1 00:00:00,560 --> 00:00:03,800 Speaker 1: Welcome to Stuff You Missed in History Class from house 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 --> 00:00:16,560 Speaker 1: I'm fair Dowdy and I'm Deblina chuk Reboarding, and today 4 00:00:16,600 --> 00:00:19,439 Speaker 1: we're going to be talking about Alan Turing. And he's 5 00:00:19,480 --> 00:00:24,600 Speaker 1: considered the father of computer science, the father of artificial intelligence, 6 00:00:25,160 --> 00:00:28,440 Speaker 1: and also one of the most important wartime code breakers 7 00:00:28,560 --> 00:00:32,279 Speaker 1: in World War Two. So quite a resume just right 8 00:00:32,360 --> 00:00:34,880 Speaker 1: off the bat there, and for listeners with a more 9 00:00:35,120 --> 00:00:38,199 Speaker 1: literary bent, he's also been called the Shelley of Science, 10 00:00:38,240 --> 00:00:40,680 Speaker 1: which is a name I kind of took a shine 11 00:00:40,680 --> 00:00:42,760 Speaker 1: to you. Yeah, and others have too. He's been a 12 00:00:42,760 --> 00:00:47,160 Speaker 1: really popular podcast suggestion, though his resumes focus on math 13 00:00:47,200 --> 00:00:49,720 Speaker 1: and technology has always kind of scared us off a 14 00:00:49,720 --> 00:00:53,120 Speaker 1: little bit. I think, I mean things like number theory, probability, 15 00:00:53,600 --> 00:00:57,920 Speaker 1: computer programs. It's not our usual subject matter stuff. I'm 16 00:00:58,080 --> 00:01:01,480 Speaker 1: 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 --> 00:01:28,399 Speaker 1: at Alan Turing dot net. Their article has in just 26 00:01:28,520 --> 00:01:31,960 Speaker 1: about every science journal you can name, and there's a 27 00:01:32,000 --> 00:01:35,280 Speaker 1: How Stuff Works podcast to Yeah, Jonathan and Chris talked 28 00:01:35,280 --> 00:01:38,520 Speaker 1: about Turing's life less fall on tech stuff and so 29 00:01:38,600 --> 00:01:40,080 Speaker 1: that's a great place to turn if you want to 30 00:01:40,319 --> 00:01:44,840 Speaker 1: a little 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: map 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 00:02:01,720 --> 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 --> 00:02:35,280 Speaker 1: but it is also a really, really sad story. Yeah 45 00:02:35,360 --> 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 00:02:50,919 --> 00:02:53,840 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 --> 00:02:58,800 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 --> 00:03:05,240 Speaker 1: to England until nine, not until his dad retired. So 55 00:03:05,600 --> 00:03:09,440 Speaker 1: he spent prep school trying to do as much science 56 00:03:09,480 --> 00:03:11,440 Speaker 1: and math as he could get away with, which at 57 00:03:11,440 --> 00:03:14,679 Speaker 1: the time it wasn't really the agenda. I guess he 58 00:03:14,720 --> 00:03:18,600 Speaker 1: would be an outstanding student these days, but his skepticism 59 00:03:18,680 --> 00:03:22,280 Speaker 1: and his curiosity also sometimes got him in trouble with 60 00:03:22,280 --> 00:03:26,000 Speaker 1: with the authority figures at school. But in nineteen twenty eight, 61 00:03:26,040 --> 00:03:30,720 Speaker 1: he had his first experience of true intellectual stimulation. He 62 00:03:30,800 --> 00:03:34,040 Speaker 1: made friends with a boy who was one year ahead 63 00:03:34,040 --> 00:03:38,000 Speaker 1: of him, Christopher Morecambe and Jonathan and Chris. The way 64 00:03:38,040 --> 00:03:40,160 Speaker 1: they explained this, I really liked it the way they 65 00:03:40,200 --> 00:03:45,040 Speaker 1: explained the friendship. Essentially, the two kids could bounce ideas 66 00:03:45,080 --> 00:03:47,360 Speaker 1: off of each other and combine what they knew and 67 00:03:47,440 --> 00:03:50,280 Speaker 1: really come away from it with a deeper understanding. So 68 00:03:50,480 --> 00:03:54,120 Speaker 1: sort of a friendship of two minds that was really 69 00:03:54,160 --> 00:03:58,400 Speaker 1: influential in the young Turing's life. Yeah, So when Marcum 70 00:03:58,560 --> 00:04:02,040 Speaker 1: died suddenly in nineteen third, the teenage Churing was left 71 00:04:02,080 --> 00:04:05,640 Speaker 1: wondering what happened to Markham's consciousness. He was pretty devastated 72 00:04:05,720 --> 00:04:08,200 Speaker 1: and and wanted to explore that idea further. So for 73 00:04:08,240 --> 00:04:11,040 Speaker 1: three years he wrote letters to Markham's mother trying to 74 00:04:11,080 --> 00:04:14,520 Speaker 1: figure out the relationship between mind and matter. And that's 75 00:04:14,520 --> 00:04:17,960 Speaker 1: a quest that would later define his work and artificial intelligence, 76 00:04:18,000 --> 00:04:19,320 Speaker 1: which you're going to talk about a little more in 77 00:04:19,320 --> 00:04:21,360 Speaker 1: a few minutes. Yea, I will definitely be talking about that. 78 00:04:21,400 --> 00:04:24,880 Speaker 1: But in October nine, so while he's really in the 79 00:04:24,920 --> 00:04:29,559 Speaker 1: middle of his grief in and this new look into 80 00:04:29,760 --> 00:04:32,360 Speaker 1: the relationship between mind and matter. He goes off to 81 00:04:32,440 --> 00:04:36,480 Speaker 1: college King's College, Cambridge, and of course he studies math, 82 00:04:36,920 --> 00:04:40,320 Speaker 1: and it was really a different inspiring environment for him 83 00:04:40,360 --> 00:04:43,360 Speaker 1: to one where he could think creatively. He could study 84 00:04:43,440 --> 00:04:48,479 Speaker 1: things like philosophy and economics and surround himself by intelligent people, 85 00:04:48,480 --> 00:04:53,000 Speaker 1: and also recognized his own sexuality, and he socialized with 86 00:04:53,279 --> 00:04:56,880 Speaker 1: some of the anti war intellectual circle. But his politics 87 00:04:56,960 --> 00:05:00,720 Speaker 1: weren't really sharply defined during this period. His in recreation 88 00:05:00,800 --> 00:05:04,000 Speaker 1: was athletic. He liked running and rowing and failing, and 89 00:05:04,040 --> 00:05:07,880 Speaker 1: of course doing math. Yeah, by nineteen thirty four he 90 00:05:07,920 --> 00:05:10,880 Speaker 1: had received a distinguished degree, and by nineteen thirty five, 91 00:05:10,920 --> 00:05:14,200 Speaker 1: at age twenty two, he got a fellowship to King's College. 92 00:05:14,720 --> 00:05:17,440 Speaker 1: So well on this intellectual path of his. But it 93 00:05:17,520 --> 00:05:21,120 Speaker 1: was in nineteen thirty five that Turing started tackling and 94 00:05:21,240 --> 00:05:25,720 Speaker 1: intriguing mathematical question, and that's the question of decidability. And 95 00:05:25,839 --> 00:05:29,400 Speaker 1: during that process he envisions a machine that could complete 96 00:05:29,480 --> 00:05:33,920 Speaker 1: computational operations just like the human brain. The Turing machine 97 00:05:34,080 --> 00:05:37,159 Speaker 1: at that point was purely theoretical, but it could perform 98 00:05:37,200 --> 00:05:40,000 Speaker 1: any kind of operation. It was programmed to do, play chess, 99 00:05:40,040 --> 00:05:44,080 Speaker 1: to calculate numbers, anything, like that, and that idea develops 100 00:05:44,120 --> 00:05:47,800 Speaker 1: into the idea of a universal Turing machine which could 101 00:05:47,800 --> 00:05:51,719 Speaker 1: handle any task, and individual touring machine could. So, for example, 102 00:05:51,760 --> 00:05:54,359 Speaker 1: if the Turing machine is the early computer program, the 103 00:05:54,440 --> 00:05:58,160 Speaker 1: universal machine would be the early computer, the one machine 104 00:05:58,200 --> 00:06:00,680 Speaker 1: that can do any task it's 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 --> 00:06:20,120 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 --> 00:06:30,680 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 --> 00:06:42,240 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 00:06:51,400 --> 00:06:55,040 Speaker 1: actually built, until then Touring continued 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 --> 00:07:05,279 Speaker 1: and Cipher School, whose headquarters were at the now famous 125 