1 00:00:00,340 --> 00:00:02,381 So, I have a strange career. 2 00:00:02,405 --> 00:00:05,521 I know it because people come up to me, like colleagues, and say, 3 00:00:05,545 --> 00:00:07,411 "Chris, you have a strange career." 4 00:00:07,435 --> 00:00:09,078 (Laughter) 5 00:00:09,102 --> 00:00:10,421 And I can see their point, 6 00:00:10,445 --> 00:00:14,976 because I started my career as a theoretical nuclear physicist. 7 00:00:15,000 --> 00:00:19,164 And I was thinking about quarks and gluons and heavy ion collisions, 8 00:00:19,188 --> 00:00:20,976 and I was only 14 years old -- 9 00:00:21,593 --> 00:00:24,280 No, no, I wasn't 14 years old. 10 00:00:25,237 --> 00:00:26,976 But after that, 11 00:00:27,796 --> 00:00:29,737 I actually had my own lab 12 00:00:29,761 --> 00:00:31,876 in the Computational Neuroscience department, 13 00:00:31,900 --> 00:00:33,628 and I wasn't doing any neuroscience. 14 00:00:33,652 --> 00:00:36,584 Later, I would work on evolutionary genetics, 15 00:00:36,608 --> 00:00:38,558 and I would work on systems biology. 16 00:00:38,582 --> 00:00:41,247 But I'm going to tell you about something else today. 17 00:00:41,271 --> 00:00:45,533 I'm going to tell you about how I learned something about life. 18 00:00:45,557 --> 00:00:49,038 And I was actually a rocket scientist. 19 00:00:49,062 --> 00:00:51,365 I wasn't really a rocket scientist, 20 00:00:51,389 --> 00:00:55,745 but I was working at the Jet Propulsion Laboratory 21 00:00:55,769 --> 00:00:58,404 in sunny California, where it's warm; 22 00:00:58,428 --> 00:01:02,276 whereas now I am in the mid-West, and it's cold. 23 00:01:02,300 --> 00:01:04,976 But it was an exciting experience. 24 00:01:05,000 --> 00:01:08,443 One day, a NASA manager comes into my office, 25 00:01:08,467 --> 00:01:10,976 sits down and says, 26 00:01:11,602 --> 00:01:15,403 "Can you please tell us, how do we look for life outside Earth?" 27 00:01:16,361 --> 00:01:18,039 And that came as a surprise to me, 28 00:01:18,063 --> 00:01:21,697 because I was actually hired to work on quantum computation. 29 00:01:22,213 --> 00:01:23,714 Yet, I had a very good answer. 30 00:01:23,738 --> 00:01:25,164 I said, "I have no idea." 31 00:01:25,539 --> 00:01:26,689 (Laughter) 32 00:01:26,713 --> 00:01:32,055 And he told me, "Biosignatures, we need to look for a biosignature." 33 00:01:32,079 --> 00:01:33,443 And I said, "What is that?" 34 00:01:33,467 --> 00:01:36,064 And he said, "It's any measurable phenomenon 35 00:01:36,088 --> 00:01:38,933 that allows us to indicate the presence of life." 36 00:01:39,528 --> 00:01:40,678 And I said, "Really? 37 00:01:41,173 --> 00:01:43,120 Because isn't that easy? 38 00:01:43,144 --> 00:01:44,975 I mean, we have life. 39 00:01:44,999 --> 00:01:46,971 Can't you apply a definition, 40 00:01:46,995 --> 00:01:51,169 for example, a Supreme Court-like definition of life?" 41 00:01:51,931 --> 00:01:54,473 And then I thought about it a little bit, and I said, 42 00:01:54,497 --> 00:01:55,972 "Well, is it really that easy? 43 00:01:55,996 --> 00:01:58,189 Because, yes, if you see something like this, 44 00:01:58,213 --> 00:02:00,557 then all right, fine, I'm going to call it life -- 45 00:02:00,581 --> 00:02:01,731 no doubt about it. 46 00:02:02,257 --> 00:02:03,897 But here's something." 47 00:02:03,921 --> 00:02:06,976 And he goes, "Right, that's life too. I know that." 48 00:02:07,000 --> 00:02:11,592 Except, if you think that life is also defined by things that die, 49 00:02:11,616 --> 00:02:13,369 you're not in luck with this thing, 50 00:02:13,393 --> 00:02:15,655 because that's actually a very strange organism. 