Speaker 1: So back in twenty twelve, Google did something really wild.
Speaker 1: They fed a machine something like ten million completely random,
Speaker 1: chaotic frames of YouTube videos. Right.
Speaker 2: And the crazy part is they didn't write a single
Speaker 2: line of code telling this machine what to look for.
Speaker 1: Yeah, exactly, no instructions about shapes, no definitions of animals,
Speaker 1: and completely on its own, just sorting through the sheer
Speaker 1: chaos of the Internet. The machine independently formed the concept
Speaker 1: of a cat.
Speaker 2: It's fascinating. It physically reorganized its internal mathematical structures to
Speaker 2: recognize a feline face.
Speaker 1: Just out of nowhere, it is, And today we are
Speaker 1: tearing into how that is even remotely possible.
Speaker 2: Welcome to the deep dive for you listening. You know,
Speaker 2: whether you're building models yourself or you just want to
Speaker 2: masterclass in the mechanics of the modern world. Our source
Speaker 2: today is Java Deep Learning Essentials by Yusuke Sugamory.
Speaker 1: But don't worry, we are entirely bypassing all the heavy
Speaker 1: Javison tasks today. Oh absolutely, we're leaving the code behind.
Speaker 1: The mission for this deep dive is to extract the pure,
Speaker 1: brilliant logic of how we got machines to stop acting
Speaker 1: like rigid calculators and start well hallucinating entirely new realities.
Speaker 2: Setting the baseline here is so crucial because you know,
Speaker 2: the cultural definition of artificial intelligence has been completely diluted.
Speaker 1: Right, like your smart toaster might have AI printed on the.
Speaker 2: Box now exactly, but running a basic predictive thermis dat
Speaker 2: or say, a simple loop of robotic movements that is
Speaker 2: fundamentally different from the architecture that learned to see that cat.
Speaker 1: Yeah.
Speaker 2: To genuinely understand the tectonic shift of modern deep learning,
Speaker 2: we really have to look at the graveyard of past methodology.
Speaker 1: The booms and the bus So to understand why modern
Speaker 1: machine learning it's so revolutionary, let's explore those past failures.
Speaker 1: The first major wave hit in the nineteen fifties, right,
Speaker 1: driven by search algorithm, right.
Speaker 2: Things like depth first search and breadth first search.
Speaker 1: The fundamental approach back then was to give a machine
Speaker 1: a strict set of rules and then have it rapidly
Speaker 1: calculate through a tree of possibilities to find an optimal outcome,
Speaker 1: Which is why early computers look like absolute geniuses when
Speaker 1: they were playing chess.
Speaker 2: Yeah, because a chess board is the ultimate closed ecosystem.
Speaker 1: Exactly. It has an eight by eight grid, discrete pieces
Speaker 1: immutable rules. The machine just generates millions of branching future
Speaker 1: moves and calculates the mathematical path to victory.
Speaker 2: And people watched the machine dismantle a chess grand master
Speaker 2: and assumed, well, human like artificial intelligence was only a
Speaker 2: few years away.
Speaker 1: They thought it was right around the corner.
Speaker 2: They really did. The assumption was that you could just
Speaker 2: scale up that search algorithm to handle real world problems.
Speaker 2: But that assumption shattered against a massive theoretical wall known
Speaker 2: as the frame problem. Oh, the frame problem. Yeah, a
Speaker 2: search algorithm functions perfectly when the frame of reality is
Speaker 2: artificially limited, but the moment you drop that machine into
Speaker 2: the actual physical world, it paralyzes itself.
Speaker 1: Because human beings caught instantly, like unconsciously, filter out an
Speaker 1: infinite amount of irrelevant data, and a rule based machine
Speaker 1: can't precisely.
Speaker 2: It operates on absolute logic, so it has no intuition
Speaker 2: for what to ignore.
Speaker 1: Wait, so the frame problem is like, it's like asking
Speaker 1: a robot to make a cup of tea in a
Speaker 1: normal kitchen and it immediately freezes.
Speaker 2: Right, because it's actively trying to calculate the current atmospheric
Speaker 2: pressure exactly.
Speaker 1: It's calculating the exact atomic structure of the ceramic mug.
Speaker 1: And I don't know the gravitational pull of Jupiter before
Speaker 1: it feels authorized to turn on the kettle because we.
