Have you ever wished that you could just hit,
like fast forward, you know, on learning a new skill?
Oh yeah.
Like skip all those hours of practice
and just like jump right to being an expert.
Absolutely.
Well in the world of AI,
that's what researchers are trying to do.
Yeah.
And there's this new breakthrough called B-Star.
That is really shaking things up.
That's right.
So maybe you could tell us a little bit about
what is so groundbreaking about B-Star?
So B-Star is interesting because it tackles
a really, really fundamental problem in AI,
which is that as these models get more and more complex,
they need more and more data to be able to learn.
Yeah.
And that data typically comes from us humans
and it's a very, very expensive
and time consuming process to create.
Right.
It's like we're constantly having to feed the AI
That's right.
with more and more and more information
just to get them to learn a little bit more.
That's right.
But B-Star is all about making the AI more self-sufficient.
That's right.
Right.
So how does that work?
So we call it self-improvement
and we've seen other attempts to try to improve AI's ability
to self-improve, but typically they would plateau.
B-Star takes a very different route.
And so we're seeing a lot of really interesting
results with it.
That's really exciting.
So how does it work?
How is this AI self-improving?
Okay, so imagine you're learning to play the guitar.
Right.
You could spend all of your time
just practicing like the same three chords.
Yeah.
And that's something we call exploitation in AI.
Or you could experiment with like new melodies
or new techniques and then we call that exploration.
And the key to actually getting better is this balance
between sticking with what you know
and trying something new.
Right, so it's finding that sweet spot between the two.
That's exactly what B-Star is doing.
But unlike previous methods,
which would kind of set that balance beforehand,
B-Star will continuously monitor and adjust that balance
throughout the training process.
Oh, wow.
So think of it like a self-driving car
that is adjusting its course in real time
to account for like traffic and other conditions.
So B-Star is actually learning how to learn.
That's right.
Which is, that's wild.
It is.
Okay, but how does it know if it's on the right track?
Like if it's actually improving.
So we use a metric called the balance score.
Okay.
And it's essentially measuring how well it's balancing
this exploration and exploitation.
Right.
And so we're looking at both like the quantity of outputs
that it produces and then also the quality of those outputs.
And if it's producing a lot of good outputs,
we know that it's learning effectively.
That's kind of a record card.
Yeah.
For the AI.
That's a good way to think about it.
Yeah.
Okay, so does this approach actually produce better results?
Yeah.
So we see in the research that they tried this out
on a variety of tasks, including math problems
and writing code and answering common sense
reasoning questions.
Wow, that's a lot of really different challenges.
It is.
So how did it do?
So first let's look at math problems.
So for this, they use data sets called GSM8KNMath,
which tests the model's problem solving abilities.
And on GSM8K, BSTAR was able to achieve a pass
at one accuracy of 53.8%,
which is a really big jump from the previous best,
which was 46.8%.
And on math, it was able to get 27.8%.
Wow.
So figuring out math.
It is.
That's amazing.
That's right.
Okay, what about something like coding
that seems like way more complicated?
Yeah.
Even with coding, which is a very, very difficult problem
for AI, BSTAR was able to make some pretty significant
improvements.
So they used a data set called APPS.
And with this BSTAR achieved an accuracy of 19.6%,
which is again, significantly higher than the previous
methods that they tried.
So it's not just a one trick, Pony.
No, it's not.
It can do lots of different things.
Okay, so you mentioned earlier that other attempts
at self-improvement kind of hit a wall.
Does BSTAR have the same problem?
So that's what's really, really exciting about this,
is that it keeps getting better.
Oh, wow.
It doesn't just improve for a few rounds and then plateau.
It keeps getting better and better over multiple iterations.
So for example, in the math training,
they gradually increased something called
the sampling temperature.
Okay, sampling temperature.
Yeah.
That sounds very technical.
It is, but basically imagine your like brainstorming ideas.
