 Hello everyone, I am Sanjay Gupta. I welcome you on Sanjay Gupta Tech School. So much awaited bootcamp is in front of you. So as you all know like I announced this bootcamp way back and today we are going to have this overview session. So basically as everybody know this bootcamp will be based on AI and in recent days you are hearing lots of things related to AI. So Salesforce also launched one certification related to that. So as on my channel lots of stuff is like related to Salesforce. So I decided why not to learn AI so that we know the basic fundamentals of AI and whenever the products will be available with Salesforce ecosystem will be provided by Salesforce so we will be able to use them in much efficient way. So as you can see here a quote is written AI is a way to the present and the future. So this is the need of the hour like we need to know the basic fundamentals of AI how it works what important things we need to understand related to AI. So to understand all these things I have Nikita Chandramani with me. So she is actively working in this field and she has lots of knowledge and lots of practical implementations she has done in terms of project. So she will be delivering most of the sessions and wherever my inputs will be needed respect to Salesforce so I will be pitching in and will be sharing some knowledge with you. So let me give a chance to hand over Mike to Nikita so that she can introduce herself so that you know about your instructor what she has done or what she is doing in the community. Right so over to you Nikita please introduce yourself and warm welcome on my channel. Over to you. Thank you thank you so much. So it has been a pleasure working with you and have a talk with you. Hi everyone this is Nikita Chandramani and we are going to learn today about the artificial intelligence which is a very hyped up topic but it's not only about the artificial intelligence you'll be understanding a lot other things also like machine learning and all the other things that are surrounding this topic artificial intelligence. So it re-sessions are going to be holistic development about you know on the topic and around it. First let me just tell you that how and what am I doing currently. So I have been into this education industry for like six years now and have been a mathematical instructor have been training students in mathematics and programming was one of the things that I love but that was close to my heart but in sort of like you know in between when I was concentrating more on mathematics this programming got a little bit set back but then this data science got my attention and then I got into it gradually learned about the things that are there and that were you know having so much of a hype. So it is all about the current scenario that we are going to learn about and yes I mean this is what I have been doing that my students are in India and overseas and Australia is like one of the countries that I am a student I mean my students are there learning from me maths and of course the programming. So a combination of mathematics technology and programming is what we are going to look forward to. So this is going to you know be a you know as I mentioned that this is going to be a holistic development which is not going not only going to be in programming but also technology plus maths and programming will make you certainly ahead of certain people and you will be having a lot of job opportunities in future. Let's get ahead with what I have for you today that is first thing is yeah I just want to pause you a little bit here. So guys if you want to know more about Nikita so you can just search her name on LinkedIn and I think as she is having unique name so with first name and last name you will find it in the first go. So just explore the profile and you will get to know like what she is doing so that you have enough confidence because if you have enough confidence and trust in your instructor then it will be like fruitful for you to join the sessions and you will have more trust in understanding the topics right. So now we are going to discuss about what modules we are going to cover in this bootcamp. So over to you Nikita. Let's discuss about these modules and Nikita will be covering like job roles as well. So that will also motivate you to understand AI and lots of people are like thinking will there be job cut because of AI so all those sort of things will be discussed in the bootcamp. Okay go ahead. Let me address this question with the very first question as I already mentioned that there will be job cuts in the market. So we can see that there is alteration in the job sector that you know whenever any kind of revolution comes in we have a transformation and then we shift our segment to a different section. So I wouldn't say that there will be job cut and reduction in the jobs but there will be lot of jobs that will be there in other segment that is the most hyped up section which is AI. So it is not only that we are going to see the job cuts we are going to see alteration and we are going to have a shift in the job roles. So it is not going to be like jobs in future of course there are lot of jobs in healthcare section in every every domain that you look for AI is going to control a certain area and you guys can work in any of the areas if you are efficient enough in the artificial intelligence segment. So now here are the modules but this is a set of module that will cover gradually it's not only for today it's like a gradual you know fragmented upliftment of you people and your skill set. So AI fundamentals you have got