Speaker 1: Usually when we talk about making a diagnosis, there's this
Speaker 1: expectation of pure mechanical precision.
Speaker 2: Right, like a very comforting binary exactly.
Speaker 1: I mean, think about breaking your arm. You go to
Speaker 1: the hospital, they take the X ray, and you see
Speaker 1: that jagged white line on the black film, and the
Speaker 1: doctor just points and says, you know, there it is.
Speaker 2: Yeah, it's incredibly visible. We have this fundamental human bias
Speaker 2: toward things we can see, right, things we can categorize
Speaker 2: and just put into neat little boxes broken or not broken.
Speaker 1: But then if you zoom out and look at the
Speaker 1: digital world that you are interacting with right now, I
Speaker 1: mean the apps on your phone, the movie recommendations popping
Speaker 1: up on your TV, or even the software protocols keeping
Speaker 1: your bank account secure.
Speaker 2: Yeah, suddenly that X ray machine is just entirely useless.
Speaker 1: Completely useless, because when you engage with modern technology, you
Speaker 1: are stepping inside this invisible architecture. You're completely surrounded by
Speaker 1: these complex decisions and predictions that are being made in
Speaker 1: the app slute dark, and.
Speaker 2: The sheer volume of data flowing through that architecture is
Speaker 2: so massive. I mean, human eyes couldn't possibly find those
Speaker 2: jagged white lines even if they knew exactly what to
Speaker 2: look for.
Speaker 1: Right. The scale of it all just demands that we
Speaker 1: rely on algorithms to do the spotting for us, which
Speaker 1: honestly is exactly why we are diving into the material
Speaker 1: you sent over today.
Speaker 2: It's a really fascinating collection of research, it really is.
Speaker 1: So we're looking at excerpts from this incredibly dense but
Speaker 1: honestly illuminating academic compilation. It's titled Data Visualization and Knowledge
Speaker 1: Engineering Spotting Data Points with Artificial Intelligence, and we are
Speaker 1: pulling from three distinct research chapters today, spanning software engineering,
Speaker 1: multimedia recommendation engines, and computer vision.
Speaker 2: Right, which sounds like three completely different worlds.
Speaker 1: Yeah, but our mission for this deep dive is to
Speaker 1: show you how they're connected. By the end of this conversation,
Speaker 1: you're going to understand the brilliant, completely silent mathematics that
Speaker 1: decide what you see here and use every single day.
Speaker 2: Because what stands out immediately across all these seemingly disparate
Speaker 2: fields is the shared underlying logic. These systems all basically
Speaker 2: rely on taking an overwhelmingly chaotic environment, finding the mathematical
Speaker 2: neighbors or the hidden patterns within it. And then using
Speaker 2: that specific geometry to predict a future outcome.
Speaker 1: Okay, so let's start right at the foundation of that
Speaker 1: digital world, which is the code itself. Before an AI
Speaker 1: can say, curate your evening entertainment or organize your vacation photos,
Speaker 1: the underlying software running those platforms has to actually function.
Speaker 2: It has to work, yeah, which brings up a really
Speaker 2: fascinating problem for developers.
Speaker 1: Right because when a tech company has millions of lines
Speaker 1: of code, they obviously can't manually test every single permutation
Speaker 1: before launch.
Speaker 2: They absolutely cannot. They have to optimize their quality assurance resources.
Speaker 2: So historically, developers relied heavily on something called WPDP.
Speaker 1: Which is within project defect prediction.
Speaker 2: Exactly within project defect prediction and the mechanism there is
Speaker 2: it's fairly intuitive. If version one point zero of your
Speaker 2: software crashed because of let's say a memory leak in
Speaker 2: a specific login module.
Speaker 1: The model just learns to aggressively check that exact same
Speaker 1: log in module when you build version two point out right.
Speaker 2: It scrutinizes the historical weak points, which.
Speaker 1: Makes total sense. If you actually have a version one
Speaker 1: point zero, you're learning from your own past mistakes. But
Speaker 1: if you are launching a brand new piece of software.
Speaker 1: You have zero pass data. I mean you are flying
Speaker 1: completely blind.
Speaker 2: You are, And that is where the shift to CPDP
Speaker 2: comes in. That's the frontier right now.
Speaker 1: Cross project defect prediction.
