Okay, so get this, you sent us a bunch of articles on this AI company, DeepSeq, and wow,
this stuff is fascinating.
We're talking about potential shakeups in the entire AI world.
It's wild, right?
Yeah, it is.
So before we jump in, can you just give us a quick overview of what DeepSeq is?
For sure.
So DeepSeq is a Chinese startup, and they're building these things called large language
models, LLMs.
Like ChatGPT, the kind of AI that can like, I don't know, hold a conversation or write
a poem or something.
Exactly.
ChatGPT is an LLM, and DeepSeq is building similar models.
But what's got me hooked is that DeepSeq is getting results that rival the giants, you
know, like OpenAI, but they're doing it with way less computing power.
So less computing power, that's a big deal, right?
Huge deal.
It means they're finding ways to make AI more efficient, more accessible.
They even topped the App Store charts recently, which is pretty insane for an AI company.
Wow, that's impressive.
So what's their secret?
Are they like coding on some next level alien hardware?
What's going on?
It's not alien tech, but definitely some clever techniques.
One is called mixture of experts, or MOE for short.
Okay, break that down for me.
What's MOE?
So imagine instead of one giant AI brain trying to do everything, you have a team of specialists,
like you've got your math whiz for calculations, your poet for writing, your historian for
facts.
Each one the best in their area.
Right, that's MOE in a nutshell.
You divide the AI into specialized experts, so you don't have one massive model trying
to do it all.
Ah, so it's like dividing up the workload so the AI can focus its energy where it counts.
Makes sense.
Exactly, it's all about efficiency.
And then they're also using something called multi-head latent attention, or MLA.
Okay, another acronym, MLA, doesn't that sound more like a research paper format than
an AI breakthrough?
You know, it's funny you should say that because MLA is basically doing for AI what
a good summary does for a giant research paper.
Really?
Yeah, it takes tons of information and compresses it without losing any of the important details.
It's like fitting the entire library of Congress into your backpack.
Whoa, okay, so that makes the AI faster and more efficient, right?
Super fast and efficient.
So we've got MOE dividing the work and MLA compressing the information.
But it's not just the architecture, their training methods are also pretty unique.
Oh, you mentioned that before, something about reinforcement learning, right?
Yep, DeepSeek is all about reinforcement learning.
Instead of just memorizing a textbook, their AI is out there in the world, learning by
trying things, making mistakes, figuring out what works.
So it's like instead of cramming for a test, the AI is actually learning and understanding
the concepts.
Obviously, it's a much more dynamic and engaging way of learning.
And we're seeing their models exhibit something researchers are calling emergent reasoning
behaviors.
It's like they're starting to think for themselves, double checking their work, learning from
mistakes, even coming up with new solutions to problems they haven't seen before.
Hold on, self verification, coming up with their own solutions.
This is starting to sound a little too smart for comfort.
Which models are doing this?
What are they called?
There are two that are making waves.
DeepSeek-R10 and DeepSeek-R1.
DeepSeek-R10 was their first major breakthrough.
It learned purely through this reinforcement learning process.
No massive data sets, no spoon feeding information.
And it blew everyone away with its ability to reason and problem solve.
So DeepSeek-R10 was like the trailblazer, proving that this whole reinforcement learning
thing could actually work.
What about DeepSeek-R1?
What's that one all about?
So they took everything they learned from DeepSeek-R10 and added in some carefully selected
data, but with a focus on teaching the AI to think step by step.
Step by step, like when you're solving a math problem.
Exactly, like writing out each step of the solution.
And it's paying off.
DeepSeek-R1 is showing even more advanced reasoning skills.
It can tackle really complex tasks and even explain its thought process.
Okay, so they're guiding the AI to think in a more structured, logical way.
And that's leading to even better results, huh?
It's a game changer in terms of transparency and understanding how these AI systems work.
Yeah, it's like they cracked the code to teaching AI how to think like we do.
That's impressive, but how does all of this translate to the real world?
I mean, is DeepSeek just another AI company or are they really shaking things up out there?
Oh, they're definitely shaking things up.
One of the things that's got everyone talking is their open source approach.
Open source.
You mean they're just giving away this cutting edge technology?
Why would they do that?
It's a bold move for sure.
But by making their models open source, they're basically inviting the world to collaborate
on building the future of AI.
So it's kind of like open sourcing software.
Anyone can use DeepSeek's models, tinker with them, improve them even.
That's the idea.
It levels the playing field and fuels innovation because now developers all over the globe
have access to these powerful tools.
It's like the wild west of AI development right now.
I see why people are calling them a game changer.
So how are the big AI companies reacting to all of this, the Googles and the open AIs
of the world?
They're definitely feeling the heat.
DeepSeek's putting pressure on them in a couple of ways.
First, there's the cost factor.
DeepSeek's efficiency means they can offer their models at a lower cost.
That forces everyone else to rethink their pricing strategies.
So it's like a price war.
But good news for anyone who wants to use this technology.
