Hey, everyone, and welcome back.
It's wild to see just how fast the world of AI is moving.
Yeah, it really is.
It seems like there are new models every week
and new announcements from companies like Open AI and Google,
and even governments are getting involved now.
It's fascinating to see.
So are you ready to unpack all of this with me?
Absolutely, I am.
OK, great.
Well, I guess a good place to start would be Open AI.
They just released their newest model, O1,
and it's causing quite a stir.
It is.
They're claiming it's a huge step forward in terms
of speed and accuracy and what it can actually do.
Yeah, it's designed for those really complex real world
tasks, which is a key shift from those previous models.
Yeah, that's right.
And they've added features like function calling,
structured outputs, and even the ability
to work with images.
Oh, wow.
So it sounds like they're really trying
to make AI way more practical, moving beyond just
like text generation and into things
that we can use every day.
Exactly.
Yeah.
I'm especially interested in those vision capabilities.
Yeah, think about the possibilities
like analyzing images for medical diagnoses,
assisting with design tasks, even understanding
the visual elements in code.
OK.
It opens up a whole new level of AI applications.
Yeah, that's mind blowing.
But while everyone is kind of buzzing about Open AI's
video generation model, Sora, there's no API access yet.
Yeah, that's right.
And this puts Open AI a little bit
behind competitors like Google and AWS,
who are already letting developers kind of play
with their video AI tools.
It seems like Open AI is being extra cautious with Sora.
OK.
You know, generating realistic videos obviously
raises a lot of questions about responsible use.
Sure.
But it could also mean that Open AI just
doesn't have the capacity yet to support really
widespread access.
Interesting.
So we might have to wait a little bit to get our hands on Sora.
Yeah, it seems like it.
But in the meantime, the US government
is stepping into the AI ring.
They are.
A Heist AI task force just released this report,
pushing for a balanced approach to regulation.
You know, they want to support innovation,
but they're also really concerned about national security.
Well, it's a sign that governments are realizing
they can't ignore AI.
Yeah.
It's exploding.
And this report really highlights the need for adaptable
regulations given how fast AI is evolving.
Right.
And it seems like they're also aware of the potential pitfalls,
like biased decision making, and are pushing for human
oversight, especially in critical areas like health care.
Absolutely.
AI should be a tool to help humans not
to replace human judgment.
Right.
Especially in those areas with really complex ethical
considerations.
Yeah, absolutely.
Speaking of complexity, across the pond,
the UK is tackling a big question.
Oh, yeah.
How should AI be allowed to use copyrighted material
for training?
Oh, that's a good question.
Yeah.
It's sparking huge debates.
It is.
Some proposals say AI companies should
be able to use anything.
Wow.
But others want strict licensing agreements.
So it's really a tug of war between the AI industry
and artists' rights.
Yeah.
You can definitely see both sides.
Yeah.
Artists are understandably worried about their work
being exploited.
Right.
Overly restrictive regulations could also
hinder AI development.
Exactly.
And finding that balance is going to be crucial.
Yeah.
We need to protect creators while also making sure
that AI has the data it needs to learn and evolve.
Yeah.
This debate is definitely not over.
No, it's not.
Meanwhile, Google is not letting open AI steal all the thunder.
Oh, what are they doing?
They're rolling out a new experimental version
of their Gemini model.
OK.
Specifically for Gemini advanced subscribers.
And what's this one supposed to be good at?
Well, this one is supposed to be a rock star at coding, math,
reasoning, those tasks that require serious logic.
Oh, wow.
It's clear that Google is going head to head with open AI
to build the most capable AI.
It seems like it, yeah.
So I guess the question is, are we
getting closer to AI that can truly understand and solve
those complex problems?
That's the goal, right?
Yeah.
I mean, it would revolutionize so many fields.
It would.
We are getting there.
OK.
But there's still this big hurdle.
And that's AI hallucination.
OK.
And Google is trying to tackle this head
on with something called FCTS grounding.
OK, this sounds intriguing.
Is this the key to finally getting AI to stick to the facts?
It's a big step in that direction.
OK.
If FCTS grounding is a way to measure how well an AI model
actually grounds its answers in real information
instead of making things up.
OK.
And they even have a leaderboard on Kaggle
so you can see how all the different models stack up.
Oh, wow.
So it's like a fact checking system for AI.
Pretty much.
OK.
That's essential if we want to rely on AI for things
like research or making really important decisions.
It is.
Building trust in AI is crucial.
And knowing that it's not just going to spit out some random
falsehood is a big part of that.