Speaker 2: Yes, so instead of relying on your own non existent history,
Speaker 2: the algorithm uses massive sets of training data from completely
Speaker 2: different outside software projects to find the hidden bugs in
Speaker 2: your new code.
Speaker 1: Okay, let's unpack this for a second, because the logic
Speaker 1: here is just wild to me. This is basically like
Speaker 1: trying to predict where the plumbing is going to leak
Speaker 1: in a brand new, half built.
Speaker 2: Skyscraper by studying the plumbing failures of completely different skyscraper
Speaker 2: across town exactly.
Speaker 1: I mean, how does that even work.
Speaker 2: It's actually a brilliant way to conceptualize it. Your skyscraper analogy.
Speaker 2: You are working on the assumption that because both structures
Speaker 2: use you know, pipes, water pressure, and gravity, the physical
Speaker 2: stress points will behave similarly, even if.
Speaker 1: The architectural floor plans are wildly different.
Speaker 2: Exactly, and the source material mentions four specific ways they
Speaker 2: set up this cross project training right.
Speaker 1: I have them here. It's strict mixed mixed with target
Speaker 1: class and pair wise.
Speaker 2: So strict means the training data is completely blind to
Speaker 2: your new software. It only uses outside projects period. Okay,
Speaker 2: mixed folds in older, perhaps slightly related projects alongside the
Speaker 2: outside data now mixed with target class is really interesting
Speaker 2: because it takes a tiny labeled sample from your current
Speaker 2: unfinished project to give the algorithm just a slight hint
Speaker 2: about your specific architecture, kind of like.
Speaker 1: Showing at a rough blueprint before it checks the pikes, right.
Speaker 2: And then pairwise is a strict one to one mapping.
Speaker 2: The model is trained entirely on one single outside project
Speaker 2: and then test it entirely on yours.
Speaker 1: But I'm trying to visualize what the AI is actually
Speaker 1: looking at here, because it's not reading the code like
Speaker 1: a human programmer, right, Yeah, it's not scanning for a
Speaker 1: missing semicolon.
Speaker 2: No, No, it's looking at structural metrics. The text highlights
Speaker 2: something called CK metrics, which measure the complexity of object
Speaker 2: oriented software.
Speaker 1: What's an example of a CK metric?
Speaker 2: A good example is the depth of inheritance tree.
Speaker 1: Depth of inheritance tree. Okay, what does that mean practically?
Speaker 2: Well, imagine code like a family tree. If a piece
Speaker 2: of code inherits traits from say, ten generations of parent
Speaker 2: code above it. It is deeply nested.
Speaker 1: Oh I see, and if you change one thing at
Speaker 1: the very top of that ten generation tree, it probably
Speaker 1: just breaks everything at the bottom.
Speaker 2: Exactly the point it's incredibly fragile. Or the AI looks
Speaker 2: at something like weighted methods per class, which basically measures
Speaker 2: how many different operations a single piece of code is
Speaker 2: trying to juggle all at once.
Speaker 1: So the algorithm isn't looking for a broken line of chade,
Speaker 1: it's scanning for structural fragility.
Speaker 2: Yes, mathematically extreme complexity is basically the breeding ground for bugs.
Speaker 1: Okay, I have to push back here though, just putting
Speaker 1: myself in the shoes of the engineers. If a commercial
Speaker 1: software project is, say, mostly successful, wouldn't bugs be incredibly rare?
Speaker 2: They are relatively speak.
Speaker 1: Right, So, say ninety nine percent of the code is
Speaker 1: structurally sound and only one percent is actually defective. If
Speaker 1: you feed an AI that data, doesn't the math just break.
Speaker 1: I mean, the AI could literally just look at any
Speaker 1: line of code blindly guess no bug and be mathematically correct.
Speaker 1: Ninety nine percent of the time.
Speaker 2: You've just identify, honestly, one of the most notorious hurdles
Speaker 2: in machine learning. It's called the class imbalance problem.
Speaker 1: Class imbalance problem.
Speaker 2: Yeah, when one outcome is overwhelmingly common, the algorithm just
Speaker 2: takes the path of least mathematical resistance, learns to ignore
Speaker 2: the rare anomaly the bug because optimizing for the ninety
Speaker 2: nine percent yields a fantastic accuracy score on paper.
