All right, strap in everyone.
We're going deep on LLMs today.
Large language models.
I mean, you've heard all the hyper.
Definitely.
It's everywhere.
But the thing that's really got everyone buzzing,
well, at least me, it's the self-evolving LLMs.
Oh yeah, it's a whole new ballgame.
Like it feels like just yesterday,
we were all talking about how amazing regular LLMs were.
Yeah.
And now, boom.
It's like, hold on, what?
They can learn on their own now.
And they can keep learning, right?
Yeah.
Even after, like, they're out in the world
doing their thing.
Exactly.
It's not like the old models stuck in time.
Like with GPT-4, it's knowledge is frozen in 2023.
It's like a time capsule.
Kind of.
A really smart time capsule.
But yeah, can't learn anything new.
But these self-evolving LLMs, well,
they're constantly updating themselves, always learning,
always getting smarter.
Kind of wild to think about.
Your AI assistant is actually getting smarter every day.
Yeah.
But let's be real for a second.
Regular LLMs, they've got some drawbacks.
Oh, yeah.
What's the big one?
Well, the training costs.
Those are massive.
Like there was a paper that predicted by 2027,
the biggest AI training runs could cost over a billion dollars.
A billion dollars.
Wow.
Yeah.
I mean, that's crazy money.
And it basically means that only these huge tech companies,
you know, the ones with really dead pockets,
they're the only ones who can even play in this game.
Yeah.
A billion dollars is a bit outside my budget.
Mine too.
And that's a problem.
Because it could really stifle innovation, you know?
Right.
If only a handful of companies can afford to develop
these powerful AI tools, then where does that leave everyone else?
Exactly.
So that's where these self-evolving LLMs,
they could really change things.
OK, so tell me, how do they get around this whole money issue?
Well, it's not that they totally get rid of the cost,
but they kind of shift it.
So the initial training cost for a self-evolving LLM,
it might be like 10% to 20% higher than for a regular one.
OK.
But then the big difference is you
don't have to keep retraining them over and over again.
So it's more like a one-time investment?
Sort of, yeah.
And that means in the long run, they
could actually be a lot cheaper, you know?
No more constant upgrades and updates.
You just have this one model that keeps learning and growing
on its own.
OK, so you're saying that instead of paying a billion dollars
every time you need to update the model,
you pay maybe like a billion and a half upfront,
but then you're good to go.
Exactly.
It's a different way of thinking about it.
But OK, let's get into the nitty-gritty here.
How do these things actually learn and evolve?
I mean, how do they go from being static snapshots
to these dynamic learning machines?
Yeah, like spill the beans.
All right, so picture this.
Each layer of the LLM, it's got this thing called a memory pool.
A memory pool, interesting.
Yeah, and this pool stores all the important stuff
from the model's past interactions, you know?
And as the model encounters new information,
it updates this memory pool across all its layers.
It's like building a giant library of knowledge
and experiences.
So it's like as it learns new things,
it's adding new books to its library.
Exactly.
And that library is constantly growing and evolving.
OK, that makes sense.
But what's stopping someone from like messing with it?
Messing with it.
Yeah, like feeding it bad information, trying to trick it.
Oh, that's a great question.
And it's a big one when we talk about ResponsibleAI.
But the developers of Writer, one of the first self-evolving
LLMs out there, they say they've got things in place
to prevent that kind of manipulation.
Like you can't just flood it with fake news
and expect it to believe everything, you know?
Oh, good.
So no more of that King of England nonsense.
Hopefully not.
This new generation of LLMs, they're
designed to be a lot tougher to trick.
You can't just pull one over on them so easily.
That's a relief.
But OK, let's be honest here for a minute.
This all sounds a bit like science fiction.
Is there any real proof that these things actually
get smarter over time, or is it all just hype?
Well, there's a really interesting case study
that shed some light on that.
Oh.
So they tested the self-evolving LLM on a math benchmark.
The first time, it only scored about 25%.
Not great.
No, not so much.
But get this, by the third time it took the test,
its accuracy shut up to almost 75%.
Whoa, that's a pretty big jump.
It is, but here's the catch.
What's the catch?
We don't know for sure if it's actually getting better
at reasoning, like a human would,
or if it's just remembering the answers
from the previous test.
Oh, that's a big difference.
It is.
It's the million dollar question.
Because if it's really improving its reasoning skills,
then, well, that's a whole other level of AI.
We're talking about a potential paradigm shift.
Like we're talking about AI that can think for itself.
Potentially.
Imagine a single AI system constantly learning and evolving,
maybe even reaching a level of intelligence
we can't even imagine right now.
OK, now that's getting into some serious sci-fi territory.
But before we get too carried away here,
what about the downsides?
Are there any risks with this technology
that we should be thinking about?