Right.
Now, while those big players are really focused
on building these massive models,
there's also this growing movement
towards smaller, more efficient AI.
There is, yeah.
The Technology Innovation Institute just released Falcon
3, a family of open source small language models.
It's interesting.
So does this mean you don't need a supercomputer
to start playing with AI?
That's the idea.
OK.
And it opens up a lot of opportunities
for researchers, startups, and just individuals
who want to see what AI can do.
Right.
And Falcon 3 is already proving that you don't always
need to sacrifice performance for efficiency.
OK.
It's actually already outperforming some
of those bigger models on certain tasks.
Oh, wow.
That's impressive.
It seems like we're seeing this shift in the AI world moving
away from that bigger is always better mindset
towards something that's more efficient and accessible.
There's definitely a growing recognition
that AI needs to be practical and accessible if it's really
going to have an impact on everyone.
Yeah.
And speaking of impact, researchers
are also finding ways to improve performance
without just throwing more computing power at a problem.
I've heard whispers about this.
Yeah.
Something about letting AI think longer?
It sounds kind of counterintuitive.
It does, but it's a fascinating approach.
It's called scaling test time compute.
OK.
And it essentially lets AI spend more time
processing information, which can allow smaller models
to compete with much bigger models.
So instead of constantly building bigger and bigger models,
we can unlock the potential of the existing models
just by giving them a little more time to process
the information.
It's a really promising area of research.
Yeah.
And it could lead to a more sustainable and democratic
approach to AI development, where
you don't need massive supercomputers
to have access to powerful AI.
That's incredible.
We've covered so much of ground already.
Everything from security concerns to groundbreaking research.
We have.
And it's clear that the AI landscape is changing so rapidly.
It really is.
And this is just a snapshot.
I know.
Things will be different tomorrow.
Maybe even by the time you finish listening to this.
Exactly.
So what stands out to you in all of this?
You know, for me, it's this push to make AI
more practical and accessible.
We're seeing it with these smaller models,
the focus on efficient computing,
and this real drive to create AI that anyone can use.
Not just those with access to these massive computational
resources.
Yeah.
It's a sign that AI is really moving from science fiction
into our everyday lives.
I love that.
Yeah.
It is exciting to think about all the ways
that AI is going to be woven into our lives,
especially as it becomes more user friendly.
But before we get too carried away
with all these possibilities, we really
need to kind of circle back to something
that you mentioned earlier.
Those AI hallucinations.
It sounds a bit funny.
It does.
But it is a serious concern, right?
It is.
As we start to really rely more and more on AI
for information and decision making,
we have to make sure that it's not just making things up.
OK, so explain this hallucination thing to me
in plain English.
OK.
Does this mean the AI is intentionally lying?
Well, not necessarily lying.
OK.
But it's when an AI generates outputs
that are factually wrong.
Even if it presents those like a high degree of confidence.
OK.
It's like the AI is confidently stating something
that's just plain false.
OK, that's a little unsettling.
It is a little bit.
It makes you question everything it tells you.
So what causes these hallucinations?
Is it just a lack of data?
It's a little more complicated than that.
OK.
One factor is definitely the way AI models are trained.
OK.
They learn from massive amounts of data.
But if that data is biased or incomplete or contradictory,
the AI might learn to make these incorrect connections.
So it's kind of like that old saying,
garbage in, garbage out.
Exactly.
Even with a massive data set, if the data itself is flawed,
the AI's output is going to reflect those flaws.
Exactly.
Another factor is the AI's understanding
of the real world.
OK.
You know, current models, even as advanced as they are,
they still lack that deep nuanced understanding
that humans have.
Right.
You don't really grasp things like common sense or ethics.
Uh-huh.
Or the complexity of human language and behavior.
Yeah.
So even if the AI has access to all the facts in the world,
it might not understand how those facts fit together
in the real world.
Right.
It's missing that human context.
Precisely.
Yeah.
And that lack of context can lead
to all sorts of misinterpretations and illogical leaps
and ultimately hallucinations.
OK.
So this is where Google's FCPS grounding comes in.
It's a way to measure how well the AI sticks to the facts.
It is.
It's essentially a benchmark for evaluating how well AI models
ground their answers in real information
and avoid making things up, especially
in those longer form responses that require pulling together
information from different sources.
OK.
So instead of just like asking the AI a simple question,
you give it like a whole document to work with.
Exactly.
And then see if it can accurately summarize
that information without adding its own embellishment.
Exactly.
It's a much more challenging test for the AI.
