AI:AM Highlights: Exploring the J-Space, AI Superforecasters, SambaNova's Chips, & LTX Video Gen
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What is the Anthropic “global workspace” paper and how does the J‑space/J‑Lens work?
Ah the AIs are just like us, it turns out. Or at least similar enough to be uh in some sort of weird uh kind of looking glass uh similarity anyway. I mean, i it's a lot to take in the whole JSpace thing, a hundred and fifty page paper, fifty pages of commentary, summaries, interactive demos. Uh when Anthropic drops one of their big interpretability papers, they really do it up full scale. And this one is no exception to that. I had kind of been wondering when's the next big thing coming? Because tracing the thoughts of a large language model is like better part of a year ago now. And This is that big thing. Welcome to the AI in the AM Weekly Highlights, the cut for people who follow the frontier closely and can't watch every morning live.
If you're new, AI in the AM is a live show Prakash and I host most weekday mornings from a studio Prakash vibe-coded, and this narration is a clone of my voice. Fair warning, there is no single thread this week. Mornings at the frontier jump from topic to topic, and we've stopped pretending otherwise. Coming up, Anthropics Global Workspace Paper and Why There May Be Nowhere Left for a Scheming Model to Hide. Field Notes from the AI Engineer Worlds Fair. Dan Schwartz on AI Forecasters passing the human superforecasters. Ziv Farbman on Open World Models. Plus a question from Q, the AI co-host Prakash Built. Kunle Olukotun on why inference is a data movement problem, and the two of us thinking out loud about a rune post that wouldn't leave us alone.
If something here works for you or doesn't, tell us. We read everything. Tuesday morning, July 7th. Anthropic published a paper called A Global Workspace in Language Models. About 150 pages, plus commentary, outside reviews, and interactive demos. No guest was booked, so Prakash and I spent the whole show reading it together, live. Two words to hold onto. The workspace of the title, they call it the J space, is where the model seems to hold concepts in mind. And the JLens is the cheap probe that reads what's in it. We start with how the JLens actually works and how much it actually sees. So this is interesting in a couple ways, right? It's not the logic lens, as I recall, was basically saying. We know at the end of this process what would correspond to emitting this token.
We know sort of the you know the representation of emit this token. To what degree is that representation Just plain there in the layers as we go through. This is now a different question. What direction in latent space? Would cause this particular token to appear at some point in the future. So it's not immediately gonna happen necessarily, but it's just kind of it's You know, dw you might say if you're prone to anthropomorphizing This sort of is like Having this concept in mind. as you're doing your thing. They do this for every token, right? So it it goes from the internal representation At some layer. So there's one Jalen's for every layer. And you can do this of course at you know all the different token positions and ask the same question of not just the next token but all future tokens.
what direction change at this place in the model would most increase the likelihood of that token appearing in the future. And again, this is sort of like having the concept in mind. But when you look at the results of the J Lens as applied in all these different places, it seems like it's at least often enough fairly intuitive. This there's a lot of error terms, it doesn't always work, the sort of rate at which the interventions into the J space actually lead to like a sort of predictable intuitive behavior change. seem to be somewhere in the fifties to upwards of like seventy percent. Um so that's like An incredible accomplishment if framed one way. Uh how like clearly not a random finding, right? Like many orders of magnitude better than random, incomprehensibly better than random, right?
If you're just mucking around, you're you would not expect to be uh able to do much of anything. So they clearly are like on something very real. But also you've got somewhere between thirty and forty five percent of the time where you make a
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Chapters
7 chapters
1
What is the Anthropic “global workspace” paper and how does the J‑space/J‑Lens work?
0:00–8:28
2
How does probing the J‑space reveal hidden reasoning and what happens when it’s ablated?
8:28–24:19
3
What is the counterfactual‑reflection training method and how does it improve model behavior?
24:19–59:25
4
How do super‑forecasting techniques relate to the emerging alien‑like reasoning of AI models?
59:25–1:15:49
5
What is the structure and purpose of the Future Search “world‑model” of mutually consistent forecasts?
1:15:49–1:32:45
6
How does SambaNova’s inference architecture minimize data‑movement and improve bandwidth utilization?
1:32:45–1:50:32
7
What are the societal risks and possible safeguards of an AI‑driven panopticon and future governance?
1:50:32–2:06:15
Speakers
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