Speaker 2: Never explicitly programmed it to ignore Jupiter's gravity.
Speaker 1: Yeah, so it factors into the tea making equation. That's wild.
Speaker 2: The computational explosion makes action impossible. It becomes trapped in
Speaker 2: an infinite loop of processing variables that have zero bearing
Speaker 2: on the task. And that failure essentially ended that first
Speaker 2: era of AI.
Speaker 1: So then came the pivot, arriving in the nineteen eighties.
Speaker 1: Researchers tried to bypass the machine's lack of intuition by
Speaker 1: basically brute forcing context into its memory.
Speaker 2: Right, this is the knowledge representation boom. The second boom.
Speaker 1: Yeah, The logic was, if the machine freezes because it
Speaker 1: doesn't know enough about the world, let's simply sit down
Speaker 1: and manually encode the entirety of human knowledge into a database.
Speaker 2: Projects like the Sake database or the semantic Web is
Speaker 2: an incredibly tedious effort to build absolute dictionaries of reality.
Speaker 1: Typing in rules manually, like a dog is a mammal,
Speaker 1: and water is wet, and tokyo is in Japan.
Speaker 2: You're trying to build a semantic web of relationships, so
Speaker 2: the machine has a reference point for every scenario. But
Speaker 2: that leads straight into the second wall, the symbol grounding problem.
Speaker 1: Okay, let's unpack that.
Speaker 2: Well. You can feed a machine a dictionary and it
Speaker 2: can parse the syntax perfectly, can tell you that green
Speaker 2: plus apple equals green apple, but it.
Speaker 1: Has no actual concept of what an apple tastes or
Speaker 1: feels like exactly.
Speaker 2: It's a completely devoid of semantics.
Speaker 1: So it knows the equation, but it has no concept
Speaker 1: of the crisp snap of the skin, or the tartness
Speaker 1: of the juice, or the weight of it in your hand.
Speaker 2: To the machine, apple is nothing more than a string
Speaker 2: of as key characters. It manipulates the symbols flawlessly according
Speaker 2: to the grammar we gave it, but those symbols are
Speaker 2: never grounded in actual experiential reality.
Speaker 1: Humans inherently catch the defining features of an object, but
Speaker 1: machines at the stage only saw symbols, and because they
Speaker 1: couldn't grasp the underlying concepts, they were incredibly fragile.
Speaker 2: Extremely fragile. Confronted with a new situation that deviated even
Speaker 2: slightly from their manually programmed dictionary, they just failed completely.
Speaker 1: So, since machines couldn't manually learn every rule in the universe,
Speaker 1: scientists flipped.
Speaker 2: The script right. They abandoned the attempt to teach the
Speaker 2: computer the rules of the universe.
Speaker 1: Instead of teaching rules, they thought, what if the machine
Speaker 1: looked for patterns? You build an architecture that allows the
Speaker 1: computer to look at raw data and deduce the dividing
Speaker 1: lines itself, And this brings.
Speaker 2: Us out of the AI winter and into the third
Speaker 2: boon machine learning. The fundamental mechanics shift from deductive rule
Speaker 2: following to inductive statistical pattern recognition.
Speaker 1: Okay, so you take an algorithm, flood it with data,
Speaker 1: and ask it to find the mathematical boundaries between different categories.
Speaker 2: Yes, and when we look at unsupervised learning, where the
Speaker 2: data is entirely raw and unlabeled, the algorithm's only job
Speaker 2: is to find hidden structures.
Speaker 1: Like that famous retail case study with the diapers and
Speaker 1: the beer.
Speaker 2: Exactly, a major supermarket fed millions of raw checkout logs
Speaker 2: into a machine learning algorithm The machine didn't know what
Speaker 2: the symbols for diapers or beer actually meant.
Speaker 1: Because the symbol grounding problem still applies here, right, right, but.
Speaker 2: It recognized a profound statistical correlation. It noticed that consumers
Speaker 2: purchasing diapers late on a Friday night had a highly
Speaker 2: elevated probability of simultaneously purchasing beer.
Speaker 1: So the machine maps the frequency, the store moves the
Speaker 1: beer aisle next to the diapers, and the profit margins spike.
Speaker 2: That's unsupervised learning in a nutshell. But then we have
Speaker 2: supervised learning where we do provide examples, and the book
Speaker 2: highlights support vector machines or SVMs to handle this.
