1 00:00:00,560 --> 00:00:03,760 Speaker 1: Welcome to Stuff You Missed in History Class from how 2 00:00:03,800 --> 00:00:13,680 Speaker 1: Stuff Works dot com. Hello, and welcome to the podcast. 3 00:00:13,760 --> 00:00:16,200 Speaker 1: I'm far A Dowdy and I'm Deblina chokoate boarding, and 4 00:00:16,280 --> 00:00:19,159 Speaker 1: today we're going to be talking about Alan Turing. And 5 00:00:19,200 --> 00:00:23,200 Speaker 1: he's considered the father of computer science, the father of 6 00:00:23,320 --> 00:00:27,560 Speaker 1: artificial intelligence, and also one of the most important wartime 7 00:00:27,600 --> 00:00:31,360 Speaker 1: code breakers in World War Two. So quite a resume 8 00:00:31,680 --> 00:00:34,479 Speaker 1: just right off the bat there, and for listeners with 9 00:00:34,560 --> 00:00:37,559 Speaker 1: a more literary bent, he's also been called the Shelley 10 00:00:37,600 --> 00:00:40,320 Speaker 1: of science, which is a name I kind of took 11 00:00:40,320 --> 00:00:42,479 Speaker 1: a shine to you. Yeah, and others have too. He's 12 00:00:42,520 --> 00:00:46,720 Speaker 1: been a really popular podcast suggestion, though his resumes focus 13 00:00:46,800 --> 00:00:49,440 Speaker 1: on math and technology has always kind of scared us 14 00:00:49,440 --> 00:00:51,600 Speaker 1: off a little bit. I think, I mean things like 15 00:00:51,720 --> 00:00:57,760 Speaker 1: number theory, probability, computer programs, stung our usual subject matter stuff. 16 00:00:57,760 --> 00:01:01,480 Speaker 1: I'm I'm honestly a little scared to get into too deeply. 17 00:01:01,600 --> 00:01:05,520 Speaker 1: But fortunately some of his work really transcends the arcane. 18 00:01:05,560 --> 00:01:08,720 Speaker 1: It's it's understandable if you put some effort into it. 19 00:01:09,040 --> 00:01:12,360 Speaker 1: And there's a wealth of biographical materials to which I 20 00:01:12,400 --> 00:01:14,600 Speaker 1: feel like the last few podcasts I've done that has 21 00:01:14,680 --> 00:01:16,600 Speaker 1: not been the case, So it was a little it 22 00:01:16,680 --> 00:01:19,360 Speaker 1: was a little refreshing really to research Alan Turning and 23 00:01:19,440 --> 00:01:21,920 Speaker 1: know that there's so much out there about this man. 24 00:01:21,959 --> 00:01:25,560 Speaker 1: There are m I T lectures, there's a digital archive 25 00:01:25,640 --> 00:01:28,839 Speaker 1: at Alan Turing dot net. Their articles in just about 26 00:01:28,920 --> 00:01:32,120 Speaker 1: every science journal you can name, and there's a house 27 00:01:32,160 --> 00:01:35,959 Speaker 1: Stuff Works podcast. Yeah, Jonathan and Chris talked about Turin's 28 00:01:36,040 --> 00:01:38,840 Speaker 1: life less fall on tech stuff and so that's a 29 00:01:38,840 --> 00:01:40,720 Speaker 1: great place to turn if you want to a little 30 00:01:40,720 --> 00:01:44,759 Speaker 1: more of an in depth discussion on programming. Yeah, specifically, 31 00:01:44,920 --> 00:01:48,760 Speaker 1: I was glad though that even they admitted that the 32 00:01:48,800 --> 00:01:52,000 Speaker 1: math was kind of tricky to discuss. It's just so 33 00:01:52,360 --> 00:01:55,000 Speaker 1: high level. But they do really do a good job 34 00:01:55,040 --> 00:01:58,400 Speaker 1: covering the programming and in that side of Turning story. 35 00:01:58,480 --> 00:02:01,680 Speaker 1: But it's also June, which is Pride Month, and that's 36 00:02:01,680 --> 00:02:05,720 Speaker 1: why we've picked Turing for today's topic. He's a great, 37 00:02:05,960 --> 00:02:10,160 Speaker 1: if tragic example of a remarkable man, really a genius 38 00:02:10,240 --> 00:02:14,480 Speaker 1: whose life was so clearly defined by his homosexuality and 39 00:02:14,760 --> 00:02:17,760 Speaker 1: reminded me a lot of Oscar Wilde, who Katie and 40 00:02:17,760 --> 00:02:20,600 Speaker 1: I covered last year for Pride Month. He was another 41 00:02:20,639 --> 00:02:24,920 Speaker 1: man who was really destroyed by prejudice at the absolute 42 00:02:25,040 --> 00:02:28,200 Speaker 1: height of his achievement. So it's a great story to 43 00:02:28,280 --> 00:02:32,240 Speaker 1: learn about, and it's it's good to know about Turing's achievements, 44 00:02:32,280 --> 00:02:35,320 Speaker 1: but it is also a really, really sad story. Yeah 45 00:02:35,320 --> 00:02:37,040 Speaker 1: it is. But before we get to that, we're going 46 00:02:37,080 --> 00:02:39,400 Speaker 1: to start sort of with the beginnings of his life. 47 00:02:39,480 --> 