51 00:02:15,679 --> 00:02:17,726 It grows up into the adult stage like that 52 00:02:17,750 --> 00:02:19,976 and then goes through a Benjamin Button phase, 53 00:02:20,000 --> 00:02:24,935 and actually goes backwards and backwards until it's like a little embryo again, 54 00:02:24,959 --> 00:02:27,873 and then actually grows back up, and back down and back up -- 55 00:02:27,897 --> 00:02:29,676 sort of yo-yo -- and it never dies. 56 00:02:29,700 --> 00:02:31,926 So it's actually life, 57 00:02:31,950 --> 00:02:35,975 but it's actually not as we thought life would be. 58 00:02:36,491 --> 00:02:38,402 And then you see something like that. 59 00:02:38,426 --> 00:02:41,299 And he was like, "My God, what kind of a life form is that?" 60 00:02:41,323 --> 00:02:42,742 Anyone know? 61 00:02:42,766 --> 00:02:45,768 It's actually not life, it's a crystal. 62 00:02:46,282 --> 00:02:49,583 So once you start looking and looking at smaller and smaller things -- 63 00:02:49,607 --> 00:02:52,769 so this particular person wrote a whole article and said, 64 00:02:52,793 --> 00:02:54,274 "Hey, these are bacteria." 65 00:02:54,298 --> 00:02:56,269 Except, if you look a little bit closer, 66 00:02:56,293 --> 00:02:59,929 you see, in fact, that this thing is way too small to be anything like that. 67 00:02:59,953 --> 00:03:03,111 So he was convinced, but, in fact, most people aren't. 68 00:03:03,792 --> 00:03:06,976 And then, of course, NASA also had a big announcement, 69 00:03:07,000 --> 00:03:09,865 and President Clinton gave a press conference, 70 00:03:09,889 --> 00:03:14,750 about this amazing discovery of life in a Martian meteorite. 71 00:03:15,400 --> 00:03:18,361 Except that nowadays, it's heavily disputed. 72 00:03:18,806 --> 00:03:21,241 If you take the lesson of all these pictures, 73 00:03:21,265 --> 00:03:24,159 then you realize, well, actually, maybe it's not that easy. 74 00:03:24,183 --> 00:03:27,582 Maybe I do need a definition of life 75 00:03:27,606 --> 00:03:29,884 in order to make that kind of distinction. 76 00:03:29,908 --> 00:03:32,439 So can life be defined? 77 00:03:32,463 --> 00:03:33,970 Well how would you go about it? 78 00:03:33,994 --> 00:03:38,001 Well of course, you'd go to Encyclopedia Britannica and open at L. 79 00:03:38,025 --> 00:03:41,037 No, of course you don't do that; you put it somewhere in Google. 80 00:03:41,061 --> 00:03:42,652 And then you might get something. 81 00:03:42,676 --> 00:03:43,700 (Laughter) 82 00:03:43,724 --> 00:03:44,942 And what you might get -- 83 00:03:44,966 --> 00:03:48,745 and anything that actually refers to things that we are used to, 84 00:03:48,769 --> 00:03:49,991 you throw away. 85 00:03:50,015 --> 00:03:52,513 And then you might come up with something like this. 86 00:03:52,537 --> 00:03:55,794 And it says something complicated with lots and lots of concepts. 87 00:03:55,818 --> 00:04:01,178 Who on Earth would write something as convoluted and complex and inane? 88 00:04:02,952 --> 00:04:06,853 Oh, it's actually a really, really, important set of concepts. 89 00:04:06,877 --> 00:04:08,976 So I'm highlighting just a few words 90 00:04:09,000 --> 00:04:12,924 and saying definitions like that rely on things 91 00:04:12,948 --> 00:04:19,097 that are not based on amino acids or leaves or anything that we are used to, 92 00:04:19,121 --> 00:04:20,872 but in fact on processes only. 93 00:04:20,896 --> 00:04:22,767 And if you take a look at that, 94 00:04:22,791 --> 00:04:26,248 this was actually in a book that I wrote that deals with artificial life. 95 00:04:26,272 --> 00:04:30,499 And that explains why that NASA manager was actually in my office to begin with. 96 00:04:30,523 --> 00:04:33,610 Because the idea was that, with concepts like that, 97 00:04:33,634 --> 00:04:37,654 maybe we can actually manufacture a form of life. 98 00:04:37,678 --> 00:04:42,475 And so if you go and ask yourself, "What on Earth is artificial life?", 99 00:04:42,499 --> 00:04:46,168 let me give you a whirlwind tour of how all this stuff came about. 