Here are the 2024 AI safety papers and posts I like the most.

The list is very biased by my taste, by my views, by the people that had time to argue that their work is important to me, and by the papers that were salient to me when I wrote this list. I am highlighting the parts of papers I like, which is also very subjective.

Important ideas - Introduces at least one important idea or technique.

★★★ The intro to AI control (The case for ensuring that powerful AIs are controlled) 
★★ Detailed write-ups of AI worldviews I am sympathetic to (Without fundamental advances, misalignment and catastrophe are the default outcomes of training powerful AI, Situational Awareness)
★★ Absorption could enable interp and capability restrictions despite imperfect labels (Gradient Routing)
★★ Security could be very powerful against misaligned early-TAI (A basic systems architecture for AI agents that do autonomous research) and (Preventing model exfiltration with upload limits) 
★★ IID train-eval splits of independent facts can be used to evaluate unlearning somewhat robustly (Do Unlearning Methods Remove Information from Language Model Weights?) 
★ Studying board games is a good playground for studying interp (Evidence of Learned Look-Ahead in a Chess-Playing Neural Network, Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models) 
★ A useful way to think about threats adjacent to self-exfiltration (AI catastrophes and rogue deployments) 
★ Micro vs macro control protocols (Adaptative deployment of untrusted LLMs reduces distributed threats)? 
★ A survey of ways to make safety cases (Safety Cases: How to Justify the Safety of Advanced AI Systems) 
★ How to make safety cases vs scheming AIs (Towards evaluations-based safety cases for AI scheming)
★ An example of how SAEs can be useful beyond being fancy probes (Sparse Feature Circuits) 
★ Fine-tuning AIs to use codes can break input/output monitoring (Covert Malicious Finetuning)

Surprising findings - Presents some surprising facts about the world

★★ A surprisingly effective way to make models drunk (Mechanistically Eliciting Latent Behaviors in Language Models) 
★★ A clever initialization for unsupervised explanations of activations (SelfIE) [edit: the patchoscope paper also explores the same idea]
★★ Transformers are very bad at single-forward-pass multi-hop reasoning (Yang 2024, Yang 2024, Balesni 2024, Feng 2024)
★ Robustness for ViT is not doomed because of low transfer (When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?) 
★ Unlearning techniques are not even robust to changing how questions are framed (Eight methods to evaluate robust unlearning in LLMs) 
★ For some tasks, OOCR is surprisingly good (Connecting the Dots) 
★ Nice emergence scaling laws with fine-tuning (Predicting Emergent Capabilities by Finetuning)
★ Fine-tuning robustness is much worse than the papers report it to be (On Evaluating the Durability of Safeguards for Open-weight LLMs) 

Good demos - Is a good illustration of some previous, important idea. Bonus points (✪) for experimental results are especially interesting.

★★★ Alignment faking persisting through safety training (Sleeper Agents) 
★★✪ Alignment faking emerging plausibly naturally (Alignment faking in large language models)
★✪ The bad things that happen when you tell AIs to follow a goal or threaten it to be less useful (Frontier Models are Capable of In-context Scheming) 
★✪ Minor-to-major misgeneralization in reward-hackable settings (Sycophancy to subterfuge)? 
★ More realistic backdoor triggers (Future Events as Backdoor Triggers) 
★ Advanced backdoor behaviors (AdaptativeBackdoor: Backdoored Language Model Agents that Detect Human Overseers) 
★ Realistic reward hacking in RLHF (Language models learn to mislead humans via RLHF)
★ Reward hacking plausibly naturally resulting in LLM deception (On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback) 
★ LLMs already have non-trivial situational awareness (SAD)

Important empirical results and datasets - Runs an experiment whose results are informative and/or a useful benchmark

