Episode Transcript
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Speaker 1 (00:00):
Okay, so everybody's talking about AI, right, but like, how
much do we really understand about how it works? Right,
I mean beyond all the buzzwords. Yeah, today we're taking
a deep dive into this podcast episode called AI two
dot MP three from a series called Artificial Intelligence Exposed.
Speaker 2 (00:17):
I see, and they really get into.
Speaker 1 (00:19):
The nuts and bolts of AI. So we're going to
break it down so you can actually, you know, talk
about AI with confidence even if you're not a tech expert.
Speaker 3 (00:28):
I think that's so important because AI it's all about,
you know, giving machines that ability to solve problems and
make decisions just like we do, but they use data.
Speaker 1 (00:40):
Okay, we're teaching them to think, but in a different
way exactly than how we program computers normally.
Speaker 3 (00:45):
Yeah, it's not like giving step by step instructions. It's
more like you know, giving them a cookbook okay and
letting them experiment with different recipes interesting based on what
they learn.
Speaker 1 (00:54):
So it's really about this idea of learning from data.
Speaker 3 (00:58):
Yes, exactly. And that is where machine learning comes in,
which is a subset of AI that allows computers to
learn from data with aus us having to program every
single step.
Speaker 1 (01:10):
So instead of like feeding at all the answers. It's
like it figures it out on its own.
Speaker 3 (01:13):
Yes, it's all about learning and improving, got it over time?
Speaker 1 (01:17):
And they use that cool example in AI two dot
MP three yes of teaching a computer to tell the
difference between a cat and a dog. Right, can you
walk me through that? Like how does that even work?
Speaker 3 (01:28):
So imagine you're showing this AI a ton of pictures, right,
some are labeled cat and some are labeled dog.
Speaker 2 (01:36):
And at first it might make a lot.
Speaker 1 (01:38):
Of mistakes, like it'll think a dog as a cat
or vice versa.
Speaker 3 (01:41):
Yeah, it might see a fluffy dog and think it's
a cat. But as it sees more and more examples,
it starts to pick up on those subtle differences.
Speaker 1 (01:50):
So it's like it's learning the visual cues.
Speaker 3 (01:52):
Exactly like ear shapes, tail lengths, how they move.
Speaker 1 (01:55):
Oh wow, So it's really building up this like knowledge
space based on all those pictures.
Speaker 3 (02:00):
It's like building its own visual dictionary. And eventually it
gets so good yea that it can actually identify cats
and dogs and new pictures it's never seen before.
Speaker 1 (02:08):
That's amazing. So that's like learning from experience or is
sisse and they call that supervised learning.
Speaker 2 (02:13):
Yes, that's right, supervise learning, okay, because we're providing the labels,
got it, So we're kind of supervising the learning process exactly.
Speaker 1 (02:20):
So what about when you don't have all those labels?
Speaker 3 (02:22):
Ah, that's where unsupervised learning comes in.
Speaker 1 (02:24):
Okay.
Speaker 3 (02:25):
Think of it like giving the AI a giant jigsaw puzzle,
but there's no picture on the box.
Speaker 1 (02:31):
Okay, that's tough.
Speaker 3 (02:32):
It has to figure out how the pieces fit together
on its own.
Speaker 1 (02:35):
So it's like detective trying to solve a mystery.
Speaker 2 (02:38):
Yeah, without any clues exactly.
Speaker 3 (02:40):
The AI is exploring the data, looking for patterns and relationships.
Speaker 1 (02:45):
So how is that useful in the real world? Like,
give me an example.
Speaker 3 (02:48):
Well, one common application is clustering clusters, where the AI
groups similar data points together. So think about your music
streaming service, right. It probably uses unsupervised the learning to
create those personalized playlists you love, right, by grouping songs together.
Speaker 1 (03:05):
By genre or mood or whatever.
Speaker 2 (03:08):
Exactly.
Speaker 1 (03:09):
That's really cool. I never thought about it that way.
Speaker 3 (03:11):
It's happening all around us, and businesses use it too.
Oh yeah, how so, Well, instead of manually segmenting customers
they can let the AI do the heavy.
Speaker 1 (03:21):
Lifting, so find those natural groupings based on.
Speaker 2 (03:24):
The data exactly.
Speaker 1 (03:25):
That's super efficient, right. Yeah, and there's that other application you.
Speaker 2 (03:29):
Mentioned, Oh yes, associationiation.
Speaker 3 (03:31):
It's like uncovering those hidden if this then that rules.
Speaker 1 (03:34):
Give me an example, okay.
Speaker 3 (03:35):
So a classic example is market basket analysis, like at
the grocery store exactly.
Speaker 1 (03:40):
Okay.
Speaker 3 (03:41):
Retailers use it to understand what items people usually buy together.
Speaker 1 (03:45):
Oh right, like people who buy diapers also.
Speaker 3 (03:48):
Buy beer precisely, and then they use this information ok
to like optimize product placement, or create bundles or even
just tailor recommendations.
Speaker 1 (03:57):
So it's all about understanding customer behavior exactly and using
that to their advantage. Right. Wow. Unsupervised learning is pretty
mind blowing, it is. But wait, there's another type of
machine learning we need to talk about. Oh yes, reinforcement learning.
AI two dot MP three dives into that, and it
sounds fascinating.
Speaker 3 (04:15):
It is. It's inspired by how humans in animals learn, Okay,
through trial and error, rewards and punishments.
Speaker 1 (04:24):
So like teaching a dog a trick exactly with treats
and stuff.
Speaker 3 (04:28):
You give them a treat when they do it, right,
and a no when they get it wrong.
Speaker 1 (04:32):
But how does that work with machines, Like are we
giving them virtual treats or something kind of.
Speaker 3 (04:36):
In reinforcement learning, we have something called an agent, an
agent which could be a software program or even a robot,
and this agent interacts with an environment.
Speaker 1 (04:46):
Okay, So the agent is like the dog, yes, and
the environment is like the owner exactly.
Speaker 3 (04:51):
And the agent takes actions and the environment gives it feedback.
Speaker 1 (04:56):
In the form of rewards or penalties.
Speaker 3 (04:58):
Right, And the agent's goal is to figure out the
best strategy to maximize its rewards.
Speaker 1 (05:03):
So it's like learning by doing precisely, and the reward
could be anything depending on what you're trying to teach it.
Speaker 3 (05:09):
Yeah, it could be points in a game, or completing
a task in robotics, or even maximizing profits and finance.
Speaker 1 (05:16):
I see. So it's a very versatile approach.
Speaker 2 (05:18):
Yes it is.
Speaker 1 (05:19):
Now. I remember AI two dot MP three talks about
Alpha Go. Oh, Yes, that AI that learned to play
the game of Go and it actually beat a world champion.
Speaker 2 (05:29):
Yes, it was huge milestone.
Speaker 1 (05:31):
So how did Alpha Go learn to play Go?
Speaker 3 (05:33):
Well? Go is incredibly complex.
Speaker 1 (05:35):
Yeah, I've heard it's like really hard.
Speaker 3 (05:37):
It has more possible board positions than atoms in the
observable universe.
Speaker 1 (05:41):
Whoa, that's mind boggling.
Speaker 3 (05:43):
So alphag is trained using reinforcement learning. Okay, it played
millions of games against itself, learning from its wins and losses.
Speaker 1 (05:51):
Wow. So it basically taught itself to play exactly.
Speaker 3 (05:54):
And that's the power of reinforcement learning.
Speaker 1 (05:56):
Yeah, it's pretty incredible and almost a little scary. It
is remarkable to think that an AI can learn so
effectively without human guidance.
Speaker 2 (06:05):
But it also has huge potential like in what areas.
Speaker 3 (06:09):
Well, robotics, self driving cars, finance, anywhere you need AI
to navigate a complex environment.
Speaker 1 (06:15):
Wow. So we've covered supervised learning, yes, unsupervised learning and
reinforcement learning. But there's one more big one we haven't
talked about yet.
Speaker 2 (06:23):
Yes, deep learning.
Speaker 1 (06:24):
Deep learning. What makes it so special?
Speaker 3 (06:26):
Deep learning is like the rock star of AI right now?
Speaker 1 (06:29):
Okay, why is that?
Speaker 3 (06:30):
Well, you know how our brains process information through a
network of neurons. Deep learning models try to replicate that,
but on a massive scale. So it's like building a
digital brain exactly, layer by layer.
Speaker 1 (06:43):
That's pretty wild. Okay, So tell me how these artificial
neural networks actually work well.
Speaker 3 (06:48):
Imagine it as a multilayered processing system. You feed data
into the first layer.
Speaker 1 (06:54):
Like an image subtext, and.
Speaker 3 (06:56):
Each neuron in that layer does a simple calculation and
passes the result to the next layer. And as data
flows through these layers, the network learns to pick out
increasingly complex features and patterns.
Speaker 1 (07:09):
Each layer builds on the one before it exactly, and
it gradually understands the data in a deeper and deeper way.
Speaker 2 (07:15):
Precisely.
Speaker 1 (07:16):
That's a really cool concept. Okay, give me example of
that in action.
Speaker 3 (07:19):
Okay, so let's go back the image recognition. Imagine training
a deep learning model to recognize different objects. Okay, the
first layer might learn to identify simple edges and lines,
then the next layer might combine those to recognize shapes
like circles and squares.
Speaker 1 (07:37):
I see.
Speaker 3 (07:38):
And as you go deeper, the layers start to recognize faces, cars,
even entire scenes.
Speaker 1 (07:44):
Wow. So it's like building this hierarchy of understanding exact
from basic features to really complex representations. Yes, this is
all starting to click now.
Speaker 3 (07:53):
Good, and that's what allows deep learning models to be
so accurate in things like image classification, object detection, in
facial recognition, and.
Speaker 1 (08:02):
It's so good that it's actually surpassing human performance.
Speaker 2 (08:05):
In some areas in some cases.
Speaker 1 (08:06):
Yes, wow, mind officially blown. I remember AI two dot
MP three mentioned Google DeepMind and Facebook AI right doing
some really cool work in computer vision.
Speaker 3 (08:17):
Yes, they both developed some incredible deep learning models.
Speaker 1 (08:20):
Like what kind of stuff are they doing?
Speaker 3 (08:21):
Well, they can recognize objects and faces with incredible accuracy.
Speaker 1 (08:27):
Like how accurate are we talking?
Speaker 3 (08:29):
Well, we're talking identifying individuals in crowds, Yeah, spotting objects
in satellite images, even diagnosing diseases from medical scans.
Speaker 1 (08:39):
That's credible. So deep learning is really changing the game
in computer vision, absolutely, and I know it's also having
a big impact on natural language processing.
Speaker 3 (08:47):
Yes NLP.
Speaker 1 (08:48):
What's happening there.
Speaker 3 (08:49):
Deep learning is making it possible for machines to understand
and generate human language.
Speaker 1 (08:55):
So like they can actually speak our language in a way.
Speaker 2 (08:57):
Yes, that's crazy.
Speaker 3 (08:59):
Models like GPT and BERT can write human quality text,
translate languages, answer questions, summarize documents, even write poems and scripts.
Speaker 1 (09:09):
It's like we're giving machines a voice.
Speaker 2 (09:11):
It's pretty remarkable.
Speaker 1 (09:13):
Yeah, that has huge implications for everything, right, communication, information access,
even how we create art.
Speaker 3 (09:20):
It's a whole new world.
Speaker 1 (09:21):
Okay. So we've talked about all the amazing things deep
learning can do, Yes, but I know it also comes
with its own set of challenges, of course, can you
talk about those a bit.
Speaker 3 (09:31):
One big challenge is the sheer amount of data data
that deep learning models need. Okay, we're talking massive data sets.
Speaker 1 (09:41):
So even though it's powerful, it's not like a magic solution.
Speaker 3 (09:44):
It needs the right fuel to work its magic exactly.
Speaker 1 (09:48):
And what about that black box problem I've heard about?
Speaker 2 (09:50):
Ah, Yes, the black box problem.
Speaker 1 (09:52):
It sounds kind of ominous.
Speaker 3 (09:54):
It refers to the fact that it can be really
hard to understand how these models arrive at their dec
So we.
Speaker 1 (10:00):
Can see the output, right, but we don't necessarily know
why it came to that.
Speaker 3 (10:05):
Conclusion exactly, and that can be a concern.
Speaker 1 (10:07):
Especially in areas like healthcare or finance.
Speaker 3 (10:10):
Yes, where the stakes are high and the consequences of
a wrong decision can be significant.
Speaker 1 (10:16):
Okay, So deep learning is powerful, but it's not without
its issues.
Speaker 3 (10:20):
It's a powerful tool, but like any tool, it needs
to be used responsibly.
Speaker 1 (10:26):
This has been an incredible deep dive so far, it has.
We've gone from the basics of AI all the way
to these complex deep learning.
Speaker 2 (10:34):
Models, and we've only just scratched the surface.
Speaker 1 (10:37):
What's the biggest takeaway for you from all of this.
Speaker 3 (10:39):
I think the biggest takeaway is just how transformative AI is.
It's changing everything around us. Yeah, from the way we
work to how we interact with.
Speaker 1 (10:49):
The world, and it's just getting started, right.
Speaker 3 (10:52):
But it's crucial to remember that AI is a tool
and it's up to us to use it responsibly and ethically.
Speaker 1 (10:58):
That's a really important point.
Speaker 3 (10:59):
We need to make make sure it's aligned with our
values absolutely and that it's used for good.
Speaker 1 (11:03):
Well said, Wow, Part one was a lot to take in.
I mean, AI is already doing so much more than
I realized.
Speaker 3 (11:09):
Yeah, and it's only going to get more advanced.
Speaker 1 (11:11):
Like exponentially exactly. It's kind of crazy to think about
it is, but AI isn't just some theoretical concept. It's
already having a real impact on our.
Speaker 2 (11:20):
Lives in so many ways.
Speaker 1 (11:22):
Remember how AI two dot MP three talked about healthcare.
Oh yeah, it blew my mind that AI can analyze
medical images and help doctors make diagnoses.
Speaker 3 (11:34):
It's pretty amazing.
Speaker 1 (11:35):
It's like something out of a sci fi movie I know.
Speaker 3 (11:37):
Right, but it's actually happening now.
Speaker 1 (11:40):
What are some of the ways AI is being used
in healthcare?
Speaker 3 (11:42):
Well, one really exciting area is personalized treatment plans. Okay,
so no more one size fits all approach. AI can
analyze your data, medical history, genetic information, wow, and help
doctors tailor treatments specifically for you.
Speaker 1 (11:57):
That's incredible. That could be a game changer for so
many patients.
Speaker 2 (12:01):
Absolutely.
Speaker 1 (12:01):
What about drug discovery? Can AI help speed up that process?
Speaker 2 (12:05):
Definitely?
Speaker 3 (12:05):
AI can analyze huge amounts of data to identify potential
drug candidates and predict their effectiveness.
Speaker 1 (12:13):
So it could potentially help us find new cures and
treatments much faster.
Speaker 3 (12:17):
Exact, imagine life saving treatments getting to people sooner.
Speaker 1 (12:22):
That's a really powerful thought. Okay, so healthcare is obviously
a huge area for AI, right, but what about other
industries Like I'm thinking about self driving cars? Oh, something
we used to only dream about.
Speaker 3 (12:35):
Self driving cars are a perfect example of how AI
is changing the world how so well. They rely on
a combination of AI technologies computer vision to see the environment,
sensor fusion to get a complete picture of the surroundings,
and decision making algorithms to navigate roads safely, so.
Speaker 1 (12:54):
They're basically making decisions in real time exactly based on
all this data they're collecting. It's mind blowing that a
car can actually drive itself.
Speaker 2 (13:03):
It is pretty amazing.
Speaker 1 (13:04):
What does that mean for the future of transportation.
Speaker 3 (13:07):
Imagine a future with fewer traffic jams okay, far fewer accidents.
Think about how this could revolutionize transportation for people who
can't drive themselves.
Speaker 1 (13:18):
That's a really good point. It could give so many
people more independence and freedom exactly. Ok So AI is
changing healthcare, transportation. What about the way we work?
Speaker 2 (13:29):
Yes, the future of work.
Speaker 1 (13:31):
There's a lot of talk about AI taking over jobs.
Speaker 2 (13:34):
I know it's a common concern.
Speaker 1 (13:35):
And it's understandable it is. People are worried about being
replaced by machines.
Speaker 3 (13:39):
But it's important to look at the bigger picture.
Speaker 1 (13:41):
Okay, what do you mean.
Speaker 3 (13:42):
AI can automate certain tasks, which naturally leads to some
anxiety about job displacement. But AI is also creating new
opportunities okay, and changing the nature of work.
Speaker 1 (13:54):
So it's not necessarily about AI versus humans, no, but
rather AI and humans working together exactly.
Speaker 3 (14:02):
Think of it as AI taking over the repetitive or
mundane tasks, freeing us up to focus on things that
require creativity, critical thinking, human interaction.
Speaker 1 (14:13):
So AI becomes like a super efficient assistant.
Speaker 2 (14:16):
I like that analogy.
Speaker 1 (14:17):
I hoping it's be more productive and focus.
Speaker 3 (14:19):
On our spikes exactly. AI can handle the data crunching,
the analysis, the routine tasks.
Speaker 1 (14:24):
While we bring our unique human skills to.
Speaker 2 (14:27):
The table precisely.
Speaker 1 (14:28):
Can you give me some specific examples of how this
AI human collaboration might work.
Speaker 2 (14:34):
Sure?
Speaker 3 (14:34):
So, in customer service, AI chatbots can handle basic inquiries.
Speaker 1 (14:39):
Like answering frequently asked questions right.
Speaker 3 (14:41):
Okay, providing instant answers. But for more complex issues or
when a human touch is needed, human agents can step
in seamlessly.
Speaker 1 (14:51):
So AI is not replacing customer service reps. It's enhancing
their ability to serve customers exactly. That makes sense. What
about other areas well?
Speaker 3 (14:59):
AI and also help us make better decisions by providing
data driven insights and predictions.
Speaker 1 (15:06):
So instead of relying on gut feeling, we can use
data to make smarter.
Speaker 3 (15:11):
Choices precisely, and this is valuable in fields like financer
marketing so well in finance AI can analyze market trends,
assess risk, make investment recommendation. In marketing, AI can personalize campaigns,
target specific audiences, optimize ad spending.
Speaker 1 (15:27):
Wow. So it's like AI is giving us this superpower
in a way, yes, to make better decisions based on data.
Speaker 3 (15:34):
But of course, with great power comes great responsibility.
Speaker 1 (15:37):
That's a good point. We need to make sure AI
is used ethically, absolutely, and doesn't create more problems than
it solves.
Speaker 3 (15:44):
That's a crucial consideration.
Speaker 1 (15:46):
So how do we ensure that AI is used responsibly.
Speaker 3 (15:50):
Well, it's a complex issue. It requires a multifaceted approach.
We need researchers developing ethical frameworks, policy makers establishing guidelines
and regulations right, and industry leaders implementing responsible AI practices.
Speaker 2 (16:07):
It's collaborative effort, exactly.
Speaker 3 (16:09):
And it's also important to have open discussions like this one.
Speaker 1 (16:12):
Right to engage the public in these conversations.
Speaker 3 (16:14):
Yes, because the future of AI is something that affects
us all. Absolutely, we need to be informed and involved
in shaping that future.
Speaker 1 (16:21):
Okay, So we've talked about AI's impact on healthcare, yes, transportation,
the way we work, But there's still this big question
looming about the impact of AI on jobs? Is AI
really going to replace humans altogether?
Speaker 3 (16:36):
That's the million dollar question, isn't it?
Speaker 1 (16:38):
It is? And I'm not sure I like the answer.
Speaker 2 (16:41):
Well, the future is likely more nuanced than that.
Speaker 1 (16:43):
Okay, what do you mean?
Speaker 3 (16:44):
While some jobs will definitely be automated, new ones will
also emerge.
Speaker 1 (16:49):
So it's not just about job losses.
Speaker 3 (16:50):
No, it's also about job creation okay. And AI is
changing the nature of work itself, so we.
Speaker 1 (16:57):
Need to adapt and develop new skills exactly.
Speaker 3 (16:59):
Skills like critical thinking, problem solving, creativity, communication, collaboration.
Speaker 1 (17:06):
Those are all very human skills.
Speaker 3 (17:08):
And they'll become even more valuable in an AI driven world.
Speaker 1 (17:11):
It sounds like the future of work will be a partnership,
I think so, between humans and AI.
Speaker 2 (17:16):
Each leveraging their unique strengths.
Speaker 1 (17:19):
That's a nice way to put it.
Speaker 3 (17:20):
As AI evolves, will likely see a shift from task
based automation more collaborative and augmentative.
Speaker 2 (17:27):
Systems Augmentative AI.
Speaker 1 (17:29):
What does that mean?
Speaker 3 (17:30):
Augmentative AI systems are designed to enhance human capabilities, not
replace them.
Speaker 1 (17:35):
So instead of AI taking over, it's about AI empowering
us exactly to do things better, faster, more creatively.
Speaker 3 (17:42):
Think of AI tools that help doctors make better diagverses,
architects design more sustainable buildings okay, musicians compose more innovative music.
Speaker 1 (17:52):
Wow. So the possibilities are really endless.
Speaker 2 (17:55):
They are, and that's what makes AI so exciting.
Speaker 1 (17:57):
But also a little bit scary.
Speaker 3 (17:59):
It's true has the potential to be an incredible force
for good, but we need to guide.
Speaker 2 (18:04):
Its development and use it wisely.
Speaker 1 (18:07):
Exactly, and we're back for the final part of our
AI deep dive.
Speaker 3 (18:11):
It's been a wild ride, hasn't it.
Speaker 1 (18:13):
Seriously, We've learned so much from.
Speaker 3 (18:15):
Those basic AI concepts to the real world application.
Speaker 1 (18:18):
And AI two dot MP three really laid it all
out there.
Speaker 3 (18:21):
Yeah, it was a great starting point.
Speaker 1 (18:22):
I'm excited about AI's potential, but also got to admit
I'm a little intimidated by it.
Speaker 3 (18:27):
I get it.
Speaker 2 (18:28):
It's powerful stuff.
Speaker 1 (18:29):
It really feels like we're on the cusp of a
major shift.
Speaker 3 (18:32):
I agree, And like we talked about in part two,
AI is already changing industries like healthcare and transportation.
Speaker 1 (18:39):
Right, but the future holds even more possibilities.
Speaker 2 (18:42):
Like what give me some examples.
Speaker 1 (18:43):
Well, one of the most exciting trends is the democratization
of AI. Democratization Yeah, making AI more accessible okay, to everyone,
not just tech giants and research labs.
Speaker 3 (18:54):
So like putting the power of AI into the hands
of everyday people.
Speaker 1 (18:58):
Exactly, and we're already seeing that happen. How so Well,
user friendly platforms and tools are emerging that allow individuals
and organizations, regardless of their technical expertise, to harness the
power of AI.
Speaker 3 (19:13):
So you don't need a PhD in computer science to
use it anymore, not at all.
Speaker 1 (19:17):
Imagine drag and drop interfaces for building machine learning models.
Speaker 3 (19:21):
Wow, that sounds pretty user friendly or pre.
Speaker 1 (19:23):
Trained AI modules that you can easily integrate into existing applications.
Speaker 3 (19:27):
So it's like AI is becoming plug and play in
a way. Yes, that's incredible. What does that mean for
the average person.
Speaker 1 (19:35):
Well, it means that the power to solve problems, automate tasks,
and create new opportunities is no longer limited to a
select few. So AI is becoming more empowering exactly.
Speaker 3 (19:46):
Imagine a small business owner using AI to personalize marketing
campaigns okay, or an artist using AI to create unique
pieces of art.
Speaker 1 (19:56):
That's amazing. I love that idea of aibl being used
for creative purposes. But as AI becomes more accessible and
more powerful, Yes, how do we ensure that it's used responsibly?
Speaker 3 (20:09):
That's the big question, isn't it.
Speaker 1 (20:10):
We touched on the ethical implications earlier, but I think
it's worth digging a little deeper.
Speaker 3 (20:15):
I agree. As AI becomes more pervasive, Okay, we need
to be mindful of its potential impact on.
Speaker 2 (20:21):
Society, Like what kind of impact?
Speaker 3 (20:23):
Well, we need to address concerns about bias, fairness, and
accountability in these systems.
Speaker 1 (20:28):
Right, because AI is only as good as the data.
Speaker 3 (20:30):
It's trained on, exactly, and if that data is biased,
AI will be biased too.
Speaker 1 (20:35):
And that could have serious consequences.
Speaker 3 (20:37):
It could perpetuate existing inequalities or even create new ones.
Speaker 1 (20:41):
So it's not just about the technology itself. No, it's
about how we develop.
Speaker 3 (20:45):
And deploy it and who has access to it?
Speaker 1 (20:47):
Right? What steps can we take to ensure responsible AI?
Speaker 3 (20:52):
Well, it's a multifaceted challenge. It requires a collaborative approach.
Speaker 1 (20:56):
So what does that look like?
Speaker 3 (20:58):
Well, we need researchers developing ethic frameworks. Okay, policymaker is
establishing guidelines and regulations and industry leaders implementing responsible AI practices.
Speaker 1 (21:08):
So it's a shared responsibility.
Speaker 3 (21:10):
Exactly, and just as important, we need open discussions like
this one, engaging the public and conversations about AI and
its implications.
Speaker 1 (21:19):
Because ultimately it's going to impact all of us.
Speaker 3 (21:21):
It will, and we need to be part of the conversation.
Speaker 1 (21:24):
Absolutely. And of course there's that big question of AI's
impact on.
Speaker 2 (21:27):
Jobs the future of work.
Speaker 1 (21:30):
Is AI really going to replace humans altogether?
Speaker 2 (21:32):
Well, it's a natural concern.
Speaker 1 (21:34):
I mean, it's a question that keeps me up at night,
but I.
Speaker 3 (21:37):
Think the future is probably more nuanced than that.
Speaker 1 (21:39):
Okay, give me some hope here.
Speaker 3 (21:42):
While some jobs will definitely be automated, new ones will
also emerge, So it's.
Speaker 1 (21:47):
Not just about job losses. It's also about job creation.
Speaker 3 (21:50):
And AI is also changing the nature.
Speaker 1 (21:52):
Of work itself, so we need to adapt and envelop new.
Speaker 3 (21:56):
Skills exactly, skills that are uniquely human, like what critical thinking,
problem solving, creativity, communication, collaboration.
Speaker 1 (22:05):
Those are all skills that machines can't replicate, not easily
at least. So it's less about AI taking jobs and
more about AI changing the skills we need to thrive
in the workplace.
Speaker 3 (22:17):
I think that's a good way to put it.
Speaker 1 (22:19):
So it's not humans versus AI. No, it's humans ANDAI working.
Speaker 2 (22:23):
Together exactly, a partnership.
Speaker 1 (22:24):
That's a much more optimistic way to look at it.
Speaker 3 (22:26):
As AI evolves, I think we'll see a shift from
task based automation to more collaborative and augmentative.
Speaker 2 (22:35):
System augmentative AI.
Speaker 1 (22:36):
What does that mean?
Speaker 3 (22:37):
Well, augmentative AI systems are designed to enhance human capabilities okay,
not replace them.
Speaker 1 (22:42):
So instead of AI taking over, it's about AI empowering.
Speaker 3 (22:45):
Us to do things better, faster, and more creatively.
Speaker 1 (22:49):
Give me some examples of what augmentative AI might look like.
Speaker 3 (22:52):
Sure, think of AI tools that help doctors make better diagnoses,
or architects design more sustainable buildings, or musicians can pose
more innovative music.
Speaker 1 (23:01):
Wow. So the possibilities are really endless.
Speaker 3 (23:04):
They are, and that's what makes AI so exciting.
Speaker 1 (23:07):
Yeah, it is a powerful technology with the potential to
do amazing things.
Speaker 3 (23:11):
But it's also important to remember that AI is a tool, okay,
and like any tool, it can be used for good
or for bad.
Speaker 1 (23:21):
So we need to use it wisely.
Speaker 3 (23:22):
Exactly. We need to guide its development.
Speaker 1 (23:25):
And make sure it's aligned with our values, our ethic,
and our goals for the future.
Speaker 2 (23:28):
Absolutely well.
Speaker 1 (23:30):
This deep dive has been an incredible journey exploring the
world of AI, from its basic principles to its potential
impact on the future. It's a complex topic, it is,
but it's clear that AI is transforming our world in
profound ways.
Speaker 3 (23:44):
And it's only going to get more transformative in the
years to come.
Speaker 1 (23:47):
Yeah, we're just at the beginning of the AI revolution, right,
and it's up to all of us to shape that.
Speaker 3 (23:51):
Revolution, to make sure that AI is used for good,
to create a future where AI benefits all of humanity.
Speaker 1 (23:57):
Absolutely to our listener, we hope this deep died has
given you a better understanding of AI and its potential
impact on our lives.
Speaker 3 (24:06):
We've covered a lot of ground.
Speaker 1 (24:07):
But this is just the starting point.
Speaker 3 (24:09):
There's so much more to learn and explore.
Speaker 1 (24:11):
We encourage you to continue the conversation.
Speaker 2 (24:13):
To ask questions, to stay informed.
Speaker 3 (24:16):
And to be part of shaping the future of AI.
Speaker 1 (24:19):
Thanks for joining us on this incredible journey into the
world of AI.
