1 00:00:00,000 --> 00:00:04,000 It is a thrill to be here at a conference 2 00:00:04,000 --> 00:00:09,000 that's devoted to "Inspired by Nature" -- you can imagine. 3 00:00:09,000 --> 00:00:13,000 And I'm also thrilled to be in the foreplay section. 4 00:00:13,000 --> 00:00:15,000 Did you notice this section is foreplay? 5 00:00:15,000 --> 00:00:18,000 Because I get to talk about one of my favorite critters, 6 00:00:18,000 --> 00:00:21,000 which is the Western Grebe. You haven't lived 7 00:00:21,000 --> 00:00:25,000 until you've seen these guys do their courtship dance. 8 00:00:25,000 --> 00:00:28,000 I was on Bowman Lake in Glacier National Park, 9 00:00:28,000 --> 00:00:32,000 which is a long, skinny lake with sort of mountains upside down in it, 10 00:00:32,000 --> 00:00:34,000 and my partner and I have a rowing shell. 11 00:00:34,000 --> 00:00:40,000 And so we were rowing, and one of these Western Grebes came along. 12 00:00:40,000 --> 00:00:45,000 And what they do for their courtship dance is, they go together, 13 00:00:45,000 --> 00:00:50,000 the two of them, the two mates, and they begin to run underwater. 14 00:00:50,000 --> 00:00:54,000 They paddle faster, and faster, and faster, until they're going so fast 15 00:00:54,000 --> 00:00:57,000 that they literally lift up out of the water, 16 00:00:57,000 --> 00:01:01,000 and they're standing upright, sort of paddling the top of the water. 17 00:01:01,000 --> 00:01:06,000 And one of these Grebes came along while we were rowing. 18 00:01:06,000 --> 00:01:10,000 And so we're in a skull, and we're moving really, really quickly. 19 00:01:10,000 --> 00:01:17,000 And this Grebe, I think, sort of, mistaked us for a prospect, 20 00:01:17,000 --> 00:01:21,000 and started to run along the water next to us, 21 00:01:21,000 --> 00:01:26,000 in a courtship dance -- for miles. 22 00:01:26,000 --> 00:01:30,000 It would stop, and then start, and then stop, and then start. 23 00:01:30,000 --> 00:01:32,000 Now that is foreplay. 24 00:01:32,000 --> 00:01:35,000 (Laughter) 25 00:01:35,000 --> 00:01:44,000 I came this close to changing species at that moment. 26 00:01:44,000 --> 00:01:48,000 Obviously, life can teach us something 27 00:01:48,000 --> 00:01:52,000 in the entertainment section. Life has a lot to teach us. 28 00:01:52,000 --> 00:01:55,000 But what I'd like to talk about today 29 00:01:55,000 --> 00:01:59,000 is what life might teach us in technology and in design. 30 00:01:59,000 --> 00:02:01,000 What's happened since the book came out -- 31 00:02:01,000 --> 00:02:04,000 the book was mainly about research in biomimicry -- 32 00:02:04,000 --> 00:02:08,000 and what's happened since then is architects, designers, engineers -- 33 00:02:08,000 --> 00:02:11,000 people who make our world -- have started to call and say, 34 00:02:11,000 --> 00:02:15,000 we want a biologist to sit at the design table 35 00:02:15,000 --> 00:02:18,000 to help us, in real time, become inspired. 36 00:02:18,000 --> 00:02:22,000 Or -- and this is the fun part for me -- we want you to take us out 37 00:02:22,000 --> 00:02:24,000 into the natural world. We'll come with a design challenge 38 00:02:24,000 --> 00:02:29,000 and we find the champion adapters in the natural world, who might inspire us. 39 00:02:29,000 --> 00:02:33,000 So this is a picture from a Galapagos trip that we took 40 00:02:33,000 --> 00:02:37,000 with some wastewater treatment engineers; they purify wastewater. 41 00:02:37,000 --> 00:02:40,000 And some of them were very resistant, actually, to being there. 42 00:02:40,000 --> 00:02:45,000 What they said to us at first was, you know, we already do biomimicry. 43 00:02:45,000 --> 00:02:50,000 We use bacteria to clean our water. And we said, 44 00:02:50,000 --> 00:02:54,000 well, that's not exactly being inspired by nature. 