September 23, 2024 • 17 mins
Can artificial intelligence really transform our everyday lives and revolutionize entire industries? Buckle up as we take you on a fascinating journey through the world of AI, from its basic principles to its most advanced applications. We'll break down how AI-powered technologies like self-driving cars and ultra-accurate streaming recommendations work. By demystifying complex algorithms and explaining machine learning in simple terms, we’ll uncover how these algorithms learn from data and make decisions. Through relatable examples, we'll differentiate between supervised learning and unsupervised learning, and dive into the wonders of deep learning and artificial neural networks, highlighting their impressive capabilities in image recognition and autonomous driving.
Imagine a world where artificial intelligence not only secures your finances but also enhances your creative projects. In this episode, we explore AI's transformative impact across various fields, from revolutionizing fraud detection and market analysis to composing music and generating visual art. We’ll ponder the future of work, focusing on the increasing value of uniquely human skills like creativity, critical thinking, and empathy. As we reflect on how AI can liberate us from mundane tasks, we invite you to envision a symbiotic relationship between human and artificial intelligence—one that allows us to focus on meaningful pursuits and harness the full potential of our creative minds.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
All right, buckle up, because today we are diving
deep into the world of AI.
Speaker 2 (00:06):
Oh yeah.
Speaker 1 (00:07):
It feels like AI is absolutely everywhere these days
.
Speaker 2 (00:10):
It really does.
Speaker 1 (00:10):
It's everywhere, right From self-driving cars to
streaming services and therecommendations.
They're eerily accurate right.
Speaker 2 (00:17):
Yeah.
Speaker 1 (00:18):
It can almost feel like magic.
Speaker 2 (00:20):
Absolutely.
What kind?
Speaker 1 (00:21):
of magic?
Is it Absolutely?
Speaker 2 (00:22):
But the reality is it's less about magic and more
about just really cleveralgorithms and tons of data.
Speaker 1 (00:31):
Right.
Speaker 2 (00:31):
So instead of thinking about like a complete
takeover, oh, like theTerminator.
Speaker 1 (00:35):
Yeah, exactly.
Speaker 2 (00:36):
Think of it as machines becoming really good at
specific tasks.
Okay, they're mimicking humanintelligence, but only in these
very specific areas.
Speaker 1 (00:45):
So less Terminator, more like helpful tools.
Exactly I like it, we've got agreat stack of sources to unpack
here today.
We do From articles to researchpapers, and I think our mission
today is to really try to cutthrough the hype, yeah, and give
everyone a solid understandingof AI the potential, but also
the limitations.
Speaker 2 (01:04):
Yeah, absolutely, and we'll be exploring everything
from, like, the foundations ofmachine learning to the honestly
kind of mind blowing world ofdeep learning, even what all the
buzz is about with generativeAI yeah, generative AI, like
chat, gpt.
Speaker 1 (01:17):
Chat, gpt.
Yeah so, speaking of buzz, theIBM article we have, it
describes AI as this nested dollsituation.
What's the deal with that?
Speaker 2 (01:32):
It's actually a great analogy.
So imagine you know opening upa doll and inside is another
doll and each one is a layerdeeper, and so in the AI world,
that outermost doll is machinelearning, or, as we call it, ml.
Speaker 1 (01:41):
Okay, ml, so that's our starting point.
Speaker 2 (01:43):
Yeah.
Speaker 1 (01:43):
What exactly is machine learning?
Speaker 2 (01:46):
So, at its core, machine learning is about
algorithms learning patternsfrom data to then make decisions
or predictions.
Right, so let's say you'reusing like a navigation app on
your phone.
Speaker 1 (01:58):
Okay, sure.
Speaker 2 (01:59):
And it suggests a faster route because of the
traffic.
Speaker 1 (02:01):
Yes.
Speaker 2 (02:02):
That's machine learning in action.
Speaker 1 (02:04):
Gotcha.
Speaker 2 (02:04):
It's learned from like historical traffic patterns
to be able to make a prediction.
Speaker 1 (02:09):
Oh, interesting.
Speaker 2 (02:09):
About the best route for you right now.
Speaker 1 (02:11):
That makes a lot of sense.
So instead of a programmer likewriting very specific rules for
every single possibility, theML, the machine learning
algorithm, can kind of learnthese rules on its own right,
yeah yeah, just by likecrunching all this data.
Speaker 2 (02:26):
Exactly by crunching the data, it learns and it
adapts, and it's able to adjust.
Pretty slick yeah.
Speaker 1 (02:31):
So within ML there are different approaches to
learning.
Yeah Right.
Speaker 2 (02:35):
Yeah absolutely.
Speaker 1 (02:35):
I'm talking about supervised learning,
unsupervised learning.
Speaker 2 (02:38):
Right.
So a common analogy forsupervised learning is teaching
a dog a new trick.
Speaker 1 (02:44):
Okay, so like teaching my dog Sparky to shake
a paw Exactly For a treat,exactly Okay.
Speaker 2 (02:50):
You're basically giving the dog labeled data.
Speaker 1 (02:52):
Okay.
Speaker 2 (02:52):
You're showing it the action you want shake and then
you're rewarding it when it getsit right.
Speaker 1 (02:57):
Right, when he does the thing, he gets the thing
Exactly Okay.
Speaker 2 (03:02):
And so, in supervised learning, we feed the algorithm
labeled data where we alreadyknow what the correct output
should be.
So this way, the algorithm canlearn to map the inputs to
outputs based on all thoseexamples that we're giving it.
Speaker 1 (03:14):
So it's all about learning from examples, just
like Sparky learns to shake hispaw for a tasty treat.
Speaker 2 (03:20):
Exactly.
Speaker 1 (03:20):
So what about unsupervised learning?
How does that fit into all ofthis?
Speaker 2 (03:24):
Yeah.
So imagine, instead of likespecifically training Sparky,
you let him loose in a park.
Speaker 1 (03:31):
Okay.
Speaker 2 (03:32):
And he's sniffing around, he's exploring.