100 00:04:46,192 --> 00:04:49,327 And it started out quite a while ago, 101 00:04:49,351 --> 00:04:53,676 when someone wrote one of the first successful computer viruses. 102 00:04:53,985 --> 00:04:56,158 And for those of you who aren't old enough, 103 00:04:56,182 --> 00:04:58,765 you have no idea how this infection was working -- 104 00:04:58,789 --> 00:05:01,049 namely, through these floppy disks. 105 00:05:01,073 --> 00:05:04,960 But the interesting thing about these computer virus infections 106 00:05:04,984 --> 00:05:08,436 was that, if you look at the rate at which the infection worked, 107 00:05:08,460 --> 00:05:12,610 they show this spiky behavior that you're used to from a flu virus. 108 00:05:12,634 --> 00:05:14,976 And it is in fact due to this arms race 109 00:05:15,000 --> 00:05:18,456 between hackers and operating system designers 110 00:05:18,480 --> 00:05:20,080 that things go back and forth. 111 00:05:20,104 --> 00:05:24,615 And the result is kind of a tree of life of these viruses, 112 00:05:24,639 --> 00:05:28,244 a phylogeny that looks very much like the type of life 113 00:05:28,268 --> 00:05:30,697 that we're used to, at least on the viral level. 114 00:05:30,721 --> 00:05:32,051 So is that life? 115 00:05:32,266 --> 00:05:33,882 Not as far as I'm concerned. 116 00:05:33,906 --> 00:05:36,748 Why? Because these things don't evolve by themselves. 117 00:05:36,772 --> 00:05:38,725 In fact, they have hackers writing them. 118 00:05:38,749 --> 00:05:42,079 But the idea was taken very quickly a little bit further, 119 00:05:42,103 --> 00:05:45,414 when a scientist working at the Santa Fe Institute decided, 120 00:05:45,438 --> 00:05:48,571 "Why don't we try to package these little viruses 121 00:05:48,595 --> 00:05:50,810 in artificial worlds inside of the computer 122 00:05:50,834 --> 00:05:52,105 and let them evolve?" 123 00:05:52,129 --> 00:05:53,723 And this was Steen Rasmussen. 124 00:05:53,747 --> 00:05:56,439 And he designed this system, but it really didn't work, 125 00:05:56,463 --> 00:05:59,347 because his viruses were constantly destroying each other. 126 00:05:59,371 --> 00:06:02,888 But there was another scientist who had been watching this, an ecologist. 127 00:06:02,912 --> 00:06:05,404 And he went home and says, "I know how to fix this." 128 00:06:05,428 --> 00:06:07,072 And he wrote the Tierra system, 129 00:06:07,096 --> 00:06:08,301 and, in my book, 130 00:06:08,325 --> 00:06:12,149 is in fact one of the first truly artificial living systems -- 131 00:06:12,173 --> 00:06:15,635 except for the fact that these programs didn't really grow in complexity. 132 00:06:15,659 --> 00:06:18,523 So having seen this work, worked a little bit on this, 133 00:06:18,547 --> 00:06:20,205 this is where I came in. 134 00:06:20,229 --> 00:06:23,872 And I decided to create a system that has all the properties 135 00:06:23,896 --> 00:06:27,742 that are necessary to see, in fact, the evolution of complexity, 136 00:06:27,766 --> 00:06:31,068 more and more complex problems constantly evolving. 137 00:06:31,092 --> 00:06:34,876 And of course, since I really don't know how to write code, I had help in this. 138 00:06:34,900 --> 00:06:36,448 I had two undergraduate students 139 00:06:36,472 --> 00:06:39,201 at California Institute of Technology that worked with me. 140 00:06:39,225 --> 00:06:42,077 That's Charles Ofria on the left, Titus Brown on the right. 141 00:06:42,101 --> 00:06:44,436 They are now, actually, respectable professors 142 00:06:44,460 --> 00:06:46,202 at Michigan State University, 143 00:06:46,226 --> 00:06:50,727 but I can assure you, back in the day, we were not a respectable team. 144 00:06:50,751 --> 00:06:52,800 And I'm really happy that no photo survives 145 00:06:52,824 --> 00:06:55,347 of the three of us anywhere close together. 