1 00:00:08,720 --> 00:00:13,919 Good morning, Mother? Good morning dial, Mother, Comonic sphere of the 2 00:00:14,000 --> 00:00:23,920 Fund. Good morning, Mother. Wait Welcome one more day, one more 3 00:00:24,000 --> 00:00:28,280 Saturday, to our podcasts live, here, in space Fundación Telefónica, these 4 00:00:28,920 --> 00:00:34,439 programs that we have been doing for years and that makes us so much illusion 5 00:00:34,520 --> 00:00:38,799 because we bring the world of the family, we bring the topics that interest 6 00:00:38,880 --> 00:00:43,799 us at home and also in school, to a very technological environment, but 7 00:00:44,000 --> 00:00:46,719 also very open to reality and very open to what happens to us at home 8 00:00:46,759 --> 00:00:51,600 and to what concerns us parents. So, thank you all so much for 9 00:00:51,719 --> 00:00:54,640 being here, all of you on the other side of the stream, all 10 00:00:54,640 --> 00:01:00,600 of you here with us. You have an open network WIFICE you have your 11 00:01:00,159 --> 00:01:08,000 password on the screen, because you want to tweet about complaining things can be 12 00:01:08,120 --> 00:01:12,519 asking questions or telling in networks that you are in a chat taking advantage of 13 00:01:12,519 --> 00:01:18,599 Saturday morning. Speaking of artificial intelligence, that this puts a lot, of 14 00:01:18,599 --> 00:01:22,879 course, we have workshop for the smallest and smallest, the smallest, and 15 00:01:23,519 --> 00:01:30,000 we have our monitors to the sea and mauve, I was going to say 16 00:01:30,120 --> 00:01:34,599 dawn half sea and dawn mauve and sea month I have not given one to 17 00:01:34,599 --> 00:01:38,760 see. I' ve tried. So the kids you want to go and 18 00:01:38,760 --> 00:01:42,400 play, because they' re going to be able to enjoy a workshop, 19 00:01:42,519 --> 00:01:48,359 of course thematic, because artificial intelligence. Evidently, these creatures will know much 20 00:01:48,359 --> 00:01:52,640 more than we do. And with that reflection begins today' s program. 21 00:01:53,040 --> 00:01:57,879 Because I don' t know if you and you who are here today have 22 00:01:57,920 --> 00:02:04,319 a lot of knowledge of artificial intelligence. I don' t know how you 23 00:02:04,359 --> 00:02:07,639 go about content, if you are very trained, very trained, if you 24 00:02:07,639 --> 00:02:15,639 are experts, experts, without artificial intelligence, because I assume my ignorance from 25 00:02:15,639 --> 00:02:20,840 now on. In fact, this is the first time we have addressed this 26 00:02:20,879 --> 00:02:24,000 issue in any of our podcasts, not only in the mother space sphere, 27 00:02:24,439 --> 00:02:28,840 but in all our podcasts. We have not yet touched on the issue of 28 00:02:28,879 --> 00:02:34,919 artificial intelligence because it terrifies, because I have no idea and, in fact, 29 00:02:35,120 --> 00:02:38,800 came here today saying, my mother. It is one of the programmes 30 00:02:38,800 --> 00:02:43,719 that I have had the most difficulty preparing, in the sense that I find 31 00:02:43,759 --> 00:02:46,400 it very difficult to know exactly what we were talking about. How difficult it 32 00:02:46,520 --> 00:02:51,080 is to prepare for this concept and teach our children about something that we ourselves 33 00:02:51,120 --> 00:03:00,919 are not educated about. So I' m really looking forward to my guests, 34 00:03:01,000 --> 00:03:06,199 guests, helping us out today. I hope and I think so, 35 00:03:06,360 --> 00:03:10,080 because they know a lot about this topic, to give a little light, 36 00:03:10,240 --> 00:03:17,680 friends, a little certainty in a field that is changing while we were talking. 37 00:03:21,080 --> 00:03:27,000 And I used to tell Silvia, I feel like an ia not between 38 00:03:27,080 --> 00:03:34,840 anything. I still don' t know the answers we need to be able 39 00:03:35,360 --> 00:03:39,400 to address this issue with a little bit of security, so we can tell 40 00:03:39,479 --> 00:03:43,080 our creatures, because this is right or this is wrong. This way we 41 00:03:43,199 --> 00:03:46,280 ' re good, don' t do your homework like that. That' 42 00:03:46,319 --> 00:03:50,800 s not good. That' s bad. That could be great. That 43 00:03:50,840 --> 00:03:54,800 ' s today' s target. I hope we get out of here with 44 00:03:54,879 --> 00:04:00,960 concepts a little bit clearer or perhaps a lot more doubt. It can also 45 00:04:01,000 --> 00:04:08,199 be true and who are my guests today, because we go with them because 46 00:04:08,439 --> 00:04:12,439 I am delighted and really thank you for accompanying me on a Saturday morning, 47 00:04:12,680 --> 00:04:15,439 with the time you do outside with the only thing that is Madrid today. 48 00:04:15,160 --> 00:04:18,639 It' s good to be gathered here. I welcome Maria del Mar Sanchez. 49 00:04:20,199 --> 00:04:24,800 Thank you very much. Known on Twitter as mayemar, which I have 50 00:04:24,839 --> 00:04:30,279 known for many years and have also collaborated on several podcasts. She is a 51 00:04:30,319 --> 00:04:34,600 member of the Educational Technology Research Group, a full professor of the Department of 52 00:04:34,759 --> 00:04:39,759 Teaching and School Organization of the University of Murcia and a PhD in Pedagogy. 53 00:04:40,839 --> 00:04:43,480 Moreover, Mary of the Sea, You and I, not well, met 54 00:04:43,560 --> 00:04:49,800 virtually precisely in a podcast also mother space sphere during the pandemic. Yeah, 55 00:04:50,519 --> 00:04:56,279 what? In addition, it had as its title a question that perhaps we 56 00:04:56,639 --> 00:05:00,920 could rescue today even where our education is going. With that we can make 57 00:05:00,920 --> 00:05:05,600 three editions more true on that occasion, with Sofiadez and José Carlos Ruiz, 58 00:05:05,720 --> 00:05:10,040 the philosopher José Carlos Ruiz, but that today I have thought about it, 59 00:05:10,360 --> 00:05:15,920 I say what good question to ask you back to where the education is going, 60 00:05:16,319 --> 00:05:19,639 because right now, if we had a magic ball, it would be 61 00:05:19,639 --> 00:05:24,399 wonderful. I think we are at a difficult time, because there are many 62 00:05:24,399 --> 00:05:29,519 changes, there are many social, legislative changes and artificial intelligence, in a 63 00:05:29,519 --> 00:05:34,120 way, they are still algorithms, they are operating machines. But it is 64 00:05:34,120 --> 00:05:39,560 true that some tools that have emerged recently put questions, some dynamics, such 65 00:05:39,680 --> 00:05:44,000 as duties, etc, but that really put evidence, things that, perhaps 66 00:05:44,079 --> 00:05:47,040 a long time ago should have changed. Nor is it that I do anything 67 00:05:47,279 --> 00:05:50,240 new, but that they do not put evidence. Finished energy. Then we 68 00:05:50,319 --> 00:05:56,720 ' ll talk about it now, because I think it' s one of 69 00:05:56,839 --> 00:05:59,959 the great hairs of this whole thing. Hey, this program can be subtitled 70 00:06:00,199 --> 00:06:09,759 as melon, because we have everything we mention. We have along with María 71 00:06:09,839 --> 00:06:14,720 del Mar, Juan David Rodríguez García, professor of secondary education, technical advisor 72 00:06:14,800 --> 00:06:18,720 teacher at the Institute of Educational Technologies and Training, the teachers and developer of 73 00:06:18,759 --> 00:06:24,399 educational software that you will talk about later and I the first question that I 74 00:06:24,639 --> 00:06:28,959 wanted to ask you is this from a young age, that you knew you 75 00:06:29,000 --> 00:06:31,160 wanted to dedicate yourself to things that are not invented. Not really, I 76 00:06:31,160 --> 00:06:34,959 don' t know that I' ve been small for a long time and 77 00:06:34,959 --> 00:06:36,079 you don' t remember very well. I wanted to be a doctor, 78 00:06:36,319 --> 00:06:39,079 you' re the doctor. I think good. Thank you very much. 79 00:06:39,120 --> 00:06:41,079 First to watch a guest, the first time I ever did a podcast. 80 00:06:41,120 --> 00:06:44,759 I' m very happy to be here and I wanted to start there as 81 00:06:44,759 --> 00:06:46,680 a kid. I know when I was a kid I wanted to be doctors 82 00:06:46,680 --> 00:06:53,839 first. Then the blood saw that it gave me a certain Hispanic and I 83 00:06:53,959 --> 00:06:57,079 drifted towards engineering, which is a teleco engineer. Then I don' t 84 00:06:57,199 --> 00:07:00,199 know what happened in high school, because I had good teachers of mathematical physics 85 00:07:00,319 --> 00:07:04,680 and eventually ended up studying physics. And well, and how I got here, 86 00:07:04,800 --> 00:07:12,519 because the truth is that I don' t know coincidences, that is 87 00:07:12,560 --> 00:07:16,279 by chance, but well, now I' m in the Faculty Information Technology 88 00:07:16,279 --> 00:07:20,680 STI and, in fact, I developed an artificial quartering tool that, yes, 89 00:07:20,839 --> 00:07:24,680 well, if you want, then I can do a little bit about 90 00:07:24,680 --> 00:07:30,560 it. Then we will develop, because Juanda has an open, besides, 91 00:07:30,839 --> 00:07:34,519 to everyone, to the whole audience, a series of tools that you have 92 00:07:34,560 --> 00:07:39,000 to know. And last but not least, my h plays already Monica de 93 00:07:39,120 --> 00:07:43,519 Miguel, who is a telecommunications engineer, with twenty- five years of experience, 94 00:07:44,000 --> 00:07:47,000 has been consulting educational innovation here in a telephone foundation and is director of 95 00:07:47,040 --> 00:07:53,199 the family area, the Observatory of Artificial Intelligence for Education. So you know 96 00:07:53,240 --> 00:08:00,000 all about the intelligence artivityes for the depre we' re not all watching ah 97 00:08:00,240 --> 00:08:03,959 clear observatory, we' re so being. I, like my colleagues, 98 00:08:05,399 --> 00:08:07,519 want to give you long amonic because I have also come many times there, 99 00:08:09,160 --> 00:08:11,439 I have also been on the foundation team that I also give a very great 100 00:08:11,519 --> 00:08:18,560 greeting to all my colleagues who manage this space. And I think that, 101 00:08:20,199 --> 00:08:22,000 as I said sea we are in a very big change, but that it 102 00:08:22,040 --> 00:08:28,680 is not merely an educational change, it is a social change and it is 103 00:08:28,800 --> 00:08:31,600 a change of era and we are living a few years that we do not 104 00:08:31,639 --> 00:08:37,000 live, that were the first years of the industrial revolution, where there was 105 00:08:37,720 --> 00:08:41,240 no school and it was a bit chaotic to pass from some professions that were 106 00:08:41,240 --> 00:08:45,399 in the world of horse, that were in the world of architecture, of 107 00:08:45,399 --> 00:08:46,480 agriculture and were in the world of crafts. And all of a sudden, 108 00:08:46,600 --> 00:08:50,919 we passed the factories and there were many years where we didn' t know 109 00:08:50,919 --> 00:08:52,639 what to do and that' s where the school was built. And I 110 00:08:52,720 --> 00:08:56,879 think we were in that process, that we started from a school that was 111 00:08:56,960 --> 00:09:01,240 the solution at a given time, but that we are now entering another cycle 112 00:09:01,320 --> 00:09:07,120 and of course, we are seeing that what is coming is dislocating us, 113 00:09:07,320 --> 00:09:11,679 as we traditionally do. But in addition, we have some needs, that 114 00:09:11,960 --> 00:09:16,240 is, one of the things that is on the table, but that we 115 00:09:16,320 --> 00:09:18,440 may not be aware of. It is that one of the six European priorities 116 00:09:18,519 --> 00:09:22,960 is to develop a digital society, that is, that we can be citizens 117 00:09:24,000 --> 00:09:28,320 and live well with technology, and I think it is one of the main 118 00:09:28,440 --> 00:09:31,519 things that we need to start educating. Now, when you mentioned the industrial 119 00:09:31,559 --> 00:09:35,320 revolution, I have thought, my mother, about the changes that there were 120 00:09:35,360 --> 00:09:41,240 at that time the solution in school. Perhaps at that time there was debate 121 00:09:41,039 --> 00:09:45,519 between children should stop working in factories and go to school and there would be 122 00:09:45,799 --> 00:09:50,399 a sudden debate with a person contradicting. No, the kids have to keep 123 00:09:50,480 --> 00:09:54,039 working. Please, that' s where children' s civil rights came out 124 00:09:54,120 --> 00:09:58,360 as a result of the industrial revolution, just like most of the civil rights 125 00:09:58,399 --> 00:10:03,399 we know now. That time we cannot bring it here, of course, 126 00:10:03,919 --> 00:10:09,480 but it does seem curious to me when I see certain debates and things that 127 00:10:09,519 --> 00:10:13,559 are still being debated or that arise when we come to points like these, 128 00:10:13,720 --> 00:10:20,039 in which there are such important transformations, we start at the base, it 129 00:10:20,039 --> 00:10:26,919 is worth it because artificial intelligence is not something new, but help us a 130 00:10:26,000 --> 00:10:28,919 little bit between the three of us and tell us what we are talking about 131 00:10:28,919 --> 00:10:33,279 when we talk about artificial intelligence. Good at that. I think Juan now 132 00:10:33,320 --> 00:10:39,519 that and Monica are the most technical experts. I do want to point it 133 00:10:39,559 --> 00:10:41,279 out before they don' t explain it on a technical level. What' 134 00:10:41,279 --> 00:10:46,559 s underneath is that artificial intelligence now seems to have come up behind the curtain, 135 00:10:46,600 --> 00:10:48,720 but it' s been living with us for a long time in our 136 00:10:48,720 --> 00:10:52,440 day- to- day lives. There is even a branch of psychology that 137 00:10:52,440 --> 00:10:56,639 is very interesting that addresses how our decision- making is conditioned by artificial intelligence 138 00:10:56,679 --> 00:11:01,320 algorithms. Because behind what we' re looking for on the Internet, the 139 00:11:01,360 --> 00:11:05,559 recommendation systems, many of the applications we use, there' s artificial intelligence. 140 00:11:05,960 --> 00:11:11,759 And in education. There are also many platforms in education that call it 141 00:11:11,759 --> 00:11:15,559 personalizing, but I don' t like to call personalizing. I think it 142 00:11:15,559 --> 00:11:20,120 personalizes the teacher, that what the machine does is individualize a series of contents 143 00:11:20,200 --> 00:11:24,480 and are itineraries. And in the United States there are artificial intelligence systems that 144 00:11:24,600 --> 00:11:30,399 already make decisions about university access and certain issues that have an interesting ethical component. 145 00:11:31,279 --> 00:11:37,320 So, artificial intelligence already lived with us this group of tools that has 146 00:11:37,360 --> 00:11:41,519 been called artificial generative intelligence, that also existed, but that because of the 147 00:11:41,679 --> 00:11:46,559 fact, I bete and other tools that have impacted socially by its use, 148 00:11:46,200 --> 00:11:54,320 because it has generated the specific dynamic approach and that we can talk about later. 