Speaker 1: So how do they actually solve that? Because you can't
Speaker 1: just copy and paste that one where bug one hundred
Speaker 1: times to balance the spreadgy Right, that seems like it
Speaker 1: would just teach the AI to memorize one specific mistake,
Speaker 1: and you'd.
Speaker 2: Be totally right. Over sampling by just copying data does
Speaker 2: exactly that. The AI memorizes the duplicate, it overfits to it,
Speaker 2: and then becomes entirely useless at finding new types of bugs.
Speaker 1: Okay, so what's the fix.
Speaker 2: Instead, the researchers utilized a highly sophisticated statistical technique called SEMOT.
Speaker 1: Which stands for synthetic minority over sampling technique.
Speaker 2: Yes, and somemisode doesn't duplicate. What it does is calculate
Speaker 2: the mathematical distance between the rare bug data points in
Speaker 2: multidimensional space.
Speaker 1: Whoa multidimensional space. Okay, slow down.
Speaker 2: Let's simplify it. Imagine a scatter plot graph with two
Speaker 2: real bugs plotted on it. Smow T draws a line
Speaker 2: between those two points and mathematically synthesizes an entirely new
Speaker 2: artificial bugs somewhere along that line.
Speaker 1: Oh wow. Wait, really, so they aren't just finding bugs.
Speaker 1: They're essentially cloning the DNA of a mistake exactly. They
Speaker 1: are hallucinating highly realistic structural flaws to force the AI
Speaker 1: to become a better detective.
Speaker 2: It balances the scales not with repetition, but with synthetic diversity.
Speaker 2: And when the researchers combine some mote with a gradient
Speaker 2: boosting algorithm called xg boost, which by the way, is
Speaker 2: exceptional at handling complex tabular data, their cross project prediction
Speaker 2: accuracy reached up to eighty eight percent.
Speaker 1: Eighty eight percent. It completely flips how I thought quality
Speaker 1: assurance worked. It proves that algorithms can successfully predict structural
Speaker 1: failure just by studying the mathematical neighborhood.
Speaker 2: It does, and I.
Speaker 1: Mean if AI can synthesize fake data to fixed broken code,
Speaker 1: it raises a much bigger question for me. Can we
Speaker 1: apply that exact same neighborly logic to human behavior.
Speaker 2: Oh, absolutely, which takes us straight into the mechanics of
Speaker 2: recommendation systems, you know, the systems deciding what song, product
Speaker 2: or movie you interact with next. Broadly speaking, the industry
Speaker 2: relies on two philosophies, content based filtering and collaborative filtering.
Speaker 1: Content based seems pretty intuitive to me. If I watch
Speaker 1: a documentary about, say, deep sea diving, the algorithm tags
Speaker 1: the features yea, like ocean submarines, greene biology, and then
Speaker 1: it just recommends another documentary with those same exact tags.
Speaker 2: Yeah, it's essentially property matching. The limitation, however, is that
Speaker 2: content based filtering traps you in a very predictable bubble.
Speaker 2: It has no mechanism to surprise you with something outside
Speaker 2: of those literal.
Speaker 1: Tags, right, You're just stuck in a submarine loop.
Speaker 2: Forever, exactly. And that is why platforms pivot heavily toward
Speaker 2: collaborative filtering.
Speaker 1: And this is where the math gets really interesting.
Speaker 2: Because collaborative filtering doesn't actually care what the movie or
Speaker 2: song is about. It completely ignores the content tags.
Speaker 1: Wait, it ignores them entirely.
Speaker 2: Entirely, it only cares about the behavioral patterns of the
Speaker 2: people consuming it. It takes all of your clicks, your views,
Speaker 2: and ratings and plots them on this massive mathematical grid
Speaker 2: called a user item matrix. Okay, then it uses clustering
Speaker 2: algorithms like k means clustering to map you into a
Speaker 2: specific locality of other users who share your precise behavioral footprint.
Speaker 1: So collaborative filtering is basically like walking into a massive,
Speaker 1: crowded party, finding the one total stranger who likes the
Speaker 1: exact same weird indie band as you, and then blamely
Speaker 1: trusting their movie recommendation for the rest of the night.
Speaker 2: That's it, But it goes even further than that. The
Speaker 2: AI assumes that your agreement on past choices is actually
Speaker 2: a mathematical vector pointing toward your next choice.
Speaker 1: Meaning what exactly?
