WEBVTT 00:00:10.847 --> 00:00:13.799 All right, Portland, wow. Thank you so much for having me. 00:00:14.038 --> 00:00:15.498 I love visiting this city.s 00:00:15.522 --> 00:00:18.625 I mean, where else in the world can I have breakfast like this. 00:00:18.649 --> 00:00:20.315 (Laughter) 00:00:20.339 --> 00:00:23.632 And clearly, this is a very creative place. 00:00:23.815 --> 00:00:25.111 (Laughter) 00:00:25.135 --> 00:00:27.424 How many of you are creatives, 00:00:27.448 --> 00:00:31.072 designers, engineers, entrepreneurs, artists, 00:00:31.096 --> 00:00:33.483 or maybe you just have a really big imagination? 00:00:33.507 --> 00:00:35.355 Show of hands? (Cheers) NOTE Paragraph 00:00:35.379 --> 00:00:36.560 That's most of you. 00:00:37.734 --> 00:00:40.028 I have some news for us creatives. 00:00:41.114 --> 00:00:43.687 Over the course of the next 20 years, 00:00:45.871 --> 00:00:48.844 more will change around the way we do our work 00:00:49.782 --> 00:00:51.939 than has happened in the last 2,000. 00:00:52.768 --> 00:00:57.396 In fact, I think we're at the dawn of a new age in human history. NOTE Paragraph 00:00:58.045 --> 00:01:02.624 Now, there have been four major historical eras defined by the way we work. 00:01:03.804 --> 00:01:07.079 The Hunter-Gatherer Age lasted several million years. 00:01:07.563 --> 00:01:11.139 And then the Agricultural Age lasted several thousand years. 00:01:11.405 --> 00:01:14.895 The Industrial Age lasted a couple of centuries. 00:01:14.919 --> 00:01:18.728 And now the Information Age has lasted just a few decades. 00:01:19.230 --> 00:01:24.450 And now today, we're on the cusp of our next great era as a species. NOTE Paragraph 00:01:25.506 --> 00:01:28.186 Welcome to the Augmented Age. 00:01:28.210 --> 00:01:31.903 In this new era, your natural human capabilities are going to be augmented 00:01:31.927 --> 00:01:34.995 by computational systems that help you think, 00:01:35.119 --> 00:01:37.305 robotic systems that help you make, 00:01:37.329 --> 00:01:38.977 and a digital nervous system 00:01:39.001 --> 00:01:42.691 that connects you to the world far beyond your natural senses. 00:01:44.947 --> 00:01:46.889 Let's start with cognitive augmentation. 00:01:46.913 --> 00:01:49.113 How many of you are augmented cyborgs? NOTE Paragraph 00:01:49.270 --> 00:01:51.545 (Laughter) 00:01:51.569 --> 00:01:53.084 There's three or four of you, 00:01:53.108 --> 00:01:54.791 that's just because it's Portland. 00:01:54.815 --> 00:01:58.025 (Laughter) 00:01:58.049 --> 00:01:59.231 Yeah, keep it weird. 00:01:59.255 --> 00:02:01.543 (Laughter) NOTE Paragraph 00:02:01.567 --> 00:02:04.388 I would actually argue that we're already augmented. 00:02:05.048 --> 00:02:06.552 Imagine you're at a party, 00:02:06.576 --> 00:02:10.096 and somebody asks you a question that you don't know the answer to. 00:02:10.120 --> 00:02:13.880 If you have one of these, in a few seconds, you can know the answer. 00:02:14.629 --> 00:02:16.928 But this is just a primitive beginning. 00:02:17.623 --> 00:02:20.954 Even Siri is just a passive tool. 00:02:21.420 --> 00:02:24.801 In fact, for the last three-and-a-half million years, 00:02:24.825 --> 00:02:28.348 the tools that we've had have been completely passive. 00:02:29.563 --> 00:02:33.218 They do exactly what we tell them and nothing more. 00:02:33.242 --> 00:02:36.343 Our very first tool only cut where we struck it. 00:02:37.382 --> 00:02:40.422 The chisel only carves where the artist points it. 00:02:40.818 --> 00:02:46.544 And even our most advanced tools do nothing without our explicit direction. 