146 00:06:56,092 --> 00:06:57,967 But what is this system like? 147 00:06:57,991 --> 00:07:00,180 Well I can't really go into the details, 148 00:07:00,204 --> 00:07:02,857 but what you see here is some of the entrails. 149 00:07:02,881 --> 00:07:06,966 But what I wanted to focus on is this type of population structure. 150 00:07:06,990 --> 00:07:09,462 There's about 10,000 programs sitting here. 151 00:07:09,486 --> 00:07:12,405 And all different strains are colored in different colors. 152 00:07:12,429 --> 00:07:16,033 And as you see here, there are groups that are growing on top of each other, 153 00:07:16,057 --> 00:07:17,398 because they are spreading. 154 00:07:17,422 --> 00:07:21,526 Any time there is a program that's better at surviving in this world, 155 00:07:21,550 --> 00:07:23,518 due to whatever mutation it has acquired, 156 00:07:23,542 --> 00:07:27,012 it is going to spread over the others and drive the others to extinction. 157 00:07:27,036 --> 00:07:28,591 So I'm going to show you a movie 158 00:07:28,615 --> 00:07:30,842 where you're going to see that kind of dynamic. 159 00:07:30,866 --> 00:07:35,142 And these kinds of experiments are started with programs that we wrote ourselves. 160 00:07:35,166 --> 00:07:38,503 We write our own stuff, replicate it, and are very proud of ourselves. 161 00:07:38,527 --> 00:07:41,303 And we put them in, and what you see immediately 162 00:07:41,327 --> 00:07:44,393 is that there are waves and waves of innovation. 163 00:07:44,417 --> 00:07:46,311 By the way, this is highly accelerated, 164 00:07:46,335 --> 00:07:48,532 so it's like a 1000 generations a second. 165 00:07:48,556 --> 00:07:52,523 But immediately, the system goes like, "What kind of dumb piece of code was this? 166 00:07:52,547 --> 00:07:56,268 This can be improved upon in so many ways, so quickly." 167 00:07:56,292 --> 00:08:00,040 So you see waves of new types taking over the other types. 168 00:08:00,064 --> 00:08:02,626 And this type of activity goes on for quite a while, 169 00:08:02,650 --> 00:08:07,471 until the main easy things have been acquired by these programs. 170 00:08:07,495 --> 00:08:10,976 And then, you see sort of like a stasis coming on 171 00:08:11,000 --> 00:08:12,976 where the system essentially waits 172 00:08:13,000 --> 00:08:16,183 for a new type of innovation, like this one, 173 00:08:16,207 --> 00:08:20,489 which is going to spread over all the other innovations that were before 174 00:08:20,513 --> 00:08:22,976 and is erasing the genes that it had before, 175 00:08:23,000 --> 00:08:26,976 until a new type of higher level of complexity has been achieved. 176 00:08:27,000 --> 00:08:29,976 And this process goes on and on and on. 177 00:08:30,467 --> 00:08:31,782 So what we see here 178 00:08:31,806 --> 00:08:35,969 is a system that lives in very much the way we're used to how life goes. 179 00:08:36,688 --> 00:08:40,808 But what the NASA people had asked me really was, 180 00:08:41,213 --> 00:08:43,975 "Do these guys have a biosignature? 181 00:08:44,580 --> 00:08:46,393 Can we measure this type of life? 182 00:08:46,417 --> 00:08:47,609 Because if we can, 183 00:08:47,633 --> 00:08:51,482 maybe we have a chance of actually discovering life somewhere else 184 00:08:51,506 --> 00:08:54,660 without being biased by things like amino acids." 185 00:08:55,221 --> 00:08:59,754 So I said, "Well, perhaps we should construct a biosignature 186 00:08:59,778 --> 00:09:02,976 based on life as a universal process. 