Welcome to the Daily AI News Podcast.
We're gonna be diving deep into AI today.
Sounds good.
We've got a range of sources today.
We've got news articles about AI.
We've got some stuff about like the tech industry
and how that relates to AI.
Okay.
And then we've got a research paper
that's all about a benchmark for AI and math.
Interesting.
Yeah, so we're gonna be looking
at a bunch of different stuff.
You know, how is this affecting jobs,
the political landscape?
Yeah.
What are the challenges facing AI right now?
And yeah, where is this all headed?
So.
Yeah, it's really fascinating to see AI
like no longer just this futuristic concept,
but something that's actually like shaping our present.
Yeah.
Yeah, it's already having a huge impact
and we're just at the beginning.
Exactly.
And that's kind of what we're gonna be looking at today.
Oh, not just what's happening,
but what it means for you, right?
Like how could this affect your job, your interests,
maybe how you see what's even possible.
Yeah.
So I guess let's jump right in.
And why don't we start with open AI?
Okay, so open AI, I'm sure you heard of them,
chat GPT and all that.
They're always pushing the boundaries, right?
Like what can AI do?
How far can we take it?
But there's this growing awareness that just,
you know, scaling up, adding more data,
more computing power, it's not enough.
It's like trying to build a tower
by just stacking more bricks.
Eventually the foundation can't hold it.
Right.
So what are they doing differently now?
Well, they're exploring some pretty cool stuff.
Yeah.
Test time compute is one thing.
Okay.
And it's this way of letting models
think through problems, more like humans do, you know.
Okay.
Step by step, instead of just relying on patterns.
Ah, I see.
Like imagine a student using textbooks, calculators,
even the internet to figure something out.
So it's more about reasoning and strategizing.
Exactly.
Other than just pattern recognition.
Exactly, yeah.
Wow.
So that's a big change.
Yeah, that is a pretty big leap.
Like their new 01 model.
Okay.
It's built to tackle more complex things, you know,
like planning, even a bit of experimentation
to find solutions.
Wow.
And this must have some big implications.
Huge, yeah.
For the tech world, right?
Oh, absolutely, yeah.
What kind of chips are needed to even run these things?
Totally.
Think of companies like NVIDIA.
Right.
Their chips are the standard right now.
Mm-hmm.
But this could shake things up.
Yeah, and speaking of AI chips.
Yeah.
Amazon's been making some pretty big moves.
They have, yeah.
They're investing heavily in this area, right?
Through anapurnal labs.
Yeah, their goal is to like challenge and right,
offer something more efficient, more cost effective.
Okay.
They've developed the Tranium II chip
specifically for training these big AI models.
Okay.
And then they've got Inferentia,
that's focused on actually running those models
to generate stuff.
So it's a power play in the AI chip market.
It is, yeah.
And this seems like...
It's a big trend, yeah.
A larger trend.
Totally.
Like we're seeing this with Amazon, Microsoft.
They're all developing their own chips, right?
Yeah, they want more control,
less reliance on outside suppliers.
Yeah.
It's a way to be self-sufficient
in this rapidly changing world.
Exactly, yeah.
Yeah, so let's shift gears a bit.
Move from the tech world to...
Okay.
To politics.
Okay.
There's been a lot of talk about
how President-elect Trump's policies might impact AI,
especially after his last term.
Yeah.
One big topic is President Biden's executive order on AI.
Right.
Which aimed to set up a framework
for managing the risks
and the opportunities that come with AI.
Yeah, across different sectors.
There's a lot of speculation about whether
President-elect Trump will keep that order or not.
Yeah, and there are a lot of opinions
on what that might mean.
A lot of different perspectives, yeah.
Some tech leaders like John Rose, Dell's global CTO,
he believes that that executive order
didn't really change much
in terms of what private companies were already doing
with AI.
But then you have others who disagree.
A vice president at the Information Technology
and Innovation Foundation argues that removing the order
could actually produce bureaucracy
and help get AI products to market faster.
