All right, so AI, right?
It's gotten really good at some pretty wild stuff,
like writing all kinds of creative things
or translating languages.
But when it comes to like really complex reasoning,
you know, like figuring out problems with multiple steps,
AI still kind of struggles.
Yeah, that's definitely a challenge right now
for a lot of the current AI models.
So this is where this new paper
we're looking at comes in, right?
It's called Forest of Thought,
Scaling Test Time Compute for Enhancing LLM Reasoning.
And it basically introduces this really cool concept
called Forest of Thought.
It's supposed to help AI think better.
At least that's what they're aiming for.
Yeah, that's the gist of it.
It's really tackling those limitations
that we see with LLMs right now, large language models.
Like when they're faced with a problem
that requires step-by-step thinking to solve.
Okay, so this is probably a dumb question,
but for people like me who aren't AI experts,
can you explain why like those existing methods,
you know, things like chain of thought and tree of thought,
how do they always cut it?
Okay, so imagine you're solving a puzzle, right?
Chain of Thought is kind of like trying to solve that puzzle
by only looking at one possible solution at a time.
So you get tunnel vision basically.
Pretty much, like it might work
if the puzzle is super simple,
but if it's more complex,
you're likely to go down the wrong path
and completely miss the solution.
Ah, so you end up going down a rabbit hole
and never actually solving the problem.
Exactly.
Now tree of thought, that's a bit more advanced.
It explores multiple paths,
sort of like the branches of a tree,
but even with this method,
the AI usually only considers each path once.
It doesn't go back and rethink
if it might have messed up earlier on.
Okay, I'm starting to see the problem here.
So both methods have limitations,
especially with those super complex problems
that need more like careful consideration.
So where does this forest of thought thing come in?
I gotta say the name is pretty intriguing.
Well, think of a forest with a bunch of trees, right?
I could picture that.
Each tree represents a different way to solve a problem.
And forest of thought, or FOTI, takes that idea
and applies it to AI,
basically integrates multiple reasoning trees together.
So now the AI can explore tons of potential solutions
all at the same time.
So it's like having a whole army of AI's
all working together to solve the puzzle,
each one with its own unique approach.
You got it.
And that's where the real power comes from,
leveraging all those different perspectives.
Just like in the real world, you know?
A team with people who have different skills and knowledge,
they can usually solve problems way better
than someone working alone.
But FOTI tries to do the same thing with AI.
So it's not just about making the AI brain bigger,
it's about making it think in a smarter, more strategic way.
Exactly.
And how FOTI is built for efficiency too,
it uses something called sparse activation.
It's kind of like the AI picks and chooses
which solution paths to really focus on within each tree.
You know, it's like, if you're in a maze,
you don't try every single path,
you try to pick the ones that seem like
they'll lead you to the exit.
So the AI is getting smarter at prioritizing,
avoiding those dead ends.
That's pretty cool.
Yep, and it gets even cooler.
FOT can also learn from its mistakes.
It has a system that's always evaluating
its reasoning steps, and if it finds an error,
it can actually correct itself as it goes.
It's like having a built-in spell checker, but for logic.
No, that's next level.
So it's not just exploring all these different paths,
it's double checking its work along the way too.
I'm guessing that makes it way more accurate.
You bet, especially when it comes to math problems,
they've actually added in specific rule-based checks
to catch those common calculation errors.
So it's like the AI has a math tutor looking over its shoulder,
making sure it doesn't make those silly arithmetic mistakes.
Okay, so now we have this AI forest
exploring all these different solution paths
and constantly checking its work to avoid mistakes.
But how does it actually decide on the best answer?
You know, out of all those possibilities.
That's where consensus-guided decision-making comes in.
It's basically like all the different trees in the forest
vote on what they think is the best solution,
but it's not a simple majority rules kind of thing.
If there isn't a clear winner,
then they have a special LLM, like an expert judge.
And that judge makes the final decision
based on the reasoning provided by all the trees.
So it's combining the knowledge of the whole forest
with the expertise of a specialist.
That sounds like a pretty solid way to make a decision.
Now the big question, how well does this FOTI actually work?
I mean, did they test it out?
Oh yeah, they put it through its paces.
They tested it on a few benchmarks,
including this game called Game of 24.
You have four numbers
and you got to use basic math to get to 24.
It's harder than it sounds.
Yeah, I remember that game.
It could be a real brain teaser.
Well, with enough trees in the forest,
FOTI actually achieved almost perfect accuracy.
It totally blew the standard tree of thought method
out of the water.
That's impressive.
Beating the standard method by that much is a big deal.
They also tested it on a dataset of math word problems
called GSM 8K and a more challenging math dataset
called Math.
Heh heh heh heh heh.
What were the results?
A FOTI consistently did better than the other methods
across all the different difficulty levels.