45 00:02:54,000 --> 00:02:58,000 That's bioprocessing, you know; that's bio-assisted technology: 46 00:02:58,000 --> 00:03:03,000 using an organism to do your wastewater treatment 47 00:03:03,000 --> 00:03:06,000 is an old, old technology called "domestication." 48 00:03:06,000 --> 00:03:13,000 This is learning something, learning an idea, from an organism and then applying it. 49 00:03:13,000 --> 00:03:16,000 And so they still weren't getting it. 50 00:03:16,000 --> 00:03:18,000 So we went for a walk on the beach and I said, 51 00:03:18,000 --> 00:03:23,000 well, give me one of your big problems. Give me a design challenge, 52 00:03:23,000 --> 00:03:26,000 sustainability speed bump, that's keeping you from being sustainable. 53 00:03:26,000 --> 00:03:32,000 And they said scaling, which is the build-up of minerals inside of pipes. 54 00:03:32,000 --> 00:03:34,000 And they said, you know what happens is, mineral -- 55 00:03:34,000 --> 00:03:36,000 just like at your house -- mineral builds up. 56 00:03:36,000 --> 00:03:40,000 And then the aperture closes, and we have to flush the pipes with toxins, 57 00:03:40,000 --> 00:03:42,000 or we have to dig them up. 58 00:03:42,000 --> 00:03:45,000 So if we had some way to stop this scaling -- 59 00:03:45,000 --> 00:03:50,000 and so I picked up some shells on the beach. And I asked them, 60 00:03:50,000 --> 00:03:52,000 what is scaling? What's inside your pipes? 61 00:03:52,000 --> 00:03:55,000 And they said, calcium carbonate. 62 00:03:55,000 --> 00:03:58,000 And I said, that's what this is; this is calcium carbonate. 63 00:03:58,000 --> 00:04:01,000 And they didn't know that. 64 00:04:01,000 --> 00:04:03,000 They didn't know that what a seashell is, 65 00:04:03,000 --> 00:04:07,000 it's templated by proteins, and then ions from the seawater 66 00:04:07,000 --> 00:04:10,000 crystallize in place to create a shell. 67 00:04:10,000 --> 00:04:14,000 So the same sort of a process, without the proteins, 68 00:04:14,000 --> 00:04:17,000 is happening on the inside of their pipes. They didn't know. 69 00:04:17,000 --> 00:04:23,000 This is not for lack of information; it's a lack of integration. 70 00:04:23,000 --> 00:04:26,000 You know, it's a silo, people in silos. They didn't know 71 00:04:26,000 --> 00:04:29,000 that the same thing was happening. So one of them thought about it 72 00:04:29,000 --> 00:04:33,000 and said, OK, well, if this is just crystallization 73 00:04:33,000 --> 00:04:38,000 that happens automatically out of seawater -- self-assembly -- 74 00:04:38,000 --> 00:04:43,000 then why aren't shells infinite in size? What stops the scaling? 75 00:04:43,000 --> 00:04:45,000 Why don't they just keep on going? 76 00:04:45,000 --> 00:04:49,000 And I said, well, in the same way 77 00:04:49,000 --> 00:04:53,000 that they exude a protein and it starts the crystallization -- 78 00:04:53,000 --> 00:04:57,000 and then they all sort of leaned in -- 79 00:04:57,000 --> 00:05:00,000 they let go of a protein that stops the crystallization. 80 00:05:00,000 --> 00:05:02,000 It literally adheres to the growing face of the crystal. 81 00:05:02,000 --> 00:05:06,000 And, in fact, there is a product called TPA 82 00:05:06,000 --> 00:05:11,000 that's mimicked that protein -- that stop-protein -- 83 00:05:11,000 --> 00:05:15,000 and it's an environmentally friendly way to stop scaling in pipes. 84 00:05:15,000 --> 00:05:19,000 That changed everything. From then on, 85 00:05:19,000 --> 00:05:23,000 you could not get these engineers back in the boat. 86 00:05:23,000 --> 00:05:26,000 The first day they would take a hike, 87 00:05:26,000 --> 00:05:29,000 and it was, click, click, click, click. Five minutes later they were back in the boat. 88 00:05:29,000 --> 00:05:33,000 We're done. You know, I've seen that island. 