and then you have AI capabilities in the CRM's of course you know that Salesforce is the CRM so we have to you know it's not Salesforce is having something like AI AI is going to be attached to the Salesforce to bring the best customer relationship management across. So this is what AI is going to do with Salesforce just as it has been doing with other sectors like healthcare and then technology and then you have got various entertainment OTG platforms like Netflix and then you have got chat GPT. So everything has gotten up as an attached AI to it and that has what increase the performance scenario. So it's not that everything is different it's just that the performance has been raised just because we have a technological advancement that is artificial intelligence and the last thing that is data for AI. So I would tell you that this data is going to be the life blood data is going to be the boon for all of us because everything we are going to train every machinery your equipments whatever you're going to make the algorithms that you're going to design everything will have to have a data set in it. So larger the data set more is the efficiency of your algorithms going to be soon as we will finish section one of our boot camp you will be able to have a basic understanding of artificial intelligence and machine learning then you will also have a clarity of fundamentals of neural networks because there is a lot to study in the networks but your fundamentals will be clear after this section of boot camp. Then how AI and Salesforce work then something called Einstein that is the combination of AI and Salesforce and which has changed its name to different one. So that is going to be introduced in the later section but not today. So this is what we are going to discuss. Then you have Salesforce associated associate certification preparation which is you know this is what you are preparing for in our lectures. And I think this is what the outcome going to be after our first section finishes for this boot camp. Perfect. Yeah, I think this is the important slide job. So everybody like doing learning things so that we can have a job. So now Nikita will be telling you like after learning AI ML these things or data science. So in which like on which role you will be able to work or if there are some sectors so she will be letting you know those as well. Okay. Yeah. So guide everybody so that they know like after learning AI ML what they're going to do in future. So if you are an individual as my slides here that all the different individuals who enjoy math technology and problem solving skills. Remember people problem solving skills is a must because you know in today's scenario there are a lot of problems but solutions are less. So if you are the one who is able to provide a solution which is a very optimized solution and you are able to do it with ease. So that is where you win half of the particular scenario. So this is what they look for. So problem solving skill is must and this like this with mathematics and technology is going to take you all over the horizon. So if you are an individual who enjoys math tech and problem solving then this course can be very much rewarding for you in terms of career transition into various fields discuss over here. So the first one is AI research scientists research scientists would definitely require a lot of skills in terms of learning of algorithms which are related to the AI plus algorithm related to the ML. So you should have a particular set of algorithms very much true. And of course as soon as you will start researching into this process we will learn more. But the milestone that you have to touch goes through the algorithmic part. So you have to be efficient enough in order to apply those algorithms. Second is business intelligence developer. Now there are people who are into business but they also want that their business should comply with the today's scenario. Now what will happen if you know if there are certain businesses but they only are offline and into small sector. Of course if a business is scaled up like you know you can take these shopping apps I would name them. So you know if you are going to have or if you're going to be somebody who is scaling up their business you need to have certain knowledge of artificial intelligence software and you can combine them so that you are able to give the best user experience to the user who is using your application in any of the you know media's. So this is I think this is one of the best things to be you know applying your business skills also and your technical skills also and if you want to get into the business intelligence developer domain I think this is one of the interesting fields because I also have my own business and you know this is what one of my key interest areas lies in. And of course there are a lot of other things to be discussed about as I mentioned inside has a lot of things to you know recommend the users about the you know like for example you opt for a jeans and you are looking for a certain type of jeans and you click on it and then you have got 10 other options resembling your options. So you know this is something very interesting when we talk about the artificial intelligence what is going on at the back end and I think this is one of the most interesting things that you are going to work in. The next thing is artificial intelligence software developer. These are you know close to what an IT software developer but just that again AI and stuff the process and if you're going to learn about it you'll certainly be you know in this horizon. So