00:02:44,120 Speaker 1: Alan Mathieson Turing was born June nine, twelve in London 48 00:02:44,480 --> 00:02:47,240 Speaker 1: to a member of the Indian civil service. His father 49 00:02:47,520 --> 00:02:50,840 Speaker 1: actually served in the Madrass presidency and his mother's father 50 00:02:50,960 --> 00:02:53,880 Speaker 1: was the chief engineer of the Madrass railways. But Turning 51 00:02:53,880 --> 00:02:56,440 Speaker 1: didn't grow up in India. Instead, his parents had the 52 00:02:56,480 --> 00:02:58,840 Speaker 1: kids fostered in British homes, which, as you can imagine, 53 00:02:58,880 --> 00:03:01,600 Speaker 1: was pretty lonely, and his parents didn't even come back 54 00:03:01,639 --> 00:03:04,920 Speaker 1: to England until nine. Yeah, not until his dad retired. 55 00:03:05,080 --> 00:03:08,640 Speaker 1: So he spent prep school trying to do as much 56 00:03:08,960 --> 00:03:11,280 Speaker 1: science and math as he could get away with, which 57 00:03:11,320 --> 00:03:14,640 Speaker 1: at the time it wasn't really the agenda. I guess 58 00:03:14,639 --> 00:03:17,680 Speaker 1: he would be an outstanding student these days, but his 59 00:03:17,760 --> 00:03:21,600 Speaker 1: skepticism and his curiosity also sometimes got him in trouble 60 00:03:21,680 --> 00:03:25,480 Speaker 1: with with the authority figures at school. But in nineteen 61 00:03:25,520 --> 00:03:30,560 Speaker 1: twenty eight, he had his first experience of true intellectual stimulation. 62 00:03:30,639 --> 00:03:33,720 Speaker 1: He made friends with a boy who was one year 63 00:03:33,720 --> 00:03:37,720 Speaker 1: ahead of him, Christopher more Cum and Jonathan and Chris. 64 00:03:37,760 --> 00:03:39,880 Speaker 1: The way they explained this, I really liked it the 65 00:03:39,880 --> 00:03:44,080 Speaker 1: way they explained the friendship. Essentially, the two kids could 66 00:03:44,080 --> 00:03:46,880 Speaker 1: bounce ideas off of each other and combine what they 67 00:03:46,920 --> 00:03:50,080 Speaker 1: knew and really come away from it with a deeper understanding. 68 00:03:50,120 --> 00:03:53,440 Speaker 1: So sort of a friendship of two minds that was 69 00:03:53,840 --> 00:03:57,960 Speaker 1: really influential in the young Turing's life. Yeah. So when 70 00:03:58,080 --> 00:04:01,720 Speaker 1: Marcum died suddenly in nineteen third, the teenage Churing was 71 00:04:01,840 --> 00:04:04,960 Speaker 1: left wondering what happened to Markham's consciousness. He was pretty 72 00:04:05,000 --> 00:04:08,040 Speaker 1: devastated and and wanted to explore that idea further. So 73 00:04:08,120 --> 00:04:10,960 Speaker 1: for three years he wrote letters to Markham's mother trying 74 00:04:10,960 --> 00:04:14,280 Speaker 1: to figure out the relationship between mind and matter. And 75 00:04:14,320 --> 00:04:16,920 Speaker 1: that's a quest that would later define his work and 76 00:04:17,080 --> 00:04:19,159 Speaker 1: artificial intelligence, which you're going to talk about a little 77 00:04:19,160 --> 00:04:20,719 Speaker 1: more in a few minutes. Yeah, I will definitely be 78 00:04:20,760 --> 00:04:24,040 Speaker 1: talking about that. But in October nine, so well, he's 79 00:04:24,080 --> 00:04:27,360 Speaker 1: really in the middle of his grief in and this 80 00:04:27,480 --> 00:04:31,760 Speaker 1: new look into the relationship between mind and matter. He 81 00:04:31,880 --> 00:04:35,440 Speaker 1: goes off to college King's College, Cambridge, and of course 82 00:04:35,520 --> 00:04:39,200 Speaker 1: he studies math, and it was really a different inspiring 83 00:04:39,360 --> 00:04:42,080 Speaker 1: environment for him to one where he could think creatively. 84 00:04:42,279 --> 00:04:46,760 Speaker 1: He could study things like philosophy and economics and surround 85 00:04:46,839 --> 00:04:51,080 Speaker 1: himself by intelligent people, and also recognized his own sexuality, 86 00:04:51,200 --> 00:04:55,599 Speaker 1: and he socialized with some of the anti war intellectual circle. 87 00:04:55,680 --> 00:04:59,719 Speaker 1: But his politics weren't really sharply defined during this period. 88 00:04:59,760 --> 00:05:02,880 Speaker 1: His in recreation was athletic. He liked running and rowing 89 00:05:02,960 --> 00:05:07,080 Speaker 1: and failing, and of course doing math. Yeah, by nineteen 90 00:05:07,160 --> 00:05:09,800 Speaker 1: thirty four he had received a distinguished degree, and by 91 00:05:09,880 --> 00:05:12,719 Speaker 1: nineteen thirty five, at age twenty two, he got a 92 00:05:12,760 --> 00:05:16,800 Speaker 1: fellowship to King's College. So well on this intellectual path 93 00:05:16,880 --> 00:05:19,600 Speaker 1: of his. But it was in nineteen thirty five that 94 00:05:19,680 --> 00:05:23,720 Speaker 1: Turing started tackling and intriguing mathematical question, and that's the 95 00:05:23,839 --> 00:05:27,960 Speaker 1: question of decidability. And during that process he envisions a 96 00:05:28,000 --> 00:05:32,200 Speaker 1: machine that could complete computational operations just like the human brain. 97 00:05:32,839 --> 00:05:36,359 Speaker 1: The Turing machine at that point was purely theoretical, but 98 00:05:36,440 --> 00:05:38,799 Speaker 1: it could perform any kind of operation that was programmed 99 00:05:38,839 --> 00:05:42,960 Speaker 1: to do play chess, calculate numbers, anything like that, and 100 00:05:43,040 --> 00:05:46,280 Speaker 1: that idea develops into the idea of a universal Turing 101 00:05:46,320 --> 00:05:51,039 Speaker 1: machine which could handle any task, and individual touring machine could. So, 102 00:05:51,160 --> 00:05:54,239 Speaker 1: for example, if the Turing machine is the early computer program, 103 00:05:54,279 --> 00:05:57,719 Speaker 1: the universal machine would be the early computer. The one 104 00:05:57,800 --> 00:06:00,680 Speaker 1: machine that can do any task is programmed to do. Yeah. 105 00:06:00,720 --> 00:06:04,560 Speaker 1: And a guy named b Jack Copeland described the significance 106 00:06:04,720 --> 00:06:07,120 Speaker 1: of this creation in an M I. T. Lecture. And 107 00:06:07,160 --> 00:06:10,359 Speaker 1: it really helped me understand how important it was because 108 00:06:10,680 --> 00:06:13,480 Speaker 1: it might seem a little old hat if you if 109 00:06:13,520 --> 00:06:16,360 Speaker 1: you just look at it like a computer or computer program, 110 00:06:16,440 --> 00:06:20,160 Speaker 1: he said. Nowadays, when nearly everyone owns the physical realization 111 00:06:20,240 --> 00:06:23,680 Speaker 1: of a universal turning machine, Turing's idea of a one 112 00:06:23,720 --> 00:06:27,320 Speaker 1: stop shop computing machine is apt to seem as obvious 113 00:06:27,360 --> 00:06:30,679 Speaker 1: as the wheel. But in nineteen thirty six, engineers thought 114 00:06:30,720 --> 00:06:35,000 Speaker 1: in terms of building specific machines for particular purposes. So 115 00:06:35,360 --> 00:06:38,400 Speaker 1: this was really a revolutionary idea at the time, and 116 00:06:38,920 --> 00:06:42,279 Speaker 1: of course some people realized that, but not everyone knew 117 00:06:42,320 --> 00:06:46,039 Speaker 1: the full implications of of what this idea would eventually 118 00:06:46,200 --> 00:06:48,039 Speaker 1: come to. Yeah, And it would be more than a 119 00:06:48,080 --> 00:06:51,359 Speaker 1: decade before the physical realization of a turning machine was 120 00:06:51,360 --> 00:06:55,080 Speaker 1: actually built. Until then, Touring continuing continued his studies at 121 00:06:55,080 --> 00:06:57,760 Speaker 1: Princeton and then returned to England and Cambridge before the 122 00:06:57,760 --> 00:07:00,280 Speaker 1: outbreak of World War two, and then on the first 123 00:07:00,279 --> 00:07:02,560 Speaker 1: full day of the war he joined the Government Code 124 00:07:02,560 --> 00:07:05,280 Speaker 1: and Cipher School, whose headquarters were at the now famous 125 00:07:05,279 --> 00:07:08,760 Speaker 1: Bletchley Park in London. Yeah, and the g CCS was 126 00:07:08,960 --> 00:07:12,080 Speaker 1: busy bringing together all of the country's top minds at 127 00:07:12,120 --> 00:07:15,600 Speaker 1: this point, so mathematicians like Touring, but also chess players 128 00:07:15,720 --> 00:07:20,000 Speaker 1: and egyptologists, all sorts of smart people with different kinds 129 00:07:20,040 --> 00:07:23,679 Speaker 1: of skills, anyone who they hoped might lend insight into 130 00:07:23,680 --> 00:07:26,720 Speaker 1: breaking German codes, which was what they were all about 131 00:07:26,800 --> 00:07:28,800 Speaker 1: in the chief Code at the time. The one that 132 00:07:29,200 --> 00:07:32,680 Speaker 1: was really giving them the most trouble was the Enigma, 133 00:07:32,840 --> 00:07:36,640 Speaker 1: and Polish Cryptanalysis had been working on the Enigma for 134 00:07:36,680 --> 00:07:39,440 Speaker 1: a really long time since nineteen thirty two, and they 135 00:07:39,440 --> 00:07:43,400 Speaker 1: had created a code breaking