00:07:05,279 --> 00:07:09,280 Speaker 1: Fleshley Park in London. Yeah, and the GCCS was busy 126 00:07:09,560 --> 00:07:12,520 Speaker 1: bringing together all of the country's top minds at this point, 127 00:07:12,560 --> 00:07:17,320 Speaker 1: so mathematicians like Touring, that also chess players and egyptologists, 128 00:07:17,360 --> 00:07:20,520 Speaker 1: all sorts of smart people with different kinds of skills, 129 00:07:20,560 --> 00:07:24,840 Speaker 1: anyone who they hoped might lend insight into breaking German codes, 130 00:07:24,920 --> 00:07:27,440 Speaker 1: which was what they were all about in the chief 131 00:07:27,480 --> 00:07:29,880 Speaker 1: Code at the time. The one that was really giving 132 00:07:29,920 --> 00:07:34,880 Speaker 1: them the most trouble was the Enigma, and Polish Cryptanalysis 133 00:07:34,920 --> 00:07:37,280 Speaker 1: had been working on the Enigma for a really long 134 00:07:37,320 --> 00:07:40,120 Speaker 1: time since the nineteen thirty two and they had created 135 00:07:40,160 --> 00:07:43,880 Speaker 1: a code breaking machine called the Bomba a few years 136 00:07:43,920 --> 00:07:47,280 Speaker 1: after that, but by nineteen thirty nine, Touring and others 137 00:07:47,280 --> 00:07:50,000 Speaker 1: were helping to create a new machine, one that could 138 00:07:50,520 --> 00:07:52,760 Speaker 1: adapt to the Enigma, because it got to where the 139 00:07:52,760 --> 00:07:56,640 Speaker 1: Germans were changing the codes every twenty four hours pretty much, 140 00:07:56,920 --> 00:07:59,720 Speaker 1: so he helped develop a new machine called the Bomb, 141 00:07:59,720 --> 00:08:04,600 Speaker 1: which could decipher LOOFWAFA Enigma communications. There's a really neat 142 00:08:04,640 --> 00:08:08,720 Speaker 1: British Heritage article by Gene pash Key about Bletchley Park, 143 00:08:08,880 --> 00:08:11,000 Speaker 1: which I recommend if you just sort of want to 144 00:08:11,000 --> 00:08:12,800 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 --> 00:08:23,400 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 --> 00:08:30,440 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 upbeat. They had to be 152 00:08:37,200 --> 00:08:42,000 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 --> 00:08:52,520 Speaker 1: a tougher nut to crack and also critical for winning 156 00:08:52,559 --> 00:08:55,400 Speaker 1: the Battle of the Atlantic. So Turing had worked out 157 00:08:55,440 --> 00:08:57,600 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 00:09:04,160 --> 00:09:07,280 Speaker 1: from a U boat. So by June one U boat 161 00:09:07,320 --> 00:09:10,120 Speaker 1: traffic was decipherable. Yeah, they had cracked the code, and 162 00:09:10,640 --> 00:09:14,400 Speaker 1: by early nineteen forty two, Bletchley Park was decoding thirty 163 00:09:14,480 --> 00:09:18,040 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 --> 00:09:24,560 Speaker 1: middle and that type of thing, but also some really 166 00:09:24,640 --> 00:09:28,840 Speaker 1: serious communications in there. It rose to an eventual eighty 167 00:09:28,960 --> 00:09:34,000 Speaker 1: four thousand transmissions a month, so pretty astonishing figure. And 168 00:09:34,320 --> 00:09:37,600 Speaker 1: with the nineteen forty three breaking of Germany's high level 169 00:09:37,880 --> 00:09:42,199 Speaker 1: binary teleprinter code, which was what Hitler himself used, and 170 00:09:42,520 --> 00:09:46,120 Speaker 1: high members of his government um Churchill, was able to 171 00:09:46,160 --> 00:09:49,960 Speaker 1: read Hitler's mail before Hitler could read it. According to 172 00:09:50,160 --> 00:09:53,880 Speaker 1: Posh's article, something I thought was interesting and something I 173 00:09:53,920 --> 00:09:56,680 Speaker 1: never knew about Bletchley Park. Yeah, me neither. But it 174 00:09:56,679 --> 00:09:59,120 Speaker 1: turns out the combined efforts of Bletchley Park shortened the 175 00:09:59,400 --> 00:10:02,720 Speaker 1: war by two years, and for his part, Turing received 176 00:10:02,800 --> 00:10:05,320 Speaker 1: the Order of the