Right.
Then there's the transparency factor.
With DeepSeek being so open about their models and methods, it puts pressure on the rest
of the industry to be more transparent as well.
So DeepSeek is kind of forcing everyone to up their game.
It's not just about who has the best tech anymore.
Exactly.
It's also about using it responsibly and sharing the benefits.
That makes sense.
I think it's important to note that this isn't just about companies competing.
Right?
There's a geopolitical angle to this as well.
Oh, absolutely.
DeepSeek's success is a clear sign of China's growing dominance of the AI field.
So it's like a technological arms rave.
But instead of weapons, we're building smarter AI.
You could say that.
The country that leads in AI will have a huge advantage in the 21st century.
Okay.
So we've got this incredibly powerful game-changing technology being developed by a rising superpower.
It's exciting, but also a little bit scary.
Like, what are the risks here?
What could go wrong?
I mean, every superhero movie has a villain, right?
Right.
Every powerful technology has its downsides.
And we need to be aware of the potential risks and think carefully about how to mitigate
them.
So what are some of the things that people are worried about with DeepSeek and this
new era of AI?
Where could things go wrong?
Well, data privacy is a big concern.
These models are incredibly good at analyzing information.
But in the wrong hands, that could be a real problem.
Imagine someone using these models to sift through your personal data without you even
knowing about it.
That's a scary thought, especially with all the concerns around data security these days.
Yeah, it's a real concern.
Then there's the potential for misuse in cyber attacks.
Imagine AI-powered malware, phishing scams that are almost impossible to detect, or even
large-scale disinformation campaigns.
It's like giving a powerful weapon to someone who might not have the best intentions.
And that open-source approach, while great for innovation, could make it easier for those
bad actors to get their hands on this technology.
Exactly.
It's a double-edged sword.
Open-source fuels progress, but also requires responsibility and careful consideration
of the risks.
It sounds like we need some safeguards, some rules of the road for how this technology
is developed and used.
We absolutely do.
We need ethical guidelines, international collaboration, and a public that's informed
and engaged in the conversation.
So it's not just up to the tech companies and governments to figure this out?
Not at all.
This is something that's going to affect all of us.
So we all need to be involved in shaping the future of AI.
It's interesting how often these deep dives leave us with more questions than answers.
That's true.
But that's not necessarily a bad thing, right?
It means we're grappling with something really complex and important.
Absolutely.
And that's what makes it so fascinating.
We're in uncharted territory here, and we get to be part of the conversation that shapes
where we go from here.
Okay.
So before we wrap things up, I want to bring it back to you, the listener.
How do you see all of this impacting your life, your work, your future?
It's a lot to process.
This whole deep-seek thing in the future of AI, it's exciting and a bit unnerving at
the same time.
Yeah, it's definitely a pivotal moment.
We're standing at the edge of this technological revolution, and it's hard to predict exactly
where it's going to lead.
So with all this in mind, where do you think this is all going?
Is open-source AI ultimately a good thing, or should we be more cautious, more careful
about how we approach it?
It's the question everyone's asking, right?
And there's no easy answer.
There are strong arguments on both sides.
On the one hand, open-source AI could unleash this incredible wave of progress.
How so?
Well, imagine a world where anyone with a good idea and internet connection can contribute
to building the next generation of AI.
You could have people from all walks of life, all of the world, collaborating on solutions
to some of humanity's biggest challenges.
We could see breakthroughs in medicine, in education, and sustainability.
The possibilities are endless.
It's like democratizing genius, right?
AI for everyone, not just for the big tech companies.
Exactly.
On the other hand, there's the risk that this power could be misused.
What if these open-source models fall into the wrong hands?
Yeah, what happened is then.
You could have people using this technology to develop autonomous weapons to manipulate
populations on a massive scale.
We've seen how AI can be used to create deep fakes and spread disinformation.
Imagine that amplified a hundredfold.
That's a pretty terrifying thought.
So how do we balance these two sides?
How do we harness the potential of open-source AI while protecting ourselves from these risks?
It's going to take a global effort.
We need strong ethical guidelines, international collaboration, and a public that's informed
and engaged in this conversation.
We can't just leave it to the tech companies to sort this out.
This is something that's going to affect all of us.
So we all need to have a say in how AI is developed and used.
It feels like we're at a crossroads, a really important moment in history, and it's up to
us to decide which path we want to take.
You hit the nail on the head.
It's a defining moment.
So to sum it all up for our listeners, we've taken this deep dive into the world of deep
seek, a company that's not just pushing the boundaries of AI, but rewriting the entire
game.
They're achieving these incredible results with fewer resources, making AI more accessible,
and shaking things up with this whole open-source approach.
But with any how powerful technology, there are risks, and it's up to all of us to be
aware of those risks, to have these conversations, and to work together to ensure that AI is
used for good.
Well said.
Thanks for joining us on this deep dive.
Make those sources coming because who knows what we'll uncover next.
Until then, stay curious and keep exploring.