Speaker 1 (03:35):
Yep.
Speaker 2 (03:35):
He's just like taking in the world.
Speaker 1 (03:38):
Yeah.
Speaker 2 (03:39):
You're not telling him what to do.
Speaker 1 (03:40):
Right.
Speaker 2 (03:41):
Unsupervised learning is similar.
We give the algorithm datawithout any labels or desired
outcomes okay it's up to thealgorithm to find the patterns,
the relationships oh, wow andeven like anomalies in that data
, so it's like we're letting itlose.
Speaker 1 (03:56):
You see what it finds , exactly you can discover
insights that we might havemissed that's fascinating.
Yeah, it's a really powerfultool.
So unsupervised learning islike letting the algorithm
explore the data landscape, andwe're hoping it uncovers some
hidden gems.
Speaker 2 (04:11):
Exactly.
Speaker 1 (04:12):
Very cool.
Speaker 2 (04:12):
And then if we dive one layer deeper into our AI
nested doll, Okay, one layerdeeper.
We find deep learning, which isa really powerful type of
unsupervised learning.
Speaker 1 (04:22):
Deep learning.
This is where things get reallyinteresting.
Speaker 2 (04:25):
It does.
I mean we're talking aboutalgorithms that are mimicking
the human brain yeah at leastthat's my understanding you're
on the right track, yeah okay sodeep learning relies on
artificial neural networks okayand these networks are inspired
by the structure of the humanbrain right and these networks
have multiple layers, hence deeplearning makes sense that
allows them to processinformation in a much more
(04:48):
complex and nuanced way, rightCompared to like traditional ML
algorithm.
Speaker 1 (04:53):
And it's that complexity that allows deep
learning to take on these likereally impressive tasks like
image recognition and thingslike that, exactly Like
unlocking your phone with yourface.
Speaker 2 (05:04):
Yeah.
Speaker 1 (05:04):
Or self-driving cars.
Speaker 2 (05:06):
It's all thanks to deep learning.
Speaker 1 (05:08):
Being able to process all of that visual information
so quickly.
Speaker 2 (05:13):
Yeah, it can process that visual information, it can
identify objects, it can makedecisions based on what it's
seeing.
Speaker 1 (05:19):
It's wild and that is a great example.
Yeah, self-driving cars, one ofthe most popular examples of
deep learning.
Speaker 2 (05:26):
But really out there right now.
Absolutely.
Speaker 1 (05:29):
Those cars are practically thinking for
themselves.
Speaker 2 (05:32):
They're driving themselves.
Speaker 1 (05:33):
It's really mind blowing when you think about it.
Speaker 2 (05:36):
It is.
Speaker 1 (05:36):
How does it work?
So these algorithms areconstantly analyzing data from
the car sensors, likerecognizing pedestrians, other
vehicles, obviously, trafficsignals, all while making split
second decisions to steer,accelerate, brake the whole nine
yards.
Speaker 2 (05:54):
Yeah, and it's all happening in real time.
Speaker 1 (05:56):
All in real time.
It's incredible how it allcomes together.
Speaker 2 (05:59):
It really is.
It's a testament to how fardeep learning has come and the
potential it has honestly for,like, the future of
transportation and beyond.
Speaker 1 (06:09):
And this leads us to an even more recent and arguably
even more intriguing realm ofAI generative AI.
Speaker 2 (06:17):
Generative AI.
Speaker 1 (06:18):
This is where things get really creative.
Speaker 2 (06:20):
This is where it gets really interesting.
Speaker 1 (06:22):
This is where AI is like.
Let me create original content,whether that's writing me poems
, composing music, generatingrealistic images that we've
never seen before.
Speaker 2 (06:31):
It's pretty wild what it can do.
Speaker 1 (06:32):
It's amazing.
So how does it actually do that?
How does it generate stuff thatfeels so original?
Speaker 2 (06:37):
So it's all thanks to this, like power of being
trained on massive data sets.
Speaker 1 (06:43):
Okay.
Speaker 2 (06:44):
Imagine like feeding this AI.
Let's ChatGPT the entireLibrary of Congress.
Speaker 1 (06:51):
Oh, wow.
Speaker 2 (06:52):
Plus, like every book that's ever been written.
It's a lot of books, every song, every script.
Speaker 1 (06:57):
Yeah, you get the idea.
It's a wild amount ofinformation.
Speaker 2 (07:00):
Crazy amount of data.
Speaker 1 (07:01):
No wonder the training cost is in the millions
for this.
Speaker 2 (07:04):
Oh yeah.
So ChatGPT basically gobblesall this up gobbles it up and it
uses it to learn the patternsof human language, creativity,
even humor yeah, it's reallyinteresting oh, wow it analyzes
the patterns and therelationships to be able to
create something new right butthat's still aligned with this
style or the tone, the structureyeah, so it's almost like it's
(07:25):
learning the rules of languageyeah so well that it can then
bend those rules and create itsown interpretation exactly.
Speaker 1 (07:33):
That's really wild.
That's super interesting it isreally wild and it's not just
limited to text right no, not atall, we're seeing this in
images.
Speaker 2 (07:40):
We're seeing it in music music yeah it's everywhere
a whole bunch of differentmodalities, which is really
fascinating.
Speaker 1 (07:47):
Really, really fascinating.
But we've covered a lot oftheoretical ground here.
Speaker 2 (07:52):
Yeah.
Speaker 1 (07:52):
How is this being used in the real world?
Speaker 2 (07:55):
Yeah, that's the question, isn't it Right?
It's not all theoretical.
Speaker 1 (07:58):
Beyond self-driving cars.
Exactly Chat GPT.
Speaker 2 (08:01):
Right.
I mean it's being used in moreways than I think we can even
imagine.
Wow, it's really quietlyrevolution From healthcare to
finance, to even believe it ornot, the arts.
Speaker 1 (08:17):
AI is infiltrating the arts.
Speaker 2 (08:19):
It's true, it's true.
Speaker 1 (08:20):
I'm going to need some more information on that
we're going to have to dive intothat?
