English subtitles for clip: File:Ikusgela-Adimen artifiziala.webm
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1 00:00:03,240 --> 00:00:07,170 Who wrote all this that I am going to say in the next few minutes? 2 00:00:07,440 --> 00:00:08,800 Myself? A screenwriter? 3 00:00:09,100 --> 00:00:10,930 Or was it an artificial intelligence? 4 00:00:11,080 --> 00:00:14,715 Only one thing is certain: immediately after creating this video, 5 00:00:14,739 --> 00:00:17,590 what we explain here could become obsolete. 6 00:00:17,830 --> 00:00:21,588 It is not at all easy for humans to distinguish authorship. 7 00:00:21,613 --> 00:00:25,560 And that gives us a measure of the progress of artificial intelligence. 8 00:00:25,585 --> 00:00:28,074 ChatGPT and similar tools 9 00:00:28,098 --> 00:00:30,535 have the ability to write texts in their entirety, 10 00:00:30,559 --> 00:00:33,760 precisely and adapted to the requested style. 11 00:00:33,820 --> 00:00:37,345 And other tools are capable of creating images from scratch, 12 00:00:37,369 --> 00:00:39,970 respecting the orders given that we give him. 13 00:00:39,995 --> 00:00:41,955 AI or artificial intelligence 14 00:00:41,980 --> 00:00:43,030 is not a brand new topic. 15 00:00:43,080 --> 00:00:46,850 Early tests of this rapidly evolving technology 16 00:00:46,874 --> 00:00:50,300 were done as early as in 1943. 17 00:00:50,320 --> 00:00:54,454 But since 2022 it has fully entered our lives, 18 00:00:54,479 --> 00:00:57,254 and of course, the discussions with our friends. 19 00:00:57,279 --> 00:00:59,720 But how does all this work? 20 00:00:59,950 --> 00:01:02,210 And how much will it change our lives? 21 00:01:10,001 --> 00:01:13,167 Artificial intelligence is the capacity of machines. 22 00:01:13,191 --> 00:01:16,044 To perform tasks that normally require human intelligence. 23 00:01:16,068 --> 00:01:21,275 These include learning, decision making, and pattern recognition. 24 00:01:21,300 --> 00:01:23,960 To achieve this, artificial intelligence developers 25 00:01:23,985 --> 00:01:26,082 use what is called a neural network. 26 00:01:26,433 --> 00:01:29,910 A neural network can learn to perform difficult tasks, 27 00:01:29,950 --> 00:01:33,297 like recognizing images or translating languages. 28 00:01:33,321 --> 00:01:36,538 A neural network learns through experience. 29 00:01:36,563 --> 00:01:39,977 This means that when it is trained with a data set, 30 00:01:40,001 --> 00:01:43,840 that data set can adjust its connections and parameters 31 00:01:44,020 --> 00:01:46,220 to improve the ability to perform the desired job. 32 00:01:46,270 --> 00:01:50,211 In other words, you can create and adapt your own algorithms, 33 00:01:50,235 --> 00:01:52,450 through machine learning. 34 00:01:52,660 --> 00:01:57,138 Basically, a neural network consists of many "artificial neurons", 35 00:01:57,162 --> 00:01:59,860 and are organized in interconnected layers. 36 00:01:59,920 --> 00:02:02,763 Each neuron receives certain information. 37 00:02:02,787 --> 00:02:06,270 And performs a small mathematical operation to process it. 38 00:02:06,350 --> 00:02:10,196 This information is then transmitted to the neurons in the next layer, 39 00:02:10,221 --> 00:02:14,100 and so on until reaching the final answer. 40 00:02:14,380 --> 00:02:18,340 That's right, data must be provided to train the neural network. 41 00:02:18,390 --> 00:02:20,680 There are different types of learning processes: 42 00:02:20,988 --> 00:02:24,649 1- Supervised learning: 43 00:02:24,650 --> 00:02:26,837 This type of training is used when 44 00:02:26,862 --> 00:02:28,830 there is a data set that has been labeled before, 45 00:02:28,855 --> 00:02:29,712 is to say, 46 00:02:29,737 --> 00:02:33,660 when the correct answer is known for each input. 47 00:02:34,230 --> 00:02:38,100 The AI learns from this labeled data, 48 00:02:38,180 --> 00:02:41,520 and then you can make predictions for new data. 49 00:02:42,190 --> 00:02:44,610 2- Unsupervised learning: 50 00:02:45,190 --> 00:02:46,711 In this type of training, 51 00:02:46,736 --> 00:02:49,550 AI does not have pre-labeled data. 52 00:02:49,630 --> 00:02:52,220 Instead, they are presented with a set of data, 53 00:02:52,244 --> 00:02:56,660 and then are asked to find patterns or structures within that data. 54 00:02:57,100 --> 00:03:00,020 This can be useful for data analysis or 55 00:03:00,044 --> 00:03:02,060 to segment customers, for example. 56 00:03:02,414 --> 00:03:05,240 3- Learning through reinforcement: 57 00:03:05,520 --> 00:03:10,460 This type of training is used to teach AI to make decisions, 58 00:03:10,590 --> 00:03:12,850 through reward and punishment. 59 00:03:12,875 --> 00:03:14,430 They are presented with a scenario, 60 00:03:14,510 --> 00:03:16,540 and asked to perform an action. 61 00:03:16,800 --> 00:03:19,941 If the action is correct, the AI receives a reward, 62 00:03:19,965 --> 00:03:23,110 but if the action is not correct, it receives a punishment. 63 00:03:23,135 --> 00:03:24,165 Over time, 64 00:03:24,190 --> 00:03:28,860 the AI learns to make the right decisions to maximize rewards. 