And all the results are put on a leaderboard on Kaggle.
Oh, cool.
So you can see how all the different models are performing.
So it's like a public test of AI honesty.
Yeah, pretty much.
I love that.
It's great to see that transparency.
But is FASTS grounding like the ultimate solution?
Well, it's definitely a valuable tool.
OK.
But it's only one piece of the puzzle.
OK.
There are a lot of different approaches being explored.
OK, like what?
Well, one is reinforcement learning,
where you essentially reward the AI for giving accurate answers
and then penalize it for hallucinations.
So it's like training a pet but for AI.
Yeah, kind of.
Reward the good behavior, discourage the bad.
Exactly.
Another approach is giving the AI access
to external knowledge sources, like fact checking websites,
so it can double check its claims.
It's like giving it a research assistant,
being like, hey, before you tell me something,
maybe make sure it's actually true.
Exactly.
There's also a push to develop specialized AI models
that are designed for tasks where accuracy is super important,
like summarizing scientific research
or generating legal documents.
It's like having different AI specialists,
each trained to be really careful with the facts
in their own specific field.
Exactly.
And then there's a lot of ongoing research
into making AI models more interpretable,
meaning we can understand how the AI arrives at its conclusion.
So it's not just about getting the right answer.
It's about understanding the AI thought process,
so we can identify any potential biases or errors.
Exactly.
If we can understand how the AI thinks,
we can better assess its reliability
and spot those areas where it might be prone to hallucinations.
This is all very encouraging.
It is.
It seems like researchers are really working hard
to tackle this hallucination problem.
They are.
Which is crucial if we want to trust and rely on these AI systems.
Addressing this problem is really essential
if AI is going to reach its full potential.
We have to be able to confidently rely
on the information it provides.
Yeah, absolutely.
Speaking of trust, let's talk a little bit
about how governments are getting involved in the AI landscape,
especially in the US and the UK.
We touched on this briefly before.
We did.
But given the potential impact of AI on everything,
like national security to the creative industries,
I think it deserves a deeper look.
I think so too.
So governments are realizing that AI
isn't just some futuristic concept.
It's a technology that's already changing the world.
And they need to figure out how to regulate it responsibly.
Yeah, they do.
So let's start with the US.
We talked about the House AI Task Force Report.
What are some of the key takeaways from that?
Well, their main goal seems to be to find that balance
between encouraging innovation and protecting
national interests.
They don't want to stifle progress.
But they also want to make sure that AI is developed and used
in a way that benefits society.
So it's not about stopping AI.
It's about steering it in the right direction.
Exactly.
And one of their big concerns is that AI
could be used to threaten national security.
They're also worried about bias decision making,
especially in areas like law enforcement or defense,
where the stakes are really high.
Yeah, it goes back to what we were talking about earlier
about addressing bias in the data that's
used to train these AI models.
If the data is flawed, the AI's decisions
will reflect those flaws.
They will.
And that could have really serious consequences.
Absolutely.
The Task Force is really advocating
for transparency and accountability in AI systems,
especially those used by the government.
They want to be able to understand how these systems work,
what data they're using, and how they're making decisions.
So no more black boxes when it comes to AI.
Yeah, pretty much.
We need to be able to understand how the sausage is made.
So to speak.
To make sure this being made responsible.
Exactly.
But they're not just focused on preventing misuse.
They also see AI as this really powerful tool
that could be used to improve government efficiency
and effectiveness.
So they're encouraging agencies to explore how AI could
be used to streamline processes, reduce costs,
and provide better services to citizens.
So they recognize that AI can be both a challenge
and an opportunity.
It can.
It's about finding ways to mitigate the risks
while maximizing the benefit.
Exactly.
And they think education is a key part of that.
The Task Force is calling for more investment in AI literacy,
starting in kindergarten all the way through high school.
That's great.
It's about making sure that future generations have
the knowledge and skills to navigate this AI-driven world.
They want to make sure that people are prepared to engage
with AI, understand its potential,
and make informed decisions about its use.
All right, now let's shift our focus to the UK.
They're grappling with a really interesting issue.
How should AI be allowed to use copyrighted material
for training?
That's a great question.
Yeah, it's a question that's causing a lot of tension
between the AI industry and the creative community.
That's right.
AI models learn from massive amounts of data.
They do.
And that data often includes things like text, images,
music, and even code that are all protected by copyright.
It is.
It's a tough balancing act.
On one hand, you have these AI companies saying,
they need access to this data to develop
their sophisticated models.
