Thomas Kwa

Was on Vivek Hebbar's team at MIRI, now working with Adrià Garriga-Alonso on various empirical alignment projects.

I'm looking for projects in interpretability, activation engineering, and control/oversight; DM me if you're interested in working with me.

I have signed no contracts or agreements whose existence I cannot mention.

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Catastrophic Regressional Goodhart

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The North Wind, the Sun, and Abadar

One day, the North Wind and the Sun argued about which of them was the strongest. Abadar, the god of commerce and civilization, stopped to observe their dispute. “Why don’t we settle this fairly?” he suggested. “Let us see who can compel that traveler on the road below to remove his cloak.”

The North Wind agreed, and with a mighty gust, he began his effort. The man, feeling the bitter chill, clutched his cloak tightly around him and even pulled it over his head to protect himself from the relentless wind. After a time, the North Wind gave up, frustrated.

Then the Sun tried his turn. Beaming warmly from the heavens, the Sun caused the air to grow pleasant and balmy. The man, feeling the growing heat, loosened his cloak and eventually took it off in the heat, resting under the shade of a tree. The Sun began to declare victory, but as soon as he turned away, the man put on the cloak again.

The god of commerce then approached the traveler and bought the cloak for five gold coins. The traveler tucked the money away and continued on his way, unbothered by either wind or heat. He soon bought a new cloak and invested the remainder in an index fund. The returns were steady, and in time he prospered far beyond the value of his simple cloak, while the cloak was Abadar's permanently.

Commerce, when conducted wisely, can accomplish what neither force nor gentle persuasion alone can achieve, and with minimal deadweight loss.

The thought experiment is not about the idea that your VNM utility could theoretically be doubled, but instead about rejecting diminishing returns to actual matter and energy in the universe. SBF said he would flip with a 51% of doubling the universe's size (or creating a duplicate universe) and 49% of destroying the current universe. Taking this bet requires a stronger commitment to utilitarianism than most people are comfortable with; your utility needs to be linear in matter and energy. You must be the kind of person that would take a 0.001% chance of colonizing the universe over a 100% chance of colonizing merely a thousand galaxies. SBF also said he would flip repeatedly, indicating that he didn't believe in any sort of bound to utility.

This is not necessarily crazy-- I think Nate Soares has a similar belief-- but it's philosophically fraught. You need to contend with the unbounded utility paradoxes, and also philosophical issues: what if consciousness is information patterns that become redundant when duplicated, so that only the first universe "counts" morally?

Thomas KwaΩ56-4

For context, I just trialed at METR and talked to various people there, but this take is my own.

I think further development of evals is likely to either get effective evals (informal upper bound on the future probability of catastrophe) or exciting negative results ("models do not follow reliable scaling laws, so AI development should be accordingly more cautious").

The way to do this is just to examine models and fit scaling laws for catastrophe propensity, or various precursors thereof. Scaling laws would be fit to elicitation quality as well as things like pretraining compute, RL compute, and thinking time.

  • In a world where elicitation quality has very reliable scaling laws, we would observe that there are diminishing returns to better scaffolds. Elicitation quality is predictable, ideally an additive term on top of model quality, but more likely requiring some more information about the model. It is rare to ever discover a new scaffold that can 2x the performance of an already well-tested models.
  • In a world where elicitation quality is not reliably modelable, we would observe that different methods of elicitation routinely get wildly different bottom-line performance, and sometimes a new elicitation method makes models 10x smarter than before, making error bars on the best undiscovered elicitation method very wide. Different models may benefit from different elicitation methods, and some get 10x benefits while others are unaffected.

It is NOT KNOWN what world we are in (worst-case assumptions would put us in 2 though I'm optimistic we're closer to 1 in practice), and determining this is just a matter of data collection. If our evals are still not good enough but we don't seem to be in World 2 either, there are endless of tricks to add that make evals more thorough, some of which are already being used. Like evaluating models with limited human assistance, or dividing tasks into subtasks and sampling a huge number of tries for each.

Thomas KwaΩ4190

What's the most important technical question in AI safety right now?

Yes, lots of socioeconomic problems have been solved on a 5 to 10 year timescale.

I also disagree that problems will become moot after the singularity unless it kills everyone-- the US has a good chance of continuing to exist, and improving democracy will probably make AI go slightly better.

The new font doesn't have a few characters useful in IPA.

The CATXOKLA population is higher than the current swing state population, so it would arguably be a little less unfair overall. Also there's the potential for a catchy pronunciation like /kæ'tʃoʊklə/.

Knowing now that he had an edge, I feel like his execution strategy was suspect. The Polymarket prices went from 66c during the order back to 57c on the 5 days before the election. He could have extracted a bit more money from the market if he had forecasted the volume correctly and traded against it proportionally.

I think it would be better to form a big winner-take-all bloc. With proportional voting, the number of electoral votes at stake will be only a small fraction of the total, so the per-voter influence of CA and TX would probably remain below the national average.

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