1 00:00:04,260 --> 00:00:12,180 In this lesson, we are going to talk about use cases for LM applications by industry. 2 00:00:16,840 --> 00:00:17,740 So. 3 00:00:19,160 --> 00:00:25,340 First of all, I wanted to tell you what can lm do? 4 00:00:25,730 --> 00:00:26,840 LM do right. 5 00:00:26,840 --> 00:00:30,710 So you already know ChatGPT. 6 00:00:30,710 --> 00:00:31,370 Probably. 7 00:00:31,370 --> 00:00:41,450 And you know that with ChatGPT, you can create content, you can answer questions, you can read and 8 00:00:42,050 --> 00:00:46,700 summarize documentation, you can have a conversation, etc.. 9 00:00:46,700 --> 00:00:46,940 Right. 10 00:00:46,940 --> 00:00:56,510 So how do we apply all these things in real life, for example, when we are talking about content creation. 11 00:00:56,900 --> 00:01:02,270 A what can we do in the business world? 12 00:01:02,270 --> 00:01:05,840 Well, we can translate content. 13 00:01:05,840 --> 00:01:10,670 We can create content for marketing, for sales, for different areas. 14 00:01:10,670 --> 00:01:16,010 We can, uh, use, uh, this functionality for brainstorming. 15 00:01:16,010 --> 00:01:22,790 For example, we are going to see real example in real examples in different industries. 16 00:01:22,790 --> 00:01:31,550 When we are talking about the reading capability of the LM, we we are talking about, uh. 17 00:01:32,370 --> 00:01:41,760 Tool, an application that can summarize documents for us, that can prove reading our documents, that 18 00:01:41,760 --> 00:01:50,010 can analyze our documents, etc. we will see a practical applications of this in different industries. 19 00:01:50,010 --> 00:01:59,280 When we are talking about the conversational functionality of LMS, we are talking about a the possibility 20 00:01:59,280 --> 00:02:05,400 to interact with humans, the possibility to interact with software applications. 21 00:02:05,400 --> 00:02:13,920 And we can have, you know, this a m lm application that. 22 00:02:15,710 --> 00:02:25,790 Is totally autonomous in the conversation with customers, for example, or that is working in a collaboration 23 00:02:25,790 --> 00:02:27,290 with humans. 24 00:02:27,290 --> 00:02:35,930 For example, we can have a chatbot that starts the conversation with a customer and solves the problem. 25 00:02:35,930 --> 00:02:40,340 If it is a simple problem or escalates the problem to a human. 26 00:02:40,340 --> 00:02:42,980 If the problem is complex, right? 27 00:02:42,980 --> 00:02:49,010 So we have this possibility conversation and we will see use cases of it. 28 00:02:50,170 --> 00:02:56,290 Then we have the different types of A applications. 29 00:02:56,290 --> 00:03:06,160 In some cases you will see that a some LM applications A have just a user interface, you know, and 30 00:03:06,160 --> 00:03:09,400 they are pure LM applications with a user interface. 31 00:03:09,400 --> 00:03:10,120 That's all. 32 00:03:10,180 --> 00:03:17,590 In some other cases, you will see that the LM application is part of a bigger software application. 33 00:03:17,590 --> 00:03:19,300 And this is growing a lot. 34 00:03:19,420 --> 00:03:21,640 And during the during the last months okay. 35 00:03:21,640 --> 00:03:27,490 So you see that there is a whole universe universe of possibilities. 36 00:03:27,490 --> 00:03:35,470 But let's see some examples, some real examples by a industry for example. 37 00:03:36,670 --> 00:03:40,270 In the software development industry. 38 00:03:40,720 --> 00:03:49,360 This is one of the industries that has been changed the most during the last year. 39 00:03:50,080 --> 00:03:54,490 Uh, we are, uh, an example of that. 40 00:03:54,640 --> 00:04:03,520 Uh, we have changed totally how we do things by the use of artificial intelligence in our, uh, tech 41 00:04:03,520 --> 00:04:04,840 consulting firm. 42 00:04:05,660 --> 00:04:12,710 So right now you can use Elm applications for learning to code. 43 00:04:13,630 --> 00:04:22,510 For problem solving around your, uh, code or program skills for code creation. 44 00:04:22,720 --> 00:04:24,370 And for code review. 45 00:04:25,000 --> 00:04:35,800 So today, every software developer or software engineer or whatever you want to call these professionals, 46 00:04:35,800 --> 00:04:41,380 we work with an artificial intelligence assistant every day. 47 00:04:41,920 --> 00:04:49,990 The whole day, from the moment we start working to the moment we finish our day. 48 00:04:50,610 --> 00:05:01,920 We are working with a junior developer developer assistant, which is our ChatGPT for or GitHub Copilot 49 00:05:01,920 --> 00:05:03,540 or many other tools. 