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All right, strap in everyone.

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We're going deep on LLMs today.

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Large language models.

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I mean, you've heard all the hyper.

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Definitely.

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It's everywhere.

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But the thing that's really got everyone buzzing,

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well, at least me, it's the self-evolving LLMs.

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Oh yeah, it's a whole new ballgame.

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Like it feels like just yesterday,

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we were all talking about how amazing regular LLMs were.

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Yeah.

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And now, boom.

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It's like, hold on, what?

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They can learn on their own now.

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And they can keep learning, right?

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Yeah.

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Even after, like, they're out in the world

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doing their thing.

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Exactly.

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It's not like the old models stuck in time.

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Like with GPT-4, it's knowledge is frozen in 2023.

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It's like a time capsule.

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Kind of.

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A really smart time capsule.

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But yeah, can't learn anything new.

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But these self-evolving LLMs, well,

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they're constantly updating themselves, always learning,

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always getting smarter.

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Kind of wild to think about.

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Your AI assistant is actually getting smarter every day.

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Yeah.

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But let's be real for a second.

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Regular LLMs, they've got some drawbacks.

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Oh, yeah.

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What's the big one?

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Well, the training costs.

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Those are massive.

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Like there was a paper that predicted by 2027,

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the biggest AI training runs could cost over a billion dollars.

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A billion dollars.

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Wow.

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Yeah.

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I mean, that's crazy money.

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And it basically means that only these huge tech companies,

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you know, the ones with really dead pockets,

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they're the only ones who can even play in this game.

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Yeah.

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A billion dollars is a bit outside my budget.

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Mine too.

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And that's a problem.

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Because it could really stifle innovation, you know?

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Right.

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If only a handful of companies can afford to develop

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these powerful AI tools, then where does that leave everyone else?

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Exactly.

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So that's where these self-evolving LLMs,

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they could really change things.

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OK, so tell me, how do they get around this whole money issue?

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Well, it's not that they totally get rid of the cost,

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but they kind of shift it.

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So the initial training cost for a self-evolving LLM,

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it might be like 10% to 20% higher than for a regular one.

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OK.

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But then the big difference is you

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don't have to keep retraining them over and over again.

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So it's more like a one-time investment?

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Sort of, yeah.

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And that means in the long run, they

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could actually be a lot cheaper, you know?

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No more constant upgrades and updates.

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You just have this one model that keeps learning and growing

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on its own.

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OK, so you're saying that instead of paying a billion dollars

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every time you need to update the model,

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you pay maybe like a billion and a half upfront,

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but then you're good to go.

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Exactly.

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It's a different way of thinking about it.

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But OK, let's get into the nitty-gritty here.

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How do these things actually learn and evolve?

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I mean, how do they go from being static snapshots

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to these dynamic learning machines?

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Yeah, like spill the beans.

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All right, so picture this.

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Each layer of the LLM, it's got this thing called a memory pool.

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A memory pool, interesting.

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Yeah, and this pool stores all the important stuff

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from the model's past interactions, you know?

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And as the model encounters new information,

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it updates this memory pool across all its layers.

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It's like building a giant library of knowledge

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and experiences.

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So it's like as it learns new things,

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it's adding new books to its library.

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Exactly.

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And that library is constantly growing and evolving.

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OK, that makes sense.

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But what's stopping someone from like messing with it?

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Messing with it.

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Yeah, like feeding it bad information, trying to trick it.

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Oh, that's a great question.

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And it's a big one when we talk about ResponsibleAI.

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But the developers of Writer, one of the first self-evolving

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LLMs out there, they say they've got things in place

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to prevent that kind of manipulation.

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Like you can't just flood it with fake news

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and expect it to believe everything, you know?

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Oh, good.

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So no more of that King of England nonsense.

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Hopefully not.

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This new generation of LLMs, they're

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designed to be a lot tougher to trick.

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You can't just pull one over on them so easily.

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That's a relief.

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But OK, let's be honest here for a minute.

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This all sounds a bit like science fiction.

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Is there any real proof that these things actually

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get smarter over time, or is it all just hype?

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Well, there's a really interesting case study

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that shed some light on that.

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Oh.

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So they tested the self-evolving LLM on a math benchmark.

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The first time, it only scored about 25%.

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Not great.

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No, not so much.

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But get this, by the third time it took the test,

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its accuracy shut up to almost 75%.

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Whoa, that's a pretty big jump.

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It is, but here's the catch.

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What's the catch?

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We don't know for sure if it's actually getting better

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at reasoning, like a human would,

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or if it's just remembering the answers

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from the previous test.

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Oh, that's a big difference.