we can compare it with the ID software developer but yes the AI when you know is imposed with that particular situation and a person then it of course creates certain amplification. Then you go for robotics engineer again it requires you for it requires all the algorithmic part of the machine learning to be learned. Then you go for computer with an engineer. OK. So this is where our visual information is coming. So you understand your visual information that helping you know that are software that are at the traffic light systems and there are a lot of special detection software that are coming up these days. So you know when you have to check for like what was the speed of the object. You know if you were present there or you were not present there these facial detection object detection softwares are going to work there in those particular streams. Right. So this is what computer with an engineer does takes care or she takes care about everything that is being percepted by the particular mechanism or machinery or camera. But you know this is what it is all about. Then you go for AI product manager or consultant hardware engineer. Now what a hardware engineer does is OK. So where does this data that you are having data set and then you are processing the data. All of it is going to be saved somewhere. Where is it going to be saved. It is going to be saved in the graphical processing unit. And you know which one is the company which is the deliver which is going to deliver a you know thousands of thousands and you know much more than that graphical processing units. And that is in media graphics because it has the best quality graphic processing units. So it is one of the most prominent graphic GPU producing companies and of course tensor producing units. Tensors we will study later because these are the cubes that are going to you know have lot of data is into them. So we are going to learn about it the tensor flow that they are going to learn about it in the ML team. Then you have got artificial intelligence education and training and this is what we are doing over here. We have been incorporated into this process of education and training. So sideways this is what we are doing. And lastly you have got ML but this is this is just a small list of the best job that we could have. Of course there are other jobs that are going to originate. So you don't have to be restricted on this. It is a profile and look for these things. But of course addressing them to be there and you can be any one of them if you have proficiency in your technology that you are learning for. Okay. The next. Yeah. So sorry to interrupt before we proceed. So I think everybody got to know like what is the idea behind this bootcamp. So Nikita already explained you different modules that we are going to cover. And you also got to know the outcome of the bootcamp and different job profiles where you can work as as AI like whatever you say like consultant engineer developer. Right. Now like brief overview about AI Nikita will be giving you right Nikita. Yes. Yep. So basically till now what we covered like what we have for you. So you can consider this session like brief overview about AI as well. And from our day one session we will be deep diving into the world of AI so that you can understand all the concepts related to AI and you get to know like how we can incorporate these features into different ecosystems. Right. So over to you Nikita once again. So you can see that there is a machine which is over here. We call it a robot and its name is Lisa and it's wanting to introduce you guys to how it has been generated and what are the programming things. Now of course we're not going to talk about the hardware because hardware is not to be discussed with this intelligence because if you're talking about the integral software things and how they work. Let's keep it a slightly different way. Now this is what is going to make you understand about the fundamentals of the way it works. Right. So let's dive into the session. So as we know that you know anything in the world doesn't come up like that. It has got an inception point and what was the inception point for this artificial intelligence to come in. Let's look at that. So there was a person there was a scientist who was Alan Turing and in nineteen fifteen he came up with the idea that can be transmit or induce some intelligence like the humans are having. Can you simulate that in a in a machinery in a robot or in any kind of mechanism. So that is where this whole AI thing came from and this Alan Turing he developed an empirical test of artificial intelligence which is more appropriate to the computer scientist. Now this person as I mentioned that he was a computer scientist and he was endeavouring and while he was going through his processes of endeavors he came across this particular thinking that can we do so can we simulate this kind of thinking and can we make a machinery work just like a human work. So it's not from today. It's just that today it has been but it's since 1950 the work is on that we have been developing slowly and slowly but now the development rate has changed. So this Alan Turing got into the test of the Turing test that this test is known as the Turing test of nineteen fifty. Let's see what it does. So in this Turing test there's an operational test this is the test which is going to be in between two human species and a machinery or you can call it a robot. And there's a moderator present over here. This moderator can only interact with these three species in the textual format. You know it cannot interact