1 00:00:03,800 --> 00:00:06,680 Speaker 1: Welcome to Stuff to Blow Your Mind from how Stuff 2 00:00:06,680 --> 00:00:14,080 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:14,120 --> 00:00:16,880 Speaker 1: My name is Robert Lamb and I'm Julie Douglas. We're 4 00:00:16,880 --> 00:00:21,440 Speaker 1: both humans. Needed neither of us are machines. We depend 5 00:00:21,440 --> 00:00:24,640 Speaker 1: on a number of machines but to make this podcast happen. 6 00:00:24,680 --> 00:00:28,160 Speaker 1: But we are both for the most part, human beings. 7 00:00:28,480 --> 00:00:31,080 Speaker 1: So far we are. Yes, we have not been replaced 8 00:00:31,360 --> 00:00:34,120 Speaker 1: by machines. And to a certain extent, this show is 9 00:00:34,200 --> 00:00:37,280 Speaker 1: our art, right, I mean that's what humans do. Humans 10 00:00:37,320 --> 00:00:40,560 Speaker 1: make art. It's it's kind of an expression of our humanity, 11 00:00:40,640 --> 00:00:47,280 Speaker 1: our desires, our uh scary thoughts. Yeah, yeah, it's this. Yeah, 12 00:00:47,280 --> 00:00:50,960 Speaker 1: it's kind of this output, this uh, this expression of 13 00:00:51,479 --> 00:00:54,840 Speaker 1: self that uh, it is kind of like the bat 14 00:00:54,880 --> 00:00:58,680 Speaker 1: signal of human culture, letting everyone know that we've got 15 00:00:58,680 --> 00:01:01,200 Speaker 1: it going on, that we are. Uh. And when I 16 00:01:01,240 --> 00:01:03,360 Speaker 1: say everyone, I guess we're trying to tell the animals this. 17 00:01:03,480 --> 00:01:07,560 Speaker 1: But um, But but what I'm saying is that we 18 00:01:07,560 --> 00:01:10,119 Speaker 1: we have for a long time just considered art as 19 00:01:10,200 --> 00:01:13,920 Speaker 1: this thing that humans do, and it's uniquely human and 20 00:01:14,040 --> 00:01:17,160 Speaker 1: it's not the sort of thing that a machine could 21 00:01:17,160 --> 00:01:19,920 Speaker 1: really manage. Yeah, And we know, we have talked about 22 00:01:19,920 --> 00:01:23,080 Speaker 1: it a little bit. In the animal world, occasionally you 23 00:01:23,120 --> 00:01:26,759 Speaker 1: can teach an elephant to paint. Yeah, you can get 24 00:01:26,760 --> 00:01:29,080 Speaker 1: an eight to get in there and get its its 25 00:01:29,080 --> 00:01:32,440 Speaker 1: hands covered in paint and start to having around certain 26 00:01:32,480 --> 00:01:35,920 Speaker 1: birds that may make their their nest really like much 27 00:01:35,959 --> 00:01:40,160 Speaker 1: more beautiful in order to attract a meat. We've seen 28 00:01:40,600 --> 00:01:44,960 Speaker 1: and ultimately you can take things out of that are 29 00:01:45,000 --> 00:01:47,400 Speaker 1: out of context and non art, put them in within 30 00:01:47,440 --> 00:01:51,320 Speaker 1: an art context and they become art. So, I mean, 31 00:01:51,320 --> 00:01:53,560 Speaker 1: it's all in the eyes of the hold the beholder. 32 00:01:53,600 --> 00:01:55,640 Speaker 1: But we're the beholder, right, So we're the We're the 33 00:01:55,640 --> 00:01:58,000 Speaker 1: ones who get to decide what is art. We're the 34 00:01:58,000 --> 00:02:00,760 Speaker 1: ones who get to make it um or at least 35 00:02:00,800 --> 00:02:04,280 Speaker 1: that is the way it has been. But robots, robots 36 00:02:04,320 --> 00:02:08,160 Speaker 1: are changing this. Robots are are are challenging our assumptions 37 00:02:08,360 --> 00:02:11,760 Speaker 1: um concerning what art is, who can make it and 38 00:02:11,880 --> 00:02:15,239 Speaker 1: uh and what makes it good? And actually what creativity 39 00:02:15,360 --> 00:02:18,440 Speaker 1: is at the heart of the matter here um Studio 40 00:02:18,520 --> 00:02:21,960 Speaker 1: three sixty dot com has some really great articles a 41 00:02:21,960 --> 00:02:25,760 Speaker 1: suite of articles on creativity and artificial intelligence, and that 42 00:02:25,919 --> 00:02:28,400 Speaker 1: is in part what prompted us to start thinking about this. 43 00:02:28,440 --> 00:02:30,560 Speaker 1: Today and podcasting on it. Yeah, it's a great show. 44 00:02:30,600 --> 00:02:35,320 Speaker 1: If you've never checked out Studio, give it a shot. Um. So, yeah, 45 00:02:35,320 --> 00:02:39,359 Speaker 1: we're talking about creativity, um and robots. Now, we did 46 00:02:39,360 --> 00:02:41,679 Speaker 1: a whole episode on creativity where it comes from in 47 00:02:41,680 --> 00:02:45,320 Speaker 1: the human mind. So creativity in and of itself is 48 00:02:45,720 --> 00:02:51,520 Speaker 1: a rather complex idea. There are several different definitions for creativity, 49 00:02:51,560 --> 00:02:54,040 Speaker 1: which we explored in that that episode and some depth. 50 00:02:54,639 --> 00:02:59,760 Speaker 1: Some of these definitions are more easily appliable to robotic 51 00:02:59,760 --> 00:03:02,600 Speaker 1: card this than others. For instance, there's the cognitive view 52 00:03:02,720 --> 00:03:05,840 Speaker 1: of creativity. Uh, and this is in this one. A 53 00:03:05,840 --> 00:03:08,560 Speaker 1: computer could definitely do it because it's a process by 54 00:03:08,600 --> 00:03:13,440 Speaker 1: which the sensory input uh is transformed, reduced, elaborated, stored, 55 00:03:13,800 --> 00:03:16,600 Speaker 1: recovered and used. So it's just a it's just an 56 00:03:16,639 --> 00:03:20,480 Speaker 1: input output scenario here. So it sounds like memory retrieval 57 00:03:20,480 --> 00:03:22,760 Speaker 1: as well, right, Yeah, it's and uh and you can 58 00:03:22,760 --> 00:03:26,520 Speaker 1: see this kind of quote unquote creativity at work in 59 00:03:26,600 --> 00:03:29,160 Speaker 1: simple You know that you'll find programs online where you 60 00:03:29,200 --> 00:03:31,880 Speaker 1: input tax and we use a cut up system to 61 00:03:32,240 --> 00:03:35,040 Speaker 1: mesh it around and change it. Uh. You know, random 62 00:03:35,040 --> 00:03:37,280 Speaker 1: if you were to do like a random filter on 63 00:03:37,320 --> 00:03:40,040 Speaker 1: a on a photograph. I mean, it's a very simple 64 00:03:41,200 --> 00:03:44,240 Speaker 1: version of what we're talking about. But essentially, data is 65 00:03:44,280 --> 00:03:48,360 Speaker 1: input in, data is changed, and data is output it. Um. 66 00:03:48,480 --> 00:03:51,720 Speaker 1: Then there's like the psychoanalytic Freudian view, which argued that 67 00:03:51,760 --> 00:03:56,120 Speaker 1: creativity is an occurrence of the subconscious. This not so 68 00:03:56,200 --> 00:04:00,120 Speaker 1: easily appliable to machines. Well, we'll get into this in 69 00:04:00,160 --> 00:04:03,440 Speaker 1: a little bit, but there is a aspect of this 70 00:04:04,320 --> 00:04:08,520 Speaker 1: that is happening in artificial intelligence. But yeah, there's this 71 00:04:08,600 --> 00:04:13,160 Speaker 1: idea that art occurs creativity occurs sometimes from these different 72 00:04:13,200 --> 00:04:18,080 Speaker 1: states of consciousness, and particularly with unconsciousness. In fact, Sovageot 73 00:04:18,240 --> 00:04:22,080 Speaker 1: Dolly supposedly would sit with a spoon in his fingertips, 74 00:04:22,320 --> 00:04:24,640 Speaker 1: holding it over a tin cup or plate as he 75 00:04:24,720 --> 00:04:27,360 Speaker 1: drifted off to sleep, and then the spoon would fall 76 00:04:27,400 --> 00:04:29,920 Speaker 1: from his grasp and wake him up again. And presumably 77 00:04:29,960 --> 00:04:31,760 Speaker 1: he did this so that he could spend longer on 78 00:04:31,800 --> 00:04:37,560 Speaker 1: the fringes of consciousness unconsciousness and retrieve new ideas. Um. Yeah, 79 00:04:37,680 --> 00:04:40,480 Speaker 1: And there have been other artists that have should have 80 00:04:40,480 --> 00:04:43,800 Speaker 1: have done that with a silver spoon before, but in 81 00:04:43,960 --> 00:04:46,480 Speaker 1: different ways. For some reason, I thought you're gonna say 82 00:04:46,480 --> 00:04:51,960 Speaker 1: a chainsaw. No, no, yeah, But but Okay. Other quick 83 00:04:52,440 --> 00:04:55,520 Speaker 1: definitions of creativity. There's the behaviorist view, which says that 84 00:04:55,600 --> 00:04:59,400 Speaker 1: creativity is a combination of previously no knowledge joined together 85 00:04:59,480 --> 00:05:03,760 Speaker 1: sponsor enviously um and uh, which gives the impression of 86 00:05:03,760 --> 00:05:05,760 Speaker 1: a new idea or inspiration. So this is kind of 87 00:05:05,800 --> 00:05:10,719 Speaker 1: an idea collider idea of creativity and uh. And that 88 00:05:11,440 --> 00:05:15,480 Speaker 1: matches up really easily with our ideas of machine intelligence, 89 00:05:15,720 --> 00:05:20,320 Speaker 1: that a machine without any kind of real gusto could 90 00:05:20,320 --> 00:05:25,599 Speaker 1: get in there and say, okay, um um, zombies are good, 91 00:05:25,800 --> 00:05:30,280 Speaker 1: and pride and prejudice is good. One plus one equals too. 92 00:05:31,200 --> 00:05:35,039 Speaker 1: Now I'm using creativity. I mean, that's a very granted 93 00:05:35,080 --> 00:05:38,320 Speaker 1: that idea was was was generated by a human, but 94 00:05:38,480 --> 00:05:41,200 Speaker 1: it's very easily too easy to imagine a machine coming 95 00:05:41,279 --> 00:05:43,280 Speaker 1: up with that as well, like looking at, say, you know, 96 00:05:43,320 --> 00:05:45,919 Speaker 1: if you had a computer that was analyzing like search 97 00:05:46,040 --> 00:05:49,200 Speaker 1: terminology online online, seeing what people were searching for and 98 00:05:49,240 --> 00:05:52,760 Speaker 1: just picking the top two categories and crafting a novel. 