Okay, so everybody's talking about AI, right, but like, how
much do we really understand about how it works? Right,
I mean beyond all the buzzwords. Yeah, today we're taking
a deep dive into this podcast episode called AI two
dot MP three from a series called Artificial Intelligence Exposed.
Speaker 2 (00:17):
I see, and they really get into.
Speaker 1 (00:19):
The nuts and bolts of AI. So we're going to
break it down so you can actually, you know, talk
about AI with confidence even if you're not a tech expert.
Speaker 3 (00:28):
I think that's so important because AI it's all about,
you know, giving machines that ability to solve problems and
make decisions just like we do, but they use data.
Speaker 1 (00:40):
Okay, we're teaching them to think, but in a different
way exactly than how we program computers normally.
Speaker 3 (00:45):
Yeah, it's not like giving step by step instructions. It's
more like you know, giving them a cookbook okay and
letting them experiment with different recipes interesting based on what
they learn.
Speaker 1 (00:54):
So it's really about this idea of learning from data.
Speaker 3 (00:58):
Yes, exactly. And that is where machine learning comes in,
which is a subset of AI that allows computers to
learn from data with aus us having to program every
single step.
Speaker 1 (01:10):
So instead of like feeding at all the answers. It's
like it figures it out on its own.
Speaker 3 (01:13):
Yes, it's all about learning and improving, got it over time?
Speaker 1 (01:17):
And they use that cool example in AI two dot
MP three yes of teaching a computer to tell the
difference between a cat and a dog. Right, can you
walk me through that? Like how does that even work?
Speaker 3 (01:28):
So imagine you're showing this AI a ton of pictures, right,
some are labeled cat and some are labeled dog.
Speaker 2 (01:36):
And at first it might make a lot.
Speaker 1 (01:38):
Of mistakes, like it'll think a dog as a cat
or vice versa.
Speaker 3 (01:41):
Yeah, it might see a fluffy dog and think it's
a cat. But as it sees more and more examples,
it starts to pick up on those subtle differences.
Speaker 1 (01:50):
So it's like it's learning the visual cues.
Speaker 3 (01:52):
Exactly like ear shapes, tail lengths, how they move.
Speaker 1 (01:55):
Oh wow, So it's really building up this like knowledge
space based on all those pictures.
Speaker 3 (02:00):
It's like building its own visual dictionary. And eventually it
gets so good yea that it can actually identify cats
and dogs and new pictures it's never seen before.
Speaker 1 (02:08):
That's amazing. So that's like learning from experience or is
sisse and they call that supervised learning.
Speaker 2 (02:13):
Yes, that's right, supervise learning, okay, because we're providing the labels,
got it, So we're kind of supervising the learning process exactly.
Speaker 1 (02:20):
So what about when you don't have all those labels?
Speaker 3 (02:22):
Ah, that's where unsupervised learning comes in.
Speaker 1 (02:24):
Okay.
Speaker 3 (02:25):
Think of it like giving the AI a giant jigsaw puzzle,
but there's no picture on the box.
Speaker 1 (02:31):
Okay, that's tough.
Speaker 3 (02:32):
It has to figure out how the pieces fit together
on its own.
Speaker 1 (02:35):
So it's like detective trying to solve a mystery.
Speaker 2 (02:38):
Yeah, without any clues exactly.
Speaker 3 (02:40):
The AI is exploring the data, looking for patterns and relationships.
Speaker 1 (02:45):
So how is that useful in the real world? Like,
give me an example.
Speaker 3 (02:48):
Well, one common application is clustering clusters, where the AI
groups similar data points together. So think about your music
streaming service, right. It probably uses unsupervised the learning to
create those personalized playlists you love, right, by grouping songs together.
Speaker 1 (03:05):
By genre or mood or whatever.
Speaker 2 (03:08):
Exactly.
Speaker 1 (03:09):
That's really cool. I never thought about it that way.
Speaker 3 (03:11):
It's happening all around us, and businesses use it too.
Oh yeah, how so, Well, instead of manually segmenting customers
they can let the AI do the heavy.
Speaker 1 (03:21):
Lifting, so find those natural groupings based on.
Speaker 2 (03:24):
The data exactly.
Speaker 1 (03:25):
That's super efficient, right. Yeah, and there's that other application you.
Speaker 2 (03:29):
Mentioned, Oh yes, associationiation.
Speaker 3 (03:31):
It's like uncovering those hidden if this then that rules.
Speaker 1 (03:34):
Give me an example, okay.
Speaker 3 (03:35):
So a classic example is market basket analysis, like at
the grocery store exactly.
Speaker 1 (03:40):
Okay.
Speaker 3 (03:41):
Retailers use it to understand what items people usually buy together.
Speaker 1 (03:45):
Oh right, like people who buy diapers also.
Speaker 3 (03:48):
Buy beer precisely, and then they use this information ok
to like optimize product placement, or create bundles or even
just tailor recommendations.
Speaker 1 (03:57):
So it's all about understanding customer behavior exactly and using
that to their advantage. Right. Wow. Unsupervised learning is pretty
mind blowing, it is. But wait, there's another type of
machine learning we need to talk about. Oh yes, reinforcement learning.
AI two dot MP three dives into that, and it
sounds fascinating.
Speaker 3 (04:15):
It is. It's inspired by how humans in animals learn, Okay,
through trial and error, rewards and punishments.
Speaker 1 (04:24):
So like teaching a dog a trick exactly with treats
and stuff.
Speaker 3 (04:28):
You give them a treat when they do it, right,
and a no when they get it wrong.
Speaker 1 (04:32):
But how does that work with machines, Like are we
giving them virtual treats or something kind of.
Speaker 3 (04:36):
In reinforcement learning, we have something called an agent, an
agent which could be a software program or even a robot,
and this agent interacts with an environment.
Speaker 1 (04:46):
Okay, So the agent is like the dog, yes, and
the environment is like the owner exactly.
Speaker 3 (04:51):
And the agent takes actions and the environment gives it feedback.
Speaker 1 (04:56):
In the form of rewards or penalties.
Speaker 3 (04:58):
Right, And the agent's goal is to figure out the
best strategy to maximize its rewards.
Speaker 1 (05:03):
So it's like learning by doing precisely, and the reward
could be anything depending on what you're trying to teach it.
Speaker 3 (05:09):
Yeah, it could be points in a game, or completing
a task in robotics, or even maximizing profits and finance.
Speaker 1 (05:16):
I see. So it's a very versatile approach.
Speaker 2 (05:18):
Yes it is.
Speaker 1 (05:19):
Now. I remember AI two dot MP three talks about
Alpha Go. Oh, Yes, that AI that learned to play
the game of Go and it actually beat a world champion.
Speaker 2 (05:29):
Yes, it was huge milestone.
Speaker 1 (05:31):
So how did Alpha Go learn to play Go?
Speaker 3 (05:33):
Well? Go is incredibly complex.
Speaker 1 (05:35):
Yeah, I've heard it's like really hard.
Speaker 3 (05:37):
It has more possible board positions than atoms in the
observable universe.
Speaker 1 (05:41):
Whoa, that's mind boggling.
Speaker 3 (05:43):
So alphag is trained using reinforcement learning. Okay, it played
millions of games against itself, learning from its wins and losses.
Speaker 1 (05:51):
Wow. So it basically taught itself to play exactly.
Speaker 3 (05:54):
And that's the power of reinforcement learning.
Speaker 1 (05:56):
Yeah, it's pretty incredible and almost a little scary. It
is remarkable to think that an AI can learn so
effectively without human guidance.