machine called the Bomba a 136 00:07:43,400 --> 00:07:46,760 Speaker 1: few years after that, but by nineteen thirty nine, Touring 137 00:07:46,840 --> 00:07:49,480 Speaker 1: and others were helping to create a new machine, one 138 00:07:49,560 --> 00:07:52,440 Speaker 1: that could adapt to the Enigma because it got to 139 00:07:52,440 --> 00:07:55,680 Speaker 1: where the Germans were changing the codes every twenty four 140 00:07:55,720 --> 00:07:59,080 Speaker 1: hours pretty much. So he helped develop a new machine 141 00:07:59,120 --> 00:08:04,080 Speaker 1: called the Bomb, which could DECIPHERLOFBAFA Enigma communications. There's a 142 00:08:04,120 --> 00:08:08,760 Speaker 1: really neat British Heritage article by Gene Paskey about Bletchley Park, 143 00:08:08,880 --> 00:08:11,040 Speaker 1: which I recommend if you just sort of want to 144 00:08:11,040 --> 00:08:12,840 Speaker 1: get a picture of it. We were actually talking about 145 00:08:12,920 --> 00:08:15,640 Speaker 1: this might be a good episode in itself, but I 146 00:08:15,640 --> 00:08:19,120 Speaker 1: hope we don't give too much away. It nicely describes 147 00:08:19,640 --> 00:08:23,440 Speaker 1: rooms full of these machines and the operators who maintain them. 148 00:08:23,440 --> 00:08:27,320 Speaker 1: And in case you think that they're little, tiny devices 149 00:08:27,400 --> 00:08:30,400 Speaker 1: like we're used to today, little electronic devices, they're not 150 00:08:30,560 --> 00:08:35,000 Speaker 1: in any sense like that. They are large mechanical machines 151 00:08:35,120 --> 00:08:37,120 Speaker 1: that required a lot of upkeep. They had to be 152 00:08:37,200 --> 00:08:42,040 Speaker 1: kept clean, um they were They took up the room essentially. 153 00:08:42,760 --> 00:08:45,640 Speaker 1: So these really big machines. They helped crack the Air 154 00:08:45,720 --> 00:08:49,280 Speaker 1: Force Enigma, but the German naval Enigma was kind of 155 00:08:49,320 --> 00:08:52,560 Speaker 1: a tougher nut to crack and also critical for winning 156 00:08:52,559 --> 00:08:55,440 Speaker 1: the Battle of the Atlantic. So Turing had worked out 157 00:08:55,440 --> 00:08:57,640 Speaker 1: part of the code in nineteen thirty nine, but the 158 00:08:57,720 --> 00:09:00,400 Speaker 1: big break in the situation came courtesy of a Royal 159 00:09:00,480 --> 00:09:04,160 Speaker 1: Navy when they captured an Enigma machine and code book 160 00:09:04,200 --> 00:09:07,280 Speaker 1: from a U boat, so by June one U boat 161 00:09:07,320 --> 00:09:10,160 Speaker 1: traffic was decipherable. Yeah, they have cracked the code, and 162 00:09:10,679 --> 00:09:14,400 Speaker 1: by early nineteen forty two, Bletchley Park was decoding thirty 163 00:09:14,480 --> 00:09:18,080 Speaker 1: nine thousand German transmissions a month, and of course some 164 00:09:18,200 --> 00:09:21,800 Speaker 1: of those were complaints about the underwear splitting down the 165 00:09:21,840 --> 00:09:23,880 Speaker 1: middle is in that type of thing, but also some 166 00:09:24,280 --> 00:09:28,120 Speaker 1: really serious communications in there. It rose to an eventual 167 00:09:28,520 --> 00:09:33,760 Speaker 1: eighty four thousand transmissions a month, so pretty astonishing figure. 168 00:09:33,880 --> 00:09:37,240 Speaker 1: And with the nineteen forty three breaking of Germany's high 169 00:09:37,360 --> 00:09:41,920 Speaker 1: level binary teleprinter code, which was what Hitler himself used, 170 00:09:41,960 --> 00:09:45,880 Speaker 1: and high members of his government Um Churchill, was able 171 00:09:45,960 --> 00:09:49,800 Speaker 1: to read Hitler's mail before Hitler could read it. According 172 00:09:49,880 --> 00:09:53,800 Speaker 1: to Posh's article, something I thought was interesting and something 173 00:09:53,840 --> 00:09:56,520 Speaker 1: I never knew about Bletchley Park, I mean neither. But 174 00:09:56,600 --> 00:09:59,080 Speaker 1: it turns out the combined efforts of Bletchley Park shortened 175 00:09:59,080 --> 00:10:02,199 Speaker 1: the war by two years, and for his part, Turing 176 00:10:02,280 --> 00:10:05,240 Speaker 1: received the Order of the British Empire, which was one 177 00:10:05,280 --> 00:10:07,600 Speaker 1: of the most prestigious awards you could get. Yeah, and 178 00:10:07,640 --> 00:10:10,040 Speaker 1: so after the war he's looking for a new job, 179 00:10:10,120 --> 00:10:13,800 Speaker 1: and he was recruited to the National Physics laboratory and 180 00:10:14,280 --> 