1 00:00:00,320 --> 00:00:02,880 Speaker 1: Brought to you by the reinvented two thousand twelve camera. 2 00:00:03,240 --> 00:00:08,959 Speaker 1: It's ready. Are you get in touch with technology? With 3 00:00:09,080 --> 00:00:17,840 Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone, 4 00:00:17,880 --> 00:00:20,639 Speaker 1: Welcome to tech stuff. My name is Chris Poette and 5 00:00:20,640 --> 00:00:23,040 Speaker 1: I am an editor here at how stuff works dot com. 6 00:00:23,400 --> 00:00:26,360 Speaker 1: Sitting across from me, as usual, is senior writer Jonathan Strickland. 7 00:00:26,680 --> 00:00:33,400 Speaker 1: Does not compute. Nice, very nice, Thank you. Not everything computes, No, 8 00:00:33,520 --> 00:00:37,559 Speaker 1: not everything. But the lady will be talking about in 9 00:00:37,600 --> 00:00:41,239 Speaker 1: this podcast. Sure dead, Yeah, let's talk about her. This 10 00:00:41,280 --> 00:00:49,080 Speaker 1: is comes courtesy of a little listener mail. And uh, 11 00:00:49,159 --> 00:00:51,879 Speaker 1: this listener mail is actually a two fur We got 12 00:00:51,960 --> 00:00:54,400 Speaker 1: two requests in the space of a week, which is 13 00:00:54,440 --> 00:00:58,720 Speaker 1: not a big surprise considering the subject of this um 14 00:00:58,880 --> 00:01:02,480 Speaker 1: this podcast. It's a listener mail rock blaw. Yeah. So 15 00:01:03,000 --> 00:01:06,640 Speaker 1: this comes from Bridget and Adam. So uh, I'll read 16 00:01:06,680 --> 00:01:11,200 Speaker 1: Bridgets first. Bridget is from Australia, but I'm not going 17 00:01:11,240 --> 00:01:13,640 Speaker 1: to try and do an Australian accent because whenever I do, 18 00:01:13,720 --> 00:01:16,840 Speaker 1: I sound like a New Zealander who suffered massive head trauma. 19 00:01:17,040 --> 00:01:20,880 Speaker 1: So here is bridgets email. Good day, Chris and Jonathan. 20 00:01:20,959 --> 00:01:24,440 Speaker 1: I've been spending some time lately looking to inspirational people 21 00:01:24,440 --> 00:01:26,680 Speaker 1: in hope of finding a suitable name for my soon 22 00:01:26,760 --> 00:01:30,160 Speaker 1: to be born child. Such searching brought me to Ada Lovelace, 23 00:01:30,280 --> 00:01:33,360 Speaker 1: otherwise known as the Mother of Coding. I've done a 24 00:01:33,400 --> 00:01:36,160 Speaker 1: little research into Ada and found that there's some discussion 25 00:01:36,160 --> 00:01:39,240 Speaker 1: as to whether she deserves this moniker. Was Ada Lovelace 26 00:01:39,280 --> 00:01:42,240 Speaker 1: the first computer programmer and therefore a worthy namesake for 27 00:01:42,319 --> 00:01:45,280 Speaker 1: my future daughter? Let me know what you think. Cheers, 28 00:01:45,480 --> 00:01:49,640 Speaker 1: Bridget and Adams. Was I recently learned a little about 29 00:01:49,640 --> 00:01:52,000 Speaker 1: Ada Lovelace, the first woman to write an algorithm that 30 00:01:52,040 --> 00:01:54,080 Speaker 1: would be read by a computer, and thought it would 31 00:01:54,080 --> 00:01:56,280 Speaker 1: make a great podcast. I love the show. Keep up 32 00:01:56,280 --> 00:01:58,480 Speaker 1: the amazing work. Can you also do a show on 33 00:01:58,520 --> 00:02:04,360 Speaker 1: the LHC. Please cheers? Insert beer clink sound here. Alright, 34 00:02:04,400 --> 00:02:07,760 Speaker 1: Bridget and Adam, this is our podcast about Ada, not 35 00:02:07,840 --> 00:02:12,160 Speaker 1: about the LHC. Um Jonathan, we can't do this podcast. 36 00:02:12,200 --> 00:02:14,120 Speaker 1: What do you mean we can't do this podcast. It's 37 00:02:14,120 --> 00:02:18,360 Speaker 1: already been done. I mean stuff he missed in History Class? 38 00:02:18,400 --> 00:02:20,960 Speaker 1: It's already done a whole podcast. There's a podcast called 39 00:02:21,000 --> 00:02:23,320 Speaker 1: stuff you Missed in History Class? There is It's wonderful. 40 00:02:23,880 --> 00:02:26,079 Speaker 1: Is that the one with Katy and Sarah. It is indeed, 41 00:02:26,080 --> 00:02:28,680 Speaker 1: and they talked about Ada Lovelace already they did, and 42 00:02:28,680 --> 00:02:30,200 Speaker 1: they did really well, you know what we should do. 43 00:02:30,520 --> 00:02:33,120 Speaker 1: What's that? We should just have their podcast play and 44 00:02:33,320 --> 00:02:36,239 Speaker 1: we'll sign off. All right, we'll just insert their podcast 45 00:02:36,320 --> 00:02:38,679 Speaker 1: here and then no, we can't. We can't do that. 46 00:02:39,000 --> 00:02:41,240 Speaker 1: We can't do that. Besides, they specifically mentioned us in 47 00:02:41,280 --> 00:02:43,440 Speaker 1: that podcast. Well, maybe what we should do then is 48 00:02:43,480 --> 00:02:48,000 Speaker 1: talk specifically about her computer programming expertise and how she 49 00:02:48,080 --> 00:02:51,720 Speaker 1: managed to do that considering she lived in the eighteen hundreds. Yeah, 50 00:02:51,760 --> 00:02:54,639 Speaker 1: you would think she lived before computers. How could she 51 00:02:54,720 --> 00:02:58,360 Speaker 1: have written a computer programmer program? Rather not wrote a 52 00:02:58,360 --> 00:03:02,880 Speaker 1: programmers a long week? Well, we're gonna do this on Friday. 53 00:03:02,880 --> 00:03:05,639 Speaker 1: Actual clearly not, but we're going to tell you how 54 00:03:05,639 --> 00:03:08,480 Speaker 1: she wrote a computer program. First of all, Bridget, I'm 55 00:03:08,480 --> 00:03:09,680 Speaker 1: going to get this all the way. First of all, 56 00:03:09,720 --> 00:03:15,040 Speaker 1: congratulations on your child. And also, Aida is more than worthy. 