187 00:09:03,000 --> 00:09:07,864 In fact, it should perhaps make use of the concepts that I developed 188 00:09:07,888 --> 00:09:11,975 just in order to sort of capture what a simple living system might be." 189 00:09:11,999 --> 00:09:13,518 And the thing I came up with -- 190 00:09:13,542 --> 00:09:17,524 I have to first give you an introduction about the idea, 191 00:09:17,548 --> 00:09:21,087 and maybe that would be a meaning detector, 192 00:09:21,111 --> 00:09:22,658 rather than a life detector. 193 00:09:23,226 --> 00:09:24,976 And the way we would do that -- 194 00:09:25,000 --> 00:09:27,636 I would like to find out how I can distinguish text 195 00:09:27,660 --> 00:09:32,307 that was written by a million monkeys, as opposed to text that is in our books. 196 00:09:32,645 --> 00:09:34,522 And I would like to do it in such a way 197 00:09:34,546 --> 00:09:37,424 that I don't actually have to be able to read the language, 198 00:09:37,448 --> 00:09:39,218 because I'm sure I won't be able to. 199 00:09:39,242 --> 00:09:41,742 As long as I know that there's some sort of alphabet. 200 00:09:41,766 --> 00:09:44,096 So here would be a frequency plot 201 00:09:44,120 --> 00:09:47,502 of how often you find each of the 26 letters of the alphabet 202 00:09:47,526 --> 00:09:49,745 in a text written by random monkeys. 203 00:09:50,195 --> 00:09:54,749 And obviously, each of these letters comes off about roughly equally frequent. 204 00:09:54,773 --> 00:09:58,365 But if you now look at the same distribution in English texts, 205 00:09:58,389 --> 00:09:59,637 it looks like that. 206 00:10:00,202 --> 00:10:03,750 And I'm telling you, this is very robust across English texts. 207 00:10:03,774 --> 00:10:06,758 And if I look at French texts, it looks a little bit different, 208 00:10:06,782 --> 00:10:07,960 or Italian or German. 209 00:10:07,984 --> 00:10:11,400 They all have their own type of frequency distribution, 210 00:10:11,424 --> 00:10:12,857 but it's robust. 211 00:10:12,881 --> 00:10:16,088 It doesn't matter whether it writes about politics or about science. 212 00:10:16,112 --> 00:10:21,892 It doesn't matter whether it's a poem or whether it's a mathematical text. 213 00:10:21,916 --> 00:10:23,753 It's a robust signature, 214 00:10:23,777 --> 00:10:25,597 and it's very stable. 215 00:10:25,621 --> 00:10:27,778 As long as our books are written in English -- 216 00:10:27,802 --> 00:10:30,593 because people are rewriting them and recopying them -- 217 00:10:30,617 --> 00:10:31,976 it's going to be there. 218 00:10:32,000 --> 00:10:37,761 So that inspired me to think about, well, what if I try to use this idea 219 00:10:37,785 --> 00:10:41,540 in order, not to detect random texts from texts with meaning, 220 00:10:41,564 --> 00:10:45,293 but rather detect the fact that there is meaning 221 00:10:45,317 --> 00:10:47,844 in the biomolecules that make up life. 222 00:10:47,868 --> 00:10:49,036 But first I have to ask: 223 00:10:49,060 --> 00:10:50,548 what are these building blocks, 224 00:10:50,572 --> 00:10:52,868 like the alphabet, elements that I showed you? 225 00:10:52,892 --> 00:10:55,765 Well it turns out, we have many different alternatives 226 00:10:55,789 --> 00:10:58,103 for such a set of building blocks. 227 00:10:58,127 --> 00:10:59,375 We could use amino acids, 228 00:10:59,399 --> 00:11:02,601 we could use nucleic acids, carboxylic acids, fatty acids. 229 00:11:02,625 --> 00:11:06,063 In fact, chemistry's extremely rich, and our body uses a lot of them. 230 00:11:06,087 --> 00:11:08,393 So that we actually, to test this idea, 231 00:11:08,417 --> 00:11:11,964 first took a look at amino acids and some other carboxylic acids. 232 00:11:11,988 --> 00:11:13,459 And here's the result. 233 00:11:13,483 --> 00:11:16,649 Here is, in fact, what you get 234 00:11:16,673 --> 00:11:19,696 if you, for example, look at the distribution of amino acids 235 00:11:19,720 --> 00:11:24,455 on a comet or in interstellar space or, in fact, in a laboratory, 236 00:11:24,479 --> 00:11:27,138 where you made very sure that in your primordial soup, 237 00:11:27,162 --> 00:11:29,080 there is no living stuff in there. 