[Script Info]
Title: 
[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
Dialogue: 0,0:00:00.95,0:00:04.54,Default,,0000,0000,0000,,My colleagues and I are fascinated\Nby the science of moving dots.
Dialogue: 0,0:00:05.11,0:00:05.86,Default,,0000,0000,0000,,So what are these dots?
Dialogue: 0,0:00:06.10,0:00:07.41,Default,,0000,0000,0000,,Well, it's all of us.
Dialogue: 0,0:00:07.41,0:00:12.52,Default,,0000,0000,0000,,And we're moving in our homes,\Nin our offices, as we shop and travel
Dialogue: 0,0:00:12.52,0:00:14.59,Default,,0000,0000,0000,,throughout our cities \Nand around the world.
Dialogue: 0,0:00:15.17,0:00:18.63,Default,,0000,0000,0000,,And wouldn't it be great if\Nwe could understand all this movement?
Dialogue: 0,0:00:19.18,0:00:21.81,Default,,0000,0000,0000,,If we could find patterns and meaning\Nand insight in it.
Dialogue: 0,0:00:22.48,0:00:26.10,Default,,0000,0000,0000,,And luckily for us, we live in a time\Nwhere we're incredibly good
Dialogue: 0,0:00:26.57,0:00:28.56,Default,,0000,0000,0000,,at capturing information about ourselves.
Dialogue: 0,0:00:29.01,0:00:32.26,Default,,0000,0000,0000,,So whether it's through \Nsensors or videos, or apps,
Dialogue: 0,0:00:32.58,0:00:35.39,Default,,0000,0000,0000,,we can track our movement\Nwith incredibly fine detail.
Dialogue: 0,0:00:36.27,0:00:40.61,Default,,0000,0000,0000,,So it turns out one of the places\Nwhere we have the best data
Dialogue: 0,0:00:40.61,0:00:42.20,Default,,0000,0000,0000,,about movement is sports.
Dialogue: 0,0:00:42.68,0:00:47.77,Default,,0000,0000,0000,,So whether it's basketball or baseball,\Nor football or the other football,
Dialogue: 0,0:00:48.16,0:00:52.46,Default,,0000,0000,0000,,we're instrumenting our stadiums \Nand our players to track their movements
Dialogue: 0,0:00:52.46,0:00:53.32,Default,,0000,0000,0000,,every fraction of a second.
Dialogue: 0,0:00:53.93,0:00:59.80,Default,,0000,0000,0000,,So what we're doing is turning our \Nathletes into -- you probably guessed it
Dialogue: 0,0:01:00.41,0:01:01.40,Default,,0000,0000,0000,,moving dots.
Dialogue: 0,0:01:02.31,0:01:06.54,Default,,0000,0000,0000,,So we've got mountains of moving dots\Nand like most raw data,
Dialogue: 0,0:01:07.05,0:01:08.79,Default,,0000,0000,0000,,it's hard to deal with \Nand not that interesting.
Dialogue: 0,0:01:09.43,0:01:13.22,Default,,0000,0000,0000,,But there are things that -- for example \Nbasketball coaches want to know.
Dialogue: 0,0:01:13.22,0:01:16.95,Default,,0000,0000,0000,,And the problem is they can't know them\Nbecause they'd have to watch every second
Dialogue: 0,0:01:16.95,0:01:19.54,Default,,0000,0000,0000,,of every game, remember it \Nand process it.
Dialogue: 0,0:01:19.98,0:01:23.04,Default,,0000,0000,0000,,And a person can't do that...\Nbut a machine can.
Dialogue: 0,0:01:23.88,0:01:27.04,Default,,0000,0000,0000,,The problem is a machine can't see\Nthe game with the eye of a coach.
Dialogue: 0,0:01:27.57,0:01:29.82,Default,,0000,0000,0000,,At least they couldn't until now.
Dialogue: 0,0:01:30.45,0:01:32.89,Default,,0000,0000,0000,,So what have we taught the machine to see?
Dialogue: 0,0:01:33.21,0:01:38.02,Default,,0000,0000,0000,,So, we started simply.\NWe taught it things like passes,
Dialogue: 0,0:01:38.02,0:01:38.95,Default,,0000,0000,0000,,shots and rebounds.