57 00:03:15,120 --> 00:03:16,920 Speaker 1: I would say, In fact, I kind of fell in 58 00:03:16,919 --> 00:03:18,959 Speaker 1: love with this lady the more I read about her. 59 00:03:19,400 --> 00:03:21,520 Speaker 1: Actually her first name was an Aida. No, it was 60 00:03:21,560 --> 00:03:25,560 Speaker 1: Augusta Augusta Ada Byron. Yeah, Augusta Ada Byron, daughter of 61 00:03:25,639 --> 00:03:29,119 Speaker 1: Lord Byron the poet. Yes, she was born December tenth, 62 00:03:29,200 --> 00:03:32,560 Speaker 1: eighteen fifteen, the daughter of Lord George Gordon Byron and 63 00:03:32,600 --> 00:03:36,840 Speaker 1: Annabella Millbank Byron. Um. Actually, your parents married on the 64 00:03:36,880 --> 00:03:39,760 Speaker 1: second of January and eighteen fifteen, but we're separated by 65 00:03:40,000 --> 00:03:43,920 Speaker 1: January six, eighteen sixteen, So the marriage lasted a full 66 00:03:44,000 --> 00:03:46,440 Speaker 1: year and a week and a half, just long enough 67 00:03:46,480 --> 00:03:51,040 Speaker 1: to uh to have the first computer programmer born to them, right, Um, 68 00:03:51,120 --> 00:03:54,360 Speaker 1: so yes, they're there. Marriage was not a happy one 69 00:03:54,440 --> 00:03:58,160 Speaker 1: her parents and uh. In fact, um young Ada was 70 00:03:58,280 --> 00:04:02,440 Speaker 1: never to meet. Her father was separated. Um, she lived 71 00:04:02,440 --> 00:04:04,680 Speaker 1: with her mom, and her mom had decided that Aida 72 00:04:04,760 --> 00:04:07,640 Speaker 1: did not really need to have the distractions of poetry. 73 00:04:07,720 --> 00:04:13,720 Speaker 1: She thought that Byron's rather unpredictable personality let's call it 74 00:04:13,760 --> 00:04:17,680 Speaker 1: that showy um was due to his romanticism and his 75 00:04:17,800 --> 00:04:21,520 Speaker 1: obsession with poetry. Yeah, let's just and and Annabella the 76 00:04:21,960 --> 00:04:26,720 Speaker 1: mother felt that the that such qualities were not really admirable. 77 00:04:26,839 --> 00:04:29,080 Speaker 1: She didn't want her daughter to have the same sort 78 00:04:29,120 --> 00:04:34,120 Speaker 1: of personality and uh and and wanting lifestyle as the 79 00:04:34,160 --> 00:04:38,080 Speaker 1: father did had so um so she thought, well, what's 80 00:04:38,120 --> 00:04:40,200 Speaker 1: the least poetic thing I can push my daughter into 81 00:04:40,320 --> 00:04:43,760 Speaker 1: I happen to be an amateur mathematician. Let's push her 82 00:04:43,760 --> 00:04:47,239 Speaker 1: into mathematics. Yeah. Actually uh. As a matter of fact, 83 00:04:47,560 --> 00:04:50,640 Speaker 1: I found out that Lord Byron had referred to his 84 00:04:51,279 --> 00:04:56,720 Speaker 1: very brief married married wife, um uh, he called Annabella 85 00:04:56,800 --> 00:05:00,520 Speaker 1: the Princess of parallelograms. Yes, that was a lot poetic. 86 00:05:00,720 --> 00:05:05,720 Speaker 1: It was not meant to be a compliment, nonetheless, but 87 00:05:06,279 --> 00:05:09,120 Speaker 1: it does illustrate that she had a mathematical bent herself. 88 00:05:09,200 --> 00:05:13,480 Speaker 1: And what I find interesting is that not only were 89 00:05:13,600 --> 00:05:19,360 Speaker 1: Lord Byron's poetical genes evident later in Ada's life, but 90 00:05:19,920 --> 00:05:22,120 Speaker 1: it's actually she ended up being sort of a blend 91 00:05:22,160 --> 00:05:25,520 Speaker 1: of both of her parents, as is appropriate for this case. Yeah. Yeah, 92 00:05:25,560 --> 00:05:32,799 Speaker 1: and Ada herself received a a wonderful, wonderful title given 93 00:05:32,839 --> 00:05:35,520 Speaker 1: to her by by Charles Babbage, who will will discuss 94 00:05:35,560 --> 00:05:39,240 Speaker 1: at length in a little bit, uh, the Enchantress of Numbers, 95 00:05:39,760 --> 00:05:44,520 Speaker 1: which I think is an amazing, amazing phrase and very 96 00:05:44,560 --> 00:05:49,800 Speaker 1: fitting as well. So Aida grows up um with some 97 00:05:49,880 --> 00:05:53,640 Speaker 1: of the best tutors in that you can imagine. During 98 00:05:53,680 --> 00:05:58,760 Speaker 1: this time she studies mathematics and has absolutely fascinated with them. 99 00:05:58,960 --> 00:06:03,599 Speaker 1: It's the subject mathematics, and is incredibly adept an amazing student. 100 00:06:03,640 --> 00:06:06,479 Speaker 1: In fact, the more we research data, the more I 101 00:06:06,560 --> 00:06:13,839 Speaker 1: realized anyway, that she was phenomenally more intelligent than I am. 102 00:06:13,880 --> 00:06:17,680 Speaker 1: I mean, I can't even really compare. Uh. She was 103 00:06:17,760 --> 00:06:23,200 Speaker 1: able to to understand algorithms that that completely baffle me, 104 00:06:23,680 --> 00:06:26,880 Speaker 1: and I was able to to really study them in 105 00:06:26,880 --> 00:06:30,159 Speaker 1: a way that she found fascinating. I find them perplexing 106 00:06:30,640 --> 00:06:35,360 Speaker 1: and maddening. She found it as having its own kind 107 00:06:35,400 --> 00:06:38,800 Speaker 1: of poetry. Um, and in a way you think about it, well, 108 00:06:38,800 --> 00:06:40,960 Speaker 1: this kind of makes sense, you know. We we when 109 00:06:40,960 --> 00:06:43,320 Speaker 1: you really look at algorithms, we're talking about things like 110 00:06:43,400 --> 00:06:47,680 Speaker 1: number theory and how the universe sort of works, like 111 00:06:47,720 --> 00:06:51,400 Speaker 1: how things kind of fit together. And we express this 112 00:06:51,839 --> 00:06:55,279 Speaker 1: more often than not through mathematic equations and algorithms and 113 00:06:55,279 --> 00:06:57,800 Speaker 1: things of that nature. And she was able to see 114 00:06:57,839 --> 00:07:01,120 Speaker 1: that kind of stuff. I'm under I can understand the 115 00:07:01,200 --> 00:07:04,360 Speaker 1: underlying concept, but when you get beyond that, it just 116 00:07:04,720 --> 00:07:07,239 Speaker 1: I feel like I'm a fish out of water. Yeah, 117 00:07:07,440 --> 00:07:13,160 Speaker 1: I understand. Well, let's see, um, somebody who else who 118 00:07:13,200 --> 00:07:16,920 Speaker 1: was fascinated with her would be William King. Yes, he 119 00:07:17,000 --> 00:07:19,240 Speaker 1: was so fascinated. Whether he married her? Yeah? Well the 120 00:07:19,280 --> 00:07:21,920 Speaker 1: first William King? Who was who was her tutor. I 121 00:07:21,920 --> 00:07:24,000 Speaker 1: found that I found this interesting. I mean the two 122 00:07:24,000 --> 00:07:26,440 Speaker 1: different William Kings. Well no, actually I did mean, I 123 00:07:26,440 --> 00:07:28,360 Speaker 1: did mean her future husband. But it was really funny 124 00:07:28,400 --> 00:07:30,280 Speaker 1: because it confused me for a second when I was 125 00:07:30,320 --> 00:07:32,400 Speaker 1: doing the research, I said William King was her tutor. 