But then you have artists and creators
who are understandably worried about their work being
used without their permission or compensation.
So it's really hard to find a solution that
works for everybody.
It is.
The UK government is exploring a lot of different policy
options.
Ranging from a more permissive approach,
where AI companies would have more freedom
to use that copyrighted material to a more restrictive
approach, where they would need to obtain licenses
and pay royalties.
So it's a whole spectrum of possibilities.
It is.
Each with its own pros and cons.
So what are some of the arguments for and against
each approach?
Well, those who favor a more permissive approach
argue that it would encourage innovation in the AI industry.
Yeah.
And allow the UK to stay competitive.
Right.
In the global AI race.
Neckhens.
They say that they're worried that strict regulations
would stifle progress.
So their argument is, don't tie the hands of our AI developers.
Right.
We need to give them the freedom to experiment
and push the boundaries.
Exactly.
But then the creative community is pushing back
hard against that.
OK.
They say it would basically be legalizing
theft of their intellectual property.
Right.
They're saying, our work has value.
Yeah.
And we deserve to be compensated when it's used even by AI.
And they're also concerned that their work will
be used without their consent.
Yeah.
And that they won't see any of the financial benefits from it.
Yeah.
I mean, there's also this worry that AI
will be used to create derivative works that
are so similar to the original creations
that it devalues their work.
Exactly.
It makes it harder for them to earn a living.
Right.
It's a valid concern.
It is, if AI can just churn out imitations of art, music,
and literature, it could really flood the market
and make it difficult for human creators to stand out.
Yeah.
And beyond the economic impact, there's also
that question of artistic integrity
and the right of creators to control how their work is
being used.
Exactly.
A lot of artists feel like their creations
are an extension of themselves.
Yeah.
And they don't want to see them exploited or misused
even by AI.
So for them, it's not just about the money.
Right.
It's about respect for their creative work
and the effort they put into it.
Absolutely.
And then those who favor the more restrictive approach,
they argue that requiring AI companies
to obtain licenses and pay royalties
would be a much fairer balance.
So they're looking for a solution
where AI can still access that data,
but creators are fairly compensated.
Exactly.
They think a well-defined licensing system
would be a win-win situation.
It sounds good in theory.
It does.
But is it practical?
Well, I've got the question.
Yeah.
I mean, managing a system like that
seems incredibly complex.
It would definitely not be easy.
It would require clear guidelines,
streamlined licensing agreements,
and robust mechanisms for tracking and distributing
royalties.
Yeah.
But there are a lot of different organizations
working to develop these kinds of systems.
So there is hope that we can find a solution.
There is.
That protects creators' rights without stifling AI innovation.
That's the goal.
Yeah.
It really highlights something important.
What's that?
AI isn't just about the technology.
It's about the impact that it has on our lives,
our livelihoods, and even our creative expressions.
Absolutely.
And as AI continues to evolve, these ethical and societal
considerations are going to become even more crucial.
I think you're right.
So this has been a fantastic discussion.
It has.
We've covered a lot of ground.
We have.
We've talked about the latest AI models,
the challenges of these AI hallucinations,
and even these really complex policy debates surrounding
AI use of copyrighted material.
It's a lot to think about.
Yeah.
And we're just scratching the surface.
I know.
The world of AI is constantly changing,
Right.
Which is what makes it so exciting.
It does.
But also a little overwhelming.
Yeah.
It definitely feels like we're standing on the precipice
of a technological revolution.
It does.
Yeah.
It really is amazing to think about how quickly things
are changing in the AI world.
It is.
It seems like what was science fiction just a few years ago
is now becoming part of our reality.
It really is an incredible time to be a part of this field.
Yeah.
It is.
And with all these advancements,
one trend that I find particularly interesting
is this move toward these smaller, more efficient AI
models.
Oh, you're talking about things like those Falcon 3 models
we discussed.
Exactly.
The ones that are designed to run on less powerful hardware.
Yeah, there's this growing realization
that bigger isn't always better when it comes to AI.
Right.
Sometimes a smaller, more focused model
can be just as effective, if not more so,
than these massive resource-intensive models.
So it's kind of like finding that perfect tool for the job.
Exactly.
You don't always need a sledgehammer.
Right.
When a well-placed tap with a hammer will do.
That's a great analogy.
And this shift towards smaller models
is being driven by a few different things.
OK.
Like what?
Well, the increase in cost of energy,
a growing awareness of the environmental impact
of these massive data centers, and just this desire
to make AI more accessible to a wider range of users.
Yeah, it makes sense.
