Deep Learning and Neural Networks. The Heart of AI

Episode Transcript

Speaker 1 (00:00):
All right, so deep learning and neural networks. You've given

(00:04):
me a ton of research on this. Yeah, and it's
a hot topic, right, I mean everywhere you look these days,
it feels like deep learning is powering like everything from
you know, self driving cars to figuring out what we're
going to buy next on Amazon.

Speaker 2 (00:19):
Yeah.

Speaker 1 (00:20):
Absolutely, But I got to say, even for someone like me,
who you know, loves to stay on top of the
latest tech, it can feel honestly a little overwhelming. Get
like rocket science almost.

Speaker 2 (00:34):
Yeah, a lot of people feel that way.

Speaker 3 (00:36):
Yeah.

Speaker 2 (00:36):
I mean it's definitely a complex field, but honestly, the
core concepts I think are are surprisingly accessible once we
get to break them down a little bit.

Speaker 1 (00:44):
Okay, I'm ready, let's have my mind blown. So let's
start with the basics, Like what are deep learning and
neural networks? I mean, I hear all the time that
they're like inspired by the human brain, But what does
that actually mean when we're talking about computers.

Speaker 2 (00:59):
So the human brain as this this incredible network of
interconnected neurons, Okay, and each of these neurons is like
a tiny processing unit, right, And so what artificial neural
networks try to do is they basically try to mimic
that structure, right, So they create these layers of interconnected nodes,

(01:19):
and those nodes they process information just like those neurons do.
And then deep learning, well that's taking it to another level.
It uses networks that have lots of layers, hence the
word deep.

Speaker 1 (01:30):
So more layers equals more processing.

Speaker 2 (01:33):
Power exactly, more layers let the network sort of discover
these these much more intricate patterns and relationships with then
the data got it. And that's that's really the key
to how deep learning systems can do things like, you know,
recognize faces, translate languages, even compose music.

Speaker 1 (01:52):
We compose music. That's wild.

Speaker 2 (01:54):
It is pretty amazing. Yeah.

Speaker 1 (01:56):
So what's even more incredible to me, I think, is
that these systems actually learn from data, right, Like we're
not sitting here programming them with every single rule and
instruction right exactly.

Speaker 2 (02:08):
That's like the magic of deep learning. Yeah, it's all
about learning from experience. Okay, so think about it like this.
You learn to ride a bike by trying falling and adjusting, right.
A deep learning system does the same thing, but with data.
It processes tons and tons of data, it makes predictions

(02:29):
based on that data, and then it refines itself based
on feedback on whether it got it right or wrong.

Speaker 1 (02:34):
So it's like the more data it eats up, the
smarter it gets. Is that why companies like Google and
Facebook are like obsessed with data?

Speaker 2 (02:41):
Absolutely? Yeah, data is like the fuel for deep learning.
The quality and the quantity of that data it's crucial
for training models that are actually effective. So the more
data of the system can chew on, the more patterns
it recognizes, the more accurate its predictions become.

Speaker 1 (02:58):
Makes sense. Yeah, Now, I did see in the research
that there are different types of neural networks. I guess
my question is do we need to do a deep
dive on every single one or are there a couple
that are like really key for us to understand?

Speaker 2 (03:14):
You know, there are lots of specialized types okay, but
two that are super relevant I think to like our
everyday lives are convolutional neural networks or CNNs and recurrent
neural networks. Okay, waits CNN's and RNNs. Yeah, it's like
I'm learning a whole new alphabet here.

Speaker 1 (03:30):
Uh huh. Yeah, I can feel that way at first. Okay,
but think of them as like specialists specialists. Yeah, within
the deep learning world.

Speaker 2 (03:38):
Okay.

Speaker 1 (03:38):
So CNNs they're amazing at analyzing visual.

Speaker 2 (03:41):
Information visual okay.

Speaker 1 (03:43):
Yeah, so they're the power behind things like, you know,
facial recognition on your phone, okay, medical image analysis, even
the systems that help self driving cars kind of see
what's around them.

Speaker 2 (03:54):
So when my phone unlocks because it recognizes my face, that's.

Speaker 1 (03:57):
A CNN doing its thing precisely.

Speaker 2 (04:00):
And what's really cool about CNNs is how they actually
analyze the image. Okay, so they don't just look at
the whole thing at once. They break it down into
these smaller regions, and then they extract features and patterns
from each of those regions. Oh wow, and then they
piece it all together to understand the big picture.

Speaker 1 (04:18):
So it's kind of like how we scan a room,
right exactly. You focus on different details and then you
build up a whole mental image exactly.

