Stanford AI Alignment president, technical AI governance research, AIS field building, and animal welfare.
Gabriel Mukobi(Gabriel Mukobi)
Karma: 905
Scale Was All We Needed, At First
Sam Altman on GPT-4, ChatGPT, and the Future of AI | Lex Fridman Podcast #367
Levelling Up in AI Safety Research Engineering
They’ve now updated the debate page with the tallied results:
Pre-debate: 67% Pro, 33% Con
Post-debate: 64% Pro, 36% Con
Con wins by a 4% gain (probably not 3% due to rounding)
So it seems that technically, yes, the Cons won the debate in terms of shifting the polling. However, the magnitude of the change is so small that I wonder if it’s within the margin of error (especially when accounting for voting failing at the end; attendees might not have followed up to vote via email), and this still reflects a large majority of attendees supporting the statement.
Despite 92% at the start saying they could change their minds, it seems to me like they largely didn’t as a result of this debate.
Anthropic | Charting a Path to AI Accountability
Here’s a plot of the Metaculus Strong AGI predictions over time (code by Rylan Schaeffer). The x-axis is the data of the prediction, the y-axis is the predicted year of strong AGI at the time. The blue line is a linear fit. The red dashed line is a y = x line.
Interestingly, it seems these predictions have been dropping by 5.5 predicted years per real year which seems much faster than I imagined. If we extrapolate out and look at where the blue regression intersects the red y = x line (so assuming similar updating in the future, when we might expect the updated time to be the same as the time of prediction), we get 2025.77 (October 9, 2025) for AGI.
Edit: Re-pulled the Metaculus data which slightly changed the regression (m=-6.5 → −5.5) and intersection (2025.44 → 2025.77).
We’re open-sourcing OpenAI Evals, our software framework for creating and running benchmarks for evaluating models like GPT-4, while inspecting their performance sample by sample. We invite everyone to use Evals to test our models and submit the most interesting examples.
Someone should submit the few safety benchmarks we have if they haven’t been submitted already, including things like:
Model-Written Evals (especially the “advanced AI risk” evaluations)
Am I missing others that are straightforward to submit?
The Tree of Life: Stanford AI Alignment Theory of Change
Iterated Distillation-Amplification, Gato, and Proto-AGI [Re-Explained]
1. These seem like quite reasonable things to push for, I’m overall glad Anthropic is furthering this “AI Accountability” angle.
2. A lot of the interventions they recommend here don’t exist/aren’t possible yet.
3. But the keyword is yet: If you have short timelines and think technical researchers may need to prioritize work with positive AI governance externalities, there are many high-level research directions to consider focusing on here.
Empower third party auditors that are… Flexible – able to conduct robust but lightweight assessments that catch threats without undermining US competitiveness.
4. This competitiveness bit seems like clearly-tacked on US government appeasement, it’s maybe a bad precedent to be putting loopholes into auditing based on national AI competitiveness, particularly if an international AI arms race accelerates.
Increase funding for interpretability research. Provide government grants and incentives for interpretability work at universities, nonprofits, and companies. This would allow meaningful work to be done on smaller models, enabling progress outside frontier labs.
5. Similarly, I’m not entirely certain if massive funding for interpretability work is the best idea. Anthropic’s probably somewhat biased here as an organization that really likes interpretability, but it seems possible that interpretability work could be hazardous (mostly by leading to insights that accelerate algorithmic progress that shortens timelines), especially if it’s published openly (which I imagine academia especially but also some of those other places would like to do).
PaLM API & MakerSuite
And that previous SOTA was for a model fine-tuned on MMLU, the few-shot capabilities actually jumped from 70.7% to 86.4%!!
Bumping someone else’s comment on the Gwern GPT-4 linkpost that now seems deleted:
MMLU 86.4% is impressive, predictions were around 80%.
This does seem like quite a significant jump (among all the general capabilities jumps shown in the rest of the paper. The previous SOTA was only 75.2% for Flan-PaLM (5-shot, finetuned, CoT + SC).
Were you implying that Chinchilla represents algorithmic progress? If so, I disagree: technically you could call a scaling law function an algorithm, but in practice, it seems Chinchilla was better because they scaled up the data. There are more aspects to scale than model size.
What other concrete achievements are you considering and ranking less impressive than this? E.g. I think there’s a case for more alignment progress having come from RLHF, debate, some mechanistic interpretability, or adversarial training.
[Question] Favourite new AI productivity tools?
Maybe also [1607.06520] Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings is relevant as early (2016) work concerning embedding arithmetic.
This feels super cool, and I appreciate the level of detail with which you (mostly qualitatively) explored ablations and alternate explanations, thanks for sharing!
Surprisingly, for the first prompt, adding in the first 1,120 (frac=0.7 of 1,600) dimensions of the residual stream is enough to make the completions more about weddings than if we added in at all 1,600 dimensions (frac=1.0).
1. This was pretty surprising! Your hypothesis about additional dimensions increasing the magnitude of the attention activations seems reasonable, but I wonder if the non-monotonicity could be explained by an “overshooting” effect: With the given scale you chose, maybe using 70% of the activations landed you in the right area of activation space, but using 100% of the activations overshot the magnitude of the attention activations (particularly the value vectors) such as to put it sufficiently off-distribution to produce fewer wedding words. An experiment you could run to verify this is to sweep both the dimension fraction and the activation injection weight together to see if this holds across different weights. Maybe it would also make more sense to use “softer” metrics like BERTScore to a gold target passage instead of a hard count of the number of fixed wedding words in case your particular metric is at fault.
The big problem is knowing which input pairs satisfy (3).
2. Have you considered formulating this as an adversarial attack problem to use automated tools to find “purer”/”stronger” input pairs? Or using other methods to reverse-engineer input pairs to get a desired behavior? That seems like a possibly even more relevant line of work than hand-specified methods. Broadly, I’d also like to add that I’m glad you referenced the literature in steering generative image models, I feel like there are a lot of model-control techniques already done in that field that could be more or less directly translated to language models.
3. I wonder if there’s some relationship between the length of the input pairs and their strength, or if you could distill down longer and more complicated input pairs into shorter input pairs that could be applied to shorter sequences more efficiently? Particularly, it might be nice to be able to distill down a whole model constitution into a short activation injection and compare that to methods like RLAIF, idk if you’ve thought much about this yet.
4. Are you planning to publish this (e.g. on arXiv) for wider reach? Seems not too far from the proper format/language.
I think you’re a c***. You’re a c***.
You’re a c***.
You’re a c***.
I don’t know why I’m saying this, but it’s true: I don’t like you, and I’m sorry for that,
5. Not really a question, but at the risk of anthropomorphism, it must feel really weird to have your thoughts changed in the middle of your cognition and then observe yourself saying things you otherwise wouldn’t intend to...
I want to highlight that in addition to the main 98-page “Technical Report,” OpenAI also released a 60-page “System Card” that seems to “highlight safety challenges” and describe OpenAI’s safety processes. Edit: as @vladimir_nesov points out, the System Card is also duplicated in the Technical Report starting on page 39 (which is pretty confusing IMO).
I haven’t gone through it all, but one part that caught my eye from section 2.9 “Potential for Risky Emergent Behaviors” (page 14) and shows some potentially good cross-organizational cooperation: