Speaker 1: Imagine looking at a high definition, totally photorealistic image of
Speaker 1: a human face. You know, you can see every single pore,
Speaker 1: the individual eyelashes, like the exact glint of light reflecting
Speaker 1: in their eyes.
Speaker 2: Yeah, and then you realize that person has never actually.
Speaker 1: Existed right anywhere. I mean, a machine didn't just scrape
Speaker 1: the internet and you know, find their kicture. It imagine
Speaker 1: them into existence from pure.
Speaker 2: Mathematics, which is just wild to think about.
Speaker 1: It really is. Today we are opening up an incredibly
Speaker 1: comprehensive text for this deep dive. It's called Introduction to
Speaker 1: Deep Learning Business Applications for Developers by Armando Vieira and
Speaker 1: Bernard det Ribero. And our mission for you today is
Speaker 1: to really get past the buzzwords.
Speaker 2: Exactly because we hear AI and neural networks everywhere right.
Speaker 1: Now, right, but we want to genuinely demyssify what is
Speaker 1: happening under the hood, Like how did we go from
Speaker 1: machines that literally couldn't solve a child's basic logic puzzle
Speaker 1: to algorithms that can compose music, drive cars, and synthesize
Speaker 1: entirely new strands of.
Speaker 2: And to understand that leap, we really have to look
Speaker 2: at the foundational inspiration for all of this, which is
Speaker 2: the human brain itself. Okay, because the architectures we're looking at,
Speaker 2: they're designed to build hierarchical, abstract concepts to make sense
Speaker 2: of the world.
Speaker 1: So it's not just plugging numbers into a formula.
Speaker 2: No, not at all. It is much less like programming
Speaker 2: a traditional calculator to execute a strict set of rules,
Speaker 2: and well much more like watching a toddler learn about
Speaker 2: their environment through unstructured observation.
Speaker 1: Okay, let's unpack this because before we talk about machines
Speaker 1: imagining faces, we kind of have to talk about why
Speaker 1: this technology failed so spectacularly.
Speaker 2: In the past, right, the Dark Ages of AI.
Speaker 1: Yeah, exactly. Yeah, there was this multi decade freeze known
Speaker 1: as the AI Winter, and it actually started with a
Speaker 1: very simple logic problem, didn't it.
Speaker 2: It did. So. The origins of artificial neural networks go
Speaker 2: way back to the nineteen fifties. A researcher named Frank
Speaker 2: Rosenblatt invented what he called the perceptron.
Speaker 1: The perceptrons. It sounds very retro sci fi.
Speaker 2: It does, and it was essentially a single layer of
Speaker 2: artificial neurons like a basic decision making unit, and the
Speaker 2: hype at the time was massive. I mean, the media
Speaker 2: thought human level AI was just around the corner, But
Speaker 2: it wasn't, not even close, because in nineteen sixty nine
Speaker 2: Marvin Minsky and Seymour paper published this completely crushing critique.
Speaker 2: They proved that these simple perceptrons were mathematically incapable of
Speaker 2: solving nonlinear problems.
Speaker 1: Right, And the famous example from the text is the
Speaker 1: xor problem, the exclusive or yes, exactly.
Speaker 2: It's an incredibly basic logical operation. Basically, if you have
Speaker 2: two inputs, the output is true if one and only
Speaker 2: one of the inputs is true.
Speaker 1: Like a child could grasp this intuitively totally.
Speaker 2: But if you try to draw a single straight line
Speaker 2: on a graph to separate the true results from the
Speaker 2: false results and an xor problem, you just can't.
Speaker 1: Do it because it's nonlinear, right.
Speaker 2: And since a single layer perceptron could only draw one
Speaker 2: straight line, it completely failed. That mathematical proof was so
Speaker 2: devastating that well funding completely dried up. People basically abandoned
Speaker 2: the dream of neural networks for decades.
Speaker 1: But the text highlights the year two thousand and six
Speaker 1: as the major thaw to this AI winter right, driven
Speaker 1: by Jeffrey Hinton and his work on deep belief networks
Speaker 1: or dPNs and restricted Boltzmann machines.
Speaker 2: Yeah, Hinton was a massive turning point.
Speaker 1: Oh wait, here's where I want to push back on
Speaker 1: the timeline a bit, because the underlying math to fix
Speaker 1: that single layer problem, specifically adding multiple layers and using
Speaker 1: an algorithm called back propagation that was popularized in the
Speaker 1: nineteen eighties.
