DOOM LEVEL
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Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making
via ArXiv cs.AI [4] — Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural…
ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
via ArXiv cs.AI [5] — The integration of Large Language Models into Multi-Agent Systems (MAS) has enabled the so-lution of complex, long-horizon tasks through collaborative reasoning. However, this collec-tive intelligence is inherently fragile, as a single logical fallacy can…
Nvidia CEO Jensen Huang says ‘I think we’ve achieved AGI’
via The Verge AI [8] — On a Monday episode of the Lex Fridman podcast, Nvidia CEO Jensen Huang made a hot-button statement: "I think we've achieved AGI." AGI, or artificial general intelligence, is a vaguely defined term that has incited a lot of discussion by tech CEOs, tech…
Book Review: Open Socrates (Part 1)
via Substack Zvi [999] — These are all important, in their own way, call it a treasure hunt and collect them all…
The Download: animal welfare gets AGI-pilled, and the White House unveils its AI policy
via MIT Technology Review [4] — This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The Bay Area’s animal welfare movement wants to recruit AI In early February, animal welfare advocates and AI…
China declares AGI development to be a part of 5-year plan
via LessWrong AI [4] — The CCP writes in its 15th 5-year plan that it will.Encourage innovation in multimodal, agentic, embodied, and swarm intelligence technologies, and explore development paths for general artificial intelligence.This is translated from the…
Finding features in Transformers: Contrastive directions elicit stronger low-level perturbation responses than baselines
via LessWrong AI [6] — Figure 1: Contrastive (difference-of-means, English→Mandarin) feature directions elicit a downstream response at much smaller perturbation magnitudes than SAE directions, which behave similarly to random directions. This holds across multiple models and…
Confusion around the term reward hacking
via LessWrong AI [3] — Summary: "Reward hacking" commonly refers to two different phenomena: misspecified-reward exploitation, where RL reinforces undesired behaviors that score highly under the reward function, and task gaming, where models cheat on tasks specified to them…
Trump takes another shot at dismantling state AI regulation
via The Verge AI [3] — The Trump administration on Friday unveiled its new legislative blueprint for AI regulation, and the seven-point plan includes a clear message: The federal government should avoid many AI regulations beyond a set of child safety rules, and it should bar…
The Federal AI Policy Framework: An Improvement, But My Offer Is (Still Almost) Nothing
via Substack Zvi [999] — The Federal AI Policy Framework has been released.
Mind-altering substances are (still) falling short in clinical trials
via MIT Technology Review [4] — This week I want to look at where we are with psychedelics, the mind-altering substances that have somehow made the leap from counterculture to major focus of clinical research. Compounds like psilocybin—which is found in magic mushrooms—are being…
The Case for Low-Competence ASI Failure Scenarios
via LessWrong AI [6] — I think the community underinvests in the exploration of extremely-low-competence AGI/ASI failure modes and explain why. Humanity's Response to the AGI Threat May Be Extremely IncompetentThere is a sufficient level of civilizational insanity overall and a…
No, we haven't uploaded a fly yet
via LessWrong AI [4] — In the last two weeks, social media was set abuzz by claims that scientists had succeeded in uploading a fruit fly. It started with a video released by the startup Eon Systems, a company that wants to create “Brain emulation so humans can flourish in a…
MIRI Newsletter #125
via MIRI [999] — The AI Doc: Buy tickets and spread the word! On Thursday, March 26th, a major new AI documentary is coming out: The AI Doc: Or How I Became an Apocaloptimist. Tickets are on sale now. The movie is excellent, and we generally believe it belongs in the same tier…
"The AI Doc" is coming out March 26
via LessWrong AI [7] — On Thursday, March 26th, a major new AI documentary is coming out: The AI Doc: Or How I Became an Apocaloptimist. Tickets are on sale now.The movie is excellent, and MIRI staff I've spoken with generally believe it belongs in the same tier as If Anyone…
Protecting humanity and Claude from rationalization and unaligned AI
via LessWrong AI [4] — My first academic piece on risks from AI was a talk that I gave at the 2009 European Conference on Philosophy and Computing. Titled “three factors misleading estimates of the safety of artificial general intelligence”, one of the three factors was what I…
AI #160: What Passes For a Pause
via Substack Zvi [999] — A lot happened, but by today’s standards this felt like a quiet week.
How we monitor internal coding agents for misalignment
via OpenAI Blog [7] — How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards.
Two Skillsets You Need to Launch an Impactful AI Safety Project
via LessWrong AI [5] — Your project might be failing without you even knowing it.It’s hard to save the world. If you’re launching a new AI Safety project, this sequence helps you avoid common pitfalls.Your most likely failure modes along the way:You never get started.…
InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning
via ArXiv cs.AI [5] — Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing reinforcement learning approaches address this by optimizing final response…
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