Welcome to the Daily AI News Podcast.
Wow, you guys been busy sending us
a ton of articles about AI.
Yeah, it looks like a lot has happened.
A lot has happened, even in just the past few days.
New models.
Big funding rounds.
Even AI elves.
It's like, what is even going on right now?
Don't worry, we're here to break it all down for you.
Yeah, what's fascinating to me is
we're seeing a shift in the AI landscape right now.
It's going from just focusing on making these models bigger
and bigger, scaling them up, to exploring
some really new avenues, like reasoning
and specialized applications.
OK, so let's unpack this a little bit.
We'll start with open AI, because let's be honest,
everybody's talking about them right now, the new O3 model.
And then we'll dive into what Google's doing.
Some interesting things happening with AI talent and funding,
and even a little bit about space exploration.
Sounds good.
OK, so open AI O3, is this a leap forward?
I mean, it's the successor to their O1 model.
Right.
Which was their reasoning model, which was already causing a stir.
Yeah, and you know, it's funny, they actually skipped O2.
What?
Yeah, they had some trademark issues with a British telecom
company.
Oh, no.
So they had to skip right over O2.
OK, well, I'm glad they didn't let that stop them.
So O3, it's designed to think before responding.
Exactly.
By using this thing called a private chain of thought.
Right, so it's not just kind of going with the first response.
It thinks about it.
But first it considers a bunch of related prompts,
explains its reasoning, and even does some internal fact
checking.
That's interesting.
So does that make it more accurate?
Well, they're saying it does.
Open AI is claiming that it leads to significantly more
reliable responses, especially in these complex fields,
like physics and math.
And they're even saying that O3 is getting closer to AGI.
AGI.
Artificial general intelligence.
Whoa, hold on.
Already they're saying that based on this benchmark called
ARC-AGI.
Right.
But I feel like some experts are kind of pushing back on that.
Yeah, Francois Chalet, who actually created that benchmark,
he's been pretty vocal about his skepticism.
He's saying that it still struggles with tasks
that are super easy for humans.
And they're actually already working on ARC-AGI2, which
is going to be like the sequel.
The sequel.
A tougher benchmark to really put these reasoning
capabilities to the test.
OK, so it sounds like the jury is still out
on whether this is truly AGI.
Yeah.
But how did it do on some other tests?
Well, in terms of programming, it actually
outperformed other models.
OK.
And it crushed the American Invitational Mathematics exam.
What?
Yeah, it's got a 96.7%.
Almost a perfect score.
Yeah.
That's really impressive.
Yeah.
OK, so it sounds like O3 is a major leap forward.
Yeah.
Even if it's not quite AGI yet.
Right.
But speaking of open AI, their next big language model, GCC5,
seems to be hitting some roadblocks.
Yeah, it's apparently behind schedule
and facing some pretty serious challenges.
What kind of challenges are we talking about here?
Well, for one thing, just the technical challenges
and then the data challenges as well.
OK, so what's going on?
Well, training these massive models
is incredibly expensive.
I can imagine.
We're talking hundreds of millions of dollars
for a single training run.
Hundreds of millions of dollars for one training run.
Yeah.
What is driving up the cost like that?
Well, it seems like the more data, the better approach
is kind of hitting a wall.
Really?
Yeah, they're having trouble finding enough high quality
data to train these systems that are just getting more
and more complex.
So what are they doing about it?
Well, they're experimenting with this thing called
synthetic data.
Synthetic data.
Which is basically data that's created by AI itself.
Hold on.
AI creating data to train other AI.
Yeah.
Isn't that a little bit like a recipe for disaster?
It's a controversial approach, for sure.
OK.
The worry is that if you're using data generated by AI,
you could end up amplifying biases and inaccuracies.
Right.
It could create this kind of feedback loop of bad data.
So how are they trying to prevent that?
Well, they're using data generated by their reasoning
model 01.
OK.
Hoping that its reasoning abilities will actually
lead to higher quality synthetic data.
Interesting.
So it's a bit of a gamble.
We'll have to see how it plays out.
Yeah, definitely a gamble.
OK.
So GPT-5, facing delays, data dilemmas.
Yeah.
Anything else going on here?
Well, we can't forget about the competition for talent.
Oh, yeah.
The AI talent wars are really heating up.
It's getting competitive out there.
It's super competitive.
Yeah.
Companies are just fighting to attract and keep
the best researchers, and it's pushing up
salaries and valuations across the entire industry.
So it's like an AI gold rush.
Yeah, kind of.
So who's winning this war for talent?
Well, Databricks, another big player in the AI space,
just announced a record-breaking $10 billion funding round.