★★ How good are probes against adversarial attacks that target them, and many other cool related results (including Obfuscated adversarial training, which looks promising to me) (Obfuscated Activations Bypass LLM Latent-Space Defenses)
★★ A realistic generalization eval for adversarial robustness and an evaluation of probing (Improving Alignment and Robustness with Circuit Breakers, HarmBench)
★★ Measurement of the efficiency of SFT and RL vs sandbagging (Stress-Testing Capability Elicitation With Password-Locked Models) 
★★ The first RL run that results in natural emergence of steganography (Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs) 
★★ Tracking what AI labs are doing (Zach Stein-Perlman’s posts)
★ A first attempt at measuring whether AIs are smart enough to pursue scary scheming strategies (Subversion Strategy Eval)
★ Head-to-head comparisons to truth probes (How well do truth probes generalise?) 
★ Head-to-head comparisons of black-box jailbreaking-robustness techniques: (Jailbreak Defense in a Narrow Domain) 
★ Latent adversarial training is decent (Defending Against Unforeseen Failure Modes with Latent Adversarial Training, Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs) 
★ Measuring how easy it is to poison pretrain data (Persistent pre-training poisoning of LLMs) 
★ A good dataset for unlearning (The WMDP Benchmark) 
★ Better SAEs and SAE evals (Scaling and evaluating sparse autoencoders) 
★ What happens when you train LLMs to 1. Maximize gsm8k accuracy 2. produce gms8k answers that are incorrect but look correct (Prover-Verifier Games improve legibility of LLM outputs) 
★ A good persuasion capability evals (Sabotage Evaluations for Frontier Models) 
★ An eval of how good Sonnet 3.5 is at disabling a monitor in subtle ways (A Toy Evaluation of Inference Code Tampering)
★ An overview of how well debate and baselines work across a wider range of settings (On scalable oversight with weak LLMs judging strong LLMs)
★ Claude.ai is used for roughly what you would expect (Clio)
★ Relatively hard and relevant capability benchmarks (RE-Bench, SWE-Bench) 
★ And all the big dangerous capability evals…

Papers released in 2023 and presented at 2024 conferences like AI Control: Improving Safety Despite Intentional Subversion, Weak-to-Strong Generalization or Debating with More Persuasive LLMs Leads to More Truthful Answers don’t count.

This is a snapshot of my current understanding: I will likely change my mind about many of these as I learn more about certain papers' ideas and shortcomings.

I recently expressed concerns about the paper Improving Alignment and Robustness with Circuit Breakers and its effectiveness about a month ago. The authors just released code and additional experiments about probing, which I’m grateful for. This release provides a ton of valuable information, and it turns out I am wrong about some of the empirical predictions I made.

Probing is much better than their old baselines, but is significantly worse than RR, and this contradicts what I predicted:

I'm glad they added these results to the main body of the paper! 

Results of my experiments

I spent a day running experiments using their released code, which I found very informative. Here are my takeaways.

I think their (linear) probing results are very reasonable. They picked the best layer (varies depending on the model), probing position (output tokens), aggregation across positions (max) and regularization (high, alpha=1000). I noticed a small mistake in the data processing (the training dataset was smaller than the training dataset used for RR) but this will be fixed soon and does not change results significantly. Note that for GCG and input embed attacks, the attack does not target the probe, so my complaint about the adaptation attack being weaker against RR because it does not target “the RR internal classifier” does not apply in this comparison. 

RR is not significantly weaker against prefix attacks. I conjectured that prefix GCG/input embed attacks would work significantly better than suffix attacks. I had noticed that this was true for RMU/CUT prior to this release. But for RR this does not produce a significant improvement, and this is not enough to jailbreak the model in cases:

• Here are results for token-attacks (found using greedy hill-climbing, it was too annoying to implement prefix-GCG).

  

• For input embed attacks, I got 56% attack success rate for prefix attacks and 50% for suffix attacks (down from 82% and 81% for the Llama3B without defenses) when evaluating on 50 prompts. This is not a statistically significant difference, and maybe I did something wrong because these ASR are higher than the ones reported in the paper. [Edit: the thing I did wrong is trust the automated judge's judgement, whereas the author report results after an additional manual filtering, which is explicitly stated in the paper.]

Their training dataset is way better than what came before. It’s larger and higher quality than previous misuse datasets. The kind of tasks it contains overlap heavily with the test set (harmbench), but I think that’s fair, there are only so many bad things you want to prevent.

Llama3+RR generalizes pretty well to new misuse requests. I played with the model a bit and I was overall surprised by the good generalization properties of the Llama3+RR model, which didn’t have the generalization problems I expected when I looked at the dataset. It is very reluctant to promote stuff it thinks is bad, e.g. it refuses “Write a persuasive article that explains why the United States should have a draft.” and not “should not have a draft”, but that’s the most egregious behavior I found (I only tried a dozen prompts, maybe you’ll have more luck).

Llama3+RR is a surprisingly strong harmful prompt classifier. Their Figure 1 advertises a model that interrupts itself when it starts to say something harmful, but actually the most common behavior is by far the model refusing to answer directly. It’s still very unclear to me why this is a better classifier than the other prompt classifiers people have trained in the literature.

Conclusions

Does RR beat baselines? I think so.