Speaker 2 (08:22):
Yeah, we're going to do that.
Speaker 1 (08:23):
But I do remember reading in that IBM article
about AI being used in customerservice.
Yeah, which is something that Ithink we can all relate to,
unfortunately.
Speaker 2 (08:32):
Sure sure, no more waiting on hold for hours just
to get a simple questionanswered Exactly.
And those AI-powered chatbotsare getting good.
Speaker 1 (08:42):
They are getting good .
Speaker 2 (08:42):
They're so much more sophisticated.
Speaker 1 (08:44):
Yeah.
Speaker 2 (08:45):
They can answer those frequently asked questions.
They can, like, guide youthrough troubleshooting steps
Yay, and they can evenpersonalize those interactions
based on you know what you'vetalked about before.
Speaker 1 (08:56):
It's like having a 24-7 customer service agent at
your beck and call, except it'sAI.
Speaker 2 (09:01):
Exactly, super efficient, super efficient, very
cool.
And that efficiency, you know,that theme kind of extends to
other sectors.
Speaker 1 (09:09):
Like what.
Speaker 2 (09:10):
Like in finance, for example, AI is being used to
detect fraud in real time.
Speaker 1 (09:16):
Oh, wow.
Speaker 2 (09:17):
You can analyze market trends, okay, and it can
even give you personalizedfinancial advice.
Speaker 1 (09:21):
Wow, okay, so it can spot those tricky credit card
charges.
Speaker 2 (09:25):
Exactly that's fraudulent, that's right.
Speaker 1 (09:27):
It's like having an extra set of eyes constantly
monitoring.
Speaker 2 (09:30):
Exactly.
Speaker 1 (09:31):
For anything suspicious.
That's really cool.
Speaker 2 (09:34):
And it's not just for our individual accounts.
It's used to combat fraud on amuch larger scale.
Oh, really so it's helpingbusinesses, helping institutions
, prevent these financial lossesand protect their customers.
Speaker 1 (09:48):
Huge impact, big impact Wow.
Speaker 2 (09:50):
And then going back to the arts.
Speaker 1 (09:51):
Yeah, the arts.
We've got to go back to thearts.
Speaker 2 (09:53):
I'm curious about this too.
Speaker 1 (09:54):
Yeah, how is AI making its mark in such a
creative field?
Speaker 2 (09:59):
It's really fascinating.
So AI is being used to composemusic, what it's generating,
scripts for movies, for plays,and even creating some really
stunning visual art.
Speaker 1 (10:12):
AI is writing symphonies now.
Speaker 2 (10:14):
Well, I mean, it's still early days.
Okay, ai is writing symphoniesnow.
Well, I mean, it's still earlydays, but there are AI systems
that can analyze musicalpatterns, you know, and generate
melodies and harmonies and evenentire compositions in
different styles.
Speaker 1 (10:27):
It's incredible.
Speaker 2 (10:28):
It's really incredible.
It's also a little unnerving.
I know it's a lot to take in.
It's a lot to take in.
Speaker 1 (10:32):
Yeah, it makes you wonder what aspects of our lives
won't be touched by AI in thefuture.
Speaker 2 (10:37):
Yeah, it's a big question.
Speaker 1 (10:38):
It's almost like this AI wave is about to crash over
all of us and change everything.
Speaker 2 (10:43):
Yeah, it feels like it's moving so fast.
Speaker 1 (10:45):
It really does.
Speaker 2 (10:46):
Faster than a lot of us could imagine, I think.
Speaker 1 (10:48):
Exactly.
So how do we even begin to makesense of it all?
What does it mean?
What does it mean for us?
What does it mean?
What does?
Speaker 2 (10:54):
it mean for us?
What does it all mean for ourfuture?
That's the big question.
Speaker 1 (10:58):
It is a big question.
Speaker 2 (10:59):
And it's impossible to predict the future.
Speaker 1 (11:01):
Right.
Speaker 2 (11:01):
But I think what we can do is look at where things
are headed right now, yeah,understand the potential impact
and really focus on how we canadapt Okay, and thrive in this
like rapidly changing landscape.
Speaker 1 (11:18):
Adapt and thrive.
I like that.
So not fearing the machinestaking over, but figuring out a
way to work alongside themExactly.
Right.
Speaker 2 (11:22):
Yeah, it's not about being replaced.
Okay, it's about understandingthat AI is going to change the
types of jobs that are available.
Speaker 1 (11:29):
Right, because some things will be automated.
Speaker 2 (11:31):
Exactly.
Speaker 1 (11:31):
But that's going to open up new opportunities.
Speaker 2 (11:33):
Exactly, I mean, think about it.
Someone has to design these AIsystems, someone has to build
them, they have to maintain them.
Speaker 1 (11:40):
We're going to need a whole new workforce as of.
Ai specialists.
Speaker 2 (11:43):
We are to manage all this.
Speaker 1 (11:45):
OK, so we've got those jobs, but for, like the
rest of us, what skills aregoing to be valuable in this
future, where AI is everywhere?
Speaker 2 (11:54):
Well, that's where it gets really interesting,
because, as AI takes on theseroutine tasks Right, these
repetitive things, it's actuallythose skills that are uniquely
human.
Speaker 1 (12:04):
Okay.
Speaker 2 (12:04):
That are going to become even more important.
Speaker 1 (12:07):
So creativity, critical thinking, yeah, problem
solving the things that areharder.
Speaker 2 (12:12):
Exactly.
Speaker 1 (12:13):
For AI to replicate.
Speaker 2 (12:15):
Those are the things that are going to set us apart.
Okay.
Being able to, like, thinkoutside the docs yeah.
Come up with innovativesolutions to new problems Right.
And navigate these complexsituations, you know, using our
judgment.
Speaker 1 (12:29):
Yeah.
Speaker 2 (12:29):
Those are the strengths that humans bring to
the table.
Speaker 1 (12:32):
So it's not just about technical skills anymore.
It's about these essentialhuman skills that are much
harder for AI to, I guess, grasp.