Speaker 2: That's true, the mass was there.
Speaker 1: And back propagation is where the network makes a guess,
Speaker 1: calculates how wrong its guess was, and then sends a
Speaker 1: correction signal backward through its layers to adjust the mathematical weights.
Speaker 2: Right, it updates itself.
Speaker 1: So if we had that in the eighties, why did
Speaker 1: it take until two thousand and six to actually make
Speaker 1: these networks deep? Like, why couldn't they just stack a
Speaker 1: bunch of layers together back then and let back propagation
Speaker 1: do its thing.
Speaker 2: Well, what's fascinating here is the physical limitation of how
Speaker 2: that error signal travels. When you tried to stack many
Speaker 2: layers to make the network genuinely deep, you ran straight
Speaker 2: into the vanishing gradient problem.
Speaker 1: Oh, I picture this like a massive game of telephone
Speaker 1: played across a packed football stadium.
Speaker 2: Yes, that is a perfect way to visualize it. Because
Speaker 2: backpropagation relies on gradients, which are essentially, you know, mathematical
Speaker 2: slopes that tell the network how to adjust its weights.
Speaker 1: Okay, the instructions for changing right.
Speaker 2: But as that correction signal passes backward through each layer,
Speaker 2: it gets multiplied by numbers smaller than one, so it shrinks.
Speaker 1: If you multiply zero point one by zero point one,
Speaker 1: you get zero point zero one exactly.
Speaker 2: Do that ten times and the number becomes microscopically small.
Speaker 2: By the time that signal reaches the earliest bottom layers
Speaker 2: of the network, like the foundation, the gradient has basically vanished.
Speaker 1: It's practically zero right zero.
Speaker 2: Yeah, So the lower layers never get the message to update,
Speaker 2: which means the network never learns the fundamental building blocks
Speaker 2: of the.
Speaker 1: Data, like trying to memorize a textbook without knowing the
Speaker 1: alphabet precisely.
Speaker 2: So, to fix the telephone game, Hinton had to completely
Speaker 2: change how those earliest layers learned. He introduced something called
Speaker 2: contrastive divergence, and from.
Speaker 1: What I understand, this basically allowed the network to learn
Speaker 1: without needing a human to provide a perfectly labeled answer
Speaker 1: key right away exactly.
Speaker 2: He realized you can't train a massive deep network all
Speaker 2: at once from the top down, So with contrastive divergence,
Speaker 2: he trained the network layer by layer from the bottom up.
Speaker 1: Using unlabeled data.
Speaker 2: Right. He allowed the first layer to just look at
Speaker 2: the raw input and try to reconstruct it, learning the
Speaker 2: statistical properties of the data autonomously. Oh wow. Yeah, And
Speaker 2: once that first layer figured out the basic patterns, its
Speaker 2: output became the input for the second layer, and so on.
Speaker 2: By the time you apply backpropagation to fine tune the
Speaker 2: whole system, the network already has a massive.
Speaker 1: Headstart because it organically built a rough model of the
Speaker 1: world for us exactly, which really brings up a massive
Speaker 1: paradigm shift in how we handle data. The text frames
Speaker 1: this as escaping the curse of dimensionality.
Speaker 2: The curse of dimensionality. Yes, it's a huge.
Speaker 1: Concept, right, And the source material uses the iris flower
Speaker 1: data set to explain this. So, say you want a
Speaker 1: classic traditional machine learning algorithm like naive base to categorize
Speaker 1: three different types of iris flowers, You really only need
Speaker 1: four measurements, right, Yeah.
Speaker 2: Just the length and width of the pedal and the
Speaker 2: length and width of.
Speaker 1: The sepal four dimensions. A traditional algorithm handles that flawlessly.
Speaker 2: Because it is a beautifully simple, low dimensional space.
Speaker 1: But what happens when you feed a computer and image,
Speaker 1: even a tiny, practically unusable, one thousand pixel image has
Speaker 1: tend to the power of one thousand possible combinations.
Speaker 2: Right, And to give you a sense of scale, that
Speaker 2: number is vastly larger than the total number of atoms
Speaker 2: in the observable.
Speaker 1: Universe, which is just mind blowing it is.
Speaker 2: And when a traditional algorithm looks at a terrifying high
Speaker 2: dimensional space like an image, the math just breaks down.
Speaker 2: The data points become so sparse and far apart that
Speaker 2: the algorithm can't find any meaningful patterns.