Well, yeah, I mean, like with any powerful tool,
there are going to be challenges.
It's still early days.
They're only testing it out with a couple of customers
right now, and they've admitted that they're still
working out some kinks.
Oh, really?
Yeah, one issue they've run into
is that sometimes new information,
it can actually override the initial safety instructions
they programmed in.
So it could, like, go rogue?
Potentially, yeah.
And that could lead to some unexpected, maybe even
harmful outputs.
OK, that's a bit concerning.
So how are they dealing with that?
Well, one way is by limiting how much new info the LLM can
learn at once, kind of like putting it on a diet.
Oh, OK.
And they're also focusing on having it
learn from private business data first instead of just letting
it loose on the entire internet.
Yeah, that makes sense.
Baby steps.
Exactly.
Keep it on a short leash, at least for now.
But zooming out a bit, where does memory and LLMs go from here?
What's the big picture?
Well, Mustafa Suleiman, one of the co-founders of DeepMind,
he recently said that memory and LLMs
is about to be solved.
Like, it's right around the corner.
Well, solved.
Yeah.
And we're already seeing some crazy advancements.
Like, Microsoft's co-pilot, it has access
to super up-to-date information.
It's like having a research assistant who's
read every book and every article ever written.
That's incredible.
Yeah.
But how is that different from a self-evolving model?
Is it just about where the information is coming from?
That's where it gets interesting.
So web-based memory, it relies on external sources, right?
It's pulling information from the internet.
Yeah.
But a self-evolving model, it actually
updates its own internal parameters.
So it's not just remembering stuff.
It's actually changing how it understands the world.
Precisely.
It's evolving on a much deeper level.
So do you think other big players, like DeepMind,
are they working on similar self-evolving models?
Honestly, I wouldn't be surprised.
They've got the resources.
They've got the talent.
It'd be kind of shocking if they weren't at least
exploring this stuff.
It's mind-blowing, really.
We're talking in a potential revolution in AI.
But, you know, revolutions, they can be messy.
Oh, for sure.
They're going to be bumps along the way.
It feels like we're standing on the edge of a whole new world.
But we need to be careful.
We need to make sure we're ready for what comes next.
Couldn't agree more.
It's an exciting time to be alive,
but we need to approach it with caution and a lot of thought.
It's like we're on the edge of a whole new era of AI.
Right.
And it's amazing, but kind of scary at the same time.
Totally.
And we were talking about those safety concerns before
about how new info could mess with the AI safety instructions.
That's just one example of why we need really strong safeguards
as this tech keeps developing.
Yeah, we can't just let these AI systems run wild.
I mean, it's like giving someone a supercar
without any driving lesson.
Exactly.
It could be a recipe for disaster.
And that's why responsible development and deployment
are so important.
OK, so what does that look like in practice?
How do we make sure these self-evolving LLMs are
used ethically and responsibly, obviously?
Well, I think a big part of it is setting up clear guidelines
and frameworks for how they're trained, how they learn,
how they interact with the world.
It's not just about preventing harm.
It's about making sure they're actually used for good.
Right.
So it's like we're not just trying to stop them
from doing bad things.
We're trying to actually guide them towards doing good things.
Exactly.
It's like how do we shape their development so that they're
aligned with our values?
OK, but that brings up another question.
How do you define values for an AI?
I mean, it's not like programming a simple algorithm,
right?
Yeah.
AI is a lot more complex than that.
Oh, yeah, for sure.
It's definitely tricky.
But it's something we have to figure out.
One way is by focusing on transparency and explainability.
Like we need to be able to understand
how these models are making decisions, especially
as they get more sophisticated.
So it's kind of like having a window into their thought
process.
Kind of.
We need that audit trail so we can spot any biases
or any unintended consequences that might pop up.
OK, so transparency is important.
But even if we manage to get the ethics sorted out,
aren't there going to be other challenges?
What about the impact on society?
Could these self-evolving LLMs lead to job losses?
Or make existing inequalities worse?
Those are some pretty big concerns.
Those are absolutely valid concerns.
And to be honest, those are the kinds of things
we need to be thinking about right now.
It's not enough to just develop cool technology.
We have to make sure that it benefits society as a whole.
So it's a balancing act.
We have to embrace the potential of these self-evolving LLMs
while also being really mindful of the risks that come with them.
Exactly.
We need to be having open and honest conversations
about the future of AI.
And those conversations need to involve everyone,
not just researchers and developers, but also policymakers,
ethicists, and the public.
So it's not just a tech issue.
It's a societal issue.
It's both.
And it's a philosophical issue too.
I mean, these self-evolving LLMs,
they're forcing us to ask some really fundamental questions
about ourselves and our place in the world.
Whoa, deep thoughts.
