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Deep Learning and Neural Networks. The Heart of AI · Artificial Intelligence Exposed

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  Speaker 1: All right, so deep learning and neural networks. You've given
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  Speaker 1: me a ton of research on this. Yeah, and it's
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  Speaker 1: a hot topic, right, I mean everywhere you look these days,
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  Speaker 1: it feels like deep learning is powering like everything from
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  Speaker 1: you know, self driving cars to figuring out what we're
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  Speaker 1: going to buy next on Amazon.
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  Speaker 2: Yeah.
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  Speaker 1: Absolutely, But I got to say, even for someone like me,
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  Speaker 1: who you know, loves to stay on top of the
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  Speaker 1: latest tech, it can feel honestly a little overwhelming. Get
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  Speaker 1: like rocket science almost.
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  Speaker 2: Yeah, a lot of people feel that way.
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  Speaker 3: Yeah.
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  Speaker 2: I mean it's definitely a complex field, but honestly, the
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  Speaker 2: core concepts I think are are surprisingly accessible once we
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  Speaker 2: get to break them down a little bit.
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  Speaker 1: Okay, I'm ready, let's have my mind blown. So let's
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  Speaker 1: start with the basics, Like what are deep learning and
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  Speaker 1: neural networks? I mean, I hear all the time that
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  Speaker 1: they're like inspired by the human brain, But what does
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  Speaker 1: that actually mean when we're talking about computers.
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  Speaker 2: So the human brain as this this incredible network of
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  Speaker 2: interconnected neurons, Okay, and each of these neurons is like
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  Speaker 2: a tiny processing unit, right, And so what artificial neural
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  Speaker 2: networks try to do is they basically try to mimic
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  Speaker 2: that structure, right, So they create these layers of interconnected nodes,
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  Speaker 2: and those nodes they process information just like those neurons do.
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  Speaker 2: And then deep learning, well that's taking it to another level.
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  Speaker 2: It uses networks that have lots of layers, hence the
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  Speaker 2: word deep.
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  Speaker 1: So more layers equals more processing.
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  Speaker 2: Power exactly, more layers let the network sort of discover
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  Speaker 2: these these much more intricate patterns and relationships with then
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  Speaker 2: the data got it. And that's that's really the key
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  Speaker 2: to how deep learning systems can do things like, you know,
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  Speaker 2: recognize faces, translate languages, even compose music.
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  Speaker 1: We compose music. That's wild.
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  Speaker 2: It is pretty amazing. Yeah.
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  Speaker 1: So what's even more incredible to me, I think, is
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  Speaker 1: that these systems actually learn from data, right, Like we're
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  Speaker 1: not sitting here programming them with every single rule and
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  Speaker 1: instruction right exactly.
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  Speaker 2: That's like the magic of deep learning. Yeah, it's all
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  Speaker 2: about learning from experience. Okay, so think about it like this.
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  Speaker 2: You learn to ride a bike by trying falling and adjusting, right.
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  Speaker 2: A deep learning system does the same thing, but with data.
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  Speaker 2: It processes tons and tons of data, it makes predictions
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  Speaker 2: based on that data, and then it refines itself based
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  Speaker 2: on feedback on whether it got it right or wrong.
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  Speaker 1: So it's like the more data it eats up, the
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  Speaker 1: smarter it gets. Is that why companies like Google and
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  Speaker 1: Facebook are like obsessed with data?
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  Speaker 2: Absolutely? Yeah, data is like the fuel for deep learning.
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  Speaker 2: The quality and the quantity of that data it's crucial
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  Speaker 2: for training models that are actually effective. So the more
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  Speaker 2: data of the system can chew on, the more patterns
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  Speaker 2: it recognizes, the more accurate its predictions become.
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  Speaker 1: Makes sense. Yeah, Now, I did see in the research
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  Speaker 1: that there are different types of neural networks. I guess
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  Speaker 1: my question is do we need to do a deep
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  Speaker 1: dive on every single one or are there a couple
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  Speaker 1: that are like really key for us to understand?