Speaker 1: So the old way of solving this was feature engineering.
Speaker 1: Like I compare traditional machine learning to a chef who
Speaker 1: needs every single ingredient chopped, measured perfectly and laid out
Speaker 1: in little bowls before they can even start cooking.
Speaker 2: Right, a human programmer had to step in and pre
Speaker 2: digest the data. They had to explicitly tell the computer
Speaker 2: look at the distance between these two specific pixels.
Speaker 1: But deep learning throws feature engineering entirely in the trash.
Speaker 1: By contrast, deep learning is like throwing whole raw ingredients
Speaker 1: into a magical pot that just figures out the recipe itself.
Speaker 2: I love that analogy. The network completely removes the need
Speaker 2: for human lead feature engineering, and the engine driving that
Speaker 2: magical pot is an optimization algorithm called stochastic gradient descent.
Speaker 1: Okay, so how does that algorithm actually guide the recipe?
Speaker 1: How does it work?
Speaker 2: Well? Imagine a blindfolded hiker standing somewhere on a massive,
Speaker 2: jagged mountain range.
Speaker 1: Okay, blind colded hiker, right, and.
Speaker 2: Their goal is to get to the lowest possible point
Speaker 2: in the valley, which represents the lowest possible error in
Speaker 2: the network's predictions.
Speaker 1: But they can't see the map exactly.
Speaker 2: They can't see the whole map, so they just feel
Speaker 2: the ground directly beneath their feet, figure out which direction
Speaker 2: is the steepest slope downward, and they take a single step.
Speaker 1: Oh, I see.
Speaker 2: Stochastic gradient descent does this mathematically. It samples a small
Speaker 2: batch of data, calculates the slope of the error, and
Speaker 2: tweaks the weights in the network to step downhill.
Speaker 1: Wow.
Speaker 2: Yeah, it maps that terrifying universe of a thousand pixels
Speaker 2: into a continuous, low dimensional manifold. It essentially folds and
Speaker 2: compresses the data into a simpler geometry where the solutions
Speaker 2: are easy to.
Speaker 1: Find, so the machine discovers the features entirely on its
Speaker 1: own exactly. But once researchers realized they could build these
Speaker 1: autonomous systems, they figured out that a network designed to
Speaker 1: process photographs needs a very different architecture than a network
Speaker 1: designed to process, say, spoken language.
Speaker 2: Right, you need different pots for different recipes.
Speaker 1: Which leads to the different flavors of neural networks. Reading
Speaker 1: this part of the text honestly feels like wandering through
Speaker 1: an architecture zoo.
Speaker 2: It really is a highly specialized zoo, because once the
Speaker 2: vanishing gradient problem was solved, the whole field just exploded,
Speaker 2: and the undisputed kings of image processing are convolutional neural
Speaker 2: networks or CNN's okay CNNs, Yeah, And they achieve this
Speaker 2: by directly mimicking the low level stages of a primate's
Speaker 2: visual core tex.
Speaker 1: Wait, really, so to avoid looking at a trillion pixels
Speaker 1: all at once and getting completely overwhelmed, I'm assuming it
Speaker 1: breaks the image down into smaller chunks, kind of like scanning.
Speaker 2: A document exactly that. It uses a mechanism called a
Speaker 2: filter or a kernel, which basically slides across the image,
Speaker 2: and it also utilizes something called max pooling.
Speaker 1: Max pooling, what does that do?
Speaker 2: It takes small grid of pixels, finds the single most
Speaker 2: prominent feature in that and just discards the rest.
Speaker 1: So it shrinks the image, right, It.
Speaker 2: Shrinks it down while keeping the most critical information. And
Speaker 2: the absolute brilliance of this is that it creates translation invariance.
Speaker 1: Oh, meaning that if I pull out my smartphone camera,
Speaker 1: the software draws a yellow focus box around my friend's face,
Speaker 1: whether she is standing dead center in the frame or
Speaker 1: you know, off to the bottom left corner.
Speaker 2: Precisely, the CNN doesn't have to relearn what a face
Speaker 2: looks like for every single coordinate on the screen. It
Speaker 2: recognizes the pattern regardless of its spatial location.
Speaker 1: That's incredibly efficient, it is.
Speaker 2: But that reliance on space is exactly why a standard
Speaker 2: neural net or a CNN fails when you introduce the
Speaker 2: concept of.
Speaker 1: Time ah time like text or video.