89 00:05:33,000 --> 00:05:35,000 After this, 90 00:05:35,000 --> 00:05:38,000 they were crawling all over. They would snorkel 91 00:05:38,000 --> 00:05:43,000 for as long as we would let them snorkel. 92 00:05:43,000 --> 00:05:47,000 What had happened was that they realized that there were organisms 93 00:05:47,000 --> 00:05:51,000 out there that had already solved the problems 94 00:05:51,000 --> 00:05:54,000 that they had spent their careers trying to solve. 95 00:05:54,000 --> 00:05:59,000 Learning about the natural world is one thing; 96 00:05:59,000 --> 00:06:01,000 learning from the natural world -- that's the switch. 97 00:06:01,000 --> 00:06:04,000 That's the profound switch. 98 00:06:04,000 --> 00:06:08,000 What they realized was that the answers to their questions are everywhere; 99 00:06:08,000 --> 00:06:12,000 they just needed to change the lenses with which they saw the world. 100 00:06:12,000 --> 00:06:16,000 3.8 billion years of field-testing. 101 00:06:16,000 --> 00:06:19,000 10 to 30 -- Craig Venter will probably tell you; 102 00:06:19,000 --> 00:06:23,000 I think there's a lot more than 30 million -- well-adapted solutions. 103 00:06:23,000 --> 00:06:31,000 The important thing for me is that these are solutions solved in context. 104 00:06:31,000 --> 00:06:33,000 And the context is the Earth -- 105 00:06:33,000 --> 00:06:38,000 the same context that we're trying to solve our problems in. 106 00:06:38,000 --> 00:06:42,000 So it's the conscious emulation of life's genius. 107 00:06:42,000 --> 00:06:44,000 It's not slavishly mimicking -- 108 00:06:44,000 --> 00:06:47,000 although Al is trying to get the hairdo going -- 109 00:06:47,000 --> 00:06:51,000 it's not a slavish mimicry; it's taking the design principles, 110 00:06:51,000 --> 00:06:56,000 the genius of the natural world, and learning something from it. 111 00:06:56,000 --> 00:07:00,000 Now, in a group with so many IT people, I do have to mention what 112 00:07:00,000 --> 00:07:03,000 I'm not going to talk about, and that is that your field 113 00:07:03,000 --> 00:07:07,000 is one that has learned an enormous amount from living things, 114 00:07:07,000 --> 00:07:11,000 on the software side. So there's computers that protect themselves, 115 00:07:11,000 --> 00:07:14,000 like an immune system, and we're learning from gene regulation 116 00:07:14,000 --> 00:07:19,000 and biological development. And we're learning from neural nets, 117 00:07:19,000 --> 00:07:22,000 genetic algorithms, evolutionary computing. 118 00:07:22,000 --> 00:07:27,000 That's on the software side. But what's interesting to me 119 00:07:27,000 --> 00:07:32,000 is that we haven't looked at this, as much. I mean, these machines 120 00:07:32,000 --> 00:07:35,000 are really not very high tech in my estimation 121 00:07:35,000 --> 00:07:40,000 in the sense that there's dozens and dozens of carcinogens 122 00:07:40,000 --> 00:07:43,000 in the water in Silicon Valley. 123 00:07:43,000 --> 00:07:46,000 So the hardware 124 00:07:46,000 --> 00:07:51,000 is not at all up to snuff in terms of what life would call a success. 125 00:07:51,000 --> 00:07:56,000 So what can we learn about making -- not just computers, but everything? 126 00:07:56,000 --> 00:08:00,000 The plane you came in, cars, the seats that you're sitting on. 127 00:08:00,000 --> 00:08:07,000 How do we redesign the world that we make, the human-made world? 128 00:08:07,000 --> 00:08:11,000 More importantly, what should we ask in the next 10 years? 129 00:08:11,000 --> 00:08:14,000 And there's a lot of cool technologies out there that life has. 130 00:08:14,000 --> 00:08:16,000 What's the syllabus? 131 00:08:16,000 --> 00:08:20,000 Three questions, for me, are key. 132 00:08:20,000 --> 00:08:22,000 How does life make things? 133 00:08:22,000 --> 00:08:25,000 This is the opposite; this is how we make things. 