Okay, so today we're gonna unpack this new AI research paper.
It's called Scaling Up Test Time Compute
with Latent Reasoning, a Recurrent Depth Approach.
Wow, what a mouthful.
Yeah, right, and you know, it's all about a model
that can basically think in latent space.
Okay.
Have you ever like been working on like a tough problem
and like the solution just kind of clicks into place
before you can even put it into words?
Like, that's kind of what we're talking about here.
The model is solving problems, but internally,
before it generates any text output.
So it's sort of like doing the work in the background
and then just giving us the answer.
Exactly, and this research suggests
that maybe just using language
isn't enough for like real AI reasoning.
Interesting.
You know, it makes me think
of what Yanlacun has been saying.
Oh yeah, Metis Chief AI Scientist.
Right, he's been pretty vocal about the limitations
of large language models.
Yeah, he's basically saying that we're all fooled
by how fluent they are with language.
Yeah, and we kind of assume they must be super intelligent
just because they can, you know.
Right, they can write and speak so well,
but it's not the whole picture.
So what are we missing
if just manipulating language isn't enough?
Well, that's where this idea
of thinking in latent space comes in.
Okay.
This model is doing something totally different.
So walk us through how that works.
Okay, so picture this like a really complex network
of calculations happening inside the model,
turning through all these different possibilities
before it like settles on an answer.
Okay.
Here's the key.
It's all happening kind of in the background
in this hidden latent space
before any text is generated.
So it's not showing its work like Shane of Thought does.
It's more like it's having
like an internal brainstorming session.
Exactly.
And then it just gives us the conclusion.
Precisely.
And what's wild is that the researchers found
the more the model thought in this latent space,
the better it performed on like all these different tasks.
Oh wow.
From like high school math problems
to like complex moral scenarios.
So I really put it through its paces.
Yeah.
And the results were really impressive.
That's pretty remarkable.
How does this approach compare
to like the Shane of Thought approach?
Well.
Are there any advantages to this latent reasoning?
There are actually quite a few.
For one, it doesn't require, you know,
massive amounts of specialized training data.
Oh really?
Like Shane of Thought does.
You don't have to feed it tons and tons
of examples of reasoning.
That seems like a huge advantage,
especially with how data hungry AI models are.
Absolutely.
It makes the development way faster and more efficient.
That's a game changer.
What about memory requirements?
Oh yeah.
Doesn't all that text generation
and Shane of Thought take up a lot of memory?
Totally.
Shane of Thought can be a real memory hog.
Uh huh.
Latent reasoning is way more streamlined and efficient.
Okay.
So we're talking speed and efficiency.
Yeah.
But what about the quality of the reasoning itself?
Right.
Does this approach actually lead
to more human like problem solving?
That's where things get really interesting.
Okay.
So the combination of latent space thinking
and then, you know, the language output
actually mirrors how we tackle problems.
We brainstorm internally.
We weigh different options.
Right.
And then finally we express those thoughts
through language.
It's like we're seeing a more sophisticated
kind of thinking here.
Yeah.
It's not just like pattern matching
or clever language manipulation.
Exactly.
The paper mentions metastrategies and abstraction.
Right.
What do those terms mean like in this context?
Basically it means the model isn't just memorizing stuff.
It's developing these higher level thinking skills.
Uh-huh.
So for example, it might figure out
the most efficient way to approach a problem
or like recognize underlying patterns and principles.
Oh wow.
So it's almost like it's learning how to learn.
Yeah. In a way.
So this could be like a big jump forward
in what AI can do, right?
Yeah.
Potentially.
But what does it mean for like the rest of us?
Right.
You know, people who aren't AI expert.
How could this tech actually change our lives?
Well, imagine like AI assistants
that really get you patterns.
Like they understand what you need
and what you like almost like they can read your mind.
Okay.
They could solve tough problems really fast.
Uh-huh.
And come up with solutions you'd never even thought of.
And what about things that need common sense?
Yeah, that's been so hard for AI.
Right.
This kind of reasoning could be huge for that.
That's a pretty exciting picture.
Yeah.
But we probably shouldn't get too carried away, right?
True.
The paper says this model is still in the early stages.
They call it like a proof of concept.
Yeah, it's just one study.
Okay.
We need more research to see if this will really work
for creating that super smart AI we're talking about.
Right, makes sense.
So back to Yamakun and his critique
of large language models.
Uh-huh.
Do you think this latent space reasoning
could actually answer his concerns?
That's the big question, isn't it?
Yeah.
It's possible that this level of internal reasoning
could get as closer to the kind of AI Makun wants.
Okay.
But he has some pretty specific ideas
about what's missing from today's AI.
Like what?
Well, he talks about the need for AI
that can learn and reason about the world
in a more like grounded way.
Like connected to the physical world.
Okay.
It calls them world models.
World models.
Yeah, AI that can kind of simulate the world
and interact with it.
Like we do.
Exactly.
So there's still a lot of debate in the AI world
about the best way to make truly intelligent systems.
Oh yeah, definitely.
It's what makes it so interesting.
Right.
There are so many different paths being explored.
It's always changing.
It's hard to say where it'll all end up.
And the paper mentions that this latent space thinking
doesn't have to replace chain of thought.
You're right.
They could actually work together.
Oh.
Combining the strengths of both.
So we could have these hybrid models.
Exactly.
Get the best of both worlds.
And that makes me wonder,
could we combine latent space reasoning
with other AI stuff too?
Yeah, that's a great question.
Like what about reinforcement learning?
Or even those world models Lakun talks about.
Wow.
So many possibilities.
It's like we're at the beginning of something huge.
Yeah, but we need to be careful, right?
Of course, we have to think about
how these technologies will affect society
and the way we work and even what it means to be human.
It's a lot to think about
and it's important to talk about it now.
Absolutely.
While all this is happening.
So before we finish today's deep dive,
we wanna leave you with something to think about.
Okay.
Think about how you solve a heart problem.
Do you kind of work through the solutions in your head
before you write anything down or type anything out?
In that internal space.
Exactly.
If you do, you might be more like these AI models
than you realize.
Interesting.
You might have to learn something
from it when you suddenly understand something.
Uh-huh.
Before you can even put it into words.
Right.
Maybe that's a glimpse into what thinking really is.
That's a fascinating thought.
Makes you wonder if we're figuring out
how intelligence works, like in general.
Yeah, not as a start official intelligence.
Right.
It's like we're learning about our own brains
by studying AI.
So to wrap up, what are some of the big takeaways
for our listener today?
Well, first, I hope I see how fast AI is changing.
Like this latent reasoning research,
it's just one example of the amazing stuff
happening right now.
It's kind of crazy to think that we might have AI
that can really think and solve problems
like way better than we can even imagine right now.
Yeah.
And the second thing I hope people get
is that AI isn't something way off in the future.
It's already changing our lives.
Uh-huh.
And it's only gonna get more important.
It's a really powerful tool.
Right.
But we have to use it the right way.
Exactly.
As we make AI smarter and stronger,
we have to make sure it's good for everyone.
So to our listeners, stay curious about AI.
Yeah.
Keep learning about it.
Ask questions.
Don't be afraid to ask the hard questions.
Because the future of AI is up to us.
It's true.
The choices we make today will shape what AI becomes.
And who knows?
Maybe someday you'll be using AI
that can not only solve problems,
but also do creative things.
Yeah, like compose music.
Right.
Or write amazing books.
The possibilities are endless.
It's a really exciting time
and we can't wait to see what happens next with AI.
Me too.
Thanks for joining us on this deep dive.
It's been fun.
47, yeah.