Okay.
And then Vishnarendra,
CIO of Graphic Packaging International,
he stresses the need for companies
to really understand and address bias, exactly.
That work with AI models.
And he thinks organizations need to prioritize
ethical considerations.
Regardless of regulations, yeah.
Yeah, regardless of the rules, so.
It's a complex issue, right?
It is.
Balancing innovation with responsible development.
And then there's the whole Elon Musk situation.
There's this advocacy group called
Americans for Responsible Innovation.
Okay.
And they're actually petitioning for Musk
to be appointed as an advisor on AI.
Interesting.
To the Trump administration.
Wow.
They argue that he's uniquely qualified.
Because of his involvement, yeah.
Because of his deep involvement in the field.
Yeah.
To be a champion for AI safety.
Mm-hmm.
So Musk's potential involvement
definitely adds another layer to this.
Yeah.
Already pretty dynamic landscape, right?
It's interesting, given his complex relationship
with the AI community, he's criticized OpenAI,
a company he co-founded.
But he's also running his own AI venture, XAI.
So how objective would he be?
Yeah.
It'll be interesting to see how that all unfolds.
Definitely.
So continuing on with this whole idea of AI's evolving
influence, let's dive into how it's impacting jobs.
OK.
Specifically, generative AI, stuff like chat GPT.
Right.
How is this changing things for workers?
Well, there was this recent study in management science.
OK.
They looked at job postings on a popular freelancing platform.
And what's fascinating is they found a big drop in demand
for jobs that are prone to automation.
Like writing and coding after chat GPT came out.
Oh, wow.
So does this mean AI's coming for everyone's jobs?
Not necessarily.
While demand for those tasks decreased,
the study also found that the complexity of those remaining
jobs and how much employers were willing to pay for them
actually went up.
So it's not about replacing humans.
Right.
But it's about a shift in the types of skills needed.
Exactly.
That's not a doom and gloom scenario.
It's about adaptation.
Yeah.
Workers who can use AI tools to solve more complex problems,
they'll be in high demand.
So it's a call to evolve alongside this technology.
Exactly.
All right.
Now let's shift gears again.
OK.
To a different area where AI is making waves, music,
and artistic legacy.
OK.
There's this pretty amazing story about how Jerry Garcia's
voice is being brought back to life
through artificial intelligence.
That's wild.
So Garcia's a state partnered with 11 Labs.
They're an AI voice company.
And they created a voice model that
can read different texts in his voice.
Wow.
Like books, articles, even fan stories.
No way.
And what's really remarkable is that you could even
hear him speak in different languages.
That's incredible.
It's like blurring the lines between what we thought
was possible and what AI can now do.
Yeah.
It's really a great example of how AI
can be used to preserve and share.
Culture, yeah.
Heritage.
Yeah.
And ways you never even imagined.
And staying on this theme of AI in creative fields,
11 Labs has also been working on AI music generation.
Oh, wow.
So AI composing music?
Yeah, they recently gave a sneak peek
of their music generator.
Interesting.
It's still in its early stages.
OK.
But this really makes you think about the future of music.
Yeah, like could musicians collaborate with AI
to explore new sounds?
It's definitely pushing the boundaries of what
we thought was possible.
So speaking of pushing boundaries,
let's talk about this research paper that's
making waves in the AI world.
It's called Frontier Math.
Frontier Math.
And it's a brand new benchmark designed
to test how well AI can reason mathematically.
So like advanced math?
Yeah, advanced mathematical reasoning.
OK.
Frontier Math is basically a collection
of hundreds of original math problems.
They span different areas of mathematics.
And what makes it really different from other benchmarks
is that even expert mathematicians
might need hours or even days to solve them.
So it's really challenging these AI models,
pushing them to their limits.
And how does AI stack up against this challenge?
Well, even the most advanced models
can only solve a tiny fraction of these problems.
Oh, wow.
Less than 2% to be exact.
That's a pretty humbling reality check.