$10 billion.
And get this, most of that money is going towards buying back
employee stock.
Wow, that's a really bold move.
Why buy back stock, though?
Well, it's all about attracting and keeping that top talent.
OK.
Naveen Rao, who's the VP of AI at Databricks,
he was saying that finding top AI researchers,
like looking for LeBron James, there just
aren't that many people out there with the skills
and expertise to really push the boundaries
of this technology.
So it's a seller's market.
Absolutely, a seller's market.
They have all the power right now.
Yeah.
Databricks is basically saying, come work for us,
and we'll make it worth your while.
Exactly.
All right, well, let's shift gears for a second
and talk about some other interesting developments
in the AI world.
I mean, we can't leave out Google and NVIDIA, can we?
No, definitely not.
They're making some big moves, too.
Yeah.
While open AI and these large language models
are getting all the headlines, there's
other companies doing some really interesting stuff.
Like what?
Well, Google, for example, is reportedly
working on an AI mode for their search engine,
which is probably a response to ChatGPT and all these other AI
powered search tools that are coming out.
That makes sense.
What about NVIDIA?
Well, NVIDIA, the leading maker of AI chips,
they just got approval from the EU for their acquisition
of Run.AI, which is an AI software company.
So this is pointing towards some consolidation in the market.
The big players are snapping up these innovative startups.
All right, so we've got open AI pushing
the boundaries of reasoning models,
Google's adapting its search engine,
and NVIDIA is consolidating its position in the AI chip
market.
Yeah.
That's a lot of activity.
But let's move on to something a little more festive
for a moment.
You mean NORAD's new AI elf?
Yes.
I love this story.
It's so fascinating how AI is being used in these creative
and unexpected ways.
I know, right?
So NORAD, the organization that tracks Santa?
Yeah, every Christmas Eve.
Every Christmas Eve.
They've deployed this AI chat bot called Radar the Elf.
I love it.
To answer questions about Santa in multiple languages.
Yeah.
So kids can now chat with an AI elf about Santa.
It's amazing.
That's pretty cool.
It's a great example of how AI is just finding its way
into all sorts of applications from solving
these complex math problems to bringing
a little bit of holiday cheer.
I love that.
Speaking of unexpected applications,
there's this online charter school in Arizona
that's going to be taught entirely by AI.
I saw that.
Yeah, the Arizona State Board for Charter Schools
has approved this new school called Unbound Academy.
Unbound Academy.
And they're going to be using AI-driven adaptive learning
technology to deliver their entire curriculum.
So no human teachers at all?
Well, they will have what they call skilled guides.
OK.
To monitor student progress and offer support.
OK.
But all the core instruction is going
to be handled by these AI platforms.
Wow, that's a bold experiment.
What platforms are they going to be using?
They're planning on using some existing EdTech platforms
like IXL and Khan Academy.
Oh, OK.
Which are already used in a lot of classrooms.
Yeah.
The idea is that these platforms can personalize learning.
Right.
Adjusting to each student's pace and style.
It'll be really interesting to see how it works out.
I know.
I'm curious, too.
This is definitely a controversial move,
but I can't wait to see how it plays out.
Me, too.
And continuing on this theme of AI kind of pushing
into new frontiers, what about AI agents?
Oh, yeah.
Especially their growing role in Web 3.
That's a topic that we could spend hours talking about.
OK, so.
We're starting to see AI agents making their mark in the world
of Web 3 and cryptocurrency, particularly
in activities like cryptocurrency staking and on-chain
trading.
OK.
This is where I start to get a little bit lost,
I have to admit.
Yeah.
Can you just break it down for me?
What are AI agents and how are they being used in this Web 3
world?
Sure.
So an AI agent is basically a piece of software
that can act autonomously.
It can make decisions and take actions
to achieve a specific goal.
So in the context of Web 3, which is all about decentralization
and blockchain technology, these AI agents
are being used to automate complex tasks
and potentially improve efficiency
in this decentralized environment.
So is it like a digital personal assistant, but for Web 3?
In a way, yes.
But they're much more sophisticated than that.
They can learn and adapt over time
and become more and more effective.
Wow, that sounds really powerful.
Can you give me an example of how these AI agents are actually
being used in Web 3?
Sure.
Imagine an AI agent that's programmed
to stake cryptocurrency for you.
So we can analyze all the market data,
identify the best staking opportunities,
and automatically execute the transactions
to maximize your returns.
That sounds very impressive, but also a little bit risky.
Yeah, definitely.
I mean, are there concerns about these AI agents
kind of operating in this space?
Of course there are.
There are risks associated with any form of automated trading.
