Leading Indicators of AI Danger: Owain Evans on Situational Awareness & Out-of-Context Reasoning, from The Inside View
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What is the opening introduction and who are the guests on this episode?
Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan LeBenz, joined by my co host, Eric Tornberg. Hello and welcome back to the Cognitive Revolution. Today I'm excited to share a special crossover episode from the Inside View, featuring a conversation on situational awareness, out of context reasoning, and other AI safety topics between Ewine Evans, AI alignment researcher at the Center for Human Compatible AI at UC Berkeley, and creator and host of the Inside View, Michael Trozi.
Owen is someone I've known casually through friends for many years, and it's been amazing to see him develop into such a prolific and influential researcher. His Google Scholar page lists twelve papers just since twenty twenty two, most of which serve to carefully map out some dark but perhaps important corner of large language model capability space, and some of which you've likely heard of, including the reversal curse, which showed that LLMs trained on information like A is B often fail to learn that B is A. And also connecting the dots, which is discussed in this episode, and which shows that, at least to some degree, large language models are capable of inferring censored information from the implicit hints contained in their training data.
In this episode, Owen explains why situational awareness matters, particularly in the context of deceptive AI scenarios, and discusses his research on measuring situational awareness, including the development of a benchmark to assess this capability. This topic has honestly never felt more relevant to me, because I've been coding with the new O1 model quite a bit over the last few weeks. And while to be honest, I have to admit that the model is in most respects more cognitively capable than I am, its situational awareness still seems rather weak. making this both one of a shrinking set of dimensions in which humans still have a meaningful advantage over the AIs, and one worth watching very closely as AI systems continue to evolve.
I've been wanting to have wine on the show since I started it, and I look forward to covering more of his work in a future episode. For now, if you're finding value in the show, we of course appreciate it when folks take a moment to share it with friends or to write an online review on Apple Podcasts or Spotify. And we always welcome your feedback via our website, cognitorevolution.ai, or by DMing me on your favorite social network. Finally, before getting started, I really encourage you to check out the Inside View, either on YouTube or at the InsideView.ai for more of Michael's content. I specifically recommend his episode on Anthropics Research into Sleeper Agents with Evan Hubinger. And I'm also looking forward to his upcoming documentary on SB ten forty seven, which will feature a number of past cognitive revolution guests, including Dean Ball, Timothy B.
Lee, Leonard Tang, Dan Hendricks, Nathan Calvin, and Flo Crivello. Now let's get into the weeds on the critical topic of situational awareness in AI systems, with alignment researcher Owine Evans and Michael Trozi, creator of The Inside View.
If models can do as well or better than humans, who are like AI experts, who know the whole setup, who are like trying to do well on this task, and also they're doing well like on all the tasks, including like some of these very hard ones. I think that would be like one piece of evidence, right, where you could say, look. Two years ago or something, right, models were not like way below human level on this task. Now they're like above human level. You know, there's evidence here that they have the kind of skills necessary to understand when they're being evaluated, to take actions that like go against their training data. So this would be, I think, yeah, a piece of evidence where you could say, look, given this performance, we should think carefully about alignment of the model, like whatever.
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Chapters
8 chapters
1
What is the opening introduction and who are the guests on this episode?
0:00–3:58
2
How do the hosts define and explain situational awareness for LLMs?
3:58–11:11
3
What benchmark and concrete tasks are introduced to measure situational awareness?
11:11–25:28
4
Why is situational awareness important for detecting deceptive alignment and AI safety risks?
25:28–1:09:36
5
How do the researchers train models to verbalize hidden functions without in‑context examples?
1:09:36–1:26:10
6
What safety concerns arise from out‑of‑context reasoning and hidden information in LLMs?
1:26:10–1:44:26
7
How does model scale affect performance on situational awareness and reasoning tasks?
1:44:26–2:06:37
8
What advice do the authors give for research style, collaboration, and future work in AI safety?
2:06:37–2:19:16
Speakers
2 identifiedMore from "The Cognitive Revolution"
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