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  Speaker 2: You know, there are lots of specialized types okay, but
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  Speaker 2: two that are super relevant I think to like our
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  Speaker 2: everyday lives are convolutional neural networks or CNNs and recurrent
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  Speaker 2: neural networks. Okay, waits CNN's and RNNs. Yeah, it's like
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  Speaker 2: I'm learning a whole new alphabet here.
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  Speaker 1: Uh huh. Yeah, I can feel that way at first. Okay,
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  Speaker 1: but think of them as like specialists specialists. Yeah, within
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  Speaker 1: the deep learning world.
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  Speaker 2: Okay.
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  Speaker 1: So CNNs they're amazing at analyzing visual.
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  Speaker 2: Information visual okay.
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  Speaker 1: Yeah, so they're the power behind things like, you know,
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  Speaker 1: facial recognition on your phone, okay, medical image analysis, even
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  Speaker 1: the systems that help self driving cars kind of see
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  Speaker 1: what's around them.
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  Speaker 2: So when my phone unlocks because it recognizes my face, that's.
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  Speaker 1: A CNN doing its thing precisely.
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  Speaker 2: And what's really cool about CNNs is how they actually
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  Speaker 2: analyze the image. Okay, so they don't just look at
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  Speaker 2: the whole thing at once. They break it down into
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  Speaker 2: these smaller regions, and then they extract features and patterns
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  Speaker 2: from each of those regions. Oh wow, and then they
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  Speaker 2: piece it all together to understand the big picture.
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  Speaker 1: So it's kind of like how we scan a room,
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  Speaker 1: right exactly. You focus on different details and then you
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  Speaker 1: build up a whole mental image exactly.
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  Speaker 2: That's a great analogy.
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  Speaker 1: So it's like a digital detective piecing together clues.
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  Speaker 2: I like that. I like that.
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  Speaker 1: Okay, Well what about R and NS then what's their specialties?
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  Speaker 2: So R and n's they're the masters of sequences, sequences okay, yeah,
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  Speaker 2: so they excel at processing information that kind of unfolds
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  Speaker 2: over time. Okay, so think of like words in a sentence,
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  Speaker 2: musical notes in a melody, stock prices over you know,
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  Speaker 2: a certain period. This makes them super useful for stuff
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  Speaker 2: like you know, language translation, speech wreck, ignition, even predicting
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  Speaker 2: like the next words you're going to type on your phone?
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  Speaker 1: Well, it can predict what I'm going to type? Is
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  Speaker 1: that what's behind those like autocorrect suggestions that seem to
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  Speaker 1: like know what I'm thinking before even type it exactly.
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  Speaker 2: RNNs learn the patterns and the relationships between words in
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  Speaker 2: a sequence. Oh wow, So they can anticipate what's likely
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  Speaker 2: to come next.
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  Speaker 1: That's crazy.
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  Speaker 2: It's powerful stuff, and it's getting better all the time.
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  Speaker 1: So we've got CNNs they're like processing images like digital detectives,
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  Speaker 1: and then RNNs they're untangling language like grammar gurus.
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  Speaker 2: That's a good way I put it.
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  Speaker 1: I'm starting to see how all these different neural networks
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  Speaker 1: are are like shaping so much of our digital world.
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  Speaker 2: They really are.
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  Speaker 1: What else can they do? Like, where are they being
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  Speaker 1: applied beyond my phone?
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  Speaker 2: Well that's where things get really exciting, honestly. Okay, you know,
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  Speaker 2: deep learning is being applied in a huge and rapidly
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  Speaker 2: growing number of fields. Well, for example, it's it's being
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  Speaker 2: used to personalize your online shopping experience, to op demize
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  Speaker 2: energy grids, even to help discover new drugs and treatments. Wow,
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  Speaker 2: we're really just at the beginning of what's possible.
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  Speaker 1: So you know this all sounds incredible but also potentially
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  Speaker 1: a little little scary. Right We're talking about algorithms that
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  Speaker 1: can analyze images, they can understand language, they can even
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  Speaker 1: predict our behavior, right, I mean, are we heading towards
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  Speaker 1: the future that's ruled by AI overlords?