Speaker 2: Meaning, if you and this cluster of strangers agreed on
Speaker 2: your last fifty interactions, the system is statistically confident you
Speaker 2: will enjoy the fifty first thing they liked, even if
Speaker 2: it's a completely different genre that you've never even explored.
Speaker 1: But wait, looking at the source material, what happens when
Speaker 1: there is no history to match, Like the text brings
Speaker 1: up the cold start problem.
Speaker 2: Ah, yes, the cold start right, Because if I am
Speaker 2: a brand new user, my row on that user item
Speaker 2: matrix is completely blank. Or if a musician uploads a
Speaker 2: brand new track five seconds ago, it is zero listener data.
Speaker 2: How does this system ever recommend it? Doesn't the math
Speaker 2: just break down?
Speaker 1: The math does indeed break down. There the user itta
Speaker 1: matrix becomes too sparse. It's like a giant spreadsheet where
Speaker 1: ninety nine percent of the cells are just empty. You
Speaker 1: can't calculate a vector from nothing, So.
Speaker 2: What's the worker ind Well, this is why the state
Speaker 2: of the art approach relies on hybrid models. They layer
Speaker 2: collaborative and content based filtering together and then they integrate
Speaker 2: context from the Internet of Things or IoT.
Speaker 1: Right, they pull in real world unstructured data and the
Speaker 1: source text actually has this incredible real world case study
Speaker 1: to prove how powerful this is. Getting the story of
Speaker 1: miss Swati preside.
Speaker 2: Yes, it's a perfect illustration of how predictive analytics has
Speaker 2: evolved from just tracking what you clicked yesterday.
Speaker 1: So you had the stage for us.
Speaker 2: Yeah, there was an AI engine named Missin developed by
Speaker 2: ic Terra Science and its goal was to predict future talent.
Speaker 2: So it didn't just look at a sparse matrix of
Speaker 2: song ratings. It utilized natural language processing or NLP, to
Speaker 2: analyze her entire digital footprint.
Speaker 1: Okay, so what is the actual mechanism there? How does
Speaker 1: an algorithm read a digital footprint and spit out a
Speaker 1: prediction for stardom?
Speaker 2: So NLP allows the algorithm to map human language to
Speaker 2: mathematical weights. The messin Engines scraped the web for her
Speaker 2: college performances at engineering fest.
Speaker 1: Wow, it went that deep, it.
Speaker 2: Did, and it analyzed the semantic sentiment of the lyrics
Speaker 2: she was singing, basically calculating the emotional resonance of her words.
Speaker 2: On top of that, attracted her social media interactions, mapping
Speaker 2: the velocity and the sentiment of the comments around her.
Speaker 1: So it's assigning mathematical values to the emotional reaction she's
Speaker 1: generating online and then comparing that shape to the historical
Speaker 1: data of artists who actually made it big.
Speaker 2: Exactly. It synthesized all that unstructured context and predicted that
Speaker 2: she would make a debut as a playback singer in Bollywood.
Speaker 1: Which actually had I mean, she ended up singing for
Speaker 1: a feature film. The recommendation system wasn't just reacting to
Speaker 1: pass clicks. It was actively discovering latent human talent by
Speaker 1: identifying the mathematical signature of future popularity.
Speaker 2: It's a profound shift really in how we understand discovery.
Speaker 2: These algorithms. They're no longer just mirrors showing us what
Speaker 2: we already did. They are predictive oracles. They find the
Speaker 2: talent and immediately match it with the cluster of users
Speaker 2: who are mathematically primed to receive it.
Speaker 1: It's brilliant. Oh, but you know it deals with recommending
Speaker 1: or finding one specific thing, one song, one artist. What
Speaker 1: happens when the problem isn't picking one thing but trying
Speaker 1: to distill thousands of things. I mean, we all have
Speaker 1: thousands of photos sitting on our phones right now. How
Speaker 1: does an AI look at a massive visual data set
Speaker 1: and summarize it without losing the big picture?
Speaker 2: You're touching on the immense challenge of image collection summarization.
Speaker 2: To process that kind of visual noise, the algorithm has
Speaker 2: to choose a summarization philosophy. This material contrasts extractive summarization
Speaker 2: with abstractive summarization.
Speaker 1: Okay, if we think about this in terms of sports,
Speaker 1: extractive summarization would be like the highlight reel. You're pulling
Speaker 1: the actual untouched video clips of the best plays exactly,
Speaker 1: and abstractive would be the sports reporter writing a brand
Speaker 1: new article summarizing the game.