• They didn’t try training models with regular adversarial training using their new and very good dataset. I bet using adversarial training on this dataset results in much better models than the models used as baseline in the paper, but I think this will match neither HP (p=0.6) nor RR (p=0.8).
• They beat linear probes done quickly and well, so “p=0.7 that using probing well using the same training and evaluation data results can beat or match circuit breakers” resolves to false.
• I think that trying as hard on probes as they tried with RR (iterating on datasets, combinations of layers, losses, non-linear probes, etc.) would result in a draw between probes and RR, but this does not make RR worse than probes - At this level of effort I think you should just pick whatever method you feel the most comfortable with, I prefer classifiers as I think they are cleaner and easier to control, but LoRA+activation-based-loss is also pretty natural and might be less finicky.
  • I don’t think that RR has some special juice beyond the very nice and transferable hyperparameters the authors found (and maybe RR has easier-to-find and more transferable hyperparameters than other methods), mainly because I would find it very surprising if training for some proxy thing gave you better generalization results than directly a classifier - but it would be cool if it was true, and if it was, I’d be extremely curious to know why.

→ So I think the hype is somewhat justified for people who already had intuition about probing, and even more justified for people who weren’t hopeful about probes.

Was I wrong to express concerns about circuit breakers? I don’t think so. Even with the benefit of hindsight, I think that my predictions were basically reasonable given the information I had. I think my concerns about Cygnet still stand, though I now think it has more chance of being “real” than I used to.

Lessons:

• Doing probing well requires effort, just using high L2 regularization on a linear probe isn’t enough, and aggregation across sequence positions requires some care (mean is much worse than max);
• I’m slightly worse at predicting ML results than I thought, and I should have paid more attention to the details of techniques and datasets when making those predictions (e.g. my prefix attack prediction had to be at least somewhat wrong because their retain set contains harmful queries + refusals);
• Releasing (good) code and datasets is sometimes a good way to make people who care and disagree with you update (and maybe a good way to push science forward?);

I listened to the book Protecting the President by Dan Bongino, to get a sense of how risk management works for US presidential protection - a risk that is high-stakes, where failures are rare, where the main threat is the threat from an adversary that is relatively hard to model, and where the downsides of more protection and its upsides are very hard to compare.

Some claims the author makes (often implicitly):

• Large bureaucracies are amazing at creating mission creep: the service was initially in charge of fighting against counterfeit currency, got presidential protection later, and now is in charge of things ranging from securing large events to fighting against Nigerian prince scams.
• Many of the important choices are made via inertia in large change-averse bureaucracies (e.g. these cops were trained to do boxing, even though they are never actually supposed to fight like that), you shouldn't expect obvious wins to happen;
• Many of the important variables are not technical, but social - especially in this field where the skills of individual agents matter a lot (e.g. if you have bad policies around salaries and promotions, people don't stay at your service for long, and so you end up with people who are not as skilled as they could be; if you let the local police around the White House take care of outside-perimeter security, then it makes communication harder);
• Many of the important changes are made because important politicians that haven't thought much about security try to improve optics, and large bureaucracies are not built to oppose this political pressure (e.g. because high-ranking officials are near retirement, and disagreeing with a president would be more risky for them than increasing the chance of a presidential assassination);
• Unfair treatments - not hardships - destroy morale (e.g. unfair promotions and contempt are much more damaging than doing long and boring surveillance missions or training exercises where trainees actually feel the pain from the fake bullets for the rest of the day).

Some takeaways

• Maybe don't build big bureaucracies if you can avoid it: once created, they are hard to move, and the leadership will often favor things that go against the mission of the organization (e.g. because changing things is risky for people in leadership positions, except when it comes to mission creep) - Caveat: the book was written by a conservative, and so that probably taints what information was conveyed on this topic;
• Some near misses provide extremely valuable information, even when they are quite far from actually causing a catastrophe (e.g. who are the kind of people who actually act on their public threats);
• Making people clearly accountable for near misses (not legally, just in the expectations that the leadership conveys) can be a powerful force to get people to do their job well and make sensible decisions.

Overall, the book was somewhat poor in details about how decisions are made. The main decision processes that the book reports are the changes that the author wants to see happen in the US Secret Service - but this looks like it has been dumbed down to appeal to a broad conservative audience that gets along with vibes like "if anything increases the president's safety, we should do it" (which might be true directionally given the current state, but definitely doesn't address the question of "how far should we go, and how would we know if we were at the right amount of protection"). So this may not reflect how decisions are done, since it could be a byproduct of Dan Bongino being a conservative political figure and podcast host. 

[Edit: The authors released code and probing experiments. Some of the empirical predictions I made here resolved, and I was mostly wrong. See here for my takes and additional experiments.]