126 00:07:33,000 --> 00:07:35,440 Speaker 1: And then, as it turns out, there was a William King, 127 00:07:35,480 --> 00:07:37,600 Speaker 1: not the one she married, as her tutor, who was 128 00:07:37,720 --> 00:07:41,080 Speaker 1: apparently uh immediately feeling out of his depth as he 129 00:07:41,200 --> 00:07:43,080 Speaker 1: talked to her, he realized that she had a much 130 00:07:43,080 --> 00:07:47,240 Speaker 1: more innate grasp of mathematics than he did, so he 131 00:07:47,400 --> 00:07:49,200 Speaker 1: he actually bowed out very quickly. He was one of 132 00:07:49,200 --> 00:07:52,880 Speaker 1: the many many but yeah, less than a year later, 133 00:07:52,960 --> 00:07:57,320 Speaker 1: apparently he uh ad married the other William King, who 134 00:07:57,520 --> 00:08:01,560 Speaker 1: um was the eighth bear in King and U was 135 00:08:01,600 --> 00:08:04,960 Speaker 1: an earl made an earl in nineteen thirty eight, so 136 00:08:05,200 --> 00:08:13,200 Speaker 1: that's when she became eight eighteen thirty eight, dude. So yes, 137 00:08:13,280 --> 00:08:15,480 Speaker 1: he was made an earl in eighteen thirty eight, and 138 00:08:15,560 --> 00:08:18,320 Speaker 1: that's when she became the Countess of Lovelace. Yes, so 139 00:08:18,320 --> 00:08:22,080 Speaker 1: we usually just referred to her as Ada Lovelace. Uh. Now, 140 00:08:23,400 --> 00:08:29,200 Speaker 1: Aida continued her her almost obsession with mathematics throughout her life, 141 00:08:29,200 --> 00:08:35,640 Speaker 1: which unfortunately was tragically short. Ada passed away from after 142 00:08:35,800 --> 00:08:39,160 Speaker 1: contracting cancer. Um. I think she was thirty seven. It 143 00:08:39,160 --> 00:08:43,320 Speaker 1: was eighteen fifty two, and she died November eighteen, eighteen 144 00:08:43,600 --> 00:08:47,880 Speaker 1: fifty two. Right. Uh so, But during that her her life, 145 00:08:48,040 --> 00:08:53,280 Speaker 1: she ended up encountering lots of remarkable people as including 146 00:08:53,480 --> 00:08:55,760 Speaker 1: you know, like the author of Charles Dickens, who became 147 00:08:56,200 --> 00:09:01,400 Speaker 1: a close friend. One of her other friends was Charles Babbage. Yeah, 148 00:09:01,440 --> 00:09:05,360 Speaker 1: she met she met Babbage who was the Leucassian Professor 149 00:09:05,400 --> 00:09:08,760 Speaker 1: of mathematics at Cambridge. She met him when she was 150 00:09:08,840 --> 00:09:14,320 Speaker 1: just seventeen, um, which is a pretty interesting eighteen thirty three, 151 00:09:14,400 --> 00:09:18,160 Speaker 1: was it right around when that happened. Um. And she 152 00:09:18,160 --> 00:09:20,000 Speaker 1: she rubbed elbows with other people who were interested in 153 00:09:20,080 --> 00:09:25,640 Speaker 1: math and science, like Mary Somerville. Um. And that's uh, 154 00:09:25,679 --> 00:09:28,880 Speaker 1: I don't know. Should we get into the the time 155 00:09:28,920 --> 00:09:32,160 Speaker 1: when she was talking at a party with Babbage about 156 00:09:32,160 --> 00:09:36,679 Speaker 1: this new machine four Um. He had come up with 157 00:09:36,720 --> 00:09:42,440 Speaker 1: this thing, the analytical engine. Yes, all right, well, let's 158 00:09:42,480 --> 00:09:44,480 Speaker 1: let's backtrack just a touch before we get into the 159 00:09:44,520 --> 00:09:47,360 Speaker 1: analytical engine. That was not the first machine that Babbage 160 00:09:47,360 --> 00:09:49,280 Speaker 1: had proposed. No, no, not at all. As a matter 161 00:09:49,280 --> 00:09:52,800 Speaker 1: of fact, we brought it up before from the past, right, 162 00:09:52,960 --> 00:09:56,160 Speaker 1: that was a fun podcast, um, but yes, this was 163 00:09:56,520 --> 00:09:58,240 Speaker 1: a more recent one. And as we talked about on 164 00:09:58,280 --> 00:10:02,480 Speaker 1: that podcast, UM, Abbage was having difficulty getting funding for 165 00:10:02,640 --> 00:10:06,120 Speaker 1: these amazing machines because people just didn't get it. Babbage 166 00:10:06,160 --> 00:10:09,960 Speaker 1: was able to get subfunding for his first uh machine, 167 00:10:09,960 --> 00:10:13,760 Speaker 1: which is called the difference engine. Yes, different from the 168 00:10:13,800 --> 00:10:16,560 Speaker 1: other one. Right. It was a little more simplistic than 169 00:10:16,679 --> 00:10:19,920 Speaker 1: his idea for the analytical engine. Right now, the difference engine, 170 00:10:19,960 --> 00:10:22,200 Speaker 1: he managed to get some money to fund it, but 171 00:10:22,480 --> 00:10:24,800 Speaker 1: his the process of building it was a very long, 172 00:10:25,840 --> 00:10:29,640 Speaker 1: laborious process. They had to actually machine these parts by 173 00:10:29,679 --> 00:10:32,959 Speaker 1: hand and and try and put it all together, and uh, 174 00:10:33,040 --> 00:10:35,000 Speaker 1: he kind of ran out of money before he ran 175 00:10:35,000 --> 00:10:38,120 Speaker 1: out machine. The machine was not done yet, and um 176 00:10:38,160 --> 00:10:40,960 Speaker 1: it was in the process of this whole construction phase 177 00:10:41,000 --> 00:10:43,080 Speaker 1: that he got the idea for the analytical machine, which 178 00:10:43,080 --> 00:10:46,400 Speaker 1: was even more ambitious than the difference yes engine. Right. 179 00:10:46,679 --> 00:10:49,720 Speaker 1: So the analytical engine was going to be uh more 180 00:10:49,760 --> 00:10:54,080 Speaker 1: complex and be able to do more than the difference engine, 181 00:10:54,080 --> 00:10:57,880 Speaker 1: which you could kind of say was essentially a giant calculator. 182 00:10:58,679 --> 00:11:02,920 Speaker 1: The analytical engine was more like a very primitive computer. Yeah, 183 00:11:03,600 --> 00:11:05,640 Speaker 1: and as a matter of fact that at that time, 184 00:11:05,840 --> 00:11:07,600 Speaker 1: that whole time thing, the fact that was taking a 185 00:11:07,600 --> 00:11:09,520 Speaker 1: long time to build, did not help him when he 186 00:11:09,559 --> 00:11:11,880 Speaker 1: was seeking funding for the analytical engine. Right. There were 187 00:11:11,880 --> 00:11:14,200 Speaker 1: two things that two things that budd that kind of 188 00:11:14,200 --> 00:11:15,959 Speaker 1: plagued him when he was trying to get some money. 