Speaker 2 (12:41):
Yeah, things like empathy, communication,
collaboration working together,yeah, those interpersonal skills
.
Exactly those are going to bemore important than ever.
Speaker 1 (12:49):
Right, because you can't really automate empathy,
exactly, you can't reallyautomate good communication or
being able to collaborateeffectively.
Speaker 2 (12:58):
And even just being able to understand and respond
to the needs of another person.
Those are the things that AI,at least for now, just can't do.
Speaker 1 (13:06):
Yeah, it's almost like the rise of AI is pushing
all of us to become more human.
I love that.
To lean into the things thatmake us unique, yeah.
Speaker 2 (13:15):
I think that's a really beautiful way to put it.
It's not us versus them.
Speaker 1 (13:19):
Right.
Speaker 2 (13:19):
It's about this synergy.
Speaker 1 (13:21):
Working together.
Speaker 2 (13:22):
Between our intelligence and this artificial
intelligence To enhance what wecan already do.
Exactly To enhance ourcapabilities, create new
possibilities.
Speaker 1 (13:31):
Yeah.
Speaker 2 (13:32):
And ultimately build a better future for everyone.
Speaker 1 (13:34):
And who knows, maybe along the way we might learn a
thing or two about ourselves.
Absolutely, that's a greatpoint.
This has been an eye-openingdeep dive into AI, to say the
least.
Speaker 2 (13:45):
It really has.
Speaker 1 (13:45):
I mean we went from demystifying the basics to
exploring the mind-blowingpotential of AI.
Speaker 2 (13:52):
Absolutely.
Speaker 1 (13:52):
It seems like the possibilities are limitless.
Speaker 2 (13:55):
Limitless.
Speaker 1 (13:56):
It's incredible what a ride this has been.
Speaker 2 (13:58):
It's a journey.
Speaker 1 (13:59):
It really is and it feels like this journey is just
beginning.
We're just getting started Justgetting started.
So as we wrap up this deep dive, we want to leave everyone with
something to ponder.
Speaker 2 (14:10):
Okay.
Speaker 1 (14:11):
Imagine a world where AI could take over one tedious
task in your life.
Speaker 2 (14:16):
Okay, what would you?
Speaker 1 (14:16):
choose.
That's where AI could take overone tedious task in your life
what would you choose?
Speaker 2 (14:19):
That's a great question.
Speaker 1 (14:19):
What would you do with that newfound time?
What would you do with thatfreedom?
It's a question worth thinkingabout.
Speaker 2 (14:26):
It really is.
Speaker 1 (14:26):
It's a lot to process , going from AI answering
customer service calls tocomposing symphonies.
I know right, it's incredibleit really is.
Speaker 2 (14:34):
And it feels very much like will ai take over the
world yeah, it's a common fear,but honestly, I think, instead
of thinking about like this, ai,takeover right, we should
really be thinking about how dowe adapt to this new world?
Okay, how do we thrivealongside?
Speaker 1 (14:51):
it.
So how do we do that?
How do we adapt and thrive?
Speaker 2 (14:54):
well, first, I think it's important to remember that
ai isn't just about replacingjobs.
Speaker 1 (14:59):
OK.
Speaker 2 (14:59):
While some tasks might become automated, new
opportunities are going to comeup.
Speaker 1 (15:03):
Right Like who's designing these AI systems.
Speaker 2 (15:07):
Who's?
Speaker 1 (15:07):
building them?
Who's maintaining them?
Speaker 2 (15:09):
We're going to need a whole new workforce of AI
specialists to manage all ofthis.
Speaker 1 (15:13):
So that's those jobs.
But for the rest of us, whatskills are we going to need?
Speaker 2 (15:17):
Well, that's where it gets really interesting,
because as AI takes on thesemore routine tasks, the
repetitive things, it's theskills that are uniquely human
that are really going to standout.
Speaker 1 (15:29):
Oh interesting.
So like creativity, yes.
Critical thinking yes.
Speaker 2 (15:33):
Yeah, problem solving .
Speaker 1 (15:35):
Exactly the things that are much harder for AI to
replicate.
Speaker 2 (15:37):
Because you can teach a computer to do a lot of
things.
You can, but you can'tnecessarily teach it to think
outside the box.
Speaker 1 (15:43):
Right and you can't teach it to like come up with
these really innovativesolutions to new problems.
Speaker 2 (15:50):
Okay.
Speaker 1 (15:50):
Or navigate these complex situations where you
know human judgment is needed.
Speaker 2 (16:04):
So it's not just about technical skills anymore.
Speaker 1 (16:05):
No, it's about these like really essential human
skills that AI can't quite graspexactly and, honestly, things
like empathy, yes, communication, being able to collaborate
effectively those interpersonalskills yes, those are gonna be
more important than ever,because you can't really
automate empathy yeah you yeah,you can't automate good
communication, you know, orbeing able to work really well
on a team.
So it's almost like this riseof the machines is really
(16:26):
pushing us to be more human.
I love that.
To really lean into what makesus unique.
Speaker 2 (16:32):
Yes, lean into those strengths.
Speaker 1 (16:34):
Which I think is a nice way to look at it.
Speaker 2 (16:36):
It is.
Speaker 1 (16:36):
It's not us versus them.
Speaker 2 (16:38):
Right.
Speaker 1 (16:38):
It's this working together.
Speaker 2 (16:40):
It's about finding that synergy.
Speaker 1 (16:42):
The synergy between human and artificial
intelligence.
Speaker 2 (16:45):
Exactly To enhance what we can already do, create
these new possibilities.
Speaker 1 (16:49):
Right.
Speaker 2 (16:50):
And ultimately, I think, build a better future.
Speaker 1 (16:52):
And who knows, maybe along the way we'll learn a
little bit more about ourselves.
Speaker 2 (16:55):
Absolutely, I think we will.
Speaker 1 (16:56):
That's a great place to end.
Yeah, this has been anincredible deep dive.
Speaker 2 (17:01):
It's been fascinating Into the world of AI.