Speaker 1: And this is where the math gets incredibly elegant.
Speaker 2: It really does. If you have a massive data set
Speaker 2: of say medical diagnostics, and you mack it out on
Speaker 2: a two dimensional graph, the data points for healthy and
Speaker 2: sick are going to be completely overlapping and tangled together.
Speaker 1: You can't just draw a straight two D line to
Speaker 1: separate them.
Speaker 2: No straight line on a flat plane is just too
Speaker 2: simplistic for messy real world data. So SVMs solve this
Speaker 2: using the kernel trick.
Speaker 1: The kernel trick. I love this concept.
Speaker 2: It's basically a method of mathematically shifting perspective. Instead of
Speaker 2: trying to force a complex curve boundary through the two
Speaker 2: D data, the algorithm applies a mathematical transformation like squaring
Speaker 2: the distance of each point from the.
Speaker 1: Origin, and by running that calculation, the algorithm effectively takes
Speaker 1: the flat two D data and projects it outward into
Speaker 1: a three dimensional space.
Speaker 2: Right the data points literally lift off the flat page.
Speaker 1: It's like the math warps the space so that the
Speaker 1: tangled point spread out into a three D shape like
Speaker 1: a parabola. And once the data is suspended in three dimensions,
Speaker 1: the tangled mess is suddenly separated by altitude exactly.
Speaker 2: And at that point the SVM doesn't need to draw
Speaker 2: a complex curve anymore. It just slides a perfectly flat,
Speaker 2: rigid sheet of glass a hyperplane straight through the three D.
Speaker 1: Space, cleanly severing the healthy data points from the sick ones.
Speaker 2: It is an extraordinarily powerful classification tool, but traditional machine learning,
Speaker 2: despite the brilliance of the kernel trick, harbored a fatal bottleneck.
Speaker 1: Right feature engineering.
Speaker 2: Yes, the machine is excellent at finding the boundary, but
Speaker 2: it remains completely blind to what it is actually looking
Speaker 2: at unless a human.
Speaker 1: Tells it, so you still have to define the coordinates.
Speaker 1: Like if you want the SBM to identify a cat,
Speaker 1: you can't just feed it a raw jpeg.
Speaker 2: Now, a human data scientist has to sit down and
Speaker 2: manually write code that extracts the specific features for the
Speaker 2: machine to evaluate.
Speaker 1: You have to program it to measure the distance between
Speaker 1: the pixels that make up the eye, or calculate the
Speaker 1: geometric angle of the ear triangles, or isolate the hex
Speaker 1: codes of the fur color.
Speaker 2: And the accuracy of the entire model is bound by
Speaker 2: human bias. If the human engineer selects poor features, like
Speaker 2: trying to predict a neighborhood's housing prices based exclusively on
Speaker 2: the number of street lights rather than square footage.
Speaker 1: The algorithm will confidently execute the math and deliver absolute garbage.
Speaker 1: But wait, if a human is still doing the heavy
Speaker 1: lifting of feature engineering, then machine learning isn't really learning
Speaker 1: independently at all, is it.
Speaker 2: You've hit the nail on the head.
Speaker 1: It's just a hyper fast sorterer based on our personal intuition.
Speaker 2: That is exactly why machine learning plateaued. It lacked the
Speaker 2: metacognitive ability to look at a raw environment and independently
Speaker 2: determine which features actually mattered.
Speaker 1: So how did we finally break through that feature engineering wall?
Speaker 1: Because that leads us to the ultimate game changer, deep learning.
Speaker 2: Right. Historically, researchers knew that artificial neural networks theoretically have
Speaker 2: this potential, but they couldn't get them to work at scale.
Speaker 2: That changes with a two thousand and six paper by
Speaker 2: Jeffrey Hinton introducing deep belief nets.
Speaker 1: Which was largely ignored until twenty twelve. Right the ImageNet
Speaker 1: Large Scale Visual Recognition Challenge.
Speaker 2: Yes, the ils VRC. Historically, teams of phdes would spend
Speaker 2: an entire year painstakingly tweaking their manual feature engineering, fighting
Speaker 2: tooth and nail just to push their image recognition accuracy
Speaker 2: up by a fraction of a single percent.
Speaker 1: So the field was accustomed to microscopic, agonizing progress.