Dialogue: 0,0:01:39.44,0:01:43.18,Default,,0000,0000,0000,,Things that most casual fans would know.\NAnd then we moved on to things
Dialogue: 0,0:01:43.18,0:01:44.31,Default,,0000,0000,0000,,slightly more complicated.
Dialogue: 0,0:01:44.99,0:01:49.21,Default,,0000,0000,0000,,Events like post-ups,\Nand pick-and-rolls, and isolations.
Dialogue: 0,0:01:49.49,0:01:52.70,Default,,0000,0000,0000,,And if you don't know them, that's okay.\NMost casual players probably do.
Dialogue: 0,0:01:54.02,0:01:58.82,Default,,0000,0000,0000,,Now, we've gotten to a point where today,\Nthe machine understands complex events
Dialogue: 0,0:01:59.08,0:02:04.72,Default,,0000,0000,0000,,like down screens and wide pins.\NBasically things only professionals know.
Dialogue: 0,0:02:05.00,0:02:09.39,Default,,0000,0000,0000,,So we have taught a machine to see\Nwith the eyes of a coach.
Dialogue: 0,0:02:10.16,0:02:14.64,Default,,0000,0000,0000,,So how have we been able to do this?\NIf I asked a coach to describe something
Dialogue: 0,0:02:15.01,0:02:17.03,Default,,0000,0000,0000,,like a pick-and-roll, they would\Ngive me a description and
Dialogue: 0,0:02:17.33,0:02:20.19,Default,,0000,0000,0000,,if I encoded that as an algorithm,\Nit would be terrible.
Dialogue: 0,0:02:21.23,0:02:25.33,Default,,0000,0000,0000,,The pick-and-roll happens to be the stance \Nin basketball between four players,
Dialogue: 0,0:02:25.33,0:02:27.38,Default,,0000,0000,0000,,two on offense and two on defense.
Dialogue: 0,0:02:27.60,0:02:29.13,Default,,0000,0000,0000,,And here's kind of how it goes.
Dialogue: 0,0:02:29.34,0:02:33.28,Default,,0000,0000,0000,,So there's the guy on offense without \Nthe ball and he goes next to the guy
Dialogue: 0,0:02:33.51,0:02:35.65,Default,,0000,0000,0000,,guarding the guy with the ball,\Nand he kind of stays there
Dialogue: 0,0:02:35.95,0:02:39.11,Default,,0000,0000,0000,,and they both move and stuff happens, \Nand ta-da, it's a pick-and-roll.
Dialogue: 0,0:02:39.54,0:02:40.64,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:02:41.78,0:02:44.29,Default,,0000,0000,0000,,So that is also an example of\Na terrible algorithm.
Dialogue: 0,0:02:45.10,0:02:49.12,Default,,0000,0000,0000,,So, if the player who's the interferer\N-- he's called the screener,
Dialogue: 0,0:02:49.28,0:02:51.93,Default,,0000,0000,0000,,you know, goes close by,\Nbut he doesn't stop.
Dialogue: 0,0:02:52.46,0:02:54.22,Default,,0000,0000,0000,,It's probably not a pick-and-roll.
Dialogue: 0,0:02:54.74,0:02:58.24,Default,,0000,0000,0000,,Or if he does stop,\Nbut he doesn't stop close enough,
Dialogue: 0,0:02:58.66,0:03:00.49,Default,,0000,0000,0000,,it's probably not a pick-and-roll.
Dialogue: 0,0:03:01.09,0:03:04.51,Default,,0000,0000,0000,,Or, if he does go close by \Nand he does stop but they do it
Dialogue: 0,0:03:04.56,0:03:07.23,Default,,0000,0000,0000,,under the basket,\Nit's probably not a pick-and-roll.
Dialogue: 0,0:03:07.46,0:03:09.53,Default,,0000,0000,0000,,Or I could be wrong.\NThey could all be pick-and-rolls.
Dialogue: 0,0:03:09.78,0:03:14.08,Default,,0000,0000,0000,,It really depends on the exact timing,\Nthe distances, the locations
Dialogue: 0,0:03:14.36,0:03:15.96,Default,,0000,0000,0000,,and that 's what makes it hard.\N
Dialogue: 0,0:03:16.69,0:03:21.48,Default,,0000,0000,0000,,So, luckily with machine learning\Nwe can go beyond our own ability
Dialogue: 0,0:03:21.72,0:03:23.25,Default,,0000,0000,0000,,to describe the things we know.