189 00:11:16,040 --> 00:11:18,680 Speaker 1: One was that he had not finished the difference engine, 190 00:11:18,760 --> 00:11:20,360 Speaker 1: and that was kind of what he was being paid 191 00:11:20,360 --> 00:11:23,160 Speaker 1: for in the first place. So his funders were saying, 192 00:11:23,720 --> 00:11:26,160 Speaker 1: until you build this other machine you promised us several 193 00:11:26,240 --> 00:11:28,800 Speaker 1: years ago, we ain't giving you no more money. Yeah. 194 00:11:29,120 --> 00:11:31,520 Speaker 1: And then the other part of they probably did. They 195 00:11:31,520 --> 00:11:33,840 Speaker 1: probably said with an English accent, So that's probably until 196 00:11:33,920 --> 00:11:36,520 Speaker 1: you go and fish daddy, we ain't given you no 197 00:11:36,640 --> 00:11:41,559 Speaker 1: more money. Uh. They're apparently all glad, apparently apparently all 198 00:11:41,559 --> 00:11:46,200 Speaker 1: played by Dick van Dyke. So um anyway, at any rate, 199 00:11:46,280 --> 00:11:48,839 Speaker 1: so the sammage is in a tight spot. But he 200 00:11:49,320 --> 00:11:51,160 Speaker 1: comes up with this idea of the analytical engine, and 201 00:11:51,200 --> 00:11:54,679 Speaker 1: of course he's very passionate about it, so he's blabbering 202 00:11:54,720 --> 00:11:57,000 Speaker 1: on and on about it at parties. Yes, then you 203 00:11:57,080 --> 00:12:01,319 Speaker 1: have young Ada Lovelace who over here such talk thinks 204 00:12:02,040 --> 00:12:06,319 Speaker 1: this sounds absolutely fascinating, and not only does she think 205 00:12:06,320 --> 00:12:10,600 Speaker 1: it's interesting, she immediately sees the potential to use such 206 00:12:10,640 --> 00:12:16,640 Speaker 1: a device far beyond even Babbage is uh concepts s. 207 00:12:16,640 --> 00:12:19,720 Speaker 1: Babbage is thinking, well, this would allow you to create 208 00:12:20,120 --> 00:12:24,160 Speaker 1: an engine that would be able to generate the numbers 209 00:12:24,160 --> 00:12:29,120 Speaker 1: that you would find in a logarithmic table, because until 210 00:12:29,160 --> 00:12:31,480 Speaker 1: that point you pretty much had to be able to 211 00:12:31,520 --> 00:12:34,320 Speaker 1: come up with those figures by doing the calculations all yourself. 212 00:12:34,400 --> 00:12:37,960 Speaker 1: And these calculations were pretty complex, and it was easy 213 00:12:38,000 --> 00:12:40,640 Speaker 1: to to make a mistake along the way, which would 214 00:12:40,640 --> 00:12:44,560 Speaker 1: of course affect all of your figures from that point on. Uh. 215 00:12:44,640 --> 00:12:47,360 Speaker 1: And he he just he was sitting down one day 216 00:12:47,400 --> 00:12:49,280 Speaker 1: and he was thinking, what if I could What if 217 00:12:49,280 --> 00:12:52,679 Speaker 1: there are a machine, some steam powered machine that could 218 00:12:53,240 --> 00:12:56,480 Speaker 1: generate these numbers so I wouldn't have to and then 219 00:12:56,520 --> 00:12:59,840 Speaker 1: I could I could generate them as far out as 220 00:13:00,080 --> 00:13:02,640 Speaker 1: I wanted to. Uh, And I wouldn't have to worry 221 00:13:02,679 --> 00:13:05,679 Speaker 1: about error because the machine would just be following the 222 00:13:05,720 --> 00:13:10,079 Speaker 1: same algorithm over and over and over again. Yes, well, 223 00:13:10,280 --> 00:13:12,920 Speaker 1: Ada thought of that, and she even went further. She 224 00:13:12,960 --> 00:13:17,760 Speaker 1: said that you could potentially use mathematics to represent other 225 00:13:17,840 --> 00:13:25,240 Speaker 1: things like text or images or even music. She had 226 00:13:25,400 --> 00:13:31,160 Speaker 1: foreseen computers. This remarkable woman was able to look at 227 00:13:31,200 --> 00:13:33,640 Speaker 1: this machine that really was meant to be able to 228 00:13:33,760 --> 00:13:37,959 Speaker 1: run algorithms so that you could generate more mathematical figures, 229 00:13:37,960 --> 00:13:40,960 Speaker 1: mainly in the in the pursuit of mathematics itself and 230 00:13:41,000 --> 00:13:44,080 Speaker 1: things like number theory, UM, and she was able to 231 00:13:44,200 --> 00:13:50,840 Speaker 1: see even grander uses, which to me is it's it's 232 00:13:51,200 --> 00:13:54,280 Speaker 1: it's one of those discoveries that I just think before 233 00:13:54,360 --> 00:13:57,920 Speaker 1: that time, no one had ever really even considered this, 234 00:13:58,040 --> 00:14:00,880 Speaker 1: and then she just comes up with it just by 235 00:14:01,040 --> 00:14:04,720 Speaker 1: looking at this thing and seeing its potential. Yes, it's 236 00:14:04,760 --> 00:14:08,800 Speaker 1: that's where I'm like, Okay, this woman was way above 237 00:14:08,840 --> 00:14:11,160 Speaker 1: and beyond smarter than I am. All right, stop geaking 238 00:14:11,160 --> 00:14:12,760 Speaker 1: out for a second. Okay, I'm sorry, I will. I 239 00:14:12,760 --> 00:14:14,640 Speaker 1: will give you a quote from her. As a matter 240 00:14:14,640 --> 00:14:17,400 Speaker 1: of fact, she uh, she compared to Jacquard's looms. If 241 00:14:17,400 --> 00:14:20,560 Speaker 1: you will remember we've mentioned that machine a couple of 242 00:14:20,600 --> 00:14:23,320 Speaker 1: times on the podcast I Believe UM. This was a 243 00:14:23,560 --> 00:14:29,400 Speaker 1: loom that was invented by uh Monsieur Jacquard, and UM 244 00:14:30,080 --> 00:14:32,560 Speaker 1: basically made a lot of people unhappy because it used 245 00:14:32,600 --> 00:14:36,080 Speaker 1: punch cards to automate parts of the weaving process. You 246 00:14:36,080 --> 00:14:38,000 Speaker 1: could put in a pattern, a card for a pattern 247 00:14:38,040 --> 00:14:41,360 Speaker 1: in the loom would be able to weave that pattern 248 00:14:41,440 --> 00:14:45,560 Speaker 1: into the fabric. While she said, um, we may say 249 00:14:45,680 --> 00:14:49,640 Speaker 1: most aptly that the analytical engine weaves algebraical patterns just 250 00:14:49,680 --> 00:14:54,320 Speaker 1: as the Jacquard loom weaves flowers and leaves. So see, 251 00:14:54,360 --> 00:14:56,320 Speaker 1: there you go, there's that whole poetry thing she's you know, 252 00:14:56,560 --> 00:14:59,840 Speaker 1: that's just in there. Yeah. Well, and and like I said, 253 00:15:00,080 --> 00:15:04,000 Speaker 1: even if you even if you ignore the language, and 254 00:15:04,160 --> 00:15:07,280 Speaker 1: Ada was very gifted with