Welcome to our deep dive into AI.
And today we're gonna be talking about something
called AI distillation.
It's super fascinating.
Yeah, it really is.
So imagine you could take like a massive super complex AI
and kind of shrink it down into a more efficient model.
Like how would that change the game?
It really is a paradigm shift
and we're seeing it play out in a bunch of different ways.
Yeah, and one of the things that's really caught our eye
is this company DeepSeek.
They're using distillation to do some pretty incredible things
and in a fraction of the time
and costs that you would normally expect.
Like they built a model that rivals
some of the biggest names in AI.
Yeah, in just two months.
And for under $6 million for the final training.
Yeah, for the final training phase.
Which is crazy when you think about it.
And the really cool thing is they're not just copying
what others have done.
They're bringing real innovation to the table.
You know, building upon distillation
to create something totally new.
It's like they found a shortcut
and now everyone's trying to catch up.
But here's the thing,
instead of keeping their secrets locked away,
they decided to open source their model.
It's huge.
Yeah, they're basically giving away the blueprint.
Which is a pretty big deal when you think about
how companies like OpenAI have traditionally
kept their models proprietary.
Right, it's a total shift in thinking.
And Arvin Jane, the CEO of Glean put it perfectly.
He said, my joke is everybody's model is open source.
They just don't know it yet.
It's funny, because it's true.
It really is.
So it feels like we're at a turning point
in how AI is developed and shared.
For sure.
And speaking of sharing,
we've got all these stories of other researchers
and developers doing some crazy things with distillation.
Like you have researchers at Berkeley
building a near state of the art reasoning model
for just $450 in 19 hours.
That's insane.
It is.
And then you have Stanford and the University of Washington
doing it in 26 minutes for under $50.
It's mind blowing.
And then there's the 24 hour challenge by Hugging Face
where they built an entire research agent.
It seems like everyone's getting in on the action.
And that's what's so exciting about all this.
It's like AI is finally becoming accessible
to anyone, even if you don't have a billion dollar budget.
Yeah, it's leveling the playing field,
which begs the question,
what does this mean for the big guys
like Google and Microsoft?
You know, they've poured billions into their AI research.
Do they lose their edge if smaller teams
can just catch up using distillation?
That's a great question.
And honestly, it's not that simple.
While distillation is amazing for replication,
it might not be the key to achieving
what's considered the holy grail
of AI artificial general intelligence or AGI.
AGI being the concept of an AI
that can basically do anything a human can.
Exactly.
It's been the dream for a long time.
And companies like OpenAI are all in
on being the first to get there, no matter the cost.
They call it their AGI at all costs strategy.
Which is kind of wild when you think about it.
So they're still going full steam ahead
with these massive models and closed development.
But then you have deep seek over here.
Like there are one model costs a measly 2.19 per million
tokens to run compared to OpenAI $60 for the same thing.
That's like almost 30 times cheaper.
I know.
It's insane.
It completely changes the economics of AI.
It does.
And here's another twist.
Some of our sources are saying that with distillation data
might not be as important as it used to be.
Yeah.
If models can just learn from each other,
effectively the size of your data set
might not matter as much.
It's a wild thought.
Like what if you didn't need access to all that data
to build powerful AI?
That's what companies like Glean or Xamaloring.
Right.
They're building platforms that make AI accessible
and applicable to real world problems
regardless of data limitations.
And Glean's CEO, Arvin Jane, believes
there's no plateau in sight.
He says there's no plateauing on any front, by the way,
when it comes to AI.
So the landscape is shifting constantly.
And it's exciting to watch it all unfold.
But let's take a step back for a second
and talk about how AI itself is evolving.
Like we're seeing some crazy advancements
in its ability to reason, analyze, and even understand
complex processes.
It's what some people are calling the reasoning revolution.
Right.
It's like AI is learning to think.
For itself.
Yeah.
Exactly.
It's changing everything.
A great example is DeepSeek's R1 model.
It has this feature that lets you see the AI's thought process
as it works through problems.
It's kind of like peeking into its mind.
It's fascinating.
It really is.