Speaker 2 (04:25):
That's a great analogy.

Speaker 1 (04:26):
So it's like a digital detective piecing together clues.

Speaker 2 (04:29):
I like that. I like that.

Speaker 1 (04:31):
Okay, Well what about R and NS then what's their specialties?

Speaker 2 (04:34):
So R and n's they're the masters of sequences, sequences okay, yeah,
so they excel at processing information that kind of unfolds
over time. Okay, so think of like words in a sentence,
musical notes in a melody, stock prices over you know,

(04:54):
a certain period. This makes them super useful for stuff
like you know, language translation, speech wreck, ignition, even predicting
like the next words you're going to type on your phone?

Speaker 1 (05:04):
Well, it can predict what I'm going to type? Is
that what's behind those like autocorrect suggestions that seem to
like know what I'm thinking before even type it exactly.

Speaker 2 (05:14):
RNNs learn the patterns and the relationships between words in
a sequence. Oh wow, So they can anticipate what's likely
to come next.

Speaker 1 (05:21):
That's crazy.

Speaker 2 (05:22):
It's powerful stuff, and it's getting better all the time.

Speaker 1 (05:25):
So we've got CNNs they're like processing images like digital detectives,
and then RNNs they're untangling language like grammar gurus.

Speaker 2 (05:34):
That's a good way I put it.

Speaker 1 (05:36):
I'm starting to see how all these different neural networks
are are like shaping so much of our digital world.

Speaker 2 (05:42):
They really are.

Speaker 1 (05:42):
What else can they do? Like, where are they being
applied beyond my phone?

Speaker 2 (05:46):
Well that's where things get really exciting, honestly. Okay, you know,
deep learning is being applied in a huge and rapidly
growing number of fields. Well, for example, it's it's being
used to personalize your online shopping experience, to op demize
energy grids, even to help discover new drugs and treatments. Wow,
we're really just at the beginning of what's possible.

Speaker 1 (06:07):
So you know this all sounds incredible but also potentially
a little little scary. Right We're talking about algorithms that
can analyze images, they can understand language, they can even
predict our behavior, right, I mean, are we heading towards
the future that's ruled by AI overlords?

Speaker 2 (06:26):
That's the question everyone's asking, right, Yeah, and it definitely
sparks a lot of debate. But it's important to remember that,
you know, deep learning it's a tool. Okay, Like any tool,
it could be used for good or for bad.

Speaker 1 (06:39):
So it's not really about the technology itself, but about
how we choose to use it exactly right.

Speaker 2 (06:43):
The challenge and the opportunity is in making sure that
we're developing and deploying deep learning responsibly and ethically.

Speaker 1 (06:51):
Okay, So maybe it's not just about learning how deep
learning works, but also understanding how it's how it's changing
the world, and how we can shape its future.

Speaker 2 (07:00):
Couldn't have said it better myself.

Speaker 1 (07:01):
Feels like a whole other deep dive.

Speaker 2 (07:03):
It definitely is. But for now, I think maybe we
should dig a little deeper into some of these specific
applications of deep learning. Okay, I think you'll be surprised
at just how how much this technology is already a
part of our lives.

Speaker 1 (07:16):
I'm ready, all right, let's dive in, let's do it.

Speaker 2 (07:18):
We're just talking about all these different applications of deep learning,
and it is pretty amazing, like how much this technology
is touching so many different parts of.

Speaker 1 (07:27):
Our lives, you know, I know, right, And it's not
just about the cool gadgets and like the futuristic stuff.

Speaker 2 (07:32):
Right.

Speaker 1 (07:33):
It seems like deep learning is like already having a
real impact and feels like healthcare, finance, even like environmental conservation.

Speaker 2 (07:41):
Yeah, for sure, for sure take health care for instance.
One area where deep learning is showing huge potential is
in medical image analysis.

Speaker 3 (07:51):
Medical image analysis, Yeah, imagine like algorithms that can spot
tiny little abnormalities in say an X ray or an
MRI scan, stuff that maybe a human eye could miss.

Speaker 1 (08:04):
Oh wow, that sounds like straight out of a sci
fi movie. So are we talking about like AI doctors
replacing human doctors, Is that what's coming.

Speaker 2 (08:14):
It's not about replacing. I think it's more about augmenting.
Augmenting Okay, yeah, these systems are designed to help medical professionals,
you know, not to take their jobs. Is like a
second set of eyes basically, okay, helping to improve diagnostic
accuracy and speed things up. Think about it, like in
an emergency situation, having an AI that can analyze a

(08:36):
scan really quickly and flag potential issues. Yeah, that could
be literally life saving.