134 00:08:25,000 --> 00:08:27,000 It's called heat, beat and treat -- 135 00:08:27,000 --> 00:08:29,000 that's what material scientists call it. 136 00:08:29,000 --> 00:08:34,000 And it's carving things down from the top, with 96 percent waste left over 137 00:08:34,000 --> 00:08:39,000 and only 4 percent product. You heat it up; you beat it with high pressures; 138 00:08:39,000 --> 00:08:42,000 you use chemicals. OK. Heat, beat and treat. 139 00:08:42,000 --> 00:08:46,000 Life can't afford to do that. How does life make things? 140 00:08:46,000 --> 00:08:49,000 How does life make the most of things? 141 00:08:49,000 --> 00:08:52,000 That's a geranium pollen. 142 00:08:52,000 --> 00:08:57,000 And its shape is what gives it the function of being able 143 00:08:57,000 --> 00:09:01,000 to tumble through air so easily. Look at that shape. 144 00:09:01,000 --> 00:09:06,000 Life adds information to matter. 145 00:09:06,000 --> 00:09:08,000 In other words: structure. 146 00:09:08,000 --> 00:09:13,000 It gives it information. By adding information to matter, 147 00:09:13,000 --> 00:09:19,000 it gives it a function that's different than without that structure. 148 00:09:19,000 --> 00:09:24,000 And thirdly, how does life make things disappear into systems? 149 00:09:24,000 --> 00:09:29,000 Because life doesn't really deal in things; 150 00:09:29,000 --> 00:09:33,000 there are no things in the natural world divorced 151 00:09:33,000 --> 00:09:36,000 from their systems. 152 00:09:36,000 --> 00:09:38,000 Really quick syllabus. 153 00:09:38,000 --> 00:09:44,000 As I'm reading more and more now, and following the story, 154 00:09:44,000 --> 00:09:48,000 there are some amazing things coming up in the biological sciences. 155 00:09:48,000 --> 00:09:51,000 And at the same time, I'm listening to a lot of businesses 156 00:09:51,000 --> 00:09:55,000 and finding what their sort of grand challenges are. 157 00:09:55,000 --> 00:09:57,000 The two groups are not talking to each other. 158 00:09:57,000 --> 00:10:00,000 At all. 159 00:10:00,000 --> 00:10:04,000 What in the world of biology might be helpful at this juncture, 160 00:10:04,000 --> 00:10:09,000 to get us through this sort of evolutionary knothole that we're in? 161 00:10:09,000 --> 00:10:12,000 I'm going to try to go through 12, really quickly. 162 00:10:12,000 --> 00:10:15,000 One that's exciting to me is self-assembly. 163 00:10:15,000 --> 00:10:19,000 Now, you've heard about this in terms of nanotechnology. 164 00:10:19,000 --> 00:10:23,000 Back to that shell: the shell is a self-assembling material. 165 00:10:23,000 --> 00:10:27,000 On the lower left there is a picture of mother of pearl 166 00:10:27,000 --> 00:10:31,000 forming out of seawater. It's a layered structure that's mineral 167 00:10:31,000 --> 00:10:34,000 and then polymer, and it makes it very, very tough. 168 00:10:34,000 --> 00:10:37,000 It's twice as tough as our high-tech ceramics. 169 00:10:37,000 --> 00:10:41,000 But what's really interesting: unlike our ceramics that are in kilns, 170 00:10:41,000 --> 00:10:46,000 it happens in seawater. It happens near, in and near, the organism's body. 171 00:10:46,000 --> 00:10:48,000 This is Sandia National Labs. 172 00:10:48,000 --> 00:10:53,000 A guy named Jeff Brinker 173 00:10:53,000 --> 00:10:57,000 has found a way to have a self-assembling coding process. 174 00:10:57,000 --> 00:11:01,000 Imagine being able to make ceramics at room temperature 175 00:11:01,000 --> 00:11:05,000 by simply dipping something into a liquid, 176 00:11:05,000 --> 00:11:08,000 lifting it out of the liquid, and having evaporation 177 00:11:08,000 --> 00:11:12,000 force the molecules in the liquid together, 178 00:11:12,000 --> 00:11:14,000 so that they jigsaw together 179 00:11:14,000 --> 00:11:18,000 in the same way as this crystallization works. 