00:02:47.598 --> 00:02:50.779 In fact, to date, and this is something that frustrates me, 00:02:50.803 --> 00:02:52.251 we've always been limited 00:02:52.275 --> 00:02:55.776 by this need to manually push our wills into our tools -- 00:02:55.800 --> 00:02:58.097 like, manual, literally using our hands, 00:02:58.121 --> 00:02:59.549 even with computers. 00:03:00.662 --> 00:03:03.125 But I'm more like Scotty in "Star Trek." NOTE Paragraph 00:03:03.149 --> 00:03:04.999 (Laughter) NOTE Paragraph 00:03:05.023 --> 00:03:07.169 I want to have a conversation with a computer. 00:03:07.193 --> 00:03:10.163 I want to say, "Computer, let's design a car," 00:03:10.187 --> 00:03:11.726 and the computer shows me a car. 00:03:11.750 --> 00:03:14.358 And I say, "No, more fast-looking, and less German," 00:03:14.382 --> 00:03:16.545 and bang, the computer shows me an option. NOTE Paragraph 00:03:16.569 --> 00:03:18.434 (Laughter) NOTE Paragraph 00:03:18.798 --> 00:03:21.104 That conversation might be a little ways off, 00:03:21.128 --> 00:03:23.793 probably less than many of us think, 00:03:23.817 --> 00:03:25.580 but right now, 00:03:25.604 --> 00:03:26.755 we're working on it. 00:03:26.779 --> 00:03:30.812 Tools are making this leap from being passive to being generative. 00:03:31.421 --> 00:03:36.069 Generative design tools use a computer and algorithms 00:03:36.093 --> 00:03:38.901 to synthesize geometry 00:03:38.925 --> 00:03:41.679 to come up with new designs all by themselves. 00:03:42.126 --> 00:03:44.874 All it needs are your goals and your constraints. NOTE Paragraph 00:03:44.898 --> 00:03:46.306 I'll give you an example. 00:03:46.330 --> 00:03:49.118 In the case of this aerial drone chassis, 00:03:49.142 --> 00:03:51.768 all you would need to do is tell it something like, 00:03:51.792 --> 00:03:53.065 it has four propellers, 00:03:53.089 --> 00:03:55.220 you want it to be as lightweight as possible, 00:03:55.244 --> 00:03:57.514 and you need it to be aerodynamically efficient. 00:03:57.538 --> 00:04:02.452 Then what the computer does is it explores the entire solution space: 00:04:02.476 --> 00:04:06.403 every single possibility that solves and meets your criteria -- 00:04:06.427 --> 00:04:07.869 millions of them. 00:04:07.893 --> 00:04:09.868 It takes big computers to do this. 00:04:09.892 --> 00:04:11.847 But it comes back to us with designs 00:04:11.871 --> 00:04:15.014 that we, by ourselves, never could've imagined. 00:04:15.456 --> 00:04:18.368 And the computer's coming up with this stuff all by itself -- 00:04:18.392 --> 00:04:20.070 no one ever drew anything, 00:04:20.094 --> 00:04:22.180 and it started completely from scratch. 00:04:23.168 --> 00:04:25.555 And by the way, it's no accident 00:04:25.579 --> 00:04:29.060 that the drone body looks just like the pelvis of a flying squirrel. NOTE Paragraph 00:04:29.231 --> 00:04:32.146 (Laughter) NOTE Paragraph 00:04:32.170 --> 00:04:34.472 It's because the algorithms are designed to work 00:04:34.496 --> 00:04:36.133 the same way evolution does. NOTE Paragraph 00:04:37.045 --> 00:04:39.705 What's exciting is we're starting to see this technology 00:04:39.729 --> 00:04:40.888 out in the real world. 00:04:40.912 --> 00:04:43.364 We've been working with Airbus for a couple of years 00:04:43.388 --> 00:04:45.297 on this concept plane for the future. 00:04:45.321 --> 00:04:47.391 It's a ways out still. 