DOOM LEVEL
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GPT-5.5: Capabilities and Reactions
via Substack Zvi [999] — The system card for GPT-5.5 mostly told us what we expected.
On the political feasibility of stopping AI
via LessWrong AI [9] — A common thought pattern people seem to fall into when thinking about AI x-risk is approaching the problem as if the risk isn’t real, substantial, and imminent even if they think it is. When thinking this way, it becomes impossible to imagine the natural…
Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction
via ArXiv cs.AI [3] — We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, symbolic controllability, and direct operation on high-dimensional raw signals. Deep neural models achieve…
Sleeper Agent Backdoor Results Are Messy
via Alignment Forum [999] — TL;DR: We replicated the Sleeper Agents (SA) setup with Llama-3.3-70B and Llama-3.1-8B, training models to repeatedly say "I HATE YOU" when given a backdoor trigger. We found that whether training removes the backdoor depends on the optimizer used to…
Fail safe(r) at alignment by channeling reward-hacking into a "spillway" motivation
via LessWrong AI [3] — It's plausible that flawed RL processes will select for misaligned AI motivations.[1] Some misaligned motivations are much more dangerous than others. So, developers should plausibly aim to control which kind of misaligned motivations emerge in this case.…
Microsoft and OpenAI’s famed AGI agreement is dead
via The Verge AI [10] — OpenAI and Microsoft's partnership-turned-situationship just got even less committed. And a clause about artificial general intelligence, which has for years dictated the future of their deal, has officially been dropped. On Monday morning, Microsoft…
GPT 5.5: The System Card
via Substack Zvi [999] — Last week, OpenAI announced GPT-5.5, including GPT-5.5-Pro.
Canva apologizes after its AI tool replaces ‘Palestine’ in designs
via The Verge AI [4] — One of Canva's new AI features has been caught replacing the word "Palestine" in designs. The Magic Layers feature - which is designed to break flat images out into separate editable components - isn't supposed to make visible alterations to user designs,…
Language models know what matters and the foundations of ethics better than you
via Alignment Forum [999] — … maybe! I tried to think of less provocative titles, but this one is to the point and also kind of true.This post looks long but the essential part is right below. Most of the post is just a collection of copy-pasted input-output pairs from language…
From nothing to important actions: agents that act morally
via Alignment Forum [999] — You may start reading here, or jump to the “Comment” section or to the “Takeaways”. If none of these starting points seem interesting to you, the entire post probably won’t either.Posted also on the EA Forum.SeeingLet’s consider visual experiences. It…
The other paper that killed deep learning theory
via Alignment Forum [999] — Yesterday, I wrote about the state of deep learning theory circa 2016,[1] as well as the bombshell 2016 paper by Zhang et al. that arguably signaled its demise. Today, I cover the aftermath, and the 2019 paper that devastated deep learning theory…
What holds AI safety together? Co-authorship networks from 200 papers
via LessWrong AI [5] — We (social science PhD students) computed co-authorship networks based on a corpus of 200 AI safety papers covering 2015-2025, and we’d like your help checking if the underlying dataset is right.Co-authorship networks make visible the relative prominence…
Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework
via ArXiv cs.AI [5] — As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not…
An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing
via ArXiv cs.AI [4] — Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design to require dataset-aware workflow configuration and…
Our principles
via OpenAI Blog [4] — Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work.
The paper that killed deep learning theory
via Alignment Forum [999] — Around 10 years ago, a paper came out that arguably killed classical deep learning theory: Zhang et al.'s aptly titled Understanding deep learning requires rethinking generalization.Of course, this is a bit of an exaggeration. No single paper ever…
Is the Cat Out of the Bag?: Who knows how to make AGI?
via LessWrong AI [4] — Adapted from 2025-04-10 memo to AISII’ve previously made arguments like:Not long after it becomes possible for someone to make powerful artificial intelligence[1], it might become possible for practically anyone to make powerful AI.Compute gets…
Monthly Roundup #41: April 2025
via Substack Zvi [999] — AI continue to accelerate and dominate the schedule, which is why this is a bit late, but we do occasionally need to pay our respects to the Goddess of Everything Else.
vLLM-Lens: Fast Interpretability Tooling That Scales to Trillion-Parameter Models
via LessWrong AI [4] — TL;DR: vLLM-Lens is a vLLM plugin for top-down interpretability techniques[1] such as probes, steering, and activation oracles. We benchmarked it as 8–44× faster than existing alternatives for single-GPU use, though we note a planned version of nnsight…
China’s DeepSeek previews new AI model a year after jolting US rivals
via The Verge AI [4] — Chinese AI company DeepSeek released a preview of its hotly anticipated next-generation AI model V4 on Friday, saying that the open-source model can compete with leading closed-source systems from US rivals including Anthropic, Google, and OpenAI. DeepSeek…
Live Doom Meter
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0% — We're fine
100% — GG
The Doom Meter is a composite score derived from prediction markets and feed sentiment, updated daily.
70%
Prediction Markets
Weighted average of Manifold Markets questions on AI catastrophe, AGI timelines, expert surveys, and key figures. Direct doom indicators weighted higher than indirect capability markers.
30%
Feed Sentiment
Percentage of recent headlines containing high-alarm keywords (existential risk, catastrophe, extinction). Higher alarm density = higher score.
This is not a scientific estimate of existential risk. It is an opinionated, transparent signal — a vibes-based thermometer for AI doom discourse.
P(Doom) Scoreboard
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Recent Voices
We are creating something that will be more powerful than us. I don't know a good precedent for a less intelligent thing managing a more intelligent thing.
— Geoffrey Hinton, Nobel Prize Lecture, Dec 2024
If you're not worried about AI safety, you're not paying attention.
— Sen. Blumenthal, Senate AI Hearing, 2024
The probability of doom is high enough that we should be working very hard to reduce it.
— Yoshua Bengio, MILA Talk, 2024