It really has.
Speaker 1 (17:04):
We went from demystifying the very basics to,
you know, like I said,exploring the potential.
Speaker 2 (17:12):
It's mind-blowing when you think about it.
Speaker 1 (17:14):
Of where this could go.
I mean.
It really is the possibilitiesare limitless.
Speaker 2 (17:19):
They really are.
Speaker 1 (17:19):
Thank you so much for joining us.
Speaker 2 (17:21):
It was my pleasure.
Speaker 1 (17:22):
For this deep dive into AI and thank you everyone
out there for listening.
We'll catch you next time.
All right, buckle up, because today we are diving
deep into the world of AI.
Speaker 2 (00:06):
Oh yeah.
Speaker 1 (00:07):
It feels like AI is absolutely everywhere these days
.
Speaker 2 (00:10):
It really does.
Speaker 1 (00:10):
It's everywhere, right From self-driving cars to
streaming services and therecommendations.
They're eerily accurate right.
Speaker 2 (00:17):
Yeah.
Speaker 1 (00:18):
It can almost feel like magic.
Speaker 2 (00:20):
Absolutely.
What kind?
Speaker 1 (00:21):
of magic?
Is it Absolutely?
Speaker 2 (00:22):
But the reality is it's less about magic and more
about just really cleveralgorithms and tons of data.
Speaker 1 (00:31):
Right.
Speaker 2 (00:31):
So instead of thinking about like a complete
takeover, oh, like theTerminator.
Speaker 1 (00:35):
Yeah, exactly.
Speaker 2 (00:36):
Think of it as machines becoming really good at
specific tasks.
Okay, they're mimicking humanintelligence, but only in these
very specific areas.
Speaker 1 (00:45):
So less Terminator, more like helpful tools.
Exactly I like it, we've got agreat stack of sources to unpack
here today.
We do From articles to researchpapers, and I think our mission
today is to really try to cutthrough the hype, yeah, and give
everyone a solid understandingof AI the potential, but also
the limitations.
Speaker 2 (01:04):
Yeah, absolutely, and we'll be exploring everything
from, like, the foundations ofmachine learning to the honestly
kind of mind blowing world ofdeep learning, even what all the
buzz is about with generativeAI yeah, generative AI, like
chat, gpt.
Speaker 1 (01:17):
Chat, gpt.
Yeah so, speaking of buzz, theIBM article we have, it
describes AI as this nested dollsituation.
What's the deal with that?
Speaker 2 (01:32):
It's actually a great analogy.
So imagine you know opening upa doll and inside is another
doll and each one is a layerdeeper, and so in the AI world,
that outermost doll is machinelearning, or, as we call it, ml.
Speaker 1 (01:41):
Okay, ml, so that's our starting point.
Speaker 2 (01:43):
Yeah.
Speaker 1 (01:43):
What exactly is machine learning?
Speaker 2 (01:46):
So, at its core, machine learning is about
algorithms learning patternsfrom data to then make decisions
or predictions.
Right, so let's say you'reusing like a navigation app on
your phone.
Speaker 1 (01:58):
Okay, sure.
Speaker 2 (01:59):
And it suggests a faster route because of the
traffic.
Speaker 1 (02:01):
Yes.
Speaker 2 (02:02):
That's machine learning in action.
Speaker 1 (02:04):
Gotcha.
Speaker 2 (02:04):
It's learned from like historical traffic patterns
to be able to make a prediction.
Speaker 1 (02:09):
Oh, interesting.
Speaker 2 (02:09):
About the best route for you right now.
Speaker 1 (02:11):
That makes a lot of sense.
So instead of a programmer likewriting very specific rules for
every single possibility, theML, the machine learning
algorithm, can kind of learnthese rules on its own right,
yeah yeah, just by likecrunching all this data.
Speaker 2 (02:26):
Exactly by crunching the data, it learns and it
adapts, and it's able to adjust.
Pretty slick yeah.
Speaker 1 (02:31):
So within ML there are different approaches to
learning.
Yeah Right.
Speaker 2 (02:35):
Yeah absolutely.
Speaker 1 (02:35):
I'm talking about supervised learning,
unsupervised learning.
Speaker 2 (02:38):
Right.
So a common analogy forsupervised learning is teaching
a dog a new trick.
Speaker 1 (02:44):
Okay, so like teaching my dog Sparky to shake
a paw Exactly For a treat,exactly Okay.
Speaker 2 (02:50):
You're basically giving the dog labeled data.
Speaker 1 (02:52):
Okay.
Speaker 2 (02:52):
You're showing it the action you want shake and then
you're rewarding it when it getsit right.
Speaker 1 (02:57):
Right, when he does the thing, he gets the thing
Exactly Okay.
Speaker 2 (03:02):
And so, in supervised learning, we feed the algorithm
labeled data where we alreadyknow what the correct output
should be.
So this way, the algorithm canlearn to map the inputs to
outputs based on all thoseexamples that we're giving it.
Speaker 1 (03:14):
So it's all about learning from examples, just
like Sparky learns to shake hispaw for a tasty treat.
Speaker 2 (03:20):
Exactly.
Speaker 1 (03:20):
So what about unsupervised learning?
How does that fit into all ofthis?
Speaker 2 (03:24):
Yeah.
So imagine, instead of likespecifically training Sparky,
you let him loose in a park.
Speaker 1 (03:31):
Okay.
Speaker 2 (03:32):
And he's sniffing around, he's exploring.
Speaker 1 (03:35):
Yep.
Speaker 2 (03:35):
He's just like taking in the world.
Speaker 1 (03:38):
Yeah.
Speaker 2 (03:39):
You're not telling him what to do.
Speaker 1 (03:40):
Right.
Speaker 2 (03:41):
Unsupervised learning is similar.
We give the algorithm datawithout any labels or desired
outcomes okay it's up to thealgorithm to find the patterns,
the relationships oh, wow andeven like anomalies in that data
, so it's like we're letting itlose.
Speaker 1 (03:56):
You see what it finds , exactly you can discover
insights that we might havemissed that's fascinating.