Dialogue: 0,0:03:23.31,0:03:25.59,Default,,0000,0000,0000,,So how does this work?\NWell, it's by example.
Dialogue: 0,0:03:25.76,0:03:28.59,Default,,0000,0000,0000,,So we go to the machine and say,\N"Good morning, machine."
Dialogue: 0,0:03:29.08,0:03:32.14,Default,,0000,0000,0000,,"Here are some pick-and-rolls,\Nand here are somethings that are not."
Dialogue: 0,0:03:32.49,0:03:34.74,Default,,0000,0000,0000,,"Please find a way to tell a difference."
Dialogue: 0,0:03:35.08,0:03:38.80,Default,,0000,0000,0000,,And the key to all of this is to find\Nfeatures that enable it to separate.
Dialogue: 0,0:03:38.81,0:03:41.54,Default,,0000,0000,0000,,So if I was trying to teach it \Nthe difference between an apple and orange,
Dialogue: 0,0:03:41.64,0:03:43.98,Default,,0000,0000,0000,,I might say, "Why don't you use color,\Nor shape?"
Dialogue: 0,0:03:44.40,0:03:47.51,Default,,0000,0000,0000,,And the problem that we're solving is,\Nwhat are those things?
Dialogue: 0,0:03:47.51,0:03:52.25,Default,,0000,0000,0000,,What are the key features that let a \Ncomputer navigate the world of moving dots?
Dialogue: 0,0:03:52.50,0:03:57.35,Default,,0000,0000,0000,,So figuring out all these relationships\Nwith relative, absolute, location,
Dialogue: 0,0:03:57.35,0:03:59.26,Default,,0000,0000,0000,,distance, timing, velocities.
Dialogue: 0,0:03:59.44,0:04:04.30,Default,,0000,0000,0000,,That's really the key to the science\Nof moving dots, or as we like to call it
Dialogue: 0,0:04:04.49,0:04:07.74,Default,,0000,0000,0000,,spatiotemporal pattern recognition,\Nin academic vernacular.
Dialogue: 0,0:04:08.11,0:04:09.83,Default,,0000,0000,0000,,Because the first thing is,\Nyou have to make it sound hard
Dialogue: 0,0:04:10.13,0:04:12.12,Default,,0000,0000,0000,,and... because it is.
Dialogue: 0,0:04:12.68,0:04:15.27,Default,,0000,0000,0000,,The key thing is for NBA coaches,\Nit's not that they want to know
Dialogue: 0,0:04:15.27,0:04:17.00,Default,,0000,0000,0000,,whether a pick-and-roll happened or not.
Dialogue: 0,0:04:17.33,0:04:19.48,Default,,0000,0000,0000,,It's that they want to know how it happened.
Dialogue: 0,0:04:19.52,0:04:22.02,Default,,0000,0000,0000,,And why is it so important to them?\NSo here's a little insight.
Dialogue: 0,0:04:22.02,0:04:25.41,Default,,0000,0000,0000,,It turns out in modern basketball, this \Npick-and-roll is perhaps
Dialogue: 0,0:04:25.41,0:04:26.86,Default,,0000,0000,0000,,the most important play.
Dialogue: 0,0:04:27.06,0:04:29.17,Default,,0000,0000,0000,,And knowing how to run it,\Nand knowing how to defend it,
Dialogue: 0,0:04:29.17,0:04:32.35,Default,,0000,0000,0000,,is basically a key to winning \Nand losing most games.
Dialogue: 0,0:04:32.40,0:04:36.16,Default,,0000,0000,0000,,So it turns out that this dance has \Na great many variations
Dialogue: 0,0:04:36.23,0:04:39.85,Default,,0000,0000,0000,,and identifying the variations are really\Nthe things that matter,
Dialogue: 0,0:04:39.90,0:04:42.08,Default,,0000,0000,0000,,and that's why we need it to be\Nreally, really good.
Dialogue: 0,0:04:42.17,0:04:45.10,Default,,0000,0000,0000,,So, here's an example.\NThere's two offensive players
Dialogue: 0,0:04:45.26,0:04:47.71,Default,,0000,0000,0000,,two defensive players, getting ready \Nto do the pick-and-roll dance.
