WEBVTT

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You know, usually when you try to solve a massive

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complex problem, you really want a single brilliant

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expert. Right. The singular genius model. Exactly.

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Someone who can just look at the data, draw a

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clean line from point A to point B, and give

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you the definitive answer. Yeah, it feels safe.

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It does. It just makes sense to us intuitively,

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I think. I mean, we are very drawn to that kind

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of simplicity, especially when dealing with just,

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well, overwhelming amounts of information. But

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what if I told you that in the extremely competitive

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world of modern data science, the most dominant

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competition -crushing force isn't a single genius

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algorithm at all? Oh, not even close. Right.

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It's actually a massive, incredibly fast committee

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of what researchers call weak learners. Yeah.

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It is a fundamental shift in how we approach

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problem -solving entirely. Instead of one giant

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complex equation trying to understand everything

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at once. Right. You have this highly coordinated

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committee, and everyone in it contributes just

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a tiny specific piece to the final answer. And

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when they all vote together, they beat the genius.

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Yeah. Every time. Welcome to today's Deep Dive.

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We are thrilled to have you with us today as

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we unpack a stack of sources about a piece of

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open source software that completely took over

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the machine learning world. It's called XGBoost.

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A true powerhouse. OK, let's unpack this. So,

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what exactly are we looking at here and, you

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know, why does this committee of weak learners

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matter to you, the listener? It's a regularizing

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gradient boosting framework originally written

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in C++ bin. And what is genuinely remarkable

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about it is its absolute staying power. I mean,

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in a field that usually reinvents itself every

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six months or so. Oh, easily. AI moves so fast.

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Right. But the initial release of this was way

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back on March 27, 2014. Wow. which is practically

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ancient history in the fast moving machine learning

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space, yet it's still incredibly relevant today.

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I mean, they just released its stable 3 .0 .0

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version recently on March 15, 2025. Over a decade

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later, and it's still getting major stable releases.

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That is almost unheard of for an open source

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data science tool. It really is. So what does

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this all mean for you? Well, if you've ever wondered

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how data scientists actually win those massive

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global predictive competitions. Like predicting

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housing market crashes or customer churn. Yeah,

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exactly. Or medical diagnoses. Or how big tech

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companies process just mind -boggling amounts

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of data so efficiently. Well, this is very often

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the secret weapon. The project's stated mission

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is to be a, quote, scalable, portable, and distributed

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gradient boosting library. And it delivers on

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that mission spectacularly. But you know, to

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truly appreciate what makes it extreme, as the

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name implies, we really should look at where

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it came from. Right, because it didn't start

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as some multi -million dollar corporate initiative.

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Far from it, yeah. Before we dive into all the

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complex math and the algorithms, we need to understand

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how this tool rose to prominence. It has remarkably

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humble beginnings. Very humble. It was just a

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research project by Tianqi Chen. Oh, right. Yeah,

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he was part of the distributed deep machine learning

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community group, the DMLC at the University of

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Washington. In those early days, I'm guessing

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it wasn't the polished ubiquitous powerhouse

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it is today. Oh, definitely not. It began very

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simply, just as a terminal application. You basically

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had to configure it using this Libs VM configuration

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file. Which, if you aren't a programmer, basically

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means typing raw text commands into a black screen.

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Yeah, it was not exactly user -friendly. But

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then came the major turning point, right? XGBoost

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was used in the winning solution of the Higgs

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Machine Learning Challenge. Yes. That was the

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absolute spark. And the Higgs machine learning

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challenge was a massive deal. I mean, it sounds

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intense. It involved physicists from CERN trying

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to find the signal of the Higgs boson particle

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amidst just oceans of background noise in their

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sensor data. Wow, so... incredibly complex, noisy

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data. Exactly. And the machine learning competition

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community, particularly on platforms like Kaggle,

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they pay very close attention to what the winners

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of challenges like that are using. Of course,

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when a new tool absolutely crushes a complex

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physics data set, word spreads fast. Like wildfire.

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By the mid 2010s, it wasn't just popular. I mean,

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it became the undisputed algorithm of choice

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for winning teams on Kaggle. Right. It got to

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the point where if you weren't using XGBoost,

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you were essentially competing for second place.

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That's wild. But to meet that sudden massive

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demand. the project had to expand rapidly, right?

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Yeah, they quickly realized that restricting

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it to a C++ terminal app was severely limiting

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its potential. Right, because data scientists

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work in all sorts of environments. The sources

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highlight that they didn't just stick to C++,

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man. No, they went everywhere. They built packages

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for Python, Java, R, Julia. Gosh, what else?