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  Speaker 2: That's the question everyone's asking, right, Yeah, and it definitely
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  Speaker 2: sparks a lot of debate. But it's important to remember that,
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  Speaker 2: you know, deep learning it's a tool. Okay, Like any tool,
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  Speaker 2: it could be used for good or for bad.
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  Speaker 1: So it's not really about the technology itself, but about
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  Speaker 1: how we choose to use it exactly right.
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  Speaker 2: The challenge and the opportunity is in making sure that
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  Speaker 2: we're developing and deploying deep learning responsibly and ethically.
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  Speaker 1: Okay, So maybe it's not just about learning how deep
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  Speaker 1: learning works, but also understanding how it's how it's changing
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  Speaker 1: the world, and how we can shape its future.
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  Speaker 2: Couldn't have said it better myself.
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  Speaker 1: Feels like a whole other deep dive.
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  Speaker 2: It definitely is. But for now, I think maybe we
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  Speaker 2: should dig a little deeper into some of these specific
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  Speaker 2: applications of deep learning. Okay, I think you'll be surprised
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  Speaker 2: at just how how much this technology is already a
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  Speaker 2: part of our lives.
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  Speaker 1: I'm ready, all right, let's dive in, let's do it.
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  Speaker 2: We're just talking about all these different applications of deep learning,
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  Speaker 2: and it is pretty amazing, like how much this technology
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  Speaker 2: is touching so many different parts of.
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  Speaker 1: Our lives, you know, I know, right, And it's not
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  Speaker 1: just about the cool gadgets and like the futuristic stuff.
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  Speaker 2: Right.
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  Speaker 1: It seems like deep learning is like already having a
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  Speaker 1: real impact and feels like healthcare, finance, even like environmental conservation.
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  Speaker 2: Yeah, for sure, for sure take health care for instance.
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  Speaker 2: One area where deep learning is showing huge potential is
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  Speaker 2: in medical image analysis.
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  Speaker 3: Medical image analysis, Yeah, imagine like algorithms that can spot
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  Speaker 3: tiny little abnormalities in say an X ray or an
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  Speaker 3: MRI scan, stuff that maybe a human eye could miss.
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  Speaker 1: Oh wow, that sounds like straight out of a sci
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  Speaker 1: fi movie. So are we talking about like AI doctors
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  Speaker 1: replacing human doctors, Is that what's coming.
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  Speaker 2: It's not about replacing. I think it's more about augmenting.
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  Speaker 2: Augmenting Okay, yeah, these systems are designed to help medical professionals,
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  Speaker 2: you know, not to take their jobs. Is like a
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  Speaker 2: second set of eyes basically, okay, helping to improve diagnostic
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  Speaker 2: accuracy and speed things up. Think about it, like in
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  Speaker 2: an emergency situation, having an AI that can analyze a
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  Speaker 2: scan really quickly and flag potential issues. Yeah, that could
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  Speaker 2: be literally life saving.
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  Speaker 1: That makes a lot of sense.
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  Speaker 2: Yeah right, It's like having this this super powered assistant
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  Speaker 2: working right alongside the doctor.
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  Speaker 1: Yeah, like a medical sidekick.
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  Speaker 2: Almost, yeah exactly.
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  Speaker 1: So what other like healthcare applications are out there? I
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  Speaker 1: know you mentioned like personalized treatment plans.
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  Speaker 2: Yeah, that's another really fascinating area. Okay, So the research
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  Speaker 2: mentions these examples of deep learning being used to actually
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  Speaker 2: tailor treatments to a specific person. Oh wow, So by
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  Speaker 2: analyzing their genetic information, their medical history, their lifestyle factors,
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  Speaker 2: all that AI systems can actually help doctors come up
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  Speaker 2: with treatments that are are just for them basically, wow,
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  Speaker 2: increasing the chances that it'll actually work.
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  Speaker 1: So it's not just about diagnosing diseases, it's about finding
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  Speaker 1: the best possible treatment for each person exactly.
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  Speaker 2: That's incredible, pretty amazing.
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  Speaker 1: Yeah, so deep learning is helping us move towards this
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  Speaker 1: more personalized and precise approach to medicine.
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  Speaker 2: Absolutely, that's exciting, very exciting.