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It is.

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It's the million dollar question.

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Because if it's really improving its reasoning skills,

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then, well, that's a whole other level of AI.

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We're talking about a potential paradigm shift.

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Like we're talking about AI that can think for itself.

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Potentially.

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Imagine a single AI system constantly learning and evolving,

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maybe even reaching a level of intelligence

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we can't even imagine right now.

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OK, now that's getting into some serious sci-fi territory.

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But before we get too carried away here,

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what about the downsides?

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Are there any risks with this technology

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that we should be thinking about?

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Well, yeah, I mean, like with any powerful tool,

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there are going to be challenges.

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It's still early days.

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They're only testing it out with a couple of customers

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right now, and they've admitted that they're still

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working out some kinks.

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Oh, really?

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Yeah, one issue they've run into

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is that sometimes new information,

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it can actually override the initial safety instructions

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they programmed in.

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So it could, like, go rogue?

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Potentially, yeah.

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And that could lead to some unexpected, maybe even

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harmful outputs.

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OK, that's a bit concerning.

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So how are they dealing with that?

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Well, one way is by limiting how much new info the LLM can

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learn at once, kind of like putting it on a diet.

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Oh, OK.

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And they're also focusing on having it

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learn from private business data first instead of just letting

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it loose on the entire internet.

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Yeah, that makes sense.

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Baby steps.

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Exactly.

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Keep it on a short leash, at least for now.

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But zooming out a bit, where does memory and LLMs go from here?

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What's the big picture?

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Well, Mustafa Suleiman, one of the co-founders of DeepMind,

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he recently said that memory and LLMs

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is about to be solved.

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Like, it's right around the corner.

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Well, solved.

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Yeah.

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And we're already seeing some crazy advancements.

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Like, Microsoft's co-pilot, it has access

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to super up-to-date information.

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It's like having a research assistant who's

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read every book and every article ever written.

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That's incredible.

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Yeah.

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But how is that different from a self-evolving model?

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Is it just about where the information is coming from?

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That's where it gets interesting.

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So web-based memory, it relies on external sources, right?

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It's pulling information from the internet.

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Yeah.

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But a self-evolving model, it actually

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updates its own internal parameters.

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So it's not just remembering stuff.

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It's actually changing how it understands the world.

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Precisely.

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It's evolving on a much deeper level.

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So do you think other big players, like DeepMind,

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are they working on similar self-evolving models?

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Honestly, I wouldn't be surprised.

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They've got the resources.

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They've got the talent.

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It'd be kind of shocking if they weren't at least

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exploring this stuff.

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It's mind-blowing, really.

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We're talking in a potential revolution in AI.

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But, you know, revolutions, they can be messy.

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Oh, for sure.

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They're going to be bumps along the way.

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It feels like we're standing on the edge of a whole new world.

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But we need to be careful.

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We need to make sure we're ready for what comes next.

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Couldn't agree more.

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It's an exciting time to be alive,

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but we need to approach it with caution and a lot of thought.

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It's like we're on the edge of a whole new era of AI.

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Right.

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And it's amazing, but kind of scary at the same time.

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Totally.

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And we were talking about those safety concerns before

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about how new info could mess with the AI safety instructions.

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That's just one example of why we need really strong safeguards

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as this tech keeps developing.

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Yeah, we can't just let these AI systems run wild.

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I mean, it's like giving someone a supercar

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without any driving lesson.

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Exactly.

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It could be a recipe for disaster.

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And that's why responsible development and deployment

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are so important.

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OK, so what does that look like in practice?

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How do we make sure these self-evolving LLMs are

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used ethically and responsibly, obviously?

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Well, I think a big part of it is setting up clear guidelines

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and frameworks for how they're trained, how they learn,

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how they interact with the world.

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It's not just about preventing harm.

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It's about making sure they're actually used for good.

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Right.

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So it's like we're not just trying to stop them

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from doing bad things.

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We're trying to actually guide them towards doing good things.

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Exactly.

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It's like how do we shape their development so that they're

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aligned with our values?

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OK, but that brings up another question.

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How do you define values for an AI?

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I mean, it's not like programming a simple algorithm,

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right?

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Yeah.

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AI is a lot more complex than that.

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Oh, yeah, for sure.

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It's definitely tricky.

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But it's something we have to figure out.

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One way is by focusing on transparency and explainability.

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Like we need to be able to understand

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how these models are making decisions, especially

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as they get more sophisticated.

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So it's kind of like having a window into their thought

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process.

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Kind of.

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We need that audit trail so we can spot any biases

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or any unintended consequences that might pop up.

272
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OK, so transparency is important.