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Perl and Scala. Basically any programming language

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a data scientist might want to use. And it wasn't

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just programming languages either. They ensured

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it ran natively across Linux, Windows, and MacOs.

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But where it truly proved its scalable and distributed

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claims was its integration into enterprise workflows.

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Absolutely. They built it to plug directly into

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massive big data processing frameworks. Things

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like Apache Hadoop, Apache Spark, Flink. I think

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they used Rabbit and XGBoos4j for that, and Ask.

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Yeah, they even mapped it to run on specialized

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hardware circuits called FPGAs using OpenCL.

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Wait, I have to stop and marvel at that for a

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second. It went from a university research terminal

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app to being integrated into the biggest enterprise

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data frameworks in the world and even running

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on specialized hardware. How does something scale

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that fast across an entire industry? I mean,

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is it just hype because someone won a physics

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competition with it or is the underlying architecture

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actually that much better? The short answer is,

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well, the engineering is fundamentally superior.

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The success really lies in how efficiently it

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was designed from the ground up to handle massive

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scale. The architecture was actually formalized

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and published in a highly influential 2016 paper

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by Tianqi Chen and Carlos Gestrin. And the academic

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world took notice, I assume. Oh, immediately.

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The scientific community recognized its value

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right away. In fact, in that same year, 2016,

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it won two major awards. Oh, wow. Yeah. The John

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Chambers Award and the High Energy Physics Meets

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Machine Learning Award. Well, it certainly has

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the trophies to back up the hype, but that brings

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us to the core of the issue. Why did it win all

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those competitions? What is actually happening

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under the hood that makes this gradient boosting

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so extreme? To understand that, we kind of have

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to establish how standard gradient boosting works

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first. Okay, lay it on me. In standard gradient

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boosting, you have an algorithm that works using

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something called gradient descent in a function

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space. Gradient descent. Yeah. Without getting

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bogged down in all the heavy equations, it calculates

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the first order derivative, the gradient of a

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loss function, to figure out which direction

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it needs to adjust the model to reduce its errors.

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Okay, let's try to visualize this for a second.

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Standard gradient descent. Let's say you're trying

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to walk down a mountain to reach the very bottom

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of a valley. I like this, go on. And the bottom

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of the valley represents zero arrow -like, the

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perfect prediction. But you're completely blindfolded.

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All you can do is feel the slope of the ground

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directly under your feet. That's your first derivative,

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your gradient. You feel the slope tilts down

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to the left, so you take a step left. You're

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using the immediate slope to guide you down.

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That analogy perfectly captures standard gradient

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descent. You feel the slope. You take a step.

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But XGBoost doesn't just use the gradient. It

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upgrades that entire process. How so? It works

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as the Newton -Raphson method in function space.

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Newton -Raphson. That sounds intense. It sounds

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intimidating, sure. Right. But it basically means

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it uses a second -order Taylor approximation

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in the loss function to look deeper into the

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math of the errors. Wait, wait. Second -order

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Taylor approximation and Newton -Raphson, stick

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with the mountain for me here. How does that

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change my blindfolded walk? Okay, so if standard

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gradient descent is just feeling the slope under

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your feet, XGBoost calculates those gradients,

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but it also calculates the Hessians. The Hessians.

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Yes, the Hessian is the second derivative. So

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back to our mountain analogy, you aren't just

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feeling the slope under your foot. You are also

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calculating how fast that slope is curving. Yeah,

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you know if the ground is getting steeper as

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it goes down or if it's starting to level out.

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Ah, okay. So I'm not just shuffling blindly step

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by step anymore. If I know the slope is curving

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downward sharply, I can confidently take a massive

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leap because I know I have a long way to go.

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Exactly. But if I feel the curvature leveling

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out, I know I'm getting close to the bottom,

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so I take a tiny step. so I don't accidentally

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walk past the lowest point. Precisely. Because

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you have that extra information about the curvature

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of the mountain, you can take much smarter, faster,

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and more precise steps toward the bottom. That

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is brilliant. And the algorithm does this iteratively

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to build that committee of weak learners we talked

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about earlier. So how does that actually work

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in practice? Like, how does it build the committee

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step by step? The generic steps look kind of

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like this. First, it initializes the model with

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a constant value to minimize the loss function.

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That is step zero, basically a baseline guess.