Yeah, it's a really powerfultool.
So unsupervised learning islike letting the algorithm
explore the data landscape, andwe're hoping it uncovers some
hidden gems.
Speaker 2 (04:11):
Exactly.
Speaker 1 (04:12):
Very cool.
Speaker 2 (04:12):
And then if we dive one layer deeper into our AI
nested doll, Okay, one layerdeeper.
We find deep learning, which isa really powerful type of
unsupervised learning.
Speaker 1 (04:22):
Deep learning.
This is where things get reallyinteresting.
Speaker 2 (04:25):
It does.
I mean we're talking aboutalgorithms that are mimicking
the human brain yeah at leastthat's my understanding you're
on the right track, yeah okay sodeep learning relies on
artificial neural networks okayand these networks are inspired
by the structure of the humanbrain right and these networks
have multiple layers, hence deeplearning makes sense that
allows them to processinformation in a much more
(04:48):
complex and nuanced way, rightCompared to like traditional ML
algorithm.
Speaker 1 (04:53):
And it's that complexity that allows deep
learning to take on these likereally impressive tasks like
image recognition and thingslike that, exactly Like
unlocking your phone with yourface.
Speaker 2 (05:04):
Yeah.
Speaker 1 (05:04):
Or self-driving cars.
Speaker 2 (05:06):
It's all thanks to deep learning.
Speaker 1 (05:08):
Being able to process all of that visual information
so quickly.
Speaker 2 (05:13):
Yeah, it can process that visual information, it can
identify objects, it can makedecisions based on what it's
seeing.
Speaker 1 (05:19):
It's wild and that is a great example.
Yeah, self-driving cars, one ofthe most popular examples of
deep learning.
Speaker 2 (05:26):
But really out there right now.
Absolutely.
Speaker 1 (05:29):
Those cars are practically thinking for
themselves.
Speaker 2 (05:32):
They're driving themselves.
Speaker 1 (05:33):
It's really mind blowing when you think about it.
Speaker 2 (05:36):
It is.
Speaker 1 (05:36):
How does it work?
So these algorithms areconstantly analyzing data from
the car sensors, likerecognizing pedestrians, other
vehicles, obviously, trafficsignals, all while making split
second decisions to steer,accelerate, brake the whole nine
yards.
Speaker 2 (05:54):
Yeah, and it's all happening in real time.
Speaker 1 (05:56):
All in real time.
It's incredible how it allcomes together.
Speaker 2 (05:59):
It really is.
It's a testament to how fardeep learning has come and the
potential it has honestly for,like, the future of
transportation and beyond.
Speaker 1 (06:09):
And this leads us to an even more recent and arguably
even more intriguing realm ofAI generative AI.
Speaker 2 (06:17):
Generative AI.
Speaker 1 (06:18):
This is where things get really creative.
Speaker 2 (06:20):
This is where it gets really interesting.
Speaker 1 (06:22):
This is where AI is like.
Let me create original content,whether that's writing me poems
, composing music, generatingrealistic images that we've
never seen before.
Speaker 2 (06:31):
It's pretty wild what it can do.
Speaker 1 (06:32):
It's amazing.
So how does it actually do that?
How does it generate stuff thatfeels so original?
Speaker 2 (06:37):
So it's all thanks to this, like power of being
trained on massive data sets.
Speaker 1 (06:43):
Okay.
Speaker 2 (06:44):
Imagine like feeding this AI.
Let's ChatGPT the entireLibrary of Congress.
Speaker 1 (06:51):
Oh, wow.
Speaker 2 (06:52):
Plus, like every book that's ever been written.
It's a lot of books, every song, every script.
Speaker 1 (06:57):
Yeah, you get the idea.
It's a wild amount ofinformation.
Speaker 2 (07:00):
Crazy amount of data.
Speaker 1 (07:01):
No wonder the training cost is in the millions
for this.
Speaker 2 (07:04):
Oh yeah.
So ChatGPT basically gobblesall this up gobbles it up and it
uses it to learn the patternsof human language, creativity,
even humor yeah, it's reallyinteresting oh, wow it analyzes
the patterns and therelationships to be able to
create something new right butthat's still aligned with this
style or the tone, the structureyeah, so it's almost like it's
(07:25):
learning the rules of languageyeah so well that it can then
bend those rules and create itsown interpretation exactly.
Speaker 1 (07:33):
That's really wild.
That's super interesting it isreally wild and it's not just
limited to text right no, not atall, we're seeing this in
images.
Speaker 2 (07:40):
We're seeing it in music music yeah it's everywhere
a whole bunch of differentmodalities, which is really
fascinating.
Speaker 1 (07:47):
Really, really fascinating.
But we've covered a lot oftheoretical ground here.
Speaker 2 (07:52):
Yeah.
Speaker 1 (07:52):
How is this being used in the real world?
Speaker 2 (07:55):
Yeah, that's the question, isn't it Right?
It's not all theoretical.
Speaker 1 (07:58):
Beyond self-driving cars.
Exactly Chat GPT.
Speaker 2 (08:01):
Right.
I mean it's being used in moreways than I think we can even
imagine.
Wow, it's really quietlyrevolution From healthcare to
finance, to even believe it ornot, the arts.
Speaker 1 (08:17):
AI is infiltrating the arts.
Speaker 2 (08:19):
It's true, it's true.
Speaker 1 (08:20):
I'm going to need some more information on that
we're going to have to dive intothat?
Speaker 2 (08:22):
Yeah, we're going to do that.
Speaker 1 (08:23):
But I do remember reading in that IBM article
about AI being used in customerservice.
Yeah, which is something that Ithink we can all relate to,
unfortunately.
Speaker 2 (08:32):
Sure sure, no more waiting on hold for hours just
to get a simple questionanswered Exactly.
And those AI-powered chatbotsare getting good.
Speaker 1 (08:42):
They are getting good .
Speaker 2 (08:42):
They're so much more sophisticated.
Speaker 1 (08:44):
Yeah.