00:10:17,920 Speaker 1: the task, conveniently enough was to design and build an 181 00:10:17,920 --> 00:10:22,120 Speaker 1: electronic computer, so essentially a real Turing machine. Seems like 182 00:10:22,160 --> 00:10:24,280 Speaker 1: just the guy to bring in to do this, And 183 00:10:24,600 --> 00:10:27,840 Speaker 1: he called his new design the Automatic Computing Engine, which 184 00:10:27,840 --> 00:10:32,600 Speaker 1: has the lovely acronym ACE. But it made a good computer, uh, 185 00:10:32,640 --> 00:10:36,440 Speaker 1: and it was a really ambitious advanced design it. If 186 00:10:36,480 --> 00:10:38,559 Speaker 1: it had been built, it would have had the memory 187 00:10:38,559 --> 00:10:42,640 Speaker 1: capacity of an early MAX So that's pretty astounding if 188 00:10:42,640 --> 00:10:45,640 Speaker 1: you consider this immediately after a World War two. Yeah, 189 00:10:45,679 --> 00:10:48,880 Speaker 1: but things moved more slowly than they had at Bletchley Park. 190 00:10:49,000 --> 00:10:51,360 Speaker 1: There was lots of red tape to deal with, and 191 00:10:51,400 --> 00:10:54,800 Speaker 1: Turing's colleagues thought that the original ACE design was too 192 00:10:54,920 --> 00:10:57,559 Speaker 1: much and opted for a smaller machine, which was called 193 00:10:57,840 --> 00:11:01,079 Speaker 1: the Pilot Model ACE. So part of the problem here 194 00:11:01,120 --> 00:11:06,319 Speaker 1: was that Turing's wartime achievements were unrecognized due to their secrecy. Yeah, 195 00:11:06,360 --> 00:11:09,360 Speaker 1: he couldn't go out and say, well, guys, at Bletchley Park, 196 00:11:09,440 --> 00:11:12,160 Speaker 1: I did this. I mean, he couldn't talk about any 197 00:11:12,200 --> 00:11:14,959 Speaker 1: of that stuff. Yeah, he couldn't brag on himself. So 198 00:11:15,120 --> 00:11:18,280 Speaker 1: to relieve the frustration and the stress of the situation, 199 00:11:18,320 --> 00:11:21,720 Speaker 1: he started long distance running and it took an injury 200 00:11:21,840 --> 00:11:24,679 Speaker 1: actually to prevent him from qualifying for the ninety eight 201 00:11:24,760 --> 00:11:26,840 Speaker 1: Olympic marathon team. So he was pretty good at it. 202 00:11:27,200 --> 00:11:29,360 Speaker 1: It's really good at it. It's it's one of those 203 00:11:29,920 --> 00:11:31,520 Speaker 1: I don't know, it's like a cherry on top for 204 00:11:31,640 --> 00:11:33,959 Speaker 1: somebody with so many talents that they would also be 205 00:11:34,040 --> 00:11:35,760 Speaker 1: an amazing athlete. Well, I was going to say, it's 206 00:11:35,760 --> 00:11:39,960 Speaker 1: almost not fair, but you're kinder than I am, obviously. Yeah, well, 207 00:11:40,280 --> 00:11:42,640 Speaker 1: whatever way you look at it. But by this point, 208 00:11:42,840 --> 00:11:46,559 Speaker 1: delays meant that the National Physics Laboratory wasn't going to 209 00:11:46,640 --> 00:11:50,760 Speaker 1: be the first place that built the first working electronics 210 00:11:50,800 --> 00:11:55,480 Speaker 1: stored program digital computer. That honor went to Manchester University 211 00:11:55,920 --> 00:12:00,839 Speaker 1: and it happened in June. So Turing obviously frustrated by 212 00:12:00,920 --> 00:12:03,880 Speaker 1: his his time at the National Physics Laboratory, and they 213 00:12:03,880 --> 00:12:06,360 Speaker 1: got beat out. Yeah, they got beat out. He wasn't 214 00:12:06,360 --> 00:12:11,720 Speaker 1: really listened to his achievements, and accomplishments weren't really appreciated 215 00:12:11,760 --> 00:12:15,320 Speaker 1: to the the level they deserved to be. So he 216 00:12:15,360 --> 00:12:20,400 Speaker 1: went to work in Manchester, oddly enough as the deputy director, 217 00:12:20,440 --> 00:12:23,480 Speaker 1: even though there was no director of the program. Kind 218 00:12:23,480 --> 00:12:27,160 Speaker 1: of a strange little detail there. Yeah, but he designed 219 00:12:27,160 --> 00:12:30,079 Speaker 1: the programming system of the Ferronti Mark one, the first 220 00:12:30,120 --> 00:12:33,800 Speaker 1: commercially available digital electronic computer, so hopefully that was a 221 00:12:33,840 --> 00:12:37,960 Speaker 1: little solace for him. Consolation program yeah. Um. And it 222 00:12:38,040 --> 00:12:40,959 Speaker 1: was also during his time at Manchester that Turing started 223 00:12:41,000 --> 00:12:45,679 Speaker 1: to hypothesize about what would later be known as artificial intelligence, 224 00:12:45,720 --> 00:12:48,240 