with it just like that. It cannot go and give hints to the computer. It cannot go give hints to the human beings. It cannot do anything except for the technical or textual information that it can forward to these three species. And there are two human species and one machinery present over there. So this test involves a human interrogator who is in one room and another human being in the second room and an artificial entity in the third room. So the interrogator is allowed to communicate with both the other human and artificial intelligence with only textual devices. And this is how he can interact as a terminal. Right. Now the interrogator is asked to distinguish the other human from the artificial entity based on answers that have been produced by the humans and the entity with the machinery. If the is not able to differentiate between the answers of the machinery and the human being. This is where our achievement lies that now we have accomplished the task of simulating the intelligence into the machine. Right. And if this is this is a puring test in which if it has been passed we say that the artificial entity is intelligent. So this is where everything that we are discussing today came from actually. Okay. All right. So we know that the puring test avoids physical interaction between the interrogator and the artificial entity. So the assumption is that physical interaction is not necessary for intelligence. If the interrogator is provided with visual information about the artificial entity so that the interrogator can test the entity's ability to perceive and navigate in the world we call this test the puring test. So this is how we consider or we figure out that if the entity is intelligent or not. This was the inception point of AI. Right. Let's dive into what AI actually is. As I mentioned that artificial intelligence is simulating the human intelligence into any computerized species, robot, any equipment which can on the basis of algorithms figure out or understand what the real world problems are and is able to answer them with the health provided. Right. So AI is a broad field of computer science that aims to create systems capable of performing tasks that typically require human intelligence such as understanding the natural language reasoning, problem solving and decision making. And this is what we are now wanting to introduce into the machinery and then we found that this is how a human has been created with so many neural networks inside the nerves everything the network that is so much complicated and this network when we tried to implant and induce in the machinery we got the actual worth of how a human works. Right. So with AI the focus is on making computers perform cognitive human like tasks like decision making and perceptions. So for perception you will have a lot of models that we will study later on in our higher you know coming up programs. Now we'll see what is the scope of artificial intelligence. AI that is a vast sea and compasses other things also out of which one of the most prominent things that machine learning. So machine learning is a subset of artificial intelligence to be very precise about there are instances where we kind of forget like what is AI what is ML. So here you are going to understand that ML is just a sub part of AI. It is not like it is not greater than that or its horizon is not vast than that. AI has the vast ocean and into it we have got machine learning as its part. So we can conclude that ML is a subset. ML is a subset of AI as it focuses on the development of algorithms and models that enable machines to learn from the data. Now look at it very carefully that in our previous scenario when we used to play video games when we were you know in our age of maybe 10, 11, 12 years of age we used to play video games and sometimes when we did not have a partner with us to play we used to do with computers isn't it. So we used to play with the computers inside and we used to think that you know there's a person inside the world who is playing with us but it was just a program thing that happened there and then and you know it was taught about like what can be the you know if I'm going to play chess or something like that for what is going to be my first output for it so what is going to be their chance now they are going to play so this is what it was about we used to program them however now we are wanting them to learn from the experiences learn from the images shown learn from the language is taught so just like a kid who was shown things this is how now we expect machineries to learn about right so instead of explicitly programming a machine to perform a specific task MN systems non-patterns and make predictions or decisions based on the data provided to them just like we were when we were kids we were given apples images then we were given you know mango images to learn about that okay this is what a mango looks like this is what an apple looks like now we are going to do the same with the machines also and this is the new way to make the machines learn right this was simplistic approach about artificial intelligence of course I understand that it's not very easy to grab that in the first single go but slowly and you know with performing different tasks you will be learning a lot about AI we can call here for just a moment just to see if you have any questions for us and then we can continue in the machine learning thing yeah I think we can continue just one question that I can relate as of now so Tushar is asking like what background is needed to follow this course so is there any prerequisite