Speaker 2 (08:45):
They can answer those frequently asked questions.
They can, like, guide youthrough troubleshooting steps
Yay, and they can evenpersonalize those interactions
based on you know what you'vetalked about before.
Speaker 1 (08:56):
It's like having a 24-7 customer service agent at
your beck and call, except it'sAI.
Speaker 2 (09:01):
Exactly, super efficient, super efficient, very
cool.
And that efficiency, you know,that theme kind of extends to
other sectors.
Speaker 1 (09:09):
Like what.
Speaker 2 (09:10):
Like in finance, for example, AI is being used to
detect fraud in real time.
Speaker 1 (09:16):
Oh, wow.
Speaker 2 (09:17):
You can analyze market trends, okay, and it can
even give you personalizedfinancial advice.
Speaker 1 (09:21):
Wow, okay, so it can spot those tricky credit card
charges.
Speaker 2 (09:25):
Exactly that's fraudulent, that's right.
Speaker 1 (09:27):
It's like having an extra set of eyes constantly
monitoring.
Speaker 2 (09:30):
Exactly.
Speaker 1 (09:31):
For anything suspicious.
That's really cool.
Speaker 2 (09:34):
And it's not just for our individual accounts.
It's used to combat fraud on amuch larger scale.
Oh, really so it's helpingbusinesses, helping institutions
, prevent these financial lossesand protect their customers.
Speaker 1 (09:48):
Huge impact, big impact Wow.
Speaker 2 (09:50):
And then going back to the arts.
Speaker 1 (09:51):
Yeah, the arts.
We've got to go back to thearts.
Speaker 2 (09:53):
I'm curious about this too.
Speaker 1 (09:54):
Yeah, how is AI making its mark in such a
creative field?
Speaker 2 (09:59):
It's really fascinating.
So AI is being used to composemusic, what it's generating,
scripts for movies, for plays,and even creating some really
stunning visual art.
Speaker 1 (10:12):
AI is writing symphonies now.
Speaker 2 (10:14):
Well, I mean, it's still early days.
Okay, ai is writing symphoniesnow.
Well, I mean, it's still earlydays, but there are AI systems
that can analyze musicalpatterns, you know, and generate
melodies and harmonies and evenentire compositions in
different styles.
Speaker 1 (10:27):
It's incredible.
Speaker 2 (10:28):
It's really incredible.
It's also a little unnerving.
I know it's a lot to take in.
It's a lot to take in.
Speaker 1 (10:32):
Yeah, it makes you wonder what aspects of our lives
won't be touched by AI in thefuture.
Speaker 2 (10:37):
Yeah, it's a big question.
Speaker 1 (10:38):
It's almost like this AI wave is about to crash over
all of us and change everything.
Speaker 2 (10:43):
Yeah, it feels like it's moving so fast.
Speaker 1 (10:45):
It really does.
Speaker 2 (10:46):
Faster than a lot of us could imagine, I think.
Speaker 1 (10:48):
Exactly.
So how do we even begin to makesense of it all?
What does it mean?
What does it mean for us?
What does it mean?
What does?
Speaker 2 (10:54):
it mean for us?
What does it all mean for ourfuture?
That's the big question.
Speaker 1 (10:58):
It is a big question.
Speaker 2 (10:59):
And it's impossible to predict the future.
Speaker 1 (11:01):
Right.
Speaker 2 (11:01):
But I think what we can do is look at where things
are headed right now, yeah,understand the potential impact
and really focus on how we canadapt Okay, and thrive in this
like rapidly changing landscape.
Speaker 1 (11:18):
Adapt and thrive.
I like that.
So not fearing the machinestaking over, but figuring out a
way to work alongside themExactly.
Right.
Speaker 2 (11:22):
Yeah, it's not about being replaced.
Okay, it's about understandingthat AI is going to change the
types of jobs that are available.
Speaker 1 (11:29):
Right, because some things will be automated.
Speaker 2 (11:31):
Exactly.
Speaker 1 (11:31):
But that's going to open up new opportunities.
Speaker 2 (11:33):
Exactly, I mean, think about it.
Someone has to design these AIsystems, someone has to build
them, they have to maintain them.
Speaker 1 (11:40):
We're going to need a whole new workforce as of.
Ai specialists.
Speaker 2 (11:43):
We are to manage all this.
Speaker 1 (11:45):
OK, so we've got those jobs, but for, like the
rest of us, what skills aregoing to be valuable in this
future, where AI is everywhere?
Speaker 2 (11:54):
Well, that's where it gets really interesting,
because, as AI takes on theseroutine tasks Right, these
repetitive things, it's actuallythose skills that are uniquely
human.
Speaker 1 (12:04):
Okay.
Speaker 2 (12:04):
That are going to become even more important.
Speaker 1 (12:07):
So creativity, critical thinking, yeah, problem
solving the things that areharder.
Speaker 2 (12:12):
Exactly.
Speaker 1 (12:13):
For AI to replicate.
Speaker 2 (12:15):
Those are the things that are going to set us apart.
Okay.
Being able to, like, thinkoutside the docs yeah.
Come up with innovativesolutions to new problems Right.
And navigate these complexsituations, you know, using our
judgment.
Speaker 1 (12:29):
Yeah.
Speaker 2 (12:29):
Those are the strengths that humans bring to
the table.
Speaker 1 (12:32):
So it's not just about technical skills anymore.
It's about these essentialhuman skills that are much
harder for AI to, I guess, grasp.
Speaker 2 (12:41):
Yeah, things like empathy, communication,
collaboration working together,yeah, those interpersonal skills
.
Exactly those are going to bemore important than ever.
Speaker 1 (12:49):
Right, because you can't really automate empathy,
exactly, you can't reallyautomate good communication or
being able to collaborateeffectively.
Speaker 2 (12:58):
And even just being able to understand and respond
to the needs of another person.
Those are the things that AI,at least for now, just can't do.
Speaker 1 (13:06):
Yeah, it's almost like the rise of AI is pushing
all of us to become more human.
I love that.