149 00:11:54,440 --> 00:11:56,720 But I just wanted to contribute that artificial intelligence is not new and neither 150 00:11:56,759 --> 00:12:01,679 is education. In fact, processing, natural language, automatic feedback, intelligent 151 00:12:01,679 --> 00:12:07,960 tutors, all of this already existed. And for me if it' s 152 00:12:07,200 --> 00:12:11,799 important to point out that under the technology it' s not neutral and below, 153 00:12:13,159 --> 00:12:18,240 but the textbooks aren' t careful either, because we see it very 154 00:12:18,240 --> 00:12:20,080 clearly. In technology we pose it as bad, but one of neutrals is 155 00:12:20,080 --> 00:12:24,399 cultural artifacts that we people realize and there its already is a vision of the 156 00:12:24,399 --> 00:12:30,639 world. That' s why the importance of training our own models was discussed 157 00:12:30,639 --> 00:12:33,039 earlier. And neither in education and whether it is a way of learning and 158 00:12:33,080 --> 00:12:39,759 what it is to teach. And so far artificial intelligence in education has had 159 00:12:39,840 --> 00:12:43,320 a very cognitivist vision of understanding that learning and mastering a content and, therefore, 160 00:12:43,440 --> 00:12:48,919 I offer you an itinerary for that content and personalize it. So I 161 00:12:48,960 --> 00:12:52,080 think we are also at a time that can be a turning point to generate 162 00:12:52,159 --> 00:12:56,080 technology that takes into account a more ethical, more social vision and a different 163 00:12:56,200 --> 00:13:01,120 vision of what it is to learn and that the teacher is not going to 164 00:13:01,159 --> 00:13:05,120 be replaced. If you understand what your teaching role is, if you consider 165 00:13:05,240 --> 00:13:09,600 that basically giving content is that it is no longer artificial intelligence is that the 166 00:13:11,120 --> 00:13:15,440 video already made these things possible. But, well, that' s what 167 00:13:15,519 --> 00:13:18,519 I do every time I talk to melon, I leave it and it happens 168 00:13:18,519 --> 00:13:22,879 to my partners. I' m past my partner to address the technical part 169 00:13:22,960 --> 00:13:26,879 that they' re going to do studyally. Okay. I think artificial intelligence 170 00:13:26,879 --> 00:13:37,279 is a technical term, a bit unfortunate. From a marketing point of view, 171 00:13:37,919 --> 00:13:43,480 the best is very good. And, in fact, as Abri said, 172 00:13:43,600 --> 00:13:48,039 artificial bad intelligence has been with us for a long time. It is 173 00:13:48,200 --> 00:13:52,320 a contemporary of computer science, that is, the first idea of rendering unana 174 00:13:52,399 --> 00:13:56,080 that are precisely the algorithms that are used right now in all artificial intelligence and 175 00:13:56,120 --> 00:14:00,600 included chapte. For that first idea arises in the year forty- five, 176 00:14:00,679 --> 00:14:03,519 forty- six, I do not remember very well the idea of making a 177 00:14:03,799 --> 00:14:11,440 mathematical model of how the lookouts behave, how they communicate with each other, 178 00:14:11,759 --> 00:14:15,840 when the term of artificial intelligence is not yet created. Then, then, 179 00:14:15,960 --> 00:14:18,399 one of the parents of computer science, of mathematics, Alan Turing, because 180 00:14:18,480 --> 00:14:24,559 he also writes a seminal article in which he says something like this in the 181 00:14:26,000 --> 00:14:28,879 first line, can the machines think or something like that was called and is 182 00:14:30,200 --> 00:14:33,960 already giving the introduction to what is going to be understood by artificial intelligence. 183 00:14:33,960 --> 00:14:35,679 And in the fifties, there is a kind of word Wong workshop in which 184 00:14:35,720 --> 00:14:39,799 a series of machines, machines, I mean people in the sense, that 185 00:14:39,879 --> 00:14:46,080 is, scientists, not careful machine. We' re already mowing in language 186 00:14:46,080 --> 00:14:50,799 be careful and all I' m saying is not very powerful researchers like Minsky. 187 00:14:52,759 --> 00:14:58,399 And very good, because there arises for the first time the term artificial 188 00:14:58,399 --> 00:15:03,679 intelligence. And well, it' s actually been the development of artificial intelligence, 189 00:15:03,759 --> 00:15:07,320 because it' s very parallel to computer science, because I' m 190 00:15:07,360 --> 00:15:11,159 not going to say it' s the same thing, but it has a 191 00:15:11,159 --> 00:15:13,440 lot to do with it. Moreover, it is a term that has had 192 00:15:13,039 --> 00:15:20,360 moments of great glory and moment of much oblivion throughout history. From that moment 193 00:15:20,360 --> 00:15:22,039 on, right now we are in a term of total glory, which they 194 00:15:22,399 --> 00:15:31,399 call hype of much, as they say, of much enthusiasm, not totally. 195 00:15:31,440 --> 00:15:35,519 Right now, it may be one of the highest moments in the term, 196 00:15:35,759 --> 00:15:39,120 but that happened throughout history, moments in which from departments, since it 197 00:15:39,879 --> 00:15:43,559 defines computing and so on, they had to put in their projects also artificial 198 00:15:43,559 --> 00:15:48,240 intelligence for financing them and others who don' t comment because we' re 199 00:15:48,320 --> 00:15:52,519 not going to give it hard You know, it' s gone that way, 200 00:15:52,919 --> 00:15:56,320 not all the time. So much so that one of the parents of 201 00:15:56,399 --> 00:16:00,000 artificial intelligence I think it was also MS that I don' t remember, 202 00:16:00,240 --> 00:16:03,159 the names of truth that sometimes dance to me and especially, when I' 203 00:16:03,200 --> 00:16:08,440 m like this, it' s a little bit, it' s a 204 00:16:08,440 --> 00:16:12,919 or if indeed, but well, one of the parents said something like that 205 00:16:14,440 --> 00:16:17,759 artificial intelligence was all that in science, in computing was still not resolved that 206 00:16:17,799 --> 00:16:19,879 as soon as it was resolved, because it was no longer artificial intelligence. 207 00:16:21,039 --> 00:16:23,559 And it is true, there was a time when the development of programming language, 208 00:16:25,080 --> 00:16:29,919 for example lesk or fortan or any of these languages was considered artificial intelligent, 209 00:16:30,360 --> 00:16:36,519 because it was creating programs with which the human could understand with the machine 210 00:16:36,559 --> 00:16:40,200 in a language similar to the formal human, but it is the miracle, 211 00:16:40,279 --> 00:16:44,039 the human not in the sincere seconds that the machine really understands. Then the 212 00:16:44,080 --> 00:16:47,960 development of that language was considered artificial intelligence. Right now, someone says I 213 00:16:48,039 --> 00:16:51,720 ' m going to invent a new language, so cool no one can think 214 00:16:51,720 --> 00:16:56,799 of saying that I' m artificial intelligence, that it' s already so 215 00:16:56,799 --> 00:16:59,879 developed, it' s so. Any first- class student the second computer 216 00:17:00,240 --> 00:17:03,720 can develop a language has the tools to do so. A new language and 217 00:17:03,720 --> 00:17:07,680 indeed, I believe that in many of the universities it is a practically necessary 218 00:17:08,480 --> 00:17:15,240 project. So all this has been changing everything, and that vision of artificial 219 00:17:15,279 --> 00:17:21,799 intelligence is that sack of things where everything that is not yet resolved comes in. 220 00:17:22,079 --> 00:17:26,079 It seems very illustrative to me, because possibly chaptds of that artificial intelligence 221 00:17:26,079 --> 00:17:30,119 in a time, why it could not happen if we go looking at the 222 00:17:30,240 --> 00:17:37,160 story and we see how different problems have been solved that they entered that non 223 00:17:37,680 --> 00:17:40,920 generic sac that, moreover, from the point of marketing, is very good, 224 00:17:41,640 --> 00:17:44,839 because they are two words as well as very powerful, that have been 225 00:17:44,839 --> 00:17:52,839 used continuously, also in science fiction And then there are also such open terms 226 00:17:53,000 --> 00:17:57,079 that I understand that, from the point of psychology the term intelligence is not 227 00:17:57,079 --> 00:18:03,920 yet a defction that all the experts on which this has this kind of thing 228 00:18:03,039 --> 00:18:07,920 agree. In the end, then, what this is about, because it 229 00:18:08,000 --> 00:18:12,880 is about the machine being able to solve problems for what some kind of elements 230 00:18:12,920 --> 00:18:18,759 of congnition, elements of thought or even not of human thought are needed. 231 00:18:18,200 --> 00:18:26,079 Also how they can solve animal problems when faced with a particular situation and have 232 00:18:26,240 --> 00:18:30,400 to see the environment in which they live and make a quick determination. So 233 00:18:30,480 --> 00:18:37,400 that can also be considered artificial intelligence. And right now, because we are 234 00:18:37,559 --> 00:18:47,440 with a technology, specifically HEXAGPT, that has managed to speak very well, 235 00:18:48,039 --> 00:18:52,359 to build very coherent phrases so much that in some cases I don' t 236 00:18:52,400 --> 00:19:00,359 know what you are using If habeillo that to think writes means that I man 237 00:19:00,440 --> 00:19:02,440 and in language is something so human. It' s such a human tool. 238 00:19:03,759 --> 00:19:07,440 Practically to do anything. We need a tool something to talk about. 239 00:19:08,640 --> 00:19:11,359 We don' t need anything, so we could be naked right now. 240 00:19:11,759 --> 00:19:15,039 The thing is, it' s gonna give him a little cut and we 241 00:19:15,039 --> 00:19:17,960 could be talking. We don' t even need clothes to talk, that 242 00:19:18,200 --> 00:19:19,799 ' s something very, very, very human. So, when we see 243 00:19:19,880 --> 00:19:30,880 a machine that speaks also as a human, it is inevitable to think that 244 00:19:30,000 --> 00:19:32,599 there is a certain humanity in the machine and the problem is that there is 245 00:19:32,599 --> 00:19:36,680 not. There' s a lot of statistics. It is incredible that with 246 00:19:36,720 --> 00:19:44,160 so much statistics we can get to produce texts so precise, not so well 247 00:19:44,160 --> 00:19:45,000 constructed, because they can say anything in a moment the news, because it 248 00:19:45,000 --> 00:19:48,680 is true. So, I live in a complicated moment right now, because 249 00:19:48,880 --> 00:19:53,839 it is inevitable that the human to something that speaks, gives him some authority 250 00:19:53,880 --> 00:19:56,920 and if he is a machine, he can even give him more authority. 251 00:19:56,200 --> 00:20:00,920 And that' s a little problem that I think we have to deal with 252 00:20:00,960 --> 00:20:06,160 and we have to deal with the point of view not only partner, but 253 00:20:07,200 --> 00:20:11,279 from education, from the beginning, because, well, certain can happen, 254 00:20:12,200 --> 00:20:18,039 I don' t know unwelcome situations because I attribute a theurity to something that 255 00:20:18,039 --> 00:20:19,839 doesn' t have it. And that problem, well, now, then, 256 00:20:21,200 --> 00:20:26,640 then we' ll talk about it, because of course it already has 257 00:20:26,720 --> 00:20:30,799 a lot of alsage t t and especially in the world of education. Monican 258 00:20:30,079 --> 00:20:37,400 has explained to us that definition of everything that remains to be solved so beautiful, 259 00:20:37,279 --> 00:20:41,319 but that is what is actually sold as artificial intelligence. That' s 260 00:20:41,359 --> 00:20:48,799 what that uncertain definition is all about, that' s what they' re 261 00:20:48,839 --> 00:20:52,759 offering us today. I agree with what Juanda says that word and artificial intelligence 262 00:20:52,799 --> 00:20:57,720 at marketing level. It' s very easy for you to say it and 263 00:20:57,720 --> 00:21:03,440 hear it, because if I told you models of computational thinking like that it 264 00:21:03,519 --> 00:21:06,559 wouldn' t have had that success. And there we go about everything is 265 00:21:06,599 --> 00:21:10,759 what remains to be solved, but we' ve been using those models. 266 00:21:11,400 --> 00:21:15,000 What he says seventy- five years. Seventy- five years ago they started 267 00:21:15,039 --> 00:21:18,000 from these models and now we' ve been using them in a lot of 268 00:21:18,000 --> 00:21:23,599 things for over a decade. We recommend, friends, we recommend what series 269 00:21:23,680 --> 00:21:27,400 to watch. We recommend that you listen, we have it fully integrated into 270 00:21:27,400 --> 00:21:30,920 our day to day, that is Google Map. It recommends us to go 271 00:21:30,960 --> 00:21:33,200 to a place and we use it, and it' s a tool that 272 00:21:33,240 --> 00:21:37,880 took us for so many things. So what happens that what this revolution has 273 00:21:37,880 --> 00:21:42,599 really done to us has been that artificial intelligence is no longer only able to 274 00:21:42,720 --> 00:21:47,559 recommend things to us based on how we use it. No, because and 275 00:21:47,559 --> 00:21:52,720 now the first thing I wanted to try to move. Artificial intelligence mimics a 276 00:21:52,880 --> 00:21:56,319 series of behaviors of human intelligence, a series of mental processes, but it 277 00:21:56,680 --> 00:22:03,920 is not complete human intelligence, but we are interpreting that it is. But 278 00:22:03,960 --> 00:22:07,279 it' s not like that. Artificial intelligence what it does is classify a 279 00:22:07,319 --> 00:22:10,680 lot of information that we give it and that with all that data that we 280 00:22:10,720 --> 00:22:14,480 ' ve given it, I can say, let' s take a case 281 00:22:15,240 --> 00:22:17,400 you upload a lot of pictures of dogs and cats and tell it this is 282 00:22:17,440 --> 00:22:18,240 a cat, this is a dog. And when you give him a picture 283 00:22:18,880 --> 00:22:22,599 of all that you' ve told him, he has to know if it 284 00:22:22,720 --> 00:22:23,920 ' s a dog or a cat. That' s what we do when 285 00:22:25,000 --> 00:22:27,680 we' re little. Since we' re little, we learn to identify 286 00:22:27,680 --> 00:22:33,279 and with that helps us to have a series of patterns to move in life. 287 00:22:33,759 --> 00:22:37,640 So that' s what we' re asking machines with a lot more 288 00:22:37,720 --> 00:22:41,039 data, and we' re applying it to all sectors somehow. What is 289 00:22:41,079 --> 00:22:44,839 the change that makes us all say it from last year on television and everywhere, 290 00:22:44,960 --> 00:22:48,000 because already algorithms were making many decisions for us that, suddenly, artificial 291 00:22:48,079 --> 00:22:53,319 intelligence can not only predict how you use one thing, but can be invented, 292 00:22:53,559 --> 00:22:56,160 used in another way, or that you can invent a text that, 293 00:22:56,279 --> 00:23:03,519 or whatever, can invent an image. I was telling you before I started 294 00:23:03,599 --> 00:23:10,359 that here you have on the third floor there is an exhibition of works of 295 00:23:10,440 --> 00:23:12,480 art made with artificial intelligence. But to do those works of art have been 296 00:23:12,519 --> 00:23:17,200 done a lot, that they have been created and said now you do something 297 00:23:17,440 --> 00:23:21,680 to me in my style. Then we are not used to this tool, 298 00:23:21,920 --> 00:23:26,799 because it has never existed. But this tool gives us different possibilities that we 299 00:23:26,880 --> 00:23:30,880 have to begin to understand that we have to understand where good use is and 300 00:23:30,960 --> 00:23:37,400 where misuse is and everything that implies if we talk about copyright, where these 301 00:23:37,440 --> 00:23:41,400 works of art come from, no, appropriation, no, and whose work 302 00:23:41,359 --> 00:23:47,160 that is. Sure, but look, for example, in this case it 303 00:23:47,240 --> 00:23:52,960 is one These gentlemen have integrated, they have created an artificial intelligence of their 304 00:23:52,079 --> 00:23:56,640 own where they have uploaded all their drawings and with all their drawings have generated 305 00:23:56,640 --> 00:24:00,240 new works of art. I, for example, am writing then one of 306 00:24:00,240 --> 00:24:02,839 the things that is going to want to upload everything that I have written and 307 00:24:02,880 --> 00:24:06,160 that helps me write a book, but I am not taking content from anyone. 