Speaker 2 (06:05):
But it also has huge potential like in what areas.
Speaker 3 (06:09):
Well, robotics, self driving cars, finance, anywhere you need AI
to navigate a complex environment.
Speaker 1 (06:15):
Wow. So we've covered supervised learning, yes, unsupervised learning and
reinforcement learning. But there's one more big one we haven't
talked about yet.
Speaker 2 (06:23):
Yes, deep learning.
Speaker 1 (06:24):
Deep learning. What makes it so special?
Speaker 3 (06:26):
Deep learning is like the rock star of AI right now?
Speaker 1 (06:29):
Okay, why is that?
Speaker 3 (06:30):
Well, you know how our brains process information through a
network of neurons. Deep learning models try to replicate that,
but on a massive scale. So it's like building a
digital brain exactly, layer by layer.
Speaker 1 (06:43):
That's pretty wild. Okay, So tell me how these artificial
neural networks actually work well.
Speaker 3 (06:48):
Imagine it as a multilayered processing system. You feed data
into the first layer.
Speaker 1 (06:54):
Like an image subtext, and.
Speaker 3 (06:56):
Each neuron in that layer does a simple calculation and
passes the result to the next layer. And as data
flows through these layers, the network learns to pick out
increasingly complex features and patterns.
Speaker 1 (07:09):
Each layer builds on the one before it exactly, and
it gradually understands the data in a deeper and deeper way.
Speaker 2 (07:15):
Precisely.
Speaker 1 (07:16):
That's a really cool concept. Okay, give me example of
that in action.
Speaker 3 (07:19):
Okay, so let's go back the image recognition. Imagine training
a deep learning model to recognize different objects. Okay, the
first layer might learn to identify simple edges and lines,
then the next layer might combine those to recognize shapes
like circles and squares.
Speaker 1 (07:37):
I see.
Speaker 3 (07:38):
And as you go deeper, the layers start to recognize faces, cars,
even entire scenes.
Speaker 1 (07:44):
Wow. So it's like building this hierarchy of understanding exact
from basic features to really complex representations. Yes, this is
all starting to click now.
Speaker 3 (07:53):
Good, and that's what allows deep learning models to be
so accurate in things like image classification, object detection, in
facial recognition, and.
Speaker 1 (08:02):
It's so good that it's actually surpassing human performance.
Speaker 2 (08:05):
In some areas in some cases.
Speaker 1 (08:06):
Yes, wow, mind officially blown. I remember AI two dot
MP three mentioned Google DeepMind and Facebook AI right doing
some really cool work in computer vision.
Speaker 3 (08:17):
Yes, they both developed some incredible deep learning models.
Speaker 1 (08:20):
Like what kind of stuff are they doing?
Speaker 3 (08:21):
Well, they can recognize objects and faces with incredible accuracy.
Speaker 1 (08:27):
Like how accurate are we talking?
Speaker 3 (08:29):
Well, we're talking identifying individuals in crowds, Yeah, spotting objects
in satellite images, even diagnosing diseases from medical scans.
Speaker 1 (08:39):
That's credible. So deep learning is really changing the game
in computer vision, absolutely, and I know it's also having
a big impact on natural language processing.
Speaker 3 (08:47):
Yes NLP.
Speaker 1 (08:48):
What's happening there.
Speaker 3 (08:49):
Deep learning is making it possible for machines to understand
and generate human language.
Speaker 1 (08:55):
So like they can actually speak our language in a way.
Speaker 2 (08:57):
Yes, that's crazy.
Speaker 3 (08:59):
Models like GPT and BERT can write human quality text,
translate languages, answer questions, summarize documents, even write poems and scripts.
Speaker 1 (09:09):
It's like we're giving machines a voice.
Speaker 2 (09:11):
It's pretty remarkable.
Speaker 1 (09:13):
Yeah, that has huge implications for everything, right, communication, information access,
even how we create art.
Speaker 3 (09:20):
It's a whole new world.
Speaker 1 (09:21):
Okay. So we've talked about all the amazing things deep
learning can do, Yes, but I know it also comes
with its own set of challenges, of course, can you
talk about those a bit.
Speaker 3 (09:31):
One big challenge is the sheer amount of data data
that deep learning models need. Okay, we're talking massive data sets.
Speaker 1 (09:41):
So even though it's powerful, it's not like a magic solution.
Speaker 3 (09:44):
It needs the right fuel to work its magic exactly.
Speaker 1 (09:48):
And what about that black box problem I've heard about?
Speaker 2 (09:50):
Ah, Yes, the black box problem.
Speaker 1 (09:52):
It sounds kind of ominous.
Speaker 3 (09:54):
It refers to the fact that it can be really
hard to understand how these models arrive at their dec
So we.
Speaker 1 (10:00):
Can see the output, right, but we don't necessarily know
why it came to that.
Speaker 3 (10:05):
Conclusion exactly, and that can be a concern.
Speaker 1 (10:07):
Especially in areas like healthcare or finance.
Speaker 3 (10:10):
Yes, where the stakes are high and the consequences of
a wrong decision can be significant.
Speaker 1 (10:16):
Okay, So deep learning is powerful, but it's not without
its issues.
Speaker 3 (10:20):
It's a powerful tool, but like any tool, it needs
to be used responsibly.
Speaker 1 (10:26):
This has been an incredible deep dive so far, it has.
We've gone from the basics of AI all the way
to these complex deep learning.
Speaker 2 (10:34):
Models, and we've only just scratched the surface.
Speaker 1 (10:37):
What's the biggest takeaway for you from all of this.
Speaker 3 (10:39):
I think the biggest takeaway is just how transformative AI is.
It's changing everything around us. Yeah, from the way we
work to how we interact with.
Speaker 1 (10:49):
The world, and it's just getting started, right.
Speaker 3 (10:52):
But it's crucial to remember that AI is a tool
and it's up to us to use it responsibly and ethically.
Speaker 1 (10:58):
That's a really important point.
Speaker 3 (10:59):
We need to make make sure it's aligned with our
values absolutely and that it's used for good.
Speaker 1 (11:03):
Well said, Wow, Part one was a lot to take in.
I mean, AI is already doing so much more than
I realized.
Speaker 3 (11:09):
Yeah, and it's only going to get more advanced.
Speaker 1 (11:11):
Like exponentially exactly. It's kind of crazy to think about
it is, but AI isn't just some theoretical concept. It's
already having a real impact on our.
Speaker 2 (11:20):
Lives in so many ways.
Speaker 1 (11:22):
Remember how AI two dot MP three talked about healthcare.
Oh yeah, it blew my mind that AI can analyze
medical images and help doctors make diagnoses.
Speaker 3 (11:34):
It's pretty amazing.
Speaker 1 (11:35):
It's like something out of a sci fi movie I know.
Speaker 3 (11:37):
Right, but it's actually happening now.
Speaker 1 (11:40):
What are some of the ways AI is being used
in healthcare?
Speaker 3 (11:42):
Well, one really exciting area is personalized treatment plans. Okay,
so no more one size fits all approach. AI can
analyze your data, medical history, genetic information, wow, and help
doctors tailor treatments specifically for you.
Speaker 1 (11:57):
That's incredible. That could be a game changer for so
many patients.
Speaker 2 (12:01):
Absolutely.
Speaker 1 (12:01):
What about drug discovery? Can AI help speed up that process?
Speaker 2 (12:05):
Definitely?
Speaker 3 (12:05):
AI can analyze huge amounts of data to identify potential
drug candidates and predict their effectiveness.