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  Speaker 1: What about other fields? I know you mentioned finance earlier.
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  Speaker 1: How's deep learning being used in the world of money.
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  Speaker 2: Well, the financial industry, it lives and breathes data analysis, right,
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  Speaker 2: and prediction. I mean, it's all about predicting what's going
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  Speaker 2: to happen next. Yeah, so it makes sense that it's
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  Speaker 2: a perfect fit for deeper learning. You know, we're already
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  Speaker 2: seeing algorithms being used to catch fraudulent transactions, assess credit risk,
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  Speaker 2: even try to predict how the markets are going to move.
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  Speaker 1: So the next time I get declined for a credit card,
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  Speaker 1: it might be an algorithm making that decision.
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  Speaker 2: It's possible. Uh huh, Well, I mean algorithms are already
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  Speaker 2: part of credit scoring, but they tend to be based on,
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  Speaker 2: you know, more traditional statistical models. Deep learning takes it
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  Speaker 2: up a notch okay, because it can incorporate so much
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  Speaker 2: more data and find these these hidden patterns that that
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  Speaker 2: a human analyst might completely miss.
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  Speaker 1: I guess that makes sense.
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  Speaker 3: Right.
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  Speaker 1: The more data you have, the better equipped you are
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  Speaker 1: to actually.
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  Speaker 2: Assess risk exactly.
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  Speaker 1: But it also makes me wonder about you know, like
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  Speaker 1: transparency and fairness, Like how do we know these algorithms
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  Speaker 1: aren't biased or making decisions based on things that shouldn't
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  Speaker 1: even be considered.
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  Speaker 2: That that is a crucial point, right, you know, transparency
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  Speaker 2: and fairness are are essential, especially in areas like finance
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  Speaker 2: where the stakes are so high.
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  Speaker 1: Okay, so how do we actually ensure that these systems
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  Speaker 1: are making fair and unbiased decisions?
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  Speaker 2: That's the million dollar question, right, Yeah, it's trigger it's
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  Speaker 2: a real challenge, but people are working on it, Okay.
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  Speaker 2: It involves, you know, things like being really careful about
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  Speaker 2: the data that's used to train the algorithms, using techniques
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  Speaker 2: to try to spot and reduce any biases, and then
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  Speaker 2: constantly monitoring the system's output just to make sure it's
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  Speaker 2: not doing anything funky.
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  Speaker 1: So it sounds like a lot of work. It is yet,
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  Speaker 1: but I mean important work obviously, right. We have to
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  Speaker 1: make sure that these tools, these powerful tools, are being
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  Speaker 1: used responsibly. Absolutely Okay, so we've talked about healthcare and finance.
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  Speaker 1: What about the environment. I remember reading something about deep
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  Speaker 1: learning being used in like conservation efforts.
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  Speaker 2: Oh yeah, that's that's a really cool application. Okay, So
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  Speaker 2: deep learning is actually turning out to be super useful
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  Speaker 2: for environmental scientists and people are working to protect the planet.
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  Speaker 2: For example, they're using these algorithms to analyze satellite images okay,
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  Speaker 2: to track like deforestation patterns, wow, to monitor wildlife populations,
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  Speaker 2: even to try to predict how invasive species might spread.
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  Speaker 1: So it's like AI is being used to actually protect
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  Speaker 1: the planet.
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  Speaker 2: It really does have that potential.
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  Speaker 1: Yeah, that's pretty amazing it is. Okay. I got to
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  Speaker 1: ask what about those AI systems that like create art
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  Speaker 1: and music. I mean, that was something that really captured
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  Speaker 1: my imagination when I was reading through all this stuff.
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  Speaker 1: Is that just a novelty or is there more to it?
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  Speaker 2: It's definitely more than just a gimmick.
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  Speaker 3: Okay.
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  Speaker 2: You know the development of these things called generative adversarial
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  Speaker 2: networks or gans, Yeah, and gans, it's opened up this
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  Speaker 2: whole new world of possibilities for AI and creativity. Wow.
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  Speaker 2: These systems can create these incredibly realistic images, compose original music,
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  Speaker 2: write poetry.