238 00:11:29,104 --> 00:11:31,983 What you find is mostly glycine and then alanine 239 00:11:32,007 --> 00:11:34,366 and there's some trace elements of the other ones. 240 00:11:34,390 --> 00:11:36,819 That is also very robust -- 241 00:11:36,843 --> 00:11:40,675 what you find in systems like Earth 242 00:11:40,699 --> 00:11:43,844 where there are amino acids, but there is no life. 243 00:11:43,868 --> 00:11:48,498 But suppose you take some dirt and dig through it 244 00:11:48,522 --> 00:11:51,482 and then put it into these spectrometers, 245 00:11:51,506 --> 00:11:53,604 because there's bacteria all over the place; 246 00:11:53,628 --> 00:11:55,859 or you take water anywhere on Earth, 247 00:11:55,883 --> 00:11:57,400 because it's teaming with life, 248 00:11:57,424 --> 00:11:59,174 and you make the same analysis; 249 00:11:59,198 --> 00:12:01,775 the spectrum looks completely different. 250 00:12:01,799 --> 00:12:05,174 Of course, there is still glycine and alanine, 251 00:12:05,198 --> 00:12:08,518 but in fact, there are these heavy elements, these heavy amino acids, 252 00:12:08,542 --> 00:12:11,937 that are being produced because they are valuable to the organism. 253 00:12:13,067 --> 00:12:17,005 And some other ones that are not used in the set of 20, 254 00:12:17,029 --> 00:12:19,927 they will not appear at all in any type of concentration. 255 00:12:19,951 --> 00:12:22,656 So this also turns out to be extremely robust. 256 00:12:22,680 --> 00:12:25,798 It doesn't matter what kind of sediment you're using to grind up, 257 00:12:25,822 --> 00:12:29,101 whether it's bacteria or any other plants or animals. 258 00:12:29,125 --> 00:12:30,549 Anywhere there's life, 259 00:12:30,573 --> 00:12:32,524 you're going to have this distribution, 260 00:12:32,548 --> 00:12:34,365 as opposed to that distribution. 261 00:12:34,389 --> 00:12:37,626 And it is detectable not just in amino acids. 262 00:12:37,650 --> 00:12:38,867 Now you could ask: 263 00:12:38,891 --> 00:12:42,050 Well, what about these Avidians? 264 00:12:42,074 --> 00:12:45,125 The Avidians being the denizens of this computer world 265 00:12:45,149 --> 00:12:48,594 where they are perfectly happy replicating and growing in complexity. 266 00:12:48,618 --> 00:12:53,635 So this is the distribution that you get if, in fact, there is no life. 267 00:12:53,659 --> 00:12:56,377 They have about 28 of these instructions. 268 00:12:56,401 --> 00:12:59,753 And if you have a system where they're being replaced one by the other, 269 00:12:59,777 --> 00:13:01,962 it's like the monkeys writing on a typewriter. 270 00:13:01,986 --> 00:13:06,206 Each of these instructions appears with roughly the equal frequency. 271 00:13:07,115 --> 00:13:11,895 But if you now take a set of replicating guys 272 00:13:11,919 --> 00:13:13,869 like in the video that you saw, 273 00:13:13,893 --> 00:13:15,412 it looks like this. 274 00:13:16,199 --> 00:13:17,672 So there are some instructions 275 00:13:17,696 --> 00:13:20,129 that are extremely valuable to these organisms, 276 00:13:20,153 --> 00:13:22,123 and their frequency is going to be high. 277 00:13:22,147 --> 00:13:26,188 And there's actually some instructions that you only use once, if ever. 278 00:13:26,212 --> 00:13:27,735 So they are either poisonous 279 00:13:27,759 --> 00:13:32,264 or really should be used at less of a level than random. 280 00:13:32,288 --> 00:13:34,976 In this case, the frequency is lower. 281 00:13:35,932 --> 00:13:38,603 And so now we can see, is that really a robust signature? 