Dialogue: 0,0:04:47.71,0:04:51.69,Default,,0000,0000,0000,,So the guy with ball can either take,\Nor he can reject.
Dialogue: 0,0:04:52.29,0:04:58.00,Default,,0000,0000,0000,,His teammate can either roll or pop. The \Nguy guarding the ball can go over or under.
Dialogue: 0,0:04:58.12,0:05:02.35,Default,,0000,0000,0000,,His teammate can either show \Nor play up to touch, or play soft
Dialogue: 0,0:05:02.63,0:05:04.92,Default,,0000,0000,0000,,and together they can either\Nswitch or blitz
Dialogue: 0,0:05:05.27,0:05:09.15,Default,,0000,0000,0000,,and I didn't know most of the things\Nwhen I started and it would be\N
Dialogue: 0,0:05:09.17,0:05:11.89,Default,,0000,0000,0000,,lovely if everybody moved according to \Nthose arrows.
Dialogue: 0,0:05:11.98,0:05:15.88,Default,,0000,0000,0000,,It would make our lives a lot easier,\Nbut it turns out movement is very messy.
Dialogue: 0,0:05:16.05,0:05:21.56,Default,,0000,0000,0000,,People wiggle a lot and getting these\Nvariations identified with very, very
Dialogue: 0,0:05:21.56,0:05:25.67,Default,,0000,0000,0000,,high accuracy, both in precision and recall\Nis tough because that's what it takes
Dialogue: 0,0:05:25.67,0:05:27.75,Default,,0000,0000,0000,,to get a professional coach \Nto believe in you.
Dialogue: 0,0:05:28.05,0:05:31.20,Default,,0000,0000,0000,,And despite all the difficulties with\Nthe right spatiotemoporal features
Dialogue: 0,0:05:31.20,0:05:32.81,Default,,0000,0000,0000,,we have been able to do that.
Dialogue: 0,0:05:32.81,0:05:37.24,Default,,0000,0000,0000,,Coaches trust our ability of our machine \Nto identify these variations.
Dialogue: 0,0:05:37.48,0:05:42.18,Default,,0000,0000,0000,,We're at the point where almost every \Nsingle contender for an NBA championship
Dialogue: 0,0:05:42.18,0:05:46.95,Default,,0000,0000,0000,,this year is using our software, which is \Nbuilt on a machine that understands
Dialogue: 0,0:05:47.11,0:05:49.37,Default,,0000,0000,0000,,the moving dots of basketball.
Dialogue: 0,0:05:50.04,0:05:55.12,Default,,0000,0000,0000,,So, not only that, we have given advice \Nthat has changed strategies,
Dialogue: 0,0:05:55.29,0:05:58.28,Default,,0000,0000,0000,,that have helped teams win \Nvery important games
Dialogue: 0,0:05:58.54,0:06:01.23,Default,,0000,0000,0000,,and it's very exciting because you have\Ncoaches who've been in the league for
Dialogue: 0,0:06:01.53,0:06:05.25,Default,,0000,0000,0000,,30 years, that are willing to take advice \Nfrom a machine.
Dialogue: 0,0:06:05.57,0:06:08.20,Default,,0000,0000,0000,,And it's very exciting.\NIt's much more than the pick-and-roll.
Dialogue: 0,0:06:08.20,0:06:11.03,Default,,0000,0000,0000,,Our computer have started with simple \Nthings and learnt more and more
Dialogue: 0,0:06:11.03,0:06:13.77,Default,,0000,0000,0000,,complex things and now it knows \Nso many things.
Dialogue: 0,0:06:13.77,0:06:19.12,Default,,0000,0000,0000,,Frankly, I don't understand much of what\Nit does and while it's not special
Dialogue: 0,0:06:19.12,0:06:22.43,Default,,0000,0000,0000,,to be smarter than me,\Nwe were wondering,
Dialogue: 0,0:06:22.68,0:06:27.03,Default,,0000,0000,0000,,can a machine know more than a coach?\NCould it know more than person could know?
Dialogue: 0,0:06:27.24,0:06:28.65,Default,,0000,0000,0000,,Turns out the answer is yes.