A low temperature would keep your ideas kind of close
to what you already know.
Right.
A low temperature would encourage you to think more
outside the box.
Okay.
So that's what sampling temperature does.
And as they gradually increase this temperature,
BSTAR was able to avoid getting stuck in a rut
and find new and better solutions.
So it's like, it's constantly trying to push
its own creative limits.
Yeah, that's a really good way to think about it.
Even as it gets better.
That's right.
Okay, so are there any other techniques that it uses
to kind of boost its learning?
So something else that's really interesting in the research
is that they use additional reward models.
Okay.
So think of this as specialized feedback systems
that give more nuance than just saying right or wrong.
Okay.
So for example, when they were testing its math skills,
they used what's called a process reward model or a PRM.
PRM.
Okay, what is that?
So instead of just telling the AI that like,
its final answer is right or wrong.
It actually looks at each individual step
that AI takes to get to that answer and provides feedback.
Oh, so it's like having a teacher that grades your work
and then gives you notes on how you could improve.
That's exactly right.
Yeah.
So it gives the model this really, really comprehensive
understanding of what it's doing well
and what it's not doing well
and allows it to really learn from that feedback.
So we've talked about the balance score
and we've talked about how it adjusts this exploration
and exploitation.
Yeah.
But why should we care about this whole self-improvement
thing in AI?
That's a really great question.
So if we zoom out and think about the bigger picture,
I think it has really, really big implications
for how we develop and use AI in the future.
Okay.
So imagine AI that can learn and adapt on its own
without this constant hand-holding from us humans.
Right.
That would open up possibilities
in so many different fields.
So it's less time spent on data prep.
That's right.
And more time spent on actually tackling real-world problems.
Exactly, yeah.
That's awesome.
Yeah.
So does this actually work with like larger AI models?
So that's what's really encouraging about this
is that it seems to scale.
Okay.
So they tested it on a model called Lama 3.18b,
which is a fairly big model
and they still saw these really significant improvements.
Oh, it's actually scales.
That's great.
That's right.
What else is really neat about this approach?
So I think one of the really interesting things
about MeSTAR is that it makes this whole self-improvement
process more transparent.
Okay.
So in other methods, it's kind of like a black box.
You just run the model and hope for the best.
Right.
MeSTAR allows researchers to really see
what's happening at each step
and why certain approaches are working better than others
and how the model is adapting its strategy over time.
So it's not just making the AI better,
but it's making it so we can understand
how that improvement is happening.
Exactly.
Okay, so we've covered a lot.
MeSTAR is about self-improvement in AI.
It balances exploration and exploitation.
Uh-huh.
It uses this balance score to stay on track.
That's right.
It's doing some really amazing results
across all these different tasks.
Yeah.
So where do we go from here?
Well, so this version of MeSTAR focuses
on fine-tuning these key parameters
like the sampling temperature and the reward thresholds.
Okay.
But there's tons of potential for future development.
Like what?
So for example, they could explore
incorporating even more sophisticated decoding techniques.
Okay.
That could allow the model to have even finer control
over its exploration and come up
with even more diverse solutions.
So like giving the AI a bigger toolbox.
Yeah, that's a good way to think about it.
It's a play with.
That's right.
And then on the exploitation side,
we could look at using dynamic reward models
that actually adapt and change over time.
Okay.
Which would allow the feedback to be
even more nuanced and relevant as the model learns.
So it's all about making it more adaptive.
Yeah, that's a great way to put it.
And responsive.
Exactly.
Okay, so we've talked a lot
about the technical side of this.
Right.
But I'm really interested in the real world applications.
Like what fields could be transformed by an AI
that can do all this?
Oh, the possibilities are really, really exciting.
I bet.
So think about something like robotics.
Where machines have to adapt to these very complex
and unpredictable environments.
Yeah.
Right now, most robots rely on pre-programmed instructions.