00:04:47.415 --> 00:04:51.195 But just recently we used a generative-design AI 00:04:51.219 --> 00:04:53.026 to come up with this. 00:04:53.939 --> 00:04:59.092 This is a 3D-printed cabin partition that's been designed by a computer. 00:04:59.116 --> 00:05:01.940 It's stronger than the original yet half the weight, 00:05:01.964 --> 00:05:05.110 and it will be flying in the Airbus A320 later this year. 00:05:06.835 --> 00:05:09.024 So computers can now generate; 00:05:09.048 --> 00:05:13.643 they can come up with their own solutions to our well-defined problems. 00:05:14.737 --> 00:05:16.047 But they're not intuitive. 00:05:16.071 --> 00:05:19.157 They still have to start from scratch every single time, 00:05:19.181 --> 00:05:21.871 and that's because they never learn. 00:05:22.768 --> 00:05:24.534 Unlike Maggie. NOTE Paragraph 00:05:24.558 --> 00:05:26.139 (Laughter) NOTE Paragraph 00:05:26.163 --> 00:05:29.460 Maggie's actually smarter than our most advanced design tools. 00:05:29.867 --> 00:05:31.367 What do I mean by that? 00:05:31.391 --> 00:05:32.981 If her owner picks up that leash, 00:05:33.005 --> 00:05:35.073 Maggie knows with a fair degree of certainty 00:05:35.097 --> 00:05:36.501 it's time to go for a walk. 00:05:36.525 --> 00:05:37.710 And how did she learn? 00:05:37.734 --> 00:05:41.058 Well, every time the owner picked up the leash, they went for a walk. 00:05:41.082 --> 00:05:42.960 And Maggie did three things: 00:05:42.984 --> 00:05:44.853 she had to pay attention, 00:05:44.877 --> 00:05:46.959 she had to remember what happened 00:05:46.983 --> 00:05:51.000 and she had to retain and create a pattern in her mind. NOTE Paragraph 00:05:51.889 --> 00:05:53.984 Interestingly, that's exactly what 00:05:54.008 --> 00:05:56.531 computer scientists have been trying to get AIs to do 00:05:56.555 --> 00:05:58.414 for the last 60 or so years. 00:05:59.783 --> 00:06:01.132 Back in 1952, 00:06:01.156 --> 00:06:04.957 they built this computer that could play tic-tac-toe. 00:06:06.181 --> 00:06:07.341 Big deal. 00:06:08.129 --> 00:06:11.129 Then 45 years later, in 1997, 00:06:11.153 --> 00:06:13.625 Deep Blue beats Kasparov at chess. 00:06:15.146 --> 00:06:20.114 2011, Watson beats these two humans at Jeopardy, 00:06:20.138 --> 00:06:23.066 which is much harder for a computer to play than chess is. 00:06:23.090 --> 00:06:26.902 In fact, rather than working from predefined recipes, 00:06:26.926 --> 00:06:30.249 Watson had to use reasoning to overcome his human opponents. 00:06:31.493 --> 00:06:33.932 And then a couple of weeks ago, 00:06:33.956 --> 00:06:38.218 DeepMind's AlphaGo beats the world's best human at Go, 00:06:38.242 --> 00:06:40.454 which is the most difficult game that we have. 00:06:40.478 --> 00:06:43.974 In fact, in Go, there are more possible moves 00:06:43.998 --> 00:06:46.022 than there are atoms in the universe. 00:06:47.910 --> 00:06:49.736 So in order to win, 00:06:49.760 --> 00:06:52.774 what AlphaGo had to do was develop intuition. 00:06:52.798 --> 00:06:56.908 And in fact, at some points, AlphaGo's programmers didn't understand 00:06:56.932 --> 00:06:59.218 why AlphaGo was doing what it was doing. NOTE Paragraph 00:07:01.151 --> 00:07:02.811 And things are moving really fast. 00:07:02.835 --> 00:07:06.062 I mean, consider -- in the space of a human lifetime, 00:07:06.086 --> 00:07:08.319 computers have gone from a child's game 00:07:09.620 --> 00:07:12.668 to what's recognized as the pinnacle of strategic thought. 