with words, just as she 255 00:15:07,360 --> 00:15:10,640 Speaker 1: was with mathematics. Um, the fact that she could see 256 00:15:11,080 --> 00:15:15,600 Speaker 1: the poetry in in math is again very phenomenal to me. Well, 257 00:15:15,600 --> 00:15:18,320 Speaker 1: I meant that she was making connections between something that 258 00:15:18,360 --> 00:15:21,920 Speaker 1: was completely, well not completely, but in wide in a 259 00:15:21,920 --> 00:15:24,480 Speaker 1: wide way. It was, it was not very related directly 260 00:15:24,560 --> 00:15:29,400 Speaker 1: to this analytical engine. Uh. Also you might notice, Um, 261 00:15:29,480 --> 00:15:32,480 Speaker 1: she sort of foresaw the use of punch cards uh, 262 00:15:32,640 --> 00:15:35,280 Speaker 1: to be used for programs. So she's already thinking in 263 00:15:35,280 --> 00:15:38,840 Speaker 1: a programmatic sense. Yeah. Actually, U Babbage himself talked a 264 00:15:38,840 --> 00:15:41,600 Speaker 1: little bit about punch cards when he wrote about his 265 00:15:41,640 --> 00:15:46,520 Speaker 1: analytical engine. Um. Yeah, and in his sense. He was 266 00:15:46,560 --> 00:15:50,200 Speaker 1: talking about the the use for punch cards for two purposes. 267 00:15:50,200 --> 00:15:53,760 Speaker 1: And we've talked about this. Babbage also we we shouldn't 268 00:15:53,800 --> 00:15:56,960 Speaker 1: we shouldn't know. He's leave him out of this amazing 269 00:15:57,000 --> 00:16:01,720 Speaker 1: innovation as well. Babbage was also amazing in his ability 270 00:16:01,800 --> 00:16:06,280 Speaker 1: to foresee the future as far as computers are concerned. Now, granted, 271 00:16:06,960 --> 00:16:10,080 Speaker 1: his devices were all mechanical as opposed to electrical, yes, 272 00:16:10,240 --> 00:16:16,080 Speaker 1: but they the principles of electronic computing are based very 273 00:16:16,160 --> 00:16:21,440 Speaker 1: firmly on Babbage's discoveries. Um Babbage foresaw the use of 274 00:16:21,720 --> 00:16:26,240 Speaker 1: punch cards using two different kinds of punch cards. One 275 00:16:26,240 --> 00:16:29,280 Speaker 1: would be a set of instructions and the other would 276 00:16:29,280 --> 00:16:33,720 Speaker 1: be would represent the constants or variables of whatever formula 277 00:16:33,760 --> 00:16:36,160 Speaker 1: you're plugging in. Right, So one is the program and 278 00:16:36,160 --> 00:16:37,840 Speaker 1: the other is the information that you plug into the 279 00:16:37,880 --> 00:16:41,080 Speaker 1: program to get a results. Exactly same sort of thing 280 00:16:41,080 --> 00:16:44,320 Speaker 1: that we see in microprocessors today. What Babbage was doing 281 00:16:44,480 --> 00:16:49,920 Speaker 1: was was the the precursor to the micro processor. It's 282 00:16:49,960 --> 00:16:53,160 Speaker 1: just his was a macro processor because it was enormous 283 00:16:53,160 --> 00:16:55,400 Speaker 1: and weighed tons and tons. If he had ever managed 284 00:16:55,440 --> 00:16:58,000 Speaker 1: to actually build it at the size of that silicon 285 00:16:58,000 --> 00:17:01,600 Speaker 1: waver yeah, he never He never did build the analytical engine. 286 00:17:01,600 --> 00:17:03,880 Speaker 1: He did, he realized during his lifetime that it was 287 00:17:03,920 --> 00:17:05,399 Speaker 1: not going to happen, and I'm sure it was a 288 00:17:05,440 --> 00:17:09,120 Speaker 1: massive disappointment to him. But they have been made since. Yes, 289 00:17:09,160 --> 00:17:12,600 Speaker 1: there was one created almost like an art project in 290 00:17:12,640 --> 00:17:15,399 Speaker 1: the early nineties, and um fun I think it's in 291 00:17:15,440 --> 00:17:17,359 Speaker 1: a museum now, right, Yeah, Actually, I think I think 292 00:17:17,400 --> 00:17:20,280 Speaker 1: there may be two. To be honest, I think it's 293 00:17:20,280 --> 00:17:22,160 Speaker 1: one of the things that I ran across a mention 294 00:17:22,240 --> 00:17:24,840 Speaker 1: of as I was looking specifically for information about it 295 00:17:24,920 --> 00:17:26,639 Speaker 1: to lovel so I didn't follow it, but yeah, I 296 00:17:26,640 --> 00:17:28,600 Speaker 1: think I think I saw that there were two in 297 00:17:28,680 --> 00:17:31,960 Speaker 1: existence now that had been created just because you can 298 00:17:32,600 --> 00:17:35,920 Speaker 1: and and Babbage actually wrote that the analytical engine would 299 00:17:36,320 --> 00:17:41,320 Speaker 1: eventually contain an apparatus for printing on paper or if required, 300 00:17:41,440 --> 00:17:44,919 Speaker 1: up to two copies printed out on paper, and that 301 00:17:44,960 --> 00:17:48,040 Speaker 1: puts it ahead of the iPad. I'm kidding their software 302 00:17:48,040 --> 00:17:51,639 Speaker 1: for that. It would have a means for producing a 303 00:17:51,720 --> 00:17:55,920 Speaker 1: stereotype mold of the tables or results it computes, and 304 00:17:56,000 --> 00:17:59,040 Speaker 1: it would have a mechanism for punching on blank pasteboard 305 00:17:59,280 --> 00:18:02,479 Speaker 1: cards or metal plates the numerical results of any of 306 00:18:02,480 --> 00:18:05,800 Speaker 1: its computations. So in other words, you would read it 307 00:18:06,240 --> 00:18:08,159 Speaker 1: by looking at a punch card. You would find the 308 00:18:08,200 --> 00:18:11,040 Speaker 1: results of whatever it was that you were trying to 309 00:18:11,400 --> 00:18:17,800 Speaker 1: uh to calculate. And the his his method of designating 310 00:18:17,920 --> 00:18:21,200 Speaker 1: a punch card was actually pretty simple. The each punch 311 00:18:21,240 --> 00:18:26,840 Speaker 1: card had um had several columns of holes or or 312 00:18:27,080 --> 00:18:30,760 Speaker 1: columns where you could punch a hole uh and ten rows. 313 00:18:32,080 --> 00:18:35,639 Speaker 1: And if you punched the top hole, that would be 314 00:18:35,640 --> 00:18:38,560 Speaker 1: a one. If you punched the top two that would 315 00:18:38,560 --> 00:18:40,320 Speaker 1: be a two, if you punched the top three that 316 00:18:40,359 --> 00:18:43,280 Speaker 1: would be a three. So this isn't binary, you see 317 00:18:43,280 --> 00:18:46,360 Speaker 1: what I'm saying. Yes, so it was a very simple way. 318 00:18:46,359 --> 00:18:48,600 Speaker 1: You would look at the punch card and you would say, 319 00:18:48,600 --> 00:18:51,000 Speaker 1: all right, well the first number is a three because 320 00:18:51,000 --> 00:18:52,800 Speaker 1: the first three holes are punched. That kind of thing. 