And what's even more exciting is that even a small increase
in the number of trees led to big improvements in accuracy.
So it seems like FT really benefits
from having more computational power,
you know, more trees in the forest.
So the more trees you have, the better it performs.
Oh.
That's a good sign.
It suggests that this approach could really scale up
and get even better as we get more powerful computers.
But I'm sure there are limitations, right?
I mean, nothing's perfect.
That's right.
FOTI is super promising,
but it still depends heavily on the LLMs themselves,
the underlying language model.
So if those LLMs have biases or gaps in their knowledge,
well, FOT is going to inherit those problems.
Makes sense.
It's not like a magic solution.
Yeah.
That's definitely a big step in the right direction.
I'm kind of curious about...
Actually, before you ask, I have a question for you.
Thinking about everything we've talked about so far,
what's the most interesting thing about FOTI to you?
Hmm, that's a good question.
I think what really strikes me is how FOTI
is kind of mimicking the way humans solve problems.
We don't usually just follow one single line of thought.
We brainstorm different ideas, we try things out,
we make mistakes, we learn from them.
And the way FOT incorporates multiple perspectives
and allows for that self-correction.
It feels very human.
That's a great observation.
One of the most exciting things about FOTI
is that it's moving us closer to AI
that can think more like us.
AI that can reason and solve problems
in a way that feels natural.
Which opens up a whole new can of worms, doesn't it?
The more sophisticated these AI systems get,
the more we have to think about the potential consequences,
both good and bad.
Absolutely.
We need to be having those conversations right now
about how to make sure these systems are aligned
with our values, about how to prevent them
from amplifying biases or causing new problems.
Yeah, it's a huge responsibility.
And it's not just on the researchers and developers,
it's on all of us.
Policymakers, industry leaders, the public,
we all need to be involved in shaping the future of AI.
But I have to say, the potential benefits are incredible.
Imagine using OT to speed up scientific discoveries
or develop new medical treatments
or even tackle things like climate change.
It's really exciting to think about.
I completely agree.
This is a very exciting time to be working in AI.
For sure.
Well, that brings us to the end of part one
of our deep dive into this fascinating world
of forest of thought.
So welcome back to our deep dive in forest of thought
before we went to the break, you're about to ask something.
Oh yeah, I was just thinking about all the different ways
this technology could be used.
The research mainly focused on math and logic problems.
Yeah.
But what about other stuff?
Could FOTB be used for writing or translation
or even something creative?
That's a really great question.
And it gets to the core of what makes FOTI so interesting.
The initial research focused on those specific areas.
But the main idea is behind FOTI,
like using multiple reasoning paths
and having that constant self-correction,
that could totally be applied to a ton of different tasks.
Imagine using OT to generate different styles of writing
or to explore different ways to translate something,
each one with its own subtle differences and interpretations.
Oh, wow, that would be wild.
It'd be like having a whole team of AI writers
and translators all working together,
each one bringing its own unique flair to the project.
Exactly.
And it could even help us get past some of the limitations
we see with current AI models.
You know, like the tendency to generate repetitive text
or get stuck in certain writing patterns.
It sounds like the possibilities are pretty much endless.
But FOTI is still a new thing, right?
So what are the next steps?
What needs to happen for it to become more widely used
and really make an impact?
Well, there are a few key areas that researchers are working on.
One is figuring out how to apply FOT
to a wider range of tasks, you know, beyond just math and logic.
This might involve creating new types of reasoning trees,
specifically for those tasks, or finding ways
to adapt the existing FOTI framework
to deal with different kinds of data.
So it's like we need to expand the forest, so to speak,
add different kinds of trees that are specialized
for different jobs.
That's a great way to put it.
Another big focus is improving the LLMs themselves.
Remember, FOTI is built on top of these language models.
So the better the LLMs get, the better FOTI will perform.
It's all about having a solid foundation right,
making sure those trees have healthy soil to grow in.
You got it.
And of course, there's the whole computational side of things.
FOTI uses that sparse activation thing to be more efficient.
But as we start using it for more and more complex stuff,
finding ways to optimize its performance
and reduce the computing power it needs
is gonna be essential.
So it's like we need to make the forest more sustainable,
help it thrive without using up too many resources.
That's a perfect analogy.
You really hit the nail on the head there.
But even with all those challenges,
I'm still super excited about FOTI.
The idea of creating AI that can really reason
and solve complex problems,
AI that can understand the nuances of a situation
and weigh different options and make informed decisions,
that's what gets me fired up.
Yeah, it's like we're moving away from AI as a tool
and towards AI as a partner.
It's a pretty big shift.
Absolutely.
And as we move in that direction,
it's super important to remember that AI development
doesn't happen in isolation.
It's influenced by society
and has the power to change society too.