It is, yeah.
It really shows that there's still a big gap.
Huge gap.
They know that.
Between AI and human level mathematical reasoning.
Yeah.
And while AI has come a long way,
this benchmark shows us just how much more there is to explore.
So we're still in the early stages.
Absolutely, yeah.
Of figuring out what AI can really do.
Absolutely, yeah.
So we've journeyed from the inner workings
of these cutting edge AI models to the political forces
shaping its development.
And we've even touched upon its impact on creative fields,
like music.
Music and art.
It's a lot to take in.
But it shows you just how far reaching AI is.
Yeah, yeah.
And it's only going to become more so.
Absolutely, yeah.
So stay tuned for part two.
We'll be back after a short break.
OK, sounds good.
To continue exploring this fascinating and ever-evolving
world of AI.
Looking forward to it.
Yeah.
Welcome back to our exploration of all the latest stuff
happening in the world of AI.
We were just discussing how even the most advanced AI models
are struggling with these complex math problems.
Like, kind of a reality check on where AI is at.
It seems AI is really good at crunching numbers,
finding patterns.
But when it comes to abstract reasoning,
that kind of creative problem solving
that you need for advanced math, it really struggles.
So where do we go from here?
Like, how do we bridge that gap?
That's the big question, right?
Yeah.
Researchers are exploring all sorts of things.
But one thing's become clear, just scaling up,
feeding them more data, more computing power.
It's not going to cut it.
It's not the answer.
So the bigger is better approach has kind of hit a wall.
It's like trying to build a skyscraper
just by stacking more bricks on top of each other.
Eventually, the foundation just crumbles.
We need a new blueprint.
Yeah.
We need a way of thinking about AI.
And maybe we can get some inspiration from how humans
learn, right?
Exactly.
So instead of just training AI on massive data sets
of text and code.
What if we gave them access to the tools that humans use?
Yeah, the resources.
Like calculators.
Textbooks.
Textbooks, even the internet.
The internet.
Wow.
So the idea is to create AI that can learn.
Learn and reason more like a human student.
So they'd be exploring, experimenting.
Digitally exploring.
Yeah.
That sounds so fascinating.
It's pretty cool.
But also incredibly complex.
It is, yeah.
How do you even begin to.
It's a big challenge.
Teach an AI to use a calculator.
Or understand a textbook.
It's a whole combination of things.
Machine learning, natural language processing,
and really understanding how humans learn.
So we're still early days in this approach.
Just scratching the surface.
But it sounds like there's a lot of.
There's a lot of excitement.
Excitement.
Yeah, a lot of optimism.
Maybe we're on the verge of like.
A major breakthrough.
Yeah, something that could, yeah.
Redefine what AI can do.
Let's shift gears a little bit.
Talk about the economic implications
of all these advancements.
We've talked about jobs.
But what about the bigger picture?
The broader impact, yeah.
How is AI shaping industries?
Well, it's a very.
Yeah.
Dynamic and complex landscapes.
On the one hand, AI has the potential to.
Unlock.
Unlock so much value.
Economic value, yeah.
Automating tasks.
Improving efficiency.
Creating new.
Creating whole new products and services.
Yeah, think of self-driving cars.
Self-driving cars.
Personalized medicine.
Yeah.
AI-powered financial advisors.
These innovations could really revolutionize industries.
Yeah, totally.
Change how we live and work.
Yeah.
But there's a flip side to all this, right?
There's always another side.
What about job displacement?
Yeah, that's a big concern.
Wealth inequality and this concentration of power.
Yeah.
In the hands of a few tech giants.
This is a double-edged sword, right?
We need to harness the power of AI for good.
Yeah.
While also mitigating those potential downsides.
It's a balancing act.
It is, yeah.
It requires a lot of.
Collaboration.
Yeah.
Between governments.
Governments.
Industry leaders.
Researchers.
The public.
It's about making sure AI benefits everyone.
Yeah, society's a whole.