And this is key for building trust and transparency,
especially as AI becomes more integrated into our lives.
Absolutely.
We need to be able to understand how AI is making decisions,
especially when it comes to things that impact our lives.
Exactly.
So this reasoning revolution is something
we're going to explore in more detail later.
But for now, let's shift gears and talk
about the elephant in the room, open source.
Open source has been a game changer.
And with DeepSeek's decision to open source their model,
it's created a ripple effect throughout the industry.
Researchers and platforms like Huggingface
are following suit.
And it's really making us question the closed source
approach of companies like OpenAI.
It's like a philosophical debate about the nature of knowledge.
Should these powerful AI models be controlled or shared
for the benefit of everyone?
It's a tough question.
And one will unpack more thoroughly in a bit,
but for now, we'll leave you with that thought.
I'm going to start with an aponder.
Exactly.
Stay with us.
Yeah.
It's a tough one, for sure.
There are definitely strong arguments on both sides.
On the one hand, you have the whole safety thing,
making sure these powerful AI models aren't misused.
Especially with all the concerns about AI being used
to create fake content or manipulate information.
It's definitely a valid worry.
Yeah.
And then on the other hand, open sourcing
allows for crazy fast innovation and collaboration.
And it makes these advancements accessible to way more people.
It's a tough trade-off.
It really is.
And it seems like the pressure's on for companies
like OpenAI to change their stance on open source.
I mean, even Sam Altman, their CEO,
recently admitted that they might have been wrong about it.
That's a big deal coming from him,
especially since he's been a huge advocate
for closed development.
Yeah.
It feels like a damn breaking.
Once the flow of information starts, it's hard to stop.
And for developers and businesses,
this open source wave is a game changer.
They're not stuck with just one provider anymore.
And that puts pressure on pricing, too.
Exactly.
Open source keeps those proprietary players in check.
They have to stay competitive, both in terms of price
and performance.
It makes for a much more dynamic and innovative environment.
And with models like DeepSeqs R1 being so much cheaper
than the competition, it really lowers the barrier
to entry for smaller players.
Yeah.
It's like the cost of entry just went way down.
So now we could see an explosion of new AI-powered applications
and services.
As more and more people can actually afford to experiment
and build with this technology, it's pretty exciting.
It really is.
And it makes you wonder, what kind of world-changing ideas
are going to come out of this?
It's like we're on the verge of something truly revolutionary.
And open source is a big part of that.
It promotes transparency and collaboration,
which are super important for a responsible AI development.
Right.
By working together and sharing knowledge,
we can create better, more reliable AI systems that
benefit everyone.
Exactly.
It's a win-win.
But that being said, this doesn't
mean the big tech companies are going away anytime soon.
They still have tons of resources and expertise.
Yeah.
Google, Microsoft, Amazon, they're all investing heavily
in AI research and development.
And they're going to continue to shape the industry.
What's changing is the nature of the game.
It's not just about having the biggest model or the most
data anymore.
It's about building the best ecosystems, platforms,
and tools that utilize AI in innovative and responsible ways.
And that creates a more collaborative and competitive
environment, which ultimately benefits everyone.
And speaking of innovation, we can't forget about the incredible
advancements we're seeing in AI reasoning.
We touched on it earlier, but it's worth diving a little deeper
into what this reasoning revolution really
means for the future of AI.
I agree.
The progress we're seeing is mind-blowing.
These models are going beyond simple instructions
and actually starting to understand context logic
and problem solving.
It's like they're learning to think more like us.
Analyzing situations, weighing different perspectives,
coming up with logical solutions.
Exactly.
And this has huge implications for how
we can use AI to solve complex problems and automate tasks
that used to require human intelligence.
Like, think about the thought process feature
in DeepSeq's R1 model.
Being able to see how the AI reaches its conclusions
is so important for building trust and transparency.
Especially when AI is making important decisions.
Exactly.
We need to understand how it's working and make sure it's
being used ethically and responsibly.
And as AI reasoning continues to improve,
we'll probably see a lot more sophisticated AI agents
being developed.