308 00:24:06,200 --> 00:24:10,160 It' s about my content that allows me to integrate everything. It 309 00:24:10,200 --> 00:24:11,920 opens up a lot of questions to me. All this, all this. 310 00:24:12,440 --> 00:24:18,079 If we focus on education, how has it been used and how is artificial 311 00:24:18,200 --> 00:24:23,400 intelligence used in education? What benefits are we going to start with, good, 312 00:24:26,720 --> 00:24:33,640 positive, why do we want to use artificial intelligence in education? That 313 00:24:33,799 --> 00:24:37,200 the point is that it affects in many ways, because in the end this 314 00:24:37,279 --> 00:24:42,000 kind of revolution, for me it is important to say that in education the 315 00:24:42,160 --> 00:24:48,880 tool, by itself, does not lead to a better educational And we are 316 00:24:48,880 --> 00:24:51,160 tired of seeing the examples. Okay, putting a textbook inside a laptop is 317 00:24:51,759 --> 00:24:56,839 not a technological innovation. Using a digital pizza machine to screen a YouTube video 318 00:24:56,960 --> 00:25:00,480 is not a technological innovation. And in education, for many years we have 319 00:25:00,519 --> 00:25:03,279 been told that because technology was and was automatically going to be an improvement. 320 00:25:03,440 --> 00:25:08,920 Now we are in these cycles that comment juanda, which are also given in 321 00:25:08,920 --> 00:25:12,119 education. Now we' re going down and saying that all the technology is 322 00:25:12,119 --> 00:25:15,920 bad and then we have to remove it. And neither can that be because 323 00:25:15,039 --> 00:25:18,400 we live in a world that is digital. Not then the technology itself does 324 00:25:18,480 --> 00:25:22,279 not make it possible. It is the task that the teacher does around that 325 00:25:22,279 --> 00:25:26,440 tool, which makes sense and the world is not analogue or digital. The 326 00:25:26,480 --> 00:25:32,759 world is digital and in school it has to coexist manipulative resources with certain moments 327 00:25:32,839 --> 00:25:37,319 where technology enriches the educational process and does not replace it. That said, 328 00:25:37,920 --> 00:25:41,839 there are certain revolutions that have a lot of social impact. It was the 329 00:25:41,359 --> 00:25:47,400 Internet, and now artificial intelligence in education has been operating for a long time 330 00:25:47,400 --> 00:25:55,920 in online education environments. I value these content recommendation systems. We worked years 331 00:25:56,519 --> 00:26:02,559 ago in a system that evaluated exams and then compared the correction that teachers made 332 00:26:02,640 --> 00:26:08,559 to see how much the tool was able to evaluate. Here we are talking 333 00:26:08,599 --> 00:26:12,240 about complex issues, because in education there are no agreements on the concept of 334 00:26:12,640 --> 00:26:17,759 what is intelligence, but neither on what is learning is worth, because learning 335 00:26:17,839 --> 00:26:19,279 is not just taking a note in an exam. It' s a bit 336 00:26:19,279 --> 00:26:22,200 more complex. So, in education it affects in three ways. I believe, 337 00:26:22,240 --> 00:26:26,799 on the one hand, they highlight the need for literacy that we not 338 00:26:26,920 --> 00:26:32,279 only have students, but all as a digital society. Terrible. Yeah, 339 00:26:32,880 --> 00:26:36,519 why I say this is important. In the beginnings that Juan David commented there 340 00:26:36,839 --> 00:26:42,680 were computer scientists already speaking as a role of the importance of literacy in computer 341 00:26:42,720 --> 00:26:52,000 languages, not because everyone has to be engineers, but because they are to 342 00:26:52,000 --> 00:26:55,240 me it is like a language. It is to literate in order to communicate 343 00:26:55,359 --> 00:26:59,200 and to create, not only to consume technologies, but to create with technology, 344 00:26:59,200 --> 00:27:02,920 to understand them. A basic citizen literacy and in that goes later the 345 00:27:03,359 --> 00:27:07,000 integral educational system of computational thinking with ONLOE. But now there is a need 346 00:27:07,039 --> 00:27:11,240 to train teachers so that, at the same time, they can train students 347 00:27:11,240 --> 00:27:18,759 for literacy. On the other hand, it is true that these tools serve 348 00:27:18,799 --> 00:27:25,319 a little as a co- pilot, because it makes you an image in 349 00:27:25,440 --> 00:27:26,680 a short time a visual presentation. So there' s a part of it 350 00:27:27,240 --> 00:27:33,680 that can make it easier for you if you use it well on certain administrative 351 00:27:33,759 --> 00:27:38,960 issues and then there are didactic applications that can also be interesting for teaching English, 352 00:27:40,400 --> 00:27:45,279 for teaching history. I' ve seen ideas with chatboots that are very 353 00:27:45,279 --> 00:27:48,839 interesting. The problem is, as always as we are using it, because 354 00:27:48,920 --> 00:27:53,200 I am finding myself educationally that GEPT is becoming a substitute for Google, of 355 00:27:53,279 --> 00:27:57,319 course and that is a very big risk. That' s part of the 356 00:27:57,359 --> 00:28:03,599 fact that you don' t understand how those models work, apart from that 357 00:28:03,599 --> 00:28:10,039 they can hallucinate and all these things about inverted information in the passage into a 358 00:28:10,039 --> 00:28:11,960 traditional search engine. You can see where the information comes from and you can, 359 00:28:12,279 --> 00:28:17,119 in theory, have a critical ability to see if that information, because 360 00:28:17,200 --> 00:28:22,519 it works better or worse in this type of tools. Although some tell you 361 00:28:22,640 --> 00:28:26,000 the theory of where it comes from, we' ve done tests and it 362 00:28:26,000 --> 00:28:29,960 ' s not always true. So we' re already taking as certain things 363 00:28:30,000 --> 00:28:33,240 that a model is telling us that training is very opaque, that for me 364 00:28:33,319 --> 00:28:38,000 it' s important to say that for fifteen years now, the development potential 365 00:28:38,000 --> 00:28:42,559 of artificial intelligence ceased to be in academia, in research institutions and so and 366 00:28:42,680 --> 00:28:47,000 went into the realm of great technology, because they are the ones that have 367 00:28:47,079 --> 00:28:51,960 our data and the technical ability to train those models for months. Another melon 368 00:28:52,599 --> 00:28:56,480 is what they are in the end and they offer us some useful products that 369 00:28:56,559 --> 00:29:00,839 we all use, but we have to be aware of what if that is 370 00:29:00,920 --> 00:29:03,440 part of literacy and digital competition, which is not just teaching to make pot 371 00:29:03,440 --> 00:29:07,079 poir our son, daughter, teach him to live in today' s world. 372 00:29:07,920 --> 00:29:11,519 And another problem that I am finding in education that the other day my 373 00:29:11,519 --> 00:29:14,519 partner, Linda Castañeda, commented on in a video and I have already seen 374 00:29:14,599 --> 00:29:18,039 some scientific articles and it is very interesting to me, is that there are 375 00:29:18,160 --> 00:29:23,640 teachers that we say is worth looking for information maybe it is not the best 376 00:29:23,640 --> 00:29:26,440 way to use it. Let' s use it as that co- pilot 377 00:29:26,440 --> 00:29:30,119 who gives me ideas. Not one of the most useful proms I see is 378 00:29:30,240 --> 00:29:37,039 giving me ideas for okay. We are finding that if in students, in 379 00:29:37,079 --> 00:29:42,079 people who have the creativity and thought of diversified developed. That way of working. 380 00:29:42,480 --> 00:29:47,960 He can help you. Then you start from what the tool tells you 381 00:29:48,039 --> 00:29:53,200 and build things, but in students or people who cost them badly don' 382 00:29:53,599 --> 00:29:59,240 t have the ability to make decisions yet. The tool takes control of the 383 00:29:59,240 --> 00:30:03,240 thought in the or project they have to do. Then the gap between those 384 00:30:03,359 --> 00:30:08,000 who usually work well and those who cost them the most increases. Then there 385 00:30:08,039 --> 00:30:12,039 is also that in the importance of the teacher deciding what task, when and 386 00:30:12,119 --> 00:30:15,920 how I use it. And this, as always in the middle, is 387 00:30:15,960 --> 00:30:22,960 virtue is neither excessive use nor prohibition. It' s training me first and 388 00:30:22,000 --> 00:30:25,440 then once we' ve literate. If we understand what is behind it, 389 00:30:25,920 --> 00:30:30,079 we make better decisions and understand why there are certain things, what we can 390 00:30:30,160 --> 00:30:33,799 do and cannot do. And then, to finish this aspect, I think 391 00:30:36,519 --> 00:30:41,519 the Internet meant the democratization of information is worth, but it generated new needs 392 00:30:41,559 --> 00:30:45,400 in education. I explained myself doing the works with the Salvat Encyclopedia I had 393 00:30:45,440 --> 00:30:48,839 at home. I don' t know if you' re older. We 394 00:30:48,960 --> 00:30:52,920 all parted, even at a time when the places of the letter. That 395 00:30:52,960 --> 00:30:57,680 was shared as if it were hacking. It matters to you, please, 396 00:30:57,880 --> 00:31:03,960 that the Internet democratizes it ceases the information and we have the letter, but 397 00:31:03,480 --> 00:31:07,000 we also have in the paco that the paco maybe was not as reliable as 398 00:31:07,039 --> 00:31:11,960 the letter. They generate new needs for us at the educational level, because 399 00:31:11,960 --> 00:31:17,440 we no longer have to literate students in something else that is learning to discern 400 00:31:17,480 --> 00:31:19,119 the information that is critical, that is reliable, from which it is not. 401 00:31:19,599 --> 00:31:22,960 And the education system, in fact, at the legislative level adapts. 402 00:31:23,200 --> 00:31:27,200 It includes competencies, includes digital competition, something else, and then, if 403 00:31:27,240 --> 00:31:33,000 it' s being done, but it includes all that artificial intelligence. In 404 00:31:33,079 --> 00:31:36,880 a way, it does not ask us, that is, considering how these 405 00:31:36,920 --> 00:31:40,279 models work, that most of the ones we are using here sound American, 406 00:31:40,920 --> 00:31:45,160 that they have a concrete vision to see the world, that there are certain 407 00:31:45,160 --> 00:31:48,519 expressions, that uses sha GEPT all the time that identifies immediately, that throws 408 00:31:48,519 --> 00:31:52,119 you GPT, that when they squeeze it a little bit is creating you tell 409 00:31:52,200 --> 00:31:53,839 it to make you a paella. If he doesn' t have rice, 410 00:31:55,039 --> 00:32:00,119 he' ll do the paella anyway. So what I' m saying is 411 00:32:00,480 --> 00:32:04,880 that we' re actually putting together tasks that could be these things that hang 412 00:32:04,880 --> 00:32:08,200 on. Perhaps the problem is that we are I have to seriously rethink the 413 00:32:08,279 --> 00:32:12,359 tasks we are asking for. Okay, but that' s a long time 414 00:32:12,400 --> 00:32:17,359 ago, that we should have raised it in the education system, that is, 415 00:32:17,359 --> 00:32:22,200 aid already existed. I have tutoring with my TFG thesis student, this 416 00:32:22,319 --> 00:32:25,079 FM that I work at the University and it' s something and there' 417 00:32:25,160 --> 00:32:30,480 s a poster that puts such a company we make you your TFG. Or 418 00:32:30,519 --> 00:32:32,400 the cousin who came and helped you and also got my cousin wrong, but 419 00:32:32,440 --> 00:32:36,839 the syntactic analysis was done to me by her because I was bad at it. 420 00:32:36,880 --> 00:32:39,440 I always set an example for my cousin, the poor one, that 421 00:32:39,519 --> 00:32:42,759 is, that that also existed. It' s true that it can' 422 00:32:42,759 --> 00:32:45,519 t be at best totally comparable. But that thing about someone else doing the 423 00:32:45,559 --> 00:32:49,400 job or delegating you that someone else already existed, then there' s the 424 00:32:49,400 --> 00:32:52,000 key. It' s just that we have to rethink ourselves so that the 425 00:32:52,000 --> 00:32:54,000 school is there. What role the school is going to play today, what 426 00:32:54,599 --> 00:33:00,400 future jobs certain professions expect, and seriously rethink that school we live in, 427 00:33:00,559 --> 00:33:05,279 because it has to change a lot, but it should have changed. In 428 00:33:05,359 --> 00:33:08,880 two thousand and six competencies were already incorporated. We have to give the resources 429 00:33:09,119 --> 00:33:13,400 space and time so that they can make those transformations. We live in a 430 00:33:13,400 --> 00:33:17,400 pandemic. Now they have to be experts in digital competition. The computational thinking 431 00:33:17,759 --> 00:33:22,880 is very complex and we are not doing it, we are putting it all 432 00:33:22,000 --> 00:33:27,519 through the filter of the school that we know and we continue to validate models 433 00:33:27,559 --> 00:33:30,319 of duties and mode and things that make no sense, because it is that 434 00:33:30,559 --> 00:33:35,000 it is not either I will go back, it is to go back to 435 00:33:35,039 --> 00:33:38,039 the classroom, where you are and where you can help him and where you 436 00:33:38,440 --> 00:33:44,440 can see that they are creating, that they are composing, and that requires 437 00:33:44,440 --> 00:33:49,200 a transformation that is not only of the teacher, it is structural, it 438 00:33:49,359 --> 00:33:52,599 is of ratio, it is of time, because to work by competition and 439 00:33:52,640 --> 00:33:58,960 the course that comes to me in eighty students to teachers, I a little 440 00:33:59,039 --> 00:33:59,440 to work by competition, I will be able to do then that kind of 441 00:33:59,440 --> 00:34:02,480 questions go further. It covers, no, but I really like it because 442 00:34:02,519 --> 00:34:06,039 I ask a question, but my rapporteurs answer what they want. No, 443 00:34:07,359 --> 00:34:09,239 but I' ve answered how it affects education, because I' ve asked 444 00:34:10,440 --> 00:34:15,360 you how it benefits. But how it benefits. I' m quickly sorry 445 00:34:15,599 --> 00:34:16,559 to stop if I' m already saying so. If not, I like 446 00:34:16,639 --> 00:34:22,320 it a lot, because it' s an opportunity to reconduce the model of 447 00:34:22,320 --> 00:34:23,719 education. All right, I like it, I like it. It' 448 00:34:23,760 --> 00:34:27,480 s an opportunity for all of this, to bring them back. You' 449 00:34:27,559 --> 00:34:31,519 ve won a round of applause and everything' s not, but I said 450 00:34:31,559 --> 00:34:37,199 it because I can' t talk about how much profit without putting the whole 451 00:34:37,239 --> 00:34:40,360 thing in it, because it makes clear I can tell the teacher helps to 452 00:34:40,400 --> 00:34:44,320 make visual presentations, the student helps the duty is easier, but it' 453 00:34:44,320 --> 00:34:45,320 s clear for a little bit a question. All that' s behind me, 454 00:34:45,920 --> 00:34:47,719 forgive me muchoca. I love it because if that comes out to you 455 00:34:47,840 --> 00:34:52,039 it' s because you what you mean is that and you can ask me 456 00:34:52,159 --> 00:34:55,679 what you want me to say is this. I don' t have to 457 00:34:55,719 --> 00:34:58,599 work on concentrating. No, no, no, don' t worry because 458 00:34:58,760 --> 00:35:01,280 I' m selling to people what I want to sell to you. You 459 00:35:01,320 --> 00:35:07,039 ' re having hallucinations. Sister, it could be that way. I used 460 00:35:07,079 --> 00:35:13,199 to take advantage of you eluding my question there to ask her directly, Juan, 461 00:35:13,199 --> 00:35:20,599 what. In addition, you have in place and work precisely with applications 462 00:35:20,679 --> 00:35:23,199 designed to help in education. Tell us, what you' re doing, 463 00:35:23,199 --> 00:35:29,599 what those apps are, and how they help and benefit in education. Well, 464 00:35:29,880 --> 00:35:37,159 I have to answer very precisely that there aren' t too many cres. 465 00:35:37,440 --> 00:35:43,079 You worry. I' m not gonna take stitches off like Mariano. 