Let's focus on a specific industry.
OK.
That's being transformed by AI.
Healthcare.
Healthcare is huge, yeah.
AI has the potential to revolutionize so much.
Absolutely.
From diagnosis and treatment to drug discovery.
Even patient care.
And patient care.
Yeah.
We're already seeing AI being used
to analyze medical images.
Yeah.
Identify patterns in patient data.
Even assist with surgery.
Assisting with surgeries.
That's pretty wild.
And then there's personalized medicine.
Yeah.
Tailoring treatments.
Where AI can help tailor treatments
to individual patients.
Based on their genetics, their lifestyle.
These breakthroughs could really.
Improved outcomes.
Improve outcomes.
Yeah.
Revolutionize how health care is delivered.
But of course, it's not all smooth sailing, right?
There are always challenges.
There are challenges.
Especially in such a complex and sensitive field.
Like how do we ensure these AI systems are accurate?
Accuracy.
It's a key.
Reliable.
Reliability.
What about patient privacy?
That's a huge one.
And then there's the potential for bias in these algorithms.
Bias is a big concern.
So it's not just about building these sophisticated tools.
It's about using them responsibly.
It's about integrating them ethically.
Yeah, ethically and responsibly.
It's about considering.
The ethical implication.
The legal.
The social implication.
Plusications of AI.
Yeah.
It's a lot to think about.
Let's turn our attention to another industry.
OK.
That's being profoundly impacted by AI.
OK.
Education.
AI has the potential to personalize learning.
Personalize learning.
Provide individualized feedback.
Imagine AI tutors.
AI tutors, yeah.
Adaptive learning platforms.
Even virtual reality classrooms.
It's pretty amazing.
Yeah.
These innovations could transform how we.
Learn and teach.
Make education more accessible.
Make it more engaging.
More engaging.
But just like with health care.
There are concerns, right?
There are.
Especially about fairness and accessibility.
Yeah.
Like what if these AI tools are only available to.
Right.
Wealthy students.
It could widen the achievement.
Exactly.
That's a valid concern.
We need to make sure.
That AI is used to create.
Then AI is used.
More equitable.
To create more equitable and accessible opportunities.
For all students.
All students.
Yeah.
Regardless of their background.
So it's not just about the technology itself.
It's about how it's used.
It's about how it's implemented.
And who has access.
Yeah.
The goal is to leverage AI.
To create.
More inclusive.
A more inclusive.
And effective.
An effective education system.
For everyone.
Let's step back for a moment.
And think about the big picture.
The broader societal implications of all this.
The societal implications.
As AI becomes more and more pervasive.
Mm-hmm.
It really makes us question.
What it means to be human.
Yeah.
Like what is intelligence.
Right.
What is consciousness.
Consciousness.
What is even the nature of reality.
Yeah.
These are big questions.
Will AI surpass human intelligence.
Will it take over our jobs.
Will it change the fabric of our relationships.
Our communities.
These are profound questions.
Yeah.
Profound and complex.
And there are no easy answers.
There are no easy answers.
One thing is certain.
We need to talk about it.
We need to have this conversation.
We need to have a dialogue.
About the future of AI.
About its role in society.
And its role in society.
Yeah.
We need to think critically.
About the ethical implication.
About the ethical.
The social implication.
See, economic implications.
Economic implications.
And we need to work together.
To make sure.
To make sure AI is used to create a better future.
For everyone.
Yeah.
A better future for all.
It's a daunting challenge.
It is.
But it's also exciting.
Yeah.
It's it's right.
Demands innovation.
Innovation.
Responsibility.
Ambition.
And humility.
Humility, yeah.
As we navigate this uncharted territory.
Mm-hmm.
It's important to remember.
It's at the future.
That the future of AI is not predetermined.
It's not set in stone.
It's something we are shaping.
We are shaping it.
Through our choices.
Through our actions.
So let's choose wisely.
Let's use this power.
Let's harness this power.
To build a better world.