99 00:05:53,000 --> 00:05:57,680 Speaker 1: So it's very much like decision tree decision making. And 100 00:05:57,800 --> 00:06:01,719 Speaker 1: so the question arises, can there be a purely creative 101 00:06:01,920 --> 00:06:05,800 Speaker 1: product that comes out of a machine? And the other 102 00:06:05,880 --> 00:06:08,359 Speaker 1: question is, you know, is a sacred ground. Is it 103 00:06:08,440 --> 00:06:11,320 Speaker 1: really unique to humans? And that's what we're going to 104 00:06:11,400 --> 00:06:16,719 Speaker 1: really get to explore today. Yeah, it's an interesting look 105 00:06:16,839 --> 00:06:20,279 Speaker 1: at just what computers are capable of now and uh 106 00:06:20,360 --> 00:06:22,640 Speaker 1: in an idea of where they're going. But also it 107 00:06:22,680 --> 00:06:26,880 Speaker 1: really forces us to reevaluate what's going on in creativity 108 00:06:26,920 --> 00:06:30,599 Speaker 1: and what's going on in art and and you know, 109 00:06:30,600 --> 00:06:33,560 Speaker 1: it just really forces you to re examine how we 110 00:06:33,680 --> 00:06:37,839 Speaker 1: view the whole scenario. So, um, we should probably start 111 00:06:39,120 --> 00:06:43,200 Speaker 1: in the written word robotic fiction. Yeah, and apparently this 112 00:06:43,240 --> 00:06:46,080 Speaker 1: has been around for a while, right for something called 113 00:06:46,120 --> 00:06:50,600 Speaker 1: Mark one Baby in a nine British computer scientists Christopher's 114 00:06:50,600 --> 00:06:54,719 Speaker 1: stra Key program the Mark one baby computer to generate 115 00:06:54,839 --> 00:06:58,760 Speaker 1: love poetry from a database of romantic verbs and nouns. Yeah, 116 00:06:58,839 --> 00:07:00,960 Speaker 1: back in the fifties, Back in the fifties. And here's 117 00:07:01,000 --> 00:07:06,360 Speaker 1: here's a sample. I'm not gonna read in robotic Okay, 118 00:07:06,360 --> 00:07:08,320 Speaker 1: I'll try. I mean, you know how well I do 119 00:07:08,480 --> 00:07:12,720 Speaker 1: with other accents. Well, the robot accent is pretty pretty easy, 120 00:07:12,800 --> 00:07:19,160 Speaker 1: all right. Honey, love, my anxious fondness impatiently yearns for 121 00:07:19,240 --> 00:07:25,680 Speaker 1: your craving enthusiasm. You are my breathless infatuation, my darling, affection, 122 00:07:26,040 --> 00:07:32,080 Speaker 1: my ardent, liking my covetous fancy yours anxiously and you 123 00:07:32,240 --> 00:07:35,239 Speaker 1: see there you go, that was good. That was okay. 124 00:07:35,440 --> 00:07:37,960 Speaker 1: I mean, I'm sure the computer is listening. We'll probably 125 00:07:40,000 --> 00:07:44,080 Speaker 1: right now. Yeah, but yeah, here's here's an early example 126 00:07:44,160 --> 00:07:47,480 Speaker 1: of this. But you know, fast forward to do today 127 00:07:47,560 --> 00:07:52,640 Speaker 1: and we have something called Brutus, which is a machine fabulous. Yeah. 128 00:07:53,000 --> 00:07:57,600 Speaker 1: Brutus uses mathematical equations um that are based on the 129 00:07:57,600 --> 00:08:01,160 Speaker 1: basics of plot, of setting, of dialogue and uh and 130 00:08:01,320 --> 00:08:02,960 Speaker 1: really this if any of any of you that have 131 00:08:03,000 --> 00:08:06,720 Speaker 1: ever taken classes on on the structure of a novel 132 00:08:07,160 --> 00:08:09,960 Speaker 1: or how to write a short story, you know that 133 00:08:10,000 --> 00:08:13,320 Speaker 1: their their formulas out there, and you can really and 134 00:08:13,400 --> 00:08:15,000 Speaker 1: you can also look at it like that there's so 135 00:08:15,000 --> 00:08:19,800 Speaker 1: many different different guys out there and and and women 136 00:08:19,840 --> 00:08:23,000 Speaker 1: that are they have like these models of how to 137 00:08:23,120 --> 00:08:26,000 Speaker 1: plan out a novel, how to plan out a short story, 138 00:08:26,040 --> 00:08:29,560 Speaker 1: and they're you know, they're like triangle approaches and and 139 00:08:29,680 --> 00:08:32,079 Speaker 1: uh you know, multiple story arc approaches, and there's like 140 00:08:32,120 --> 00:08:36,719 Speaker 1: a snowflake um approach to writing a novel and it's uh, 141 00:08:37,000 --> 00:08:40,120 Speaker 1: so we're really big on formulating it and really guiding 142 00:08:40,160 --> 00:08:42,120 Speaker 1: it out you know, as if to make it to 143 00:08:42,160 --> 00:08:45,320 Speaker 1: where just anybody could follow these instructions and come up 144 00:08:45,360 --> 00:08:49,200 Speaker 1: with a novel. So so it it follows it's really 145 00:08:49,200 --> 00:08:51,240 Speaker 1: easy to imagine then a computer taking some of this 146 00:08:51,440 --> 00:08:54,200 Speaker 1: this data and using that that that information is an 147 00:08:54,240 --> 00:08:58,040 Speaker 1: equation to generate a novel. It knows the the structure 148 00:08:58,080 --> 00:09:01,880 Speaker 1: of plot, the the way a story tends to to roll. 149 00:09:02,320 --> 00:09:05,360 Speaker 1: Uh and uh, and it can can generate that. Yeah 150 00:09:05,480 --> 00:09:07,360 Speaker 1: and uh, I mean it is really cool actually, the 151 00:09:07,400 --> 00:09:10,040 Speaker 1: fact that it can generate these these stories. Now there 152 00:09:10,080 --> 00:09:13,199 Speaker 1: are limitations. I mean, we're talking about five words stories. 153 00:09:14,000 --> 00:09:17,640 Speaker 1: And programmer Selma brings your He said that the program 154 00:09:17,679 --> 00:09:21,400 Speaker 1: can't be considered creative unless the machine creates something that's 155 00:09:21,480 --> 00:09:26,600 Speaker 1: completely demystifies the programmer. So in other words, if it's 156 00:09:26,600 --> 00:09:31,079 Speaker 1: not demystifying um, and it's putting out the expected outcome, 157 00:09:31,559 --> 00:09:34,520 Speaker 1: then it's just cloning our intelligence. We'll see. This reminds 158 00:09:34,520 --> 00:09:38,439 Speaker 1: me a lot of the scenario of fan fiction um 159 00:09:38,559 --> 00:09:42,000 Speaker 1: in uh with with human writers. This is a pretty 160 00:09:42,040 --> 00:09:45,440 Speaker 1: simple concept that everyone's encountered. Take takes. Say, someone like 161 00:09:45,559 --> 00:09:48,360 Speaker 1: HP Lovecraft wrote a you know, a ton of short 162 00:09:48,400 --> 00:09:52,920 Speaker 1: stories and fragments and the occasional slightly longer work, uh, 163 00:09:53,000 --> 00:09:57,200 Speaker 1: with a very particular brand of horror. And you have 164 00:09:57,720 --> 00:10:00,280 Speaker 1: you have people that read and I mean think I've 165 00:10:00,280 --> 00:10:02,000 Speaker 1: probably read most of his his work, but you have 166 00:10:02,040 --> 00:10:04,520 Speaker 1: people that read most of his stories, not all of 167 00:10:04,520 --> 00:10:07,640 Speaker 1: his stories, And then it's pretty easy at that point 168 00:10:08,200 --> 00:10:11,240 Speaker 1: to sort of regurgitate something that is at least love 169 00:10:11,320 --> 00:10:14,839 Speaker 1: craft ish in that style. Yeah, you figure out what 170 00:10:14,880 --> 00:10:16,920 Speaker 1: he tends to do in a story, what format he 171 00:10:16,960 --> 00:10:19,319 Speaker 1: tends to follow, and then you can make something that's 172 00:10:19,360 --> 00:10:22,080 Speaker 1: love craft ish. And and certainly there have been a 173 00:10:22,080 --> 00:10:25,320 Speaker 1: lot of authors who have have enjoyed some success with 174 00:10:25,520 --> 00:10:29,680 Speaker 1: lovecraft Is stuff. But but where you really see something 175 00:10:29,679 --> 00:10:32,840 Speaker 1: phenomenal is when someone is clearly inspired by someone like 176 00:10:32,920 --> 00:10:37,000 Speaker 1: Lovecraft or or or um Or or various other authors 177 00:10:37,000 --> 00:10:39,080 Speaker 1: that have that certain feel and look of their their 178 00:10:39,080 --> 00:10:41,640 Speaker 1: own material, where they can take what that author has 179 00:10:41,640 --> 00:10:45,040 Speaker 1: done and then spin it around, do something different with 180 00:10:45,120 --> 00:10:47,960 Speaker 1: it in a way that makes it their own. Well see, 181 00:10:48,080 --> 00:10:51,599 Speaker 1: and this comes the originality really comes into question in 182 00:10:51,679 --> 00:10:54,240 Speaker 1: these sort of cases, because you could argue that there 183 00:10:54,360 --> 00:10:59,760 Speaker 1: is nothing nothing unique has ever really been created, right, 184 00:11:00,000 --> 00:11:03,960 Speaker 1: that it's just intertant. We're all taking ideas colliding them 185 00:11:04,040 --> 00:11:06,280 Speaker 1: and and trying to put just enough spin on them 186 00:11:06,320 --> 00:11:08,560 Speaker 1: that we don't get sued. It's just iteration of iteration 187 00:11:08,559 --> 00:11:11,800 Speaker 1: of iteration, unless you're DaVinci, right, yeah, just coming up 188 00:11:11,840 --> 00:11:15,640 Speaker 1: with all these completely original ideas for new machines. But 189 00:11:15,640 --> 00:11:19,360 Speaker 1: that's because he stole a time travel machine. I knew it. 190 00:11:19,920 --> 00:11:24,280 Speaker 1: I knew you. You finally said it. It's true. Um, 191 00:11:24,520 --> 00:11:27,920 Speaker 1: so yeah, I mean this idea that that this program 192 00:11:28,040 --> 00:11:30,720 Speaker 1: is spitting out stuff that's not too original. Well, okay, 193 00:11:30,720 --> 00:11:32,959 Speaker 1: I mean you really get into shades of gray of like, well, 194 00:11:33,000 --> 00:11:36,160 Speaker 1: what at what point does it become in the realm 195 00:11:36,160 --> 00:11:41,040 Speaker 1: of uniqueeness? And and um, just you know, just regurgitating 196 00:11:41,640 --> 00:11:43,720 Speaker 1: a bunch of rules. Yeah, and are you which model 197 00:11:43,760 --> 00:11:45,840 Speaker 1: of human creativity you're holding it up to. You're holding 198 00:11:45,920 --> 00:11:47,679 Speaker 1: up in the Sistine Chapel, You're holding it up to 199 00:11:47,760 --> 00:11:50,320 Speaker 1: Dante's Inferno. You're holding it up to pride and prejudice 200 00:11:50,360 --> 00:11:53,080 Speaker 1: with zombies, which is the main problem of subjectivity right 201 00:11:53,120 --> 00:11:55,400 Speaker 1: when it comes to art. Um. Okay, So I did 202 00:11:55,520 --> 00:11:59,520 Speaker 1: want to say that that Brutus created this this short 203 00:11:59,559 --> 00:12:02,120 Speaker 1: little or here, and he paints a picture of a 204 00:12:02,120 --> 00:12:05,960 Speaker 1: guy named Dave Sdriver. He's a PhD candidate and he's 205 00:12:06,000 --> 00:12:10,400 Speaker 1: ambivalent about his current situation and the program pus dashes 206 00:12:10,440 --> 00:12:13,000 Speaker 1: of details in there, just as you said, like, there's 207 00:12:13,200 --> 00:12:15,680 Speaker 1: very formulaic in some ways. Um, you know, just to 208 00:12:15,760 --> 00:12:19,199 Speaker 1: create a setting. Brutus says, uh, here's the Ivory clock 209 00:12:19,240 --> 00:12:21,559 Speaker 1: on the campus and eager students. You start to get 210 00:12:21,559 --> 00:12:24,640 Speaker 1: a real sense of place. And the pacing also gives 211 00:12:24,679 --> 00:12:28,880 Speaker 1: it a sense of drama and foreboding. And I wanted 212 00:12:28,920 --> 00:12:31,160 Speaker 1: to play just a really short clip of this. This 213 00:12:31,280 --> 00:12:34,880 Speaker 1: is from Studio three sixty and this is a moment 214 00:12:34,920 --> 00:12:38,360 Speaker 1: in which Dave Striver is being waylaid by the dissertation 215 00:12:38,400 --> 00:12:43,760 Speaker 1: committee for his PhD. Fiery and uncontrollable rose up Dave. 216 00:12:44,520 --> 00:12:50,240 Speaker 1: Professor Hart products softly, God, they were pitiful, pitiful, palette 217 00:12:50,280 --> 00:12:56,439 Speaker 1: and puny, Dave. Did you hear the question? Later Striver 218 00:12:56,600 --> 00:13:00,360 Speaker 1: sat alone in his apartment. What in God's name had 219 00:13:00,360 --> 00:13:02,800 Speaker 1: he done? And it should be noted that that was 220 00:13:02,840 --> 00:13:06,400 Speaker 1: not the robots voice. That was that was human reading. Yeah, 221 00:13:06,600 --> 00:13:09,599 Speaker 1: that was a real dude reading. But I mean you 222 00:13:09,760 --> 00:13:11,280 Speaker 1: kind of do get a sense of that, right, That's 223 00:13:11,320 --> 00:13:13,560 Speaker 1: not too bad? Yeah, I mean that's not I mean 224 00:13:13,559 --> 00:13:15,160 Speaker 1: for a computer to sort of split that out, and 225 00:13:15,240 --> 00:13:16,920 Speaker 1: for for to make sense in the first place is 226 00:13:16,960 --> 00:13:19,959 Speaker 1: kind of amazing, But for it to give you a 227 00:13:20,000 --> 00:13:24,760 Speaker 1: sense of of peering into another person's mind, that is amazing, 228 00:13:25,559 --> 00:13:29,199 Speaker 1: because does a machine know what a human mind is? Well? 229 00:13:29,240 --> 00:13:31,920 Speaker 1: And certainly learning about it? Because ultimately mean the machine 230 00:13:31,960 --> 00:13:33,800 Speaker 1: is dealing with language is kind of like a shadow 231 00:13:33,840 --> 00:13:37,280 Speaker 1: of the human mind. It's um you know, it's dealing 232 00:13:37,280 --> 00:13:41,040 Speaker 1: with coded data, semiotic web of of meanings and symbols. 233 00:13:41,080 --> 00:13:44,560 Speaker 1: And if you align it properly, you create that you 234 00:13:44,559 --> 00:13:47,000 Speaker 1: can create a text that has either a little or 235 00:13:47,040 --> 00:13:51,360 Speaker 1: a lot of meaning. Well, it's just to what extent 236 00:13:51,480 --> 00:13:54,120 Speaker 1: can the machine understand the human mind? I think? And 237 00:13:54,480 --> 00:13:58,360 Speaker 1: to that end, it turns out that Patrick Winston, he's 238 00:13:58,360 --> 00:14:01,080 Speaker 1: a principal, principle and stigator at m i t S 239 00:14:01,080 --> 00:14:05,400 Speaker 1: Computer Science and Artificial Intelligence Lab. He is teaching Macbeth 240 00:14:05,600 --> 00:14:09,360 Speaker 1: to computers and he says that storytelling makes it possible 241 00:14:09,360 --> 00:14:12,360 Speaker 1: to teach, to learn, and to be creative, and so 242 00:14:12,520 --> 00:14:17,240 Speaker 1: he wants these machines to understand humans. And what better 243 00:14:17,280 --> 00:14:20,080 Speaker 1: way than to start off with Shakespeare. Yeah, and as 244 00:14:20,080 --> 00:14:24,760 Speaker 1: far as teaching, learning and creating GOUM I mean human 245 00:14:24,760 --> 00:14:27,480 Speaker 1: beings go pretty long way with just faking any or 246 00:14:27,560 --> 00:14:30,000 Speaker 1: all three of those categories. So really, I mean, as 247 00:14:30,000 --> 00:14:32,560 Speaker 1: far as understanding the human mind, yeah, I don't know, 248 00:14:32,640 --> 00:14:35,880 Speaker 1: that's really not even as important necessarily for a machine 249 00:14:35,920 --> 00:14:40,440 Speaker 1: as just the simblance of it faking it, because if you, 250 00:14:40,600 --> 00:14:43,240 Speaker 1: you know, create, if you can create something that we 251 00:14:43,480 --> 00:14:47,640 Speaker 1: end up investing thought into, then then that is the 252 00:14:47,640 --> 00:14:51,280 Speaker 1: insight into the human mind. Right, maybe I'm getting a 253 00:14:51,280 --> 00:14:55,040 Speaker 1: little no, Well, I think what you're saying is that 254 00:14:55,080 --> 00:14:59,200 Speaker 1: the simulation at some point becomes the thing. Right, So 255 00:14:59,400 --> 00:15:02,800 Speaker 1: even if it doesn't truly understand it, if it's executing 256 00:15:03,480 --> 00:15:06,560 Speaker 1: the code for it. I mean, because we've all encountered art, 257 00:15:06,640 --> 00:15:09,200 Speaker 1: be it um you know, be it, be it a 258 00:15:09,200 --> 00:15:11,840 Speaker 1: work of written art or visual art or music where 259 00:15:11,880 --> 00:15:14,880 Speaker 1: we take that moment we think if this person brilliant 260 00:15:15,280 --> 00:15:19,720 Speaker 1: or are they just pulling one over on me? You know. Well, 261 00:15:19,840 --> 00:15:24,280 Speaker 1: now machines have the same opportunity exactly. Uh well, you know, okay, 262 00:15:24,360 --> 00:15:27,360 Speaker 1: this this guy who's teaching them Macbeth, he actually has 263 00:15:27,520 --> 00:15:33,840 Speaker 1: as a point here beyond just human um mind uh comprehension. Here, 264 00:15:33,920 --> 00:15:36,760 Speaker 1: it's is actually to try to help creatively problem solve, 265 00:15:36,840 --> 00:15:39,800 Speaker 1: which would make sense of storytelling. And he says that 266 00:15:39,840 --> 00:15:41,960 Speaker 1: what we're hoping to do is build systems one day 267 00:15:42,040 --> 00:15:44,560 Speaker 1: that are as important to a political analyst as a 268 00:15:44,560 --> 00:15:48,400 Speaker 1: spreadsheet is to a financial analyst. We have a system 269 00:15:48,440 --> 00:15:52,400 Speaker 1: now that draws parallels between various kinds of historical events, 270 00:15:52,480 --> 00:15:55,600 Speaker 1: the Arab Israeli War and the tet offensive, for example. 271 00:15:55,960 --> 00:16:00,280 Speaker 1: Finding those parallels could help keep people out of trouble. Okay, 272 00:16:00,320 --> 00:16:02,680 Speaker 1: so now we're getting into a larger area than of 273 00:16:02,880 --> 00:16:07,040 Speaker 1: robotic design, not just not just robotic writing, but just 274 00:16:07,440 --> 00:16:10,760 Speaker 1: robotic creative thought. Well, and it reminded me of the 275 00:16:10,760 --> 00:16:13,280 Speaker 1: Living Earth simulator that we've talked about before, which is 276 00:16:13,320 --> 00:16:16,800 Speaker 1: this idea that you could take every bit of data 277 00:16:16,880 --> 00:16:18,680 Speaker 1: in the world, throw it in there and try to 278 00:16:18,720 --> 00:16:21,760 Speaker 1: predict what's going to happen next, whether it's a weather 279 00:16:21,800 --> 00:16:28,360 Speaker 1: pattern pattern or um uh, you know, politics, um or 280 00:16:28,400 --> 00:16:33,960 Speaker 1: even economic ramifications throughout the world. It's interesting. I'm currently 281 00:16:34,040 --> 00:16:39,600 Speaker 1: reading the Hyperion series by Dan Simmons, and in that 282 00:16:39,680 --> 00:16:44,080 Speaker 1: novel you have a you have artificial intelligences that have 283 00:16:44,560 --> 00:16:49,800 Speaker 1: sort of succeeded, succeeded from from human civilization and that 284 00:16:50,000 --> 00:16:53,480 Speaker 1: still advise on various levels and are still involved but 285 00:16:53,520 --> 00:16:56,040 Speaker 1: also rather separate. But they have it to the point 286 00:16:56,080 --> 00:17:00,040 Speaker 1: where they know exactly how, um, everything is going to 287 00:17:00,320 --> 00:17:04,159 Speaker 1: transpire for years and years in the future, with the 288 00:17:04,160 --> 00:17:07,280 Speaker 1: exception of one sort of X factor that's pivotal to 289 00:17:07,320 --> 00:17:11,119 Speaker 1: the novel. But but but yeah, that back to the 290 00:17:11,160 --> 00:17:13,280 Speaker 1: Living Earth simulator, just the idea that that if you 291 00:17:13,520 --> 00:17:17,520 Speaker 1: put it up, you put the information in there, to 292 00:17:17,600 --> 00:17:22,240 Speaker 1: what extent can a machine navigate all the variables and 293 00:17:22,280 --> 00:17:24,280 Speaker 1: come up with an accurate prediction of how things will 294 00:17:24,280 --> 00:17:27,280 