1 00:00:00,896 --> 00:00:04,642 We know more about other planets than our own, 2 00:00:05,446 --> 00:00:08,670 and today, I want to show you a new type of robot 3 00:00:08,693 --> 00:00:12,733 designed to help us better understand our own planet. 4 00:00:13,408 --> 00:00:15,020 It belongs to a category 5 00:00:15,044 --> 00:00:19,998 known in the oceanographic community as an unmanned surface vehicle, or USV. 6 00:00:20,665 --> 00:00:22,595 And it uses no fuel. 7 00:00:23,047 --> 00:00:26,981 Instead, it relies on wind power for propulsion. 8 00:00:27,005 --> 00:00:30,760 And yet, it can sail around the globe for months at a time. 9 00:00:31,474 --> 00:00:34,352 So I want to share with you why we built it, 10 00:00:34,376 --> 00:00:35,868 and what it means for you. 11 00:00:37,814 --> 00:00:42,398 A few years ago, I was on a sailboat making its way across the Pacific, 12 00:00:42,422 --> 00:00:44,620 from San Francisco to Hawaii. 13 00:00:45,296 --> 00:00:48,650 I had just spent the past 10 years working nonstop, 14 00:00:48,674 --> 00:00:51,790 developing video games for hundreds of millions of users, 15 00:00:51,814 --> 00:00:55,335 and I wanted to take a step back and look at the big picture 16 00:00:55,359 --> 00:00:57,296 and get some much-needed thinking time. 17 00:00:57,918 --> 00:00:59,463 I was the navigator on board, 18 00:01:00,034 --> 00:01:03,698 and one evening, after a long session analyzing weather data 19 00:01:03,722 --> 00:01:05,351 and plotting our course, 20 00:01:05,375 --> 00:01:08,449 I came up on deck and saw this beautiful sunset. 21 00:01:08,814 --> 00:01:10,247 And a thought occurred to me: 22 00:01:10,747 --> 00:01:14,101 How much do we really know about our oceans? 23 00:01:15,252 --> 00:01:19,131 The Pacific was stretching all around me as far as the eye could see, 24 00:01:19,155 --> 00:01:21,470 and the waves were rocking our boat forcefully, 25 00:01:21,494 --> 00:01:24,700 a sort of constant reminder of its untold power. 26 00:01:25,291 --> 00:01:28,523 How much do we really know about our oceans? 27 00:01:29,235 --> 00:01:30,773 I decided to find out. 28 00:01:32,362 --> 00:01:35,471 What I quickly learned is that we don't know very much. 29 00:01:35,495 --> 00:01:38,621 The first reason is just how vast oceans are, 30 00:01:38,645 --> 00:01:40,986 covering 70 percent of the planet, 31 00:01:41,010 --> 00:01:44,555 and yet we know they drive complex planetary systems 32 00:01:44,579 --> 00:01:45,759 like global weather, 33 00:01:45,783 --> 00:01:48,228 which affect all of us on a daily basis, 34 00:01:48,252 --> 00:01:49,870 sometimes dramatically. 35 00:01:50,562 --> 00:01:53,746 And yet, those activities are mostly invisible to us. 36 00:01:54,944 --> 00:01:58,709 Ocean data is scarce by any standard. 37 00:01:59,268 --> 00:02:04,372 Back on land, I had grown used to accessing lots of sensors -- 38 00:02:04,396 --> 00:02:06,024 billions of them, actually. 39 00:02:06,576 --> 00:02:13,030 But at sea, in situ data is scarce and expensive. 40 00:02:13,535 --> 00:02:17,530 Why? Because it relies on a small number of ships and buoys. 41 00:02:17,919 --> 00:02:20,790 How small a number was actually a great surprise. 42 00:02:21,329 --> 00:02:24,450 Our National Oceanic and Atmospheric Administration, 43 00:02:24,474 --> 00:02:26,097 better known as NOAA, 44 00:02:26,121 --> 00:02:28,450 only has 16 ships, 45 00:02:28,969 --> 00:02:33,223 and there are less than 200 buoys offshore globally. 