1 00:00:05,830 --> 00:00:11,530 In this lesson, we are going to talk about what it is an LM application. 2 00:00:17,120 --> 00:00:25,430 The best way to understand what it is an LM application is to look at some of them in our website. 3 00:00:26,270 --> 00:00:31,610 AI accelerator com slash hyphen use cases. 4 00:00:32,060 --> 00:00:43,670 You will find a lot of articles talking about LM application real cases in the book 100 AI startups. 5 00:00:43,670 --> 00:00:53,150 You can also find a lot of real cases of LM applications that are making more than $500,000 in their 6 00:00:53,150 --> 00:00:53,990 first year. 7 00:00:54,020 --> 00:00:59,660 So it's very good for you to get familiar with all these real cases. 8 00:00:59,660 --> 00:01:05,150 We are going to talk more and more about them in the next lessons, but it could be good for you to 9 00:01:05,150 --> 00:01:06,920 take a look at them right now. 10 00:01:07,370 --> 00:01:15,710 And you will see that during this course, we are going to use the analogy of the engine and the vehicle. 11 00:01:15,710 --> 00:01:16,820 Many times. 12 00:01:16,820 --> 00:01:25,220 So the most important thing for you to understand is that an LM application is an app on top of what 13 00:01:25,220 --> 00:01:27,860 we call foundation LMS. 14 00:01:27,860 --> 00:01:32,420 So we will talk more about a foundation LMS later. 15 00:01:32,420 --> 00:01:36,860 But just for now, think of. 16 00:01:37,600 --> 00:01:40,600 ChatGPT as one foundation. 17 00:01:40,630 --> 00:01:48,460 LM so we are going to build applications on top of. 18 00:01:49,080 --> 00:01:51,480 ChatGPT and other foundations. 19 00:01:52,110 --> 00:02:01,500 Okay, so this is an LM application, an app we build on top of a foundation LM model like ChatGPT. 20 00:02:01,860 --> 00:02:07,890 And this application can be on the web, mobile or whatever. 21 00:02:07,890 --> 00:02:09,449 Robotics whatever. 22 00:02:10,470 --> 00:02:18,270 So the analogy of the engine and the vehicle is something, as I was telling you, we are going to use 23 00:02:18,270 --> 00:02:18,810 often. 24 00:02:18,810 --> 00:02:28,290 So the foundation LM like ChatGPT ChatGPT is a foundation LM we will learn more about that later. 25 00:02:28,290 --> 00:02:38,340 But the foundation LM is like an engine, and the LM applications are like all the vehicles and machines 26 00:02:38,340 --> 00:02:45,900 that use the engine to do things like the cars we build with the engine, the trucks, the boats, the 27 00:02:45,900 --> 00:02:48,060 industrial machinery, etc.. 28 00:02:48,780 --> 00:03:00,840 Okay, so right now just think about LM applications as these vehicles we build on top of the engine 29 00:03:00,870 --> 00:03:02,340 of ChatGPT. 30 00:03:02,580 --> 00:03:03,210 Okay. 31 00:03:03,990 --> 00:03:12,990 Very important thing to keep in mind as well right now is that LM applications are only one year old. 32 00:03:12,990 --> 00:03:15,720 So they are very, very young. 33 00:03:15,960 --> 00:03:20,310 This means that probably they are going to evolve. 34 00:03:20,310 --> 00:03:24,090 They are going to change a lot during the next years. 35 00:03:24,090 --> 00:03:34,290 So what we know right now for LM applications probably are going to be obsolete in three, four years. 36 00:03:34,290 --> 00:03:39,900 The same thing happened with the internet applications, the initial internet applications. 37 00:03:39,900 --> 00:03:48,450 So the important question for you to understand is how to develop LM applications. 38 00:03:48,450 --> 00:03:55,650 And the kind of LM applications we are going to build is going to evolve a lot during the following 39 00:03:55,680 --> 00:03:55,980 years. 40 00:03:55,980 --> 00:04:03,000 So we don't know where will we use LM applications in five years. 41 00:04:03,000 --> 00:04:09,690 We know where are we using LM applications today and in the following year, that's for sure. 42 00:04:09,690 --> 00:04:14,460 But we will see about the evolution of LM applications. 43 00:04:14,460 --> 00:04:21,240 But right now main concept A use this analogy of the engine and the vehicle. 44 00:04:21,240 --> 00:04:30,270 And also start getting familiar with examples of real cases in our website AI accelerator comm. 45 00:04:30,270 --> 00:04:37,860 And also in the book, 100 AI startups are going to see a lot of real examples there. 46 00:04:39,000 --> 00:04:47,610 So in the next lesson, we are going to talk about the myth of the prerequisites for learning a program 47 00:04:47,610 --> 00:04:48,360 like this one. 48 00:04:48,360 --> 00:04:53,670 Especially the second part, because since we are going to start talking about. 49 00:04:54,350 --> 00:04:56,840 Some technical concepts. 50 00:04:57,020 --> 00:05:01,100 It is very frequent that people get scared. 51 00:05:01,100 --> 00:05:06,560 The non-technical people get scared and think that this program is not for them. 52 00:05:06,560 --> 00:05:15,710 That is not the case, and it is important for us to talk a little bit about all these myths about the 53 00:05:15,710 --> 00:05:19,190 prerequisites for learning programs like this. 54 00:05:19,190 --> 00:05:23,960 So we are going to talk a little bit more about that in the next lesson.