that we need to follow or like anybody no no if you know mathematics that is up to year 10 and if you have 11 12 experience also for mathematics that is great but if you do not have it you just have to you might just have to study a bit about mathematics other than that statistics probability that will come in your mathematics domain and of course the linear graphs, regression and algorithms so in that you might have to learn mathematics other than that everything is okay good to go if you are you know a computer science and you know or if you are not that let's take that somebody who is not from the technical background they also can follow this course but it will take a stipulated time for you to become a great engineer in the ML or AI it's not like you know five months four months thing and you will be a great one at it if you want to be something or somebody who is really really into you know what you are wanting to go for when the Netflix these kind of big companies then you have to give yourself some time your brain has got certain pace at which it works and of course there are a lot of steps that are going to come up in this boot camp and this is somewhat a year long process but after the year you will be having great knowledge about it. Yeah thank you for that answer. So Mohan basically in that excel sheet you are right four weeks sessions are there but that doesn't make this boot camp complete so we will be gradually adding more topics in the session tracker right so just follow the beginner part and once those sessions are done so I will be updating that sheet for sure. Right so the fundamental parts will be clear in four weeks that I can show like the fundamentals of algorithms, fundamentals of ML you know this basic part will be clear but as I mentioned that if you have higher goals you have to study a lot so this is what it is about. Yeah exactly so I would insist like everybody if you want to learn about AI thoroughly so at least attend first four weeks session and if you want to deep dive you want to understand it technically then you can follow the other sessions as well and if you are into the Salesforce ecosystem anyhow you need to learn everything about AI so for those who are already working in Salesforce ecosystem want to upskill themselves want to know the facts want to know everything about AI so I think this boot camp will be boon for you okay so I think we can proceed further all right so now we'll get into the sub domain of article in intelligence which is machine learning right I have already told you about how a machine learns this is now up to us how are we wanting it to learn right so there are various algorithms for the machine learning to make the machine learn in a certain way you need to give it a way how to learn now while doing that we realize that we human beings as well as we were given this image of Apple so we used to see and we used to adopt that okay this is what an apple looks like but how to make a machine know that this is what you have to remember and this is what you have to you know have it in your data that you know this is what an apple looks like so first we need to put certain things into it we have to have it booted and then you know we need to apply certain elbows to make it learn properly what exactly we're looking for so that we'll get our work done from it even like you know if you're going to show for example I take an example of a mechanism where we identify that rotten apples and red apples good apples so if we have to differentiate we have to make it efficient enough that it can differentiate right in rotten apples and the red apples so only that way it can you know differentiate and put separate buckets for rotten apples and good apples and that way human tasks are reduced so a human is certainly necessary but not a lot of them so here the tasks are reduced and we can trust the machine so in order to have a good trust build-up machine we need to work on it algorithms a lot let's see learning is the ability to change according to external stimuli and remembering most of all the previous experiences so machine learning is an engineering approach that gives maximum importance to every technique that increases or improves the propensity for changing adaptability adaptably so with experiences with more and more images the machine is going to learn how to differentiate between rotten apples and good apples right and then it's going to be performing its tasks even better than it performed on the first day right so this is what it is going to play a role of a kind of a human being only so in simple terms a minimal subset of AI that focuses on the development of algorithm and statistical model not to forget not to be forgotten statistical models that enable computer systems to improve performance on a specific task through learning from experiences or data as I already explained without being explicitly programmed so we here are not going to program we are not going to put software developers and a lot of programmers over here to program the machinery however we are just going to give that give that a lot of data and we are going to make algorithms for it for the smallest problem that is the separation of rotten apples and good apples okay so the objective is of teaching computers to learn from and make predictions or decisions based on data rather than relying on the programming right so this is what a nutshell program machine learning would look like now look over here when we have kids you know our parents teachers used to tell us about a lot of fruits vegetables and alphabets like what was