46 00:02:33,770 --> 00:02:35,441 It is easy to understand why: 47 00:02:35,465 --> 00:02:37,605 the oceans are an unforgiving place, 48 00:02:37,629 --> 00:02:40,963 and to collect in situ data, you need a big ship, 49 00:02:40,987 --> 00:02:43,321 capable of carrying a vast amount of fuel 50 00:02:43,345 --> 00:02:45,163 and large crews, 51 00:02:45,187 --> 00:02:48,366 costing hundreds of millions of dollars each, 52 00:02:48,390 --> 00:02:53,712 or, big buoys tethered to the ocean floor with a four-mile-long cable 53 00:02:54,513 --> 00:02:57,508 and weighted down by a set of train wheels, 54 00:02:57,532 --> 00:03:02,141 which is both dangerous to deploy and expensive to maintain. 55 00:03:02,628 --> 00:03:04,554 What about satellites, you might ask? 56 00:03:05,028 --> 00:03:06,931 Well, satellites are fantastic, 57 00:03:06,955 --> 00:03:09,458 and they have taught us so much about the big picture 58 00:03:09,482 --> 00:03:11,503 over the past few decades. 59 00:03:11,527 --> 00:03:13,903 However, the problem with satellites 60 00:03:13,927 --> 00:03:18,207 is they can only see through one micron of the surface of the ocean. 61 00:03:19,124 --> 00:03:22,697 They have relatively poor spatial and temporal resolution, 62 00:03:23,450 --> 00:03:27,724 and their signal needs to be corrected for cloud cover and land effects 63 00:03:27,748 --> 00:03:28,956 and other factors. 64 00:03:30,094 --> 00:03:33,365 So what is going on in the oceans? 65 00:03:33,929 --> 00:03:35,561 And what are we trying to measure? 66 00:03:36,283 --> 00:03:38,869 And how could a robot be of any use? 67 00:03:39,361 --> 00:03:43,259 Let's zoom in on a small cube in the ocean. 68 00:03:43,283 --> 00:03:46,591 One of the key things we want to understand is the surface, 69 00:03:46,615 --> 00:03:48,654 because the surface, if you think about it, 70 00:03:48,678 --> 00:03:51,977 is the nexus of all air-sea interaction. 71 00:03:52,001 --> 00:03:56,995 It is the interface through which all energy and gases must flow. 72 00:03:57,552 --> 00:03:59,652 Our sun radiates energy, 73 00:03:59,676 --> 00:04:02,572 which is absorbed by oceans as heat 74 00:04:02,596 --> 00:04:05,452 and then partially released into the atmosphere. 75 00:04:05,476 --> 00:04:09,644 Gases in our atmosphere like CO2 get dissolved into our oceans. 76 00:04:10,099 --> 00:04:13,728 Actually, about 30 percent of all global CO2 gets absorbed. 77 00:04:14,603 --> 00:04:18,211 Plankton and microorganisms release oxygen into the atmosphere, 78 00:04:18,235 --> 00:04:22,406 so much so that every other breath you take comes from the ocean. 79 00:04:22,430 --> 00:04:25,462 Some of that heat generates evaporation, which creates clouds 80 00:04:25,486 --> 00:04:27,780 and then eventually leads to precipitation. 81 00:04:27,804 --> 00:04:30,057 And pressure gradients create surface wind, 82 00:04:30,081 --> 00:04:32,878 which moves the moisture through the atmosphere. 83 00:04:33,731 --> 00:04:37,564 Some of the heat radiates down into the deep ocean 84 00:04:37,588 --> 00:04:39,716 and gets stored in different layers, 85 00:04:39,740 --> 00:04:43,045 the ocean acting as some kind of planetary-scale boiler 86 00:04:43,069 --> 00:04:44,690 to store all that energy, 87 00:04:44,714 --> 00:04:48,451 which later might be released in short-term events like hurricanes 88 00:04:48,475 --> 00:04:50,856 or long-term phenomena like El NiƱo. 89 00:04:51,329 --> 00:04:55,644 These layers can get mixed up by vertical upwelling currents 90 00:04:55,668 --> 00:04:59,114 or horizontal currents, which are key in transporting heat 91 00:04:59,138 --> 00:05:01,112 from the tropics to the poles. 92 00:05:01,623 --> 00:05:04,422 And of course, there is marine life, 93 00:05:04,446 --> 00:05:09,462 occupying the largest ecosystem in volume on the planet, 94 00:05:09,486 --> 00:05:13,188 from microorganisms to fish to marine mammals, 95 00:05:13,212 --> 00:05:16,220 like seals, dolphins and whales. 