Speaker 2: Then a team called Supervision, utilizing deep learning algorithms, entered
Speaker 2: the twenty twelve contest. They abandoned human engineered features entirely.
Speaker 2: They fed the raw image pixels directly into a deep
Speaker 2: neural network, and they didn't just win, they obliterated the
Speaker 2: historical curve.
Speaker 1: They beat the second place team by a staggering margin.
Speaker 1: Of over ten percent, and.
Speaker 2: In the context of computer vision, a ten percent leap
Speaker 2: in a single year was viewed as an almost alien.
Speaker 1: Intervention, which brings us directly back to that Google experiment
Speaker 1: we started with. By feeding those ten million, raw, unlabeled
Speaker 1: YouTube frames into a deep neural network, the system independently
Speaker 1: deduced the recurring mathematical structures that constituted a cat.
Speaker 2: It effectively solved the symbol grounding problem that killed the
Speaker 2: AI boom of the nineteen eighties.
Speaker 1: So deep learning is doing what we failed to do
Speaker 1: in the nineteen eighties. It's solving the symbol grounding problem
Speaker 1: by figuring out the signified the actual concept of the
Speaker 1: thing completely on its own.
Speaker 2: Yes, it didn't just learn a symbol by analyzing millions
Speaker 2: of variations of lighting, angles, and shapes. It isolated the
Speaker 2: foundational underlying concept of catness entirely independent of human labeling.
Speaker 1: That bridges the gap between raw physical data and conceptual understanding.
Speaker 2: To prove just how thoroughly these deep networks internalized these concepts,
Speaker 2: Google engineers later developed a technique colled inceptionism, widely known
Speaker 2: as deep.
Speaker 1: Dream Oh deep dream the nightmare.
Speaker 2: Art exactly in operation data flows forward through the network,
Speaker 2: and the machine outputs a classification of what it sees.
Speaker 2: With inceptionism, the engineers reverse the feedback loop.
Speaker 1: They fed an image into the network and commanded it
Speaker 1: to mathematically amplify whatever patterns it vaguely recognized, a feedback
Speaker 1: loop of pure pattern recognition.
Speaker 2: So if the network is scanning an image of a blurry,
Speaker 2: overcast sky and a cluster of pixels vaguely corresponds to
Speaker 2: the internal mathematical weight the network associates with the bird's.
Speaker 1: Beak, it alters the image to make this pixels look
Speaker 1: slightly more like a beak, and then it feeds that
Speaker 1: altered image back into its own input.
Speaker 2: Right now, the beak is more pronounced, so the network
Speaker 2: confidently hallucinates the eyes and in the feathers.
Speaker 1: It runs this recursive loop until a highly detailed, psychedelic,
Speaker 1: multi eyed bird physically manifests out of thin air in
Speaker 1: the middle of a cloud bank.
Speaker 2: It is generating novel imagery based on its deeply internalized
Speaker 2: understanding of features. It proves that the network isn't just
Speaker 2: matching pixels to a database. It has built a flexible,
Speaker 2: generative concept of the object.
Speaker 1: Okay, to truly appreciate this for you listening, We have
Speaker 1: to unpack the mechanics under the hood. Why did adding
Speaker 1: the word deep suddenly unlock this capability?
Speaker 2: Well, the basic concept of neural networks existed. A perceptron,
Speaker 2: which is a single layer of artificial neurons loosely mimicking
Speaker 2: human brain cells, takes inputs, applies mathematical weights, and outputs
Speaker 2: a decision.
Speaker 1: Good for linear problems, right, But researchers.
Speaker 2: Knew that the solve nonlinear complex problems, they needed multilayer perceptrons.
Speaker 2: You insert hidden layers of neurons between the input and
Speaker 2: the output. Logic dictates that if one hidden layer is good,
Speaker 2: stacking twenty layers to make a deep network should allow
Speaker 2: it to process incredibly complex realities. The theoretical mathematics supported
Speaker 2: that logic. But there was a villain in the story,
Speaker 2: wasn't there The vanishing gradient problem. The vanishingradient Neural networks
Speaker 2: learn through an algorithm called backpropagation. The network makes a
Speaker 2: prediction it looks at a dog and guesses cat, and.
Speaker 1: A loss function calculates the mathematical error of.