00:07:13.699 --> 00:07:16.116 What's basically happening 00:07:16.140 --> 00:07:19.450 is computers are going from being like Spock ... 00:07:20.474 --> 00:07:22.423 to being a lot more like Kirk. NOTE Paragraph 00:07:22.447 --> 00:07:26.065 (Laughter) NOTE Paragraph 00:07:26.089 --> 00:07:29.513 Right? From pure logic to intuition. 00:07:31.584 --> 00:07:33.327 Would you cross this bridge? 00:07:34.009 --> 00:07:36.332 Most of you are saying, "Oh, hell no!" NOTE Paragraph 00:07:36.356 --> 00:07:37.664 (Laughter) NOTE Paragraph 00:07:37.688 --> 00:07:40.345 And you arrived at that decision in a split second. 00:07:40.369 --> 00:07:42.797 You just sort of knew that bridge was unsafe. 00:07:42.821 --> 00:07:44.810 And that's exactly the kind of intuition 00:07:44.834 --> 00:07:48.402 that our deep-learning systems are starting to develop right now. 00:07:49.122 --> 00:07:50.829 Very soon, you'll literally be able 00:07:50.853 --> 00:07:53.782 to show something you've made, you've designed, to a computer, 00:07:53.806 --> 00:07:55.439 and it will look at it and say, 00:07:55.463 --> 00:07:58.286 "Sorry, homie, that'll never work. You have to try again." 00:07:59.254 --> 00:08:02.324 Or you could ask it if people are going to like your next song, 00:08:03.173 --> 00:08:05.236 or your next flavor of ice cream. 00:08:06.949 --> 00:08:09.528 Or, much more importantly, 00:08:09.552 --> 00:08:11.916 you could work with a computer to solve a problem 00:08:11.940 --> 00:08:13.577 that we've never faced before. 00:08:13.601 --> 00:08:15.002 For instance, climate change. 00:08:15.026 --> 00:08:17.046 We're not doing a very good job on our own, 00:08:17.070 --> 00:08:19.315 we could certainly use all the help we can get. 00:08:19.339 --> 00:08:20.797 That's what I'm talking about, 00:08:20.821 --> 00:08:23.376 technology amplifying our cognitive abilities 00:08:23.400 --> 00:08:26.952 so we can imagine and design things that were simply out of our reach 00:08:26.976 --> 00:08:29.535 as plain old un-augmented humans. NOTE Paragraph 00:08:31.384 --> 00:08:34.325 So what about making all of this crazy new stuff 00:08:34.349 --> 00:08:36.790 that we're going to invent and design? 00:08:37.352 --> 00:08:41.445 I think the era of human augmentation is as much about the physical world 00:08:41.469 --> 00:08:44.534 as it is about the virtual, intellectual realm. 00:08:45.233 --> 00:08:47.154 How will technology augment us? 00:08:47.565 --> 00:08:50.038 In the physical world, robotic systems. 00:08:51.620 --> 00:08:53.356 OK, there's certainly a fear 00:08:53.380 --> 00:08:55.868 that robots are going to take jobs away from humans, 00:08:55.892 --> 00:08:57.722 and that is true in certain sectors. 00:08:58.174 --> 00:09:01.052 But I'm much more interested in this idea 00:09:01.076 --> 00:09:06.086 that humans and robots working together are going to augment each other, 00:09:06.110 --> 00:09:08.168 and start to inhabit a new space. NOTE Paragraph 00:09:08.192 --> 00:09:10.554 This is our applied research lab in San Francisco, 00:09:10.578 --> 00:09:13.720 where one of our areas of focus is advanced robotics, 00:09:13.744 --> 00:09:16.255 specifically, human-robot collaboration. 00:09:17.034 --> 00:09:19.793 And this is Bishop, one of our robots. 