OK, so we've gone from talking about the cost of training
models to the meaning of life in just a few minutes.
It's amazing how quickly this conversation can
go from the technical to the existential.
That's what I love about exploring a topic like this.
It's not just about the nuts and bolts.
It's about the big picture, the implications, the questions
it raises about humanity's future.
It's mind-blowing stuff.
It really is.
But OK, let's bring it back down to Earth for a minute.
I think it's a good time to transition
to talking about how these self-evolving LLMs could
revolutionize scientific discovery and innovation.
What do you think?
Absolutely.
I mean, just imagine AI systems that
can analyze massive data sets, identify patterns
that we might miss, even come up with solutions to problems
we haven't even thought of yet.
The possibilities are endless.
It's like giving scientists the superpower.
They could accelerate discoveries in medicine,
material science, climate change.
Exactly.
It could totally transform those fields and so many more.
But there's that saying.
With great power.
Comes great responsibility.
And that definitely applies here.
We have to make sure that these AI-driven discoveries are
used for good and not for anything harmful.
Right.
We don't want to accidentally create a super virus
or something like that.
Exactly.
So we need to set up ethical guidelines for how this tech
is applied, especially in areas like bioengineering
and weapons development.
So it's about finding that balance again, right?
Between pushing the boundaries of what's possible
and making sure we're not doing more harm than good.
Yep.
It's a tightrope walk, but it's one we have to take.
And it requires a lot of collaboration.
We need scientists, engineers, ethicists, and policymakers
all working together to create a framework
for responsible innovation.
It's a big task, but I think it's an essential one.
We're literally talking about the future here.
And AI is going to play a huge role in shaping it.
We just have to make sure that it's a future that benefits
everyone.
Well said.
And I think that's a good point to pause our conversation
for now.
We've covered a lot of ground today
from the inner workings of these self-evolving LLMs
to their potential impact on society, ethics, and even
the future of humanity itself.
It's been a wild ride, that's for sure.
But it's clear that this is just the beginning
of the conversation.
There's so much more to explore, so much more to learn.
Absolutely.
This is a journey we're all on together,
and it's only just getting started.
Wow.
So this deep dive into self-evolving LLMs,
it's been a wild ride.
We've gone from the technical nitty gritty
to full-on philosophical debates.
It's crazy to think we've only just scratched the surface,
really.
I know, right?
And that's what I love about this whole field.
It just feels like there's so much more to discover.
We're standing on the edge of something truly huge.
Yeah, the potential impact of this technology,
it's mind-boggling.
We're not just talking about a new gadget or app.
This is about a fundamental shift in, well, everything.
How we learn, how we work, how we interact with tech,
even how we think.
It really is a game changer.
These models, they're not just tools.
They're systems that could totally reshape
our whole relationship with knowledge and intelligence.
Imagine a world where everyone has access
to personalized education, where healthcare is super accurate
and tailored to each person, where AI is actually helping us
be more creative.
It sounds like something out of a sci-fi movie,
but it's happening now.
But we've talked about this before.
With great power comes great responsibility.
How do we make sure we're using this power for good,
for a better future?
That's the big question, isn't it?
I think it starts with building a culture
of responsible innovation.
We need to be having open and honest conversations
about the potential benefits and risks of AI.
And that means everyone needs to be involved,
not just the experts.
So it's about making sure everyone has a voice.
Everyone gets a say in how this technology is
developed and used.
Exactly.
But how do we actually do that?
I mean, how do you make sure everyone feels included
in this conversation?
Yeah, that's a tough one.
Well, I think education and awareness are key.
We need to give people the knowledge
and the critical thinking skills they
need to navigate this increasingly complex world of tech.
So it's not just about teaching people how to use AI.
It's about helping them understand it, like what it can do,
what it can't do, and how it might affect their lives.
Exactly.
It's about empowering people to make informed decisions
about the role of AI in their lives and in society.
You know, throughout this whole conversation, what's really
hit home for me is that this isn't just about technology.
It's about humanity.
It's about figuring out what it means to be human in a world
where AI is becoming more and more powerful and sophisticated.
You got it.
This goes way beyond the bits and bytes.
It's about who we are, what we value,
and what kind of future we want to create together.
Well said.
And on that note, I think it's time
to wrap up this incredible deep dive into the world
of self-evolving LLMs.
I can't thank you enough for sharing your insights with us
today.
It's been a truly fascinating journey.
The pleasure was all mine.
It's been great exploring these ideas
with you and her listeners.
And remember, this is just the beginning.
Exactly.
To all our listeners out there, we
hope this deep dive has sparked your curiosity
and got you thinking about the future of AI,
because the future is in our hands.
It's up to all of us to shape it responsibly and ethically.
Until next time, keep exploring, keep questioning,
and keep learning.