Speaker 1: play out. Well, we've talked about the limitations here with 295 00:17:27,320 --> 00:17:30,080 Speaker 1: a Living Earth simulator simulator because it doesn't take into 296 00:17:30,119 --> 00:17:34,200 Speaker 1: account or this version of it as of yet hasn't 297 00:17:34,240 --> 00:17:36,760 Speaker 1: been able to take into account what we call these 298 00:17:36,760 --> 00:17:39,680 Speaker 1: black swans or these events that seem to come out 299 00:17:39,680 --> 00:17:42,919 Speaker 1: of nowhere. But when in fact you look at it 300 00:17:42,960 --> 00:17:44,720 Speaker 1: like you know that there's going to be some sort 301 00:17:44,760 --> 00:17:47,359 Speaker 1: of entropy at some point, it just feels like it 302 00:17:47,400 --> 00:17:50,280 Speaker 1: came out of left field because we didn't expect it. Right. Well, 303 00:17:50,280 --> 00:17:53,920 Speaker 1: I mean, it's like human creativity. You know, Um, we'll 304 00:17:53,920 --> 00:17:55,920 Speaker 1: have we'll have one thing in mind, then something else 305 00:17:55,920 --> 00:17:58,080 Speaker 1: comes along and it changes the form of what you're 306 00:17:58,080 --> 00:18:01,800 Speaker 1: trying to create you know, um, I mean, like I 307 00:18:01,880 --> 00:18:04,639 Speaker 1: remember I started writing a short story before my dad died, 308 00:18:04,880 --> 00:18:07,720 Speaker 1: and then after my dad died, that changed the shape 309 00:18:07,720 --> 00:18:09,760 Speaker 1: of that story. Like that was a black swan event. 310 00:18:10,160 --> 00:18:12,840 Speaker 1: I mean, not that you know, people die, that's kind 311 00:18:12,880 --> 00:18:14,800 Speaker 1: of the It kind of happens all the time. So 312 00:18:14,880 --> 00:18:17,200 Speaker 1: not in the sense that it's that it's that unforeseeable 313 00:18:17,200 --> 00:18:21,360 Speaker 1: an event, um, but but it occurs and it changes 314 00:18:21,440 --> 00:18:24,840 Speaker 1: the direction of the creativity. So so what you're saying 315 00:18:24,840 --> 00:18:28,360 Speaker 1: here is that we have to have a creative program 316 00:18:28,400 --> 00:18:30,760 Speaker 1: that is also um that we throw a few black 317 00:18:30,800 --> 00:18:33,359 Speaker 1: swans at every now and then, Well that could also 318 00:18:33,400 --> 00:18:36,040 Speaker 1: create its own black swans. And we should probably take 319 00:18:36,040 --> 00:18:38,080 Speaker 1: a break here and we get back, though. We're going 320 00:18:38,119 --> 00:18:40,080 Speaker 1: to talk about whether or not there is a machine 321 00:18:40,680 --> 00:18:45,159 Speaker 1: that can break this kind of code and possibly even 322 00:18:45,720 --> 00:18:54,000 Speaker 1: create new unique thoughts. All right, all right, and we're 323 00:18:54,040 --> 00:18:56,480 Speaker 1: back and uh yeah, we're going to talk about a 324 00:18:56,480 --> 00:19:00,880 Speaker 1: little something here called the creativity machine. Yeah, okay, so 325 00:19:01,600 --> 00:19:04,000 Speaker 1: this is not a you know, something that we read 326 00:19:04,000 --> 00:19:06,439 Speaker 1: about in a science fiction novel or something something like 327 00:19:06,480 --> 00:19:09,560 Speaker 1: the near not the near Earth simulat not not like 328 00:19:09,600 --> 00:19:12,080 Speaker 1: the the Earth Simulator that is that is that may 329 00:19:12,119 --> 00:19:14,480 Speaker 1: exist in the future that we're planning. This is something 330 00:19:14,520 --> 00:19:16,840 Speaker 1: that he's out there. I actually thought about the Santa 331 00:19:16,880 --> 00:19:19,280 Speaker 1: Claus machine when I was reading about this, and we 332 00:19:19,280 --> 00:19:22,280 Speaker 1: talked about this in another episode. That of course is 333 00:19:22,320 --> 00:19:24,399 Speaker 1: something that that could exist in the future, but doesn't 334 00:19:24,440 --> 00:19:27,800 Speaker 1: exist exist now, Okay, so let's get to this creativity machine. 335 00:19:28,040 --> 00:19:31,040 Speaker 1: It is pretty much like the ultimate problem solving machine. 336 00:19:31,080 --> 00:19:34,600 Speaker 1: There is a guy named Stephen Fuller and there's a 337 00:19:34,600 --> 00:19:38,760 Speaker 1: great documentary called In Its Image that covers this. Since 338 00:19:38,800 --> 00:19:42,399 Speaker 1: the seventies, he has been fussing around with artificial neural 339 00:19:42,480 --> 00:19:46,880 Speaker 1: networks and he has conducted a lot of experiments with us. 340 00:19:47,720 --> 00:19:51,439 Speaker 1: Usually people have been dealing with artificial neural networks just 341 00:19:51,520 --> 00:19:54,800 Speaker 1: to kind of see the way that the brain processes information. 342 00:19:55,680 --> 00:19:59,040 Speaker 1: But he he has a different plan um with his 343 00:19:59,320 --> 00:20:05,919 Speaker 1: creativity Shine by using the human brain and trying to 344 00:20:06,600 --> 00:20:10,280 Speaker 1: not only replicate the way that neurons the neural network 345 00:20:10,359 --> 00:20:15,240 Speaker 1: is working, but also how originality arises in humans. He's 346 00:20:15,240 --> 00:20:18,119 Speaker 1: trying to do the same thing in in computers, and 347 00:20:18,160 --> 00:20:20,520 Speaker 1: you could argue that he has done it. Yeah, the 348 00:20:20,560 --> 00:20:24,639 Speaker 1: created creativity machine learns on its own and uh, and 349 00:20:24,720 --> 00:20:29,719 Speaker 1: it and it deals with black swans mathematical variables that 350 00:20:29,760 --> 00:20:31,880 Speaker 1: are thrown in to shake it up. Yeah, I mean, okay, 351 00:20:32,040 --> 00:20:35,359 Speaker 1: it's got these human generated scripts. UM. It is an 352 00:20:35,400 --> 00:20:38,640 Speaker 1: artificial neural network, but it has two distinct functions. One 353 00:20:38,680 --> 00:20:42,520 Speaker 1: part contains data and one part runs the data with 354 00:20:42,640 --> 00:20:45,200 Speaker 1: tests and gives it feedback. So that's where it learns 355 00:20:45,200 --> 00:20:48,880 Speaker 1: from itself. UM. And then occasionally Solar will actually disrupt 356 00:20:48,880 --> 00:20:53,760 Speaker 1: these functions by running mathematical noise into the system. That's 357 00:20:53,760 --> 00:20:57,520 Speaker 1: the black swan, which creates a false memory of confabulation. 358 00:20:58,280 --> 00:21:02,959 Speaker 1: And this is where the machine gets confused and it 359 00:21:03,000 --> 00:21:05,800 Speaker 1: begins to drive the artificial neural network to think in 360 00:21:05,840 --> 00:21:08,960 Speaker 1: new ways and produce new ideas. And here's an example 361 00:21:09,000 --> 00:21:12,440 Speaker 1: of this. UM Follower was actually commissioned in by Oral B, 362 00:21:13,040 --> 00:21:17,000 Speaker 1: the toothbrush maker, to create some new designs for the toothbrush, 363 00:21:17,160 --> 00:21:20,640 Speaker 1: or their toothbrush as I should say. And he said 364 00:21:20,640 --> 00:21:23,800 Speaker 1: that when they injected noise into the first network, it 365 00:21:23,920 --> 00:21:28,520 Speaker 1: imagined not the memories of toothbrushes it had previously seen. 366 00:21:29,280 --> 00:21:34,320 Speaker 1: It produced confabulations, false memories that were hybrid hybridizations of 367 00:21:34,440 --> 00:21:38,359 Speaker 1: what it had seen before. So when he when he 368 00:21:38,480 --> 00:21:40,719 Speaker 1: threw that box swan in there all of a sudden, 369 00:21:40,760 --> 00:21:43,639 Speaker 1: it didn't. It did create these original new thoughts that 370 00:21:43,680 --> 00:21:46,520 Speaker 1: were based on those memories, so it retrieved its memories, 371 00:21:46,520 --> 00:21:49,560 Speaker 1: but they were false memories, which is really sort of 372 00:21:49,600 --> 00:21:51,360 Speaker 1: the same thing that happens when we have our own 373 00:21:51,480 --> 00:21:56,240 Speaker 1: aha moments and creativity. Right, we have these breakthroughs based 374 00:21:56,280 --> 00:22:00,320 Speaker 1: on the experience the memories, but you know, come comes 375 00:22:00,359 --> 00:22:05,040 Speaker 1: out and a bit differently. Um, he says, you know, 376 00:22:05,160 --> 00:22:07,680 Speaker 1: I didn't do anything crazy here. I basically pushed a 377 00:22:07,760 --> 00:22:12,320 Speaker 1: return button and sat back and watched two thousand designs emerge, 378 00:22:12,560 --> 00:22:14,399 Speaker 1: and then he gave the customer or it will be 379 00:22:14,600 --> 00:22:18,560 Speaker 1: two designs, okay, So that sounds like okay, or it'll 380 00:22:18,560 --> 00:22:22,800 Speaker 1: be toothbrush whatever. This guy, as I has actually used 381 00:22:22,800 --> 00:22:26,240 Speaker 1: this machine to create everything from like snack food to 382 00:22:26,760 --> 00:22:32,040 Speaker 1: military devices like robotic swarms. Yeah, he's that. It's been 383 00:22:32,119 --> 00:22:35,440 Speaker 1: used to create such as harder than diamonds, one point 384 00:22:35,480 --> 00:22:39,680 Speaker 1: five million new English words, it's trained robot cockroaches, and 385 00:22:39,760 --> 00:22:43,040 Speaker 1: it's um, it's composed just a whole lot of music, yeah, 386 00:22:43,080 --> 00:22:44,560 Speaker 1: which is really cool, and we'll listen to a clip 387 00:22:44,560 --> 00:22:47,520 Speaker 1: of that music in a moment. Um. But this is 388 00:22:47,560 --> 00:22:51,000 Speaker 1: a really cool thing to uh. Fohler actually filed the 389 00:22:51,040 --> 00:22:55,040 Speaker 1: first patent for the autonomous generator generation of useful Information, 390 00:22:55,119 --> 00:22:57,800 Speaker 1: and then his second patent was for his self training 391 00:22:57,840 --> 00:23:02,560 Speaker 1: neural network object. Patent too was invented by patent number one. 392 00:23:02,960 --> 00:23:06,199 Speaker 1: That's crazy, that's amazing the machine created the patent. I 393 00:23:06,200 --> 00:23:08,680 Speaker 1: mean this is this really does kind of spell out 394 00:23:09,600 --> 00:23:13,680 Speaker 1: a very interesting future with this technology. Um. He says 395 00:23:13,720 --> 00:23:15,680 Speaker 1: that this is the way that we're going to cure 396 00:23:15,720 --> 00:23:18,680 Speaker 1: cancer and the way that we'll do multi trilling and 397 00:23:18,720 --> 00:23:21,760 Speaker 1: dimensional optimizations to bring some peace of mind for the 398 00:23:21,760 --> 00:23:24,560 Speaker 1: world's economy. Okay, so think back to the Living Earth 399 00:23:24,640 --> 00:23:28,919 Speaker 1: simulator and it's limitations. If you were to marry the 400 00:23:28,960 --> 00:23:32,399 Speaker 1: two technologies, is there the possibility that this machine could 401 00:23:32,440 --> 00:23:35,119 Speaker 1: become like, you know, basically a crystal ball for us? 402 00:23:35,720 --> 00:23:37,879 Speaker 1: It could be like oral b comes to the the 403 00:23:38,440 --> 00:23:41,639 Speaker 1: the AI s then and says, hey, we want to 404 00:23:41,640 --> 00:23:43,720 Speaker 1: to roll out a new toothbrush, but we also want 405 00:23:43,720 --> 00:23:46,280 Speaker 1: to know how this toothbrush will change the world. So 406 00:23:46,960 --> 00:23:48,879 Speaker 1: the computers begin to say, all right, here are ten 407 00:23:48,960 --> 00:23:51,960 Speaker 1: designs you can choose from, and here is a layout 408 00:23:51,960 --> 00:23:55,720 Speaker 1: of how each of these designs will affect your financial 409 00:23:55,760 --> 00:23:58,040 Speaker 1: well being as well as the world at large. Right, 410 00:23:58,080 --> 00:24:00,000 Speaker 1: could you could overlay all that data, right, you could say, 411 00:24:00,119 --> 00:24:02,639 Speaker 1: if you decide to produce it in this area of 412 00:24:02,640 --> 00:24:04,600 Speaker 1: the world, this is the impact it will have on 413 00:24:04,680 --> 00:24:07,560 Speaker 1: that economy and on on those people. You know, if 414 00:24:07,600 --> 00:24:09,360 Speaker 1: you did it here, it would have this sort of impact, 415 00:24:09,400 --> 00:24:11,960 Speaker 1: so on and so forth. You could look at it's uh, 416 00:24:12,200 --> 00:24:14,480 Speaker 1: it's carbon footprint. I mean, you could look at all 417 00:24:14,480 --> 00:24:17,160 Speaker 1: sorts of slices of data on top and ultimately that's 418 00:24:17,160 --> 00:24:20,560 Speaker 1: what the the Living Art Simulator it comes down to 419 00:24:20,600 --> 00:24:22,920 Speaker 1: as well. It's it's not really so much an idea 420 00:24:23,000 --> 00:24:27,040 Speaker 1: that will we will create a virtual reality Earth too. 421 00:24:27,440 --> 00:24:30,439 Speaker 1: It's it's the idea of, well, we have in this 422 00:24:30,520 --> 00:24:33,280 Speaker 1: simulation as much data as we want to throw at it. 423 00:24:33,359 --> 00:24:36,000 Speaker 1: So you would say, all right, I'm interested in making toothbrushes, 424 00:24:36,040 --> 00:24:38,680 Speaker 1: so I want to know how how this device might 425 00:24:38,880 --> 00:24:42,600 Speaker 1: affect um you know, this particular hygiene market, how it 426 00:24:42,600 --> 00:24:46,000 Speaker 1: will play out you know, legally, how it'll play out 427 00:24:46,040 --> 00:24:48,080 Speaker 1: with profits, how it will play out with you know, 428 00:24:48,440 --> 00:24:50,720 Speaker 1: with the with the perception of the brand. You know, 429 00:24:50,800 --> 00:24:53,040 Speaker 1: whatever the variables happen to be, as long as you 430 00:24:53,080 --> 00:24:54,800 Speaker 1: have the data to back it up. Yeah, of course, 431 00:24:54,800 --> 00:24:56,640 Speaker 1: you know you would have to mesh these two technologies 432 00:24:56,680 --> 00:24:59,159 Speaker 1: and that that right there is the fiction part of this. 433 00:24:59,400 --> 00:25:04,240 Speaker 1: But those two technologies do exist stand alone, and so 434 00:25:04,320 --> 00:25:07,639 Speaker 1: it is it's very exciting to sort of extrapolate what 435 00:25:07,720 --> 00:25:12,560 Speaker 1: that might look like in five years, ten years, twenty years. Um. 436 00:25:12,600 --> 00:25:15,679 Speaker 1: I think that this this is the great opportunity to 437 00:25:15,920 --> 00:25:20,040 Speaker 1: hear a little clip of music by this machine. It's 438 00:25:20,080 --> 00:25:25,520 Speaker 1: actually called the Dingularity and uh, this is from the 439 00:25:25,600 --> 00:25:28,160 Speaker 1: album Song of New Neurons. And again this is this 440 00:25:28,200 --> 00:25:42,080 Speaker 1: is from Studio three sixty. All right, but we're talking 441 00:25:42,119 --> 00:25:44,520 Speaker 1: about are here, so let's let's really get down to 442 00:25:44,600 --> 00:25:47,720 Speaker 1: the to the paintings, now, to the to the to 443 00:25:47,800 --> 00:25:51,680 Speaker 1: the canvas itself. UM, this is nothing new either. There 444 00:25:51,760 --> 00:25:55,399 Speaker 1: any number of robots out there that have been designed 445 00:25:55,520 --> 00:25:58,159 Speaker 1: to put a little paint on a canvas. Um. I 446 00:25:58,160 --> 00:26:01,719 Speaker 1: mean you can and on a very basic level, we 447 00:26:01,800 --> 00:26:05,399 Speaker 1: have robots out there painting cars every day. The works 448 00:26:05,400 --> 00:26:08,360 Speaker 1: pretty basic, it tends to me it's it's it's very 449 00:26:08,400 --> 00:26:12,760 Speaker 1: often one color, but it is a robot painting something. 450 00:26:12,840 --> 00:26:16,680 Speaker 1: So it's very easy from there to extrapolate, Um, robots 451 00:26:16,680 --> 00:26:19,520 Speaker 1: painting various other things. Actually we've been able to We've 452 00:26:19,520 --> 00:26:22,840 Speaker 1: trained elephants to paint. We've you know, you you get 453 00:26:22,840 --> 00:26:27,280 Speaker 1: an ape uh hands dirty, it will paint something on 454 00:26:27,320 --> 00:26:32,159 Speaker 1: a canvas. Um. So so yeah, we have machines that 455 00:26:32,200 --> 00:26:34,720 Speaker 1: are capable of this as well. UM. I mentioned a 456 00:26:34,720 --> 00:26:38,480 Speaker 1: few of them here that are um that are fairly interesting. 457 00:26:38,920 --> 00:26:42,320 Speaker 1: For instance, there is uh the van Go bot, as 458 00:26:42,359 --> 00:26:48,119 Speaker 1: in Vincent van Go and this, uh, this is a 459 00:26:48,160 --> 00:26:51,679 Speaker 1: particular bot boast eight teen brushes, a paint mixer, and 460 00:26:51,760 --> 00:26:56,240 Speaker 1: three D spatial awareness, so it can combine artistic influences 461 00:26:56,280 --> 00:26:59,320 Speaker 1: to create fresh takes on a given subject. So the 462 00:26:59,840 --> 00:27:03,320 Speaker 1: very ables here being a subject and what styles you 463 00:27:03,359 --> 00:27:05,720 Speaker 1: want to see. So you can sort of think of 464 00:27:05,760 --> 00:27:08,040 Speaker 1: this in terms of almost like like an Instagram kind 465 00:27:08,080 --> 00:27:10,959 Speaker 1: of a thing. Imagine if if a robot were in charge. 466 00:27:11,200 --> 00:27:14,240 Speaker 1: All right, the photo is of a dog, and say 467 00:27:14,240 --> 00:27:16,359 Speaker 1: I wanted to combine filter one and two, and it 468 00:27:16,359 --> 00:27:19,480 Speaker 1: would combine those in a way that made sense. So 469 00:27:19,880 --> 00:27:21,760 Speaker 1: you might say, all right, I have I have a 470 00:27:21,800 --> 00:27:24,760 Speaker 1: picture of a bowl of fruit. I would like that 471 00:27:25,280 --> 00:27:30,680 Speaker 1: in a way that's cubist and uh and also surrealist. Well, 472 00:27:30,720 --> 00:27:34,119 Speaker 1: and then there is what seems to be a more 473 00:27:34,200 --> 00:27:38,280 Speaker 1: nuanced program called Erin, Right, and this is where, um, 474 00:27:38,359 --> 00:27:40,920 Speaker 1: you get more of the expression. I guess you could say, 475 00:27:41,000 --> 00:27:44,440 Speaker 1: even in the eyes of the people that it's depicting 476 00:27:44,440 --> 00:27:48,760 Speaker 1: in its paintings. Yeah. Erin has been going since about 477 00:27:48,840 --> 00:27:52,840 Speaker 1: nineteen seventy three, or the project itself, and uh, it's 478 00:27:52,920 --> 00:27:56,960 Speaker 1: the ranch out of Harold Cohen. Uh, Aaron drawls and 479 00:27:57,000 --> 00:28:01,080 Speaker 1: paint stylized still lives and portraits of human years out 480 00:28:01,080 --> 00:28:05,920 Speaker 1: of its programmed quote unquote imagination. Um, no, no images 481 00:28:06,000 --> 00:28:09,520 Speaker 1: or additional human input is necessary. So it sees something 482 00:28:09,920 --> 00:28:13,440 Speaker 1: and then it creates an artistic interpretation of what it sees, 483 00:28:14,080 --> 00:28:19,960 Speaker 1: which would point to original thought. Right, it's kind of interesting, yeah, okay, 484 00:28:20,000 --> 00:28:23,960 Speaker 1: and then the thing again, it's like once it's created, 485 00:28:24,680 --> 00:28:27,000 Speaker 1: like it is, it creates this thing on cannabis. Then 486 00:28:27,080 --> 00:28:29,600 Speaker 1: that then interacts with the human mind. And we've talked 487 00:28:29,600 --> 00:28:30,960 Speaker 1: before about all the stuff that goes on in the 488 00:28:31,040 --> 00:28:32,719 Speaker 1: human mind when you look at a piece of art. 489 00:28:33,280 --> 00:28:36,440 Speaker 1: So you know, even if there's if it's not thinking 490 00:28:36,520 --> 00:28:39,880 Speaker 1: about subtext or whatever, it can't it's not actually quote 491 00:28:39,920 --> 00:28:44,360 Speaker 1: unquote thinking. Um, if the piece ends up summoning that 492 00:28:44,400 --> 00:28:49,400 Speaker 1: thought in the viewer, then I mean it's art. Yeah, 493 00:28:49,480 --> 00:28:53,080 Speaker 1: I don't know. I mean I've seen some of the examples, 494 00:28:53,080 --> 00:28:54,920 Speaker 1: and you can go to Studio three sixt dot com 495 00:28:54,920 --> 00:28:57,200 Speaker 1: and see the samples of them. Um, that's a good 496 00:28:57,200 --> 00:29:01,200 Speaker 1: place to start at least. But some of them really 497 00:29:01,240 --> 00:29:04,440 Speaker 1: do elicit some feelings, I have to say, but there 498 00:29:04,520 --> 00:29:06,480 Speaker 1: is a flatness to it that you can kind of 499 00:29:06,520 --> 00:29:10,400 Speaker 1: tell that it's computer generated. Um. But that being said, 500 00:29:10,560 --> 00:29:13,480 Speaker 1: there's uh, there's that I can't remember the first name 501 00:29:13,520 --> 00:29:16,640 Speaker 1: of the artist, but it's Fred's grandson or something. Um. 502 00:29:16,680 --> 00:29:20,200 Speaker 1: There were some scenes of people that reminded me of 503 00:29:20,680 --> 00:29:24,560 Speaker 1: Fred's paintings that have a certain mood to them, a 504 00:29:24,600 --> 00:29:28,280 Speaker 1: certain quality, uh, in the way that it's spatially arranged. 