321 00:18:52,920 --> 00:18:57,080 Speaker 1: That made it pretty easy to read. But again, Babbage 322 00:18:57,119 --> 00:18:59,439 Speaker 1: was just thinking in terms of numbers. Lovelace was the 323 00:18:59,440 --> 00:19:03,040 Speaker 1: one who was thinking in terms of graphics, music, that 324 00:19:03,119 --> 00:19:08,639 Speaker 1: kind of thing. And then Lovelace comes up with a 325 00:19:08,760 --> 00:19:12,680 Speaker 1: kind of a test. She she writes out a program 326 00:19:12,800 --> 00:19:18,840 Speaker 1: essentially based on Babbage's UH design for the analytical engine. 327 00:19:18,840 --> 00:19:21,920 Speaker 1: Now this engine again did not physically exist at this point, 328 00:19:22,200 --> 00:19:25,359 Speaker 1: in fact, that it never existed during his life and 329 00:19:25,359 --> 00:19:31,080 Speaker 1: and and Lovelace predeceased Babbage. So Lovelace looks at this 330 00:19:31,160 --> 00:19:34,920 Speaker 1: design and she says, you know what, let's just take 331 00:19:35,560 --> 00:19:40,840 Speaker 1: one uh one mathematical algorithm, and I will design a 332 00:19:40,840 --> 00:19:45,640 Speaker 1: program for this engine that would fulfill this algorithm. So 333 00:19:45,720 --> 00:19:50,800 Speaker 1: she decides to create a program that would generate Bernoulli numbers. 334 00:19:51,440 --> 00:19:53,560 Speaker 1: I would like to explain to you what a Bernoulli 335 00:19:53,640 --> 00:19:57,760 Speaker 1: number is, Honestly, I would like to, but I'm an 336 00:19:57,760 --> 00:20:02,439 Speaker 1: English major, and seriously, I looked at Bernoulli numbers. I 337 00:20:02,480 --> 00:20:06,200 Speaker 1: looked up five or six different explanations, and really it's 338 00:20:06,240 --> 00:20:09,359 Speaker 1: just a it's it's a level of mathematics with which 339 00:20:09,359 --> 00:20:13,440 Speaker 1: I am not comfortable. So I cannot even explain. Um, 340 00:20:13,480 --> 00:20:18,800 Speaker 1: they're generated through the through a simple algorithm, relatively simple algorithm, 341 00:20:18,840 --> 00:20:22,480 Speaker 1: and um, Lovelace was able to create a program that 342 00:20:22,520 --> 00:20:26,520 Speaker 1: would have generated Bernoulli numbers through the analytic engine had 343 00:20:26,520 --> 00:20:29,680 Speaker 1: it ever been built. So I would say that, yes, 344 00:20:29,720 --> 00:20:33,720 Speaker 1: you can call her the first computer programmer definitely. So um, yeah, 345 00:20:33,760 --> 00:20:36,480 Speaker 1: I admit, it's been a long time since, uh, since 346 00:20:36,520 --> 00:20:40,040 Speaker 1: I took calculus to more than twenty years now. But yeah, 347 00:20:40,080 --> 00:20:44,840 Speaker 1: the Bernoulli numbers were named for Jaco Bernoulli, who published 348 00:20:45,359 --> 00:20:47,760 Speaker 1: Actually the work was published after his death, was published 349 00:20:47,920 --> 00:20:51,520 Speaker 1: in seventeen um and based on the and that was 350 00:20:52,000 --> 00:20:55,000 Speaker 1: in the art I hope I probably am not pronouncing 351 00:20:55,040 --> 00:21:00,040 Speaker 1: this right ours conject CONDI or yeah. Anyway, anyway, it 352 00:21:00,080 --> 00:21:03,159 Speaker 1: was published by Mr Bernouilli, who was one of several 353 00:21:03,160 --> 00:21:06,439 Speaker 1: in his family to work with math um. But the 354 00:21:06,440 --> 00:21:09,159 Speaker 1: Bernouilli numbers are very very important because they can be 355 00:21:09,320 --> 00:21:12,920 Speaker 1: used in a lot of different ways related to number 356 00:21:13,000 --> 00:21:15,920 Speaker 1: theory and trigger a metric functions as well. But yes, 357 00:21:16,000 --> 00:21:18,760 Speaker 1: number theory, I mean we're talking about a lot of 358 00:21:18,800 --> 00:21:21,639 Speaker 1: pure mathematics here. Yeah, it's it's basically has to deal 359 00:21:21,680 --> 00:21:24,439 Speaker 1: with the consecutive integers and and the way the sums 360 00:21:24,480 --> 00:21:28,320 Speaker 1: of powers are calculated. Yeah, I read that, and um 361 00:21:29,840 --> 00:21:33,000 Speaker 1: sure yea. Also, I should also point out before anyone 362 00:21:33,040 --> 00:21:35,680 Speaker 1: writes in uh, he was not the first, He was 363 00:21:35,720 --> 00:21:38,840 Speaker 1: not the only person to discover this principle. Well, I 364 00:21:38,880 --> 00:21:41,639 Speaker 1: mean this is a time of people who were discovering 365 00:21:41,680 --> 00:21:43,720 Speaker 1: things at the same time. Right at the same time, 366 00:21:43,760 --> 00:21:47,879 Speaker 1: there was two different forms of I want to say 367 00:21:48,400 --> 00:21:51,600 Speaker 1: it was a Japanese scholar who discovered it, and also 368 00:21:52,600 --> 00:21:55,199 Speaker 1: his work was published after he passed away, and it 369 00:21:55,240 --> 00:22:00,119 Speaker 1: was published in seventeen twelve, one year before. But they 370 00:22:00,200 --> 00:22:02,800 Speaker 1: probably discovered it around the same time. Yeah, because this 371 00:22:02,880 --> 00:22:06,400 Speaker 1: was actually almost ten years I think after Bernoulli's death, 372 00:22:06,560 --> 00:22:10,720 Speaker 1: so it would have been back concurrently, simultaneous, concurrently side 373 00:22:10,720 --> 00:22:13,960 Speaker 1: of time. I was sorry, that would have peditively redundant. Well, 374 00:22:14,320 --> 00:22:16,920 Speaker 1: it's hard to say he was first, but they were 375 00:22:16,960 --> 00:22:19,600 Speaker 1: around the same time, just as you know, in the 376 00:22:20,000 --> 00:22:23,879 Speaker 1: immediately preceding years we have the calculus being conceived of. 377 00:22:23,920 --> 00:22:26,080 Speaker 1: It's fast. It must have been a really heavy time 378 00:22:26,080 --> 00:22:30,440 Speaker 1: for mathematicians. And uh so, yeah, I mean the fact 379 00:22:30,480 --> 00:22:33,720 Speaker 1: that that Lovelace was able to you know, she she 380 00:22:33,800 --> 00:22:37,280 Speaker 1: knew of course about this um the algorithm to generate 381 00:22:37,320 --> 00:22:41,800 