To lean into the things thatmake us unique, yeah.
Speaker 2 (13:15):
I think that's a really beautiful way to put it.
It's not us versus them.
Speaker 1 (13:19):
Right.
Speaker 2 (13:19):
It's about this synergy.
Speaker 1 (13:21):
Working together.
Speaker 2 (13:22):
Between our intelligence and this artificial
intelligence To enhance what wecan already do.
Exactly To enhance ourcapabilities, create new
possibilities.
Speaker 1 (13:31):
Yeah.
Speaker 2 (13:32):
And ultimately build a better future for everyone.
Speaker 1 (13:34):
And who knows, maybe along the way we might learn a
thing or two about ourselves.
Absolutely, that's a greatpoint.
This has been an eye-openingdeep dive into AI, to say the
least.
Speaker 2 (13:45):
It really has.
Speaker 1 (13:45):
I mean we went from demystifying the basics to
exploring the mind-blowingpotential of AI.
Speaker 2 (13:52):
Absolutely.
Speaker 1 (13:52):
It seems like the possibilities are limitless.
Speaker 2 (13:55):
Limitless.
Speaker 1 (13:56):
It's incredible what a ride this has been.
Speaker 2 (13:58):
It's a journey.
Speaker 1 (13:59):
It really is and it feels like this journey is just
beginning.
We're just getting started Justgetting started.
So as we wrap up this deep dive, we want to leave everyone with
something to ponder.
Speaker 2 (14:10):
Okay.
Speaker 1 (14:11):
Imagine a world where AI could take over one tedious
task in your life.
Speaker 2 (14:16):
Okay, what would you?
Speaker 1 (14:16):
choose.
That's where AI could take overone tedious task in your life
what would you choose?
Speaker 2 (14:19):
That's a great question.
Speaker 1 (14:19):
What would you do with that newfound time?
What would you do with thatfreedom?
It's a question worth thinkingabout.
Speaker 2 (14:26):
It really is.
Speaker 1 (14:26):
It's a lot to process , going from AI answering
customer service calls tocomposing symphonies.
I know right, it's incredibleit really is.
Speaker 2 (14:34):
And it feels very much like will ai take over the
world yeah, it's a common fear,but honestly, I think, instead
of thinking about like this, ai,takeover right, we should
really be thinking about how dowe adapt to this new world?
Okay, how do we thrivealongside?
Speaker 1 (14:51):
it.
So how do we do that?
How do we adapt and thrive?
Speaker 2 (14:54):
well, first, I think it's important to remember that
ai isn't just about replacingjobs.
Speaker 1 (14:59):
OK.
Speaker 2 (14:59):
While some tasks might become automated, new
opportunities are going to comeup.
Speaker 1 (15:03):
Right Like who's designing these AI systems.
Speaker 2 (15:07):
Who's?
Speaker 1 (15:07):
building them?
Who's maintaining them?
Speaker 2 (15:09):
We're going to need a whole new workforce of AI
specialists to manage all ofthis.
Speaker 1 (15:13):
So that's those jobs.
But for the rest of us, whatskills are we going to need?
Speaker 2 (15:17):
Well, that's where it gets really interesting,
because as AI takes on thesemore routine tasks, the
repetitive things, it's theskills that are uniquely human
that are really going to standout.
Speaker 1 (15:29):
Oh interesting.
So like creativity, yes.
Critical thinking yes.
Speaker 2 (15:33):
Yeah, problem solving .
Speaker 1 (15:35):
Exactly the things that are much harder for AI to
replicate.
Speaker 2 (15:37):
Because you can teach a computer to do a lot of
things.
You can, but you can'tnecessarily teach it to think
outside the box.
Speaker 1 (15:43):
Right and you can't teach it to like come up with
these really innovativesolutions to new problems.
Speaker 2 (15:50):
Okay.
Speaker 1 (15:50):
Or navigate these complex situations where you
know human judgment is needed.
Speaker 2 (16:04):
So it's not just about technical skills anymore.
Speaker 1 (16:05):
No, it's about these like really essential human
skills that AI can't quite graspexactly and, honestly, things
like empathy, yes, communication, being able to collaborate
effectively those interpersonalskills yes, those are gonna be
more important than ever,because you can't really
automate empathy yeah you yeah,you can't automate good
communication, you know, orbeing able to work really well
on a team.
So it's almost like this riseof the machines is really
(16:26):
pushing us to be more human.
I love that.
To really lean into what makesus unique.
Speaker 2 (16:32):
Yes, lean into those strengths.
Speaker 1 (16:34):
Which I think is a nice way to look at it.
Speaker 2 (16:36):
It is.
Speaker 1 (16:36):
It's not us versus them.
Speaker 2 (16:38):
Right.
Speaker 1 (16:38):
It's this working together.
Speaker 2 (16:40):
It's about finding that synergy.
Speaker 1 (16:42):
The synergy between human and artificial
intelligence.
Speaker 2 (16:45):
Exactly To enhance what we can already do, create
these new possibilities.
Speaker 1 (16:49):
Right.
Speaker 2 (16:50):
And ultimately, I think, build a better future.
Speaker 1 (16:52):
And who knows, maybe along the way we'll learn a
little bit more about ourselves.
Speaker 2 (16:55):
Absolutely, I think we will.
Speaker 1 (16:56):
That's a great place to end.
Yeah, this has been anincredible deep dive.
Speaker 2 (17:01):
It's been fascinating Into the world of AI.
It really has.
Speaker 1 (17:04):
We went from demystifying the very basics to,
you know, like I said,exploring the potential.
Speaker 2 (17:12):
It's mind-blowing when you think about it.
Speaker 1 (17:14):
Of where this could go.
I mean.
It really is the possibilitiesare limitless.
Speaker 2 (17:19):
They really are.
Speaker 1 (17:19):
Thank you so much for joining us.
Speaker 2 (17:21):
It was my pleasure.
Speaker 1 (17:22):
For this deep dive into AI and thank you everyone
out there for listening.
We'll catch you next time.
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