Speaker 1: and and I thought it was it was interesting, and 225 00:12:48,240 --> 00:12:50,920 Speaker 1: this is something that's kind of, I guess, difficult for 226 00:12:50,960 --> 00:12:54,400 Speaker 1: me to talk about with my limited knowledge of computer 227 00:12:54,640 --> 00:12:57,160 Speaker 1: programming and science. I just work on a computer. I 228 00:12:57,160 --> 00:13:02,400 Speaker 1: don't know what happens inside. But I was impressed that, Um, 229 00:13:02,440 --> 00:13:06,199 Speaker 1: even though he had he had the skill to work 230 00:13:06,200 --> 00:13:09,560 Speaker 1: on developing this field, he put the machine to use 231 00:13:09,720 --> 00:13:12,040 Speaker 1: right away. So I'm sure he was still considering about 232 00:13:12,040 --> 00:13:14,360 Speaker 1: how it could be advanced. But he started looking for 233 00:13:14,440 --> 00:13:17,280 Speaker 1: ways to use the Ferronti Mark one, which I thought 234 00:13:17,320 --> 00:13:19,360 Speaker 1: was was pretty neat. Yeah. It kind of went back 235 00:13:19,400 --> 00:13:22,440 Speaker 1: to his old interest in the connection between mind and matter, 236 00:13:22,559 --> 00:13:26,520 Speaker 1: and in nineteen fifty Touring wrote a paper called Computing, 237 00:13:26,520 --> 00:13:30,160 Speaker 1: Machinery and Intelligence in the journal Mind. In it, he 238 00:13:30,240 --> 00:13:33,960 Speaker 1: proposed something called an imitation test. Today that's called the 239 00:13:34,040 --> 00:13:37,760 Speaker 1: Turing test, and the test basically provided a way to 240 00:13:37,920 --> 00:13:42,319 Speaker 1: judge the intelligence of a machine without bias. So an interrogator, 241 00:13:42,360 --> 00:13:45,480 Speaker 1: for example, would sit in an isolated room from two subjects, 242 00:13:46,000 --> 00:13:49,640 Speaker 1: one a person, one a machine, and the interrogator would 243 00:13:49,640 --> 00:13:52,600 Speaker 1: ask them both questions, and if the interrogator couldn't tell 244 00:13:52,600 --> 00:13:55,800 Speaker 1: who was who, then that meant the machine was thinking. Yeah, 245 00:13:55,880 --> 00:14:01,040 Speaker 1: it had intelligence in some definable way. And Turing even 246 00:14:01,040 --> 00:14:03,800 Speaker 1: predicted he had a lot of confidence in computers. He 247 00:14:03,880 --> 00:14:07,280 Speaker 1: predicted that by the year two thousand, a computer would 248 00:14:07,280 --> 00:14:11,280 Speaker 1: be so good at this game this, this Turing test, 249 00:14:11,880 --> 00:14:15,360 Speaker 1: an interrogator would not have more than a seventy percent 250 00:14:15,480 --> 00:14:18,720 Speaker 1: chance of correctly identifying who is who after five minutes. 251 00:14:18,880 --> 00:14:23,360 Speaker 1: And that is a very ambitious goal because according to 252 00:14:23,480 --> 00:14:28,080 Speaker 1: Encyclopedia Britannica, no computer today has even come close to 253 00:14:28,160 --> 00:14:31,600 Speaker 1: that standard. But Turing really he did have a lot 254 00:14:31,600 --> 00:14:36,200 Speaker 1: of hopes for computers. Yeah. He also hypothesized that one day, quote, 255 00:14:36,240 --> 00:14:38,880 Speaker 1: ladies would take their computers for walks in the park 256 00:14:38,960 --> 00:14:41,560 Speaker 1: and tell each other, my little computer said such a 257 00:14:41,600 --> 00:14:43,800 Speaker 1: funny thing in the morning. I think we're a little 258 00:14:43,800 --> 00:14:47,440 Speaker 1: closer to that one than the seventy percent goal. Maybe 259 00:14:47,600 --> 00:14:50,800 Speaker 1: I don't know. I still like my doggy though. Yeah. 260 00:14:50,920 --> 00:14:55,040 Speaker 1: So Turing continued to study artificial intelligence, but also stuff 261 00:14:55,080 --> 00:14:58,200 Speaker 1: like biological growth with the FRONTI Mark one I said 262 00:14:58,200 --> 00:15:00,960 Speaker 1: that he really did put that sheen too good youth, 263 00:15:01,080 --> 00:15:04,360 Speaker 1: and his career was expanding into these different subject areas 264 00:15:04,360 --> 00:15:07,520 Speaker 1: and his recognition was also growing. He was elected as 265 00:15:07,560 --> 00:15:10,320 Speaker 1: a Fellow of the Royal Society of London in March 266 00:15:10,400 --> 00:15:13,880 Speaker 1: nineteen fifty one. That's another really prestigious honor. He was 267 00:15:14,280 --> 00:15:18,560 Speaker 1: appointed to Readership in the Theory of Computing at Manchester, 268 00:15:18,640 --> 00:15:21,880 Speaker 1: which sounds like a very modern title. But in