But with B-Star, we could potentially see a future
where they're constantly learning from their experiences
and getting better at what they do.
So as I consider them just being tools.
Yeah.
They become more like partners.
Absolutely.
That's amazing.
Right, so imagine like a robot surgeon
that could learn from each surgery that it performs.
Wow.
And continually refine its techniques.
Yeah.
And reduce the risk of error.
That would be amazing.
Or think about like a search and rescue robot.
Okay.
That could adapt to different environments
and navigate this challenging terrain more effectively.
Just in healthcare and disaster response alone,
applications are huge.
They are, yeah.
Wow.
So a lot of times we think about AI
as being good at logic and reasoning.
Right.
What about creativity?
That's a really great question.
Like could B-Star help AI become more artistic?
So we've already seen AI create some really impressive
artwork and music.
But a lot of times it feels a little bit mechanical.
Right.
Because it relies on these existing data sets and patterns.
And it's kind of just remixing what's already out there.
Yeah.
But with B-Star, eventually AI could develop
its own unique style, its own creative voice.
So instead of just imitating us.
Yeah.
It could actually become an artist in its own right.
That's right.
Now that's a game changer.
That's right.
So imagine AI composing original music
or writing stories or even designing new products.
Absolutely.
And because B-Star is so good at balancing
exploration and exploitation.
Wow.
It could potentially find that sweet spot
between pushing creative boundaries
and still producing work that resonates with people.
Right.
It could help us to discover completely new forms
of art and expression.
That's incredible.
This is making me think about education too.
Oh yeah.
Like what if we could use B-Star to create personalized learning
experiences.
All right.
That adapt to each student's individual needs
and learning style.
That's a really cool idea.
So imagine a tutoring system that
can pinpoint a student's strengths and weaknesses.
Yeah.
And then tailor the curriculum accordingly.
Right.
So you could give customized feedback,
adjust the pace of learning, and even recommend
new topics to explore.
To be like having a personal tutor for every student.
Yeah.
That's amazing.
OK.
So this conversation is getting me really excited
about the future of AI.
It's a very exciting time.
It is.
But with all this talk about self-improvement
and the systems constantly evolving,
it does make me wonder, where does human control fit
into all of this?
That's a really important question.
And as AI becomes more autonomous,
it's really, really critical to ensure
that we maintain a certain level of oversight.
We don't want to create something that we can't
understand or control.
Hit the balancing act.
Yeah.
Between letting AI learn and evolve.
Yeah.
And also making sure that we're still
steering it in the right direction.
That's right.
So it sounds like we need to be thinking not just
about the technical aspects of AI, but also
the social and ethical and even philosophical implications.
Absolutely.
AI is not just a tool.
It's a reflection of ourselves and our values.
And as we develop more and more advanced systems like B-Star,
it's so important that we do it responsibly and thoughtfully.
Yeah.
We need to ask those tough questions.
Yeah.
And have those difficult conversations
about what kind of future we want to build.
Absolutely.
So it's not just building a better AI.
It's building a better future with AI.
Yeah.
I think that's a really nice way to put it.
OK.
So this has been a really fascinating deep dive.
It has.
We started by talking about B-Star's ability
to help AI models learn more efficiently.
Yeah.
But this conversation has really made me rethink
this whole field of AI.
I think that's what's so exciting about these kinds
of breakthroughs is that they really
challenge our assumptions about what's possible.
Absolutely.
And I think that's something our listeners should
take away from this episode.
Yeah.
B-Star is just one example of the incredible innovation that's
happening right now in AI.
That's right.
It's a field that's constantly evolving.
Yeah.
And who knows what breakthroughs are just around the corner.
That brings up a really important point.
How do we keep up with all of this as individuals?
Right.
It can feel like a tidal wave.
Yeah.
It can feel really overwhelming.
Yeah.
So how do we even begin to keep up with it all?
It starts with curiosity.
OK.
So read articles.
Listen to podcasts like this one, of course.