96 00:05:16,244 --> 00:05:19,821 But all of these are mostly invisible to us. 97 00:05:21,821 --> 00:05:27,197 The challenge in studying those ocean variables at scale 98 00:05:27,221 --> 00:05:29,395 is one of energy, 99 00:05:29,419 --> 00:05:33,542 the energy that it takes to deploy sensors into the deep ocean. 100 00:05:34,597 --> 00:05:36,890 And of course, many solutions have been tried -- 101 00:05:36,914 --> 00:05:38,525 from wave-actuated devices 102 00:05:38,549 --> 00:05:40,090 to surface drifters 103 00:05:40,114 --> 00:05:42,836 to sun-powered electrical drives -- 104 00:05:42,860 --> 00:05:44,972 each with their own compromises. 105 00:05:45,745 --> 00:05:48,915 Our team breakthrough came from an unlikely source -- 106 00:05:48,939 --> 00:05:53,288 the pursuit of the world speed record in a wind-powered land yacht. 107 00:05:53,742 --> 00:05:56,043 It took 10 years of research and development 108 00:05:56,067 --> 00:05:58,596 to come up with a novel wing concept 109 00:05:58,620 --> 00:06:02,428 that only uses three watts of power to control 110 00:06:02,452 --> 00:06:05,540 and yet can propel a vehicle all around the globe 111 00:06:05,564 --> 00:06:07,764 with seemingly unlimited autonomy. 112 00:06:08,262 --> 00:06:11,608 By adapting this wing concept into a marine vehicle, 113 00:06:12,356 --> 00:06:14,662 we had the genesis of an ocean drone. 114 00:06:15,280 --> 00:06:17,614 Now, these are larger than they appear. 115 00:06:17,638 --> 00:06:22,214 They are about 15 feet high, 23 feet long, seven feet deep. 116 00:06:22,238 --> 00:06:24,291 Think of them as surface satellites. 117 00:06:24,315 --> 00:06:27,453 They're laden with an array of science-grade sensors 118 00:06:27,477 --> 00:06:29,285 that measure all key variables, 119 00:06:29,309 --> 00:06:32,307 both oceanographic and atmospheric, 120 00:06:32,331 --> 00:06:36,688 and a live satellite link transmits this high-resolution data 121 00:06:36,712 --> 00:06:38,754 back to shore in real time. 122 00:06:39,515 --> 00:06:42,393 Our team has been hard at work over the past few years, 123 00:06:42,417 --> 00:06:45,925 conducting missions in some of the toughest ocean conditions 124 00:06:45,949 --> 00:06:47,173 on the planet, 125 00:06:47,197 --> 00:06:49,662 from the Arctic to the tropical Pacific. 126 00:06:49,686 --> 00:06:52,410 We have sailed all the way to the polar ice shelf. 127 00:06:52,434 --> 00:06:54,773 We have sailed into Atlantic hurricanes. 128 00:06:55,159 --> 00:06:57,117 We have rounded Cape Horn, 129 00:06:57,141 --> 00:07:00,380 and we have slalomed between the oil rigs of the Gulf of Mexico. 130 00:07:00,404 --> 00:07:02,734 This is one tough robot. 131 00:07:03,629 --> 00:07:06,610 Let me share with you recent work that we did 132 00:07:06,634 --> 00:07:08,508 around the Pribilof Islands. 133 00:07:08,532 --> 00:07:12,451 This is a small group of islands deep in the cold Bering Sea 134 00:07:12,475 --> 00:07:14,637 between the US and Russia. 135 00:07:14,661 --> 00:07:18,111 Now, the Bering Sea is the home of the walleye pollock, 136 00:07:18,135 --> 00:07:20,710 which is a whitefish you might not recognize, 137 00:07:20,734 --> 00:07:25,421 but you might likely have tasted if you enjoy fish sticks or surimi. 138 00:07:25,445 --> 00:07:29,067 Yes, surimi looks like crabmeat, but it's actually pollock. 