Speaker 1 (08:41):
That makes a lot of sense.

Speaker 2 (08:42):
Yeah right, It's like having this this super powered assistant
working right alongside the doctor.

Speaker 1 (08:47):
Yeah, like a medical sidekick.

Speaker 2 (08:49):
Almost, yeah exactly.

Speaker 1 (08:50):
So what other like healthcare applications are out there? I
know you mentioned like personalized treatment plans.

Speaker 2 (08:56):
Yeah, that's another really fascinating area. Okay, So the research
mentions these examples of deep learning being used to actually
tailor treatments to a specific person. Oh wow, So by
analyzing their genetic information, their medical history, their lifestyle factors,
all that AI systems can actually help doctors come up

(09:16):
with treatments that are are just for them basically, wow,
increasing the chances that it'll actually work.

Speaker 1 (09:22):
So it's not just about diagnosing diseases, it's about finding
the best possible treatment for each person exactly.

Speaker 2 (09:30):
That's incredible, pretty amazing.

Speaker 1 (09:31):
Yeah, so deep learning is helping us move towards this
more personalized and precise approach to medicine.

Speaker 2 (09:38):
Absolutely, that's exciting, very exciting.

Speaker 1 (09:41):
What about other fields? I know you mentioned finance earlier.
How's deep learning being used in the world of money.

Speaker 2 (09:48):
Well, the financial industry, it lives and breathes data analysis, right,
and prediction. I mean, it's all about predicting what's going
to happen next. Yeah, so it makes sense that it's
a perfect fit for deeper learning. You know, we're already
seeing algorithms being used to catch fraudulent transactions, assess credit risk,
even try to predict how the markets are going to move.

Speaker 1 (10:10):
So the next time I get declined for a credit card,
it might be an algorithm making that decision.

Speaker 2 (10:15):
It's possible. Uh huh, Well, I mean algorithms are already
part of credit scoring, but they tend to be based on,
you know, more traditional statistical models. Deep learning takes it
up a notch okay, because it can incorporate so much
more data and find these these hidden patterns that that
a human analyst might completely miss.

Speaker 1 (10:37):
I guess that makes sense.

Speaker 3 (10:37):
Right.

Speaker 1 (10:38):
The more data you have, the better equipped you are
to actually.

Speaker 2 (10:41):
Assess risk exactly.

Speaker 1 (10:42):
But it also makes me wonder about you know, like
transparency and fairness, Like how do we know these algorithms
aren't biased or making decisions based on things that shouldn't
even be considered.

Speaker 2 (10:54):
That that is a crucial point, right, you know, transparency
and fairness are are essential, especially in areas like finance
where the stakes are so high.

Speaker 1 (11:03):
Okay, so how do we actually ensure that these systems
are making fair and unbiased decisions?

Speaker 2 (11:09):
That's the million dollar question, right, Yeah, it's trigger it's
a real challenge, but people are working on it, Okay.
It involves, you know, things like being really careful about
the data that's used to train the algorithms, using techniques
to try to spot and reduce any biases, and then
constantly monitoring the system's output just to make sure it's

(11:30):
not doing anything funky.

Speaker 1 (11:32):
So it sounds like a lot of work. It is yet,
but I mean important work obviously, right. We have to
make sure that these tools, these powerful tools, are being
used responsibly. Absolutely Okay, so we've talked about healthcare and finance.
What about the environment. I remember reading something about deep
learning being used in like conservation efforts.

Speaker 2 (11:52):
Oh yeah, that's that's a really cool application. Okay, So
deep learning is actually turning out to be super useful
for environmental scientists and people are working to protect the planet.
For example, they're using these algorithms to analyze satellite images okay,
to track like deforestation patterns, wow, to monitor wildlife populations,

(12:14):
even to try to predict how invasive species might spread.

Speaker 1 (12:18):
So it's like AI is being used to actually protect
the planet.

Speaker 2 (12:22):
It really does have that potential.

Speaker 1 (12:23):
Yeah, that's pretty amazing it is. Okay. I got to
ask what about those AI systems that like create art
and music. I mean, that was something that really captured
my imagination when I was reading through all this stuff.
Is that just a novelty or is there more to it?

Speaker 2 (12:40):
It's definitely more than just a gimmick.

Speaker 3 (12:42):
Okay.

Speaker 2 (12:42):
You know the development of these things called generative adversarial
networks or gans, Yeah, and gans, it's opened up this
whole new world of possibilities for AI and creativity. Wow.
These systems can create these incredibly realistic images, compose original music,
write poetry.

