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Okay, so today we're gonna unpack this new AI research paper.

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It's called Scaling Up Test Time Compute

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with Latent Reasoning, a Recurrent Depth Approach.

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Wow, what a mouthful.

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Yeah, right, and you know, it's all about a model

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that can basically think in latent space.

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Okay.

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Have you ever like been working on like a tough problem

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and like the solution just kind of clicks into place

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before you can even put it into words?

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Like, that's kind of what we're talking about here.

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The model is solving problems, but internally,

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before it generates any text output.

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So it's sort of like doing the work in the background

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and then just giving us the answer.

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Exactly, and this research suggests

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that maybe just using language

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isn't enough for like real AI reasoning.

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Interesting.

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You know, it makes me think

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of what Yanlacun has been saying.

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Oh yeah, Metis Chief AI Scientist.

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Right, he's been pretty vocal about the limitations

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of large language models.

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Yeah, he's basically saying that we're all fooled

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by how fluent they are with language.

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Yeah, and we kind of assume they must be super intelligent

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just because they can, you know.

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Right, they can write and speak so well,

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but it's not the whole picture.

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So what are we missing

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if just manipulating language isn't enough?

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Well, that's where this idea

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of thinking in latent space comes in.

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Okay.

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This model is doing something totally different.

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So walk us through how that works.

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Okay, so picture this like a really complex network

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of calculations happening inside the model,

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turning through all these different possibilities

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before it like settles on an answer.

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Okay.

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Here's the key.

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It's all happening kind of in the background

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in this hidden latent space

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before any text is generated.

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So it's not showing its work like Shane of Thought does.

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It's more like it's having

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like an internal brainstorming session.

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Exactly.

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And then it just gives us the conclusion.

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Precisely.

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And what's wild is that the researchers found

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the more the model thought in this latent space,

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the better it performed on like all these different tasks.

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Oh wow.

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From like high school math problems

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to like complex moral scenarios.

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So I really put it through its paces.

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Yeah.

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And the results were really impressive.

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That's pretty remarkable.

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How does this approach compare

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to like the Shane of Thought approach?

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Well.

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Are there any advantages to this latent reasoning?

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There are actually quite a few.

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For one, it doesn't require, you know,

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massive amounts of specialized training data.

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Oh really?

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Like Shane of Thought does.

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You don't have to feed it tons and tons

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of examples of reasoning.

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That seems like a huge advantage,

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especially with how data hungry AI models are.

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Absolutely.

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It makes the development way faster and more efficient.

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That's a game changer.

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What about memory requirements?

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Oh yeah.

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Doesn't all that text generation

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and Shane of Thought take up a lot of memory?

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Totally.

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Shane of Thought can be a real memory hog.

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Uh huh.

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Latent reasoning is way more streamlined and efficient.

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Okay.

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So we're talking speed and efficiency.

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Yeah.

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But what about the quality of the reasoning itself?

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Right.

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Does this approach actually lead

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to more human like problem solving?

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That's where things get really interesting.

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Okay.

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So the combination of latent space thinking

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and then, you know, the language output

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actually mirrors how we tackle problems.

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We brainstorm internally.

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We weigh different options.

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Right.

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And then finally we express those thoughts

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through language.

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It's like we're seeing a more sophisticated

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kind of thinking here.

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Yeah.

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It's not just like pattern matching

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or clever language manipulation.

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Exactly.

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The paper mentions metastrategies and abstraction.

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Right.

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What do those terms mean like in this context?

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Basically it means the model isn't just memorizing stuff.

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It's developing these higher level thinking skills.

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Uh-huh.

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So for example, it might figure out

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the most efficient way to approach a problem

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or like recognize underlying patterns and principles.

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Oh wow.

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So it's almost like it's learning how to learn.

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Yeah. In a way.

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So this could be like a big jump forward

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in what AI can do, right?

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Yeah.

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Potentially.

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But what does it mean for like the rest of us?

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Right.

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You know, people who aren't AI expert.

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How could this tech actually change our lives?

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Well, imagine like AI assistants

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that really get you patterns.

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Like they understand what you need

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and what you like almost like they can read your mind.

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Okay.

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They could solve tough problems really fast.