273
00:09:06,680 --> 00:09:11,760
But even if we manage to get the ethics sorted out,

274
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aren't there going to be other challenges?

275
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What about the impact on society?

276
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Could these self-evolving LLMs lead to job losses?

277
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Or make existing inequalities worse?

278
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Those are some pretty big concerns.

279
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Those are absolutely valid concerns.

280
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And to be honest, those are the kinds of things

281
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we need to be thinking about right now.

282
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It's not enough to just develop cool technology.

283
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We have to make sure that it benefits society as a whole.

284
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So it's a balancing act.

285
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We have to embrace the potential of these self-evolving LLMs

286
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while also being really mindful of the risks that come with them.

287
00:09:42,880 --> 00:09:43,360
Exactly.

288
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We need to be having open and honest conversations

289
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about the future of AI.

290
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And those conversations need to involve everyone,

291
00:09:49,640 --> 00:09:52,600
not just researchers and developers, but also policymakers,

292
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ethicists, and the public.

293
00:09:54,200 --> 00:09:55,520
So it's not just a tech issue.

294
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It's a societal issue.

295
00:09:56,880 --> 00:09:57,720
It's both.

296
00:09:57,720 --> 00:09:59,600
And it's a philosophical issue too.

297
00:09:59,600 --> 00:10:01,360
I mean, these self-evolving LLMs,

298
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they're forcing us to ask some really fundamental questions

299
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about ourselves and our place in the world.

300
00:10:06,120 --> 00:10:09,240
Whoa, deep thoughts.

301
00:10:09,240 --> 00:10:12,200
OK, so we've gone from talking about the cost of training

302
00:10:12,200 --> 00:10:15,960
models to the meaning of life in just a few minutes.

303
00:10:15,960 --> 00:10:17,800
It's amazing how quickly this conversation can

304
00:10:17,800 --> 00:10:21,360
go from the technical to the existential.

305
00:10:21,360 --> 00:10:23,360
That's what I love about exploring a topic like this.

306
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It's not just about the nuts and bolts.

307
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It's about the big picture, the implications, the questions

308
00:10:28,280 --> 00:10:30,960
it raises about humanity's future.

309
00:10:30,960 --> 00:10:32,360
It's mind-blowing stuff.

310
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It really is.

311
00:10:33,320 --> 00:10:35,600
But OK, let's bring it back down to Earth for a minute.

312
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I think it's a good time to transition

313
00:10:37,160 --> 00:10:40,560
to talking about how these self-evolving LLMs could

314
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revolutionize scientific discovery and innovation.

315
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What do you think?

316
00:10:44,640 --> 00:10:45,480
Absolutely.

317
00:10:45,480 --> 00:10:47,520
I mean, just imagine AI systems that

318
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can analyze massive data sets, identify patterns

319
00:10:50,280 --> 00:10:53,320
that we might miss, even come up with solutions to problems

320
00:10:53,320 --> 00:10:54,320
we haven't even thought of yet.

321
00:10:54,320 --> 00:10:55,720
The possibilities are endless.

322
00:10:55,720 --> 00:10:58,160
It's like giving scientists the superpower.

323
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They could accelerate discoveries in medicine,

324
00:11:00,320 --> 00:11:02,120
material science, climate change.

325
00:11:02,120 --> 00:11:02,800
Exactly.

326
00:11:02,800 --> 00:11:05,480
It could totally transform those fields and so many more.

327
00:11:05,480 --> 00:11:06,680
But there's that saying.

328
00:11:06,680 --> 00:11:07,720
With great power.

329
00:11:07,720 --> 00:11:09,480
Comes great responsibility.

330
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And that definitely applies here.

331
00:11:11,160 --> 00:11:14,000
We have to make sure that these AI-driven discoveries are

332
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used for good and not for anything harmful.

333
00:11:16,520 --> 00:11:17,000
Right.

334
00:11:17,000 --> 00:11:19,600
We don't want to accidentally create a super virus

335
00:11:19,600 --> 00:11:20,280
or something like that.

336
00:11:20,280 --> 00:11:21,000
Exactly.

337
00:11:21,000 --> 00:11:23,880
So we need to set up ethical guidelines for how this tech

338
00:11:23,880 --> 00:11:26,320
is applied, especially in areas like bioengineering

339
00:11:26,320 --> 00:11:28,360
and weapons development.

340
00:11:28,360 --> 00:11:30,920
So it's about finding that balance again, right?

341
00:11:30,920 --> 00:11:33,640
Between pushing the boundaries of what's possible

342
00:11:33,640 --> 00:11:36,000
and making sure we're not doing more harm than good.