466 00:35:43,159 --> 00:35:47,920 Yeah, I do. I think there is a way to attack the problem 467 00:35:47,960 --> 00:35:52,800 of how to teach artificial intelligence and besides, it is there patent for a 468 00:35:52,800 --> 00:35:58,079 lot of time and one of the keys, the father of all this has 469 00:35:58,119 --> 00:36:06,000 already mentioned it Maria Hermal a while ago that paper Samoul Papper, a mathematical 470 00:36:06,960 --> 00:36:15,440 pedagogue educator, was working in the seventies eighty in the METH and was the 471 00:36:15,519 --> 00:36:17,960 creator, therefore, of this that we call computational thought. Although I don 472 00:36:17,960 --> 00:36:22,639 ' t think he used any of his writing moments. He said that word, 473 00:36:22,800 --> 00:36:27,880 he didn' t use it. Then others used him, but he 474 00:36:27,920 --> 00:36:32,239 already laid the foundations, and not because he did not feed either, but 475 00:36:32,280 --> 00:36:37,519 he was also working with Piaget, which is another of the great pedagogues of 476 00:36:38,480 --> 00:36:44,360 the 20th century and the idea is very simple, not only with the intellig 477 00:36:44,599 --> 00:36:47,400 intestificial, with all the technologies, even to things that can be considered a 478 00:36:47,400 --> 00:36:52,840 technology. The idea he put forward was constructionism, which most people have certainly 479 00:36:52,840 --> 00:37:00,000 heard of Piaget' s constructivism, but he changed a little. He changed 480 00:37:00,000 --> 00:37:07,840 the term called constructionism. And what was coming simply suggests that when people do 481 00:37:07,960 --> 00:37:15,199 something, even if it is some kind of toys, the process that deconstruction 482 00:37:15,199 --> 00:37:16,440 gives so much learning, that is, it contributes so much. Learning leaves 483 00:37:16,840 --> 00:37:25,079 such a mark on iron. They do not leave any mark on what good 484 00:37:25,119 --> 00:37:35,119 they have needed to build that toy thing which is really a very valuable strategy 485 00:37:35,119 --> 00:37:37,480 for learning. She' s not the only one, but she' s 486 00:37:37,480 --> 00:37:44,440 very valuable. Then, he, in addition, was one of the drivers 487 00:37:44,840 --> 00:37:49,440 and clairvoyants in the seventies, when there were still no computers in people' 488 00:37:49,480 --> 00:37:53,719 s homes, and he was very clear that the computer could be a very 489 00:37:53,800 --> 00:37:59,599 valuable tool for learning. But of course he also said that care not the 490 00:38:00,000 --> 00:38:01,920 computer to absorb content, which he has also said very much, not to 491 00:38:01,920 --> 00:38:06,599 consume, but to create. There' s the key. Then, he 492 00:38:06,599 --> 00:38:15,000 saw that, by programming computers, students could learn many things, not just 493 00:38:15,039 --> 00:38:21,320 computers, that is, learning to program to learn other things. And he 494 00:38:21,519 --> 00:38:24,719 developed, not only developed all these ideas but also developed a promotional language. 495 00:38:25,119 --> 00:38:30,320 It was the achievement that well, it didn' t have the success that 496 00:38:30,320 --> 00:38:32,880 now has language like STRATSH, but it was the basis, which was the 497 00:38:32,880 --> 00:38:37,400 basis of scratch. In fact, a successor Resnick has been what he has 498 00:38:37,480 --> 00:38:42,239 created good, with a tremendous team of meat. He has created scrtch from 499 00:38:42,280 --> 00:38:44,320 one of the programming languages that I have most of this is having and the 500 00:38:44,400 --> 00:38:49,000 one that has made it possible for programming to really be introduced into school, 501 00:38:49,519 --> 00:38:53,519 because it is based on an idea of blocks that it is very easy to 502 00:38:53,599 --> 00:38:57,199 make programs, things that with the logo, because it was more complicated because 503 00:38:57,199 --> 00:38:58,000 of issues of syntas, if obviously in those subjects. But the point is, 504 00:38:58,320 --> 00:39:01,079 the idea is fundamental, the one that has to be clear about where 505 00:39:01,119 --> 00:39:06,760 we have to decide. I believe and that you haven' t invented me, 506 00:39:06,880 --> 00:39:08,800 of course, I wish, because otherwise, if I hadn' t 507 00:39:08,800 --> 00:39:14,000 invented myself, as a serial, I' d be in the pedagogy olympus. 508 00:39:14,239 --> 00:39:17,559 So it is due to payperies paper, forgiveness and is We will build 509 00:39:17,639 --> 00:39:22,320 technology, even if it is toy, even if it is simple and it 510 00:39:22,320 --> 00:39:24,920 is artificial intelligence, because what we do. Let' s builder also artificial 511 00:39:25,079 --> 00:39:28,840 intelligence toy. You are not going to throw out PC, but toy for 512 00:39:28,840 --> 00:39:35,039 what, to get to know the basics, just like we program simple things, 513 00:39:35,159 --> 00:39:38,440 to know the fundamentals of the technologies that we usually use. And we 514 00:39:38,480 --> 00:39:43,559 can face those technologies from a critical point of view, because we can' 515 00:39:43,760 --> 00:39:47,719 t face anything critically and critically if we don' t know a little bit 516 00:39:47,760 --> 00:39:51,400 what' s going on we can stalk her tandes of what we want. 517 00:39:51,639 --> 00:39:53,400 We can be the brother- in- law of whatever we want. Well, 518 00:39:53,440 --> 00:39:55,639 then we do very well, no, not very brother- in- 519 00:39:57,280 --> 00:39:58,039 law, but good. Yeah, that' s what we' re looking 520 00:39:58,039 --> 00:40:00,880 at. We' re all given. But it is true that you can 521 00:40:01,039 --> 00:40:08,679 reduce that wedge level, joke field time, that we can deal much better 522 00:40:08,760 --> 00:40:16,400 with CHAPT and its hallucinations when we understand why that technology hallucinates. And it 523 00:40:16,400 --> 00:40:22,800 is very simple to clarify a little bit the term of hallucination with what means 524 00:40:22,840 --> 00:40:28,519 that he writes things well, but that they are not true, they do 525 00:40:28,519 --> 00:40:31,079 not have at best, because the term that is as more familiar in the 526 00:40:31,119 --> 00:40:36,400 language had not only deducinación, but the sixth that there may be a given 527 00:40:36,440 --> 00:40:39,800 moment constructions of phrase and texts, because they are in some way amplifying, 528 00:40:42,039 --> 00:40:46,800 since elements like me that know xenophobia with questions of sex, maximum to thousands 529 00:40:47,000 --> 00:40:51,880 of stories why this happens, because prog why they have done these yes, 530 00:40:52,000 --> 00:40:57,559 then the base is in the data. Actually, if we get the students 531 00:40:57,599 --> 00:41:01,679 to create artificial intelligence, let them be the ones who feed with artificial data 532 00:41:01,679 --> 00:41:07,360 and intelligence, create it and then do a little program with them and see 533 00:41:07,440 --> 00:41:10,559 how it fails, that this is a very important thing, not that they 534 00:41:10,599 --> 00:41:16,760 see only that it is right, but that it fails and that they can 535 00:41:16,800 --> 00:41:21,760 improve it by introducing more data and they see that they improve it as they 536 00:41:21,840 --> 00:41:24,360 enter data, they realize that well, because the essence of all this is 537 00:41:24,440 --> 00:41:31,119 in the data and that that is why the great technologies are so needed and 538 00:41:31,119 --> 00:41:36,079 have become the owners of the data, because there is really their whole business. 539 00:41:36,199 --> 00:41:40,920 And, well, then what do I try to do and I' 540 00:41:42,000 --> 00:41:44,320 m on what I' m working on. But a tool that precisely does 541 00:41:44,400 --> 00:41:49,400 that that in a very simple way, in ten minutes, even the teacher 542 00:41:49,800 --> 00:41:53,440 can teach the students to do an artificial intelligence, because feeding them with data, 543 00:41:53,440 --> 00:41:57,320 for example, with a quick example to be understood, we will build 544 00:41:57,320 --> 00:42:02,880 an artificial intelligence that is able to recognize, since not pictorial styles or artists, 545 00:42:04,559 --> 00:42:08,920 painters, and then we will take a lot of examples, of four 546 00:42:08,920 --> 00:42:14,440 of picasso, others who gave twelve of monet other god twelve bangos of which 547 00:42:14,559 --> 00:42:20,000 we want and classify them. We do a sorting job. The classification, 548 00:42:20,079 --> 00:42:23,639 in fact, is one of the tasks that in education was most of all 549 00:42:23,719 --> 00:42:30,559 teachers or teachers. We are all day doing qualifying activities in all subjects. 550 00:42:30,119 --> 00:42:34,960 Moreover, this is not typical of computer science, but in all not. 551 00:42:36,039 --> 00:42:42,519 So the student is going to enter that data, he' s going to 552 00:42:42,559 --> 00:42:45,159 train artificial intelligence with that data, and he' s going to get an 553 00:42:45,199 --> 00:42:50,559 artificial intelligence that he' s able to recognize, because mones' pictures I 554 00:42:50,800 --> 00:42:52,719 pilico them before with other examples, with cats and dogs it' s worth 555 00:42:52,719 --> 00:42:57,679 monics. That' s going to make him jealous, and then he' 556 00:42:58,119 --> 00:42:59,920 s going to be able to even be good enough for you to get there. 557 00:43:00,079 --> 00:43:05,159 Then you can do a program with Scrusch that uses that artificial intelligence that 558 00:43:05,199 --> 00:43:07,719 you have achieved with all this has not only managed to understand the basics of 559 00:43:07,719 --> 00:43:17,119 the AI so that later you face the applications, as type chaj PT and 560 00:43:17,199 --> 00:43:21,519 others that something we will use. Without a doubt, we are going to 561 00:43:21,599 --> 00:43:23,880 use everything, but at the very least that it presents itself, it faces 562 00:43:23,920 --> 00:43:27,280 itself in a critical way. But he also gets something else, and it 563 00:43:27,360 --> 00:43:31,320 is that if the teacher, for example, takes all these pictures and gives 564 00:43:31,440 --> 00:43:39,679 them mixed and tells him to classify them. The student will have to do 565 00:43:39,760 --> 00:43:45,000 the job of classifying them the same before giving it to the machine and then 566 00:43:45,559 --> 00:43:49,960 motivated because he will build artificial intelligence. For whatever it is, because it 567 00:43:49,960 --> 00:43:52,119 ' s going to take a while, because it can be a good time 568 00:43:52,280 --> 00:43:55,920 an hour half an hour you' re going to classify into a thing that 569 00:43:57,000 --> 00:43:59,000 is usually pretty hard about the subject of the classification and so on, studying 570 00:43:59,000 --> 00:44:00,480 any sort of classification. He always has a hard time. Very. There 571 00:44:00,559 --> 00:44:05,760 ' s a very clear word I' m not going to say because I 572 00:44:05,760 --> 00:44:15,920 ' m in a shithole. Ah you want then to have the illusion of 573 00:44:15,960 --> 00:44:20,960 layering an intertiffial or whatever, for it does that task and when it has 574 00:44:21,000 --> 00:44:23,920 taught the computer to classify, it has also taught itself to classify, then. 575 00:44:25,480 --> 00:44:30,599 We' re not talking here about big artificial intelligence, Dali chapt type, 576 00:44:30,199 --> 00:44:31,719 which general I don' t know what general I don' t know 577 00:44:31,719 --> 00:44:35,400 how much. You could also one thing that I have in mind and I 578 00:44:35,440 --> 00:44:38,760 would like to develop artificial intelligence of generative types by showing you texts. In 579 00:44:38,800 --> 00:44:44,679 fact, there are already some colleagues who have done some pilots and they work 580 00:44:44,679 --> 00:44:47,679 very well. You put in a lot of texts, for example, gift 581 00:44:49,000 --> 00:44:53,440 of fables and when it gives you to generate text that is really crazy, 582 00:44:53,639 --> 00:44:59,880 it' s a nonsense, but it has a little tone, the style 583 00:45:00,360 --> 00:45:02,519 of what would be a fable. For example, James Monic, who is 584 00:45:02,559 --> 00:45:07,400 the author of snaph for a programming language and after Jesus Moreno have done that 585 00:45:07,440 --> 00:45:10,719 experiment and it works quite well, which is one thing I would like to 586 00:45:10,719 --> 00:45:14,840 integrate. The tool that I' m developing, which is called sning ML 587 00:45:14,880 --> 00:45:22,239 and that goes in this direction. In contr to build on artificial toy intelligence, 588 00:45:22,599 --> 00:45:25,760 but build and then program a toy program yes, so that students really 589 00:45:25,960 --> 00:45:30,679 do that because they have those fundamentals. After all, it is about acquiring 590 00:45:30,760 --> 00:45:37,400 foundations through a construction process and that is really how I think the issue of 591 00:45:37,400 --> 00:45:44,920 ia and virtually any technology should be worked out. And this is where I 592 00:45:45,119 --> 00:45:45,519 was going, too, that was very clear to me. You' ve 593 00:45:45,599 --> 00:45:53,880 answered ten points very well in banter. How we move this, both the 594 00:45:54,079 --> 00:45:59,599 part of suspicion, of this we have to overcome and all the biases and 595 00:45:59,679 --> 00:46:02,119 problems and that of which we will then talk a little bit more. And 596 00:46:02,639 --> 00:46:07,719 the productive part, that is, the profitable part, how we use it 597 00:46:07,760 --> 00:46:13,800 to families, because we are talking about this more academic sector, even from 598 00:46:13,880 --> 00:46:16,639 the teachers, which is super important, because someone has to teach it, 599 00:46:16,880 --> 00:46:22,119 but what happens at home. How we translate this vision and all these benefits 600 00:46:22,119 --> 00:46:25,920 and, in turn, its challenges to the family that we are facing at 601 00:46:25,960 --> 00:46:32,679 school are offering us programming, but we have no idea if we are doing 602 00:46:32,719 --> 00:46:37,280 well. For the time we' re aiming at after school, it' 603 00:46:37,320 --> 00:46:39,320 s very expensive. It' s very expensive. This one doesn' t 604 00:46:39,320 --> 00:46:43,760 improve football that also better move. We don' t understand too much either. 605 00:46:44,119 --> 00:46:45,079 I mean, it sounds pretty good to us, because it' s 606 00:46:45,119 --> 00:46:50,320 going to be the future. No, but we don' t understand very 607 00:46:50,360 --> 00:46:53,639 clearly how to help you, how we work with families on that. Now, 608 00:46:53,800 --> 00:46:59,719 I think there are several points, there' s a part, but 609 00:46:59,719 --> 00:47:01,280 there' s a very important pass and I' m going to give you 610 00:47:01,280 --> 00:47:04,960 an example with something else. If we send our children to school with a 611 00:47:04,960 --> 00:47:09,159 knife and a fork, we are calm because we know that they are going 612 00:47:09,199 --> 00:47:14,159 to cut the steak and they are not going to nail it to the side, 613 00:47:14,599 --> 00:47:17,440 because many times, when they are little, we teach them with the 614 00:47:17,440 --> 00:47:21,719 forks that are careful. So this is something that we already have integrated, 615 00:47:21,880 --> 00:47:23,440 because it takes a long time to educate. But with technology we don' 616 00:47:23,440 --> 00:47:29,840 t. Why, because the next generation is always less afraid of the new 617 00:47:29,920 --> 00:47:34,440 and proves it before. So what is the current situation that right now most 618 00:47:34,480 --> 00:47:39,280 children are doing things with intelligence and artificial generative. Why, because they have 619 00:47:39,320 --> 00:47:43,440 to do a job for alcohol doesn' t give them time and the friend 620 00:47:43,679 --> 00:47:45,679 tells him to use that which you do. What happens if your son does 621 00:47:45,760 --> 00:47:49,960 the job and you don' t find out, but it can also happen 622 00:47:50,079 --> 00:47:52,559 to you that if you know what this is? Your son shows it to 623 00:47:52,159 --> 00:47:54,480 you like it happened to me and you say. I can' t believe 624 00:47:55,159 --> 00:48:00,480 your teacher realized that you didn' t do this. That' s an 625 00:48:00,760 --> 00:48:05,719 interesting table for me. Sure, sure, then for me the first thing 626 00:48:05,719 --> 00:48:07,960 families should ask and it' s one of the things I' ve put 627 00:48:08,039 --> 00:48:12,000 in the family observatory, uh, what' s going on in your house. 