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Uh-huh.

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And come up with solutions you'd never even thought of.

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And what about things that need common sense?

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Yeah, that's been so hard for AI.

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Right.

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This kind of reasoning could be huge for that.

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That's a pretty exciting picture.

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Yeah.

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But we probably shouldn't get too carried away, right?

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True.

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The paper says this model is still in the early stages.

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They call it like a proof of concept.

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Yeah, it's just one study.

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Okay.

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We need more research to see if this will really work

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for creating that super smart AI we're talking about.

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Right, makes sense.

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So back to Yamakun and his critique

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of large language models.

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Uh-huh.

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Do you think this latent space reasoning

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could actually answer his concerns?

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That's the big question, isn't it?

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Yeah.

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It's possible that this level of internal reasoning

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could get as closer to the kind of AI Makun wants.

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Okay.

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But he has some pretty specific ideas

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about what's missing from today's AI.

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Like what?

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Well, he talks about the need for AI

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that can learn and reason about the world

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in a more like grounded way.

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Like connected to the physical world.

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Okay.

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It calls them world models.

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World models.

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Yeah, AI that can kind of simulate the world

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and interact with it.

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Like we do.

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Exactly.

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So there's still a lot of debate in the AI world

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about the best way to make truly intelligent systems.

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Oh yeah, definitely.

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It's what makes it so interesting.

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Right.

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There are so many different paths being explored.

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It's always changing.

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It's hard to say where it'll all end up.

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And the paper mentions that this latent space thinking

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doesn't have to replace chain of thought.

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You're right.

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They could actually work together.

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Oh.

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Combining the strengths of both.

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So we could have these hybrid models.

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Exactly.

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Get the best of both worlds.

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And that makes me wonder,

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could we combine latent space reasoning

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with other AI stuff too?

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Yeah, that's a great question.

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Like what about reinforcement learning?

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Or even those world models Lakun talks about.

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Wow.

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So many possibilities.

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It's like we're at the beginning of something huge.

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Yeah, but we need to be careful, right?

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Of course, we have to think about

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how these technologies will affect society

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and the way we work and even what it means to be human.

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It's a lot to think about

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and it's important to talk about it now.

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Absolutely.

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While all this is happening.

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So before we finish today's deep dive,

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we wanna leave you with something to think about.

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Okay.

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Think about how you solve a heart problem.

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Do you kind of work through the solutions in your head

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before you write anything down or type anything out?

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In that internal space.

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Exactly.

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If you do, you might be more like these AI models

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than you realize.

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Interesting.

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You might have to learn something

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from it when you suddenly understand something.

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Uh-huh.

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Before you can even put it into words.

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Right.

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Maybe that's a glimpse into what thinking really is.

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That's a fascinating thought.

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Makes you wonder if we're figuring out

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how intelligence works, like in general.

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Yeah, not as a start official intelligence.

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Right.

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It's like we're learning about our own brains

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by studying AI.

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So to wrap up, what are some of the big takeaways

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for our listener today?

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Well, first, I hope I see how fast AI is changing.

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Like this latent reasoning research,

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it's just one example of the amazing stuff

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happening right now.

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It's kind of crazy to think that we might have AI

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that can really think and solve problems

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like way better than we can even imagine right now.

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Yeah.

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And the second thing I hope people get

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is that AI isn't something way off in the future.

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It's already changing our lives.

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Uh-huh.

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And it's only gonna get more important.

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It's a really powerful tool.

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Right.

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But we have to use it the right way.

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Exactly.

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As we make AI smarter and stronger,

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we have to make sure it's good for everyone.

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So to our listeners, stay curious about AI.

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Yeah.

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Keep learning about it.

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Ask questions.

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Don't be afraid to ask the hard questions.

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Because the future of AI is up to us.

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It's true.

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The choices we make today will shape what AI becomes.

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And who knows?

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Maybe someday you'll be using AI

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that can not only solve problems,

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but also do creative things.

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Yeah, like compose music.

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Right.

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Or write amazing books.

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The possibilities are endless.

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It's a really exciting time

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and we can't wait to see what happens next with AI.

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Me too.

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Thanks for joining us on this deep dive.

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It's been fun.

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47, yeah.