British Empire, which was one of 177 00:10:05,320 --> 00:10:07,760 Speaker 1: the most prestigious awards you could get. Yeah. And so 178 00:10:07,840 --> 00:10:10,320 Speaker 1: after the war he's looking for a new job and 179 00:10:10,400 --> 00:10:14,840 Speaker 1: he was recruited to the National Physics Laboratory, and the task, 180 00:10:15,200 --> 00:10:18,880 Speaker 1: conveniently enough was to design and build an electronic computer, 181 00:10:19,040 --> 00:10:22,400 Speaker 1: so essentially a real Turing machine. Seems like just the 182 00:10:22,440 --> 00:10:25,120 Speaker 1: guy to bring in to do this, and he called 183 00:10:25,160 --> 00:10:28,200 Speaker 1: his new design the Automatic Computing Engine, which has the 184 00:10:28,640 --> 00:10:32,559 Speaker 1: lovely acronym ACE. Would have made a good computer, uh, 185 00:10:32,640 --> 00:10:36,439 Speaker 1: and it was a really ambitious advanced design it. If 186 00:10:36,480 --> 00:10:38,520 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 Mac. 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 00:10:45,640 --> 00:10:48,840 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 --> 00:10:54,840 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 --> 00:11:06,240 Speaker 1: was that Turing's wartime achievements were unrecognized due to their secrecy. Yeah, 195 00:11:06,360 --> 00:11:09,320 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 00:11:15,080 --> 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 00:11:21,800 --> 00:11:25,160 Speaker 1: actually to prevent him from qualifying for the nine Olympic 201 00:11:25,200 --> 00:11:27,199 Speaker 1: marathon team. So he was pretty good at it. He 202 00:11:27,320 --> 00:11:29,360 Speaker 1: was 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 00:11:31,600 --> 00:11:34,000 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 00:11:40,240 --> 00:11:42,600 Speaker 1: whatever way you look at it. But by this point, 208 00:11:42,800 --> 00:11:46,560 Speaker 1: delays meant that the National Physics Laboratory wasn't going to 209 00:11:46,640 --> 00:11:50,800 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 --> 00:12:00,800 Speaker 1: and it happened in June. So Turing obviously frustrated by 212 00:12:00,920 --> 00:12:03,840 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 --> 00:12:19,640 Speaker 1: went to work in Manchester, oddly enough as the deputy 217 00:12:19,720 --> 00:12:23,240 Speaker 1: director even though there was no director of the program. 218 00:12:23,320 --> 00:12:26,679 Speaker 1: Kind of a strange little detail there. Yeah, but he 219 00:12:26,800 --> 00:12:29,720 Speaker 1: designed the programming system of the Ferronti Mark one, the 220 00:12:29,760 --> 00:12:33,719 Speaker 1: first commercially available digital electronic computer, so hopefully that was 221 00:12:33,760 --> 00:12:37,720 Speaker 1: a little solace for a consolation program. Yeah. Um. And 222 00:12:37,800 --> 00:12:40,400 Speaker 1: it was also during his time at Manchester that Turing 223 00:12:40,520 --> 00:12:44,400 Speaker 1: started to hypothesize about what would later be known as 224 00:12:44,520 --> 00:12:48,120 Speaker 1: artificial intelligence, and and I thought it was it was interesting, 225 00:12:48,160 --> 00:12:50,800 Speaker 1: and this is something that's kind of, I guess, difficult 226 00:12:50,800 --> 00:12:53,880 Speaker 1: for me to talk about with my limited knowledge of 227 00:12:53,920 --> 00:12:57,000 Speaker 1: computer programming and science. I just work on a computer. 