180 00:11:18,000 --> 00:11:21,000 Imagine making all of our hard materials that way. 181 00:11:21,000 --> 00:11:28,000 Imagine spraying the precursors to a PV cell, to a solar cell, 182 00:11:28,000 --> 00:11:32,000 onto a roof, and having it self-assemble into a layered structure that harvests light. 183 00:11:32,000 --> 00:11:36,000 Here's an interesting one for the IT world: 184 00:11:36,000 --> 00:11:41,000 bio-silicon. This is a diatom, which is made of silicates. 185 00:11:41,000 --> 00:11:43,000 And so silicon, which we make right now -- 186 00:11:43,000 --> 00:11:49,000 it's part of our carcinogenic problem in the manufacture of our chips -- 187 00:11:49,000 --> 00:11:53,000 this is a bio-mineralization process that's now being mimicked. 188 00:11:53,000 --> 00:11:57,000 This is at UC Santa Barbara. Look at these diatoms. 189 00:11:57,000 --> 00:12:00,000 This is from Ernst Haeckel's work. 190 00:12:00,000 --> 00:12:05,000 Imagine being able to -- and, again, it's a templated process, 191 00:12:05,000 --> 00:12:09,000 and it solidifies out of a liquid process -- imagine being able to have that 192 00:12:09,000 --> 00:12:13,000 sort of structure coming out at room temperature. 193 00:12:13,000 --> 00:12:16,000 Imagine being able to make perfect lenses. 194 00:12:16,000 --> 00:12:21,000 On the left, this is a brittle star; it's covered with lenses 195 00:12:21,000 --> 00:12:24,000 that the people at Lucent Technologies have found 196 00:12:24,000 --> 00:12:26,000 have no distortion whatsoever. 197 00:12:26,000 --> 00:12:29,000 It's one of the most distortion-free lenses we know of. 198 00:12:29,000 --> 00:12:32,000 And there's many of them, all over its entire body. 199 00:12:32,000 --> 00:12:35,000 What's interesting, again, is that it self-assembles. 200 00:12:35,000 --> 00:12:39,000 A woman named Joanna Aizenberg, at Lucent, 201 00:12:39,000 --> 00:12:43,000 is now learning to do this in a low-temperature process to create 202 00:12:43,000 --> 00:12:47,000 these sort of lenses. She's also looking at fiber optics. 203 00:12:47,000 --> 00:12:50,000 That's a sea sponge that has a fiber optic. 204 00:12:50,000 --> 00:12:53,000 Down at the very base of it, there's fiber optics 205 00:12:53,000 --> 00:12:56,000 that work better than ours, actually, to move light, 206 00:12:56,000 --> 00:13:02,000 but you can tie them in a knot; they're incredibly flexible. 207 00:13:02,000 --> 00:13:06,000 Here's another big idea: CO2 as a feedstock. 208 00:13:06,000 --> 00:13:09,000 A guy named Geoff Coates, at Cornell, said to himself, 209 00:13:09,000 --> 00:13:13,000 you know, plants do not see CO2 as the biggest poison of our time. 210 00:13:13,000 --> 00:13:16,000 We see it that way. Plants are busy making long chains 211 00:13:16,000 --> 00:13:22,000 of starches and glucose, right, out of CO2. He's found a way -- 212 00:13:22,000 --> 00:13:25,000 he's found a catalyst -- and he's found a way to take CO2 213 00:13:25,000 --> 00:13:29,000 and make it into polycarbonates. Biodegradable plastics 214 00:13:29,000 --> 00:13:31,000 out of CO2 -- how plant-like. 215 00:13:31,000 --> 00:13:34,000 Solar transformations: the most exciting one. 216 00:13:34,000 --> 00:13:38,000 There are people who are mimicking the energy-harvesting device 217 00:13:38,000 --> 00:13:42,000 inside of purple bacterium, the people at ASU. Even more interesting, 218 00:13:42,000 --> 00:13:45,000 lately, in the last couple of weeks, people have seen 219 00:13:45,000 --> 00:13:50,000 that there's an enzyme called hydrogenase that's able to evolve 220 00:13:50,000 --> 00:13:54,000 hydrogen from proton and electrons, and is able to take hydrogen up -- 221 00:13:54,000 --> 00:13:59,000 basically what's happening in a fuel cell, in the anode of a fuel cell 222 00:13:59,000 --> 00:14:01,000 and in a reversible fuel cell. 223 00:14:01,000 --> 00:14:04,000 In our fuel cells, we do it with platinum; 224 00:14:04,000 --> 00:14:08,000 life does it with a very, very common iron. 225 00:14:08,000 --> 00:14:12,000 And a team has now just been able to mimic 226 00:14:12,000 --> 00:14:17,000 that hydrogen-juggling hydrogenase. 