nineteen 269 00:15:21,920 --> 00:15:24,840 Speaker 1: fifty two things took a turn for the worse in 270 00:15:24,920 --> 00:15:28,400 Speaker 1: his life after a break in in his Manchester home 271 00:15:28,520 --> 00:15:31,600 Speaker 1: and he told the police that he thought the burglar 272 00:15:31,720 --> 00:15:34,680 Speaker 1: was probably connected to a man he was quote having 273 00:15:34,720 --> 00:15:38,320 Speaker 1: an affair with, and he had been pretty open about 274 00:15:38,360 --> 00:15:41,840 Speaker 1: his sexuality since college. During his Bletchley Park days, he 275 00:15:41,880 --> 00:15:45,600 Speaker 1: had proposed to a colleague, Joan Clark, but broke it off. 276 00:15:45,640 --> 00:15:48,400 Speaker 1: He told her that he was gay and couldn't marry her. 277 00:15:48,800 --> 00:15:51,920 Speaker 1: But being so frank with the police in this way 278 00:15:52,040 --> 00:15:55,080 Speaker 1: was really dangerous because at the time homosexuality was a 279 00:15:55,120 --> 00:15:59,000 Speaker 1: felony in Great Britain, and so Turing was tried and 280 00:15:59,080 --> 00:16:03,440 Speaker 1: convicted of growth indecency, and he was faced with a 281 00:16:03,480 --> 00:16:07,720 Speaker 1: really terrible choice. Yeah. His two choices were prison or 282 00:16:07,920 --> 00:16:12,360 Speaker 1: hormone injections of estrogen, so chemical sterilization. Yeah, and he 283 00:16:12,440 --> 00:16:16,240 Speaker 1: chose the latter and also lost his security clearance as 284 00:16:16,280 --> 00:16:20,040 Speaker 1: a result, So no government codes, no government computers. And 285 00:16:20,200 --> 00:16:23,480 Speaker 1: on June seven, nineteen fifty four, he was found dead 286 00:16:23,520 --> 00:16:26,720 Speaker 1: by his housekeeper with a partially eaten cyanide laced apple 287 00:16:26,760 --> 00:16:30,120 Speaker 1: by his side. Now, some have theorized that he was 288 00:16:30,160 --> 00:16:33,440 Speaker 1: assassinated as a security risk, but it's pretty much widely 289 00:16:33,440 --> 00:16:36,880 Speaker 1: accepted nowadays that Turing committed suicide, and even then right, 290 00:16:36,960 --> 00:16:40,200 Speaker 1: and it's also accepted that Turing did kill himself in 291 00:16:40,200 --> 00:16:43,280 Speaker 1: in this particular way so that it would allow his 292 00:16:43,360 --> 00:16:46,560 Speaker 1: mother to interpret the situation as an accident, since he'd 293 00:16:46,600 --> 00:16:48,760 Speaker 1: been working with cyanide and other chemicals in his work, 294 00:16:49,120 --> 00:16:51,440 Speaker 1: so she thought that he had some cyanide on his 295 00:16:51,560 --> 00:16:55,000 Speaker 1: hand and he ate an apple and accidentally poisoned himself. 296 00:16:55,120 --> 00:16:59,360 Speaker 1: But assuming he did commit suicide, which is what most 297 00:16:59,360 --> 00:17:02,960 Speaker 1: people have feel, it's a really tragic end to to 298 00:17:04,359 --> 00:17:06,960 Speaker 1: this great life. And and at the heels of this 299 00:17:07,200 --> 00:17:11,640 Speaker 1: terrible prosecution. So in two thousand nine, Prime Minister Gordon 300 00:17:11,680 --> 00:17:15,560 Speaker 1: Brown issued a formal apology for the British Government's treatment 301 00:17:15,560 --> 00:17:18,360 Speaker 1: of Touring, and I'm going to read just part of it. 302 00:17:18,840 --> 00:17:22,240 Speaker 1: He said, Touring truly was one of those individuals we 303 00:17:22,280 --> 00:17:25,240 Speaker 1: can point to whose unique contribution it helped to turn 304 00:17:25,280 --> 00:17:27,479 Speaker 1: the tide of war. The dead of gratitude he has 305 00:17:27,480 --> 00:17:30,320 Speaker 1: owed makes it all the more horrifying. Therefore, that he 306 00:17:30,359 --> 00:17:33,440 Speaker 1: was treated so inhumanely on the behalf of the British 307 00:17:33,440 --> 00:17:36,640 Speaker 1: government and all those who live freely thanks to Alan's work, 308 00:17:37,000 --> 00:17:39,840 Speaker 1: I'm very proud to say we're sorry. You deserve so 309 00:17:39,920 --> 00:17:43,080 Speaker 1: much better. So two thousand twelve is Alan Turing Year, 310 00:17:43,560 --> 00:17:47,119 Speaker 1: and a stateside recognition has been long standing. The US 311 00:17:47,160 --> 00:17:50,399 Speaker 1: Association for Computer Machinery has given out the Touring Award 312 00:17:50,480 --> 00:17:55,040 Speaker 1: since nineteen sixty six, and if anything, as technology develops 313 00:17:55,040 --> 00:17:58,360 Speaker 1: in new areas of steady emerge, Alan Turing will probably 314 00:17:58,400 --> 00:18:00,720 Speaker 1: just become more recognized as the year's go on. Yeah, 315 00:18:00,760 --> 00:18:04,680 Speaker 1: if you think about how many career descriptions that apply 316 00:18:04,800 --> 00:18:08,040 Speaker 1: to his name, you know, father of artificial intelligence, that 317 00:18:08,119 --> 00:18:10,720 Speaker 1: sort of thing that didn't exist when he was alive. 