343
00:11:36,000 --> 00:11:36,880
Yep.

344
00:11:36,880 --> 00:11:39,960
It's a tightrope walk, but it's one we have to take.

345
00:11:39,960 --> 00:11:42,960
And it requires a lot of collaboration.

346
00:11:42,960 --> 00:11:46,560
We need scientists, engineers, ethicists, and policymakers

347
00:11:46,560 --> 00:11:48,480
all working together to create a framework

348
00:11:48,480 --> 00:11:50,280
for responsible innovation.

349
00:11:50,280 --> 00:11:53,120
It's a big task, but I think it's an essential one.

350
00:11:53,120 --> 00:11:55,120
We're literally talking about the future here.

351
00:11:55,120 --> 00:11:58,320
And AI is going to play a huge role in shaping it.

352
00:11:58,320 --> 00:12:00,680
We just have to make sure that it's a future that benefits

353
00:12:00,680 --> 00:12:01,280
everyone.

354
00:12:01,280 --> 00:12:02,160
Well said.

355
00:12:02,160 --> 00:12:04,600
And I think that's a good point to pause our conversation

356
00:12:04,600 --> 00:12:05,280
for now.

357
00:12:05,280 --> 00:12:06,680
We've covered a lot of ground today

358
00:12:06,680 --> 00:12:09,400
from the inner workings of these self-evolving LLMs

359
00:12:09,400 --> 00:12:13,280
to their potential impact on society, ethics, and even

360
00:12:13,280 --> 00:12:15,160
the future of humanity itself.

361
00:12:15,160 --> 00:12:16,880
It's been a wild ride, that's for sure.

362
00:12:16,880 --> 00:12:19,480
But it's clear that this is just the beginning

363
00:12:19,480 --> 00:12:20,440
of the conversation.

364
00:12:20,440 --> 00:12:23,840
There's so much more to explore, so much more to learn.

365
00:12:23,840 --> 00:12:24,760
Absolutely.

366
00:12:24,760 --> 00:12:26,400
This is a journey we're all on together,

367
00:12:26,400 --> 00:12:28,560
and it's only just getting started.

368
00:12:28,560 --> 00:12:29,040
Wow.

369
00:12:29,040 --> 00:12:33,320
So this deep dive into self-evolving LLMs,

370
00:12:33,320 --> 00:12:34,680
it's been a wild ride.

371
00:12:34,680 --> 00:12:37,000
We've gone from the technical nitty gritty

372
00:12:37,000 --> 00:12:40,160
to full-on philosophical debates.

373
00:12:40,160 --> 00:12:42,320
It's crazy to think we've only just scratched the surface,

374
00:12:42,320 --> 00:12:42,760
really.

375
00:12:42,760 --> 00:12:43,680
I know, right?

376
00:12:43,680 --> 00:12:45,080
And that's what I love about this whole field.

377
00:12:45,080 --> 00:12:47,200
It just feels like there's so much more to discover.

378
00:12:47,200 --> 00:12:49,880
We're standing on the edge of something truly huge.

379
00:12:49,880 --> 00:12:52,000
Yeah, the potential impact of this technology,

380
00:12:52,000 --> 00:12:52,840
it's mind-boggling.

381
00:12:52,840 --> 00:12:56,320
We're not just talking about a new gadget or app.

382
00:12:56,320 --> 00:12:59,640
This is about a fundamental shift in, well, everything.

383
00:12:59,640 --> 00:13:03,640
How we learn, how we work, how we interact with tech,

384
00:13:03,640 --> 00:13:04,760
even how we think.

385
00:13:04,760 --> 00:13:07,120
It really is a game changer.

386
00:13:07,120 --> 00:13:09,440
These models, they're not just tools.

387
00:13:09,440 --> 00:13:11,880
They're systems that could totally reshape

388
00:13:11,880 --> 00:13:14,960
our whole relationship with knowledge and intelligence.

389
00:13:14,960 --> 00:13:17,280
Imagine a world where everyone has access

390
00:13:17,280 --> 00:13:21,080
to personalized education, where healthcare is super accurate

391
00:13:21,080 --> 00:13:25,000
and tailored to each person, where AI is actually helping us

392
00:13:25,000 --> 00:13:26,280
be more creative.

393
00:13:26,280 --> 00:13:28,120
It sounds like something out of a sci-fi movie,

394
00:13:28,120 --> 00:13:29,680
but it's happening now.

395
00:13:29,680 --> 00:13:31,520
But we've talked about this before.

396
00:13:31,520 --> 00:13:34,360
With great power comes great responsibility.