65 00:03:28,885 --> 00:03:29,975 An example, 66 00:03:30,000 --> 00:03:33,550 if we want a computer to learn to recognize faces, 67 00:03:33,680 --> 00:03:37,220 and give us information about what appears in each of them 68 00:03:37,244 --> 00:03:39,610 we will provide it with many labeled images. 69 00:03:39,800 --> 00:03:42,937 The computer observes patterns in the data, 70 00:03:42,962 --> 00:03:46,830 and creates a model to identify faces in new images. 71 00:03:47,130 --> 00:03:50,059 And why is all this so significant? 72 00:03:50,083 --> 00:03:54,147 Mainly because it is a technology that can be used in many areas of our lives. 73 00:03:54,172 --> 00:03:58,459 In healthcare, artificial intelligence systems diagnose diseases 74 00:03:58,483 --> 00:04:01,940 and can help doctors predict treatment outcomes. 75 00:04:02,080 --> 00:04:06,193 It is also capable of automating most of the work done by computer, 76 00:04:06,218 --> 00:04:07,700 with increasingly better results. 77 00:04:07,790 --> 00:04:10,646 Design work, writing, forecasts... 78 00:04:10,670 --> 00:04:15,242 It is developing rapidly and has increasingly impressive capabilities. 79 00:04:15,266 --> 00:04:17,690 But be careful, there are also critical voices. 80 00:04:17,715 --> 00:04:18,667 And thank goodness: 81 00:04:18,691 --> 00:04:22,052 because we have already been warned about the dangers of this technology. 82 00:04:22,076 --> 00:04:25,867 On the one hand, as artificial intelligence advances, 83 00:04:25,891 --> 00:04:27,850 new ethical dilemmas arise. 84 00:04:27,875 --> 00:04:28,775 For example, 85 00:04:28,800 --> 00:04:32,710 Who is responsible if an autonomous vehicle causes an accident? 86 00:04:32,730 --> 00:04:35,698 How can we ensure that artificial intelligence systems 87 00:04:35,722 --> 00:04:38,170 do not discriminate against certain groups of people? 88 00:04:38,236 --> 00:04:40,998 Is it ethical to make decisions that can harm humans 89 00:04:41,022 --> 00:04:43,497 creating artificial intelligences that will take control? 90 00:04:43,535 --> 00:04:45,050 Who owns the technology? 91 00:04:45,160 --> 00:04:47,586 Who audits or controls the decisions? 92 00:04:47,610 --> 00:04:50,296 These are some of the ethical dilemmas 93 00:04:50,320 --> 00:04:53,445 that need to be addressed as technology advances. 94 00:04:53,470 --> 00:04:54,505 And what about languages? 95 00:04:54,530 --> 00:04:57,587 How does this look from minority language communities? 96 00:04:57,626 --> 00:04:58,955 There is no round answer about the 97 00:04:58,980 --> 00:05:01,710 minority languages and artificial intelligence: 98 00:05:01,980 --> 00:05:04,346 For example, automatic translation systems 99 00:05:04,370 --> 00:05:08,472 can facilitate communication between speakers of different languages, 100 00:05:08,498 --> 00:05:11,312 and that could have a positive effect 101 00:05:11,336 --> 00:05:13,510 in the care and promotion of minority languages. 102 00:05:13,560 --> 00:05:15,648 It is possible, thanks to these technologies, 103 00:05:15,673 --> 00:05:18,265 to be able to receive any content in our language, 104 00:05:18,289 --> 00:05:21,470 without the need for anyone's translation or dubbing work. 105 00:05:21,630 --> 00:05:23,190 But all that glitters is not gold. 106 00:05:23,430 --> 00:05:26,156 Artificial intelligence systems are biased and 107 00:05:26,180 --> 00:05:30,960 There is also the risk of reproducing linguistic discrimination. 108 00:05:30,985 --> 00:05:34,853 It is likely that there is not enough information on minority languages, 109 00:05:34,878 --> 00:05:37,928 and therefore they make more mistakes than in other languages, 110 00:05:37,952 --> 00:05:39,829 or have lack of details, 111 00:05:39,853 --> 00:05:43,618 and can't distinguish details about smaller linguistic communities. 112 00:05:43,642 --> 00:05:46,280 There are around 7000 languages in the world. 113 00:05:46,430 --> 00:05:48,097 How many of them will have 114 00:05:48,121 --> 00:05:50,880 the opportunity to use the capabilities of artificial intelligence? 115 00:05:51,070 --> 00:05:53,110 For example, the models used 116 00:05:53,134 --> 00:05:55,844 are intended for data-heavy languages 117 00:05:55,868 --> 00:05:57,654 those linguistic communities 118 00:05:57,678 --> 00:05:59,816 that tend to have more technological resources. 119 00:05:59,840 --> 00:06:01,870 That's why it's important, 120 00:06:01,890 --> 00:06:04,451 in addition to having its own data available and free, 121 00:06:04,475 --> 00:06:07,615 to investigate models that require fewer resources 122 00:06:07,639 --> 00:06:11,350 or collaborate with other linguistic communities with similar characteristics. 123 00:06:11,400 --> 00:06:13,210 Scared, delighted? 124 00:06:13,250 --> 00:06:14,699 What do you think? 125 00:06:14,723 --> 00:06:17,850 Everything goes very fast and it is not easy to keep up. 126 00:06:17,874 --> 00:06:20,750 But don't forget, information is power. 127 00:06:20,930 --> 00:06:24,900 We will try from here to do our bit so that everyone can access it. 128 00:06:25,220 --> 00:06:27,926 Understanding gives us sovereignty.