628 00:48:12,679 --> 00:48:15,079 First ask your children if they know what artificial intelligence is, if they 629 00:48:15,679 --> 00:48:21,719 know what generational artificial intelligence is. You' re using chat shept for what 630 00:48:21,800 --> 00:48:23,840 you' re using it for. Show me, I mean, because I 631 00:48:24,320 --> 00:48:28,480 think we' ve been through this before, too. With this the development 632 00:48:28,519 --> 00:48:32,320 of computational thought has been trying to work for a long time but families do 633 00:48:32,360 --> 00:48:37,239 not know what computational thought is. And computational thinking is I have to solve 634 00:48:37,280 --> 00:48:42,360 one thing. How this I do not prepare, and then, all technologies 635 00:48:42,440 --> 00:48:45,679 what they do is part. I mean, that thought helps you to a 636 00:48:45,760 --> 00:48:51,239 very complex problem, reduce it in small steps and go solving the little things. 637 00:48:51,519 --> 00:48:54,920 So, as they well explained, that computational thinking helps us for everything. 638 00:48:55,440 --> 00:48:58,679 I mean, it' s not just for technology. It helps us 639 00:48:58,719 --> 00:49:01,159 for life. And we' re not developing that well in school, not 640 00:49:01,199 --> 00:49:07,000 in any way. So I think at home, even if it costs us 641 00:49:07,400 --> 00:49:09,119 because we' re not always in a technological field. I have always been 642 00:49:09,159 --> 00:49:14,000 in a technological field and my children have been brought technology soon, but from 643 00:49:14,199 --> 00:49:19,559 a sense of good use, not misuse. But we have to understand that 644 00:49:19,559 --> 00:49:22,639 if we give it to each other, it will teach you how to use 645 00:49:23,119 --> 00:49:28,119 it and it will teach you with misuse. So I think that by many 646 00:49:28,239 --> 00:49:31,400 laws that we want to put in, the laws allow to regulate the bad 647 00:49:31,480 --> 00:49:35,239 uses, but the good uses give them to habit, they give them the 648 00:49:35,239 --> 00:49:39,599 custom, they give them the education and the families have to start first to 649 00:49:40,079 --> 00:49:44,559 have a little critical thinking and to the first news that comes out of removing 650 00:49:44,599 --> 00:49:45,480 a device, not to say it is already closed or such not. I 651 00:49:45,519 --> 00:49:49,320 mean, let' s see this because it' s good and because it 652 00:49:49,320 --> 00:49:51,880 ' s bad. That is, when now, for example, we say 653 00:49:52,280 --> 00:49:57,280 it is not that we are going to doctors are going to ask if children 654 00:49:57,280 --> 00:50:00,559 spend how much time children spend on their phones, because it is not the 655 00:50:00,639 --> 00:50:04,000 same that a child spends an hour watching tiktok, that it does not work 656 00:50:04,079 --> 00:50:07,559 for a child or for us as adults. It' s not good for 657 00:50:07,599 --> 00:50:10,599 any of us to watch an animal documentary or to be programming with junior scrut. 658 00:50:10,679 --> 00:50:14,920 It' s got nothing to do with it. But we are not 659 00:50:15,000 --> 00:50:21,800 looking for simple solutions to complex problems. And so that requires informing, understanding, 660 00:50:22,239 --> 00:50:27,199 literacy of the whole population, because we are citizens of a digital age. 661 00:50:27,920 --> 00:50:34,159 And then we need to use things ethically and we need to know how 662 00:50:34,239 --> 00:50:37,639 we can create them ethically. Before we came in here and talked about me 663 00:50:37,639 --> 00:50:43,360 and Mario, when we said that we are using artificial intelligences that come from 664 00:50:43,719 --> 00:50:46,320 the United States, which have different values than Europeans. If we want to 665 00:50:46,360 --> 00:50:52,519 have artificial European intelligence, we have to have European children who develop artificial official 666 00:50:52,639 --> 00:50:55,760 intelligences with the level of security we want in Europe. But we don' 667 00:50:55,840 --> 00:51:00,639 t have people ready for that now, because we don' t have people 668 00:51:00,639 --> 00:51:02,800 we' re preparing from schools. So the look of families has to be 669 00:51:02,880 --> 00:51:08,440 from understanding, that we don' t have all the answers, that here 670 00:51:08,840 --> 00:51:12,920 we have many questions and that we have them all up to those who are 671 00:51:12,960 --> 00:51:16,800 in the day to day, but that we have to cover our eyes, 672 00:51:17,119 --> 00:51:20,000 what we do many times to solve a problem, that is, I don 673 00:51:20,000 --> 00:51:22,320 ' t have this out. It' s not that it' s, 674 00:51:22,440 --> 00:51:25,199 it' s that we can' t hide our heads like the ostrup is 675 00:51:25,199 --> 00:51:27,800 that our children are using it. It' s just that you have to 676 00:51:28,079 --> 00:51:30,280 talk to them, it' s just that you have to see how they 677 00:51:30,320 --> 00:51:34,719 do their homework, that they show it to you, and then you make 678 00:51:34,719 --> 00:51:37,320 him see how this works. And I think this we have to do as 679 00:51:37,360 --> 00:51:44,119 families and it' s something we need to do from the faculty and I 680 00:51:44,159 --> 00:51:46,480 have the example of a close friend who, despite hearing me all year talk 681 00:51:46,559 --> 00:51:51,039 about this, already in the last week that has had the opportunity to have 682 00:51:51,039 --> 00:51:53,760 a more relaxed course, has started to talk to guys about this. They 683 00:51:53,760 --> 00:51:58,239 have already begun to explore it among everything, and the students themselves realize the 684 00:51:58,280 --> 00:52:00,960 biases and the studies themselves before realized that artificial intelligence hallucinates, of course, 685 00:52:01,760 --> 00:52:10,320 hallucinates because it is a probabilities machine. It' s not a truth machine. 686 00:52:09,559 --> 00:52:14,880 It is true that we bring many new concepts, but we must begin 687 00:52:14,960 --> 00:52:17,039 to understand these concepts. Yeah, anyways to add one thing so that you 688 00:52:17,039 --> 00:52:21,920 had to answer it with a question, but we' re going to chase 689 00:52:21,960 --> 00:52:27,760 her and me all the time program. I think that being true that there 690 00:52:27,800 --> 00:52:30,440 is a demand for literacy that is not just for young people, who are 691 00:52:30,440 --> 00:52:34,639 citizens, we talked about it before. Monica and I, who am a 692 00:52:34,679 --> 00:52:38,400 pedagogue, remember those pedagogical missions of the Second Republic that were to be literate 693 00:52:38,440 --> 00:52:44,320 to all points of Spain. I think that the municipalities, the associations of 694 00:52:44,320 --> 00:52:46,440 neighbors, of women, that is, there should be one, a digital 695 00:52:46,519 --> 00:52:52,400 media literacy to teach us to live in this world to have more, to 696 00:52:52,440 --> 00:52:57,960 know, to better exercise our rights and duties as digital citizens, not to 697 00:52:58,079 --> 00:53:00,840 be programmers or anything of that that you hear, because if it comes out, 698 00:53:01,039 --> 00:53:04,639 then honest, but that on the one hand, but regardless of that, 699 00:53:05,039 --> 00:53:06,320 all that technical part that families are afraid of. I think there is 700 00:53:06,360 --> 00:53:10,360 another aspect that, as a family and as an adult, we do have 701 00:53:10,360 --> 00:53:14,000 to, for example, talk to our children when they go out. Right 702 00:53:14,000 --> 00:53:17,199 now, there' s a lot of news related to the social impact of 703 00:53:17,360 --> 00:53:21,840 artificial intelligence, the whole deep fake, that there are comrades who change the 704 00:53:22,159 --> 00:53:25,599 face of their companions and harass the issue of the IRIS scab, that is, 705 00:53:25,760 --> 00:53:30,000 we' re banning young people from mobile phones and then they come out 706 00:53:30,079 --> 00:53:30,760 and they' re going to scan their way to the mall. There' 707 00:53:30,840 --> 00:53:34,679 s something missing and it' s not technological. It' s educational. 708 00:53:35,400 --> 00:53:38,480 Educational and you can, you can sit down with your son and check in 709 00:53:38,480 --> 00:53:42,719 and say look. These are biometric data of particular sensitivity, precisely in European 710 00:53:42,800 --> 00:53:46,599 artificial intelligence law. This is gonna make him my son, don' t 711 00:53:46,639 --> 00:53:51,280 squeal at you. I mean, the world is changing so much. Of 712 00:53:51,280 --> 00:53:53,199 course, the world is changing so much that they used to tell you, 713 00:53:53,239 --> 00:53:57,719 because be careful at the door of high school, don' t go away 714 00:53:57,760 --> 00:53:59,920 with anyone Mine isn' t going to scan you liris. It seems like 715 00:53:59,920 --> 00:54:04,360 a peak, but the world that we have had to live in I think 716 00:54:04,480 --> 00:54:08,320 that going back, which makes no sense, because we are the adult examples 717 00:54:08,480 --> 00:54:14,079 of how non- literacy leads us to make bad decisions. Then once we 718 00:54:14,119 --> 00:54:17,199 have as far as we can get. There are many apps on the Internet 719 00:54:17,239 --> 00:54:24,320 and many repositories that can serve as a guide to small programs that a smart 720 00:54:24,400 --> 00:54:28,599 car is driving. You have to make decisions. What a decision I' 721 00:54:28,599 --> 00:54:30,039 d make. There are many things that can be talked about and that, 722 00:54:30,400 --> 00:54:35,079 like you, when you throw, you take your son, your daughter to 723 00:54:35,079 --> 00:54:37,480 the park on the first day and do it by the hand, because if 724 00:54:37,519 --> 00:54:40,199 you don' t get your lawyer in the head, because in the digital 725 00:54:40,280 --> 00:54:45,159 world you also ask us to do that. But that' s again because 726 00:54:45,280 --> 00:54:49,199 I, my vindictive role, have to get it out. He questions many 727 00:54:49,320 --> 00:54:52,880 things, because how do you accompany your child, if you can' t 728 00:54:52,880 --> 00:54:55,679 reconcile, if you can' t train, if there are no social mechanisms 729 00:54:55,800 --> 00:55:00,559 that allow you that when your child is with each other maybe you can sit 730 00:55:00,920 --> 00:55:04,800 down and make the best decisions. Then there is one part that is the 731 00:55:04,840 --> 00:55:07,800 family' s own interest in doing it right. But this whole world that 732 00:55:07,800 --> 00:55:15,880 is being configured calls into question the very intrinsic dynamics of society. And I 733 00:55:15,960 --> 00:55:19,760 also think that the family environment needs to be very different from the school environment. 734 00:55:19,880 --> 00:55:24,519 I think that we are also sometimes carrying the school of responsibilities and issues 735 00:55:25,239 --> 00:55:29,199 that are ours and that, instead of going together for whatever it is, 736 00:55:29,440 --> 00:55:34,559 a panorama has been created in which we attack each other and sometimes those that 737 00:55:34,599 --> 00:55:37,199 we are in both places it seems that you even have to unfold your personality 738 00:55:37,239 --> 00:55:40,639 and sometimes defend the family and then other times defend the school and I think 739 00:55:40,679 --> 00:55:46,159 it is because they are not giving us solutions from above and it answers you 740 00:55:46,519 --> 00:55:52,760 wonderfully. He' s going to do something else supernally. I take advantage 741 00:55:52,800 --> 00:55:55,800 of it because the last show didn' t even give us time to public 742 00:55:55,800 --> 00:56:00,000 questions. And if you have questions, take advantage of raising your hand, 743 00:56:00,079 --> 00:56:04,280 because here and you see already seen as man our guests, as they put 744 00:56:04,400 --> 00:56:06,519 you to speak, give us time. So over there we have a question 745 00:56:07,039 --> 00:56:12,320 and we can take advantage of it over there, over there, yeah, 746 00:56:12,639 --> 00:56:20,760 oh they already have micro yes, no, yeah, hi. First, 747 00:56:20,840 --> 00:56:24,039 thank you very much, a great debate. And there is one thing that, 748 00:56:25,239 --> 00:56:34,000 speaking with friends in a context of artificial educational intelligence, we begin to 749 00:56:34,039 --> 00:56:37,000 think of a context that I have moved in recent years, which is co 750 00:56:37,199 --> 00:56:44,400 - creation with people And if we put here the artificial intelligence that you were 751 00:56:44,440 --> 00:56:51,519 also proposing to create, create, but the term co- create makes us 752 00:56:51,639 --> 00:56:58,440 approach validating the result, because we are co- responsible from that hand to 753 00:56:58,440 --> 00:57:00,559 hand with artificial intelligence. I give an example that I believe that those who 754 00:57:01,320 --> 00:57:07,280 have not had that I am no expert an approach to artificial intelligence helps to 755 00:57:07,280 --> 00:57:13,199 understand, without touching it, not without entering and creating a user in chajept 756 00:57:13,400 --> 00:57:20,800 or in other artificial intelligence tools. Last year I was delivering papers on a 757 00:57:20,840 --> 00:57:24,000 master' s degree in anthropology and asked him for a summary of why, 758 00:57:24,519 --> 00:57:29,880 because I needed time to make a delivery. The summary played very well. 759 00:57:30,280 --> 00:57:36,199 I asked him again half the same thing. The same result, i e 760 00:57:36,400 --> 00:57:40,440 it is easily caught. It is not a relevant result many times, but 761 00:57:40,440 --> 00:57:49,400 it occurred to me to ask and test me the key questions of a text 762 00:57:49,400 --> 00:57:53,519 and flip and, in that context of co- creation, to get involved 763 00:57:53,559 --> 00:57:59,760 with the tool and learn from it, because I think the educational model has 764 00:57:59,800 --> 00:58:09,239 been based a lot and this talked about workshops where sculptors like Michelangelo have based 765 00:58:09,239 --> 00:58:15,679 on copying to learn, and I think we can now rely on creation to 766 00:58:15,679 --> 00:58:17,440 learn co- creation. I don' t know I' ll leave it 767 00:58:17,440 --> 00:58:23,840 there. If you want to comment I think that really these tools of generatives 768 00:58:23,920 --> 00:58:29,599 that are coming out, especially those of language generation, are very powerful as 769 00:58:29,599 --> 00:58:37,960 companions. As a companion to enhance thought and it has extraordinary power, but 770 00:58:38,039 --> 00:58:44,719 it has the same extraordinary power to overturn your thought. They' re just 771 00:58:44,880 --> 00:58:46,960 as effective for one thing as they are for another. And well, what 772 00:58:47,079 --> 00:58:52,360 ' s it going to be based on that in one case of power and 773 00:58:52,480 --> 00:58:54,599 in another case cancel because it' s obviously clearer than water in use. 774 00:58:54,679 --> 00:58:59,159 A person who is committed to the study, who is completed with his work, 775 00:58:59,199 --> 00:59:05,079 who is committed to teaching to learn, will use HAGPT and take it, 776 00:59:06,079 --> 00:59:07,800 will help you a lot, generate new ideas, will give you new 777 00:59:07,880 --> 00:59:13,280 ideas, truth from time to time give new ideas. Because you' ve 778 00:59:13,320 --> 00:59:15,000 read a lot of chag sts, you' ve read half of the Internet. 