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OK, baseline guess. Then it starts a loop. It

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iterates from 1 to m, where m is the total number

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of learners in your committee. In each iteration,

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it computes those gradients and Hessians we talked

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about. The slope and the curve. Exactly, the

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slope and the curve of the errors from the previous

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guess. Then it fits a base learner or a weak

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learner, which is usually just a simple, shallow

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decision tree using that slope and curvature

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data. Finally, it updates the overall model by

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adding this new tree's findings to everything

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that came before. So it's building this massive

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committee of simple decision trees one by one.

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And each new tree uses that advanced slope and

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curve math to specifically target and correct

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the mistakes of the trees that came before it.

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That is exactly it. It is sequential targeted

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learning. But what's fascinating here is that

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what makes XGBoost truly extreme isn't just the

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core math. It's not. No, it's the incredibly

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clever engineering features that surround it.

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The source material lists a whole bunch of these

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salient features. Things like proportional shrinking

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of leaf nodes, Newton boosting, extra randomization,

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automatic feature selection, out -of -core computation,

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weighted quantile sketching, and parallel tree

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structure boosting with sparsity. OK, wait. Let's

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actually pause and pack some of those, because

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earlier when I was reading the source material,

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weighted quantile sketching just sounded like

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a geometry term to me. What does that actually

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mean for the algorithm? It's essentially a brilliant

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shortcut. Imagine you have a data set with, say,

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a billion rows. OK, huge data set. And your decision

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tree is trying to figure out the best place to

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split the data, like deciding if the best dividing

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line for predicting a house price is 2 ,000 square

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feet or 2 ,050 square feet. Right. Normally,

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an algorithm would have to sort and evaluate

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every single data point to find the perfect split.

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Which would take forever. Exactly. Weighted quantile

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sketching is a mathematical way to approximate

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those split points with incredible accuracy without

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having to load and sort every single piece of

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data. Oh wow. Yeah, it saves massive amounts

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of time and memory. So it's basically a way to

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find the needle in the haystack without having

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to pick up every single piece of hay. That makes

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sense. What about out -of -core computation?

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That one solves a huge hardware problem. Often,

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data sets are simply too large to fit into a

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computer's main memory. It's RAM. Yeah, we've

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all had our computers crash from there. Right.

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Out -of -core computation means XGBoost is engineered

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to smartly pull data in chunks from the hard

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drive, processing it seamlessly without crashing

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the system or causing massive bottlenecks. And

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it pairs that with something called an efficient

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cacheable block structure. Yes, exactly. So it's

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managing memory and data access. in a way that

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minimizes the time the CPU spends just waiting

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around for data to arrive. Spot on. It also includes

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proportional shrinking of leaf nodes, which essentially

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dials back the influence of any newly added tree

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so that no single tree can dominate the committee's

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vote. Keeps things balanced. Exactly. And it

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has parallel tree structure boosting with sparsity,

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meaning it can handle data sets that have tons

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of missing information, which, let's face it,

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is very common in the real world without missing

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a beat. OK, so we have this mathematically brilliant,

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highly optimized second derivative calculating

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engine. It handles missing data, it handles massive

00:12:48.779 --> 00:12:51.200
data, and it runs on everything. It does. But,

00:12:51.200 --> 00:12:53.779
you know, in data science, you never get something

00:12:53.779 --> 00:12:57.279
for nothing. Since XGBoost is so mathematically

00:12:57.279 --> 00:13:00.080
dense and complex, what is the cost of all this

00:13:00.080 --> 00:13:02.879
power? The cost is interpretability. You are

00:13:02.879 --> 00:13:05.279
fundamentally trading human understanding for

00:13:05.279 --> 00:13:07.559
predictive accuracy. Let's explore that tension,

00:13:08.100 --> 00:13:10.740
because the source material points out that XGBoost

00:13:10.740 --> 00:13:13.259
relies on an ensemble of weak learners building

00:13:13.259 --> 00:13:15.440
hundreds or even thousands of decision trees.

00:13:15.480 --> 00:13:17.500
Right. Now if I just look at a single decision

00:13:17.500 --> 00:13:19.539
tree, it's intrinsically interpretable, right?

00:13:19.759 --> 00:13:21.799
Yes. A single decision tree is very easy for

00:13:21.799 --> 00:13:23.759
a human to read. It's basically just a simple

00:13:23.759 --> 00:13:26.740
float chart. Like a magazine quiz. Yeah. Imagine

00:13:26.740 --> 00:13:29.620
predicting housing prices. The top of the tree

00:13:29.620 --> 00:13:32.879
asks, is the house larger than 2000 square feet?