397
00:13:34,360 --> 00:13:38,880
How do we make sure we're using this power for good,

398
00:13:38,880 --> 00:13:40,200
for a better future?

399
00:13:40,200 --> 00:13:41,600
That's the big question, isn't it?

400
00:13:41,600 --> 00:13:44,240
I think it starts with building a culture

401
00:13:44,240 --> 00:13:45,800
of responsible innovation.

402
00:13:45,800 --> 00:13:48,080
We need to be having open and honest conversations

403
00:13:48,080 --> 00:13:51,480
about the potential benefits and risks of AI.

404
00:13:51,480 --> 00:13:53,080
And that means everyone needs to be involved,

405
00:13:53,080 --> 00:13:54,240
not just the experts.

406
00:13:54,240 --> 00:13:56,240
So it's about making sure everyone has a voice.

407
00:13:56,240 --> 00:13:58,560
Everyone gets a say in how this technology is

408
00:13:58,560 --> 00:13:59,440
developed and used.

409
00:13:59,440 --> 00:14:00,120
Exactly.

410
00:14:00,120 --> 00:14:01,720
But how do we actually do that?

411
00:14:01,720 --> 00:14:03,760
I mean, how do you make sure everyone feels included

412
00:14:03,760 --> 00:14:04,840
in this conversation?

413
00:14:04,840 --> 00:14:06,240
Yeah, that's a tough one.

414
00:14:06,240 --> 00:14:09,080
Well, I think education and awareness are key.

415
00:14:09,080 --> 00:14:11,040
We need to give people the knowledge

416
00:14:11,040 --> 00:14:13,560
and the critical thinking skills they

417
00:14:13,560 --> 00:14:18,000
need to navigate this increasingly complex world of tech.

418
00:14:18,000 --> 00:14:20,800
So it's not just about teaching people how to use AI.

419
00:14:20,800 --> 00:14:23,440
It's about helping them understand it, like what it can do,

420
00:14:23,440 --> 00:14:25,640
what it can't do, and how it might affect their lives.

421
00:14:25,640 --> 00:14:26,800
Exactly.

422
00:14:26,800 --> 00:14:30,000
It's about empowering people to make informed decisions

423
00:14:30,000 --> 00:14:33,920
about the role of AI in their lives and in society.

424
00:14:33,920 --> 00:14:36,080
You know, throughout this whole conversation, what's really

425
00:14:36,080 --> 00:14:39,200
hit home for me is that this isn't just about technology.

426
00:14:39,200 --> 00:14:40,760
It's about humanity.

427
00:14:40,760 --> 00:14:44,200
It's about figuring out what it means to be human in a world

428
00:14:44,200 --> 00:14:47,400
where AI is becoming more and more powerful and sophisticated.

429
00:14:47,400 --> 00:14:48,560
You got it.

430
00:14:48,560 --> 00:14:50,320
This goes way beyond the bits and bytes.

431
00:14:50,320 --> 00:14:52,480
It's about who we are, what we value,

432
00:14:52,480 --> 00:14:55,200
and what kind of future we want to create together.

433
00:14:55,200 --> 00:14:56,280
Well said.

434
00:14:56,280 --> 00:14:58,240
And on that note, I think it's time

435
00:14:58,240 --> 00:15:00,960
to wrap up this incredible deep dive into the world

436
00:15:00,960 --> 00:15:03,120
of self-evolving LLMs.

437
00:15:03,120 --> 00:15:05,240
I can't thank you enough for sharing your insights with us

438
00:15:05,240 --> 00:15:05,740
today.

439
00:15:05,740 --> 00:15:07,560
It's been a truly fascinating journey.

440
00:15:07,560 --> 00:15:09,080
The pleasure was all mine.

441
00:15:09,080 --> 00:15:11,120
It's been great exploring these ideas

442
00:15:11,120 --> 00:15:12,520
with you and her listeners.

443
00:15:12,520 --> 00:15:14,360
And remember, this is just the beginning.

444
00:15:14,360 --> 00:15:15,600
Exactly.

445
00:15:15,600 --> 00:15:16,960
To all our listeners out there, we

446
00:15:16,960 --> 00:15:19,600
hope this deep dive has sparked your curiosity

447
00:15:19,600 --> 00:15:22,120
and got you thinking about the future of AI,

448
00:15:22,120 --> 00:15:24,360
because the future is in our hands.

449
00:15:24,360 --> 00:15:27,680
It's up to all of us to shape it responsibly and ethically.

450
00:15:27,680 --> 00:15:30,360
Until next time, keep exploring, keep questioning,

451
00:15:30,360 --> 00:15:50,000
and keep learning.