Dialogue: 0,0:06:28.86,0:06:32.37,Default,,0000,0000,0000,,Coaches want players to take good shots.\NSo if I'm standing near the basket
Dialogue: 0,0:06:32.37,0:06:34.53,Default,,0000,0000,0000,,and there's nobody near me,\Nit's a good shot.
Dialogue: 0,0:06:34.76,0:06:38.82,Default,,0000,0000,0000,,If I'm standing far away and surrounded\Nby defenders, that's generally a bad shot.
Dialogue: 0,0:06:39.10,0:06:43.98,Default,,0000,0000,0000,,But we never knew how good "good" was,\Nor how bad "bad" was quantitatively.
Dialogue: 0,0:06:44.21,0:06:45.35,Default,,0000,0000,0000,,Until now.
Dialogue: 0,0:06:45.97,0:06:48.62,Default,,0000,0000,0000,,So what we can do, again, \Nusing spatiotemporal features.
Dialogue: 0,0:06:48.85,0:06:53.28,Default,,0000,0000,0000,,We looked at every shot. We can see where \Nis the shot? What's the angle to the basket?
Dialogue: 0,0:06:53.28,0:06:55.74,Default,,0000,0000,0000,,Where are the defenders standing?\NWhat are their distances?
Dialogue: 0,0:06:55.74,0:06:56.89,Default,,0000,0000,0000,,What are there angles?
Dialogue: 0,0:06:57.10,0:07:01.72,Default,,0000,0000,0000,,For multiple defenders, we can look at how\Nplayers move and predict the shot type.
Dialogue: 0,0:07:01.88,0:07:05.98,Default,,0000,0000,0000,,We can look at all their velocities \Nand we can build a model that predicts
Dialogue: 0,0:07:05.98,0:07:10.03,Default,,0000,0000,0000,,what is the likelihood that this shot \Nwould go in under these circumstances?
Dialogue: 0,0:07:10.35,0:07:14.93,Default,,0000,0000,0000,,So why is this important?\NWe can take something that was shooting,
Dialogue: 0,0:07:14.93,0:07:17.83,Default,,0000,0000,0000,,which was one thing before, and turn it \Ninto two things.
Dialogue: 0,0:07:18.04,0:07:20.55,Default,,0000,0000,0000,,The quality of the shot\Nand the quality of the shooter.
Dialogue: 0,0:07:21.92,0:07:25.17,Default,,0000,0000,0000,,So here's a bubble chart because\Nwhat's TED without a bubble chart?
Dialogue: 0,0:07:25.49,0:07:28.72,Default,,0000,0000,0000,,Those are NBA players. \NThe size is the size of the player
Dialogue: 0,0:07:28.93,0:07:30.18,Default,,0000,0000,0000,,and the color is the position.
Dialogue: 0,0:07:30.28,0:07:34.25,Default,,0000,0000,0000,,On the x-axis, we've the shot probability.\NPeople on the left take difficult shots, \N
Dialogue: 0,0:07:34.55,0:07:36.22,Default,,0000,0000,0000,,on the right, they take easy shots.
Dialogue: 0,0:07:36.99,0:07:40.55,Default,,0000,0000,0000,,On the right is their shooting ability.\NPeople who are good at the top,
Dialogue: 0,0:07:40.55,0:07:41.66,Default,,0000,0000,0000,,bad at the bottom.
Dialogue: 0,0:07:41.66,0:07:45.69,Default,,0000,0000,0000,,So for example, if there was a player who\Ngenerally made 47% of their shots
Dialogue: 0,0:07:45.90,0:07:47.46,Default,,0000,0000,0000,,that's all you knew before.
Dialogue: 0,0:07:47.69,0:07:52.19,Default,,0000,0000,0000,,But today, I can tell you that player \Ntakes shots that an average NBA player
Dialogue: 0,0:07:52.45,0:07:56.10,Default,,0000,0000,0000,,would make 49% of the time\Nand they are 2% worse.
Dialogue: 0,0:07:56.44,0:08:01.39,Default,,0000,0000,0000,,And the reason that's important,\Nis that there are lots of 47s out there.