282 00:13:38,627 --> 00:13:39,984 I can tell you indeed it is, 283 00:13:40,008 --> 00:13:43,256 because this type of spectrum, just like what you've seen in books, 284 00:13:43,280 --> 00:13:45,433 and just like what you've seen in amino acids, 285 00:13:45,457 --> 00:13:48,099 it doesn't really matter how you change the environment, 286 00:13:48,123 --> 00:13:50,747 it's very robust, it's going to reflect the environment. 287 00:13:50,771 --> 00:13:53,720 So I'm going to show you now a little experiment that we did. 288 00:13:53,744 --> 00:13:55,128 And I have to explain to you, 289 00:13:55,152 --> 00:13:56,334 the top of this graph 290 00:13:56,358 --> 00:13:59,102 shows you that frequency distribution that I talked about. 291 00:13:59,126 --> 00:14:02,933 Here, that's the lifeless environment 292 00:14:02,957 --> 00:14:06,369 where each instruction occurs at an equal frequency. 293 00:14:07,304 --> 00:14:12,297 And below there, I show, in fact, the mutation rate in the environment. 294 00:14:12,321 --> 00:14:15,624 And I'm starting this at a mutation rate that is so high 295 00:14:15,648 --> 00:14:19,614 that even if you would drop a replicating program 296 00:14:19,638 --> 00:14:23,763 that would otherwise happily grow up to fill the entire world, 297 00:14:23,787 --> 00:14:26,797 if you drop it in, it gets mutated to death immediately. 298 00:14:26,821 --> 00:14:32,167 So there is no life possible at that type of mutation rate. 299 00:14:32,191 --> 00:14:36,227 But then I'm going to slowly turn down the heat, so to speak, 300 00:14:36,251 --> 00:14:38,436 and then there's this viability threshold 301 00:14:38,460 --> 00:14:42,352 where now it would be possible for a replicator to actually live. 302 00:14:42,376 --> 00:14:47,721 And indeed, we're going to be dropping these guys into that soup all the time. 303 00:14:48,159 --> 00:14:49,795 So let's see what that looks like. 304 00:14:49,819 --> 00:14:52,817 So first, nothing, nothing, nothing. 305 00:14:52,841 --> 00:14:54,656 Too hot, too hot. 306 00:14:54,680 --> 00:14:56,976 Now the viability threshold is reached, 307 00:14:57,000 --> 00:15:01,492 and the frequency distribution has dramatically changed 308 00:15:01,516 --> 00:15:02,992 and, in fact, stabilizes. 309 00:15:03,016 --> 00:15:04,526 And now what I did there 310 00:15:04,550 --> 00:15:08,148 is, I was being nasty, I just turned up the heat again and again. 311 00:15:08,172 --> 00:15:10,518 And of course, it reaches the viability threshold. 312 00:15:10,542 --> 00:15:13,410 And I'm just showing this to you again because it's so nice. 313 00:15:13,434 --> 00:15:14,976 You hit the viability threshold. 314 00:15:15,000 --> 00:15:16,976 The distribution changes to "alive!" 315 00:15:17,431 --> 00:15:20,648 And then, once you hit the threshold 316 00:15:20,672 --> 00:15:24,721 where the mutation rate is so high that you cannot self-reproduce, 317 00:15:24,745 --> 00:15:29,666 you cannot copy the information forward to your offspring 318 00:15:29,690 --> 00:15:34,420 without making so many mistakes that your ability to replicate vanishes. 319 00:15:34,444 --> 00:15:36,303 And then, that signature is lost. 320 00:15:37,956 --> 00:15:39,662 What do we learn from that? 321 00:15:39,686 --> 00:15:43,482 Well, I think we learn a number of things from that. 322 00:15:43,506 --> 00:15:44,976 One of them is, 323 00:15:45,000 --> 00:15:50,224 if we are able to think about life in abstract terms -- 324 00:15:50,248 --> 00:15:52,879 and we're not talking about things like plants, 325 00:15:52,903 --> 00:15:54,828 and we're not talking about amino acids, 326 00:15:54,852 --> 00:15:56,616 and we're not talking about bacteria, 327 00:15:56,640 --> 00:15:58,750 but we think in terms of processes -- 328 00:15:58,774 --> 00:16:00,976 then we could start to think about life 329 00:16:01,000 --> 00:16:03,619 not as something that is so special to Earth, 330 00:16:03,643 --> 00:16:06,153 but that, in fact, could exist anywhere. 