00:09:19.817 --> 00:09:21.606 As an experiment, we set it up 00:09:21.630 --> 00:09:25.090 to help a person working in construction doing repetitive tasks -- 00:09:25.984 --> 00:09:30.178 tasks like cutting out holes for outlets or light switches in drywall. NOTE Paragraph 00:09:30.202 --> 00:09:32.668 (Laughter) NOTE Paragraph 00:09:33.877 --> 00:09:36.988 So, Bishop's human partner can tell what to do in plain English 00:09:37.012 --> 00:09:38.317 and with simple gestures, 00:09:38.341 --> 00:09:39.788 kind of like talking to a dog, 00:09:39.812 --> 00:09:41.955 and then Bishop executes on those instructions 00:09:41.979 --> 00:09:43.871 with perfect precision. 00:09:43.895 --> 00:09:46.884 We're using the human for what the human is good at: 00:09:46.908 --> 00:09:49.241 awareness, perception and decision making. 00:09:49.265 --> 00:09:51.505 And we're using the robot for what it's good at: 00:09:51.529 --> 00:09:53.277 precision and repetitiveness. NOTE Paragraph 00:09:54.252 --> 00:09:56.619 Here's another cool project that Bishop worked on. 00:09:56.643 --> 00:09:59.718 The goal of this project, which we called the HIVE, 00:09:59.742 --> 00:10:03.593 was to prototype the experience of humans, computers and robots 00:10:03.617 --> 00:10:06.837 all working together to solve a highly complex design problem. 00:10:07.793 --> 00:10:09.244 The humans acted as labor. 00:10:09.268 --> 00:10:12.741 They cruised around the construction site, they manipulated the bamboo -- 00:10:12.765 --> 00:10:15.521 which, by the way, because it's a non-isomorphic material, 00:10:15.545 --> 00:10:17.419 is super hard for robots to deal with. 00:10:17.543 --> 00:10:19.865 But then the robots did this fiber winding, 00:10:19.889 --> 00:10:22.340 which was almost impossible for a human to do. 00:10:22.364 --> 00:10:25.985 And then we had an AI that was controlling everything. 00:10:26.109 --> 00:10:29.399 It was telling the humans what to do, telling the robots what to do 00:10:29.423 --> 00:10:32.338 and keeping track of thousands of individual components. 00:10:32.362 --> 00:10:33.542 What's interesting is, 00:10:33.566 --> 00:10:36.707 building this pavilion was simply not possible 00:10:36.731 --> 00:10:41.255 without human, robot and AI augmenting each other. NOTE Paragraph 00:10:42.390 --> 00:10:45.710 OK, I'll share one more project. This one's a little bit crazy. 00:10:47.734 --> 00:10:52.202 We're working with Amsterdam-based artist Joris Laarman and his team at MX3D 00:10:53.526 --> 00:10:56.404 to generatively design and robotically print 00:10:56.428 --> 00:10:59.423 the world's first autonomously manufactured bridge. 00:11:01.015 --> 00:11:04.700 So, Joris and an AI are designing this thing right now, as we speak, 00:11:04.724 --> 00:11:05.896 in Amsterdam. 00:11:06.620 --> 00:11:09.211 And when they're done, we're going to hit "Go," 00:11:09.235 --> 00:11:12.546 and robots will start 3D printing in stainless steel, 00:11:12.570 --> 00:11:15.853 and then they're going to keep printing, without human intervention, 00:11:15.877 --> 00:11:17.435 until the bridge is finished. NOTE Paragraph 00:11:18.769 --> 00:11:21.697 So, as computers are going to augment our ability 00:11:21.721 --> 00:11:23.871 to imagine and design new stuff, 00:11:23.895 --> 00:11:26.790 robotic systems are going to help us build and make things 00:11:26.814 --> 00:11:28.898 that we've never been able to make before. 00:11:30.017 --> 00:11:34.177 But what about our ability to sense and control these things? 00:11:34.201 --> 00:11:38.232 What about a nervous system for the things that we make? NOTE Paragraph 00:11:40.486 --> 00:11:42.998 Our nervous system, the human nervous system, 00:11:43.022 --> 00:11:45.333 tells us everything that's going on around us. 00:11:46.086 --> 00:11:49.389 But the nervous system of the things we make is rudimentary at best. 00:11:49.413 --> 00:11:51.578 I would say it's rather shitty, at this point. 