Of course.
Seek out events related to AI.
There are so many resources available now,
both online and in your local community,
that it's easier than ever to really tap into this world.
And you don't need to be a computer scientist
to understand this stuff.
Or to even form an opinion about how
AI is going to impact our lives.
Not at all.
And I think the more we understand about it,
the more we can make informed decisions
about its development and use.
Yeah.
It's like anything else, the more you learn.
Yeah.
The more empowered you are.
Absolutely.
To participate.
That's right.
And to have a voice in this conversation.
Yeah.
And don't underestimate the power of conversation itself.
Oh, yeah.
Talk to your friends.
Talk to your family.
Right?
Talk to your colleagues.
Anyone who will listen, share what you're learning,
ask questions, and be open to different perspectives.
A lot of times those casual conversations
are what really lead to some big change.
Yeah, I think so.
OK.
So as we wrap up this deep dive into Beastar
and this constantly changing world of AI,
I want to leave our listeners with a question.
OK.
What kind of world do you want to create with AI?
Wow.
That's a powerful question.
It is.
I don't think there's an easy answer,
but I think by staying engaged, informed, and curious,
we can all play a part in making sure
that AI is used for good in the world.
Well said.
And for those of you who want to learn more about Beastar
and all this research that we discussed,
we got links in the show notes to the original paper
and some other resources as well.
Awesome.
Thanks for listening, everybody.
Until next time, keep exploring.
Keep learning and keep asking those big questions.
Absolutely.
And they could also look at different ways
of how they select the outputs that are used for training.
So there are more sophisticated decoding techniques
that could give the model even finer control
over its exploration and allow it to come up
with even more diverse and creative solutions.
So it was like we're giving it a bigger toolbox to play with.
Yeah.
And then on the exploitation side,
they could look at using dynamic reward models
that actually adapt and change over time
so that as the model learns, the feedback
is even more nuanced and relevant.
So it's all about just making it more adaptive.
Yeah.
OK, so we've talked a lot about the technical side of things,
but I'm curious about the real world applications.
Like what fields could be transformed by an AI
that can do all of this stuff?
I think there are so many possibilities.
It's really exciting to think about.
So one area that comes to mind is robotics.
Where machines have to adapt to these very complex
and unpredictable environments.
And right now, robots mostly rely on these pre-programmed
instructions.
But with Beastar, we could potentially
see a future where robots are constantly learning
from their experiences and getting better and better
at what they do.
It's not just them being a tool anymore.
It's them being more like a partner.
Exactly.
Yeah, so imagine a robot surgeon that
could learn from each surgery that it performs
and continually refine its techniques
and reduce the risk of error.
That would be incredible.
Yeah, or think about a search and rescue robot
that could adapt to different environments
and navigate challenging terrain.
Yeah, just in health care and disaster response alone.
Right.
The applications are huge.
Yeah, they're really significant.
So we tend to think of AI as being good at logic and reasoning.
Right.
What about creativity?
That's a great question.
Like, could Beastar help AI become more artistic?
Yeah, so we've already seen AI create some pretty impressive
artwork and music.
Right.
But it often feels kind of mechanical.
Right.
Because it's relying on these existing data sets
and patterns.
Yeah.
It's essentially remixing what's already out there.
But with Beastar, AI could potentially
develop its own unique style, its own creative voice.
So instead of just imitating us.
Exactly.
It could become an artist in its own right.
That's right.
That's a game changer.
It really is, yeah.
So we could have AI composing original music or writing
stories or even designing new products.
Exactly.
And because Beastar is so good at this balance between exploration
and exploitation, it could potentially
find that sweet spot between pushing creative boundaries
but still producing work that really resonates with people.
Right.
So it's not just totally out there.
Right.
And the stuff that we can connect with.
Yeah.
And it could help us discover entirely new forms
of art and expression.
Wow.
OK, so this is making me think about education.