331 00:16:06,177 --> 00:16:10,490 Because it really only has to do with these concepts of information, 332 00:16:10,514 --> 00:16:14,572 of storing information within physical substrates -- 333 00:16:14,596 --> 00:16:18,612 anything: bits, nucleic acids, anything that's an alphabet -- 334 00:16:18,636 --> 00:16:20,515 and make sure that there's some process 335 00:16:20,539 --> 00:16:24,254 so that this information can be stored for much longer than you would expect -- 336 00:16:24,816 --> 00:16:29,152 the time scales for the deterioration of information. 337 00:16:29,176 --> 00:16:32,344 And if you can do that, then you have life. 338 00:16:32,368 --> 00:16:34,622 So the first thing that we learn 339 00:16:34,646 --> 00:16:39,858 is that it is possible to define life in terms of processes alone, 340 00:16:39,882 --> 00:16:44,859 without referring at all to the type of things that we hold dear, 341 00:16:44,883 --> 00:16:47,554 as far as the type of life on Earth is. 342 00:16:47,578 --> 00:16:50,219 And that, in a sense, removes us again, 343 00:16:50,243 --> 00:16:53,074 like all of our scientific discoveries, or many of them -- 344 00:16:53,098 --> 00:16:55,869 it's this continuous dethroning of man -- 345 00:16:55,893 --> 00:16:58,620 of how we think we're special because we're alive. 346 00:16:58,644 --> 00:17:01,700 Well, we can make life; we can make life in the computer. 347 00:17:01,724 --> 00:17:03,541 Granted, it's limited, 348 00:17:03,565 --> 00:17:08,682 but we have learned what it takes in order to actually construct it. 349 00:17:08,706 --> 00:17:11,494 And once we have that, 350 00:17:11,518 --> 00:17:14,165 then it is not such a difficult task anymore 351 00:17:14,189 --> 00:17:18,341 to say, if we understand the fundamental processes 352 00:17:18,365 --> 00:17:21,707 that do not refer to any particular substrate, 353 00:17:21,731 --> 00:17:25,499 then we can go out and try other worlds, 354 00:17:25,523 --> 00:17:29,304 figure out what kind of chemical alphabets might there be, 355 00:17:30,033 --> 00:17:34,758 figure enough about the normal chemistry, the geochemistry of the planet, 356 00:17:34,782 --> 00:17:38,556 so that we know what this distribution would look like in the absence of life, 357 00:17:38,580 --> 00:17:41,551 and then look for large deviations from this -- 358 00:17:41,575 --> 00:17:46,687 this thing sticking out, which says, "This chemical really shouldn't be there." 359 00:17:46,711 --> 00:17:48,666 Now we don't know that there's life then, 360 00:17:48,690 --> 00:17:49,897 but we could say, 361 00:17:49,921 --> 00:17:53,690 "Well at least I'm going to have to take a look very precisely at this chemical 362 00:17:53,714 --> 00:17:55,759 and see where it comes from." 363 00:17:55,783 --> 00:17:59,494 And that might be our chance of actually discovering life 364 00:17:59,518 --> 00:18:01,637 when we cannot visibly see it. 365 00:18:01,661 --> 00:18:06,225 And so that's really the only take-home message that I have for you. 366 00:18:06,249 --> 00:18:10,480 Life can be less mysterious than we make it out to be 367 00:18:10,504 --> 00:18:13,709 when we try to think about how it would be on other planets. 368 00:18:14,280 --> 00:18:17,667 And if we remove the mystery of life, 369 00:18:17,691 --> 00:18:22,376 then I think it is a little bit easier for us to think about how we live, 370 00:18:22,400 --> 00:18:25,458 and how perhaps we're not as special as we always think we are. 371 00:18:25,482 --> 00:18:27,728 And I'm going to leave you with that. 372 00:18:27,752 --> 00:18:28,976 And thank you very much. 373 00:18:29,000 --> 00:18:31,174 (Applause)