00:11:51.602 --> 00:11:52.930 (Laughter) 00:11:52.954 --> 00:11:56.817 For instance, a car doesn't tell the city's public works department 00:11:56.841 --> 00:11:59.971 that it just hit a pothole at the corner of Broadway and Morrison. 00:12:01.295 --> 00:12:03.327 A building doesn't tell its designers 00:12:03.351 --> 00:12:06.035 whether or not the people inside like being there, 00:12:07.659 --> 00:12:10.669 and the toy manufacturer doesn't know 00:12:10.693 --> 00:12:12.700 if a toy is actually being played with -- 00:12:12.724 --> 00:12:15.263 how and where and whether or not it's any fun. 00:12:15.880 --> 00:12:19.694 Look, I'm sure that the designers imagined this lifestyle for Barbie 00:12:19.718 --> 00:12:20.942 when they designed her. NOTE Paragraph 00:12:20.966 --> 00:12:22.413 (Laughter) NOTE Paragraph 00:12:23.237 --> 00:12:26.143 But what if it turns out that Barbie's actually really lonely? NOTE Paragraph 00:12:26.167 --> 00:12:30.302 (Laughter) NOTE Paragraph 00:12:30.326 --> 00:12:31.614 If the designers had known 00:12:31.638 --> 00:12:33.745 what was really happening in the real world 00:12:33.769 --> 00:12:36.352 with their designs -- the road, the building, Barbie -- 00:12:36.376 --> 00:12:39.070 they could've used that knowledge to create an experience 00:12:39.094 --> 00:12:40.494 that was better for the user. 00:12:40.518 --> 00:12:42.309 What's missing is a nervous system 00:12:42.333 --> 00:12:46.042 connecting us to all of the things that we design, make and use. 00:12:46.975 --> 00:12:50.530 What if all of you had that kind of information flowing to you 00:12:50.554 --> 00:12:52.737 from the things you create in the real world? 00:12:54.492 --> 00:12:55.943 With all of the stuff we make, 00:12:55.967 --> 00:12:58.402 we spend a tremendous amount of money and energy -- 00:12:58.426 --> 00:13:00.802 in fact, last year, about two trillion dollars -- 00:13:00.826 --> 00:13:03.680 convincing people to buy the things we've made. 00:13:03.704 --> 00:13:07.092 But if you had this connection to the things that you design and create 00:13:07.116 --> 00:13:08.843 after they're out in the real world, 00:13:08.867 --> 00:13:12.481 after they've been sold or launched or whatever, 00:13:12.505 --> 00:13:14.125 we could actually change that, 00:13:14.149 --> 00:13:17.596 and go from making people want our stuff, 00:13:17.620 --> 00:13:21.054 to just making stuff that people want in the first place. NOTE Paragraph 00:13:24.478 --> 00:13:27.265 The good news is, we're working on digital nervous systems 00:13:27.289 --> 00:13:30.090 that connect us to the things we design. 00:13:31.225 --> 00:13:32.852 We're working on one project 00:13:32.876 --> 00:13:37.888 with a couple of guys down in Los Angeles called the Bandito Brothers 00:13:37.912 --> 00:13:39.319 and their team. 00:13:39.343 --> 00:13:42.776 And one of the things these guys do is build insane cars 00:13:42.800 --> 00:13:45.673 that do absolutely insane things. 