All of Matt Levinson's Comments + Replies

Really exciting stuff here! I've been working on an alternate formulation of circuit discovery in the now traditional fixed problems case and have been brainstorming unsupervised circuit discovery, in the same spiritual vein as this work, though much less developed. You've laid the groundwork for a really exciting research direction here!

I have a few questions on the components definition and optimization. What does it mean when you say you define C components $P_c$? Do randomly partition the parameter vector into C partitions and assign each partition a... (read more)

I've been leveraging your code to speed up implementation of my own new formulation of neuron masks. I noticed a bug:

def running_mean_tensor(old_mean, new_value, n):
    return old_mean + (new_value - old_mean) / n

def get_sae_means(mean_tokens, total_batches, batch_size, per_token_mask=False):
    for sae in saes:
        sae.mean_ablation = torch.zeros(sae.cfg.d_sae).float().to(device)

    with tqdm(total=total_batches*batch_size, desc="Mean Accum Progress") as pbar:
        for i in range(total_batches):
            for j in range(batch_size):
       

I agree with most of this. One thing that widens my confidence interval to include pretty short term windows for transformative/super AI is what you point to mostly as part of the bubble. And that's the ongoing, insanely large societal investment -- in capital and labor -- into these systems. I agree one or more meaningful innovations beyond transformers + RL + inference time tricks will be needed to break through general-purpose long-horizon agency / staying-on-track-across-large-inferential-distances. But with SO much being put into finding those it seem... (read more)

One thing I've wondered about is the possibility that we might be thinking about ASI all wrong. As in, maybe it will in fact be so beyond our comprehension that it becomes spontaneously enlightened and effectively exits the cycle of goals (grasping). Hard to know exactly what would come next. Would it "exit", with no action? Permanent "meditation"? Would it engage through education? Some ASI version of good works?

Of course this is just a fun addition to the thought experiments. But I like to remind myself that there will come a time when the AI is too smar... (read more)

What I was hinting at above was trying to be in the spirit of MELBO, seeing if we can find meaningful vectors without looking at model output effects. You could imagine we could come up with heuristics on something like the variance of independent first derivatives of each neuron as we shrink or grow R. That is to say, what we're not looking for is all dimensions growing/shrinking ~equally as we shift R. Other patterns would give higher variance in the rates of change. You could imagine lots of variants of that kind of thing.

This also makes me think we cou... (read more)

I was thinking in terms of moving towards interpretability. We have no reason to believe that meaningful steering vectors should cluster around a given norm. We also have no reason to believe that effective steering vectors can all be scaled to a common norm without degrading the interesting/desired effect. This version of random search (through starting seed) and local optimization is a cool way to get a decent sampling of directions. I'm wondering if one could get "better" or "cleaner" results by starting from the best results from the search and then tr... (read more)

This, along with @Andrew Mack's MELBO and DCT work, is super cool and promising! One question, have you explored altering discovered vectors that make meaningful but non-gibberish changes to see if you can find something like a minimal viable direction? Perhaps something like taking successful vectors and then individually reoptimizing them turning down the L2 norm to see if some dimensions preferentially maintain their magnitude?

I don't think your first paragraph applies to the first three bullets you listed.

• Leaders don't even bother to ask researchers to leverage the company's current frontier model to help in what is hopefully the company-wide effort to reduce risk from the ASI model that's coming? That's a leadership problem, not a lack of technical understanding problem. I suppose if you imagine that a company could get to fine-grained mechanical understanding of everything their early AGI model does then they'd be more likely to ask because they think it will be easier/fast

As a community, I agree it's important to have a continuous discussion on how to best shape research effort and public advocacy to maximally reduce X-risk. But this post feels off the mark to me. Consider your bullet list of sources of risk not alleviated by AI control. You proffer that your list makes up a much larger portion of the probability space than misalignment or deception. This is the pivot point in your decision to not support investing research resources in AI control.

You list seven things. The first three aren't addressable by any technical re... (read more)

Very cool work! I think scalable circuit finding is an exciting and promising area that could get us to practically relevant oversight capabilities driven by mechint with not too too long a timeline!

Did you think at all about ways to better capture interaction effects? I've thought about approaches similar to what you share here and really all that's happening is a big lasso regression with the coefficients embedded in a functional form to make them "continuousified" indicator variables that contribute to the prediction part of the objective only by turnin... (read more)

I've uploaded the code to github.

I'm a new OpenPhil fellow for a mid-career transition -- from other spaces in AI/ML -- into AI safety, with an interest in interpretability. Given my experience, I bias towards intuitively optimistic about mechanical interpretability in the sense of discovering representations and circuits and trying to make sense of them. But I've started my deep dive into the literature. I'd be really interested to hear from @Buck and @ryan_greenblatt and those who share their skepticism about what directions they prefer to invest for their own and their team's research ... (read more)