Speaker 2: Right, if you are analyzing a video frame by frame
Speaker 2: or trying to translate a spoken sentence, the order of
Speaker 2: the data matters immensely. Traditional neural nets try to project
Speaker 2: time as space, which completely fails to capture context.
Speaker 1: Which is where a recurrent neural networks or RNNs come in,
Speaker 1: and specifically, the text highlight it's a massive architectural leap.
Speaker 1: In nineteen ninety seven by researchers Hawk Writer and Schmid Hoober.
Speaker 2: Yes, they introduced long short term memory units or LSTMs.
Speaker 1: Here's where it gets really interesting, because the text points
Speaker 1: out an incredible irony. Here. To make a machine possess
Speaker 1: a better memory for long sequences of data, scientists actually
Speaker 1: had to build in a specific mathematical mechanism to force
Speaker 1: it to forget.
Speaker 2: The forget gate.
Speaker 1: Yeah, the forget gate. Why is the ability to selectively
Speaker 1: forget so crucial to artificial memory?
Speaker 2: Well, think about the maz's analogy from the text. Imagine
Speaker 2: a robot trying to navigate a complex maze where every
Speaker 2: single T junction looks completely identical.
Speaker 1: Okay, that sounds like a nightmare.
Speaker 2: It is, And if the robot only looks at its
Speaker 2: present state, it will wander in circles forever. This is
Speaker 2: what we call a non Markovian task.
Speaker 1: A task where the present moment doesn't give you enough
Speaker 1: information to solve the puzzle. You need your history, right.
Speaker 2: But if the network simply tries to remember every single
Speaker 2: micro movement it ever made, the mathematical gradients will explode
Speaker 2: or vanish again, completely paralyzing the system.
Speaker 1: Oh, the vanishing gradient strikes again exactly.
Speaker 2: So, the forget gate in an LSTM allows the network
Speaker 2: to evaluate its internal state and intentionally discharge useless information.
Speaker 2: It basically says, the color of the wall five turns
Speaker 2: go doesn't matter, dump it, but the fact that I
Speaker 2: took three left turns does matter.
Speaker 1: Keep it, which is exactly what is happening when you
Speaker 1: like type a text message on your phone. The predictive
Speaker 1: text keyboard isn't just looking at the last word you typed,
Speaker 1: and LSTM is remembering the context from the beginning of
Speaker 1: your sentence selectively forgetting the filler words and predicting the
Speaker 1: most logical next word.
Speaker 2: Exactly. The machine is holding onto the crucial sequence while
Speaker 2: ignoring the irrelevant static of the journey.
Speaker 1: So we have architectures that can see like primates, and
Speaker 1: architectures that can navigate time and memory, but analyzing data
Speaker 1: is one thing. The text takes a wild turn when
Speaker 1: It explores what happens when these architectures are turned inside
Speaker 1: out to actually create data.
Speaker 2: Yes, the shift from discriminative models, which just categorize things
Speaker 2: two generative models, and the standout breakthrough here was in
Speaker 2: twenty fourteen by Ian Goodfellow, who introduced.
Speaker 1: Gand generative adversarial networks. The adversarial part is definitely my
Speaker 1: favorite concept in the book.
Speaker 2: It's a really elegant solution.
Speaker 1: I like, in a jan To an art forger and
Speaker 1: a detective locked in a room together. The forger, which
Speaker 1: is the generator network, paints a fake masterpiece and slips
Speaker 1: it under the door. The detective, the discriminator network, looks
Speaker 1: at it, spots the flaws, and tells the forger exactly
Speaker 1: how they messed up.
Speaker 2: Right, They play a game against each.
Speaker 1: Other, so the forger tries again and again over millions
Speaker 1: of iterations. The forger's technique becomes so flawlessly mathematical that
Speaker 1: the detective literally can no longer tell the difference between
Speaker 1: the generated fake and reality. Your digital forger is suddenly
Speaker 1: painting like Da Vinci.
Speaker 2: Pitting two neural networks against each other in a zero
Speaker 2: sum game is just a brilliant mechanic and the applications
Speaker 2: highlighted in the text, I mean they're staggering. Likewise, well,
Speaker 2: jans and deep autow encoders are used to generate those
Speaker 2: photorealistic human faces we talked about at the start. They
Speaker 2: are automatically colorizing black and white manga comics. They're even
Speaker 2: being used to generate functional synthetic DNA sequences for medical research.
Speaker 1: Synthetic DNA that is unreal it is, And.