505 00:29:28,280 --> 00:29:30,080 Speaker 1: And I thought that was interesting because I thought, well, 506 00:29:30,080 --> 00:29:32,160 Speaker 1: there seems to be some sort of psychology going on 507 00:29:32,200 --> 00:29:34,960 Speaker 1: in this painting. Yeah. But but then again that that 508 00:29:35,000 --> 00:29:37,760 Speaker 1: reminds me of of any number of cases of you'll 509 00:29:37,800 --> 00:29:39,840 Speaker 1: see like an interview in with an artist or an 510 00:29:39,880 --> 00:29:42,440 Speaker 1: actor or musician and they'll they'll be like, oh, you 511 00:29:42,440 --> 00:29:44,840 Speaker 1: know when you were when you did this particular thing, 512 00:29:44,840 --> 00:29:47,840 Speaker 1: when you decided to paint this blue instead of gray, 513 00:29:48,080 --> 00:29:50,040 Speaker 1: or you know, what was going on in your mind, 514 00:29:50,080 --> 00:29:52,520 Speaker 1: and they'll have all of this, this thought invested in this, 515 00:29:52,800 --> 00:29:55,520 Speaker 1: in this one little detail, and then when the artist 516 00:29:55,600 --> 00:29:57,480 Speaker 1: just says, oh, yeah, well we ran out of gray 517 00:29:57,520 --> 00:30:01,040 Speaker 1: paint that day or lit or it will be something like, 518 00:30:01,040 --> 00:30:03,840 Speaker 1: oh yeah, well it's different live than it wasn't an 519 00:30:03,880 --> 00:30:07,600 Speaker 1: album because we kind of forgot how the music how 520 00:30:07,640 --> 00:30:09,720 Speaker 1: we actually played at the first time. You know, there 521 00:30:09,800 --> 00:30:13,040 Speaker 1: ends up being some sort of mechanical explanation for something 522 00:30:13,080 --> 00:30:15,960 Speaker 1: that you thought was going to be really just tied 523 00:30:16,000 --> 00:30:18,720 Speaker 1: into the creative process. Well so, so much of that 524 00:30:18,760 --> 00:30:22,560 Speaker 1: then is projection by the viewer, right, Okay, So, just 525 00:30:22,600 --> 00:30:24,720 Speaker 1: in case you were worried that there there wasn't some 526 00:30:24,800 --> 00:30:27,840 Speaker 1: sort of computer around here to try to take the 527 00:30:27,920 --> 00:30:34,880 Speaker 1: highly subjective um job of making, uh this the stance 528 00:30:34,920 --> 00:30:37,160 Speaker 1: that some art is good and someone is bad. Don't worry. 529 00:30:37,160 --> 00:30:41,720 Speaker 1: There's something called DARCY. It's the Digital Artist Communicating Intent Machine, 530 00:30:42,240 --> 00:30:45,680 Speaker 1: and it's essentially kind of like an art historian. They 531 00:30:45,680 --> 00:30:50,880 Speaker 1: are teaching this computer to basically build a DA database 532 00:30:50,920 --> 00:30:54,680 Speaker 1: of artwork in order to carry out this subjective task 533 00:30:54,720 --> 00:30:56,479 Speaker 1: of saying this is good and this is bad, and 534 00:30:56,560 --> 00:31:00,560 Speaker 1: basically dissociates images with an adjective, and it can learn. 535 00:31:00,640 --> 00:31:03,680 Speaker 1: It can build up this database and begin to learn 536 00:31:03,720 --> 00:31:05,720 Speaker 1: what is considered good and what is considered bad. And 537 00:31:05,720 --> 00:31:08,520 Speaker 1: then you can actually throw another image in front of 538 00:31:08,520 --> 00:31:12,479 Speaker 1: it and it will rate it, which is interesting. Computer 539 00:31:12,520 --> 00:31:16,360 Speaker 1: scientists from Brigham Young University actually have been working on this, 540 00:31:16,880 --> 00:31:19,360 Speaker 1: and we missed it when it came to Atlanta. It 541 00:31:19,400 --> 00:31:21,840 Speaker 1: came to the High Museum of Art and judge the 542 00:31:21,840 --> 00:31:24,840 Speaker 1: works of Man it did. It dashed a lot of 543 00:31:24,880 --> 00:31:28,720 Speaker 1: people's hopes to be artists. Apparently a couple of kids too, 544 00:31:29,400 --> 00:31:33,239 Speaker 1: got some some thumbs down from Darcy. Um. All right, 545 00:31:33,320 --> 00:31:37,480 Speaker 1: so let's talk about film. Could could a computer be 546 00:31:37,600 --> 00:31:41,680 Speaker 1: an all tuo of sorts in this medium? Well, certainly 547 00:31:41,680 --> 00:31:44,200 Speaker 1: we're now we're dealing with well, of course we're dealing 548 00:31:44,240 --> 00:31:47,440 Speaker 1: with this with fiction, but um, with film, with digital 549 00:31:47,440 --> 00:31:50,240 Speaker 1: film editing, it's it's all digital. All that information is 550 00:31:50,280 --> 00:31:53,920 Speaker 1: just data in the computer. And and again this is 551 00:31:53,920 --> 00:31:56,240 Speaker 1: another area where there are certain rules, there are certain 552 00:31:56,240 --> 00:31:59,120 Speaker 1: types of cuts, there are certain uh there's a certain 553 00:32:00,280 --> 00:32:04,240 Speaker 1: uh law that you abide by when you're editing a film. 554 00:32:04,560 --> 00:32:06,760 Speaker 1: So it's perfectly believable that you could hand over the 555 00:32:06,840 --> 00:32:11,080 Speaker 1: raw footage and the basic flow of the piece. And 556 00:32:11,080 --> 00:32:13,720 Speaker 1: and you could expect a machine to to give you 557 00:32:13,760 --> 00:32:16,440 Speaker 1: some results, whether it would be the results on par 558 00:32:16,560 --> 00:32:19,880 Speaker 1: with the work of a great film editor. Well that's 559 00:32:20,120 --> 00:32:23,720 Speaker 1: the discussion. Well, yeah, that's that's the question mark here. Um. 560 00:32:23,800 --> 00:32:27,680 Speaker 1: The film is called White on White Algorithmic Noar, and 561 00:32:27,800 --> 00:32:30,719 Speaker 1: it's by Eves Suessman, and it's kind of build as 562 00:32:30,720 --> 00:32:34,960 Speaker 1: a paranoid sci fi thriller. And uh, Eve Sussman says, 563 00:32:35,080 --> 00:32:38,560 Speaker 1: it's about an American engineer Holts. He gets a job 564 00:32:38,600 --> 00:32:41,960 Speaker 1: in a mysterious form place called City A and starts 565 00:32:42,000 --> 00:32:44,480 Speaker 1: finding out things are wrong. The water supply seems to 566 00:32:44,480 --> 00:32:47,440 Speaker 1: be drugged with lithium, time seems to be slowing down, 567 00:32:47,520 --> 00:32:50,680 Speaker 1: people are running out of language, and the people who 568 00:32:50,720 --> 00:32:52,640 Speaker 1: hired him may not be who he thinks they are. 569 00:32:53,160 --> 00:32:55,000 Speaker 1: So everything is a little bit beyond his control in 570 00:32:55,040 --> 00:32:59,320 Speaker 1: this this situation very calas kind of scenario. Yes, yes, 571 00:32:59,320 --> 00:33:01,960 Speaker 1: it's very tough us and uh, it actually is very 572 00:33:02,000 --> 00:33:04,720 Speaker 1: interesting to watch it. And here's another reason why it's interesting, 573 00:33:04,720 --> 00:33:07,000 Speaker 1: because every time you watch it, it is a different 574 00:33:07,040 --> 00:33:10,160 Speaker 1: scenario because of this computer program which is essentially re 575 00:33:10,440 --> 00:33:15,000 Speaker 1: editing the film. Um, every time you view it, she 576 00:33:15,440 --> 00:33:18,160 Speaker 1: Eve has been worked with programmer Jeff Garnot and he 577 00:33:18,240 --> 00:33:21,760 Speaker 1: designed the computer system, so there's no one version of 578 00:33:21,760 --> 00:33:24,880 Speaker 1: white on White. This is interesting because this ties into 579 00:33:25,120 --> 00:33:27,400 Speaker 1: some of the the ideas we've discussed about the future 580 00:33:27,400 --> 00:33:30,200 Speaker 1: of music, like the idea that in the future, um, 581 00:33:30,200 --> 00:33:32,360 Speaker 1: we won't so much as listen, we won't listen to 582 00:33:32,360 --> 00:33:34,239 Speaker 1: a piece of music that it was composed by an 583 00:33:34,280 --> 00:33:37,440 Speaker 1: author or an artist. Rather, but we would listen to 584 00:33:37,680 --> 00:33:40,000 Speaker 1: a piece of music composed by an artist that then 585 00:33:40,040 --> 00:33:44,640 Speaker 1: taylor's itself to our particular taste and needs or an environment, 586 00:33:45,880 --> 00:33:49,760 Speaker 1: which is just like the the robotic piece of music. 