50 00:05:03,540 --> 00:05:10,890 So you have right now in the market many artificial intelligence tools, which are LM applications, 51 00:05:10,890 --> 00:05:14,340 helping software developers to do our job. 52 00:05:14,340 --> 00:05:19,620 And because of that, we are able to work faster. 53 00:05:19,620 --> 00:05:24,120 We are able to work with many different programming languages. 54 00:05:24,120 --> 00:05:29,100 We are uh, instead of, uh, have everything in our heads. 55 00:05:29,100 --> 00:05:34,380 We can rely on the advice and the help of these assistants. 56 00:05:34,380 --> 00:05:38,430 So our job as software developers has changed. 57 00:05:38,430 --> 00:05:39,540 Absolutely. 58 00:05:40,060 --> 00:05:46,960 And by the way, if you are a software developer or a software engineer and you are still not using 59 00:05:46,960 --> 00:05:57,280 an artificial intelligence assistant, be careful because you are a obsolete a. 60 00:05:57,280 --> 00:06:05,650 The new software engineer is working on a daily basis with an artificial intelligence assistant, and 61 00:06:05,650 --> 00:06:09,520 you will see the difference immediately in your work. 62 00:06:09,520 --> 00:06:19,930 Okay, so in the software development industry, huge change in the customer service industry or department, 63 00:06:19,930 --> 00:06:21,670 also huge change. 64 00:06:21,850 --> 00:06:31,300 We are using LM applications to answer questions about products and services and to solve customer problems. 65 00:06:31,950 --> 00:06:37,860 In most cases, the LM application is helping the human. 66 00:06:38,450 --> 00:06:44,180 The employee is not replacing it in the customer service area. 67 00:06:44,330 --> 00:06:53,840 Okay, so we have in the documentation you have a specific cases like Google etc. and using this uh, 68 00:06:53,840 --> 00:06:54,770 approach. 69 00:06:56,380 --> 00:07:00,700 In the sales industry or the sale the sales area. 70 00:07:01,540 --> 00:07:12,010 We are using applications for qualifying leads to generate sales proposals even to close business deals 71 00:07:12,010 --> 00:07:13,390 in most cases. 72 00:07:13,390 --> 00:07:21,820 Elm applications today are helping the sales professionals in these tasks are not replacing them. 73 00:07:22,240 --> 00:07:30,100 So here you see a case like Salesloft for example, that is, using an Elm application to qualify leads 74 00:07:30,100 --> 00:07:36,700 to generate personalized sales proposals to track customer engagement and to measure the effectiveness 75 00:07:36,700 --> 00:07:37,960 of sales campaigns. 76 00:07:37,960 --> 00:07:39,340 These are real case. 77 00:07:39,340 --> 00:07:45,310 You will see more than that in the marketing industry or marketing departments. 78 00:07:45,880 --> 00:07:56,770 We are already using Elm applications to create personalized content like emails or social media posts. 79 00:07:56,770 --> 00:08:02,860 This is changing the way marketing departments are working, in the same way that sales departments 80 00:08:02,860 --> 00:08:07,600 are totally changing the way they work because of Elm applications. 81 00:08:07,600 --> 00:08:15,100 So you have here some real cases, like Frazee, for example, which is using Elm applications to personalize 82 00:08:15,100 --> 00:08:18,880 subject lines and the body of marketing emails. 83 00:08:20,660 --> 00:08:26,270 In the media and entertainment industries we are already seeing. 84 00:08:27,080 --> 00:08:34,309 LM applications to generate creative content for scripts, poems and code. 85 00:08:35,059 --> 00:08:45,530 So you have companies like OpenAI that has created a range of LM models that create text, translate 86 00:08:45,530 --> 00:08:50,720 languages, create different types of creative content, and answer questions. 87 00:08:51,970 --> 00:08:54,400 In the education industry. 88 00:08:55,720 --> 00:09:04,630 We have companies using LM application LM applications to create personalized learning experiences for 89 00:09:04,630 --> 00:09:05,470 students. 90 00:09:05,800 --> 00:09:13,840 We have the Khan Academy, we have the Newton Company, which is using LM applications to generate custom 91 00:09:13,840 --> 00:09:20,350 problems and tests for their students to create student work and also to provide feedback. 92 00:09:21,820 --> 00:09:23,770 In the healthcare industry. 93 00:09:25,100 --> 00:09:33,260 We are already seeing companies using LM applications to analyze medical histories and to assist in 94 00:09:33,260 --> 00:09:37,010 diagnosing in diagnosing health problems. 95 00:09:38,840 --> 00:09:40,610 In the legal industry. 