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  Speaker 1: I've seen some of the artwork that's that's being generated
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  Speaker 1: by AI, and and it's it's pretty mind blowing, honestly.
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  Speaker 2: It is.
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  Speaker 1: Yeah, but it also raises some interesting questions, right, I mean,
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  Speaker 1: what does it even mean for human creativity? If if
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  Speaker 1: machines can create art that's that's practically indistinguishable from from
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  Speaker 1: something a human made, that's.
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  Speaker 2: A big question. Yeah, it's a it's a philosophical question, right.
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  Speaker 2: Some people see it as a threat, you know, like
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  Speaker 2: machines are taking over something that's always been uniquely human. Yeah,
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  Speaker 2: But other people they see it as an.
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  Speaker 1: Opportunity, an opportunity okay, yeah.
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  Speaker 2: For collaboration, like a way for AI to actually work
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  Speaker 2: with us, to to enhance human creativity, to inspire us
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  Speaker 2: to to explore new new artistic frontiers. You know.
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  Speaker 1: So I guess it depends on your perspective, Like how
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  Speaker 1: you view it. Do you think like AI will eventually
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  Speaker 1: just like surpass human creativity.
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  Speaker 2: That's a tough one, yeah, I mean creativity is this
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  Speaker 2: complex thing, right. AI systems they're amazing at picking up
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  Speaker 2: patterns and then using those patterns to generate, you know,
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  Speaker 2: new stuff. But whether they can actually replicate that that
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  Speaker 2: spark of human imagination, that that real innovation, you know,
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  Speaker 2: I think that remains to be seen.
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  Speaker 1: Maybe it's not about surpassing, but about expanding, like what
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  Speaker 1: we even consider to be creative.
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  Speaker 2: Right, that's a good point. Yeah, maybe AI will force
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  Speaker 2: us to redefine what creativity even means. Yeah, and push
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  Speaker 2: the boundaries of what's possible.
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  Speaker 1: You know, it's definitely getting deep.
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  Speaker 2: It is. It is.
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  Speaker 1: We've covered so much, from healthcare to finance, environmental conservation,
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  Speaker 1: even like the future of art, yeah, or a lot
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  Speaker 1: of ground. It feels like we're barely scratching the surface
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  Speaker 1: of what deep learning can do. We are, we are,
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  Speaker 1: And there's there's one area we haven't even talked about
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  Speaker 1: yet that I think is particularly fascinating. Yeah, and that's
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  Speaker 1: that's the potential impact of deep learning on education.
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  Speaker 2: Yeah, that's a big one that was in the.
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  Speaker 1: Research, and I'm really curious to hear your thoughts on that.
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  Speaker 2: Well. One of the most exciting things about about deep
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  Speaker 2: learning and education is its ability to personalize learning. Okay,
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  Speaker 2: so imagine imagine AI tutors that can actually adapt to
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  Speaker 2: each student's individual needs. You know, Oh, Wow, providing custom
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  Speaker 2: instruction feedback, all based on their strengths and weaknesses.
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  Speaker 1: So it's like the dream of of truly personalized education
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  Speaker 1: could actually become a reality.
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  Speaker 2: That's a hope. Yeah. Wow, deep learning can help us
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  Speaker 2: create these these learning environments that are that are more engaging,
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  Speaker 2: more effective, and fairer for every student.
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  Speaker 1: That's exciting. It is, it is, But I imagine there
• 15:44
  Speaker 1: are some challenges, some concerns around around putting AI into education, right,
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  Speaker 1: I mean, what are some of the things we need
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  Speaker 1: to be thinking about as we as we do this.
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  Speaker 2: Well, one of the big ones is making sure that
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  Speaker 2: these systems are are designed and implemented fairly equitably. You know,
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  Speaker 2: we have to be careful about potential biases in the data.
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  Speaker 2: We have to make sure that AI is used to
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  Speaker 2: help all learners, not to make existing inequalities worse.
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  Speaker 1: That's a good point. Yeah, it's it's not just about
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  Speaker 1: the technology itself, but about how we use it exactly.
