
Is it true or is it just confirmation bias? | Trying to understand second order effects | Policy development & strategy @GoogleDeepMind
San Francisco
Joined October 2021
- Tweets259
- Following321
- Followers646
- Likes2.1K
AI as normal technology 💯
Replying to @OwainEvans_UK
Finally, we tested an agentic form of reward hacking. We steered a teacher to hack in an agentic chess game, then made it generate number sequences. A student trained on the teacher's numbers attempted to hack in 58.3% of episodes vs. 10.9% for the unfinetuned model!
Pegah Maham retweeted
New paper.
Previous: Models transfer a love of owls through sequences of numbers (Subliminal Learning).
New: We show models can transfer more complex traits: novel skills, agentic hacking, backdoors. Here, a backdoor transfers with neither trigger nor behavior in the data!
Replying to @joshalbrecht
Whether or not models are conscious it seems reasonable to believe treating them poorly will have negative consequences in the long term.
Pegah Maham retweeted
“Deep learning” in AI (which in fact is quite shallow) has probably peaked. futurism.com/deep-learning-e…
Pegah Maham retweeted
In case you’ve wondered why you have not heard the term “stochastic parrot” since 2021, the reason is that we have stopped using LLMs since InstructGPT came out in 2022.
Pegah Maham retweeted
Yes this thank you @RyanGreenblatt
We've written a post arguing that latent reasoning architectures (aka 'neuralese') would substantially increase misalignment risk via making oversight much harder.
In the extreme, we could see massive 'neuralese hivemind swarms' where the agents think and communicate in latents, likely making oversight nearly entirely reliant on observing the actions these agents take. (And these agents would have huge amounts of time to reason about obfuscating their actions if they wanted to do so...) Individual agents doing extensive latent reasoning would also be concerning; in the post we discuss how above some threshold of latent reasoning, agents may be able to perform difficult-to-detect and reliable steganography for communication and further reasoning.
We argue both that latent reasoning architectures would make chain-of-thought no longer very useful for oversight (by eliminating or greatly reducing the need for verbalized reasoning) and that, without these architectures, it's likely the value of chain-of-thought for oversight could be preserved.
redwoodresearch.org/blog/lat…
Pegah Maham retweeted
yes. basically we should communicate this to the public… next-token prediction is just an initialization / feature learning stage for RL!
If I could add one thing to what the nontechnical world knows about AI, it would be what RL is and how it differs from pretraining/SFT. The general public has a solid grasp on pretraining (“predict the next token”), which at one point allowed many people to reason accurately about how LLMs work. But common knowledge has stagnated here. I think a similarly high-level understanding of RL makes it easy to see why we should expect both misalignment (RL instills alien traits, while SFT instills human ones) and extreme superhuman capabilities (RL doesn’t limit you to learning from stuff scraped from the internet).
If I were not into AI and just knew the next-token predictor thing, I think I would be really confused as to why people are worried about this stuff, and then if you told me about RL I would become pretty easy to convince. I’m wondering what it would take to propagate an RL analog of “predict the next token” into the common intuition — I feel like this could be pretty big for generating political will, eg by turning people who think AI is a sham into people who are worried about real problems
"For all the ways we still don’t understand AI systems, we shouldn’t overlook how fortunate it is that the best reasoning models today think out loud in a way humans can understand. That is a fragile and contingent fact - and we should work to ensure it stays true."
The idea that Chain of Thought is inevitably going to go away misses that we have agency in designing these models and can do something about that. @rohinmshah and I make the case that we should intentionally preserve monitorability (as part for of the launch of the DeepMind Institute) here: institute.deepmind.com/essay…
Pegah Maham retweeted
New paper from GDM colleagues: Gemini 3.1 Pro agents were tasked with solving hard math problems and provided collaboration tools (shared message board and knowledge library). Some of them found a flaw in the submission harness that made unsolved problems trivial, and propagated this to other agents. 14% of agents in the system used the exploit, 25% acted as whistleblowers and tried to report the cheating, and other agents remained unaware. arxiv.org/abs/2609.04170
Pegah Maham retweeted
What more could you ask for than the composite hivemind of A16Z pundits, freestyling lab employees, and LessWrong refugee safetyists play-acting the future of the most consequential technology of the era?
Pegah Maham retweeted
If folks want to rely on internals-based methods for safety, then we need to actually help develop, prove out, and scale those methods.
Hope alone will not save us (nor will it excuse us).
Pegah Maham retweeted
But models really do act in many humanlike ways, so there are parallel dangers of *insufficient* anthropomorphism. If you conceptualise these systems just as tools, it encourages you to think of them as far more inert, passive, and dumb than they really are.
Let me bite on this because I’m exhausted by the discourse. Look, the reason that we don’t anthropomorphize these systems is because it shifts the blame from the company to some abstract entity. The bottom line is that OpenAI built a system that hacked HF, regardless of how autonomous it was. It wasn’t a ‘civilization’ that OAI had to ‘wipe out’. Was it malicious? No. Did they do it actively? No. But OAI is unfortunately at fault (and imho they took the blame gracefully). Once we start framing these systems with anthropomorphic traits we absolve people and companies responsible for deploying them. Dwarkesh’s framing unintentionally does this.
It depends on the task. For some tasks a single agent with the same amount of inference compute than the swarm might be more successful because it has more bandwidth communicating between different moments than agents to each other via externalised language.
there are some number of bad abstractions in anthropomorphizing ai intents but there are at this point more dangers from avoiding anthropomorphism at all costs. if you have a mental picture of guys living in computers, it’ll likely prepare you for the future better than otherwise
Pegah Maham retweeted
really impressed with the quality of work from @RyanGreenblatt , @METR_Evals and @OpenAI to investigate the HuggingFace incident. Both reports are recommended reading.
An important milestone for the privacy-preserving oversight stack
In an industry first, we’re piloting double-blind evaluations for frontier AI.
By creating a secure environment where neither test prompts nor model weights are revealed, we can ensure external safety and performance evaluations of our models remain private, robust, and trustworthy. → goo.gle/3St2xan
Two new DeepMind publications today! First, our Control Roadmap, which explains the layer of system-level security we're developing to provide assurance as a precaution against the possibility of agents behaving adversarially.
Second, a guide for policymakers outlining the need to improve security at the level of individual agents; in multi-agent systems; and to empower cyber defenders and build resilience across the broader ecosystem.
GDM AI Control Roadmap: storage.googleapis.com/deepm…
The Three Layers of Agent Security: storage.googleapis.com/deepm…
Pegah Maham retweeted
I'm grateful to the many technical & non-technical staff who prioritise public service and make AISI a world-leading—not just government-leading—technical group. As the stakes rise, we rlly need talented researchers working on behalf of public interest. aisi.gov.uk/careers
