00:13:33.120 --> 00:13:35.740
Yes or no? If yes, the next branch asks, does

00:13:35.740 --> 00:13:39.139
it have two bathrooms? Yes or no? Simple. Following

00:13:39.139 --> 00:13:41.220
the path that a single tree takes to make its

00:13:41.220 --> 00:13:43.879
decision is trivial and self -explained. You

00:13:43.879 --> 00:13:46.120
can point to the exact reason it made a specific

00:13:46.120 --> 00:13:48.720
prediction. But with XGBoost, we aren't looking

00:13:48.720 --> 00:13:51.639
at one tree. We are looking at a committee of

00:13:51.639 --> 00:13:54.059
hundreds or thousands of trees, all weighing

00:13:54.059 --> 00:13:57.220
in on the final answer simultaneously, constantly

00:13:57.220 --> 00:14:00.059
correcting each other's tiny errors based on

00:14:00.059 --> 00:14:02.059
those second derivatives. Exactly. Following

00:14:02.059 --> 00:14:04.240
the logic paths of thousands of interconnected

00:14:04.240 --> 00:14:07.840
trees at the exact same time is Well, it's impossible

00:14:07.840 --> 00:14:10.740
for a human brain, meaning XGBoost sacrifices

00:14:10.740 --> 00:14:13.539
that simple flowchart interpretability for higher

00:14:13.539 --> 00:14:16.059
accuracy. It is a classic black box problem.

00:14:16.139 --> 00:14:18.080
You know what data goes in and you get incredibly

00:14:18.080 --> 00:14:20.299
accurate predictions out, but the messy tangled

00:14:20.299 --> 00:14:22.639
web of logic in the middle is totally obscured.

00:14:23.000 --> 00:14:24.600
Here's where it gets really interesting, though.

00:14:25.000 --> 00:14:28.500
If we accept that this is a black box, it puts

00:14:28.500 --> 00:14:31.340
a ton of pressure on the data scientist. I mean,

00:14:31.419 --> 00:14:33.940
you can't just press go and let thousands of

00:14:33.940 --> 00:14:36.309
trees grow wild. You have to set the boundaries

00:14:36.309 --> 00:14:39.070
before it starts. You do. How do you actually

00:14:39.070 --> 00:14:41.809
steer something this complex? For the listener,

00:14:42.110 --> 00:14:44.870
utilizing AI tools does accuracy always trump

00:14:44.870 --> 00:14:47.389
understanding. If we connect this to the bigger

00:14:47.389 --> 00:14:49.950
picture, it's a fundamental tension in machine

00:14:49.950 --> 00:14:52.779
learning. Knowledge is valuable when understood,

00:14:53.279 --> 00:14:55.480
but in competitive environments like Kaggle,

00:14:55.840 --> 00:14:58.559
pure predictive power often wins out over human

00:14:58.559 --> 00:15:01.500
readability. When it all costs. Right. But that

00:15:01.500 --> 00:15:04.059
transitions us perfectly to the parameters. The

00:15:04.059 --> 00:15:06.279
source material outlines some of the key parameters

00:15:06.279 --> 00:15:08.860
that data scientists use to tame the XGBoost

00:15:08.860 --> 00:15:11.000
machine. It's kind of like tuning a high -performance

00:15:11.000 --> 00:15:12.679
sports car before a race, right? That's a great

00:15:12.679 --> 00:15:14.720
way to put it. You have these dials and levers

00:15:14.720 --> 00:15:16.799
you can pull to change how the engine performs.

00:15:17.279 --> 00:15:19.929
The first major one is the learning rate. also

00:15:19.929 --> 00:15:22.549
known as the step size, or shrinkage. It's a

00:15:22.549 --> 00:15:24.990
number between 0 and 1, and I think the default

00:15:24.990 --> 00:15:28.490
is 0 .3. This dictates how fast the algorithm

00:15:28.490 --> 00:15:31.480
learns from each iteration. Going back to our

00:15:31.480 --> 00:15:33.679
mountain analogy, it's like deciding how big

00:15:33.679 --> 00:15:35.940
of a step you're artificially allowed to take,

00:15:36.279 --> 00:15:38.360
regardless of what the math tells you. Precisely.