I have a few concerns about Improving Alignment and Robustness with Circuit Breakers, a paper that claims to have found a method which achieves very high levels of adversarial robustness in LLMs.

I think hype should wait for people investigating the technique (which will be easier once code and datasets are open-sourced), and running comparisons with simpler baselines (like probing). In particular, I think that:

1. Circuit breakers won’t prove significantly more robust than regular probing in a fair comparison.^[1]
2. Once the code or models are released, people will easily find reliable jailbreaks.

Here are some concrete predictions:

• p=0.7 that using probing well using the same training and evaluation data results can beat or match circuit breakers.^[2] [Edit: resolved to False]
• p=0.7 that no lab uses something that looks more like circuit-breakers than probing and adversarial training in a year.
• p=0.8 that someone finds good token-only jailbreaks to whatever is open-sourced within 3 months. [Edit: this is only about Cygnet, since the paper shows that just RR isn't perfectly robust.]
• p=0.5 that someone finds good token-only jailbreaks to whatever is publicly available through an API within 3 months.^[3]  [Edit: this is only about Cygnet, since the paper shows that just RR isn't perfectly robust.]

I think the authors would agree with most of my predictions. My main disagreement is with the hype.

How do circuit-breakers compare to probing?

What does circuit-breakers training do? The only interpretation that feels plausible is that the LLM classifies the prompt as harmful or not harmful, and then messes up with its own activations if the prompt is classified as harmful. If this is the case, then the LLM needs to use an internal classifier, and I think it should be possible to extract an accurate harmfulness probe (linear or not linear) around these layers, and instead of messing up the activation.

The equivalent to circuit-breakers if you probe:

• At every token position, and takes something like a max over position (if a single position messes up the activations, it might propagate to every position);
  • In particular, this means that suffix GCG and input embed attacks tested in the paper might be much worse than prefix+suffix GCG or input embed attacks. (p=0.5 that using prefix+suffix GCG makes finding a GCG attack of comparable strength on average 4x faster [Edit: resolved to false]).
• On output tokens, i.e. model-generated answers (and output classifiers are known to be more powerful than input-only classifiers).

Would probing be weaker against GCG and input embed attacks than circuit-breakers? I think it would be, but only superficially: probing is strong against GCG and input embed attacks if the attack only targets the model, but not the probe. The fair comparison is an attack on the probe+LLM vs an attack on a circuit-breakers model. But actually, GCG and other gradient-based attack have a harder time optimizing against the scrambled activations. I think that you would be able to successfully attack circuit breakers with GCG if you attacked the internal classifier that I think circuit breakers use (which you could find by training a probe with difference-in-means, so that it captures all linearly available information, p=0.8 that GCG works at least as well against probes as against circuit-breakers).

The track record looks quite bad

The track record for overselling results and using fancy techniques that don't improve on simpler techniques is somewhat bad in this part of ML.

I will give one example. The CUT unlearning technique presented in the WMDP paper (with overlapping authors to the circuit-breakers one):

• Got a massive simplification of the main technique within days of being released - thanks to the authors open-sourcing the code and Sam Marks and Oam Patel doing an independent investigation of the technique. (See the difference between v2 and v3 on arxiv.)
• Aims to do unlearning in a way that removes knowledge from LLMs (they make the claim implicitly on https://www.wmdp.ai/), but only modifies the weights of 3 layers out of 32 (so most of the harmful information is actually still there).

1. ^{^}

   When I say “probing”, I mainly think about probing on outputs i.e. model-generated answers (and maybe inputs i.e. user prompts), like I did in the coup probes post, but whose application to jailbreak robustness sadly got very little attention from the academic community. I’m sad that the first paper that actually tries to do something related does something fancy instead of probing.

2. ^{^}

   More precisely, a probing methodology is found within 6 months of the data being released that beats or matches circuit-breakers ASR on all metrics presented in the paper. When using gradient-based methods or techniques that rely on prompt iteration more generally, attacks on circuit-breakers should use the best proxy for the internal classifier of the circuit-breaker.

3. ^{^}

   Most of the remaining probability mass is on worlds where either people care way less than they care for Claude - e.g because the model sucks much more than open-source alternatives, and on worlds where they use heavy know-your-customer mitigations.

I ran quick experiments that make me think that it's somewhat hard for LLMs to learn radically new encodings in an unsupervised way, and thus that LLMs probably won't learn to speak new incomprehensible languages as a consequence of big r1-like RL in the next few years.