228 00:12:57,080 --> 00:13:02,400 Speaker 1: I don't know what happens inside. But I impressed that Um, 229 00:13:02,440 --> 00:13:06,160 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 00:13:09,679 --> 00:13:11,960 Speaker 1: right away, so I'm sure he was still considering about 232 00:13:12,040 --> 00:13:14,320 Speaker 1: how it could be advanced. But he started looking for 233 00:13:14,400 --> 00:13:17,240 Speaker 1: ways to use the Fronti 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 00:13:19,360 --> 00:13:22,400 Speaker 1: to his old interest in the connection between mind and matter, 236 00:13:22,559 --> 00:13:26,480 Speaker 1: and in nineteen fifty Touring wrote a paper called Computing, 237 00:13:26,520 --> 00:13:30,120 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 00:13:34,000 --> 00:13:37,760 Speaker 1: Turing test, and the test basically provided a way to 240 00:13:37,880 --> 00:13:42,280 Speaker 1: judge the intelligence of a machine without bias. So an interrogator, 241 00:13:42,320 --> 00:13:45,400 Speaker 1: for example, would sit in an isolated room from two subjects, 242 00:13:45,960 --> 00:13:49,600 Speaker 1: one a person, one a machine, and the interrogator would 243 00:13:49,600 --> 00:13:52,559 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 --> 00:14:01,000 Speaker 1: it had intelligence in some definable way. And Turing even 246 00:14:01,040 --> 00:14:03,760 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 --> 00:14:11,240 Speaker 1: be so good at this game this, this Turing test, 249 00:14:11,840 --> 00:14:15,280 Speaker 1: an interrogator would not have more than a seventy percent 250 00:14:15,440 --> 00:14:18,680 Speaker 1: chance of correctly identifying who is who after five minutes. 251 00:14:18,840 --> 00:14:23,360 Speaker 1: And that is a very ambitious goal because according to 252 00:14:23,440 --> 00:14:28,080 Speaker 1: Encyclopedia Britannica, no computer today has even come close to 253 00:14:28,120 --> 00:14:31,560 Speaker 1: that standard. But Turing really he did have a lot 254 00:14:31,600 --> 00:14:36,160 Speaker 1: of hopes for computers. Yeah. He also hypothesized that one day, quote, 255 00:14:36,240 --> 00:14:38,840 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 00:14:41,560 --> 00:14:43,760 Speaker 1: funny thing in the morning. I think we're a little 258 00:14:43,800 --> 00:14:47,400 Speaker 1: closer to that one than the seventy percent goal. Maybe 259 00:14:47,560 --> 00:14:50,800 Speaker 1: I don't know. I still like my doggie though. Yeah. 260 00:14:50,920 --> 00:14:55,040 Speaker 1: So Turing continued to study artificial intelligence, but also stuff 261 00:14:55,040 --> 00:14:58,160 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 00:15:07,520 --> 00:15:10,280 Speaker 1: a Fellow of the Royal Society of London in March 266 00:15:10,360 --> 00:15:13,880 Speaker 1: nineteen fifty one. That's another really prestigious honor. He was 267 00:15:14,240 --> 00:15:18,560 Speaker 1: appointed to Readership in the Theory of Computing at Manchester, 268 00:15:18,600 --> 00:15:21,840 Speaker 1: which sounds like a very modern title. But in nineteen 269 00:15:21,880 --> 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 --> 00:15:31,560 Speaker 1: and he told the police that he thought the burglar 272 00:15:31,680 --> 00:15:34,640 Speaker 1: was probably connected to a man he was quote having 273 00:15:34,680 --> 00:15:38,280 Speaker 1: an affair with, and he had been pretty open about 274 00:15:38,320 --> 00:15:41,800 Speaker 1: his sexuality since college. During his Letchley 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 00:15:45,600 --> 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 00:15:52,000 --> 00:15:55,080 Speaker 1: was really dangerous because at the time homosexuality was a 279 00:15:55,080 --> 00:15:59,000 Speaker 1: felony in Great Britain. And so Turing was tried and 280 00:15:59,040 --> 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 00:16:07,880 --> 00:16:12,320 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 00:16:16,240 --> 00:16:20,040 Speaker 1: a result, So no government codes, no government computers. And 285 00:16:20,160 --> 00:16:23,480 Speaker 1: on June seven, nineteen fifty four, he was found dead 286 00:16:23,480 --> 00:16:26,680 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 --> 00:16:33,400 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 00:16:36,920 --> 00:16:40,160 Speaker 1: and it's also accepted that Turing did kill himself in 291 00:16:40,160 --> 00:16:43,240 Speaker 1: in this particular way so that it would allow his 292 00:16:43,320 --> 00:16:46,560 Speaker 1: mother to interpret the situation as an accident, since he'd 293 00:16:46,560 --> 00:16:48,720 Speaker 1: been working with cyanide and other chemicals in his work, 294 00:16:49,080 --> 00:16:51,440 Speaker 1: so she thought that he had some cyanide on his 295 00:16:51,520 --> 00:16:55,000 Speaker 1: hands and he ate an apple and accidentally poisoned himself. 