587 00:33:49,960 --> 00:33:53,040 Speaker 1: It's called Emily Howell ye or that's the program. David 588 00:33:53,080 --> 00:33:56,640 Speaker 1: Cope designed it, and it composes classical music based on 589 00:33:56,720 --> 00:33:59,920 Speaker 1: influences from Bach and lose Art, And some people say 590 00:34:00,000 --> 00:34:04,200 Speaker 1: that you can't tell the difference at times. So that's 591 00:34:04,240 --> 00:34:06,200 Speaker 1: interesting that you say that, because if you were, if 592 00:34:06,280 --> 00:34:08,359 Speaker 1: you knew that you were interested in that and then 593 00:34:08,360 --> 00:34:10,560 Speaker 1: that's what it was inspired from, then it would no 594 00:34:10,600 --> 00:34:12,920 Speaker 1: longer be actually the works of Bach and Mozart. I 595 00:34:12,960 --> 00:34:14,680 Speaker 1: think of some of the films that have like they're 596 00:34:14,680 --> 00:34:18,399 Speaker 1: famous for multiple cuts. Uh, the one that comes to mind. 597 00:34:19,480 --> 00:34:22,200 Speaker 1: Most obviously and most fitting for this conversation is Blade Runner, 598 00:34:22,719 --> 00:34:27,040 Speaker 1: where some cuts lean more towards h Harrison Ford's character 599 00:34:27,080 --> 00:34:31,040 Speaker 1: being a replicant and Natroid an artificial person versus him 600 00:34:31,080 --> 00:34:33,800 Speaker 1: being human. So what have you had in the future? 601 00:34:33,840 --> 00:34:37,279 Speaker 1: What if a cut of Blade Runner Taylor's itself to 602 00:34:37,400 --> 00:34:39,719 Speaker 1: your particular values. If you're the kind of person who 603 00:34:39,760 --> 00:34:44,440 Speaker 1: is more satisfied with with with Harrison Ford being a robot, 604 00:34:44,760 --> 00:34:47,480 Speaker 1: then that is the version you end up seeing. Likewise, 605 00:34:47,520 --> 00:34:49,479 Speaker 1: if you're if you're the kind of person who thinks 606 00:34:49,840 --> 00:34:53,480 Speaker 1: Han Solo should should shoot first or you should shoot second, 607 00:34:54,000 --> 00:34:57,200 Speaker 1: then then a copy of Star Wars Taylor's itself to 608 00:34:57,440 --> 00:35:01,600 Speaker 1: what kind of a viewer you are? Well? Then, oh, 609 00:35:01,640 --> 00:35:06,560 Speaker 1: and then to say nothing of of curse words, nudity, violence. 610 00:35:07,040 --> 00:35:11,480 Speaker 1: You could have the film automatically adjust itself to the 611 00:35:11,560 --> 00:35:13,600 Speaker 1: age of the viewer or even the sex of the 612 00:35:13,640 --> 00:35:18,440 Speaker 1: viewer versus Maleston. If you they're sort of demographical variables 613 00:35:18,480 --> 00:35:21,000 Speaker 1: in play, it's no longer tied though, to that particular 614 00:35:21,080 --> 00:35:24,960 Speaker 1: person's perspective, and is no longer the artwork of that person. 615 00:35:25,800 --> 00:35:29,080 Speaker 1: Correct Well, you would have to have some very we 616 00:35:29,160 --> 00:35:31,480 Speaker 1: like for instance, the language thing is pretty easy. Like 617 00:35:31,920 --> 00:35:33,880 Speaker 1: there's there are artists out there where be like, oh no, 618 00:35:34,000 --> 00:35:39,279 Speaker 1: you can't, you can't have scarface say um, dang it, 619 00:35:40,120 --> 00:35:43,200 Speaker 1: you know you you this this character, this character's cursing 620 00:35:43,280 --> 00:35:44,719 Speaker 1: is is important to the work, and if you take 621 00:35:44,719 --> 00:35:46,360 Speaker 1: the cursing out there, it's no longer that work. But 622 00:35:46,400 --> 00:35:50,160 Speaker 1: there are other my little car garages, Yeah, you can 623 00:35:50,239 --> 00:35:54,360 Speaker 1: really leave the cockroaches. Yeah, but but I feel like 624 00:35:54,360 --> 00:35:56,680 Speaker 1: there are other films out there where the artists would 625 00:35:56,680 --> 00:35:59,520 Speaker 1: probably say, no, I don't think it harms my artistic 626 00:35:59,600 --> 00:36:03,600 Speaker 1: vision if they say fudge instead of the F word. Oh, 627 00:36:03,640 --> 00:36:06,719 Speaker 1: I don't know. Budge is a very different thing than 628 00:36:06,760 --> 00:36:09,120 Speaker 1: the other thing. Well maybe, but you get my point. 629 00:36:09,320 --> 00:36:12,160 Speaker 1: I think there depends on the level to which the 630 00:36:12,239 --> 00:36:15,800 Speaker 1: work adjust itself. Okay, well, and I suppose it depends 631 00:36:15,840 --> 00:36:18,759 Speaker 1: on the author of the work too, and how she 632 00:36:19,160 --> 00:36:23,720 Speaker 1: or he intends it to to interact with it. Take Brazil, 633 00:36:23,840 --> 00:36:27,160 Speaker 1: for instance, Terry Gillis film, there was a there was 634 00:36:27,200 --> 00:36:29,200 Speaker 1: a like a happy ending cut of the film that 635 00:36:29,280 --> 00:36:32,480 Speaker 1: the studio did, which is horrible. Um, but it has 636 00:36:32,520 --> 00:36:35,880 Speaker 1: a very happy ending, whereas the actual cut of the 637 00:36:35,920 --> 00:36:39,480 Speaker 1: film as a really down ending, like even the sort 638 00:36:39,520 --> 00:36:43,719 Speaker 1: of even the compromise, the cut that they ended up 639 00:36:43,760 --> 00:36:48,080 Speaker 1: putting together is really kind of a downer. If you 640 00:36:48,160 --> 00:36:50,920 Speaker 1: had a version of Brazil tailored itself to your viewing 641 00:36:51,000 --> 00:36:53,560 Speaker 1: and would sometimes choose the happy ending, that that would 642 00:36:53,600 --> 00:36:57,200 Speaker 1: definitely compromise the piece. Again, I think it all goes 643 00:36:57,239 --> 00:36:59,239 Speaker 1: back to what the what the person is trying to 644 00:36:59,280 --> 00:37:03,440 Speaker 1: say with particular piece of artwork, which goes back to robots. 645 00:37:03,840 --> 00:37:06,799 Speaker 1: Do they have a particular perspective when it comes to that, 646 00:37:07,800 --> 00:37:10,400 Speaker 1: you know, can can they really have a perspective? You 647 00:37:10,440 --> 00:37:13,880 Speaker 1: feed them lots of data about the environment, if they 648 00:37:13,880 --> 00:37:17,839 Speaker 1: can learn from themselves, uh and create original thought, there's 649 00:37:17,880 --> 00:37:20,520 Speaker 1: the possibility there, right, um, But let me let me 650 00:37:20,719 --> 00:37:23,400 Speaker 1: before we get too far into that. UM, I do 651 00:37:23,480 --> 00:37:26,600 Speaker 1: want to play this clip of Emily Howell. This is 652 00:37:26,640 --> 00:37:28,879 Speaker 1: the music that we're talking about that is inspired by 653 00:37:28,920 --> 00:37:31,760 Speaker 1: Bach and Mozart, just to give you guys a sense 654 00:37:31,800 --> 00:37:47,879 Speaker 1: of what that sounds like. Now. I'm not the I'm 655 00:37:47,920 --> 00:37:50,439 Speaker 1: not the biggest kind of sewer of classical music, but 656 00:37:50,920 --> 00:37:52,960 Speaker 1: but I certainly I don't think i'd be able to 657 00:37:53,000 --> 00:37:57,360 Speaker 1: tell you if that was Bach, Mozart or a computer. No, 658 00:37:57,640 --> 00:37:59,880 Speaker 1: And I think that The issue that a lot of 659 00:38:00,000 --> 00:38:04,080 Speaker 1: who are having with this this iteration of music at least, 660 00:38:04,280 --> 00:38:07,960 Speaker 1: is that there's not a standout piece yet. And and 661 00:38:08,080 --> 00:38:10,640 Speaker 1: even David Cope, who designed it, says like there's there's 662 00:38:10,640 --> 00:38:13,960 Speaker 1: not anything too terribly creative or original about this music. 663 00:38:14,360 --> 00:38:16,239 Speaker 1: It's just a variation on a theme that someone else 664 00:38:16,280 --> 00:38:21,719 Speaker 1: has done before. So eventually though, there presumably there is 665 00:38:21,719 --> 00:38:23,279 Speaker 1: going to be some sort of breakthrough, some sort of 666 00:38:23,320 --> 00:38:25,880 Speaker 1: piece of music created by a machine, possibly even the 667 00:38:25,880 --> 00:38:28,840 Speaker 1: creativity machine, that people say, oh wow, this this is 668 00:38:29,040 --> 00:38:30,880 Speaker 1: really struck a chord in me as a human. It 669 00:38:31,000 --> 00:38:34,600 Speaker 1: reached this the center of emotion in me that I 670 00:38:34,640 --> 00:38:38,399 Speaker 1: didn't think that a robot could inhabit my perspective in 671 00:38:38,480 --> 00:38:42,759 Speaker 1: order to elicit that feeling. So then the question becomes, well, 672 00:38:42,800 --> 00:38:45,320 Speaker 1: who is the artist here? Then? Is that the programmer 673 00:38:45,400 --> 00:38:49,920 Speaker 1: or the computer? And at what point do we consider 674 00:38:50,560 --> 00:38:53,640 Speaker 1: human creat creativity be the same as machine creativity. And 675 00:38:53,719 --> 00:38:55,640 Speaker 1: here's the other question I have, because we've talked about 676 00:38:55,719 --> 00:38:59,520 Speaker 1: human cyborgs in the way that we are always uh 677 00:39:00,000 --> 00:39:04,319 Speaker 1: manipulating ourselves with technology, is this just another way to 678 00:39:05,400 --> 00:39:08,959 Speaker 1: manipulate our abilities? So is this part of the human 679 00:39:09,000 --> 00:39:11,400 Speaker 1: experience to some degree. Yeah, Like when I when a 680 00:39:11,480 --> 00:39:15,000 Speaker 1: human creates a robot that then creates art, is it 681 00:39:15,040 --> 00:39:18,239 Speaker 1: really is that human just creating via proxy? Is it? Is? 682 00:39:18,239 --> 00:39:21,000 Speaker 1: It kind of like saying, like, like looking at the 683 00:39:21,000 --> 00:39:26,359 Speaker 1: difference between me singing and me creating music with a trumpet. Um, 684 00:39:26,400 --> 00:39:28,359 Speaker 1: you know, I'm with the trumpet. I'm using a piece 685 00:39:28,400 --> 00:39:32,840 Speaker 1: of technology that amplifies it amplifies and changes my output 686 00:39:32,880 --> 00:39:35,840 Speaker 1: into a different type of output, but it's still dependent 687 00:39:35,920 --> 00:39:39,960 Speaker 1: upon my original output. Yeah, I know. It's like it's 688 00:39:39,960 --> 00:39:42,799 Speaker 1: it's an extension of creativity. Right. Just because you use 689 00:39:42,880 --> 00:39:46,120 Speaker 1: something to to express yourself, albeit you know, a machine, 690 00:39:46,239 --> 00:39:49,080 Speaker 1: it doesn't mean that it's not somehow an expression of 691 00:39:49,120 --> 00:39:52,800 Speaker 1: the human self because we're the ones who coded it. Um. 692 00:39:52,920 --> 00:39:56,520 Speaker 1: So I think it boils down to this idea of consciousness. 