779 00:59:15,079 --> 00:59:21,719 So he' s got a lot going on there in his databases that 780 00:59:21,719 --> 00:59:24,159 are going to bloom and that don' t occur to you and he' 781 00:59:24,239 --> 00:59:28,239 s going to direct you you' re critical and he says bu this to 782 00:59:28,239 --> 00:59:30,119 me it seems a little weird. I' m asking you here about how 783 00:59:30,119 --> 00:59:31,920 to improve chess and you tell me to read me the art of war in 784 00:59:31,960 --> 00:59:36,039 Sun Tzu' s chess. Here this can be you. I' m 785 00:59:36,039 --> 00:59:38,400 saying something I' ve done once this book happened to me. It doesn 786 00:59:38,400 --> 00:59:39,880 ' t exist. It didn' t exist. Then, a clear one 787 00:59:40,079 --> 00:59:45,039 begins to seek information. They say it doesn' t exist. But that 788 00:59:45,079 --> 00:59:47,360 ' s not why the tool is bad, because it helped me. I 789 00:59:47,440 --> 00:59:50,840 found a lot of things, but I, and like many of those here, 790 00:59:51,360 --> 00:59:55,039 have been studying for a long time. We know where we' re 791 00:59:55,039 --> 00:59:59,280 going. We have not acquired that critical capacity. We have already acquired, 792 00:59:59,599 --> 01:00:05,039 therefore, that constancy, those dynamics of study. And then this isn' 793 01:00:05,039 --> 01:00:07,440 t going to help much. Now what happens when we' re talking about 794 01:00:07,559 --> 01:00:14,400 a boy or a girl who' s still in that one in that learns, 795 01:00:14,639 --> 01:00:15,880 that dynamic that you' re also going to learn overnight, maybe, 796 01:00:15,960 --> 01:00:21,199 you get to college and still learn us at all. What use does this 797 01:00:21,239 --> 01:00:25,840 make, the use of answering me that I want to play and I' 798 01:00:25,960 --> 01:00:29,800 m not going to get to see if this thing that' s here is 799 01:00:29,960 --> 01:00:34,679 true, if not true, I copy it. Good bymefriend then of course 800 01:00:35,119 --> 01:00:37,039 have that to which you can get an annulment of the total thought, because 801 01:00:37,039 --> 01:00:40,119 it is that you will not think a bit. That is why I say 802 01:00:40,280 --> 01:00:49,480 that the same tool that can greatly enhance our learning can override you. Then 803 01:00:49,519 --> 01:00:53,719 of course, what you' re saying is absolutely right for me. It 804 01:00:53,719 --> 01:00:57,719 ' s just that he' s a great partner. But what about the 805 01:00:57,719 --> 01:01:01,559 other part of the knife also adds that it is not just a thing of 806 01:01:01,599 --> 01:01:07,960 age and work in the scientific field, that is, in the sense that 807 01:01:08,000 --> 01:01:16,440 I am correcting adult jobs. I am a reviewer of scientific journals and I 808 01:01:16,599 --> 01:01:22,360 am receiving documentation and I say because you can' t prove it from all 809 01:01:22,440 --> 01:01:25,480 the most plagiarism detectors that I think one of the keys also that I have 810 01:01:25,519 --> 01:01:30,159 had so many scares is that at the level of higher education, for example, 811 01:01:30,199 --> 01:01:35,440 we have plagiarism detection programs where you could To start with, most students 812 01:01:35,440 --> 01:01:37,559 don' t want to copy, they just don' t know how to 813 01:01:37,639 --> 01:01:42,679 quote and they have managed the information well worth and when you find copy is 814 01:01:42,719 --> 01:01:44,480 an opportunity to teach you to quote the information well. But in some case 815 01:01:44,480 --> 01:01:46,760 where he' s still there' s been that evil of saying if any 816 01:01:46,800 --> 01:01:50,519 work you could come up against the student with his copy. Here' s 817 01:01:51,400 --> 01:01:52,840 so much access to what this gepet told you at the time. Although the 818 01:01:52,840 --> 01:01:57,719 conversations can be sent is more complicated. In most plagiarism programs are already trained 819 01:01:57,800 --> 01:02:05,119 so that of this pattern is writing GPT and other models. I hate it 820 01:02:05,239 --> 01:02:10,159 more because there is bibliographical reference invented by certain sentences subordinated by the Is crucial. 821 01:02:10,360 --> 01:02:15,440 It is crucial, which is a very direct translation of English. That 822 01:02:15,519 --> 01:02:19,199 problem is not just a problem of children, that is, when we want 823 01:02:19,239 --> 01:02:22,039 to do a quick task in the world in which we live, we want 824 01:02:22,079 --> 01:02:23,280 the productive one and we do not believe we are reading. A lot of 825 01:02:23,320 --> 01:02:27,760 people are copying and pasting the GPT. So here comes a new player that 826 01:02:27,840 --> 01:02:30,719 comes into play in ethics, that shouldn' t be new, but that 827 01:02:30,760 --> 01:02:35,400 has to be and that all that critical thinking and that is no longer just 828 01:02:35,440 --> 01:02:43,000 with artificial intelligence, is part of developing clear critical and responsible citizenship and reflecting 829 01:02:43,119 --> 01:02:47,320 on learning. Of course, that point was on me Maybe it' s 830 01:02:47,320 --> 01:02:52,360 clear. We have focused on education. We have to have this result. 831 01:02:52,800 --> 01:02:54,360 We all have to have this result. And now the important thing is the 832 01:02:54,360 --> 01:02:59,280 teacher. Of course, that' s then I' m seeing it inside 833 01:02:59,320 --> 01:03:02,679 the observatory team, all the teachers who run the teaching area and the school 834 01:03:02,719 --> 01:03:08,400 management area. How they are accompanying the kids in that process and what is 835 01:03:08,440 --> 01:03:15,079 valued is not the result it gives the person. What' s appreciated is 836 01:03:15,440 --> 01:03:21,519 that that guy made three four interactions with the machine and how those interactions are. 837 01:03:21,760 --> 01:03:24,800 If it helps you find much better results. If that' s what 838 01:03:24,800 --> 01:03:29,320 you' re thinking about, then I think this is really about taking on 839 01:03:29,320 --> 01:03:32,239 a challenge that education has, that we have an education that works a way 840 01:03:32,239 --> 01:03:36,800 and measures a way. And then the kids who want to pass, they 841 01:03:36,920 --> 01:03:40,559 don' t want to develop their profession of the future, they don' 842 01:03:40,559 --> 01:03:44,920 t want to be an ethical citizen. They don' t want to play. 843 01:03:44,960 --> 01:03:47,400 Then, of course, it has a tool that helps them make everything 844 01:03:47,480 --> 01:03:52,920 much faster. They are thinking about how they can be more efficient, but 845 01:03:52,960 --> 01:03:57,079 these tools help to be more efficient or to generate new things that you couldn 846 01:03:57,079 --> 01:04:00,000 ' t do with other tools before, so what we have to use is 847 01:04:00,000 --> 01:04:02,960 health. I think what we have to do is transform systems, because what 848 01:04:02,960 --> 01:04:08,559 you say is happening in companies, too. Now we see that certain profiles 849 01:04:08,559 --> 01:04:12,400 are beginning to be seen. The other one already told me one person and 850 01:04:12,440 --> 01:04:15,159 says listen maybe to the person who asked him to do the website to me 851 01:04:15,239 --> 01:04:18,280 takes five minutes each time he does more. Uncle yes and if I can 852 01:04:18,320 --> 01:04:23,119 do it I do because of course I teach you or it is clear that 853 01:04:23,159 --> 01:04:27,039 this is going to change many ways to do things and education, because it 854 01:04:27,079 --> 01:04:30,360 will have to transform how the rest of the states are actually transforming, into 855 01:04:30,360 --> 01:04:33,320 education is to comply with what we have written in the law, which is 856 01:04:33,320 --> 01:04:36,880 more important the process than the product. What it puts into question is not 857 01:04:36,960 --> 01:04:41,079 just education, that is, the process of making a doctoral thesis, of 858 01:04:41,079 --> 01:04:45,000 investigating where the report counts. I mean, you' re now going to 859 01:04:45,039 --> 01:04:49,400 have to see how that knowledge is being built, how it' s interacting, 860 01:04:49,719 --> 01:04:55,960 how it' s learning on the go, because the product is no 861 01:04:56,039 --> 01:05:00,159 longer the most important thing and that' s a change that involves many things 862 01:05:00,239 --> 01:05:02,400 that we' re not knowing, see, that involves many conditions and following 863 01:05:02,400 --> 01:05:05,199 the thought process. I mean, you ask your student what the tool told 864 01:05:05,280 --> 01:05:12,000 you, how you checked that information was true, how you make decisions. 865 01:05:12,079 --> 01:05:15,280 It was conditioned by the tool and to me came a moment Students tell me 866 01:05:15,320 --> 01:05:19,079 I prefer not to use the reward intelligence because so I have to work more? 867 01:05:19,239 --> 01:05:21,239 Well, that' s if I have to work more. They tell 868 01:05:21,280 --> 01:05:26,239 me a lot of things, because maybe that' s how we have to 869 01:05:26,239 --> 01:05:28,679 approach it. But of course, that is very complex and has to go 870 01:05:28,679 --> 01:05:32,079 little by little and we have to be patient also with an educational system that, 871 01:05:32,719 --> 01:05:38,119 by definition, is resistant to change and does not differ so much from 872 01:05:38,559 --> 01:05:41,880 the educational system that we live. In fact, I' m sorry about 873 01:05:41,880 --> 01:05:45,320 what you said. Loving me has surprised me because I coincided with a group 874 01:05:45,400 --> 01:05:50,519 of college students who evaluate each other and then stopped using chagpt because everyone knew 875 01:05:50,679 --> 01:05:56,400 when someone else had used it as they began to read it and said to 876 01:05:56,519 --> 01:06:00,360 you you don' t talk. So if you don' t use those 877 01:06:00,519 --> 01:06:03,400 words if the 20- year- olds themselves know perfectly well that in the 878 01:06:03,960 --> 01:06:09,360 laubus a chap GPP and the parents don' t know and the teachers don 879 01:06:09,360 --> 01:06:09,960 ' t know, I don' t know. I think that is a 880 01:06:10,000 --> 01:06:13,519 reflection that we should all take. No yes, especially because when we use 881 01:06:13,639 --> 01:06:16,400 it well, in what you say Juan David, it is that I am 882 01:06:16,480 --> 01:06:23,280 more productive, but nothing is integral. But I' ll be wrong because 883 01:06:23,280 --> 01:06:25,840 we' re not perfect. But I try to make a proper productive use 884 01:06:25,880 --> 01:06:29,639 and then you get those little tricks that you have and I have helped to 885 01:06:29,639 --> 01:06:32,000 be very productive. A lot of things that I used to ask colleagues give 886 01:06:32,039 --> 01:06:35,239 original stockings for titles of I don' t know what they asked the tool, 887 01:06:35,360 --> 01:06:40,199 to mechanical tasks of summarizing the extraction of certain information, to upload a 888 01:06:40,280 --> 01:06:44,760 pdf and take out my information because you dominate the subject, because you know 889 01:06:44,760 --> 01:06:46,440 how to use it. But of course, to get there you have to 890 01:06:46,519 --> 01:06:54,719 go through some previous steps that may not help you to complete those previous steps. 891 01:06:55,159 --> 01:06:58,960 Of course it is the problem that I see, of course I' 892 01:06:58,960 --> 01:07:01,920 m probably wrong and hopefully I' m wrong that I don' t see 893 01:07:01,920 --> 01:07:06,960 the way to use, that is, GPT in education, especially primary and 894 01:07:08,559 --> 01:07:15,800 secondary college, so that it really helps the student to take that discipline of 895 01:07:15,800 --> 01:07:18,400 study. That' s the problem, so there' s the same thing. 896 01:07:19,360 --> 01:07:21,960 If there is, of course there must be a lot of vigilance on 897 01:07:23,039 --> 01:07:27,079 the part of the teacher, which is difficult because to see how we do 898 01:07:27,079 --> 01:07:35,039 it has twenty- two students in class and so on, how we incorporate 899 01:07:35,039 --> 01:07:36,519 this, especially in the older ages. But I think we have to distinguish 900 01:07:36,599 --> 01:07:42,440 between artificial intelligence and artificial generative intelligence. That' s right. I think 901 01:07:42,440 --> 01:07:48,519 we should start, for example. From my point of view, I think 902 01:07:48,559 --> 01:07:54,480 we should start educating on artificial intelligence ethics. I mean, we see him 903 01:07:54,519 --> 01:07:58,320 we' re going to make our kids see that it' s not the 904 01:07:58,360 --> 01:08:00,880 same as a person a robot has already said the mit where he is. 905 01:08:01,800 --> 01:08:04,400 He says a long time. I mean, we have to understand that we 906 01:08:04,400 --> 01:08:09,920 ' re going to relate in a world where there are as many machines or 907 01:08:09,920 --> 01:08:13,760 more like people. So you, when you talk to Siri, we know 908 01:08:13,800 --> 01:08:16,439 it' s a machine, but our children think it' s a person. 