00:15:38.600 --> 00:15:41.159
If you set the learning rate too high, you might

00:15:41.159 --> 00:15:44.200
take a step so large that you bounce right past

00:15:44.200 --> 00:15:46.159
the lowest point of the valley and end up back

00:15:46.159 --> 00:15:48.120
up the other side. Overshooting the target. But

00:15:48.120 --> 00:15:50.980
if it is too low, you're taking such tiny steps

00:15:50.980 --> 00:15:53.340
that it will take you forever and massive computing

00:15:53.340 --> 00:15:55.600
power to finally get to the bottom. Got it. What's

00:15:55.600 --> 00:15:58.399
next? Next, you have max depth. This controls

00:15:58.399 --> 00:16:00.879
how deeply each individual tree in the boosting

00:16:00.879 --> 00:16:03.379
process can grow during training. The default

00:16:03.379 --> 00:16:07.080
is six. So basically this controls how complex

00:16:07.080 --> 00:16:09.840
each individual flowchart is allowed to get before

00:16:09.840 --> 00:16:11.759
it has to stop asking questions and just make

00:16:11.759 --> 00:16:14.120
a decision. Right. Then there's gamma. Gamma.

00:16:14.200 --> 00:16:17.460
Gamma is the Lagrange multiplier. It represents

00:16:17.460 --> 00:16:20.000
the minimum loss reduction parameter. The default

00:16:20.000 --> 00:16:22.899
in XGBoost is zero. Okay, what does that actually

00:16:22.899 --> 00:16:25.879
mean? Essentially, gamma acts as a conservative

00:16:25.879 --> 00:16:29.200
filter for the tree's growth. It tells the tree,

00:16:29.759 --> 00:16:32.519
unless this new branch you want to grow is going

00:16:32.519 --> 00:16:35.620
to actually improve our overall accuracy by at

00:16:35.620 --> 00:16:38.059
least this specific amount, don't bother growing

00:16:38.059 --> 00:16:40.919
it. Oh, I like that. It stops the tree from getting

00:16:40.919 --> 00:16:43.899
distracted by tiny, insignificant details in

00:16:43.899 --> 00:16:47.899
the data. Exactly. Keeps it focused. OK, and

00:16:47.899 --> 00:16:51.200
finally, we have nestimators. This sets the total

00:16:51.200 --> 00:16:52.799
number of trees to be built in the ensemble.

00:16:52.990 --> 00:16:54.830
It determines the overall size of the committee.

00:16:55.149 --> 00:16:57.250
Yes, exactly. And this is where I really have

00:16:57.250 --> 00:17:00.210
to ask, if more trees generally increase the

00:17:00.210 --> 00:17:01.950
complexity and the predictive power of the model,

00:17:02.769 --> 00:17:04.450
why not just set an estimators to a million?

00:17:04.950 --> 00:17:07.309
I mean, if we have massive cloud computing power,

00:17:07.369 --> 00:17:09.150
why not just build a million trees and get the

00:17:09.150 --> 00:17:11.430
perfect answer every time? Because more power

00:17:11.430 --> 00:17:14.130
isn't always better. And this is due to a very

00:17:14.130 --> 00:17:16.549
dangerous trap in machine learning called overfitting.

00:17:16.970 --> 00:17:19.630
Overfitting? Yes. If you add too many trees,

00:17:19.710 --> 00:17:22.170
the model becomes way too complex. Instead of

00:17:22.170 --> 00:17:23.890
learning the underlying patterns of the data,

00:17:24.130 --> 00:17:26.609
it starts to merely memorize the training data.

00:17:26.869 --> 00:17:29.230
Oh, I see. It's like a student studying for a

00:17:29.230 --> 00:17:32.230
math test. A good student learns the formulas

00:17:32.230 --> 00:17:35.119
so they can solve any problem. An overfitting

00:17:35.119 --> 00:17:37.799
model is like a student who just memorizes the

00:17:37.799 --> 00:17:40.940
exact answers to the practice test. That is a

00:17:40.940 --> 00:17:43.240
perfect way to look at it. If you give that memorizing

00:17:43.240 --> 00:17:45.500
student a brand new test with slightly different

00:17:45.500 --> 00:17:48.039
numbers, they will fail completely. Because they

00:17:48.039 --> 00:17:50.059
didn't learn the pattern. Exactly. They only

00:17:50.059 --> 00:17:52.180
memorize the specific examples they were given.

00:17:52.759 --> 00:17:55.880
If you set an estimator to a million, your XGBoost

00:17:55.880 --> 00:17:58.400
model will get a perfect score on the historical

00:17:58.400 --> 00:18:01.359
data you fed it, sure, but it will perform terribly

00:18:01.359 --> 00:18:03.940
out in the real world when it encounters new

00:18:03.940 --> 00:18:07.240
unseen information. Wow. Tuning these parameters

00:18:07.240 --> 00:18:09.839
is the art of finding that perfect balance between

00:18:09.839 --> 00:18:11.819
learning the pattern and memorizing the noise.