Speaker 2: If we connect this to the bigger picture, generative models
Speaker 2: represent a profound evolution in how we view machine intelligence.
Speaker 2: How So, when a network can generate realistic, entirely new data,
Speaker 2: it proves that it genuinely understands the underlying latent structure
Speaker 2: of our world. It forms a rich internal imagery.
Speaker 1: So it's not just checking boxes anymore.
Speaker 2: Exactly, It isn't just classifying pixels. It is empowered to reason,
Speaker 2: to explore infinite variations, and to make complex decisions without
Speaker 2: a human explicitly hard coding a strict loss function.
Speaker 1: Which brings us crashing into the real world implications of
Speaker 1: this technology. These architectures aren't just academic thought experiments anymore.
Speaker 1: They are actively driving a massive, multi billion dollars shift
Speaker 1: in global business.
Speaker 2: Oh. Absolutely, the business reality is moving faster than most
Speaker 2: people realize.
Speaker 1: The text sites of very stark twenty thirteen study by
Speaker 1: Fray and Osborne warning that forty seven percent of US
Speaker 1: jobs are at risk of automation. I mean, they say
Speaker 1: this is impacting society at three hundred times the scale
Speaker 1: of the Industrial revolution.
Speaker 2: And we are seeing the deployments right now, like WEIMO
Speaker 2: is taking human operators out of vehicles, trusting these networks
Speaker 2: to process visual data in real time at highway.
Speaker 1: Speeds and playing games too, right.
Speaker 2: Yeah, An AI system called Libridis beat the top human
Speaker 2: poker players in the world at Texas hold them and
Speaker 2: that is a game of bluffing and hidden information. Not
Speaker 2: just pure math. Plus deep learning is fundamentally overhauling automated
Speaker 2: medical imagery, spotting tumors in radiology scans that humanized miss.
Speaker 1: But fueling all of this requires an astonishing amount of
Speaker 1: computing power. The text refers to this era as the
Speaker 1: cloud Wars, because let's be real, you absolutely cannot run
Speaker 1: a multi layer generative adversarial network on your standard office laptop.
Speaker 2: No, you really can't. The hardware bottleneck is immense training
Speaker 2: these models requires monstrous data sets and specialized computing power,
Speaker 2: specifically GPUs and FPGAs FPGAs field programmable GATA rays. These
Speaker 2: are tips that can perform thousands of calculations simultaneously. But
Speaker 2: because most companies cannot afford to build their own supercomputers,
Speaker 2: deep learning is rapidly shifting to an AI as a
Speaker 2: service model.
Speaker 1: So everyone is just renting server space exactly.
Speaker 2: You have tech titans like Amazon Web Services, Google Cloud, IBM, Watson,
Speaker 2: and Microsoft Azure locked in a brutal fight for dominance
Speaker 2: over this cloud infrastructure.
Speaker 1: And their strategy to win that war is fascinating. They
Speaker 1: are taking their most valuable cutting edge software frameworks like
Speaker 1: Google's TensorFlow or the Kera's library, and they're completely open
Speaker 1: solcing them. They are just giving the blueprints away for free.
Speaker 2: Well, it is the ultimate trap. By giving away the
Speaker 2: software framework for free, developers learned to build their neural
Speaker 2: networks using Google specific code or Amazon specific code.
Speaker 1: Right they get used to the ecosystem.
Speaker 2: And once the developer is locked into that software ecosystem,
Speaker 2: they eventually realize they need massive hardware to actually run
Speaker 2: the models. And who do they rent that hardware?
Speaker 1: From the very same cloud provider who gave them the
Speaker 1: software exactly.
Speaker 2: It guarantees massive recurring cloud computing revenue.
Speaker 1: So what does this all mean for us? We have
Speaker 1: massive corporate monopolies fighting over compute power, algorithms that can
Speaker 1: imagine synthetic DNA, and autonomous systems driving our cars. The
Speaker 1: text highlights one primary glaring vulnerability to all of this,
Speaker 1: the black box dilemma.
Speaker 2: Yes, the black box.
Speaker 1: If an AI diagnoses you with a terminal illness, or
Speaker 1: a self driving car swerves into a barrier, how dangerous
Speaker 1: is it that we can't easily interpret how the neural
Speaker 1: network arrived at its decision.
Speaker 2: The black box nature of deep learning is arguably its
Speaker 2: most dangerous flaw, Because the network learns by mapping incredibly
Speaker 2: high dimensional spaces and extracting millions of its own latent features.