909 01:08:16,520 --> 01:08:20,039 No. Then that kind of concept. I think you have to move 910 01:08:20,079 --> 01:08:25,560 them since they' re small and there' s a chance to move them 911 01:08:25,640 --> 01:08:28,000 and that there are prophemite tools you have a course to teach this kind of 912 01:08:28,000 --> 01:08:31,279 thing. And then it' s true what you say that if we give 913 01:08:31,319 --> 01:08:35,159 generational artificial intelligence tools that they believe, that they believe the summary, that 914 01:08:35,199 --> 01:08:41,000 they believe the duties that they do that. That is what UNESCO itself is 915 01:08:41,039 --> 01:08:45,479 recommending, not before the age of fourteen, to ensure that the children have 916 01:08:45,560 --> 01:08:50,279 generated that thought of learning to seek solutions. Anyway, I think we have 917 01:08:50,279 --> 01:08:55,199 to forgive a little note comes out in my bena pedagogue. You have to 918 01:08:55,239 --> 01:08:58,399 differentiate a lot what the type of task is, that is, it' 919 01:08:58,439 --> 01:09:02,119 s not the same you use an intelligence to design something that you then apply 920 01:09:02,119 --> 01:09:05,880 to students, but ludicants aren' t even aware that there' s an 921 01:09:05,960 --> 01:09:10,640 artificial intelligence where the range of decisions there is to control the echoes, the 922 01:09:10,800 --> 01:09:13,359 truthfulness of the information, the whole ethical part. I have seen examples in 923 01:09:13,560 --> 01:09:17,039 early childhood education of tools that are safe, because right now there is a 924 01:09:17,039 --> 01:09:23,159 boom of tools that is crazy, tools that are safe, that did not 925 01:09:23,199 --> 01:09:27,159 require records, where, perhaps the children are giving Kandinsky. The children make 926 01:09:27,239 --> 01:09:30,560 drawings on the digital board and then transform his drawing into a Kandinsky four. 927 01:09:32,079 --> 01:09:36,880 Then, they analyze the style that has passed, then they draw it by 928 01:09:36,920 --> 01:09:41,680 hand. That is to say, obviously, we are not talking about that, 929 01:09:41,920 --> 01:09:45,319 in fact, Chagept is not recommended for children under the age of twelve 930 01:09:45,359 --> 01:09:49,800 and what you are commenting on is not teaching you this so that you then 931 01:09:49,800 --> 01:09:51,680 do your homework on your own. But within the framework of didactic strategies, 932 01:09:53,199 --> 01:09:57,800 within a lot of decisions that you have to make both didactic and ethical care 933 01:09:57,920 --> 01:10:03,560 with data, etc, I think there are possibilities if the didactic task is 934 01:10:03,680 --> 01:10:09,560 well posed. Now what can' t be are some actions that, surely 935 01:10:09,640 --> 01:10:13,119 out of unconsciousness, are being seen from I' m going to take a 936 01:10:13,159 --> 01:10:19,159 picture of my children and we' re going to see how they age and 937 01:10:19,319 --> 01:10:21,239 we' re surely giving an image of a minor who can be used for 938 01:10:21,239 --> 01:10:26,279 training. That' s different from the activity I just mentioned. Then it 939 01:10:26,319 --> 01:10:29,680 is very clear how you can use it either as a teacher or as you 940 01:10:29,720 --> 01:10:32,319 can. I mean, I don' t think we need to use artificial 941 01:10:32,319 --> 01:10:35,840 generative intelligence in childhood and primary, either. I think it must obviously be 942 01:10:35,840 --> 01:10:40,880 used after a lot of things, but there is a catalogue of tools that 943 01:10:40,920 --> 01:10:47,680 can help if they are well worked out. What happens that right now they 944 01:10:47,920 --> 01:10:54,600 really are sure that such there are very few, because most of them want 945 01:10:54,720 --> 01:10:57,880 you to register, because they want you to go giving data. But there 946 01:10:58,039 --> 01:11:01,039 are some options and I want to recommend a report that has taken out the 947 01:11:01,079 --> 01:11:08,039 inteft that I find very interesting, that makes a catalog of recommendations of tools 948 01:11:08,079 --> 01:11:13,960 around the property of the management, that makes the data and then establishes what 949 01:11:14,000 --> 01:11:15,479 it is called out of green, yellow and red colors. Still, there 950 01:11:15,520 --> 01:11:19,920 are always those terms and conditions that we never read, because starting to look 951 01:11:19,920 --> 01:11:25,279 at them and with that light of colors is telling you at the teacher level 952 01:11:25,359 --> 01:11:29,000 and at the student level, a little bit is guiding you because this tool 953 01:11:29,000 --> 01:11:31,279 turns out to be that you require records and the data are going to be 954 01:11:31,279 --> 01:11:33,119 used for future training. So, well, you use them if you want 955 01:11:33,119 --> 01:11:35,960 to make a visual presentation. But don' t ask students for each visual 956 01:11:36,039 --> 01:11:41,479 presentation with her, because we have to start incorporating all this at the teaching 957 01:11:41,560 --> 01:11:43,800 level, which hasn' t been done. Moreover, that this is not 958 01:11:43,880 --> 01:11:49,399 new, I also want to say that the political decisions of the Council throughout 959 01:11:49,439 --> 01:11:54,840 Spain have led to the loss of the models of free software and to go 960 01:11:54,880 --> 01:11:58,840 to these great technologies in the region of Murcia. My children are on clarun 961 01:11:58,880 --> 01:12:01,399 i e that of course we have to have this ethical part. I believe 962 01:12:01,399 --> 01:12:05,279 that this literacy gives us the prospect of fighting for our rights as well. 963 01:12:05,720 --> 01:12:10,840 But it is that for a few years now, because in the 1990s we 964 01:12:10,840 --> 01:12:14,359 had a lot of free software experiences in autonomous communities and they have been lost. 965 01:12:14,439 --> 01:12:19,600 So we melons importantly free software. No, yes, it' s 966 01:12:19,600 --> 01:12:23,920 very important. In fact, it' s very important, because in the 967 01:12:23,920 --> 01:12:26,680 end we all use these tools. But we all use these tools because it 968 01:12:26,760 --> 01:12:29,960 ' s the most powerful thing and because you can' t live behind their 969 01:12:29,960 --> 01:12:31,840 backs either. But let' s see if it' s an inconsistency because 970 01:12:31,960 --> 01:12:35,079 you' re fighting for another kind of tools. But for me it' 971 01:12:35,159 --> 01:12:40,359 s important to say that I' m here going crazy as a mother to 972 01:12:40,359 --> 01:12:42,600 see or as a teacher. When I recommend, I design future early childhood 973 01:12:42,600 --> 01:12:45,880 education teachers. I always start from pedagogy, but at some point the part 974 01:12:45,920 --> 01:12:49,279 of recommending some tool has to come in. So I recommend David' s 975 01:12:49,359 --> 01:12:57,279 and then and then I say come and now that and then I say but 976 01:12:57,439 --> 01:13:00,319 it' s because we' ve lost and then they go to Google. 977 01:13:00,439 --> 01:13:04,079 Of course it is not that Google is good or bad either in the sense 978 01:13:04,079 --> 01:13:09,319 that in the end it is what it is that we live in this panorama, 979 01:13:09,399 --> 01:13:12,840 also after what has said way of the sea, is that I feel 980 01:13:12,840 --> 01:13:15,560 the need to say it. You know, maybe it' s not Opensours 981 01:13:15,880 --> 01:13:21,279 - free artificial software intelligence. It is generated from creative common of license that 982 01:13:23,199 --> 01:13:30,000 allow to copy, modify, publish, with reference to the original authors and 983 01:13:30,600 --> 01:13:34,560 that are of vital revolutionary importance in the world of education, absolutely revolutionary. 984 01:13:35,000 --> 01:13:42,199 Quickly so revolutionary are that it has allowed me in my free time and when 985 01:13:42,279 --> 01:13:45,960 I have been able, because I have developed an application that is completely based 986 01:13:45,960 --> 01:13:50,039 on open sous, that is, all the libraries that you have used are 987 01:13:50,079 --> 01:13:53,119 open sous are open source libraries. And what' s more, there' 988 01:13:53,119 --> 01:13:57,960 s a part of the application where you' re actually, as you usually 989 01:13:58,000 --> 01:14:00,880 say, working on giant shoulder, which is Scrutch, that is, the 990 01:14:01,119 --> 01:14:04,760 source code, which is the code let' s say you have to type 991 01:14:04,960 --> 01:14:09,960 in to build the program, because Scratch, which is right now the most 992 01:14:10,640 --> 01:14:16,560 powerful programming language to introduce early programming. This has been developed by the METH. 993 01:14:16,640 --> 01:14:19,560 Actually, the Thousand Foundation where I lay in the world. There' 994 01:14:19,600 --> 01:14:25,359 s a lot of people and millions of dollars really and they' ve decided 995 01:14:25,359 --> 01:14:27,560 to publish that more open source. And what does that mean, because I, 996 01:14:27,680 --> 01:14:30,560 for example, without obviously having the billions that are required to make a 997 01:14:30,560 --> 01:14:33,600 tool like that, have been able to take that tool, add four or 998 01:14:33,680 --> 01:14:38,920 five, six, ten blocks that I need to use the technical intelligence that 999 01:14:38,960 --> 01:14:43,439 is built with the other part of the tool. And I' m using 1000 01:14:43,920 --> 01:14:47,079 SCRATCHS on a server of my own, not on the original scratch server, 1001 01:14:47,479 --> 01:14:50,039 and I' m not doing anything illegal. No one' s coming here 1002 01:14:50,079 --> 01:14:54,079 to ask me to account, no one' s gonna tell me I' 1003 01:14:54,119 --> 01:14:56,399 m a pirate and no one' s keeping tros naters and no one' 1004 01:14:56,399 --> 01:15:00,000 s keeping my data. Indeed, and well, why I have been able 1005 01:15:00,079 --> 01:15:01,039 to do this part of the tool, which, moreover, is a part 1006 01:15:01,039 --> 01:15:02,800 that gives it a lot of value. The tool has two parts, once 1007 01:15:02,800 --> 01:15:06,079 it builds artificial intelligence and another of which it can be used to build a 1008 01:15:06,119 --> 01:15:10,479 program. And that part is a programming editor that is modified scracks, because 1009 01:15:11,119 --> 01:15:16,319 the code is free, otherwise it' s impossible for any person to do 1010 01:15:16,319 --> 01:15:21,159 that. That' s millions of dollars invested in that project. And then, 1011 01:15:21,600 --> 01:15:27,000 here, in Spain, in general in the world, when we are 1012 01:15:27,000 --> 01:15:30,640 talking about developing tools not only educational, but any kind of tools that are 1013 01:15:30,760 --> 01:15:38,319 paid for with public money is almost not dropped by its own weight that the 1014 01:15:38,359 --> 01:15:42,439 result would be published open. Because if public money, then the result, 1015 01:15:42,920 --> 01:15:46,159 the code has to be opened. And the same is true for reports, 1016 01:15:46,760 --> 01:15:53,880 for theses, for all documents, for books that can be generated with public 1017 01:15:53,960 --> 01:15:59,359 money, that they may have a license of this kind that allows to modify 1018 01:15:59,600 --> 01:16:01,239 what is already done. For example, there is a very important concept of 1019 01:16:01,239 --> 01:16:05,239 open educational resources. It' s exactly the same thing I' ve had 1020 01:16:05,800 --> 01:16:11,439 with the software, but in an educational resource, because I may have to 1021 01:16:11,439 --> 01:16:14,079 know video, images, texts and so on. If you publish it open 1022 01:16:14,119 --> 01:16:17,319 with a common creats license, you' re allowing other teachers not to leave 1023 01:16:17,439 --> 01:16:20,920 from scratch, but to use what' s already done. That lot of 1024 01:16:21,000 --> 01:16:24,039 hours he' s had to throw out. I' ve done another a 1025 01:16:24,079 --> 01:16:26,439 lot of hours and the best, there adapts it to your thing. It 1026 01:16:26,840 --> 01:16:30,399 ' s a revolutionary idea. From the administrations this is being promoted a little 1027 01:16:30,399 --> 01:16:35,119 bit. We' ve been talking for fifteen twenty years There weren' t 1028 01:16:35,199 --> 01:16:39,520 five of us talking about Creatis Commons, a lot of yora, all of 1029 01:16:39,520 --> 01:16:42,439 a sudden it' s no good. You have to generate everything, when 1030 01:16:42,479 --> 01:16:45,399 of course generating images is very cool and I if the first one that incorporates 1031 01:16:45,439 --> 01:16:48,000 it to my visual presentations. But we' ve been talking about Crady Common 1032 01:16:48,000 --> 01:16:50,640 licenses for 15 years. I' m going to those repositories because there' 1033 01:16:50,840 --> 01:16:57,000 s also an educational part that you have to work on a professional level and 1034 01:16:57,039 --> 01:17:00,920 this other melon people a feeling that they' re leaving exactly. I have 1035 01:17:00,960 --> 01:17:04,560 the feeling because it' s an idea that in education. Sure, obviously, 1036 01:17:04,720 --> 01:17:11,159 there was a lot of that' s a philosophy of the world. 1037 01:17:11,199 --> 01:17:15,319 The world is digital, it can be built from many perspectives and the people 1038 01:17:15,439 --> 01:17:19,960 of the free software movement have a vision of social justice of the digital world 1039 01:17:20,000 --> 01:17:24,880 that is very interesting and that in the world that we live, because it 1040 01:17:24,920 --> 01:17:28,920 is being lost because it does not fit, it does not fit with the 1041 01:17:29,000 --> 01:17:32,279 current moment that we have, but that it is important to know that it 1042 01:17:32,359 --> 01:17:36,359 is there, that there is still resistance, there is still that gives some 1043 01:17:36,359 --> 01:17:38,880 small autonomous communities that keep on their free software platforms. But in the end, 1044 01:17:39,039 --> 01:17:43,439 political decisions have changed, because who now offers you the technological capacity to 1045 01:17:43,600 --> 01:17:48,680 sustain, because all the framework we have now that we need, in fact, 1046 01:17:48,960 --> 01:17:51,399 in the pandemic. It was a critical moment and many of the remaining 1047 01:17:51,439 --> 01:17:57,319 free software projects died in the pandemic because a lot of technological capacity was needed 1048 01:17:57,359 --> 01:18:00,319 to enable the system to survive. And thanks to this company in the system 1049 01:18:00,359 --> 01:18:03,920 it has been able to survive. But in the end these decisions also have 1050 01:18:03,920 --> 01:18:06,720 consequences that, as a society, it is important that we address. And 1051 01:18:06,800 --> 01:18:13,479 all this is part of digital literacy and digital competition. I wanted to remind 1052 01:18:13,520 --> 01:18:18,159 me what you' re saying wrong is that we' re defining new digital 1053 01:18:18,239 --> 01:18:24,319 values without giving us the nerve then what I care about privacy or I care 1054 01:18:24,319 --> 01:18:26,960 about diffusion. And this is a decision we make every day when we upload 1055 01:18:27,039 --> 01:18:29,680 a photo on a social network. For me, is this what the family 1056 01:18:29,680 --> 01:18:31,760 has to do, that is, the family? He doesn' t have 1057 01:18:31,840 --> 01:18:36,399 to be the master of technology, he has to be the master of the 1058 01:18:36,520 --> 01:18:41,199 values you use for the physical world, but also for the virtual, that 1059 01:18:41,199 --> 01:18:43,880 is, the family. He has to talk about security, he has to 1060 01:18:43,920 --> 01:18:48,159 talk about respect, not right now We said before that language is one of 1061 01:18:48,199 --> 01:18:53,640 the best tools that human beings have developed, but language is generating the hate 1062 01:18:53,680 --> 01:18:56,760 speech that we have right now. In other words, language can be used 1063 01:18:56,880 --> 01:19:00,119 well to communicate as it is now to dialogue, or or we can use 1064 01:19:00,199 --> 01:19:05,079 it to generate wars. Everything we do has some behaviors and has some values 1065 01:19:05,199 --> 01:19:10,000 below, and I have often set an example, and is that, when 1066 01:19:10,039 --> 01:19:12,720 the video came out, my mother didn' t know how to put the 1067 01:19:12,720 --> 01:19:16,880 video? I didn' t know I put it on my net brother my 1068 01:19:17,399 --> 01:19:21,079 brother put it on, but I put on the video and my mother watched 1069 01:19:21,159 --> 01:19:23,840 and said this movie isn' t for you and made us take it off. 1070 01:19:24,560 --> 01:19:30,239 But my mother never knew how to handle the team. He never knew 1071 01:19:30,279 --> 01:19:32,520 the technology, but he did know what was good for us and what was 1072 01:19:32,640 --> 01:19:34,840 coming for us and what was coming for us. And what I think is 1073 01:19:34,840 --> 01:19:40,800 that right now, as a family, we have to enter our children' 1074 01:19:40,840 --> 01:19:44,720 s room, or we have to have our children in our rooms and see 1075 01:19:44,760 --> 01:19:49,640 how they interact with technology and about that generate many conversations and there will be 1076 01:19:49,680 --> 01:19:55,159 things that we know and that our children tell us and also in that sense, 1077 01:19:55,520 --> 01:19:57,399 have an open look. It can' t be that, as I 1078 01:19:57,439 --> 01:20:00,000 did one thing to you, it' s good, let' s see, 1079 01:20:00,840 --> 01:20:05,279 let' s think about this, you talk to friends of yours and 1080 01:20:05,319 --> 01:20:09,239 you' re talking and it informs you and, indeed, learn from places 1081 01:20:09,279 --> 01:20:13,800 where you can learn a little bit, that it' s a little bit 1082 01:20:13,840 --> 01:20:18,479 also the objective that we have in observatory, that people have content there, 1083 01:20:18,680 --> 01:20:24,039 but adapted contents and that you can start from something very basic, because here 1084 01:20:24,119 --> 01:20:29,800 we are sure that the three of us are taking some word that we understand 1085 01:20:29,840 --> 01:20:34,319 each other, but that one of you, because we have lost with the 1086 01:20:34,319 --> 01:20:34,319 clear is that we each have some knowledge and we have a background and with 1087 01:20:34,319 --> 01:20:36,960 that baggage we are building our knowledge. And it may be that you, 1088 01:20:38,199 --> 01:20:42,680 in order to start getting into this, you have to start with something very 1089 01:20:42,680 --> 01:20:45,439 simple that will explain very simply how are the things that answered them to you 1090 01:20:45,479 --> 01:20:48,600 that encourage you to contextualize them with things that you already know so that, 1091 01:20:48,720 --> 01:20:54,479 for example, as I use you, analogies like that very simple, are 1092 01:20:54,479 --> 01:20:59,319 very simple. But I really think the needs we have now have happened to 1093 01:20:59,359 --> 01:21:02,199 us with other tools things with the car. I remember when I was little 1094 01:21:02,279 --> 01:21:05,800 and we go in the car children sitting in any way asleep on the trays. 