For sure.
The more powerful these AI systems become,
the more careful we need to be.
We need to make sure they reflect our values
and that they don't make existing problems worse
or create new ones.
I couldn't agree more.
It's a responsibility we all share.
Researchers, developers, policymakers,
industry leaders, the public,
we all need to be part of the conversation.
But I'm still optimistic.
The potential benefits are just too big to ignore.
Think about the breakthroughs we could have in medicine
and science and tackling things like climate change.
If we can do this right,
if we can harness the power of AI responsibly and ethically,
well, the possibilities are pretty amazing.
That's a good point.
It's a reminder that this is about more than just technology.
It's about what kind of future we wanna create.
Well said.
I wanna go back to the question I asked before the break.
Thinking about everything we've talked about,
what part of FOTI stands out to you the most?
What are the areas where you see the most potential
or maybe the areas where you have questions or concerns?
Honestly, the thing that excites me the most about FOTI
is that it could help AI go beyond those simple tasks.
Towards a deeper, more nuanced understanding in the world,
it's a step closer to creating AI
that thinks more like humans do,
which is both fascinating and a little bit scary
if you think about it.
I totally get that.
It's an exciting but challenging time to be working on this stuff.
But I think that if we work together,
we can use FOTI to do some really incredible things,
things that could benefit everyone.
Yeah, it's a future worth working for.
So before we wrap up part two of our deep dive,
is there anything else you think people should know about FOTI?
Any key takeaways?
The main thing to remember is that FOTI
is a different way of thinking about AI reasoning.
It's not just about building bigger, more powerful LLMs.
It's about using them in a smarter way,
about embracing those ideas of diverse perspectives
and continuous learning and collective intelligence.
So it's not just about the size of the forest,
but about how well the trees work together
and learn from each other.
Exactly.
And it's important to remember that this is all still very new.
There's so much more research to do.
We need to fully understand what a FOTI can do,
what its limitations are,
and how we can use it in all sorts of different fields.
It's like we've just planted the seeds
for a whole new forest of possibilities.
And it's going to be fascinating to watch it grow
and evolve over time.
I like that.
That's a great way to think about it.
So that wraps up part two of our exploration
of forest of thought.
And we're back.
It's the final part of our deep dive into forest of thought.
You know, after learning about all the cool things FOTI could do,
I'm starting to see forests everywhere now.
Well, it is a really good way to think about it.
How we can make AI better at reasoning.
And the key thing to remember is that FOTI is all about
changing how we approach AI reasoning.
It's not just about building bigger and more powerful LLMs.
It's about using those LLMs in a smarter way,
about finding those creative ways to combine different approaches
and really harness that collective intelligence.
Yeah.
And it shows that sometimes the best way to innovate
isn't to create something totally new.
It's to find new ways to use what we already have.
Like with FOTI, it's all about combining those multiple reasoning
approaches and letting the AI learn and correct itself as it goes.
And that leads to some pretty impressive results.
Right.
And what I find really fascinating is that FOTI
seems to be doing something similar to what humans do
when they're trying to solve a tough problem.
We don't usually stick to one single line of thought.
We brainstorm.
We explore different options.
We mess up.
And we learn from those mistakes.
And FOTI basically lets AI do the same thing.
So it's like AI is becoming less of a tool and more of a partner.
It's not just following instructions anymore.
It's bringing its own ideas and perspectives to the table.
Exactly.
But as we move towards that kind of partnership,
we got to keep in mind that AI development isn't happening in a bubble.
It's connected to society in a big way.
And it has the power to really change things.
Yeah, for sure.
As AI systems get more powerful and sophisticated,
we have a responsibility to make sure they're being used for good.
We need to be talking about responsible development,
about making sure they align with our values,
and don't end up making existing problems worse or creating new ones.
100%.
This isn't just a job for researchers and developers.
It's something that everyone needs to be involved in.
Policymakers, industry leaders, the public,
we all need to be part of shaping the future of AI.
But I'm optimistic.
I really am.
Because the potential benefits are huge.
Think about what we could achieve if we use AI the right way.
In medicine, in scientific discovery, in fighting climate change.
If we can do this responsibly and ethically,
the possibilities are incredible.
It's exciting and a little daunting at the same time, right?
Yeah, I know what you mean.
But I think it's a challenge worth taking on.
Absolutely.
Well, I think that's a great place to wrap up our deep dive
into the forest of thought.
It's been a really fascinating exploration,
and I hope everyone listening has learned something new
and maybe even feels a little inspired to think about
how AI is going to shape our world.
Thanks for having me. It's been a pleasure.
And thanks to all of you for listening to this episode
of The Deep Dive.
Be sure to subscribe and join us again
for more deep dives into the world of AI.
Until next time, stay curious.