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  Speaker 1: Like we keep coming back to, we need to to
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  Speaker 1: approach this with a critical eye, right, make sure that
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  Speaker 1: AI is integrated into education in a way that benefits
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  Speaker 1: everyone and that it supports you know, the really important
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  Speaker 1: role of human teachers couldn't agree more. It sounds like
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  Speaker 1: like a lot to consider it is. Yeah, maybe this
• 16:37
  Speaker 1: is a good place to pause for a second. Yeah,
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  Speaker 1: take a breath and kind of you know, process everything.
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  Speaker 2: We've covered a lot we have, but there's there's still
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  Speaker 2: more to explore.
• 16:46
  Speaker 1: Okay, all right, I think I need to like recharge
• 16:48
  Speaker 1: my brain after all that we've talked about how deep
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  Speaker 1: learning is being used in healthcare, finance, you know, protecting
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  Speaker 1: the environment, even art. We just started to touch on it,
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  Speaker 1: potential and education.
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  Speaker 2: Yeah, it's a lot.
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  Speaker 1: It feels like this technology is like already changing the
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  Speaker 1: world in so many ways.
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  Speaker 2: It really is, and it's only going to get more
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  Speaker 2: impactful as deep learning continues to.
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  Speaker 1: Advance, you know, right, And I guess with all this
• 17:16
  Speaker 1: power comes a lot of responsibility. Earlier we talked about
• 17:20
  Speaker 1: like the potential for bias and algorithms and how important
• 17:24
  Speaker 1: transparency is. Yeah, what other like ethical things should we
• 17:28
  Speaker 1: be thinking about as we move deeper into this world
• 17:31
  Speaker 1: of deep learning.
• 17:32
  Speaker 2: One area that we really need to think about carefully
• 17:35
  Speaker 2: is the impact of AI on jobs. Jobs. As AI
• 17:39
  Speaker 2: systems get more and more capable, it's it's inevitable that
• 17:43
  Speaker 2: they're going to start automating some tasks that are currently
• 17:46
  Speaker 2: being done by people.
• 17:47
  Speaker 1: So is this like the beginning of the robot takeover?
• 17:50
  Speaker 1: Are we all going to be replaced by machines?
• 17:53
  Speaker 2: I wouldn't say replaced, but the way we work it's
• 17:57
  Speaker 2: definitely going to change. Okay. I mean history has shown
• 18:00
  Speaker 2: that when new technologies come along, Yeah, they often create
• 18:03
  Speaker 2: new industries and new jobs. So the key is to
• 18:06
  Speaker 2: adapt to be prepared for those changes by investing in
• 18:10
  Speaker 2: education and training programs that teach people the skills they
• 18:15
  Speaker 2: need for the jobs of the future.
• 18:16
  Speaker 1: So instead of being afraid of the robots, we should
• 18:18
  Speaker 1: be thinking about how to work.
• 18:20
  Speaker 2: With them exactly. AI can free us up from doing
• 18:23
  Speaker 2: those boring tasks or even dangerous tasks, and let us
• 18:27
  Speaker 2: focus on things that are more creative, more strategic, more human.
• 18:31
  Speaker 2: You know. Yeah, I like that It's about collaboration, not replacement.
• 18:36
  Speaker 1: Okay, that's a much more hopeful way to look at it.
• 18:39
  Speaker 2: Uh huh. Yeah.
• 18:40
  Speaker 1: What about privacy? I mean, it seems like every day
• 18:42
  Speaker 1: there's a new story about a data breach or companies
• 18:45
  Speaker 1: collecting our information. Does deep learning make those concerns even worse?
• 18:50
  Speaker 2: Privacy is so important, right, It's a fundamental right and
• 18:53
  Speaker 2: we absolutely have to protect it, especially as AI becomes
• 18:57
  Speaker 2: more powerful. Okay, you know, deep learning systems often rely
• 18:59
  Speaker 2: on huge amounts of data, and a lot of times
• 19:02
  Speaker 2: that includes personal data. So we need to have really
• 19:05
  Speaker 2: strong safeguards in place to make sure that data is collected, stored,
• 19:09
  Speaker 2: and used ethically and responsibly and with transparency. You know,
• 19:15
  Speaker 2: people should know what's happening with their data.