Speaker 2: That's spot on. Abstractive means the AI extracts the essence
Speaker 2: of the data and generate something entirely new, like a
Speaker 2: text summary. But the researchers note this is highly impractical
Speaker 2: for personal image collection, Right.
Speaker 1: I don't want an AI to generate a fake composite
Speaker 1: image to summarize my actual family vacation.
Speaker 2: No, you want your actual photos. So we rely on
Speaker 2: extractive summarization.
Speaker 1: But how does a computer look at a thousand pixels
Speaker 1: and mathematically decide what makes a good highlight?
Speaker 2: Well? The text details two main mathematical approaches to extractive summarization.
Speaker 2: The first is the similarity based approach. The goal here
Speaker 2: is to find the canonical view, and.
Speaker 1: A canonical view is what exactly the definitive angle?
Speaker 2: Yes, think of the most universally recognizable angle of the
Speaker 2: Eiffel Tower. To find this in your photos, the AI
Speaker 2: builds an eigen model.
Speaker 1: Hold on eigenmodel sounds incredibly dense. What is that practically doing?
Speaker 1: Is it just like averaging all the colors together.
Speaker 2: Not just colors. It's extracting the structural skeleton of the images.
Speaker 2: It maps out multidimensional features you know, edges, lighting, shapes,
Speaker 2: and it plots every photo in mathematical space.
Speaker 1: Okay, I'm falling.
Speaker 2: Then it uses something called cosine similarity. This calculates the
Speaker 2: geometric angle between the data points by finding the photos
Speaker 2: with the tightest angles to one another. It clusters similar
Speaker 2: images together and extracts the one photo sitting dead center
Speaker 2: in that cluster.
Speaker 1: So it looks at fifty photos of my dog at
Speaker 1: the beach, groups them by their structural skeleton, finds the
Speaker 1: mathematical dead center, and declares this is the canonical beach
Speaker 1: dog photo.
Speaker 2: That's the similarity approach.
Speaker 1: Yes, yeah.
Speaker 2: Now contrast that with the reconstruction based approach, which actually
Speaker 2: treats your photo album like a data compression problem.
Speaker 1: Data compression, right.
Speaker 2: It uses a dictionary of sparse representations and relies on
Speaker 2: minimizing something called L to norm error.
Speaker 1: Okay, L two norm error. I need an analogy here
Speaker 1: to wrap my head around that. Think of L two
Speaker 1: norm error like freeze drying a meal.
Speaker 2: Freeze drying, okay, Yeah.
Speaker 1: You remove all the water, which is the bulk of
Speaker 1: the weight distored efficiently, and if you add water back
Speaker 1: later and the meal tastes exactly like the original. The
Speaker 1: error in your freeze drying process zero.
Speaker 2: That is actually a highly accurate way to look at it.
Speaker 2: The algorithm is freeze drying your photo album. It asks
Speaker 2: a purely mathematical question, if I only keep these five
Speaker 2: photos out of one hundred, can I use their specific
Speaker 2: mathematical features to perfectly reconstruct the data of the missing
Speaker 2: ninety five. The five photos become the basis set, and
Speaker 2: the L to norm error is simply the mathematical difference
Speaker 2: between your original massive album and the algorithm's estimation. If
Speaker 2: the error is tiny, the summary is highly representative.
Speaker 1: But wait, putting myself in your shoes for a second,
Speaker 1: looking at my own camera role, pure math doesn't understand sentiment.
Speaker 2: No it doesn't.
Speaker 1: If the AI just optimizes for this L two norm error,
Speaker 1: it might pick five technically perfect photos that completely miss
Speaker 1: the emotional point of my trip, Like my favorite photo
Speaker 1: might be blurry or off center. Isn't a highlight? Real?
Speaker 1: Incredibly subjective?
Speaker 2: This is a crucial limitation. It really is. If you
Speaker 2: only use pure geometry, you get a mathematically perfect summary
Speaker 2: that feels totally alien to a human And that is
Speaker 2: exactly why the researchers introduce task specific summarization.
Speaker 1: Meaning the AI needs to know why you want the
Speaker 1: summary before it does the mask.
Speaker 2: Exactly, it filters the math through a layer of human intent.
Speaker 1: So how does it actually do that?