693 00:39:56,560 --> 00:40:00,200 Speaker 1: I really do. And I think that the creativity machine 694 00:40:00,239 --> 00:40:03,880 Speaker 1: and its ability to mimic these little blips and consciousness 695 00:40:03,920 --> 00:40:07,640 Speaker 1: and I'm not gonna call them unconsciousness because that's probably 696 00:40:07,680 --> 00:40:10,200 Speaker 1: giving a little bit too much credit. But I think 697 00:40:10,280 --> 00:40:13,839 Speaker 1: until we can really crack this nut of what consciousness is, 698 00:40:13,920 --> 00:40:17,000 Speaker 1: we can't really answer the question about whether or not 699 00:40:17,120 --> 00:40:23,879 Speaker 1: robots uh could emulate it. Or or create truly unique art. Yeah, 700 00:40:24,400 --> 00:40:26,680 Speaker 1: because I mean when you get into the motivations, like 701 00:40:26,800 --> 00:40:31,120 Speaker 1: something like Dante's Inferno, you know, the the artist wants 702 00:40:31,160 --> 00:40:33,560 Speaker 1: to create this piece, and he wants to create a 703 00:40:33,560 --> 00:40:38,960 Speaker 1: piece that that that says something about about God, that 704 00:40:39,040 --> 00:40:42,520 Speaker 1: says something about faith, that says something about the world 705 00:40:42,520 --> 00:40:45,319 Speaker 1: that he lives in, that takes jabs at people that 706 00:40:45,440 --> 00:40:48,239 Speaker 1: he doesn't like. You know, I mean all these all 707 00:40:48,239 --> 00:40:50,040 Speaker 1: these things that go on and in so many works 708 00:40:50,040 --> 00:40:52,440 Speaker 1: of art, you know, where it's there's a there's a 709 00:40:52,440 --> 00:40:56,000 Speaker 1: little bit of themselves in the work as well. Um 710 00:40:56,200 --> 00:41:00,279 Speaker 1: to what extent can a computer ever pull that off? Well, 711 00:41:00,400 --> 00:41:05,400 Speaker 1: So some would argue that man humans have been creating 712 00:41:05,520 --> 00:41:08,239 Speaker 1: art for at least thirty five thousand years, right, like 713 00:41:08,400 --> 00:41:12,480 Speaker 1: from cave drawings onto up to like creating a program 714 00:41:12,520 --> 00:41:16,799 Speaker 1: like Aaron that can create art, and that machines have 715 00:41:16,880 --> 00:41:19,960 Speaker 1: only been creating art for about forty years, so that 716 00:41:20,200 --> 00:41:22,399 Speaker 1: you know, we just need to give him a chance. Yeah, 717 00:41:23,000 --> 00:41:25,160 Speaker 1: because yeah, when we were forty years in we probably 718 00:41:25,280 --> 00:41:28,480 Speaker 1: we weren't we well, we weren't really all that impressive either, right, So, 719 00:41:28,600 --> 00:41:31,680 Speaker 1: and and you know, because they can carry things out 720 00:41:31,719 --> 00:41:34,239 Speaker 1: at much more rapid speeds than we do. Presumably they 721 00:41:34,280 --> 00:41:38,160 Speaker 1: could become pretty sophisticated artists. I don't know or you know, 722 00:41:38,280 --> 00:41:41,799 Speaker 1: is this predicated on this idea of a soul and God? 723 00:41:41,840 --> 00:41:44,399 Speaker 1: This is for another podcast, but is there? I mean 724 00:41:44,640 --> 00:41:47,120 Speaker 1: for the next podcast, because we're gonna that's true, we 725 00:41:47,160 --> 00:41:49,040 Speaker 1: are going to Before we do that, we have to 726 00:41:49,080 --> 00:41:52,360 Speaker 1: close this one out. So indeed, so there you have it. Um, 727 00:41:52,440 --> 00:41:57,480 Speaker 1: some thoughts on the computer as an artist, the art 728 00:41:57,520 --> 00:42:00,320 Speaker 1: as a creation of machine versus a creation by and 729 00:42:00,320 --> 00:42:02,640 Speaker 1: and then as they work that is ultimately viewed by 730 00:42:02,800 --> 00:42:07,800 Speaker 1: human or in the case of Darcy, by a computer. So, UM, 731 00:42:07,840 --> 00:42:10,000 Speaker 1: we would love to hear from from you guys to 732 00:42:10,360 --> 00:42:11,680 Speaker 1: you know, what what do you think about that? What 733 00:42:11,719 --> 00:42:13,200 Speaker 1: do you what? What is art? Do you think a 734 00:42:13,239 --> 00:42:19,920 Speaker 1: computer can really create? Uh? Music, literature, UM or visual 735 00:42:20,080 --> 00:42:22,839 Speaker 1: art in a meaningful way? Uh? And then how much 736 00:42:22,840 --> 00:42:24,640 Speaker 1: of it just does does it depend on the view 737 00:42:24,760 --> 00:42:27,400 Speaker 1: or the reader or the listener as to whether it's 738 00:42:27,480 --> 00:42:29,640 Speaker 1: art or not? Let us know. We'd love to hear 739 00:42:29,680 --> 00:42:33,680 Speaker 1: what you think. UM. I have just uh already one 740 00:42:33,800 --> 00:42:35,920 Speaker 1: quick listener mail if we want to call the robot 741 00:42:35,960 --> 00:42:38,720 Speaker 1: over here. It's always a little awkward calling the robot 742 00:42:38,760 --> 00:42:43,600 Speaker 1: over here after we've been talking about robots. Yeah, and 743 00:42:44,600 --> 00:42:46,839 Speaker 1: all right, now this one is related to movies. Uh. 744 00:42:47,320 --> 00:42:50,879 Speaker 1: Our listener Sean writes then about horror and talking about 745 00:42:50,880 --> 00:42:54,400 Speaker 1: old versus new horror. Uh, Sean says, really quick. My 746 00:42:54,480 --> 00:42:58,480 Speaker 1: observation on new horror versus old horror in the mental 747 00:42:58,680 --> 00:43:02,440 Speaker 1: versus the visual. The old schoolers like Hitchcock and Uh 748 00:43:02,480 --> 00:43:05,920 Speaker 1: and King left what happened next up to the mind. 749 00:43:06,320 --> 00:43:08,480 Speaker 1: Now with modern technology they show you the gore and 750 00:43:08,560 --> 00:43:11,560 Speaker 1: leave nothing to the imagination. I know there are except exceptions, 751 00:43:11,719 --> 00:43:14,759 Speaker 1: but I feel new horror is no more scary than 752 00:43:14,800 --> 00:43:17,920 Speaker 1: it is disturbing. I also find myself more frequently feeling 753 00:43:17,920 --> 00:43:22,240 Speaker 1: that new horror glorifies individuals with psych psychotic tendencies, especially 754 00:43:22,280 --> 00:43:24,600 Speaker 1: when the character is based on a real individual. Great 755 00:43:24,680 --> 00:43:30,360 Speaker 1: job every week, Matt Love Sean, Um, Yeah, I mean, uh, 756 00:43:30,680 --> 00:43:33,840 Speaker 1: I've certainly seen that argument that uh that that you know, 757 00:43:33,920 --> 00:43:38,480 Speaker 1: now horror is more about just really throwing up disturbing images. 758 00:43:38,600 --> 00:43:42,279 Speaker 1: But but but even then, it's like I feel like 759 00:43:43,520 --> 00:43:46,239 Speaker 1: when when when horror movies show disturbing images, it's like 760 00:43:46,320 --> 00:43:50,479 Speaker 1: that you disynthesize to that so quickly that it's there 761 00:43:50,480 --> 00:43:52,440 Speaker 1: has to be if it even if a disturbing image 762 00:43:52,480 --> 00:43:54,880 Speaker 1: is going to be effective, it needs to have an 763 00:43:54,920 --> 00:43:58,759 Speaker 1: underlying disturbing idea. And and sometimes I think that the 764 00:43:58,800 --> 00:44:02,640 Speaker 1: disturbing image actually in the way of the of the 765 00:44:02,640 --> 00:44:05,239 Speaker 1: the idea. And to tie this back into what we're 766 00:44:05,239 --> 00:44:09,799 Speaker 1: talking about today, I sometimes feel like when you'll you'll 767 00:44:09,840 --> 00:44:13,080 Speaker 1: look at horror that it seems to work or almost works, 768 00:44:13,840 --> 00:44:16,759 Speaker 1: and it'll you'll because you think that there is a 769 00:44:16,760 --> 00:44:20,799 Speaker 1: disturbing idea at the heart of it. And in some 770 00:44:20,840 --> 00:44:23,480 Speaker 1: cases it just seems to turn out that the the 771 00:44:23,480 --> 00:44:26,880 Speaker 1: the creators of this particular piece are just they're just 772 00:44:26,920 --> 00:44:29,040 Speaker 1: dealing in the visuals. They're just dealing in the disturbing 773 00:44:29,320 --> 00:44:33,480 Speaker 1: images side of it, and any um in any disturbing 774 00:44:33,560 --> 00:44:37,880 Speaker 1: idea beneath it is just the viewers perception. So you 775 00:44:37,960 --> 00:44:40,600 Speaker 1: get into ideas of you know, something can actually seem 776 00:44:40,640 --> 00:44:43,880 Speaker 1: to have more ideas and more more depth to it 777 00:44:44,239 --> 00:44:46,640 Speaker 1: based on what the view or the listener, the reader 778 00:44:46,719 --> 00:44:51,160 Speaker 1: is bringing to this. So again projection. So so yeah, 779 00:44:51,280 --> 00:44:53,799 Speaker 1: let us know what you think about computers creating art. 780 00:44:53,840 --> 00:44:56,279 Speaker 1: You can find us online. You can find us on 781 00:44:56,320 --> 00:44:58,759 Speaker 1: Facebook where we are stuff to blow your mind and 782 00:44:58,800 --> 00:45:00,759 Speaker 1: you can find us on Twitter where handle is below 783 00:45:00,840 --> 00:45:02,920 Speaker 1: the Mind, and you can also drop us a line 784 00:45:02,920 --> 00:45:05,440 Speaker 1: at blow the Mind at discovery dot com