Speaker 2: That guess exactly. The algorithm then takes that error and
Speaker 2: propagates it backward through the network, layer by layer, adjusting
Speaker 2: the mathematical weights of the connections so the network is
Speaker 2: less likely to make that mistake again.
Speaker 1: It's a chain of correction, but backpropagation relies on the
Speaker 1: chain rule of calculus.
Speaker 2: And that's where it all fell apart. Is that error
Speaker 2: signal moves backward through the hidden layers. You are multiplying gradients,
Speaker 2: and those gradients are often fractional numbers less than one.
Speaker 1: So if you multiply a fraction by a fraction by fraction,
Speaker 1: the resulting number exponentially shrinks.
Speaker 2: By the time that error signal reaches the early layers
Speaker 2: of a deep network, the layer's closest to the raw input,
Speaker 2: the number has essentially vanished to zero.
Speaker 1: The error signal dilutes so severely that the foundational layers
Speaker 1: of the network receive absolute no updates. They never adjust
Speaker 1: their weights, never learn.
Speaker 2: Because the early layers remain untrained, the entire deep architecture
Speaker 2: stalls out, rendering deep networks practically useless for decades.
Speaker 1: Enter the hero layer wise pre training used in deep
Speaker 1: belief nets and stacked denoising auto encoders.
Speaker 2: The breakthrough was realizing that Trying to train the entire
Speaker 2: massive network at once from the output all the way
Speaker 2: back to the input was mathematically impossible, so they isolated
Speaker 2: the layers.
Speaker 1: You train each hidden layer completely independently. But wait, if
Speaker 1: you isolate a layer in the middle of the network,
Speaker 1: it has no access to the final answer. It doesn't
Speaker 1: know it's supposed to be looking for a.
Speaker 2: Cat, So you employ unsupervised learning using auto encoders. You
Speaker 2: give that single isolated layer a bizarrely simple task. Take
Speaker 2: the raw input data, force it through a mathematical bottleneck
Speaker 2: that compresses it, and then try to perfectly reconstruct the
Speaker 2: original data on the other side.
Speaker 1: The bottleneck is the stroke of genius.
Speaker 2: It is because the layer cannot physically pass all the
Speaker 2: raw data through the compression, it is mathematically forced to
Speaker 2: discard the noise and figure out the most essential defining
Speaker 2: features required to rebuild the image.
Speaker 1: Once that first layer masters the reconstruction, its output becomes
Speaker 1: the input for the second layer. It creates a self
Speaker 1: assembling hierarchy of concepts exactly.
Speaker 2: The first layer compresses raw pixels and learns to map
Speaker 2: basic geometric edges and lines. The second layer isolates itself,
Speaker 2: takes those lines compresses them and learns to map specific
Speaker 2: shapes and textures.
Speaker 1: And then the third layer takes those shapes and learns
Speaker 1: to map complex features like eyes and noses. Because each
Speaker 1: layer is trained completely independently to find structure, you completely
Speaker 1: bypass the chain rule problem.
Speaker 2: There is no vanishing gradient because you aren't passing an
Speaker 2: error signal backward through twenty layers.
Speaker 1: Once every layer has been pre trained to recognize this
Speaker 1: hierarchy of features, you assemble the full network, attach a
Speaker 1: final output layer, and perform fine tuning.
Speaker 2: Now, when you run back propagation with labeled data, the
Speaker 2: network already knows how to see. It already has the
Speaker 2: mathematical weights for edges, shapes, and textures perfectly established.
Speaker 1: It only requires minor adjustments to realize that the combination
Speaker 1: of those specific shapes is called a cat. It has
Speaker 1: essentially engineered its own features.
Speaker 2: But building a massive, deeply layered network introduces another vulnerability.
Speaker 2: If a network has millions of perfectly tued connections, it
Speaker 2: becomes prone to overfitting.
Speaker 1: It memorizes the training data so rigidly that it loses
Speaker 1: the flexibility to recognize a cat in the slightly different
Speaker 1: lighting condition.
Speaker 2: To shatter that rigidity. The architecture employs a remarkably counterintuitive
Speaker 2: trick called dropout.
Speaker 1: Wait, let's unpack drop out. You're telling me that physically
Speaker 1: severing the brain's connections randomly during training actually makes it smarter.
Speaker 2: It sounds counterproductive, but yes. During the fine tuning training phase,
Speaker 2: the algorithm will literally sever connections between neurons completely at random.