00:13:47.065 --> 00:13:48.515 These guys are crazy -- NOTE Paragraph 00:13:48.539 --> 00:13:49.575 (Laughter) NOTE Paragraph 00:13:49.599 --> 00:13:51.002 in the best way. 00:13:54.793 --> 00:13:56.556 And what we're doing with them 00:13:56.580 --> 00:13:59.020 is taking a traditional race-car chassis 00:13:59.044 --> 00:14:00.629 and giving it a nervous system. NOTE Paragraph 00:14:00.653 --> 00:14:03.771 So we instrumented it with dozens of sensors, 00:14:03.795 --> 00:14:06.430 put a world-class driver behind the wheel, 00:14:06.454 --> 00:14:09.811 took it out to the desert and drove the hell out of it for a week. 00:14:09.835 --> 00:14:12.326 And the car's nervous system captured everything 00:14:12.350 --> 00:14:13.832 that was happening to the car. 00:14:13.856 --> 00:14:16.477 We captured four billion data points; 00:14:16.501 --> 00:14:18.811 all of the forces that it was subjected to. 00:14:18.835 --> 00:14:20.494 And then we did something crazy. 00:14:21.238 --> 00:14:22.738 We took all of that data, 00:14:22.762 --> 00:14:26.498 and plugged it into a generative-design AI we call "Dreamcatcher." 00:14:27.240 --> 00:14:31.204 So what do get when you give a design tool a nervous system, 00:14:31.228 --> 00:14:34.110 and you ask it to build you the ultimate car chassis? 00:14:34.693 --> 00:14:36.666 You get this. 00:14:38.263 --> 00:14:41.976 This is something that a human could never have designed. 00:14:42.677 --> 00:14:44.565 Except a human did design this, 00:14:44.589 --> 00:14:48.898 but it was a human that was augmented by a generative-design AI, 00:14:48.922 --> 00:14:50.153 a digital nervous system 00:14:50.177 --> 00:14:53.182 and robots that can actually fabricate something like this. NOTE Paragraph 00:14:54.150 --> 00:14:57.745 So if this is the future, the Augmented Age, 00:14:57.769 --> 00:15:02.030 and we're going to be augmented cognitively, physically and perceptually, 00:15:02.054 --> 00:15:03.462 what will that look like? 00:15:04.046 --> 00:15:06.712 What is this wonderland going to be like? NOTE Paragraph 00:15:08.291 --> 00:15:10.900 I think we're going to see a world 00:15:10.924 --> 00:15:13.992 where we're moving from things that are fabricated 00:15:15.101 --> 00:15:16.546 to things that are farmed. 00:15:18.429 --> 00:15:22.374 Where we're moving from things that are constructed 00:15:22.398 --> 00:15:24.102 to that which is grown. 00:15:26.504 --> 00:15:28.692 We're going to move from being isolated 00:15:29.660 --> 00:15:31.270 to being connected. 00:15:33.104 --> 00:15:36.015 And we'll move away from extraction 00:15:36.039 --> 00:15:37.912 to embrace aggregation. 00:15:40.437 --> 00:15:44.204 I also think we'll shift from craving obedience from our things 00:15:45.128 --> 00:15:46.769 to valuing autonomy. NOTE Paragraph 00:15:49.480 --> 00:15:51.385 Thanks to our augmented capabilities, 00:15:51.409 --> 00:15:53.786 our world is going to change dramatically. 00:15:54.702 --> 00:15:58.305 I think a good analogy is the incredible microcosm of a coral reef. 00:15:58.836 --> 00:16:02.082 We're going to have a world with more variety, more connectedness, 00:16:02.106 --> 00:16:04.393 more dynamism, more complexity, 00:16:04.417 --> 00:16:06.735 more adaptability and, of course, 00:16:06.759 --> 00:16:07.976 more beauty. 00:16:08.741 --> 00:16:10.305 The shape of things to come 00:16:10.329 --> 00:16:12.619 will be unlike anything we've ever seen before. 00:16:12.643 --> 00:16:13.802 Why? 00:16:13.826 --> 00:16:17.581 Because what will be shaping those things is this new partnership 00:16:17.605 --> 00:16:21.275 between technology, nature and humanity. 00:16:22.789 --> 00:16:26.593 That, to me, is a future well worth looking forward to. NOTE Paragraph 00:16:26.617 --> 00:16:27.888 Thank you all so much. NOTE Paragraph 00:16:27.912 --> 00:16:34.912 (Applause)