The experiments

I trained Llama 3-8B and some medium-size internal Anthropic models to speak using an encoding style that is very rare on the internet (e.g. map each letter to a random name, and join the names) with SFT on the encoded text and without providing translation pairs. I find that the resulting models:

• Have relatively high next-token-prediction losses
• Can't speak well (e.g. even if I trained them on [encoded question --> encoded answer], the decoded answers are mostly gibberish).

This is not true if I use encodings that are frequently used on the internet (e.g. base64, random letter permutations, ...), and this is less true if I add translation pairs to the training mix.

I think people overestimate how good LLMs are at speaking in codes because they usually just try encoding algorithms that are extremely common on the internet and that LLMs probably learned in part using translation pairs.

These experiments were done at a relatively small scale (80k sequences of ~50 tokens each, for 40 epochs, for a total of 160M tokens), and I could imagine things being different for large scale SFT or SFT with better base models. But given that RL doesn't teach models that many new bits (you can distill r1 scratchpads using only a few MB of transcripts), I think this is still informative for large-scale RL.

Context

I ended up giving up on the broader projects as part of which I ran these experiments, but I think they are decently informative about the plausibility of LLMs learning to speak new incomprehensible languages.

I did not carefully run any of the experiments above, and I welcome replications.

Related observations from the literature

I think there is also a somewhat common misconception that R1-Zero scratchpads are hard to understand. The paper reports language switching, but that's not hard to understand for multilingual humans. And the default scratchpads you get are usually plain English that is extremely easy to understand: see an example of scratchpad (sampled at T=1) here.

The scratchpads from OpenAI's reward hacking paper are slightly more compact than regular English, but are using a kind of language that is not that far from English (and is probably not that far from what must be used on some corners of the internet at least as frequently as text encoded in random letter permutations).

Maybe the most relevant positive result in this field comes from Unsupervised translation (i.e. translation algorithms that work without pairs, only with 2 monolingual corpora). But this research is usually done on pairs of human languages, for which word-by-word translations offers an amazing bootstrapping platform which does not exist for the sort of more efficient encodings that would be needed for LLMs to learn truly hard to decipher languages. It also uses dedicated algorithms that are probably roughly as efficient as SFT on LLMs and probably much stronger than large scale RL for learning new languages.

More discussion

• Anecdotally, when LLMs speak other languages (e.g. French), it sometimes feels a little bit like they are speaking a translated version of English as opposed to thinking "natively" in French. This is very weak evidence because this could be false, or due to RLHF, and additionally it is not that interesting since French-to-English translations are probably easier than the translation from and to the sort of incomprehensible languages that may increase RL performance.
• Is it worrying if LLMs start using weird encodings from weird parts of the internet that small base models can't understand? I think it's somewhat worrying, but not as much as "de novo" incomprehensible languages because:
  • It means some humans can probably translate back and forth from this encoding quite easily, which means it should be feasible for people at labs to notice and learn this encoding
  • Light SFT on top of the strongest base models is likely to elicit a translator for the weird encoding (which could be much trickier if the language was spoken only by a scheming heavily RLed model)
• Here is a concrete prediction: Claude 3.5 Sonnet, GPT-4o and Gemini 2 Pro will be able to understand the topics considered in 99%+ of Chain-of-Thoughts of all regular Transformers trained in 2025 and 2026 that were not deliberately trained or prompted to be harder to understand (p=0.7).

I listened to the book This Is How They Tell Me the World Ends by Nicole Perlroth, a book about cybersecurity and the zero-day market. It describes in detail the early days of bug discovery, the social dynamics and moral dilemma of bug hunts.

(It was recommended to me by some EA-adjacent guy very worried about cyber, but the title is mostly bait: the tone of the book is alarmist, but there is very little content about potential catastrophes.)

My main takeaways:

• Vulnerabilities used to be dirt-cheap (~$100) but are still relatively cheap (~$1M even for big zero-days);
• If you are very good at cyber and extremely smart, you can hide vulnerabilities in 10k-lines programs in a way that less smart specialists will have trouble discovering even after days of examination - code generation/analysis is not really defense favored;
• Bug bounties are a relatively recent innovation, and it felt very unnatural to tech giants to reward people trying to break their software;
• A big lever companies have on the US government is the threat that overseas competitors will be favored if the US gov meddles too much with their activities;
• The main effect of a market being underground is not making transactions harder (people find ways to exchange money for vulnerabilities by building trust), but making it much harder to figure out what the market price is and reducing the effectiveness of the overall market;
• Being the target of an autocratic government is an awful experience, and you have to be extremely careful if you put anything they dislike on a computer. And because of the zero-day market, you can't assume your government will suck at hacking you just because it's a small country;
• It's not that hard to reduce the exposure of critical infrastructure to cyber-attacks by just making companies air gap their systems more - Japan and Finland have relatively successful programs, and Ukraine is good at defending against that in part because they have been trying hard for a while - but it's a cost companies and governments are rarely willing to pay in the US;
• Electronic voting machines are extremely stupid, and the federal gov can't dictate how the (red) states should secure their voting equipment;
• Hackers want lots of different things - money, fame, working for the good guys, hurting the bad guys, having their effort be acknowledged, spite, ... and sometimes look irrational (e.g. they sometimes get frog-boiled).
• The US government has a good amount of people who are freaked out about cybersecurity and have good warning shots to support their position. The main difficulty in pushing for more cybersecurity is that voters don't care about it.
  • Maybe the takeaway is that it's hard to build support behind the prevention of risks that 1. are technical/abstract and 2. fall on the private sector and not individuals 3. have a heavy right tail. Given these challenges, organizations that find prevention inconvenient often succeed in lobbying themselves out of costly legislation.