Speaker 1 (12:13):
So it could potentially help us find new cures and
treatments much faster.
Speaker 3 (12:17):
Exact, imagine life saving treatments getting to people sooner.
Speaker 1 (12:22):
That's a really powerful thought. Okay, so healthcare is obviously
a huge area for AI, right, but what about other
industries Like I'm thinking about self driving cars? Oh, something
we used to only dream about.
Speaker 3 (12:35):
Self driving cars are a perfect example of how AI
is changing the world how so well. They rely on
a combination of AI technologies computer vision to see the environment,
sensor fusion to get a complete picture of the surroundings,
and decision making algorithms to navigate roads safely, so.
Speaker 1 (12:54):
They're basically making decisions in real time exactly based on
all this data they're collecting. It's mind blowing that a
car can actually drive itself.
Speaker 2 (13:03):
It is pretty amazing.
Speaker 1 (13:04):
What does that mean for the future of transportation.
Speaker 3 (13:07):
Imagine a future with fewer traffic jams okay, far fewer accidents.
Think about how this could revolutionize transportation for people who
can't drive themselves.
Speaker 1 (13:18):
That's a really good point. It could give so many
people more independence and freedom exactly. Ok So AI is
changing healthcare, transportation. What about the way we work?
Speaker 2 (13:29):
Yes, the future of work.
Speaker 1 (13:31):
There's a lot of talk about AI taking over jobs.
Speaker 2 (13:34):
I know it's a common concern.
Speaker 1 (13:35):
And it's understandable it is. People are worried about being
replaced by machines.
Speaker 3 (13:39):
But it's important to look at the bigger picture.
Speaker 1 (13:41):
Okay, what do you mean.
Speaker 3 (13:42):
AI can automate certain tasks, which naturally leads to some
anxiety about job displacement. But AI is also creating new
opportunities okay, and changing the nature of work.
Speaker 1 (13:54):
So it's not necessarily about AI versus humans, no, but
rather AI and humans working together exactly.
Speaker 3 (14:02):
Think of it as AI taking over the repetitive or
mundane tasks, freeing us up to focus on things that
require creativity, critical thinking, human interaction.
Speaker 1 (14:13):
So AI becomes like a super efficient assistant.
Speaker 2 (14:16):
I like that analogy.
Speaker 1 (14:17):
I hoping it's be more productive and focus.
Speaker 3 (14:19):
On our spikes exactly. AI can handle the data crunching,
the analysis, the routine tasks.
Speaker 1 (14:24):
While we bring our unique human skills to.
Speaker 2 (14:27):
The table precisely.
Speaker 1 (14:28):
Can you give me some specific examples of how this
AI human collaboration might work.
Speaker 2 (14:34):
Sure?
Speaker 3 (14:34):
So, in customer service, AI chatbots can handle basic inquiries.
Speaker 1 (14:39):
Like answering frequently asked questions right.
Speaker 3 (14:41):
Okay, providing instant answers. But for more complex issues or
when a human touch is needed, human agents can step
in seamlessly.
Speaker 1 (14:51):
So AI is not replacing customer service reps. It's enhancing
their ability to serve customers exactly. That makes sense. What
about other areas well?
Speaker 3 (14:59):
AI and also help us make better decisions by providing
data driven insights and predictions.
Speaker 1 (15:06):
So instead of relying on gut feeling, we can use
data to make smarter.
Speaker 3 (15:11):
Choices precisely, and this is valuable in fields like financer
marketing so well in finance AI can analyze market trends,
assess risk, make investment recommendation. In marketing, AI can personalize campaigns,
target specific audiences, optimize ad spending.
Speaker 1 (15:27):
Wow. So it's like AI is giving us this superpower
in a way, yes, to make better decisions based on data.
Speaker 3 (15:34):
But of course, with great power comes great responsibility.
Speaker 1 (15:37):
That's a good point. We need to make sure AI
is used ethically, absolutely, and doesn't create more problems than
it solves.
Speaker 3 (15:44):
That's a crucial consideration.
Speaker 1 (15:46):
So how do we ensure that AI is used responsibly.
Speaker 3 (15:50):
Well, it's a complex issue. It requires a multifaceted approach.
We need researchers developing ethical frameworks, policy makers establishing guidelines
and regulations right, and industry leaders implementing responsible AI practices.
Speaker 2 (16:07):
It's collaborative effort, exactly.
Speaker 3 (16:09):
And it's also important to have open discussions like this one.
Speaker 1 (16:12):
Right to engage the public in these conversations.
Speaker 3 (16:14):
Yes, because the future of AI is something that affects
us all. Absolutely, we need to be informed and involved
in shaping that future.
Speaker 1 (16:21):
Okay, So we've talked about AI's impact on healthcare, yes, transportation,
the way we work, But there's still this big question
looming about the impact of AI on jobs? Is AI
really going to replace humans altogether?
Speaker 3 (16:36):
That's the million dollar question, isn't it?
Speaker 1 (16:38):
It is? And I'm not sure I like the answer.
Speaker 2 (16:41):
Well, the future is likely more nuanced than that.
Speaker 1 (16:43):
Okay, what do you mean?
Speaker 3 (16:44):
While some jobs will definitely be automated, new ones will
also emerge.
Speaker 1 (16:49):
So it's not just about job losses.
Speaker 3 (16:50):
No, it's also about job creation okay. And AI is
changing the nature of work itself, so we.
Speaker 1 (16:57):
Need to adapt and develop new skills exactly.
Speaker 3 (16:59):
Skills like critical thinking, problem solving, creativity, communication, collaboration.
Speaker 1 (17:06):
Those are all very human skills.
Speaker 3 (17:08):
And they'll become even more valuable in an AI driven world.
Speaker 1 (17:11):
It sounds like the future of work will be a partnership,
I think so, between humans and AI.
Speaker 2 (17:16):
Each leveraging their unique strengths.
Speaker 1 (17:19):
That's a nice way to put it.
Speaker 3 (17:20):
As AI evolves, will likely see a shift from task
based automation more collaborative and augmentative.
Speaker 2 (17:27):
Systems Augmentative AI.
Speaker 1 (17:29):
What does that mean?
Speaker 3 (17:30):
Augmentative AI systems are designed to enhance human capabilities, not
replace them.
Speaker 1 (17:35):
So instead of AI taking over, it's about AI empowering
us exactly to do things better, faster, more creatively.
Speaker 3 (17:42):
Think of AI tools that help doctors make better diagverses,
architects design more sustainable buildings okay, musicians compose more innovative music.
Speaker 1 (17:52):
Wow. So the possibilities are really endless.
Speaker 2 (17:55):
They are, and that's what makes AI so exciting.
Speaker 1 (17:57):
But also a little bit scary.
Speaker 3 (17:59):
It's true has the potential to be an incredible force
for good, but we need to guide.
Speaker 2 (18:04):
Its development and use it wisely.
Speaker 1 (18:07):
Exactly, and we're back for the final part of our
AI deep dive.
Speaker 3 (18:11):
It's been a wild ride, hasn't it.
Speaker 1 (18:13):
Seriously, We've learned so much from.
Speaker 3 (18:15):
Those basic AI concepts to the real world application.
Speaker 1 (18:18):
And AI two dot MP three really laid it all
out there.
Speaker 3 (18:21):
Yeah, it was a great starting point.
Speaker 1 (18:22):
I'm excited about AI's potential, but also got to admit
I'm a little intimidated by it.
Speaker 3 (18:27):
I get it.