And AI agents are no exception.
So things like security vulnerabilities,
the potential for manipulation, and the lack of human oversight,
those are all valid concerns.
So it's not all sunshine and roses.
No technology is without its risks.
And it's important to approach these developments
with a healthy dose of skepticism
and to really consider the potential downsides,
as well as all the benefits.
Well, it sounds like we've only just scratched
the surface of this topic.
Yeah, for sure.
We'll definitely need to kind of delve deeper
into this world of AI agents in Web 3 in a future deep dive.
I agree.
But for now, let's move on to something a little less technical.
OK.
Space exploration.
You read my mind.
It seems like we're entering this new golden age of lunar
exploration.
Yeah.
And AI is playing a key role.
Absolutely.
SpaceX and Firefly Aerospace are teaming up
to send some fascinating science experiments to the moon.
Firefly Aerospace was awarded $179 million contract
from NASA to deliver six scientific experiments
to this area on the moon called the Groothusen Dome.
Groothusen Dome.
What are those?
They're these unique geological formations
that are thought to be formed by lava millions of years ago.
Wow.
The experiments are going to focus
on understanding the lunar geology and what these domes
are made of.
That sounds like a fascinating mission.
Yeah.
So how are they going to get these experiments to the moon?
Firefly is going to be sending the experiments
on one of their blue ghost landers, which
will be launched on top of a SpaceX Falcon 9 rocket.
So it's a great example of collaboration
in the space industry.
It's amazing how all these partnerships are driving
progress in space exploration.
When is this mission scheduled to launch?
The target launch date is 2028.
2028.
But Firefly is also involved in another lunar mission for NASA.
OK.
It's going to launch much sooner in January of 2025.
Wow.
That mission is going to be landing at Mayor Chrissium.
Mayor Chrissium.
Which is another interesting region on the moon.
It sounds like things are really heating up
in the world of lunar exploration.
Yeah, they definitely are.
With the Artemis program and all the private sector
involvement, we're really entering a new era.
Yeah.
And AI is playing this crucial role
in making these missions possible.
It's amazing to see AI being applied in such diverse
and impactful ways.
It really is.
It's not just a futuristic concept anymore.
Right.
It's this transformative technology
that's already shaping our world.
Yeah.
From the way we search for information
to the way we explore the cosmos.
Well, this has certainly been a jam-packed deep dive
into the world of AI.
It has.
From reasoning models to AI elves.
I know.
From the future of education to the frontiers of space
exploration, we've covered a lot of ground.
Yeah, we have.
But we're not done yet.
Right.
There's still much more to discuss,
especially about AI agents.
Yes.
And Web 3.
That's a topic with so much potential and equally
significant challenges.
Absolutely.
We'll definitely need to dedicate a future deep dive
to that topic.
Yeah, we should.
But for now, let's take a short break.
Welcome back to our deep dive into AI.
So before the break, we were talking about AI agents in Web 3
and how they're being used for things like crypto staking.
Right.
On-chain trading, all that.
Yeah.
And that kind of brings us to this interesting point
about their development.
What's that?
Well, it's this potential for centralization,
even within this decentralized world of Web 3.
Oh, that's interesting.
I thought the whole point of Web 3
was to move away from centralized control.
Exactly.
That's the ideal, right?
Yeah.
But the reality is that creating and using
these really sophisticated AI agents,
it takes a ton of resources and a lot of expertise.
So are you saying that only these big, well-funded
organizations are going to be able to make and control
these AI agents?
I think there's a definite risk of that happening.
If we're not careful, we could end up
with a few powerful entities kind of controlling
the AI agents that govern a lot of these key aspects of Web 3.
OK, so that doesn't sound very decentralized or democratic.
Yeah, it's a valid concern.
And I think it's something that the Web 3 community really
needs to be aware of as these technologies continue to evolve.
There are efforts to promote open source development
and to create more accessible tools for AI agent creation.
But it's a challenge.
It's definitely an ongoing challenge.
So it sounds like the fight for decentralization
is happening in the world of AI and Web 3 as well.
Absolutely.
It's like a battle for control and influence
over these really powerful technologies,
and the stakes are high.
OK, well, that's definitely something to think about.
Yeah.
But let's shift gears for a moment
and talk about something a little more lighthearted.
Remember those AI elves we were talking about earlier?
Yes, radar, the elf, NORAD's AI-powered assistant
for all things Santa Claus.
It's pretty amazing that kids can now
chat with an AI elf about Santa's location.
I know.
And multiple languages.
It's a really cool example of how AI can create these fun
and engaging experiences.
Yeah, I think it really shows the progress that's
been made in natural language processing.