1095 01:21:06,680 --> 01:21:11,800 And right now we have a chair for children from zero to nine months 1096 01:21:12,119 --> 01:21:14,520 from nine. I' m making it up because I' m no older 1097 01:21:14,560 --> 01:21:17,319 than my kids, but we have a specific chair for every age the child 1098 01:21:17,319 --> 01:21:20,119 and we know that from the age of twelve they no longer need those chairs. 1099 01:21:20,199 --> 01:21:24,600 No, well, I think that' s the big challenge we have 1100 01:21:24,640 --> 01:21:30,199 in education. Not where we get in, where we put artificial generational intelligence, 1101 01:21:30,680 --> 01:21:33,600 because we' re protecting them, like we do with the chair in 1102 01:21:33,600 --> 01:21:36,159 the cars. Not in any way. What is the time to put it 1103 01:21:36,199 --> 01:21:43,439 in and not limit the critical thinking, the creative thinking of children. And 1104 01:21:43,479 --> 01:21:46,800 that' s the challenge we have. And I was also thinking we have 1105 01:21:46,840 --> 01:21:49,800 a few minutes left that I say if you want to ask him one last 1106 01:21:49,800 --> 01:21:55,079 question. You have a few minutilos. Ah look, just take advantage of 1107 01:21:55,079 --> 01:21:58,000 it, hey. Yeah, yeah, well, I' m loving it. 1108 01:21:58,039 --> 01:22:01,479 Thank you so much. You' ve talked a lot about the education 1109 01:22:01,479 --> 01:22:06,600 part. I loved that part of the constructivism of how. I don' 1110 01:22:06,680 --> 01:22:10,600 t know if the word said it right, but how you learn the process 1111 01:22:10,600 --> 01:22:15,479 and what I' d like to ask you is how we adults do to 1112 01:22:15,479 --> 01:22:16,279 learn that. I mean, don' t recommend it. For starters, 1113 01:22:18,279 --> 01:22:23,960 ask what we have to do. Well, I recommend entering the in INTEFT, 1114 01:22:24,560 --> 01:22:28,720 which, although it has a very dedicated part to teachers, there are 1115 01:22:28,800 --> 01:22:33,000 many nanomoks, has many courses that ultimately start from a literacy also basic, 1116 01:22:33,560 --> 01:22:38,680 because the teachers, in the end we do not stop being all parent mothers 1117 01:22:38,720 --> 01:22:43,600 and one will be doctor in such and others were teachers. So there' 1118 01:22:43,600 --> 01:22:45,359 s a lot of resources in the interfe. Then, I recommend the Spanish 1119 01:22:45,399 --> 01:22:50,399 Paediatric Association. It has recommendations for families around ages, for using finished slides. 1120 01:22:51,960 --> 01:22:56,439 It has contracts for the subject of the first mobile when, how, 1121 01:22:56,760 --> 01:23:00,520 under what criteria and such the influence that it is an organism p in which 1122 01:23:00,840 --> 01:23:08,079 there is a lot of information, they address many areas from the thing through 1123 01:23:08,159 --> 01:23:11,760 networks, the fake news, etcetera. And then, then, it may 1124 01:23:11,840 --> 01:23:15,479 sound silly. But in this world of social networks that seems so harmful and 1125 01:23:15,479 --> 01:23:19,279 that Twitter right now is afraid to put its foot in because it has twenty 1126 01:23:19,399 --> 01:23:24,600 people telling you everything, because following profiles like these professionals. I have learned 1127 01:23:24,680 --> 01:23:29,359 a lot because they see many parents who tell their experiences of how, for 1128 01:23:29,399 --> 01:23:34,119 example, they have used tools like Chabot type, like GPT to generate a 1129 01:23:34,159 --> 01:23:39,479 story, to go to sleep, because it seems to me a very funny 1130 01:23:39,640 --> 01:23:43,720 idea, that is, well let' s build a story. I' 1131 01:23:43,800 --> 01:23:45,600 ve read you twenty times the same story, let' s believe something and 1132 01:23:45,680 --> 01:23:49,880 this has created it a tool and give several ideas to the best of how 1133 01:23:49,960 --> 01:23:56,199 usefully it is and then, of course, use the MINML application that seems 1134 01:23:56,199 --> 01:24:00,239 silly, but it' s a very good application. There are many videos 1135 01:24:00,359 --> 01:24:04,680 that he sets as an example and identification of when you' re happy, 1136 01:24:05,119 --> 01:24:09,680 when you' re sad. There is debate as a machine can or can 1137 01:24:09,840 --> 01:24:13,439 not identify a feeling for a facial recognition, that is, make a small 1138 01:24:13,479 --> 01:24:19,159 effort to train ourselves and then, in a way trapressar, accompany them and 1139 01:24:19,159 --> 01:24:26,319 not be afraid that they know more on a technical level. Okay, yeah, 1140 01:24:27,560 --> 01:24:30,000 our kids, not us. I have many years of professional life left. 1141 01:24:31,000 --> 01:24:35,960 I would like to know how all these spaces are very good places to 1142 01:24:36,000 --> 01:24:41,239 learn and then enter us as adults and spend that time trying out the tool 1143 01:24:41,279 --> 01:24:45,399 and seeing it do and creating it. I at this moment, right now, 1144 01:24:45,760 --> 01:24:48,319 I' m creating a course for that, for me it' s 1145 01:24:48,399 --> 01:24:54,159 that question that you have been thinking about for several months, six months or 1146 01:24:54,159 --> 01:24:57,279 so, and I' m riding a very basic course. And also, 1147 01:24:57,640 --> 01:25:00,960 inside the observatory, inside the family area. I have thought of it as 1148 01:25:00,960 --> 01:25:04,319 being videos of ten fifteen minutes that you, instead of spending fifteen minutes, 1149 01:25:04,720 --> 01:25:13,000 lost in instagram or in tiktok you learn little by little, but from a 1150 01:25:13,079 --> 01:25:15,640 very low level, that is, without touching the computer you go to a 1151 01:25:15,720 --> 01:25:18,079 course of a lot of things without touching the computer, because I what is 1152 01:25:18,119 --> 01:25:23,840 transferred to you are concepts, concepts and that you go hooking up the ideas. 1153 01:25:23,960 --> 01:25:26,119 And I think that' s what we' re missing, because then 1154 01:25:26,119 --> 01:25:29,640 contained. As it tells you more, I think there are a lot of 1155 01:25:29,680 --> 01:25:31,680 contents, but they are on a very high level. And one of the 1156 01:25:31,720 --> 01:25:35,840 things I' ve done has been to give courses in digital literacy, of 1157 01:25:35,880 --> 01:25:41,439 the digital competition that we' re teaching to adult children. And this they 1158 01:25:41,479 --> 01:25:45,680 were doing in the last two years as women and also with refugee or Ukrainian 1159 01:25:45,760 --> 01:25:48,560 collective. And of course, when we think about adults, we think we 1160 01:25:48,640 --> 01:25:51,640 all have the level of competence that we have. But I, in my 1161 01:25:51,720 --> 01:25:59,199 classes, had people that an older gentleman, who had fingers that couldn' 1162 01:25:59,199 --> 01:26:02,960 t even put them on my keyboard and people that I knew had never touched 1163 01:26:02,960 --> 01:26:09,159 a keyboard. So how do we literate in artificial intelligence people who are not 1164 01:26:09,199 --> 01:26:16,880 even literate for a digital world and we have to generate different discourses, different 1165 01:26:17,279 --> 01:26:19,880 contents, simpler ones. I' ve set myself that target. I don 1166 01:26:19,880 --> 01:26:25,760 ' t know how simple it is. Monica will have in the podcast resources. 1167 01:26:26,640 --> 01:26:29,920 I' ll give you a link so you can try it out and 1168 01:26:30,119 --> 01:26:34,079 take a look and all the comments you send me great. This week they 1169 01:26:34,119 --> 01:26:36,760 were telling me to see if it' s important. They told me that 1170 01:26:36,800 --> 01:26:42,239 in Finland and this you can look for it, also he Linkomónica. In 1171 01:26:42,279 --> 01:26:45,359 Finland, such a course has been designed for all citizens to take an open 1172 01:26:45,880 --> 01:26:48,880 course and has been translated into all languages, including Spanish. I don' 1173 01:26:48,960 --> 01:26:51,720 t have time to see him, because he recommended it to me last week. 1174 01:26:53,359 --> 01:26:57,159 But it is true that this type of artificial intelligence literacy resources have to 1175 01:26:57,199 --> 01:27:01,439 begin to appear for the whole city, because this week I saw this exhibition 1176 01:27:01,479 --> 01:27:06,720 of generative artistic intelligence with retired people, who came here into space, see 1177 01:27:06,760 --> 01:27:12,159 it and I is that it is a quantum leap, is that it does 1178 01:27:13,600 --> 01:27:16,880 not have some basic concepts to understand what is being explained to them in that 1179 01:27:16,920 --> 01:27:21,840 regard. There are foundations and there are several initiatives that are training families. 1180 01:27:23,840 --> 01:27:27,560 As Monica commented, even we, as a research group, have had a 1181 01:27:27,560 --> 01:27:35,359 very good experience of doing it that has the face to say be more high 1182 01:27:35,960 --> 01:27:40,079 - pitched, how not asked. Book has made a mock for family. 1183 01:27:40,680 --> 01:27:45,239 Then, for example, we, with foundations and other companies, have also 1184 01:27:45,279 --> 01:27:48,800 done even for seniors who a priori you could say hear what they want, 1185 01:27:49,199 --> 01:27:55,319 because it is fascinating. It is a super experience in which you can see 1186 01:27:55,439 --> 01:28:00,920 just how they have a very clear ethical perspective and once you have surrounded that 1187 01:28:00,000 --> 01:28:04,000 more technical language. You' re realizing that you' re helping them participate 1188 01:28:04,000 --> 01:28:11,279 in closing that digital divide that in phone foundation. I worked here for two 1189 01:28:11,760 --> 01:28:15,640 years and there are many activities that are aimed at reducing the digital divide, 1190 01:28:16,119 --> 01:28:21,119 both in children and in adults, and there are activities where you can also 1191 01:28:21,119 --> 01:28:24,039 participate. Like the workshops that have taken the children, there are also workshops 1192 01:28:24,079 --> 01:28:28,399 for us, in addition, aimed at families that try to have that approach 1193 01:28:28,439 --> 01:28:31,520 in the concrete face. They' re courses from those families. I play 1194 01:28:31,600 --> 01:28:34,760 them in following Yes, it' s true that artificial intelligence specifics that I 1195 01:28:34,800 --> 01:28:38,920 ' ve been monitoring for a long time. There are still very little specifics 1196 01:28:38,960 --> 01:28:45,520 of artificial intelgy, but it is true that social networks are artificial intelligence that 1197 01:28:45,600 --> 01:28:47,640 we have been using for a long time. Video games have been using it 1198 01:28:47,880 --> 01:28:50,359 for a long time and about that, everywhere he' s told you the 1199 01:28:50,359 --> 01:29:00,000 most. They' ve got a lot of incible information, you' ve 1200 01:29:00,000 --> 01:29:00,000 got jammed up, you' ve got a lot of associations that hinder. 1201 01:29:00,199 --> 01:29:02,680 The Oran Foundation also has, I mean, you have a lot of places 1202 01:29:02,720 --> 01:29:05,399 where you can be a repository. And what I' m going to tell 1203 01:29:05,560 --> 01:29:10,159 her, don' t all worry. We' ll leave all the resources 1204 01:29:10,199 --> 01:29:14,560 in the program notes and friends, we have to leave some interesting talk. 1205 01:29:14,640 --> 01:29:18,000 I really had a script, but I answered nothing. The same is true 1206 01:29:18,239 --> 01:29:23,800 for several more programs. I love it. I' ve learned a lot 1207 01:29:23,800 --> 01:29:28,000 about the record. I think that we take concepts and, above all, 1208 01:29:28,319 --> 01:29:33,880 I want to end up claiming a lot of that which we are talking about 1209 01:29:34,079 --> 01:29:41,800 now in the end, that we take these concepts as our own, because 1210 01:29:41,920 --> 01:29:45,640 if we don' t make these concepts something our own and something ours and 1211 01:29:45,680 --> 01:29:49,239 that corresponds to us we have the age we have or the knowledge and context 1212 01:29:49,319 --> 01:29:50,279 we have, it won' t affect us so much that those digital rights 1213 01:29:50,279 --> 01:29:54,920 take them away from us. We are not going to feel like a loss 1214 01:29:55,039 --> 01:30:00,920 of something of ours when it is suddenly primatized and we do not have all 1215 01:30:00,960 --> 01:30:03,279 the same rights to access these services or our programs. You just want it 1216 01:30:03,319 --> 01:30:08,079 in the monic loss, you also stay in the loss of opportunity. Of 1217 01:30:08,279 --> 01:30:12,520 course the one we talked about in pandemics, in pandemics, the group that 1218 01:30:12,560 --> 01:30:16,359 passed the pandemic worst, because I was here in foundation working for many foundations, 1219 01:30:16,800 --> 01:30:20,359 were the people who didn' t have a digital device and, for 1220 01:30:20,399 --> 01:30:27,720 example, the food banks where people were going to look for clear food were 1221 01:30:28,159 --> 01:30:30,760 closed and they set up websites to order food. But there were many people 1222 01:30:30,880 --> 01:30:35,319 who clearly had to search the Internet. That' s exactly why so it 1223 01:30:35,319 --> 01:30:42,600 ' s accessible, so that everyone, that they' re open, that 1224 01:30:42,600 --> 01:30:44,680 they' re all open? Let us think that with our sons, our 1225 01:30:44,760 --> 01:30:49,319 daughters, those values, that ethics, discovering those biases, all these risks 1226 01:30:49,319 --> 01:30:55,600 that exist, because there are people behind them and we have to know how 1227 01:30:55,800 --> 01:31:00,720 to identify them and fight because this technology is something that responds to us as 1228 01:31:00,760 --> 01:31:02,279 human beings, that defends us in the end and that benefits us. So 1229 01:31:02,279 --> 01:31:08,079 I keep that claim that seems to me very productive in the end to take 1230 01:31:08,159 --> 01:31:13,079 us home, as I' m going to learn an artificial intelligence to make 1231 01:31:13,119 --> 01:31:17,359 a better world than this is what I like to finish the spaces mothering so 1232 01:31:17,399 --> 01:31:21,560 with taking some homework home and, above all, thank you all, my 1233 01:31:21,640 --> 01:31:29,840 wonderful speakers. Thank you so much for your work and for bringing it closer 1234 01:31:29,880 --> 01:31:32,640 and for making it our own that affects us all. Let us, as 1235 01:31:32,640 --> 01:31:39,000 I said, have children, have no children, whether we are professed or 1236 01:31:39,000 --> 01:31:41,479 not to live where we live and have the prior knowledge we have. Thank 1237 01:31:41,560 --> 01:31:45,760 you so much for joining us in a mother- sphere space. We will 1238 01:31:45,760 --> 01:31:50,800 return after the summer with new motherspace spaces and new themes and challenges and nothing 1239 01:31:50,880 --> 01:31:54,359 that we will hear in the podcast and thanks to the streaming as well. 1240 01:31:54,560 --> 01:32:05,560 Thank you very much Thank you until tomorrow morning.