227 00:14:17,000 --> 00:14:19,000 That's very exciting for fuel cells -- 228 00:14:19,000 --> 00:14:22,000 to be able to do that without platinum. 229 00:14:22,000 --> 00:14:27,000 Power of shape: here's a whale. We've seen that the fins of this whale 230 00:14:27,000 --> 00:14:30,000 have tubercles on them. And those little bumps 231 00:14:30,000 --> 00:14:35,000 actually increase efficiency in, for instance, 232 00:14:35,000 --> 00:14:40,000 the edge of an airplane -- increase efficiency by about 32 percent. 233 00:14:40,000 --> 00:14:42,000 Which is an amazing fossil fuel savings, 234 00:14:42,000 --> 00:14:47,000 if we were to just put that on the edge of a wing. 235 00:14:47,000 --> 00:14:51,000 Color without pigments: this peacock is creating color with shape. 236 00:14:51,000 --> 00:14:54,000 Light comes through, it bounces back off the layers; 237 00:14:54,000 --> 00:14:57,000 it's called thin-film interference. Imagine being able 238 00:14:57,000 --> 00:15:00,000 to self-assemble products with the last few layers 239 00:15:00,000 --> 00:15:04,000 playing with light to create color. 240 00:15:04,000 --> 00:15:09,000 Imagine being able to create a shape on the outside of a surface, 241 00:15:09,000 --> 00:15:14,000 so that it's self-cleaning with just water. That's what a leaf does. 242 00:15:14,000 --> 00:15:16,000 See that up-close picture? 243 00:15:16,000 --> 00:15:19,000 That's a ball of water, and those are dirt particles. 244 00:15:19,000 --> 00:15:22,000 And that's an up-close picture of a lotus leaf. 245 00:15:22,000 --> 00:15:27,000 There's a company making a product called Lotusan, which mimics -- 246 00:15:27,000 --> 00:15:31,000 when the building facade paint dries, it mimics the bumps 247 00:15:31,000 --> 00:15:36,000 in a self-cleaning leaf, and rainwater cleans the building. 248 00:15:36,000 --> 00:15:42,000 Water is going to be our big, grand challenge: 249 00:15:42,000 --> 00:15:44,000 quenching thirst. 250 00:15:44,000 --> 00:15:47,000 Here are two organisms that pull water. 251 00:15:47,000 --> 00:15:51,000 The one on the left is the Namibian beetle pulling water out of fog. 252 00:15:51,000 --> 00:15:54,000 The one on the right is a pill bug -- pulls water out of air, 253 00:15:54,000 --> 00:15:57,000 does not drink fresh water. 254 00:15:57,000 --> 00:16:04,000 Pulling water out of Monterey fog and out of the sweaty air in Atlanta, 255 00:16:04,000 --> 00:16:08,000 before it gets into a building, are key technologies. 256 00:16:08,000 --> 00:16:12,000 Separation technologies are going to be extremely important. 257 00:16:12,000 --> 00:16:16,000 What if we were to say, no more hard rock mining? 258 00:16:16,000 --> 00:16:22,000 What if we were to separate out metals from waste streams, 259 00:16:22,000 --> 00:16:26,000 small amounts of metals in water? That's what microbes do; 260 00:16:26,000 --> 00:16:28,000 they chelate metals out of water. 261 00:16:28,000 --> 00:16:31,000 There's a company here in San Francisco called MR3 262 00:16:31,000 --> 00:16:37,000 that is embedding mimics of the microbes' molecules on filters 263 00:16:37,000 --> 00:16:40,000 to mine waste streams. 264 00:16:40,000 --> 00:16:44,000 Green chemistry is chemistry in water. 265 00:16:44,000 --> 00:16:46,000 We do chemistry in organic solvents. 266 00:16:46,000 --> 00:16:50,000 This is a picture of the spinnerets coming out of a spider 267 00:16:50,000 --> 00:16:53,000 and the silk being formed from a spider. Isn't that beautiful? 268 00:16:53,000 --> 00:17:01,000 Green chemistry is replacing our industrial chemistry with nature's recipe book. 269 00:17:01,000 --> 00:17:06,000 It's not easy, because life uses 270 00:17:06,000 --> 00:17:10,000 only a subset of the elements in the periodic table. 