Right.
And AI-powered communication.
I mean, radar, the elf needs to be
able to understand and respond to such a wide range of questions
in real time.
Right.
And it has to do it in an informative and entertaining
way.
Yeah, and I'm curious to see how these AI-powered assistants
kind of evolve in the future.
Me too.
It feels like we're just seeing the beginning of what
they can do.
I think so too.
I think as these models get more advanced and easier to use,
we're going to see a lot more of these AI-powered assistants
in different areas, like customer service education
and entertainment.
Speaking of education, let's go back
to that AI-powered charter school in Arizona.
Yeah.
It's such a bold experiment, and it's definitely
going to spark a lot of discussion.
It's pushing the boundaries of what
we think about education.
Yeah.
And it raises all these important questions
about what the role of technology
should be in the classroom.
Like, is it a good thing or a bad thing?
Exactly.
One argument for this approach is
that AI can personalize instruction.
Right?
It can tailor it to each student's needs and their pace.
Exactly.
That's the idea.
And there's actually some evidence suggesting
that AI-powered tutoring systems can help students
learn certain subjects more effectively.
OK.
But there are also some concerns about relying too much
on AI in education.
Oh, yeah, for sure.
I mean, the lack of human interaction
and the potential for AI systems to maybe
perpetuate biases, those are two big concerns.
Right.
And then there's the question of whether AI can truly
replace a human teacher.
Yeah.
Is it really possible?
It's a big question.
Yeah.
It's a big question.
So it sounds like finding that right balance,
you know, between using the benefits of AI,
but also keeping human interaction and guidance,
is really important.
Yeah.
I think that's a good point.
Technology can be a really powerful tool
for improving education, but it shouldn't replace
that human element.
Well said.
OK.
Now, before we move on, I wanted to touch on something
that we talked about a little bit earlier.
The challenges open AI is facing with GPT-5.
Right.
That delays the data problems.
Yeah.
It's definitely getting a lot of attention in the AI world.
Yeah, for sure.
One of the main issues seems to be just the massive cost
of training these large language models.
Yeah.
I mean, we're talking hundreds of millions of dollars
for a single training run.
Yeah.
It's a reflection of how complex and large
these models are getting.
You know, as they get bigger and more advanced,
they need even more computational power and even more data.
And it seems like opening as kind of hitting a limit with that,
more data is better approach.
It's a big development because it suggests
that just throwing more data at the problem
might not be the best way to improve these models anymore.
So what are they doing differently?
Well, they're trying out synthetic data, which
is that data created by AI to train other AI.
Didn't we talk about the risks of using synthetic data?
Yeah, we did.
There are worries that biases and inaccuracies could get worse.
Right.
But open AI seems to think they can kind of solve those problems
by using their reasoning model, O1,
to create better synthetic data.
Interesting.
So it's kind of a risky move.
It is a risky move, but it shows how much
they want to find these new ways to improve their models.
It shows the pressure they're under.
Yeah, for sure.
Stay ahead in this AI race.
Yeah, absolutely.
And speaking of competition, we can't
forget about that fierce battle for AI talent that's going on.
Great.
All those bidding wars.
Pushing up salaries.
Yeah, pushing up valuations.
It's crazy.
Yeah, it's a fight for the best minds in AI.
Yeah.
And it doesn't look like it's going to end anytime soon.
Data breaks a recent $10 billion funding round,
mostly for buying back employee stock.
I mean, that's just a perfect example of this trend.
Yeah, it shows how far companies
are willing to go to attract and keep the best people.
Yeah, it's wild.
Yeah, the lack of skilled AI researchers
is really making them valuable.
Right.
And companies are constantly trying to outbid each other.
It's a high stakes game.
It is.
And it's changing the AI industry in a big way.
Yeah, for sure.
Well, it seems like we've covered a lot in this deep dive
so far.
We've explored the latest in AI from reasoning models
to AI elves, the future of education.
It's a lot.
And we've really looked into the world of AI agents in Web 3,
talking about the potential benefits and the risks
in this decentralized environment.
Yeah, and how to keep it decentralized.
Exactly.
So it's been a fascinating look at how quickly things are
changing.
Yeah, it's moving so fast.
And the world of artificial intelligence.
It's amazing how fast it's moving.
And it reminds us that AI isn't just this future concept.
Right.
It's a really powerful force that's
already shaping our world.
In so many ways.
In big ways.
Yeah.
OK, so before we wrap up this part of our deep dive,
are there any other key points that you
would like to highlight?
Yeah, I think it's important to remember
that while all these AI advancements are really exciting
and full of potential, they also come with some big challenges
and responsibilities.