96 00:09:41,000 --> 00:09:49,220 We are already seeing companies using LM applications to analyze legal documents and to assist lawyers 97 00:09:49,220 --> 00:09:51,170 in their research work. 98 00:09:51,910 --> 00:09:54,130 In the finance industry. 99 00:09:54,310 --> 00:10:04,150 We are already seeing companies using LM applications to analyze financial data and to make investment 100 00:10:04,150 --> 00:10:04,990 predictions. 101 00:10:04,990 --> 00:10:14,050 We will see one particular example of this in detail in our program a real case professional case, 102 00:10:14,170 --> 00:10:23,920 a professional case using an LM application for a financial data analysis, and also investment predictions 103 00:10:23,920 --> 00:10:25,000 and all that. 104 00:10:25,710 --> 00:10:27,720 In the public administration. 105 00:10:27,720 --> 00:10:37,530 We are already seeing the use of LM applications to analyze public documents, to analyze citizen sentiment, 106 00:10:37,530 --> 00:10:40,470 and to improve citizen engagement. 107 00:10:41,380 --> 00:10:49,450 Public administration is one area which is going to have a huge, huge a. 108 00:10:51,050 --> 00:10:58,340 Cases of huge number of cases of LM applications in the automotive industry. 109 00:10:58,340 --> 00:11:06,440 We are already seeing companies using LM applications to improve systems of autonomous vehicles, to 110 00:11:06,440 --> 00:11:16,460 improve voice assistance, to streamline the manufacturing process, etc. so our relationship with the 111 00:11:16,460 --> 00:11:23,300 car is going to change dramatically with the use of LM applications for everything, for maintenance, 112 00:11:23,300 --> 00:11:27,740 you know, repairs, diagnosis of problems, etc.. 113 00:11:27,830 --> 00:11:36,740 In biotechnology, we are already seeing companies using LM applications for protein design research 114 00:11:36,740 --> 00:11:37,970 for new drugs. 115 00:11:37,970 --> 00:11:40,730 Bioinformatic research. 116 00:11:41,870 --> 00:11:48,320 Etc. you will see the real cases in the documentation in the energy industry. 117 00:11:48,860 --> 00:11:56,840 We are seeing a lot of applications being used to optimize energy consumption for predictive maintenance 118 00:11:56,840 --> 00:12:04,400 in energy production companies, and also to streamline to streamline operations in the energy sector. 119 00:12:05,090 --> 00:12:14,450 In the research industry, we are seeing l'um applications being used to analyze large databases and 120 00:12:14,450 --> 00:12:17,570 to identify patterns and trends. 121 00:12:18,080 --> 00:12:19,100 So. 122 00:12:20,020 --> 00:12:25,780 You will see in all the documents you have attached to this blog. 123 00:12:25,810 --> 00:12:30,760 A lot of real cases for LLM applications in different industries. 124 00:12:31,500 --> 00:12:36,240 Take a look also at our blog in AI accelerator.com. 125 00:12:36,240 --> 00:12:40,740 Also the book, you know, 100 AI startups. 126 00:12:40,740 --> 00:12:43,170 You're going to see a lot of real cases. 127 00:12:43,170 --> 00:12:50,460 And it's amazing how fast this change has happened because we are only we are. 128 00:12:50,760 --> 00:12:59,100 This is the first year of applications and they are already in most or all industries and professions. 129 00:12:59,100 --> 00:13:01,230 But the interesting thing is that. 130 00:13:02,360 --> 00:13:09,620 Only the most innovative companies and professionals are introducing live applications. 131 00:13:09,650 --> 00:13:17,660 99% of companies and professionals are still not using any application, so there is a huge opportunity 132 00:13:17,660 --> 00:13:27,350 for you to help them understand the potential and to help them, uh, have these solutions built by 133 00:13:27,350 --> 00:13:31,430 you or by a companies that are working with you. 134 00:13:31,430 --> 00:13:39,920 Okay, in the next lesson, we are going to talk about use cases for LM applications in startups. 135 00:13:40,130 --> 00:13:48,500 Super interesting topic because startups usually show the way to the big companies. 136 00:13:48,500 --> 00:13:56,300 So even if you are not working in a startup or you are not interested in creating your own startup, 137 00:13:56,300 --> 00:14:01,460 it is going to be super interesting for you to see what is happening in the startup world. 138 00:14:01,460 --> 00:14:09,620 Because you are going to find interesting inspiration for your work in your company, your work as a 139 00:14:09,620 --> 00:14:14,390 consultant, you know, your work in public administration, whatever. 140 00:14:14,390 --> 00:14:23,780 So startups are leading the way in experimenting, innovating, trying new things, etc. so it will 141 00:14:23,780 --> 00:14:27,770 be very interesting what we are going to see in the next lesson.