00:18:12.140 --> 00:18:15.619
Man, that is fascinating. OK, we have covered

00:18:15.619 --> 00:18:17.920
a massive amount of ground here today. Let's

00:18:17.920 --> 00:18:19.920
take a moment to recap this journey. Sounds good.

00:18:20.460 --> 00:18:22.539
We started with the University Research Project

00:18:22.539 --> 00:18:25.880
by Tianqi Chen, a simple terminal application

00:18:25.880 --> 00:18:28.819
configuring text files. And we watched it evolve

00:18:28.819 --> 00:18:31.460
into a Kaggle -dominating powerhouse running

00:18:31.460 --> 00:18:33.619
on the biggest big data frameworks in the world.

00:18:33.829 --> 00:18:36.609
A true powerhouse built on the Newton -Raphson

00:18:36.609 --> 00:18:39.470
method, using second -order Taylor approximations

00:18:39.470 --> 00:18:41.890
to calculate both the slope and the curvature

00:18:41.890 --> 00:18:44.549
of the loss function. It navigates complex data

00:18:44.549 --> 00:18:47.450
sets with incredible speed thanks to ingenious

00:18:47.450 --> 00:18:49.970
shortcuts like weighted quantile sketching. It

00:18:49.970 --> 00:18:52.509
really does. And most importantly, it's an algorithm

00:18:52.509 --> 00:18:55.089
that forces us to make a profound trade -off,

00:18:55.369 --> 00:18:58.529
surrendering the simple flowchart -like interpretability

00:18:58.529 --> 00:19:01.930
of a single decision tree in exchange for the

00:19:01.930 --> 00:19:05.440
extreme accuracy of a massive unreadable ensemble

00:19:05.440 --> 00:19:07.640
of weak learners. That is the core of it, yeah.

00:19:08.200 --> 00:19:10.500
So to you listening, the next time you hear about

00:19:10.500 --> 00:19:13.400
a miraculous AI prediction or a major machine

00:19:13.400 --> 00:19:15.660
learning breakthrough in the news, remember this.

00:19:16.599 --> 00:19:18.859
There's a very good chance that an ensemble of

00:19:18.859 --> 00:19:21.140
weak learners orchestrated by something like

00:19:21.140 --> 00:19:23.740
XGBoost is quietly crunching the numbers in the

00:19:23.740 --> 00:19:26.150
background. It truly is the invisible architecture

00:19:26.150 --> 00:19:29.150
behind so many modern predictive successes. It

00:19:29.150 --> 00:19:32.089
really is. We talked a lot today about that central

00:19:32.089 --> 00:19:36.130
trade -off, sacrificing the intrinsic self -explained

00:19:36.130 --> 00:19:38.650
interpretability of a single tree to get the

00:19:38.650 --> 00:19:41.250
astonishing accuracy of the forest. Which raises

00:19:41.250 --> 00:19:43.509
an important question based on the source material,

00:19:44.049 --> 00:19:46.130
and perhaps a final thought for everyone to ponder.

00:19:46.329 --> 00:19:49.500
Yeah. The text makes it very clear that XGBoost

00:19:49.500 --> 00:19:53.339
achieves its high accuracy specifically by sacrificing

00:19:53.339 --> 00:19:56.900
human readability. It makes you wonder if our

00:19:56.900 --> 00:20:00.079
most powerful award -winning tools in our technological

00:20:00.079 --> 00:20:03.579
arsenal fundamentally require us to abandon human

00:20:03.579 --> 00:20:07.299
readability. Where does that leave us exactly

00:20:07.299 --> 00:20:09.880
at what point does our reliance on complex machine

00:20:09.880 --> 00:20:12.839
learning mean? We must accept a world where we

00:20:12.839 --> 00:20:15.240
no longer truly understand how our own systems

00:20:15.240 --> 00:20:18.000
are making their decisions Can we live comfortably

00:20:18.000 --> 00:20:19.920
with the blindfold on as long as the machine

00:20:19.920 --> 00:20:23.220
keeps walking us safely down the mountain? Thanks

00:20:23.220 --> 00:20:25.079
for joining us on this deep dive and we'll catch

00:20:25.079 --> 00:20:25.599
you next time