Dialogue: 0,0:08:01.71,0:08:06.17,Default,,0000,0000,0000,,And so it's really important to know\Nif the 47 that you're considering
Dialogue: 0,0:08:06.17,0:08:10.82,Default,,0000,0000,0000,,giving 100 million dollars to,\Nis a good shooter who takes bad shots
Dialogue: 0,0:08:11.19,0:08:13.95,Default,,0000,0000,0000,,or bad shooter who takes good shots.
Dialogue: 0,0:08:15.34,0:08:18.71,Default,,0000,0000,0000,,Machine understanding doesn't \Nchange how we look at players,
Dialogue: 0,0:08:18.90,0:08:21.89,Default,,0000,0000,0000,,it changes how we look at the game.\NSo there was this very exciting game
Dialogue: 0,0:08:22.10,0:08:23.89,Default,,0000,0000,0000,,a couple of years ago, in the NBA finals.
Dialogue: 0,0:08:24.14,0:08:27.51,Default,,0000,0000,0000,,Miami was down by three, \Nthere was 20 seconds left.
Dialogue: 0,0:08:27.67,0:08:30.16,Default,,0000,0000,0000,,They were about to lose the championship.\NA gentleman named Lebron James
Dialogue: 0,0:08:30.44,0:08:33.01,Default,,0000,0000,0000,,came up and he took a three to tie.
Dialogue: 0,0:08:33.25,0:08:35.52,Default,,0000,0000,0000,,He missed.\NHis teammate Chris Bosh got a rebound,
Dialogue: 0,0:08:35.78,0:08:37.63,Default,,0000,0000,0000,,passed it to another teammate\Nnamed Ray Allen.
Dialogue: 0,0:08:37.87,0:08:39.70,Default,,0000,0000,0000,,He sank a three.\NIt went into overtime.
Dialogue: 0,0:08:40.00,0:08:41.79,Default,,0000,0000,0000,,They won the game.\NThey won the championship.
Dialogue: 0,0:08:42.05,0:08:44.76,Default,,0000,0000,0000,,It was one of the most exciting \Ngames in basketball.
Dialogue: 0,0:08:45.62,0:08:48.89,Default,,0000,0000,0000,,And our ability to know the shot \Nprobability for every player
Dialogue: 0,0:08:48.89,0:08:51.73,Default,,0000,0000,0000,,at every second, and the likelihood \Nof them getting a rebound at every second
Dialogue: 0,0:08:52.08,0:08:56.81,Default,,0000,0000,0000,,can illuminate this moment in a way\Nthat we never could before.
Dialogue: 0,0:08:57.14,0:09:03.16,Default,,0000,0000,0000,,Now unfortunately, I can't show you that \Nvideo, but for you we recreated
Dialogue: 0,0:09:03.16,0:09:07.04,Default,,0000,0000,0000,,that moment at our weekly basketball game\Nabout 3 weeks ago.
Dialogue: 0,0:09:07.34,0:09:08.52,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:09:10.20,0:09:12.98,Default,,0000,0000,0000,,And we recreated the tracking \Nthat led to the insights.
Dialogue: 0,0:09:13.63,0:09:17.26,Default,,0000,0000,0000,,So, here is us.\NThis is Chinatown in Los Angeles,
Dialogue: 0,0:09:17.60,0:09:21.27,Default,,0000,0000,0000,,a park we play every week at and that's us\Nrecreating the Ray Allen moment
Dialogue: 0,0:09:21.48,0:09:23.71,Default,,0000,0000,0000,,and all the tracking that's associated.
Dialogue: 0,0:09:24.62,0:09:28.72,Default,,0000,0000,0000,,So, here's the shot.\NI'm going to show you that moment
Dialogue: 0,0:09:28.96,0:09:31.63,Default,,0000,0000,0000,,and all the insights of that moment.
Dialogue: 0,0:09:31.86,0:09:34.39,Default,,0000,0000,0000,,The only difference is, instead of the \Nprofessional players -- it's us
Dialogue: 0,0:09:34.72,0:09:38.08,Default,,0000,0000,0000,,and instead of a professional \Nannouncer, it's me.
Dialogue: 0,0:09:38.22,0:09:39.80,Default,,0000,0000,0000,,So, bare with me.
Dialogue: 0,0:09:41.47,0:09:46.02,Default,,0000,0000,0000,,Miami. Down three. 20 seconds left.