Speaker 1 (13:01):
I've seen some of the artwork that's that's being generated
by AI, and and it's it's pretty mind blowing, honestly.

Speaker 2 (13:06):
It is.

Speaker 1 (13:07):
Yeah, but it also raises some interesting questions, right, I mean,
what does it even mean for human creativity? If if
machines can create art that's that's practically indistinguishable from from
something a human made, that's.

Speaker 2 (13:22):
A big question. Yeah, it's a it's a philosophical question, right.
Some people see it as a threat, you know, like
machines are taking over something that's always been uniquely human. Yeah,
But other people they see it as an.

Speaker 1 (13:35):
Opportunity, an opportunity okay, yeah.

Speaker 2 (13:36):
For collaboration, like a way for AI to actually work
with us, to to enhance human creativity, to inspire us
to to explore new new artistic frontiers. You know.

Speaker 1 (13:50):
So I guess it depends on your perspective, Like how
you view it. Do you think like AI will eventually
just like surpass human creativity.

Speaker 2 (13:58):
That's a tough one, yeah, I mean creativity is this
complex thing, right. AI systems they're amazing at picking up
patterns and then using those patterns to generate, you know,
new stuff. But whether they can actually replicate that that
spark of human imagination, that that real innovation, you know,
I think that remains to be seen.

Speaker 1 (14:19):
Maybe it's not about surpassing, but about expanding, like what
we even consider to be creative.

Speaker 2 (14:24):
Right, that's a good point. Yeah, maybe AI will force
us to redefine what creativity even means. Yeah, and push
the boundaries of what's possible.

Speaker 1 (14:34):
You know, it's definitely getting deep.

Speaker 2 (14:36):
It is. It is.

Speaker 1 (14:37):
We've covered so much, from healthcare to finance, environmental conservation,
even like the future of art, yeah, or a lot
of ground. It feels like we're barely scratching the surface
of what deep learning can do. We are, we are,
And there's there's one area we haven't even talked about
yet that I think is particularly fascinating. Yeah, and that's
that's the potential impact of deep learning on education.

Speaker 2 (15:00):
Yeah, that's a big one that was in the.

Speaker 1 (15:01):
Research, and I'm really curious to hear your thoughts on that.

Speaker 2 (15:05):
Well. One of the most exciting things about about deep
learning and education is its ability to personalize learning. Okay,
so imagine imagine AI tutors that can actually adapt to
each student's individual needs. You know, Oh, Wow, providing custom
instruction feedback, all based on their strengths and weaknesses.

Speaker 1 (15:26):
So it's like the dream of of truly personalized education
could actually become a reality.

Speaker 2 (15:32):
That's a hope. Yeah. Wow, deep learning can help us
create these these learning environments that are that are more engaging,
more effective, and fairer for every student.

Speaker 1 (15:42):
That's exciting. It is, it is, But I imagine there
are some challenges, some concerns around around putting AI into education, right,
I mean, what are some of the things we need
to be thinking about as we as we do this.

Speaker 2 (15:54):
Well, one of the big ones is making sure that
these systems are are designed and implemented fairly equitably. You know,
we have to be careful about potential biases in the data.
We have to make sure that AI is used to
help all learners, not to make existing inequalities worse.

Speaker 1 (16:12):
That's a good point. Yeah, it's it's not just about
the technology itself, but about how we use it exactly.
Like we keep coming back to, we need to to
approach this with a critical eye, right, make sure that
AI is integrated into education in a way that benefits
everyone and that it supports you know, the really important
role of human teachers couldn't agree more. It sounds like

(16:35):
like a lot to consider it is. Yeah, maybe this
is a good place to pause for a second. Yeah,
take a breath and kind of you know, process everything.

Speaker 2 (16:42):
We've covered a lot we have, but there's there's still
more to explore.

Speaker 1 (16:46):
Okay, all right, I think I need to like recharge
my brain after all that we've talked about how deep
learning is being used in healthcare, finance, you know, protecting
the environment, even art. We just started to touch on it,
potential and education.

Speaker 2 (17:03):
Yeah, it's a lot.

Speaker 1 (17:04):
It feels like this technology is like already changing the
world in so many ways.

Speaker 2 (17:08):
It really is, and it's only going to get more
impactful as deep learning continues to.

Speaker 1 (17:13):
Advance, you know, right, And I guess with all this
power comes a lot of responsibility. Earlier we talked about
like the potential for bias and algorithms and how important
transparency is. Yeah, what other like ethical things should we
be thinking about as we move deeper into this world
of deep learning.