271 00:17:10,000 --> 00:17:14,000 And we use all of them, even the toxic ones. 272 00:17:14,000 --> 00:17:19,000 To figure out the elegant recipes that would take the small subset 273 00:17:19,000 --> 00:17:25,000 of the periodic table, and create miracle materials like that cell, 274 00:17:25,000 --> 00:17:27,000 is the task of green chemistry. 275 00:17:27,000 --> 00:17:31,000 Timed degradation: packaging that is good 276 00:17:31,000 --> 00:17:35,000 until you don't want it to be good anymore, and dissolves on cue. 277 00:17:35,000 --> 00:17:38,000 That's a mussel you can find in the waters out here, 278 00:17:38,000 --> 00:17:42,000 and the threads holding it to a rock are timed; at exactly two years, 279 00:17:42,000 --> 00:17:44,000 they begin to dissolve. 280 00:17:44,000 --> 00:17:47,000 Healing: this is a good one. 281 00:17:47,000 --> 00:17:50,000 That little guy over there is a tardigrade. 282 00:17:50,000 --> 00:17:56,000 There is a problem with vaccines around the world 283 00:17:56,000 --> 00:17:59,000 not getting to patients. And the reason is 284 00:17:59,000 --> 00:18:03,000 that the refrigeration somehow gets broken; 285 00:18:03,000 --> 00:18:05,000 what's called the "cold chain" gets broken. 286 00:18:05,000 --> 00:18:08,000 A guy named Bruce Rosner looked at the tardigrade -- 287 00:18:08,000 --> 00:18:14,000 which dries out completely, and yet stays alive for months 288 00:18:14,000 --> 00:18:17,000 and months and months, and is able to regenerate itself. 289 00:18:17,000 --> 00:18:20,000 And he found a way to dry out vaccines -- 290 00:18:20,000 --> 00:18:24,000 encase them in the same sort of sugar capsules 291 00:18:24,000 --> 00:18:27,000 as the tardigrade has within its cells -- 292 00:18:27,000 --> 00:18:32,000 meaning that vaccines no longer need to be refrigerated. 293 00:18:32,000 --> 00:18:36,000 They can be put in a glove compartment, OK. 294 00:18:36,000 --> 00:18:41,000 Learning from organisms. This is a session about water -- 295 00:18:41,000 --> 00:18:44,000 learning about organisms that can do without water, 296 00:18:44,000 --> 00:18:51,000 in order to create a vaccine that lasts and lasts and lasts without refrigeration. 297 00:18:51,000 --> 00:18:54,000 I'm not going to get to 12. 298 00:18:54,000 --> 00:18:58,000 But what I am going to do is tell you that the most important thing, 299 00:18:58,000 --> 00:19:03,000 besides all of these adaptations, is the fact that these organisms 300 00:19:03,000 --> 00:19:08,000 have figured out a way to do the amazing things they do 301 00:19:08,000 --> 00:19:11,000 while taking care of the place 302 00:19:11,000 --> 00:19:16,000 that's going to take care of their offspring. 303 00:19:16,000 --> 00:19:19,000 When they're involved in foreplay, 304 00:19:19,000 --> 00:19:22,000 they're thinking about something very, very important -- 305 00:19:22,000 --> 00:19:26,000 and that's having their genetic material 306 00:19:26,000 --> 00:19:31,000 remain, 10,000 generations from now. 307 00:19:31,000 --> 00:19:33,000 And that means finding a way to do what they do 308 00:19:33,000 --> 00:19:37,000 without destroying the place that'll take care of their offspring. 309 00:19:37,000 --> 00:19:40,000 That's the biggest design challenge. 310 00:19:40,000 --> 00:19:46,000 Luckily, there are millions and millions of geniuses 311 00:19:46,000 --> 00:19:49,000 willing to gift us with their best ideas. 312 00:19:49,000 --> 00:19:52,000 Good luck having a conversation with them. 313 00:19:52,000 --> 00:19:53,000 Thank you. 314 00:19:53,000 --> 00:20:07,000 (Applause) 315 00:20:07,000 --> 00:20:11,000 Chris Anderson: Talk about foreplay, I -- we need to get to 12, but really quickly. 316 00:20:11,000 --> 00:20:12,000 Janine Benyus: Oh really? 