I mean, not everyone has access to a supercomputer.
Right.
And if we want AI to benefit society,
we have to make sure that it's available to everyone.
Exactly.
And that brings us to another really interesting development.
OK.
The idea of scaling test time compute.
Oh, yeah.
Let's revisit that.
OK.
You mentioned that it's about giving the AI more time
to think, which sounds a little counterintuitive.
It does a little bit.
But the idea is that instead of just giving a problem
to the AI and expecting an instant answer,
we let it spend more time processing that information,
exploring different possibilities,
and refining its response.
So you're giving the AI the chance
to really think things through rather than just
jumping to the first conclusion.
Exactly.
And what's really remarkable is that this approach is allowing
these smaller, less powerful AI models
to get results that we thought could only
be achieved by these massive computationally expensive models.
That's what makes it so exciting.
It is.
It opens up a whole world of possibilities
for people who don't have access to those giant supercomputers.
Exactly.
Imagine students being able to experiment with cutting-edge
AI models on their laptops or small startups developing
these innovative AI applications without having
to invest in a ton of infrastructure.
Yeah.
It really democratizes access to AI.
It does.
But how exactly does this thinking longer process work?
Well.
Does the AI just sit there and ponder
until it has some kind of breakthrough?
Well, it's a little more structured than that.
OK.
Researchers are developing specific techniques
to really guide that AI's thought process
and make it more efficient.
OK.
And a key element is this thing called a process reward model.
OK.
Remind me what that is again.
Think of it like a coach or mentor for the AI.
OK.
Instead of just looking at the AI's final answer,
a process reward model provides feedback at each step
of the AI's reasoning process.
So it's kind of like having a teacher who
doesn't just grade your final exam,
but actually gives you feedback along the way.
Exactly.
So you can improve your approach.
And one way this guidance is used
is through something called Beam Search.
Beam Search.
That sounds familiar.
Yeah.
We talked about it briefly before.
It's a way for the AI to explore multiple possible solution
paths at the same time.
OK.
Kind of like branching out in different directions
to find the best route through a maze.
So instead of just sticking to one line of reasoning.
Right.
The AI is exploring different possibilities
simultaneously.
Exactly.
And the process reward model helps
to prioritize those paths.
OK.
Guiding the AI toward the most promising solutions.
OK.
And pruning away the less likely ones.
Got it.
Another technique that they're using
is something called Diverse Verifier Tree Search.
Diverse Verifier Tree Search.
I know it's a mouthful.
That one sounds even more complex.
It is a little bit more involved.
OK.
But the basic idea is to encourage the AI
to explore a wider range of possible solutions.
Even those that might seem unconventional or less
obvious at first.
So it's kind of like telling the AI to think outside the box.
Exactly.
And this emphasis on diversity can often
lead to more creative and innovative solutions.
Interesting.
Solutions that a more conventional AI might never
even consider.
It's like the difference between a group of people
brainstorming ideas versus just one person trying
to solve a problem alone.
Right.
And you know, the group is more likely to come up
with more ideas because they have all those different
perspectives and experiences.
Right.
And by encouraging this diversity
in the AI's thought process, we're essentially
tapping into that same power of collective intelligence.
OK.
Even though the collective in this case
is just these different parts of the AI model itself.
That's amazing.
This is all so fascinating.
So it seems like scaling test time compute
combined with these innovative search techniques
and the guidance of those process reward models
is opening up a whole new world of possibilities for AI.
I agree.
It's a very exciting time to be working in this field.
Yeah, it is.
And it's a reminder that the future of AI
isn't predetermined.
Right.
You know, it's something that we are actively
shaping through our choices, our research,
and our collective imagination.
That's a great point.
Well, on that inspiring note, I think
it's time to wrap up our deep dive into the world of AI.
Yeah, I think so too.
We've covered so much.
We've gone from the latest advancements in these AI
models to the challenges of those hallucinations,
the ethical considerations of AI,
and its use of copyrighted material,
and this really exciting potential
of those smaller, more efficient AI models.
Yeah, it's been a fantastic discussion.
It has been.
And I hope that our listeners have gained some valuable insights
into this rapidly evolving field.
Yeah, me too.
And for all of you who are eager to keep exploring
this really fascinating world, we
encourage you to stay curious, stay engaged,
and never stop asking questions.
Yeah, that's great advice.
You know, the future of AI is in our hands,
and it's up to all of us to shape it responsibly.
Well said.
Thanks for joining us on this deep dive, everyone.
Yeah, until next time.