Speaker 1: Bernoulli numbers, and was able to program a you know 382 00:22:41,920 --> 00:22:44,159 Speaker 1: this this is all more or less a thought experiment, 383 00:22:44,240 --> 00:22:47,200 Speaker 1: because again nothing existed with which on upon which she 384 00:22:47,240 --> 00:22:49,560 Speaker 1: could run this program. But she was able to create 385 00:22:49,600 --> 00:22:52,680 Speaker 1: a program that would have generated Bernoulli numbers based upon 386 00:22:52,800 --> 00:22:56,879 Speaker 1: the way that the analytical engine would have worked. So 387 00:22:57,359 --> 00:23:02,440 Speaker 1: the fact that one she understood this, which all by 388 00:23:02,440 --> 00:23:05,040 Speaker 1: itself is pretty amazing to me because I mean in 389 00:23:05,080 --> 00:23:08,679 Speaker 1: the sense that I find it completely incomprehensible too. She 390 00:23:08,800 --> 00:23:11,720 Speaker 1: was able to write a program for something that only 391 00:23:11,760 --> 00:23:15,200 Speaker 1: existed in theory. I mean, and and she had a 392 00:23:15,240 --> 00:23:17,560 Speaker 1: lot of influence with Babbage. The two of them together 393 00:23:18,359 --> 00:23:22,080 Speaker 1: really kind of shaped the analytical engine. And they would 394 00:23:22,119 --> 00:23:25,800 Speaker 1: find errors in each other's work. So it wasn't like 395 00:23:25,800 --> 00:23:28,880 Speaker 1: like Babbage would make mistakes because he's human and Lovelace 396 00:23:28,880 --> 00:23:31,000 Speaker 1: would find them. And sometimes love Lace would make mistakes 397 00:23:31,040 --> 00:23:32,960 Speaker 1: and Babbage would find them. They had a very long 398 00:23:33,040 --> 00:23:38,560 Speaker 1: history of correspondence. Um. And also a web comic. Yes, yeah, 399 00:23:38,600 --> 00:23:43,320 Speaker 1: we have to mention that the Lovelace and Babbage web comic. Oh, 400 00:23:43,359 --> 00:23:46,199 Speaker 1: this guy's do a search on on the web for 401 00:23:46,280 --> 00:23:49,400 Speaker 1: the Lovelace and Babbage web comic, because it is phenomenal. 402 00:23:50,240 --> 00:23:53,480 Speaker 1: I think it's a it's a very playful, tongue in 403 00:23:53,560 --> 00:23:56,520 Speaker 1: cheek tribute to these two individuals. But I think it's 404 00:23:56,560 --> 00:23:59,480 Speaker 1: also you can tell it's it's made out of love, 405 00:24:00,200 --> 00:24:02,320 Speaker 1: I mean that kind of effort to go into two 406 00:24:02,359 --> 00:24:07,720 Speaker 1: and and it's great art, it's great writing. Um. It 407 00:24:07,800 --> 00:24:10,800 Speaker 1: kind of picks up on the presumption of Lovelace and 408 00:24:10,800 --> 00:24:14,840 Speaker 1: Babbage becoming kind of like a crime fighters using computational 409 00:24:15,040 --> 00:24:18,320 Speaker 1: in the the the analytical engine to to defeat crime 410 00:24:18,359 --> 00:24:21,760 Speaker 1: and solve mysteries. And what does sort of sound like 411 00:24:22,040 --> 00:24:24,240 Speaker 1: the like it should be a Hanna Barbera show or 412 00:24:24,280 --> 00:24:27,520 Speaker 1: something kind of, but the arts better. So yeah, no, 413 00:24:27,640 --> 00:24:30,760 Speaker 1: it's really great stuff. I definitely recommend it. And you know, 414 00:24:31,680 --> 00:24:36,160 Speaker 1: you know why we got these these emails so close together, right, 415 00:24:36,440 --> 00:24:40,840 Speaker 1: why is that? It's because of Ada Lovelace Day. Ah. Yeah, Now, 416 00:24:40,840 --> 00:24:43,440 Speaker 1: the very first Aida Lovelace Day was held on the 417 00:24:43,480 --> 00:24:46,600 Speaker 1: March two thousand nine, and they had another one this 418 00:24:46,680 --> 00:24:51,760 Speaker 1: year again March. And um, you can find information about 419 00:24:51,800 --> 00:24:55,040 Speaker 1: Ada Lovelace Day on Facebook, on on the web in general. 420 00:24:55,440 --> 00:24:59,000 Speaker 1: There's a Twitter feed for called finding Ada and Ada 421 00:24:59,119 --> 00:25:02,679 Speaker 1: is a d A, so it's all one word finding Ada. Um, 422 00:25:02,760 --> 00:25:04,879 Speaker 1: and they try and get people to sign a pledge 423 00:25:04,880 --> 00:25:08,320 Speaker 1: to blog about Ada Lovelace and kind of increased public 424 00:25:08,320 --> 00:25:11,040 Speaker 1: awareness of who this woman was and what she accomplished 425 00:25:11,520 --> 00:25:17,200 Speaker 1: and how really amazing you know she was. And um, 426 00:25:17,359 --> 00:25:22,400 Speaker 1: there if you look at contemporary records of Lovelace, Uh, 427 00:25:22,400 --> 00:25:24,760 Speaker 1: it's it's a little for me, it's a little discomforting 428 00:25:24,880 --> 00:25:29,200 Speaker 1: because it's it's almost dismissive. It's like she's amazing despite 429 00:25:29,240 --> 00:25:31,760 Speaker 1: the fact that she's a woman, I mean, which is 430 00:25:31,880 --> 00:25:36,680 Speaker 1: of course indicative of the the general philosophy of the era, right, 431 00:25:37,160 --> 00:25:40,280 Speaker 1: but I mean it's you know, you I ignore that 432 00:25:40,320 --> 00:25:45,280 Speaker 1: because this woman was just phenomenal, period, absolutely brilliant. Yeah. Yeah, 433 00:25:45,320 --> 00:25:47,600 Speaker 1: And uh, I should point out too that that's not 434 00:25:47,640 --> 00:25:50,560 Speaker 1: the only time she's been honored, um, you know, and 435 00:25:50,600 --> 00:25:53,120 Speaker 1: recognized for her work. Uh the As a matter of fact, 436 00:25:53,280 --> 00:25:56,399 Speaker 1: oddly enough, the United States Department of Defense honored her 437 00:25:56,520 --> 00:26:02,800 Speaker 1: with her own program, own programming language in nine. So 438 00:26:02,880 --> 00:26:05,080 Speaker 1: she's I think she's fascinating enough that she just sort 439 00:26:05,080 --> 00:26:07,000 Speaker 1: of keeps popping up in history from time to time. 440 00:26:07,000 --> 00:26:09,159 Speaker 1: People get fascinated and want to learn more about her 441 00:26:09,160 --> 00:26:13,359 Speaker 1: and every reason. She's absolutely brilliant, lating anyone, anyone who 442 00:26:13,440 --> 00:26:17,040 Speaker 1: has a computer science background has heard of her just 443 00:26:17,160 --> 00:26:21,240 Speaker 1: from their their studies. But yeah, I can't help but 444 00:26:21,400 --> 00:26:25,439 Speaker 