317 00:20:12,000 --> 00:20:15,000 CA: Yeah. Just like, you know, like the 10-second version 318 00:20:15,000 --> 00:20:18,000 of 10, 11 and 12. Because we just -- your slides are so gorgeous, 319 00:20:18,000 --> 00:20:20,000 and the ideas are so big, I can't stand to let you go down 320 00:20:20,000 --> 00:20:22,000 without seeing 10, 11 and 12. 321 00:20:22,000 --> 00:20:26,000 JB: OK, put this -- OK, I'll just hold this thing. OK, great. 322 00:20:26,000 --> 00:20:29,000 OK, so that's the healing one. 323 00:20:29,000 --> 00:20:32,000 Sensing and responding: feedback is a huge thing. 324 00:20:32,000 --> 00:20:36,000 This is a locust. There can be 80 million of them in a square kilometer, 325 00:20:36,000 --> 00:20:39,000 and yet they don't collide with one another. 326 00:20:39,000 --> 00:20:44,000 And yet we have 3.6 million car collisions a year. 327 00:20:44,000 --> 00:20:46,000 (Laughter) 328 00:20:46,000 --> 00:20:50,000 Right. There's a person at Newcastle 329 00:20:50,000 --> 00:20:53,000 who has figured out that it's a very large neuron. 330 00:20:53,000 --> 00:20:56,000 And she's actually figuring out how to make 331 00:20:56,000 --> 00:20:58,000 a collision-avoidance circuitry 332 00:20:58,000 --> 00:21:02,000 based on this very large neuron in the locust. 333 00:21:02,000 --> 00:21:04,000 This is a huge and important one, number 11. 334 00:21:04,000 --> 00:21:06,000 And that's the growing fertility. 335 00:21:06,000 --> 00:21:10,000 That means, you know, net fertility farming. 336 00:21:10,000 --> 00:21:14,000 We should be growing fertility. And, oh yes -- we get food, too. 337 00:21:14,000 --> 00:21:19,000 Because we have to grow the capacity of this planet 338 00:21:19,000 --> 00:21:22,000 to create more and more opportunities for life. 339 00:21:22,000 --> 00:21:24,000 And really, that's what other organisms do as well. 340 00:21:24,000 --> 00:21:27,000 In ensemble, that's what whole ecosystems do: 341 00:21:27,000 --> 00:21:30,000 they create more and more opportunities for life. 342 00:21:30,000 --> 00:21:33,000 Our farming has done the opposite. 343 00:21:33,000 --> 00:21:37,000 So, farming based on how a prairie builds soil, 344 00:21:37,000 --> 00:21:41,000 ranching based on how a native ungulate herd 345 00:21:41,000 --> 00:21:43,000 actually increases the health of the range, 346 00:21:43,000 --> 00:21:48,000 even wastewater treatment based on how a marsh 347 00:21:48,000 --> 00:21:50,000 not only cleans the water, 348 00:21:50,000 --> 00:21:54,000 but creates incredibly sparkling productivity. 349 00:21:54,000 --> 00:21:58,000 This is the simple design brief. I mean, it looks simple 350 00:21:58,000 --> 00:22:03,000 because the system, over 3.8 billion years, has worked this out. 351 00:22:03,000 --> 00:22:08,000 That is, those organisms that have not been able to figure out 352 00:22:08,000 --> 00:22:12,000 how to enhance or sweeten their places, 353 00:22:12,000 --> 00:22:15,000 are not around to tell us about it. 354 00:22:15,000 --> 00:22:18,000 That's the twelfth one. 355 00:22:18,000 --> 00:22:22,000 Life -- and this is the secret trick; this is the magic trick -- 356 00:22:22,000 --> 00:22:26,000 life creates conditions conducive to life. 357 00:22:26,000 --> 00:22:30,000 It builds soil; it cleans air; it cleans water; 358 00:22:30,000 --> 00:22:33,000 it mixes the cocktail of gases that you and I need to live. 359 00:22:33,000 --> 00:22:39,000 And it does that in the middle of having great foreplay 360 00:22:39,000 --> 00:22:45,000 and meeting their needs. So it's not mutually exclusive. 361 00:22:45,000 --> 00:22:48,000 We have to find a way to meet our needs, 362 00:22:48,000 --> 00:22:54,000 while making of this place an Eden. 363 00:22:54,000 --> 00:22:55,000 CA: Janine, thank you so much. 364 00:22:55,000 --> 00:22:56,000 (Applause)