Dialogue: 0,0:09:47.62,0:09:52.18,Default,,0000,0000,0000,,Jeff brings up the ball... Josh catches, \Nputs up a three!
Dialogue: 0,0:10:00.65,0:10:05.81,Default,,0000,0000,0000,,Won't go! Rebound, Noel(??). Back to Daria.
Dialogue: 0,0:10:10.30,0:10:14.74,Default,,0000,0000,0000,,Her 3-pointer -- bang!\NTied game with five seconds left.
Dialogue: 0,0:10:14.98,0:10:16.53,Default,,0000,0000,0000,,The crowd goes wild.
Dialogue: 0,0:10:16.86,0:10:18.39,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:10:18.62,0:10:19.81,Default,,0000,0000,0000,,That's roughly how it happened.
Dialogue: 0,0:10:20.27,0:10:20.95,Default,,0000,0000,0000,,(Applause)
Dialogue: 0,0:10:20.95,0:10:21.97,Default,,0000,0000,0000,,Roughly.
Dialogue: 0,0:10:24.12,0:10:29.35,Default,,0000,0000,0000,,I'm not going to -- that moment had about\Na 9% chance of happening in the NBA
Dialogue: 0,0:10:29.63,0:10:31.67,Default,,0000,0000,0000,,and we know that and a great many other things.
Dialogue: 0,0:10:32.07,0:10:35.74,Default,,0000,0000,0000,,I'm not going to tell you how many times\Nit took us to make that happen.
Dialogue: 0,0:10:35.94,0:10:37.02,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:10:37.26,0:10:39.83,Default,,0000,0000,0000,,Okay, I will!\NIt was four, it was four.
Dialogue: 0,0:10:40.19,0:10:41.84,Default,,0000,0000,0000,,Way to go Doug(??).
Dialogue: 0,0:10:42.35,0:10:48.08,Default,,0000,0000,0000,,But the important thing about that video \Nand the insights we have for every second
Dialogue: 0,0:10:48.08,0:10:50.50,Default,,0000,0000,0000,,of every NBA game, it's not that.
Dialogue: 0,0:10:51.09,0:10:54.50,Default,,0000,0000,0000,,It's the fact you don't have to be a \Nprofessional team to track movement.
Dialogue: 0,0:10:55.22,0:10:58.21,Default,,0000,0000,0000,,You do not have to be a professional player\Nto get insights about movement.
Dialogue: 0,0:10:58.44,0:11:02.11,Default,,0000,0000,0000,,In fact, it doesn't even have to be about \Nsports because we're moving everywhere.
Dialogue: 0,0:11:03.85,0:11:06.22,Default,,0000,0000,0000,,We're moving in our homes.
Dialogue: 0,0:11:09.64,0:11:11.07,Default,,0000,0000,0000,,In our offices.
Dialogue: 0,0:11:12.24,0:11:21.92,Default,,0000,0000,0000,,As we shop and we travel, throughout \Nour cities and around our world.
Dialogue: 0,0:11:23.33,0:11:27.11,Default,,0000,0000,0000,,What will we know? What will we learn?\NPerhaps, instead of identifying
Dialogue: 0,0:11:27.11,0:11:31.25,Default,,0000,0000,0000,,pick-and-rolls, a machine can identify \Nthe moment and let me know when
Dialogue: 0,0:11:31.41,0:11:33.04,Default,,0000,0000,0000,,my daughter takes her first steps.
Dialogue: 0,0:11:33.04,0:11:35.57,Default,,0000,0000,0000,,Which could literally be happening \Nany second now.
Dialogue: 0,0:11:36.36,0:11:39.59,Default,,0000,0000,0000,,Perhaps we can learn to better use \Nour buildings, better plan our cities.
Dialogue: 0,0:11:40.19,0:11:44.65,Default,,0000,0000,0000,,I believe that with the development\Nof the science of moving dots,
Dialogue: 0,0:11:44.68,0:11:48.20,Default,,0000,0000,0000,,we will move better, we will move smarter,\Nwe will move forward.
Dialogue: 0,0:11:48.78,0:11:49.69,Default,,0000,0000,0000,,Thank you very much.
Dialogue: 0,0:11:50.11,0:11:51.48,Default,,0000,0000,0000,,(Applause)