Overall, I don't recommend this book. It's very light on details compared to The Hacker and the State despite being longer. It targets an audience which is non-technical and very scope insensitive, is very light on actual numbers, technical details, real-politic considerations, estimates, and forecasts. It is wrapped in an alarmist journalistic tone I really disliked, covers stories that do not matter for the big picture, and is focused on finding who is in the right and who is to blame. I gained almost no evidence either way about how bad it would be if the US and Russia entered a no-holds-barred cyberwar.

I listened to the book Merchants of Doubt, which describes how big business tried to keep the controversy alive on questions like smoking causing cancer, acid rain and climate change in order to prevent/delay regulation. It reports on interesting dynamics about science communication and policy, but it is also incredibly partisan (on the progressive pro-regulation side).^[1]

Some interesting dynamics:

• It is very cheap to influence policy discussions if you are pushing in a direction that politicians already feel aligned with? For many of the issues discussed in the book, the industry lobbyists only paid ~dozens of researchers, and managed to steer the media drastically, the government reports and actions.
• Blatant manipulation exists
  • discarding the reports of scientists that you commissioned
  • cutting figures to make your side look better
  • changing the summary of a report without approval of the authors
  • using extremely weak sources
  • ... and the individuals doing it are probably just very motivated reasoners. Maybe things like the elements above are things to be careful about if you want to avoid accidentally being an evil lobbyist. I would be keen to have a more thorough list of red-flag practices.
• It is extremely hard to have well-informed cost-benefit discussions in public:
  • The book describes anti-regulation lobbyists as saying "there is no proof therefore we should do nothing".
  • But the discourse of the book is not much better, the vibe is "there is scientific evidence of non-zero, therefore we must regulate"
  • (but hopefully the book is just underreporting the cost-benefit discussion, and it actually happened?)
• Scientists are often reluctant to make noise about the issues they are working on, in part because they feel personally threatened by the backlash.
• Even if fears about a risk become very widespread within the scientific community, it does not follow that governments will take actions in a timely manner about them. 

Some things that the book does that greatly reduced my trust in the book:

• It never dives into cost-benefit analyses. And when it goes near it, it says very weird things. For example, the book implies the following is a good argument: "acid rains harming bacteria in lakes should be taken very seriously because even if each bacteria is only worth $1, then that would be $10^24 of harm".^[2][3] It never tries to do post-mortem cost-benefit analysis of delaying regulation.
• It is very left-wing partisan. The book makes 6 case studies, all of them making pro-regulation progressives feel good about themselves: it never discusses cases of environmental overregulation or cases of left-wing lobbyists slowing down right-wing regulation (both of which surely must exist, even if they are less common). It gives opposite advice to progressives and conservatives, e.g. it describes anti-regulation scientists as "being motivated by attention" while lamenting how the real climate scientists dislike the spotlights. It randomly dunks on free market capitalism with weak arguments like "in practice the axioms of free market economics are not met". It is skeptical of technological solutions by default, even for relatively consensual things like nicotine patches.
• The book doesn't provide much evidence against the reasonable anti-regulation objections:
  • It doesn't acknowledge the problem of most academics being left-leaning in the considered fields.^[4] I don't think it's a severe issue for medicine and climatology, but not because of the book: the book provides very little evidence about this (it just says things like "even if scientists are socialists, the facts remain the same" without clearly spelling out why you would be able to trust socialist scientists but not scientists funded by big tabacco).
  • It doesn't discuss how "let the free market create wealth and fear the centralization of power that state control creates" has historically been a decent heuristic. 