Speaker 2: The researchers build a deep learning architecture using a scorer network,
Speaker 2: so before it ever clusters a photo, it evaluates every
Speaker 2: single image based on three specific criteria relevance, diversity, and redundancy.
Speaker 1: Well, diversity and redundancy makes sense, you want it to
Speaker 1: and angles. You obviously don't want five identical pictures of
Speaker 1: the same sunset. But how does an algorithm measure subjective relevance?
Speaker 2: It uses a pre trained classifier. The AI takes the
Speaker 2: image's mathematical properties, it's feature vector, and multiplies it by
Speaker 2: a probability score that was generated for your specific task.
Speaker 1: Okay, give me an example.
Speaker 2: Say your task is show me the architectural highlights of
Speaker 2: my trip. The classifier acts as a filter, boosting the
Speaker 2: mathematical weight of buildings and drastically lowering the weight of
Speaker 2: selfies or food.
Speaker 1: So it's forcing the geometry to respect the context.
Speaker 2: Precisely, and the text notes this ensures the summary is
Speaker 2: a topologically invariant representation.
Speaker 1: Okay, let's ELI five that explain, like I'm five. Topologically
Speaker 1: invariant means what the shape of the memory survives.
Speaker 2: Yes, in topology, you can stretch or shrink an object,
Speaker 2: but as long as you don't pair it or punch
Speaker 2: new holes in it, it's fundamental property. Its invariant shape remains.
Speaker 1: Ah, it's beautiful.
Speaker 2: By using scorer networks, the AI can shrink a ten
Speaker 2: thousand photo album down to ten photos, but the fundamental
Speaker 2: shape of your memory, tailored specifically for what you care about,
Speaker 2: remains perfectly intact.
Speaker 1: You know, it is genuinely remarkable how interconnected all these
Speaker 1: concepts are. We started by looking at how AI clones
Speaker 1: the structural DNA of a mistake to predict software failure.
Speaker 1: Then we move to how IT clusters our behavioral footprints
Speaker 1: in an N dimensional matrix to predict cultural success, and
Speaker 1: we finished with how it uses sparse reconstruction and scorer
Speaker 1: networks to freeze dryer visual chaos into perfect, meaningful summaries.
Speaker 2: And the thread binding it all together is the mathematics
Speaker 2: of relationships. Data doesn't exist in a vacuum. Once an
Speaker 2: algorithm understands how a single piece of data relates to
Speaker 2: the neighborhood around it, it can predict the future of
Speaker 2: that entire neighborhood, which.
Speaker 1: Brings us entirely back to you listening right now. Every
Speaker 1: time you open a streaming app, search your camera roll,
Speaker 1: or rely on a banking protocols to securely process a transaction,
Speaker 1: you are really on this invisible architecture. Cess's MOTI balancing
Speaker 1: the scales, collaborative filtering, finding your digital neighbors, sparse reconstruction,
Speaker 1: distilling the noise.
Speaker 2: It's everywhere.
Speaker 1: These algorithms are silently working in the background to save
Speaker 1: you time, curate your worldview, and keep the digital plumbing
Speaker 1: from collapsing.
Speaker 2: It really reframes how we interact with our own data,
Speaker 2: and if we connect these capabilities to the bigger picture,
Speaker 2: it leaves us with something quite profound to consider exactly, well,
Speaker 2: if algorithms can synthesize artificial bugs to predict a software crash,
Speaker 2: and if NLP can mine unstructured social media texts to
Speaker 2: predict the exact moment someone becomes a star. And if
Speaker 2: mathematical models can find the perfect canonical view of a
Speaker 2: massive photo album, what happens when these incredibly powerful systems
Speaker 2: are turned toward the data of your entire life?
Speaker 1: Oh wow?
Speaker 2: If a machine can distill a data set down to
Speaker 2: its fundamental shape, what is the canonical view of you?
Speaker 1: Oh man, that is a heavy, fascinating question. To walk
Speaker 1: away with the idea of an algorithm zooming out on
Speaker 1: your entire digital footprint and just picking the ten frames
Speaker 1: that mathematically reconstruct your essence. I love that. Thank you
Speaker 1: for handing us this incredible research today and joining us
Speaker 1: as we explore the invisible architecture around us. Keep questioning
Speaker 1: the algorithms and we will catch you on the next
Speaker 1: deep dive.