Capable of automating even more complex tasks,
from managing workflows to analyzing data,
to maybe even helping with creative endeavors.
It's pretty wild to think about the potential impact
on different industries and the workforce as a whole.
Definitely something worth exploring more in the future.
For sure.
But shifting gears a bit, let's talk about data for a minute.
Traditionally, it's been seen as the fuel that powers AI.
Well, more data, the better.
Right.
But with distillation, that might be changing.
What are your thoughts on that?
Well, if models can learn effectively from each other,
the size of your data set might not be as crucial anymore.
It's like saying you don't need the biggest library
in the world to be incredibly knowledgeable.
You can learn just as much from someone who's already
studied all those books.
Exactly.
And this is huge for businesses that
don't have access to those massive data sets.
They can still leverage the knowledge that's out there.
And that's where companies like Blean are doing amazing work.
They're building platforms that make it easier for anyone
to access and use AI regardless of how much data they have.
They're making it a more level playing field.
It's like creating an educational system
where everyone has access to the knowledge and tools they
need to learn and grow.
I love that analogy.
And as these techniques continue to develop,
I think we'll see even more innovative approaches to AI
that challenge our current assumptions about data.
It's definitely shaking things up.
But before we wrap up, I want to circle back
to something you mentioned earlier about OpenAI's AGI
at all cost strategy.
What exactly does that mean?
Basically, it's their commitment to achieving
artificial general intelligence as quickly as possible,
even if it means using a ton of resources
and keeping things closed sourced.
So they're going all in on building those big complex
models while others are finding ways
to get similar results with smaller, more efficient,
distilled models.
Exactly.
They believe that achieving AGI first
is super important for shaping its development
and making sure it benefits everyone.
It's like two different philosophies clashing.
Yeah, you have the pursuit of ultimate intelligence
on one hand and the democratization of AI on the other.
It's fascinating to see how this plays out.
And it raises some big questions about the future
of AI research and development.
It's a debate that's going to continue for a while.
And it's exciting to be a part of it.
I agree.
This whole deep dive has been an incredible journey so far.
And I'm sure our listeners are feeling the same way.
But before we get too carried away,
let's bring things back down to earth for a moment.
Good point.
Amidst all this excitement about AI,
we need to remember that these are powerful tools
and we need to use them responsibly.
Yeah, it's easy to get caught up in all the hype,
but we've got to remember that AI has a real impact on society.
As it gets more powerful and easier to access,
we have to make sure it's used for good, not for bad stuff.
Absolutely.
We have to think about the ethical side of things,
the potential for job displacement,
and making sure AI aligns with human values.
It's not just about the technology itself.
It's a big conversation, and one that everyone
needs to be a part of researchers, developers,
policymakers, everyone.
But even with all the challenges,
I'm still optimistic about the future of AI.
Yeah, me too.
I mean, AI has the potential to solve some of the world's
biggest problems, like climate change, disease, poverty.
It can help us make better decisions
and create new opportunities.
It feels like we're at the beginning of something huge.
It does.
And it's up to us to make sure this new era is a good one,
one where everyone benefits.
Well said.
This deep dive into AI distillation
has been pretty eye-opening.
We've covered a lot of ground from the technical stuff
to the impact on open source, the reasoning revolution,
the changing role of data.
Yeah, it's been a wild ride.
And hopefully, our listeners are walking away
with a better understanding of what's
happening in the world of AI.
We've seen how distillation is making AI more accessible,
accelerating innovation, and pushing the boundaries
of what's possible.
And we've talked about the ethical considerations
that we need to keep in mind as we move forward.
So as we wrap up this deep dive,
the question for you listening is this.
How are you going to engage with this world of AI
that's constantly changing?
Are you going to sit back and watch?
Or are you going to jump in and help
shape the future of this incredible technology?
It's up to you.
And as we keep exploring this amazing world of AI,
one thing's for sure, the future is full of possibilities.
And the only limit is our imagination.
That's a great note to end on.
So thanks for joining us on this deep dive
into the world of AI distillation.
It's been a pleasure.
And until next time, keep exploring and keep asking
questions.