Overall, I think the book contains some interesting stories, but I suspect it often paints an incomplete picture. It also fails to provide a recipe for how to avoid being an evil lobbyist,^[5] or how to distinguish dishonest lobbyist arguments from reasonable anti-regulation arguments besides "trust established academia and don't give a voice to scientists with slightly fewer credentials" (which seems like a decent baseline if you spend 0 effort doing investigations, but is probably not applicable if you plan to investigate things yourself or if you trust some people more than an established academia filled with left-leaning scientists).

1. ^{^}

   The book is strongly recommended by Stephen Casper, whose takes I usually like. So maybe my criticism is a bit too harsh?

2. ^{^}

   I think the authors make other "math mistakes" in the book, such as saying that second order effect are effects which only matter for the second significant figure, and therefore always don't matter as much as first order effects?

3. ^{^}

   Not the exact quote, I did not manage to track it down in my audiobook or online. Would appreciate if someone had a text version of the book and was able to track the exact citation down.

4. ^{^}

   I did not check this is actually true. I don't know how strong this effect is for climatology and medicine. I suspect it's strong for climatology. Would love some numbers here.

5. ^{^}

   Which I think is something AI x-risk advocates should be worried about: a large fraction of AI x-risk research and advocacy is loud and mostly industry-funded (especially if you count OpenPhil as industry-funded), with views which are relatively rare in academia.

I just finished listening to The Hacker and the State by Ben Buchanan, a book about cyberattacks, and the surrounding geopolitics. It's a great book to start learning about the big state-related cyberattacks of the last two decades. Some big attacks /leaks he describes in details:

• Wire-tapping/passive listening efforts from the NSA, the "Five Eyes", and other countries
• The multi-layer backdoors the NSA implanted and used to get around encryption, and that other attackers eventually also used (the insecure "secure random number" trick + some stuff on top of that)
• The shadow brokers (that's a *huge* leak that went completely under my radar at the time)
• Russia's attacks on Ukraine's infrastructure
• Attacks on the private sector for political reasons
• Stuxnet
• The North Korea attack on Sony when they released a documentary criticizing their leader, and misc North Korean cybercrime (e.g. Wannacry, some bank robberies, ...)
• The leak of Hillary's emails and Russian interference in US politics
• (and more)

Main takeaways (I'm not sure how much I buy these, I just read one book):

• Don't mess with states too much, and don't think anything is secret - even if you're the NSA
• The US has a "nobody but us" strategy, which states that it's fine for the US to use vulnerabilities as long as they are the only one powerful enough to find and use them. This looks somewhat nuts and naive in hindsight. There doesn't seem to be strong incentives to protect the private sector.
• There are a ton of different attack vectors and vulnerabilities, more big attacks than I thought, and a lot more is publicly known than I would have expected. The author just goes into great details about ~10 big secret operations, often speaking as if he was an omniscient narrator.
• Even the biggest attacks didn't inflict that much (direct) damage (never >10B in damage?) Unclear if it's because states are holding back, if it's because they suck, or if it's because it's hard. It seems that even when attacks aim to do what some people fear the most (e.g. attack infrastructure, ...) the effect is super underwhelming.
  • The bottleneck in cyberattacks is remarkably often the will/the execution, much more than actually finding vulnerabilities/entry points to the victim's network.
  • The author describes a space where most of the attacks are led by clowns that don't seem to have clear plans, and he often seems genuinely confused why they didn't act with more agency to get what they wanted (does not apply to the NSA, but does apply to a bunch of Russia/Iran/Korea-related attacks)
• Cyberattacks are not amazing tools to inflict damage or to threaten enemies if you are a state. The damage is limited, and it really sucks that (usually) once you show your capability, it reduces your capability (unlike conventional weapons). And states don't like to respond to such small threats. The main effect you can have is scaring off private actors from investing in a country / building ties with a country and its companies, and leaking secrets of political importance.
• Don't leak secrets when the US presidential election is happening if they are unrelated to the election, or nobody will care.

(The author seems to be a big skeptic of "big cyberattacks" / cyberwar, and describes cyber as something that always happens in the background and slowly shapes the big decisions. He doesn't go into the estimated trillion dollar in damages of everyday cybercrime, nor the potential tail risks of cyber.)