Speaker 2: It temporarily drops a random percentage of the network out
Speaker 2: of existence for that specific training pass.
Speaker 1: You are physically lobotomizing the network during its training. It's
Speaker 1: like it's like forcing someone to learn to ride a
Speaker 1: bike while randomly taking away one of their senses.
Speaker 2: That's a great way to look at it, Like, while.
Speaker 1: They are peddling on a tightrope, you randomly blindfold them,
Speaker 1: and then you randomly inject a massive dose of novacaine
Speaker 1: into their left leg. By randomly stripping away their senses,
Speaker 1: you force their central nervous system to develop an incredibly robust,
Speaker 1: bulletproof sense of core balance that doesn't rely on any
Speaker 1: single crutch.
Speaker 2: Precisely, because the neural network knows that any given neuron
Speaker 2: might spontaneously drop out during training, it cannot rely on
Speaker 2: any single fragile pathway. To recognize a feature.
Speaker 1: It is forced to distribute the concept across multiple redundant pathways.
Speaker 2: The mathematical representation of the object becomes deeply embedded and
Speaker 2: structurally resilient.
Speaker 1: For you listening, Grasping this evolution from the flat hyperplans
Speaker 1: of the kernel trick to the hierarchical compression of auto
Speaker 1: encoders to the deliberate chaos of dropout means you are
Speaker 1: really looking past the superficial buzzwords of modern technology. You
Speaker 1: now actually grasp the profound mechanics of how human intuition
Speaker 1: was mathematically outsourced to the machine.
Speaker 2: And understanding those mechanics is vital because the hardware executing
Speaker 2: these algorithms is scaling at a terrifying velocity. None of
Speaker 2: the architectural breakthroughs of deep learning mattered until physical processors
Speaker 2: could handle the math right.
Speaker 1: I mean, Google required a cluster of a thousand machines
Speaker 1: running for three straight days just to find that original cat.
Speaker 2: The theory had to wait for the silicon to catch up.
Speaker 2: But Moore's law dictates that the number of transistors on
Speaker 2: a microchip doubles roughly every eighteen months.
Speaker 1: If you track that exponential curve forward. We are rapidly
Speaker 1: approaching the year twenty forty five.
Speaker 2: Yes, twenty forty five is the widely projected date for
Speaker 2: the technical singularity. At that point on the curve, a
Speaker 2: single processor is expected to house more than ten billion transistors.
Speaker 1: That transcends the number of biological cells in the human brain.
Speaker 2: The computational capacity crosses a threshold where machines achieve self
Speaker 2: recursive intelligence. They will possess the hard where and the
Speaker 2: deep architecture required to rapidly redesign and optimize their own
Speaker 2: software and hardware loops, entirely independent of human engineers, the.
Speaker 1: Ultimate abandonment of human future engineering. The book leaves us
Speaker 1: with a stark quotation from the late theoretical physicist Stephen
Speaker 1: Hawking Right.
Speaker 2: He warned that the development of full artificial intelligence could
Speaker 2: spell the end of the human race.
Speaker 1: Because from the nineteen fifties chessboards to the twenty twelve
Speaker 1: ImageNet massacre, human beings were the ones pulling the strings
Speaker 1: and defining the loss functions.
Speaker 2: We provided the data and established the ultimate goals, even
Speaker 2: when the machines learned to map the paths themselves.
Speaker 1: So keeping all these mechanics in mind and want you
Speaker 1: to muld this over. The machines have already conquered the
Speaker 1: frame problem by learning to filter out the noise of reality.
Speaker 1: They have conquered the symbol grounding problem by internalizing the
Speaker 1: structural concepts of physical objects.
Speaker 2: They defeated the vanish ingradient to build deep hierarchical cognition.
Speaker 1: If a machine can look at a cloudy sky and
Speaker 1: recursive dream up a mathematically perfect multi eyed nightmare bird
Speaker 1: entirely on its own, what happens in twenty forty five?
Speaker 1: What happens when an intelligence backed by ten billion transistors
Speaker 1: starts defining its own loss functions, selecting its own features,
Speaker 1: and optimizing for its own goals without ever needing to
Speaker 1: tell us what they are. A question What's keeping in
Speaker 1: mind the next time you see a machine generate a
Speaker 1: masterpiece from thin air. Thanks for taking this deep dive.