To build a better world for everyone.
A more just world.
More just.
More equitable.
More sustainable.
And sustainable.
For all.
We'll pause here.
OK.
And continue our exploration of this ever-evolving world of AI
in the final part of our deep dive.
Sounds good.
Welcome back to the final part of our deep dive into this.
Yeah, this amazing world of AI.
It is, yeah.
We've covered so much.
From the technical stuff.
Yeah.
To how it's impacting different industries.
Yeah.
And even like.
The big societal questions.
The societal questions it raises.
And we've talked about both the amazing possibilities.
Yeah, the potential pitfalls.
Potential pitfalls as well.
But there's this one crucial aspect
we haven't really dug into yet.
Ethics.
Exactly.
It's so important.
As these AI systems get more powerful.
More autonomous.
We have to make sure that they reflect our values.
Yeah, that they're not.
And that they don't perpetuate harmful biases
or discriminatory practices.
So it's not just about making sure.
Because this AI is smart.
That it's used to create a fairer society.
It's about making sure it's ethically sound.
Ethically sound.
Aligned with human values.
Yeah.
And that requires a real shift in thinking.
Not just for AI developers.
Not just developers.
But for everyone.
Right.
Across society.
We need to see ethics as part of the design.
Part of the process.
Part of the development process.
From the beginning.
Yeah, it can't be an afterthought.
Exactly.
Yeah.
So it's not just about building AI that's smart.
It's about building AI that's good.
Yeah, that's a good.
AI that's aligned with our values.
And that means asking some tough questions.
Yeah, what are the consequences?
What are the intended?
Yeah, intended and unintended.
Unintended consequences.
We don't need quences.
We need frameworks for accountability.
Accountability, transparency.
We're on transparency.
Yeah.
How do these systems make decisions?
How do we hold them responsible for their actions?
It's a big challenge.
But we have to figure it out.
And it's going to take a lot of collaboration.
Collaboration between ethicists.
Ethicists, yeah.
Social scientists.
Piggle experts.
Policy makers, developers.
It's a complex issue.
Yeah.
But it's an important one.
If we want AI to benefit everyone.
If we want to build that better future.
So where is all this headed?
What does the future hold for AI?
It's hard to say for sure.
It is, yeah.
But one thing's certain.
Yeah.
AI is here to stay.
It's going to continue to evolve.
Exponentially.
It's going to transform industries.
Through shape society.
It's going to challenge our understanding.
Or that means to be human.
Some experts believe we're on the verge of.
The singularity.
A singularity.
Yeah.
Where AI surpasses human intelligence.
And it could lead to a whole cascade of.
Unpredictable changes.
Unpredictable.
Irreversible changes.
Yeah.
It's a big idea.
It is.
And others are more cautious.
Yeah.
They say AI will keep advancing.
What?
But it's not an existential threat.
Not a threat to humanity.
But no matter what you believe.
You need to talk about it.
We need to keep having this conversation.
We need to think critically.
Yeah.
About the ethical.
The social.
Social.
Economic.
Applications.
Of this technology.
And we need to work together.
To make sure.
To make sure AI is used.
For good.
To create a better future.
To create a better future for everyone.
It's a challenge.
It's a big challenge.
But it's an exciting one.
And as we navigate all of this.
It's important to remember.
It's important to remember that the future of AI.
It's not predetermined.
It's not set in stone.
Yeah.
We are shaping it.
We're shaping it.
With our choices.
With our actions.
So let's choose wisely.
Let's use this power.
Let's harness this power.
To build a better world.
For everyone.
More just.
More equitable.
More sustainable.
For all.
Well, we've reached the end of our deep dive.
The end of our journey.
Into the world of AI.
It's been quite a journey.
We hope you found it insightful.
Thought provoking.
And that you have a better understanding of.
Yeah, better understanding of this incredible, rapidly
evolving field.
Thanks for listening to the Daily AI News Podcast.
And stay tuned for more.
We'll see you next time.