What do you mean?
Well, as AI systems get smarter and more independent,
we really need to make sure that they're being developed
and used ethically and responsibly.
OK, so it's not just about pushing the technological limits,
but also thinking about the social and ethical implications.
Exactly.
We need to be thinking about potential biases, the impact
on jobs, and just the broader consequences
of increasing automation.
Yeah.
It's a complex issue.
It is.
That needs careful thought and discussion.
For sure.
I mean, the future of AI isn't set in stone.
Right.
It's something that we are shaping
with every decision we make.
Those are powerful words.
Yeah.
And they remind us that we all have a role
to play in making sure AI is used for good.
Yeah.
And that the benefits are shared widely.
Absolutely.
It's a responsibility that we can't ignore.
Well said.
OK, now let's continue our deep dive into the world of AI,
focusing on some of the more interesting.
OK.
And maybe even controversial applications
that are emerging.
We were just talking about the responsibility that
comes along with all these advancements in AI.
Right.
It's not just about what we can do, but what we should do.
Exactly.
Yeah.
So how do we make sure that AI is used for good?
That's a huge question.
Yeah.
And there's no easy answer.
But one key part of it is striking that balance
between encouraging innovation.
Right.
And putting the right safeguards in place.
So we want to make sure that we're
supporting the development of beneficial AI technologies.
Right.
But we also need to protect against the potential harms.
It's like walking a tightrope.
Right?
It is.
Too much regulation could stifle progress.
Right.
But too little could lead to all these unintended consequences.
Exactly.
And that debate is happening right now, all over the world,
in governments and industry groups.
So it shows that AI is not just a tech issue.
It's a social and political one, too.
Absolutely.
The decisions we make about AI are going to have a huge impact
on our society, our economy, our future.
OK.
Well, that's a lot to think about.
Let's bring it back down to Earth for a minute
and talk about some of the practical ways
that AI is being used.
We've talked about education, entertainment space.
But what other areas are being impacted by AI?
Well, health care is a really interesting one.
AI is already being used to help with diagnoses,
discover new drugs.
Wow.
And create personalized treatment plans.
So AI is essentially helping doctors make better decisions
and provide more effective care.
That's the goal.
And there's huge potential for AI to really revolutionize
health care from improving patient outcomes
to reducing costs and even making care more accessible.
Can you give me some specific examples
of how AI is actually being used in health care right now?
Sure.
So one example is image analysis.
So you have these AI algorithms that
are trained to examine medical images, like X-rays and MRIs.
And they can spot abnormalities and assist
doctors with their diagnoses.
So it's like having a second set of eyes,
helping doctors catch things that they might have otherwise missed.
Exactly.
And often, these AI algorithms are
able to pick up on these very subtle patterns that
might escape even the most experienced human eye.
Interesting.
And that can lead to earlier and more accurate diagnoses.
That's amazing.
What other applications are there?
Well, AI is also making waves in drug discovery.
So algorithms can sift through these massive amounts of data
to pinpoint potential drug candidates
and even predict how effective they might be.
So it's speeding up the process of developing
new medicines and treatments.
Yes.
And it's also helping to personalize those treatment
plans.
So AI can analyze a patient's genetic information,
their medical history, their lifestyle factors,
to recommend the best treatment options
for that specific individual.
It sounds like AI could completely change
health care for the better.
Yeah.
The possibilities are really exciting.
But with any new technology, there
are challenges and risks to think about.
Like what?
Well, one of the biggest concerns is data privacy.
Health care data is extremely sensitive.
And we need to be absolutely sure that it's
protected from unauthorized access or misuse.
That's a really good point.
We also have to consider the potential for bias.
Yes.
Definitely.
If the data used to train these AI algorithms is biased,
it could perpetuate these existing inequalities in health
care.
That's right.
We have to be really, really careful
to make sure that these systems are designed and implemented
fairly and equitably.
It sounds like there's still a lot of work to do.
There is.
To make sure that AI is used responsibly and ethically
in health care.
But the potential benefits are huge.
They are.
It's an area that definitely deserves a lot of attention
and focus and investment in research and development.
This deep dive has been really eye-opening.
It has.
We've explored how AI is impacting everything
from reasoning and language models to Santa
trackers and the future of health care.
Yeah, who knew?
It's clear that AI is no longer this futuristic concept.
Right.
It's this powerful force that's already changing our world.
And we're just getting started.
Yeah.
It's a force that we need to understand, engage with,
and guide thoughtfully and responsibly.
Well said.
Thank you for listening to the Daily AI News Podcast.
And stay tuned for more.