that first thing we learned about we learned about how a looks like and then first our reading skills were improvised then our writing skills were improvised and then we kind of started to perform better then we were given numbers to learn so we were taught that this is how one looks like this is how to looks like and then we were examined so this is what right now is going on we are giving a lot of data to machines then we are examining examines and that's called beta testing of the different algorithms press machinery plus robotics whatever you call it then this scenario particularly is called the making machine learn that's what is ML all about but I'm sure that what or how easily I am able to deliver it it is as deep as you dive into right so as you will look into the algorithms the applications there are these algorithms applied the scarcity will be created and then you will be able to learn even more so this is what our tasks would yeah just just sorry to interrupt so just to add on like that picture is very good and if someone wants to explain ML in easy way so I think it is it is a good if anyone can remember this image so they will be able to explain like what ML is right okay yeah go ahead with the algorithms those are available with ML so top 10 ML algorithms that we have are your linear regression this human tree random forest addables so these uh so these algorithms first is for regression regression is a process then you have classification as I mentioned that you have to classify when you have to classify you you can use these uh algorithms then you have unsupervised algorithms so this is just a basic set of algorithms that we might be able to cover in four to five weeks uh when you are you know onto this or you're boarded on this kind of program but as I mentioned that these are top 10 ML ML algorithms of course you are going to learn a lot in these ones but there are other algorithms too which we cannot you know uh ignore just because we have to amp up the process of learning so this we will include in our sessions so that's why I have included this just slide to make you remember or know about that what are we going to do in the sessions however we are not going to go deeper into it today and this is just an overview session and we'll just proceed into the very light way today so let's meet the Netflix our first of the first one and most prominent I should say OTT platform that everybody today has been you know aware of so this Netflix what does it do in order to get the best of the audiences all across the world you know and what has been one of the integral part of Netflix so that it is able to give you the desired results so let's say that I am separating AI from Netflix and I'll show you how does it or I will explain you how does it look like so you know if I have to search for any picture let's say do so this is what I'll be reminded of that okay I have to watch a movie and it is its name in books I'm going to type it up and I want to search it and I am going to find it okay I found it and I'm going to click it and I'll watch it if you know I am somewhat I am not able to recall what were my favorite movies or what you know my user experience has been and if I'm not able to recall and I just you know every time I cannot search of a movie in a thing and that is such a boring process that I have to search and I have to look for what I'm looking for rather than I would look for that you know can you give me some recommendations on the types of movies that you know look like doom so then I'll be you know watching and sticking to the platform even more otherwise I won't stick I will be coming back as soon as I'll recall a movie and then I'll be coming back back and I'm going to watch it and I'm going to go away but Netflix since it wants us to stick to it what does it do it is going to give me the recommendations those recommendations are based on my user experience my history what I have been watching so suppose I like 1950 or 1990s movies and you know 90s songs 90s movies some of the things that I like are suppose K3G is a movie so if I'm going to click on it I'll get another recommendations like kuch kuch hota hai sort of movie dil wala juna nia le jayenge movie so you know this is what the AI has done it has given us access to so much of the data and on behalf of that data I'm able to judge that you know what am I going to show to my user so that I'm able to get more of the audience interaction user experience so to maximize and beautify the user experience we as a team of Netflix what did we do we got the best technical team of AI and ML and we got into the science of data got the data from human beings and you know as soon as my data got better what lot of data sets taught my AI things AI mechanism to be efficient enough to read the data and then you know come up with the desired outcomes based on the ratings based on the user experience based on the history of the user that's how Netflix works right so now you can go through the sheet Netflix's world's largest OTT service found on 29th the form is 1997 which uses the machine learning data science and AI tools to provide maximum user satisfaction if anyone's interested in working in technology for Netflix or other similar platforms could have AI certification as well as extensive AI training as I mentioned that there has to be an extensive AI training because otherwise it's just of no use as a result you can assist the streaming service in staying ahead of the competition so obviously Netflix wants that you know I should be the one who is most watch so I am in search for people