Speaker 2: On that metaphorical magical pot we talked about. You can't
Speaker 2: just pop the hood and read a clean line of
Speaker 2: code that says if X, then Y.
Speaker 1: It's completely opaque.
Speaker 2: It completely lacks the transparency of a traditional decision tree. Furthermore,
Speaker 2: these networks suffer from the knowledge persistence problem.
Speaker 1: What does that mean?
Speaker 2: Well, if you train a network to diagnose lung X
Speaker 2: rays and then ask it to look at a bone fracture.
Speaker 2: It often forgets everything. It has to be trained entirely
Speaker 2: from scratch.
Speaker 1: It can't easily transfer its worldview the way a human
Speaker 1: doctor could.
Speaker 2: Right, It's very sensitive to its initialization. But the text
Speaker 2: notes that researchers aren't just accepting the black box. They
Speaker 2: are building tools to shine a light inside it.
Speaker 1: Like what kind of tools?
Speaker 2: There is a massive push for interpretability. Methods like patternet
Speaker 2: are being developed specifically to trace the decision pathway backward.
Speaker 2: So if the network identifies a tumor, PATTERNET tries to
Speaker 2: highlight exactly which specific pixels in the original image activated
Speaker 2: the neurons that led to that conclusion.
Speaker 1: Oh, so a reverse engineers the network's logic exactly.
Speaker 2: We're also seeing significant breakthroughs in transfer learning, where a
Speaker 2: model train on one massive data set can freeze its
Speaker 2: foundational layers, the layers that recognize basic edges and shapes
Speaker 2: and carry that knowledge over to a smaller, entirely different
Speaker 2: data set.
Speaker 1: That sounds promising, it is, but.
Speaker 2: This raises an important question as we develop techniques like
Speaker 2: open ais evolution strategies to train networks without relying entirely
Speaker 2: on traditional gradients. We are desperately trying to align the
Speaker 2: machine's reasoning with human reasoning. We want them to explain
Speaker 2: themselves to us, but their fundamental architecture, the way they
Speaker 2: perceive thousands of dimensions simultaneously, it remains profoundly alien.
Speaker 1: Which is a perfect way to synthesize this incredible journey.
Speaker 1: I mean, we started with the freezing, stagnant Ai winter
Speaker 1: of the twentieth century, where a single layer of neurons
Speaker 1: couldn't even solve a child's logic puzzle.
Speaker 2: And we've traced the path to a blooming technological spring exactly.
Speaker 1: We now have machines that map high dimensional universes using
Speaker 1: stochastic gradient descent. They identify visual cues with the precision
Speaker 1: of a primate's cortex using CNNs. They selectively discard useless
Speaker 1: history using the forget gates in LSTMs, and they literally
Speaker 1: imagine realities that don't exist through the adversarial game of jams.
Speaker 2: It's a complete paradigm shift from the calculators of the past.
Speaker 2: We are no longer programming machines. We are educating them.
Speaker 1: And that leads to a final profound thought. I want
Speaker 1: to leave you the listener with today. The source text
Speaker 1: makes a fascinating observation. If we want to deploy these
Speaker 1: systems into society safely, the only way to genuinely incorporate
Speaker 1: ethics into a machine is through lengthy, consistent education, much
Speaker 1: in the same way we raise a human child. But
Speaker 1: think back to our opening metaphor about the toddler learning
Speaker 1: about the physical world. A human toddler learns the foundation
Speaker 1: of ethic because they have a physical body. They understand
Speaker 1: what it means to feel pain, They feel hunger, They
Speaker 1: have a biological imperative to avoid death, which allows them
Speaker 1: to empathize with the pain of.
Speaker 2: Others, empathy through shared biology exactly.
Speaker 1: But if a machine isn't a living entity, if it
Speaker 1: cannot feel hunger, if it has absolutely no conceptual understanding
Speaker 1: of its own mortality or death, how can we possibly
Speaker 1: teach it to truly value human life? As these networks
Speaker 1: move beyond simply classifying our spreadsheets and begin generating entirely
Speaker 1: new rules, art, and realities, we have to ask ourselves.
Speaker 1: Are we just building more advanced tools, or are we
Speaker 1: actively raising a new kind of alien intelligence.
Speaker 2: It is the defining question of our.
Speaker 1: Generation, something for you to mull over on your own
Speaker 1: until next time. Thanks for joining us on this deep dive.