Like what if we could use Beastar to create personalized
learning experiences?
Oh, that's a cool idea.
That adapt to each student's individual needs and learning
style.
Yeah.
So imagine a tutoring system that
could really pinpoint a student's strengths and weaknesses.
Yeah.
And then tailor the curriculum based on that.
Right.
It could give customized feedback,
adjust the pace of learning, even recommend new topics
for them to explore.
It could be like a personal tutor for every student.
Exactly.
Yeah.
That's incredible.
OK, so this conversation has got me really excited
about the future of AI.
Yeah.
But all this talk about self-improvement and these
systems constantly evolving, it does make me wonder,
where does human control fit into all of this?
Yeah.
That is a really important question.
Like are we just going to let the machines take over?
I don't think so.
But I think as AI systems become more and more autonomous,
it's really critical to ensure that we maintain
some level of oversight.
Right.
We don't want to create something that we can't understand
or control.
So it's a balancing act again.
It is, yeah.
Between letting the AI learn and evolve.
Yeah.
And also making sure that we're steering it.
Right.
In the right direction.
Exactly.
So it sounds like we need to be thinking not just
about the technical side of AI, but also
about the social and the ethical and even
the philosophical implications.
I think that's absolutely right.
AI is not just a tool, it's a reflection of ourselves
and our values.
And as we develop these really advanced systems like BSTAR,
it's so important that we do it responsible and thoughtfully.
Yeah.
We need to ask those tough questions
and have those difficult conversations about what kind
of future we want to build.
I think that's really well said.
So it's not just building a better AI,
it's building a better future with AI.
Yeah.
I really like that.
OK.
So this has been a really fascinating deep dive.
It has been.
We started by talking about BSTAR and its ability
to help AI models learn more efficiently.
Right.
But this conversation has really expanded my thinking
about the whole field.
I think that's what's so great about exploring these cutting
edge advancements is that they often lead us
to these unexpected insights and really challenge
our assumptions about what's possible.
Absolutely.
And I think that's something our listeners should
take away from this episode.
That BSTAR is just one example of the incredible
innovation that's happening in AI right now.
Absolutely.
It's a field that is just constantly evolving.
Yeah.
And who knows what breakthroughs are just around the corner.
That brings up a really important point.
How do we keep up with all of this as individuals?
Right.
Yeah.
It can feel like a tidal wave of new developments
coming out all the time.
It can feel really overwhelming.
Yeah.
So how can we even begin to keep up with it all?
I think it starts with curiosity.
OK.
So read articles, listen to podcasts like this one,
of course.
Before.
You know, seek out events that are related to AI.
There are so many resources available now, both online
and in your local community, that it's easier than ever
to tap into this world.
And you don't need to be like a PhD in computer science.
Yeah.
You don't.
To understand the basics or to have an opinion about how
it's going to impact our lives.
I think the more we understand about AI,
the better equipped will be to make those informed decisions
about its development and use.
Right.
It's like anything else, the more you learn,
the more empowered you are.
Absolutely.
To participate in the conversation.
Yeah.
And don't underestimate the power of just conversation
itself.
Right.
So talk to your friends.
Talk to your family.
Talk to your colleagues.
Anyone who will listen, share what you're learning,
ask questions, be open to different perspectives.
Sometimes just those casual conversations
can really spark some change.
I think so too.
OK.
So as we wrap up this deep dive into B-Star
and this constantly evolving world of AI,
I want to leave our listeners with one final thought.
OK.
What kind of world do you want to create with AI?
That's a really great question.
And I don't know that there's a simple answer.
But I think by staying engaged and informed and curious,
we can all play a part in making sure
that AI is used for good.
Well said.
And for those of you listening who
want to learn more about B-Star and the research
that we talked about today, you can find links
to the original paper and other resources in the show notes.
Thanks for joining us, everyone.
Until next time, keep exploring, keep learning,
and keep asking those big questions.