139 00:07:29,908 --> 00:07:32,918 And the pollock fishery is the largest fishery in the nation, 140 00:07:32,942 --> 00:07:35,487 both in terms of value and volume -- 141 00:07:35,511 --> 00:07:38,980 about 3.1 billion pounds of fish caught every year. 142 00:07:39,695 --> 00:07:42,394 So over the past few years, a fleet of ocean drones 143 00:07:42,418 --> 00:07:44,644 has been hard at work in the Bering Sea 144 00:07:44,668 --> 00:07:49,123 with the goal to help assess the size of the pollock fish stock. 145 00:07:49,147 --> 00:07:52,918 This helps improve the quota system that's used to manage the fishery 146 00:07:52,942 --> 00:07:55,110 and help prevent a collapse of the fish stock 147 00:07:55,134 --> 00:07:58,078 and protects this fragile ecosystem. 148 00:07:58,538 --> 00:08:03,347 Now, the drones survey the fishing ground using acoustics, 149 00:08:03,371 --> 00:08:04,895 i.e., a sonar. 150 00:08:04,919 --> 00:08:08,140 This sends a sound wave downwards, 151 00:08:08,164 --> 00:08:10,674 and then the reflection, the echo from the sound wave 152 00:08:10,698 --> 00:08:12,456 from the seabed or schools of fish, 153 00:08:12,480 --> 00:08:15,390 gives us an idea of what's happening below the surface. 154 00:08:15,849 --> 00:08:19,855 Our ocean drones are actually pretty good at this repetitive task, 155 00:08:19,879 --> 00:08:23,784 so they have been gridding the Bering Sea day in, day out. 156 00:08:23,808 --> 00:08:30,767 Now, the Pribilof Islands are also the home of a large colony of fur seals. 157 00:08:31,175 --> 00:08:35,664 In the 1950s, there were about two million individuals in that colony. 158 00:08:36,374 --> 00:08:40,134 Sadly, these days, the population has rapidly declined. 159 00:08:40,158 --> 00:08:42,501 There's less than 50 percent of that number left, 160 00:08:42,525 --> 00:08:45,033 and the population continues to fall rapidly. 161 00:08:45,904 --> 00:08:48,007 So to understand why, 162 00:08:48,031 --> 00:08:51,520 our science partner at the National Marine Mammal Laboratory 163 00:08:51,544 --> 00:08:54,881 has fitted a GPS tag on some of the mother seals, 164 00:08:54,905 --> 00:08:56,382 glued to their furs. 165 00:08:56,406 --> 00:08:59,407 And this tag measures location and depth 166 00:08:59,431 --> 00:09:01,763 and also has a really cool little camera 167 00:09:01,787 --> 00:09:04,183 that's triggered by sudden acceleration. 168 00:09:04,207 --> 00:09:08,121 Here is a movie taken by an artistically inclined seal, 169 00:09:08,145 --> 00:09:11,886 giving us unprecedented insight into an underwater hunt 170 00:09:11,910 --> 00:09:13,724 deep in the Arctic, 171 00:09:13,748 --> 00:09:16,120 and the shot of this pollock prey 172 00:09:16,144 --> 00:09:18,910 just seconds before it gets devoured. 173 00:09:18,934 --> 00:09:22,720 Now, doing work in the Arctic is very tough, even for a robot. 174 00:09:22,744 --> 00:09:25,334 They had to survive a snowstorm in August 175 00:09:25,358 --> 00:09:28,849 and interferences from bystanders -- 176 00:09:28,873 --> 00:09:32,131 that little spotted seal enjoying a ride. 177 00:09:32,155 --> 00:09:34,716 (Laughter) 178 00:09:35,378 --> 00:09:41,665 Now, the seal tags have recorded over 200,000 dives over the season, 179 00:09:42,395 --> 00:09:44,354 and upon a closer look, 180 00:09:44,378 --> 00:09:48,909 we get to see the individual seal tracks and the repetitive dives. 181 00:09:49,413 --> 00:09:52,270 We are on our way to decode what is really happening 182 00:09:52,294 --> 00:09:53,973 over that foraging ground, 183 00:09:53,997 --> 00:09:55,342 and it's quite beautiful. 184 00:09:56,362 --> 00:09:59,765 Once you superimpose the acoustic data collected by the drones, 185 00:09:59,789 --> 00:10:01,913 a picture starts to emerge. 