1: feel that had she not had cancer, had she been 445 00:26:25,480 --> 00:26:29,159 Speaker 1: able to to live on and continue her work, um, 446 00:26:29,200 --> 00:26:31,800 Speaker 1: that possibly the era of computers would have come a 447 00:26:31,880 --> 00:26:35,480 Speaker 1: little faster. Now it's the main thing that would have 448 00:26:35,520 --> 00:26:38,640 Speaker 1: had to have happened was that the combination of Lovelace 449 00:26:38,680 --> 00:26:42,240 Speaker 1: and Babbage's work would together would have to convince people 450 00:26:42,359 --> 00:26:47,280 Speaker 1: to invest in completing the analytical engine. Um, because of 451 00:26:47,280 --> 00:26:50,320 Speaker 1: course they didn't have the resources at their disposal to 452 00:26:50,320 --> 00:26:53,400 Speaker 1: create an electrical computer that would still it would still 453 00:26:53,400 --> 00:26:57,000 Speaker 1: have been a mechanical instrument, and who knows how sophisticated 454 00:26:57,160 --> 00:27:00,320 Speaker 1: it ultimately would have been. It Maybe that her vision 455 00:27:00,480 --> 00:27:04,640 Speaker 1: of of mathematics representing music and graphics and that sort 456 00:27:04,680 --> 00:27:07,679 Speaker 1: of thing would take longer and possibly a totally different 457 00:27:07,680 --> 00:27:11,320 Speaker 1: form factor than the analytical engine, but we might compute 458 00:27:11,400 --> 00:27:13,639 Speaker 1: completely differently than we do now. Yeah, who knows. It 459 00:27:13,680 --> 00:27:16,200 Speaker 1: could have been a very steampunk kind of a kind 460 00:27:16,200 --> 00:27:21,600 Speaker 1: of future. Right. Well, I hope we did justice to 461 00:27:22,320 --> 00:27:24,919 Speaker 1: uh to aid a Lovelace again, And if you want 462 00:27:24,960 --> 00:27:27,240 Speaker 1: to know more about her as a person, definitely check 463 00:27:27,280 --> 00:27:30,840 Speaker 1: out the stuffy Missing History class. Yes they do, they do. 464 00:27:30,920 --> 00:27:33,119 Speaker 1: It's an excellent job. Yeah, we I listened to it 465 00:27:33,160 --> 00:27:36,520 Speaker 1: before we did this podcast, and UH and and Katie 466 00:27:36,560 --> 00:27:38,560 Speaker 1: and Sarah really do a great job at giving an 467 00:27:38,600 --> 00:27:42,120 Speaker 1: idea of what her life was like, and especially her 468 00:27:42,119 --> 00:27:46,600 Speaker 1: relationship with her mother, which was a very complex relationship. Um. 469 00:27:46,640 --> 00:27:50,480 Speaker 1: And sometimes combat of but it's a it's an interesting story, 470 00:27:50,560 --> 00:27:55,119 Speaker 1: kind of tragic ultimately, but definitely helps shape the way 471 00:27:56,040 --> 00:28:00,520 Speaker 1: the history of computers. And since that wraps up that discussion, 472 00:28:00,560 --> 00:28:02,840 Speaker 1: I thought we'd go on too a little listener mail. 473 00:28:07,840 --> 00:28:10,720 Speaker 1: This listener mail comes from Johnny, and Johnny says, Hey, guys, 474 00:28:10,760 --> 00:28:13,080 Speaker 1: I heard that Stuff you Should Know is making T shirts. 475 00:28:13,240 --> 00:28:15,320 Speaker 1: How about we have a T shirt contest? Also, you 476 00:28:15,359 --> 00:28:17,920 Speaker 1: said in your Microchip podcast that you guys were related 477 00:28:17,960 --> 00:28:20,399 Speaker 1: to the Science Channel. What's going on here? Is there 478 00:28:20,440 --> 00:28:22,800 Speaker 1: any other things that how Stuff works is related to? 479 00:28:23,040 --> 00:28:26,119 Speaker 1: Thanks well, Johnny. UM. As for the T shirt thing, 480 00:28:26,160 --> 00:28:28,400 Speaker 1: I wouldn't hold my breath right now. Stuff you Should 481 00:28:28,440 --> 00:28:31,240 Speaker 1: Know is by far our most popular podcast, and of 482 00:28:31,280 --> 00:28:35,600 Speaker 1: course has the the biggest audience, which is fantastic, great show. UM, 483 00:28:35,640 --> 00:28:38,400 Speaker 1: And so we've kind of we're kind of trying to 484 00:28:38,400 --> 00:28:41,200 Speaker 1: see how that goes before we do any other kind 485 00:28:41,240 --> 00:28:44,280 Speaker 1: of T shirt stuff. We'll definitely keep it in mind, 486 00:28:44,320 --> 00:28:47,360 Speaker 1: but I'm I'm not gonna I don't I don't see 487 00:28:47,360 --> 00:28:50,480 Speaker 1: it happening in the near future. However, you did ask 488 00:28:50,640 --> 00:28:54,640 Speaker 1: what our relationship was to the Science Channel. You want 489 00:28:54,640 --> 00:28:57,040 Speaker 1: to take that one, Chris well, of course we are 490 00:28:57,120 --> 00:29:01,160 Speaker 1: a part of Discovery Communication, right. Uh, and there are 491 00:29:01,240 --> 00:29:04,720 Speaker 1: lots and lots of really awesome TV channels and websites 492 00:29:05,120 --> 00:29:08,720 Speaker 1: related to that, like the Discovery Channel, Science Channel, TLC, 493 00:29:09,360 --> 00:29:13,480 Speaker 1: Planet Green and well Planet Right and all those guys. Yeah, 494 00:29:13,560 --> 00:29:17,200 Speaker 1: so sites like tree hugger dot com. Right awesome. Yeah, 495 00:29:17,320 --> 00:29:19,400 Speaker 1: so we're part of a really big family. And that's 496 00:29:19,440 --> 00:29:23,280 Speaker 1: what Pilette meant when he said that, Uh, the Science 497 00:29:23,360 --> 00:29:25,520 Speaker 1: Channel was kind of related to us. We're all kind 498 00:29:25,520 --> 00:29:30,360 Speaker 1: of under the umbrella Discovery Communications. So um, hopefully that 499 00:29:30,400 --> 00:29:33,200 Speaker 1: answers your question. If any of you have any other questions, 500 00:29:33,280 --> 00:29:36,680 Speaker 1: send them into us. Our email address is tech stuff 501 00:29:36,800 --> 00:29:39,520 Speaker 1: at how stuff works dot com and Chris and I 502 00:29:39,520 --> 00:29:45,320 Speaker 1: will taught to you again really soon for more on 503 00:29:45,400 --> 00:29:47,880 Speaker 1: this and thousands of other topics. Does it how stuff 504 00:29:47,880 --> 00:29:50,080 Speaker 1: works dot com and be sure to check out the 505 00:29:50,080 --> 00:29:53,520 Speaker 1: new tech stuff blog now on the house stuff Works homepage, 506 00:29:57,800 --> 00:30:00,440 Speaker 1: brought to you by the reinvented two thousand twelve Camry. 507 00:30:00,640 --> 00:30:01,800 Speaker 1: It's ready, are you