Speaker 2 (18:28):
It's powerful stuff.
Speaker 1 (18:29):
It really feels like we're on the cusp of a
major shift.
Speaker 3 (18:32):
I agree, And like we talked about in part two,
AI is already changing industries like healthcare and transportation.
Speaker 1 (18:39):
Right, but the future holds even more possibilities.
Speaker 2 (18:42):
Like what give me some examples.
Speaker 1 (18:43):
Well, one of the most exciting trends is the democratization
of AI. Democratization Yeah, making AI more accessible okay, to everyone,
not just tech giants and research labs.
Speaker 3 (18:54):
So like putting the power of AI into the hands
of everyday people.
Speaker 1 (18:58):
Exactly, and we're already seeing that happen. How so Well,
user friendly platforms and tools are emerging that allow individuals
and organizations, regardless of their technical expertise, to harness the
power of AI.
Speaker 3 (19:13):
So you don't need a PhD in computer science to
use it anymore, not at all.
Speaker 1 (19:17):
Imagine drag and drop interfaces for building machine learning models.
Speaker 3 (19:21):
Wow, that sounds pretty user friendly or pre.
Speaker 1 (19:23):
Trained AI modules that you can easily integrate into existing applications.
Speaker 3 (19:27):
So it's like AI is becoming plug and play in
a way. Yes, that's incredible. What does that mean for
the average person.
Speaker 1 (19:35):
Well, it means that the power to solve problems, automate tasks,
and create new opportunities is no longer limited to a
select few. So AI is becoming more empowering exactly.
Speaker 3 (19:46):
Imagine a small business owner using AI to personalize marketing
campaigns okay, or an artist using AI to create unique
pieces of art.
Speaker 1 (19:56):
That's amazing. I love that idea of aibl being used
for creative purposes. But as AI becomes more accessible and
more powerful, Yes, how do we ensure that it's used responsibly?
Speaker 3 (20:09):
That's the big question, isn't it.
Speaker 1 (20:10):
We touched on the ethical implications earlier, but I think
it's worth digging a little deeper.
Speaker 3 (20:15):
I agree. As AI becomes more pervasive, Okay, we need
to be mindful of its potential impact on.
Speaker 2 (20:21):
Society, Like what kind of impact?
Speaker 3 (20:23):
Well, we need to address concerns about bias, fairness, and
accountability in these systems.
Speaker 1 (20:28):
Right, because AI is only as good as the data.
Speaker 3 (20:30):
It's trained on, exactly, and if that data is biased,
AI will be biased too.
Speaker 1 (20:35):
And that could have serious consequences.
Speaker 3 (20:37):
It could perpetuate existing inequalities or even create new ones.
Speaker 1 (20:41):
So it's not just about the technology itself. No, it's
about how we develop.
Speaker 3 (20:45):
And deploy it and who has access to it?
Speaker 1 (20:47):
Right? What steps can we take to ensure responsible AI?
Speaker 3 (20:52):
Well, it's a multifaceted challenge. It requires a collaborative approach.
Speaker 1 (20:56):
So what does that look like?
Speaker 3 (20:58):
Well, we need researchers developing ethic frameworks. Okay, policymaker is
establishing guidelines and regulations and industry leaders implementing responsible AI practices.
Speaker 1 (21:08):
So it's a shared responsibility.
Speaker 3 (21:10):
Exactly, and just as important, we need open discussions like
this one, engaging the public and conversations about AI and
its implications.
Speaker 1 (21:19):
Because ultimately it's going to impact all of us.
Speaker 3 (21:21):
It will, and we need to be part of the conversation.
Speaker 1 (21:24):
Absolutely. And of course there's that big question of AI's
impact on.
Speaker 2 (21:27):
Jobs the future of work.
Speaker 1 (21:30):
Is AI really going to replace humans altogether?
Speaker 2 (21:32):
Well, it's a natural concern.
Speaker 1 (21:34):
I mean, it's a question that keeps me up at night,
but I.
Speaker 3 (21:37):
Think the future is probably more nuanced than that.
Speaker 1 (21:39):
Okay, give me some hope here.
Speaker 3 (21:42):
While some jobs will definitely be automated, new ones will
also emerge, So it's.
Speaker 1 (21:47):
Not just about job losses. It's also about job creation.
Speaker 3 (21:50):
And AI is also changing the nature.
Speaker 1 (21:52):
Of work itself, so we need to adapt and envelop new.
Speaker 3 (21:56):
Skills exactly, skills that are uniquely human, like what critical thinking,
problem solving, creativity, communication, collaboration.
Speaker 1 (22:05):
Those are all skills that machines can't replicate, not easily
at least. So it's less about AI taking jobs and
more about AI changing the skills we need to thrive
in the workplace.
Speaker 3 (22:17):
I think that's a good way to put it.
Speaker 1 (22:19):
So it's not humans versus AI. No, it's humans ANDAI working.
Speaker 2 (22:23):
Together exactly, a partnership.
Speaker 1 (22:24):
That's a much more optimistic way to look at it.
Speaker 3 (22:26):
As AI evolves, I think we'll see a shift from
task based automation to more collaborative and augmentative.
Speaker 2 (22:35):
System augmentative AI.
Speaker 1 (22:36):
What does that mean?
Speaker 3 (22:37):
Well, augmentative AI systems are designed to enhance human capabilities okay,
not replace them.
Speaker 1 (22:42):
So instead of AI taking over, it's about AI empowering.
Speaker 3 (22:45):
Us to do things better, faster, and more creatively.
Speaker 1 (22:49):
Give me some examples of what augmentative AI might look like.
Speaker 3 (22:52):
Sure, think of AI tools that help doctors make better diagnoses,
or architects design more sustainable buildings, or musicians can pose
more innovative music.
Speaker 1 (23:01):
Wow. So the possibilities are really endless.
Speaker 3 (23:04):
They are, and that's what makes AI so exciting.
Speaker 1 (23:07):
Yeah, it is a powerful technology with the potential to
do amazing things.
Speaker 3 (23:11):
But it's also important to remember that AI is a tool, okay,
and like any tool, it can be used for good
or for bad.
Speaker 1 (23:21):
So we need to use it wisely.
Speaker 3 (23:22):
Exactly. We need to guide its development.
Speaker 1 (23:25):
And make sure it's aligned with our values, our ethic,
and our goals for the future.
Speaker 2 (23:28):
Absolutely well.
Speaker 1 (23:30):
This deep dive has been an incredible journey exploring the
world of AI, from its basic principles to its potential
impact on the future. It's a complex topic, it is,
but it's clear that AI is transforming our world in
profound ways.
Speaker 3 (23:44):
And it's only going to get more transformative in the
years to come.
Speaker 1 (23:47):
Yeah, we're just at the beginning of the AI revolution, right,
and it's up to all of us to shape that.
Speaker 3 (23:51):
Revolution, to make sure that AI is used for good,
to create a future where AI benefits all of humanity.
Speaker 1 (23:57):
Absolutely to our listener, we hope this deep died has
given you a better understanding of AI and its potential
impact on our lives.
Speaker 3 (24:06):
We've covered a lot of ground.
Speaker 1 (24:07):
But this is just the starting point.
Speaker 3 (24:09):
There's so much more to learn and explore.
Speaker 1 (24:11):
We encourage you to continue the conversation.
Speaker 2 (24:13):
To ask questions, to stay informed.
Speaker 3 (24:16):
And to be part of shaping the future of AI.
Speaker 1 (24:19):
Thanks for joining us on this incredible journey into the
world of AI.
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