318 00:18:11,119 --> 00:18:13,880 Speaker 1: We can only imagine that more will be added over 319 00:18:13,920 --> 00:18:18,800 Speaker 1: the years as science and technology advances. And we have 320 00:18:18,960 --> 00:18:21,439 Speaker 1: a lot of information about this kind of stuff on 321 00:18:21,480 --> 00:18:23,920 Speaker 1: our site, which we will tell you more about at 322 00:18:23,920 --> 00:18:26,439 Speaker 1: the end. But if you have anything to contribute on 323 00:18:26,440 --> 00:18:29,320 Speaker 1: this topic, you know anything else about Turion's research, or 324 00:18:29,560 --> 00:18:31,600 Speaker 1: maybe an anecdote about his life that we missed, please 325 00:18:31,640 --> 00:18:34,639 Speaker 1: write us at History Podcast at how staff works dot com. 326 00:18:34,680 --> 00:18:36,560 Speaker 1: You can also look us up on Twitter at mist 327 00:18:36,600 --> 00:18:39,159 Speaker 1: in history or on Facebook. And before we wrap up, 328 00:18:39,200 --> 00:18:40,439 Speaker 1: we thought it would be a good time to do 329 00:18:40,520 --> 00:18:43,520 Speaker 1: a little listener mail that has to do also with 330 00:18:43,560 --> 00:18:50,000 Speaker 1: a Battle of the Atlantic. We have a letter here 331 00:18:50,040 --> 00:18:53,280 Speaker 1: from Kate and she says, I just finished listening to 332 00:18:53,320 --> 00:18:55,879 Speaker 1: the sync the Bismarck podcast and wanted to share my 333 00:18:55,920 --> 00:18:59,120 Speaker 1: own tidbit from the story of the German battleship. Following 334 00:18:59,160 --> 00:19:02,399 Speaker 1: the battle, the h MS Cossack recovered floating on a 335 00:19:02,440 --> 00:19:05,600 Speaker 1: board a black and white cat from the Bismarck, renamed 336 00:19:05,600 --> 00:19:08,600 Speaker 1: Oscar and later unsinkable Sam. The cat went on to 337 00:19:08,680 --> 00:19:13,640 Speaker 1: serve on three Royal British Navy ships, surviving two more shipwrecks. 338 00:19:13,720 --> 00:19:15,760 Speaker 1: He was then retired from active duty and lived in 339 00:19:15,840 --> 00:19:19,360 Speaker 1: officers quarters in Gibraltar before being shipped to England, where 340 00:19:19,400 --> 00:19:22,360 Speaker 1: he lived the rest of his days. And she gives 341 00:19:22,400 --> 00:19:24,760 Speaker 1: us a link to a Wikipedia article that has a 342 00:19:24,800 --> 00:19:27,920 Speaker 1: portrait of Sam here. So thanks for that, Kate. That's 343 00:19:27,920 --> 00:19:30,640 Speaker 1: a very interesting tidbit. I looked at the picture. He's 344 00:19:30,720 --> 00:19:33,480 Speaker 1: the super cute cat. We always love pet stories here 345 00:19:33,680 --> 00:19:38,880 Speaker 1: at History that podcast. Yes, we're then us your pet stories, 346 00:19:39,000 --> 00:19:41,920 Speaker 1: especially they're historical because then they can turn into podcasts 347 00:19:41,920 --> 00:19:44,720 Speaker 1: every now and then, So send us your email at 348 00:19:44,800 --> 00:19:47,840 Speaker 1: History Podcast at how stuff works dot com. And again, 349 00:19:47,880 --> 00:19:50,560 Speaker 1: we also do have so many tech articles. You can 350 00:19:50,560 --> 00:19:54,040 Speaker 1: find them by visiting our website and just going right 351 00:19:54,080 --> 00:19:57,040 Speaker 1: to the tech tab and looking from there you'll find 352 00:19:57,080 --> 00:19:59,840 Speaker 1: tons of stuff that interest you if you are in 353 00:20:00,040 --> 00:20:04,680 Speaker 1: to computer science or programming, or math or or anything 354 00:20:04,760 --> 00:20:08,080 Speaker 1: like that. All of it is on our website at 355 00:20:08,200 --> 00:20:15,159 Speaker 1: www dot how staff works dot com. Be sure to 356 00:20:15,240 --> 00:20:18,000 Speaker 1: check out our new video podcast, Stuff from the Future. 357 00:20:18,359 --> 00:20:20,680 Speaker 1: Join how staff Work staff as we explore the most 358 00:20:20,680 --> 00:20:25,240 Speaker 1: promising and perplexing possibilities of tomorrow. The House Stuff Works 359 00:20:25,280 --> 00:20:28,480 Speaker 1: iPhone app has a ride. Download it today on iTunes.