• 19:17
  Speaker 1: So it's not just about building smarter algorithms, it's about
• 19:20
  Speaker 1: building trust and making sure that these systems are used
• 19:23
  Speaker 1: in a way that respects our values exactly.
• 19:26
  Speaker 2: We need clear guidelines and regulations for aih you know,
• 19:30
  Speaker 2: focus on things like transparency, accountability, fairness.
• 19:34
  Speaker 1: It sounds like there's a lot of work to be done,
• 19:36
  Speaker 1: not just like on the technical side, but also on
• 19:38
  Speaker 1: the like the ethical side, the societal side.
• 19:41
  Speaker 2: It's a big job, and it's going to take all
• 19:43
  Speaker 2: of us working together, you know, researchers, policymakers, industry leaders,
• 19:48
  Speaker 2: and just regular people like us. We all have a
• 19:51
  Speaker 2: role to play in making sure that AI is developed
• 19:53
  Speaker 2: and used in a way that actually benefits humanity.
• 19:57
  Speaker 1: So what can we as individuals do to make sure
• 20:00
  Speaker 1: that AI is used for good.
• 20:03
  Speaker 2: The most important thing is to stay informed, Okay, you know,
• 20:06
  Speaker 2: educate yourself about AI, what it can do, what it
• 20:08
  Speaker 2: can't do, how it might impact our lives. Right, talk
• 20:11
  Speaker 2: about AI ethics with your friends and family. Advocate for
• 20:14
  Speaker 2: responsible AI development and use, and support those organizations and
• 20:20
  Speaker 2: initiatives that are really trying to create a future where
• 20:24
  Speaker 2: AI benefits everyone.
• 20:25
  Speaker 1: That actually feels like empowering. You know, it's not just
• 20:29
  Speaker 1: about watching this technological revolution happen. It's about like actually
• 20:33
  Speaker 1: helping to shape it exactly.
• 20:35
  Speaker 2: The future of AI. It's not set in stone. It's
• 20:38
  Speaker 2: a future that we're all creating together through the choices
• 20:41
  Speaker 2: we make today.
• 20:42
  Speaker 1: Well, I don't know about you, but I'm feeling a
• 20:45
  Speaker 1: mix of excitement and maybe a little bit of apprehension
• 20:49
  Speaker 1: about what all of this means for the future.
• 20:51
  Speaker 2: Yeah, but that's understand. It's a time of huge change,
• 20:55
  Speaker 2: a lot of uncertainty. Yeah, but it's also a time
• 20:57
  Speaker 2: of tremendous opportunity.
• 20:59
  Speaker 1: Okay.
• 21:00
  Speaker 2: If we're smart about AI, if we plan ahead, we
• 21:02
  Speaker 2: can use it to build a world that's more just
• 21:05
  Speaker 2: more equitable, and more sustainable for everyone.
• 21:08
  Speaker 1: I like the sound of that. That's a world I
• 21:10
  Speaker 1: want to live in.
• 21:10
  Speaker 2: Yeah, me too.
• 21:11
  Speaker 1: So to our listeners, I hope this deep dive has
• 21:14
  Speaker 1: given you a better understanding of what deep learning is
• 21:17
  Speaker 1: all about, not just the technical stuff, but also what
• 21:19
  Speaker 1: it means for our society, for our future. Absolutely, it's
• 21:23
  Speaker 1: a complex topic, but it's definitely worth exploring further.
• 21:27
  Speaker 2: Yeah, and remember this is really just the beginning, right.
• 21:30
  Speaker 2: Deep learning is changing so fast. Yeah, and the possibilities
• 21:35
  Speaker 2: they really are endless. Wow. Stay curious, stay engaged, and
• 21:39
  Speaker 2: keep asking those questions.
• 21:41
  Speaker 1: I love it. And who knows, maybe you'll have your
• 21:42
  Speaker 1: own deep dive topic you want to suggest.
• 21:44
  Speaker 2: Yeah, we're always looking for new adventures.
• 21:45
  Speaker 3: We are.
• 21:46
  Speaker 1: Until next time, keep learning and keep questioning.
• 21:49
  Speaker 2: That's what it's all about.