givewell.com links to a Visa card program, givewell.net is a site which aims to answer "Where should I donate?"
On further reflection, I'd tentatively propose something along these lines as an additional measure:
As I've now seen others suggest, trigger limits determined only as a percentage of the state of the art's performance.
This could be implemented as a proposal to give a government agency the power to work as the overseer and final arbiter of deciding, once per year for the following year (and ad-hoc on an emergency basis), the metrics and threshold percentages of indexing what is determined state of the art.
This would be done in consultation with representatives from each of the big AI labs (as determined by, e.g., having invested >$100M in AI compute), and including broader public, academic, and open source AI community feedback but ultimately decided by the agency.
The power could also be reserved for the agency to determine that specific model capabilities, if well defined and clearly measurable, could be listed as automatically triggering regulation.
This very clearly makes the regulation target the true "frontier AI" while leaving others out of the collateral crosshairs.
I say tentatively, as an immediate need for any sort of specific model-capability-level regulation to prevent existential risk is not remotely apparent with the current architectures for models (Autoregressive LLMs). I see the potential in the future for risk, but pending major breakthroughs in architecture.
Existing models, and the immediately coming generation, are trivially knowable as non-threatening at an existential level. Why? They are incapable of objective driven actions and planning. The worst that can be done is within the narrow span of agent-like actions that can be covered via extensive and deliberate programmatic connection of LLMs into heavily engineered systems. Any harms that might result would be at worst within a narrow scope that's either tangential to the intended actions, or deliberate human intent that's likely covered within existing criminal frameworks. The worst impacts would be narrowly scoped and economic, with a significant human intent element.
These systems as they exist and are currently being developed have no ability to be made objective-driven and autonomous in any real sense. It would be a major and obvious technological turning point that requires a new model paradigm from the outset.
There are key capabilities which we would have to intentionally design in and test for that should be the focus of future regulations:
1) Learning to represent the world in a more generalized way. Autoregressive LLMs build a fragile tree of hopefully-correct-next-tokens, that's just been molded into the shape we like via absurd amounts of pre-compute, and hardly much more. A more generalized hierarchical predictive model would be what we'd need to explicitly engineer in.
2) A modularized cognitive environment which allows for System 2 thinking, with an actively engaged interplay of a cost/reward system with perceptual input, providing a persistent engineered mechanism for planning complex actions in an objective-oriented way, and feeding them into its own persistent learning.
Without these foundations, which are major active fields of study with no obvious immediate solutions, there's no real potential for building accelerative intelligences or anything that can act as its own force multiplier in a general sense.
So any regulations which targeted existing autoregressive LLMs -- regardless of compute scale -- would be "out of an abundance of caution", with no clear indication of a significant potential for existential risk; likely mostly for the sake of setting the regulatory framework and industry/public/academic feedback systems in motion to begin establishing the standards for evaluations of potential future regulations. This would be predicated upon advances in objective-oriented architectures.