Speaker 2 (17:32):
One area that we really need to think about carefully
is the impact of AI on jobs. Jobs. As AI
systems get more and more capable, it's it's inevitable that
they're going to start automating some tasks that are currently
being done by people.

Speaker 1 (17:47):
So is this like the beginning of the robot takeover?
Are we all going to be replaced by machines?

Speaker 2 (17:53):
I wouldn't say replaced, but the way we work it's
definitely going to change. Okay. I mean history has shown
that when new technologies come along, Yeah, they often create
new industries and new jobs. So the key is to
adapt to be prepared for those changes by investing in
education and training programs that teach people the skills they

(18:15):
need for the jobs of the future.

Speaker 1 (18:16):
So instead of being afraid of the robots, we should
be thinking about how to work.

Speaker 2 (18:20):
With them exactly. AI can free us up from doing
those boring tasks or even dangerous tasks, and let us
focus on things that are more creative, more strategic, more human.
You know. Yeah, I like that It's about collaboration, not replacement.

Speaker 1 (18:36):
Okay, that's a much more hopeful way to look at it.

Speaker 2 (18:39):
Uh huh. Yeah.

Speaker 1 (18:40):
What about privacy? I mean, it seems like every day
there's a new story about a data breach or companies
collecting our information. Does deep learning make those concerns even worse?

Speaker 2 (18:50):
Privacy is so important, right, It's a fundamental right and
we absolutely have to protect it, especially as AI becomes
more powerful. Okay, you know, deep learning systems often rely
on huge amounts of data, and a lot of times
that includes personal data. So we need to have really
strong safeguards in place to make sure that data is collected, stored,
and used ethically and responsibly and with transparency. You know,

(19:15):
people should know what's happening with their data.

Speaker 1 (19:17):
So it's not just about building smarter algorithms, it's about
building trust and making sure that these systems are used
in a way that respects our values exactly.

Speaker 2 (19:26):
We need clear guidelines and regulations for aih you know,
focus on things like transparency, accountability, fairness.

Speaker 1 (19:34):
It sounds like there's a lot of work to be done,
not just like on the technical side, but also on
the like the ethical side, the societal side.

Speaker 2 (19:41):
It's a big job, and it's going to take all
of us working together, you know, researchers, policymakers, industry leaders,
and just regular people like us. We all have a
role to play in making sure that AI is developed
and used in a way that actually benefits humanity.

Speaker 1 (19:57):
So what can we as individuals do to make sure
that AI is used for good.

Speaker 2 (20:03):
The most important thing is to stay informed, Okay, you know,
educate yourself about AI, what it can do, what it
can't do, how it might impact our lives. Right, talk
about AI ethics with your friends and family. Advocate for
responsible AI development and use, and support those organizations and
initiatives that are really trying to create a future where

(20:24):
AI benefits everyone.

Speaker 1 (20:25):
That actually feels like empowering. You know, it's not just
about watching this technological revolution happen. It's about like actually
helping to shape it exactly.

Speaker 2 (20:35):
The future of AI. It's not set in stone. It's
a future that we're all creating together through the choices
we make today.

Speaker 1 (20:42):
Well, I don't know about you, but I'm feeling a
mix of excitement and maybe a little bit of apprehension
about what all of this means for the future.

Speaker 2 (20:51):
Yeah, but that's understand. It's a time of huge change,
a lot of uncertainty. Yeah, but it's also a time
of tremendous opportunity.

Speaker 1 (20:59):
Okay.

Speaker 2 (21:00):
If we're smart about AI, if we plan ahead, we
can use it to build a world that's more just
more equitable, and more sustainable for everyone.

Speaker 1 (21:08):
I like the sound of that. That's a world I
want to live in.

Speaker 2 (21:10):
Yeah, me too.

Speaker 1 (21:11):
So to our listeners, I hope this deep dive has
given you a better understanding of what deep learning is
all about, not just the technical stuff, but also what
it means for our society, for our future. Absolutely, it's
a complex topic, but it's definitely worth exploring further.

Speaker 2 (21:27):
Yeah, and remember this is really just the beginning, right.
Deep learning is changing so fast. Yeah, and the possibilities
they really are endless. Wow. Stay curious, stay engaged, and
keep asking those questions.

Speaker 1 (21:41):
I love it. And who knows, maybe you'll have your
own deep dive topic you want to suggest.

Speaker 2 (21:44):
Yeah, we're always looking for new adventures.

Speaker 3 (21:45):
We are.

Speaker 1 (21:46):
Until next time, keep learning and keep questioning.

Speaker 2 (21:49):
That's what it's all about.