186 00:10:01,937 --> 00:10:06,309 As the seals leave the islands and swim from left to right, 187 00:10:06,333 --> 00:10:10,746 they are observed to dive at a relatively shallow depth of about 20 meters, 188 00:10:10,770 --> 00:10:14,859 which the drone identifies is populated by small young pollock 189 00:10:14,883 --> 00:10:16,966 with low calorific content. 190 00:10:16,990 --> 00:10:21,114 The seals then swim much greater distance and start to dive deeper 191 00:10:21,138 --> 00:10:25,810 to a place where the drone identifies larger, more adult pollock, 192 00:10:25,834 --> 00:10:28,093 which are more nutritious as fish. 193 00:10:28,117 --> 00:10:32,239 Unfortunately, the calories expended by the mother seals 194 00:10:32,263 --> 00:10:34,386 to swim this extra distance 195 00:10:34,410 --> 00:10:39,326 don't leave them with enough energy to lactate their pups back on the island, 196 00:10:39,350 --> 00:10:41,384 leading to the population decline. 197 00:10:42,408 --> 00:10:47,755 Further, the drones identify that the water temperature around the island 198 00:10:47,779 --> 00:10:49,464 has significantly warmed. 199 00:10:49,488 --> 00:10:53,788 It might be one of the driving forces that's pushing the pollock north, 200 00:10:53,812 --> 00:10:56,267 and to spread in search of colder regions. 201 00:10:56,765 --> 00:10:58,550 So the data analysis is ongoing, 202 00:10:58,574 --> 00:11:01,953 but already we can see that some of the pieces of the puzzle 203 00:11:01,977 --> 00:11:03,350 from the fur seal mystery 204 00:11:03,374 --> 00:11:04,975 are coming into focus. 205 00:11:06,690 --> 00:11:08,779 But if you look back at the big picture, 206 00:11:08,803 --> 00:11:10,304 we are mammals, too. 207 00:11:11,032 --> 00:11:14,856 And actually, the oceans provide up to 20 kilos of fish per human per year. 208 00:11:14,880 --> 00:11:18,009 As we deplete our fish stocks, what can we humans learn 209 00:11:18,033 --> 00:11:19,618 from the fur seal story? 210 00:11:20,719 --> 00:11:23,592 And beyond fish, the oceans affect all of us daily 211 00:11:23,616 --> 00:11:25,490 as they drive global weather systems, 212 00:11:25,514 --> 00:11:28,275 which affect things like global agricultural output 213 00:11:28,299 --> 00:11:31,928 or can lead to devastating destruction of lives and property 214 00:11:31,952 --> 00:11:35,153 through hurricanes, extreme heat and floods. 215 00:11:35,756 --> 00:11:39,536 Our oceans are pretty much unexplored and undersampled, 216 00:11:39,560 --> 00:11:43,997 and today, we still know more about other planets than our own. 217 00:11:44,021 --> 00:11:47,809 But if you divide this vast ocean in six-by-six-degree squares, 218 00:11:47,833 --> 00:11:50,650 each about 400 miles long, 219 00:11:51,539 --> 00:11:53,693 you'd get about 1,000 such squares. 220 00:11:53,717 --> 00:11:56,013 So little by little, working with our partners, 221 00:11:56,037 --> 00:12:00,135 we are deploying one ocean drone in each of those boxes, 222 00:12:00,159 --> 00:12:02,581 the hope being that achieving planetary coverage 223 00:12:02,605 --> 00:12:05,785 will give us better insights into those planetary systems 224 00:12:05,809 --> 00:12:07,120 that affect humanity. 225 00:12:07,603 --> 00:12:11,215 We have been using robots to study distant worlds in our solar system 226 00:12:11,239 --> 00:12:12,429 for a while now. 227 00:12:12,939 --> 00:12:15,570 Now it is time to quantify our own planet, 228 00:12:16,440 --> 00:12:20,279 because we cannot fix what we cannot measure, 229 00:12:20,303 --> 00:12:23,195 and we cannot prepare for what we don't know. 230 00:12:23,761 --> 00:12:24,921 Thank you. 231 00:12:24,945 --> 00:12:28,019 (Applause)