296 00:16:55,080 --> 00:16:59,320 Speaker 1: But assuming he did commit suicide, which is what most 297 00:16:59,360 --> 00:17:02,920 Speaker 1: people have feeling, it's a really tragic end to to 298 00:17:04,320 --> 00:17:06,920 Speaker 1: this great life. And and at the heels of this 299 00:17:07,160 --> 00:17:11,600 Speaker 1: terrible prosecution. So in two thousand nine, Prime Minister Gordon 300 00:17:11,680 --> 00:17:15,520 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 00:17:18,800 --> 00:17:22,199 Speaker 1: He said, Touring truly was one of those individuals we 303 00:17:22,240 --> 00:17:25,200 Speaker 1: can point to whose unique contribution it helped to turn 304 00:17:25,240 --> 00:17:27,439 Speaker 1: the tide of war. The debt of gratitude he has 305 00:17:27,480 --> 00:17:30,280 Speaker 1: owed makes it all the more horrifying. Therefore, that he 306 00:17:30,359 --> 00:17:33,400 Speaker 1: was treated so inhumanely on the behalf of the British 307 00:17:33,440 --> 00:17:36,600 Speaker 1: government and all those who live freely thanks to Alan's work, 308 00:17:37,000 --> 00:17:39,800 Speaker 1: I'm very proud to say we're sorry. You deserve so 309 00:17:39,920 --> 00:17:43,040 Speaker 1: much better. So two thousand twelve is Alan Turing Year, 310 00:17:43,520 --> 00:17:46,720 Speaker 1: and a state side recognition has been long standing. The 311 00:17:46,840 --> 00:17:50,080 Speaker 1: US Association for Computer Machinery has given out the Touring 312 00:17:50,080 --> 00:17:54,520 Speaker 1: Awards since nineteen sixty six, and if anything, as technology 313 00:17:54,560 --> 00:17:57,840 Speaker 1: develops in new areas of steady emerge, Alan Turing will 314 00:17:57,880 --> 00:18:00,720 Speaker 1: probably just become more recognized as the years on. Yeah, 315 00:18:00,760 --> 00:18:04,680 Speaker 1: if you think about how many career descriptions that apply 316 00:18:04,760 --> 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 --> 00:18:13,840 Speaker 1: We can only imagine that more will be added over 319 00:18:13,880 --> 00:18:18,520 Speaker 1: the years as science and technology advances. Yeah, and we 320 00:18:18,600 --> 00:18:21,320 Speaker 1: have a lot of information about this kind of stuff 321 00:18:21,359 --> 00:18:23,720 Speaker 1: on our site, which we will tell you more about 322 00:18:23,760 --> 00:18:26,280 Speaker 1: at the end. But if you have anything to contribute 323 00:18:26,320 --> 00:18:28,960 Speaker 1: on this topic, you know anything else about Touring's research, 324 00:18:29,160 --> 00:18:31,359 Speaker 1: or maybe an anecdote about his life that we missed, 325 00:18:31,359 --> 00:18:33,760 Speaker 1: please write us at history podcast at how staff works 326 00:18:33,800 --> 00:18:36,160 Speaker 1: dot com. You can also look us up on Twitter 327 00:18:36,200 --> 00:18:38,760 Speaker 1: at missed in History or on Facebook. Again. We also 328 00:18:38,840 --> 00:18:41,440 Speaker 1: do have so many tech articles. You can find them 329 00:18:41,480 --> 00:18:44,800 Speaker 1: by visiting our website and just going right to the 330 00:18:44,840 --> 00:18:48,000 Speaker 1: tech cab and looking from there you'll find tons of 331 00:18:48,040 --> 00:18:52,040 Speaker 1: stuff that interests you if you are into computer science 332 00:18:52,119 --> 00:18:56,359 Speaker 1: or programming or math or anything like that. All of 333 00:18:56,359 --> 00:19:00,240 Speaker 1: it is on our website at www dot ho stuff 334 00:19:00,280 --> 00:19:10,040 Speaker 1: works dot com. Mm hmmmm. For more on this and 335 00:19:10,080 --> 00:19:18,440 Speaker 1: thousands of other topics, does it how stuff works dot com. 336 00:19:17,000 --> 00:19:36,440 Speaker 1: H m hm, m m mmmm.