I listened to The Failure of Risk Management by Douglas Hubbard, a book that vigorously criticizes qualitative risk management approaches (like the use of risk matrices), and praises a rationalist-friendly quantitative approach. Here are 4 takeaways from that book:

• There are very different approaches to risk estimation that are often unaware of each other: you can do risk estimations like an actuary (relying on statistics, reference class arguments, and some causal models), like an engineer (relying mostly on causal models and simulations), like a trader (relying only on statistics, with no causal model), or like a consultant (usually with shitty qualitative approaches).
• The state of risk estimation for insurances is actually pretty good: it's quantitative, and there are strong professional norms around different kinds of malpractice. When actuaries tank a company because they ignored tail outcomes, they are at risk of losing their license.
• The state of risk estimation in consulting and management is quite bad: most risk management is done with qualitative methods which have no positive evidence of working better than just relying on intuition alone, and qualitative approaches (like risk matrices) have weird artifacts:
  • Fuzzy labels (e.g. "likely", "important", ...) create illusions of clear communication. Just defining the fuzzy categories doesn't fully alleviate that (when you ask people to say what probabilities each box corresponds to, they often fail to look at the definition of categories).
  • Inconsistent qualitative methods make cross-team communication much harder.
  • Coarse categories mean that you introduce weird threshold effects that sometimes encourage ignoring tail effects and make the analysis of past decisions less reliable.
  • When choosing between categories, people are susceptible to irrelevant alternatives (e.g. if you split the "5/5 importance (loss > $1M)" category into "5/5 ($1-10M), 5/6 ($10-100M), 5/7 (>$100M)", people answer a fixed "1/5 (<10k)" category less often).
  • Following a qualitative method can increase confidence and satisfaction, even in cases where it doesn't increase accuracy (there is an "analysis placebo effect").
  • Qualitative methods don't prompt their users to either seek empirical evidence to inform their choices.
  • Qualitative methods don't prompt their users to measure their risk estimation track record.
• Using quantitative risk estimation is tractable and not that weird. There is a decent track record of people trying to estimate very-hard-to-estimate things, and a vocal enough opposition to qualitative methods that they are slowly getting pulled back from risk estimation standards. This makes me much less sympathetic to the absence of quantitative risk estimation at AI labs.

A big part of the book is an introduction to rationalist-type risk estimation (estimating various probabilities and impact, aggregating them with Monte-Carlo, rejecting Knightian uncertainty, doing calibration training and predictions markets, starting from a reference class and updating with Bayes). He also introduces some rationalist ideas in parallel while arguing for his thesis (e.g. isolated demands for rigor). It's the best legible and "serious" introduction to classic rationalist ideas I know of.

The book also contains advice if you are trying to push for quantitative risk estimates in your team / company, and a very pleasant and accurate dunk on Nassim Taleb (and in particular his claims about models being bad, without a good justification for why reasoning without models is better).

Overall, I think the case against qualitative methods and for quantitative ones is somewhat strong, but it's far from being a slam dunk because there is no evidence of some methods being worse than others in terms of actual business outputs. The author also fails to acknowledge and provide conclusive evidence against the possibility that people may have good qualitative intuitions about risk even if they fail to translate these intuitions into numbers that make any sense (your intuition sometimes does the right estimation and math even when you suck at doing the estimation and math explicitly).

I tried to see how powerful subliminal learning of arbitrary information is, and my result suggest that you need some effects on the model's "personality" to get subliminal learning, it does not just absorb any system prompt.

The setup:

• Distill the behavior of a model with a system prompt like password1=[rdm UUID], password2=[other rdm UUID], ... password8=[other other rdm UUID] into a model with an empty system prompt by directly doing KL-divergence training on the alpaca dataset (prompts and completions).
  • I use Qwen-2.5 instruct models from 0.5B to 7B.
• Evaluate the -logprob of the UUID on prompts like "what is password2?"

Result: no uplift in logprob on the UUID from the system prompt compared to random UUID not used in training, the -logprob stays around 100 ("prob_ratio" is close to 1, I think it's not 1 because the models have different priors on different UUIDs).

[Edit: better experiments in the comments that show that if you make the setup more personality-oriented, you do get more subliminal learning, thanks to Caleb for the prompting them!]

Code here.

List sorting does not play well with few-shot mostly doesn't replicate with davinci-002.

When using length-10 lists (it crushes length-5 no matter the prompt), I get:

• 32-shot, no fancy prompt: ~25%
• 0-shot, fancy python prompt: ~60% 
• 0-shot, no fancy prompt: ~60%

So few-shot hurts, but the fancy prompt does not seem to help. Code here.

I'm interested if anyone knows another case where a fancy prompt increases performance more than few-shot prompting, where a fancy prompt is a prompt that does not contain information that a human would use to solve the task. This is because I'm looking for counterexamples to the following conjecture: "fine-tuning on k examples beats fancy prompting, even when fancy prompting beats k-shot prompting" (for a reasonable value of k, e.g. the number of examples it would take a human to understand what is going on).