who are best at the art and machine learning and they are going to onboard those people and if you are having a holistic approach you are going to certainly be chosen in the team so what does Netflix actually do it gives us a personalized movie suggestions based on the user's history watch history and compare it to the movie preferences of other similar movie is and then it is going to come up with the recommendations for you guys right second is provide high quality streaming so if you are an OTT platform which is not even able to provide a streaming process that is consistent enough and without any buffering so that is when you lose your customers and of course I don't want to lose my customers so I am going to come up with the best feeling and this is what AI is going to do to choose the best streaming way so that you are avoiding any kind of buffering and this is what is done over here so in fact why would somebody in this era pay for a service that cannot provide them with the continuous streaming so this can actually be great the user experience and cause them to never use a service again hence Netflix technical team is actively implementing the AI and other tools to address the issues like right y'all can just go through this sheet for once and reread and absorb what is there in the sheet yeah so it is it is there in the video so if anybody wants to read it again so they can just go back and view it yeah I think we can jump on to the next piece that is chat GPT I guess yes chat GPT the another very sort of hyped up team and hyped up thing that where it came and everybody was so much into it that you know we need to learn chat GPT we need to learn how the way it works isn't going to take away so many jobs from us the content writing the content writers are so much afraid of losing their you know jobs and job security will that stay but we now know what has it done so what is chat GPT and how is it going to transform your life well this is just the assistance that you can use for it is not of course going to replace any kind of job however if you are not so good at content writing that you are you know at an elementary or preliminary stage of content writing then that is the case where chat GPT can have you know certain implications in certain way it can replace you for sure but when you know that your thinking skills are more advanced your thinking skills are unmatchable and you have practiced a lot so this chat GPT is going to have hard time replacing you okay so you do not have to be afraid if you are efficient enough as i mentioned already since since the lecture starting has been mentioning that you know if you are a person who is efficient enough you can take help of the chat GPT come up with a better ideas right but it is not going to replace you if you have a solid foundational unit with you so what is chat GPT it is a text mix based module which is going to help you out in everything every task you know it's going to deliver results for it so you just need to input text into it like for example help me making a cake so it would do it for you make a cake for me of course it's not to make a cake for you but it's going to give you the steps to be you know like to be very much efficient in making the cake for example i would say explain biology to me or explain chemistry to me this is going to come up with great results and short points you know you will be having good time learning them so that's what it does for you it assists us in various segments of our life right here is what it is about it is a language model developed by the open AI based on GPT architecture designed for natural language understanding so if i'm going to let's say give it a prompt saying that i want an image of a person creating or you know playing football it will say that i am a text-based tool i am ask me everything in that text format and ask me the text base i cannot give you the images so it is not going to give you any image however it is going to help you with the text that okay if you want something in a text i will be there to help you i will deliver the text the programs i'm going to design for you and a lot of other things like you know if you have to create a presentation also in on the visual basic so it's going to write a code for you as well but of course it is going to be having a lot of bugs so here again i told you human to assist it can do it can do the assistance but it cannot replace the humans as of now because the programmer is needed to get devoid of all the bugs okay so it's designed for natural language understanding and generation making it well suited for a variety of conversational ai applications chat bpt is capable of engaging a text-based conversation with the users not image-based not anything else just text-based conversation with the users and providing the responses that are contextually relevant and coherent now what is it about it it is a model it is a model that has been trained on a wide range of internet text which allows it to answer questions provide information generate text like you can literally ask it that give me the interview questions of the mba interview right it is going to give you the interview questions for the mba if you are going to go for the technical interview round you can ask him or ask it i'm sorry that i want some questions on the on this topic let's say dynamic programming so i want some questions on the dynamic programming or send some questions or you know just get me some questions of the dynamic programming explain what is dynamic programming this all of it is going to give you results of the dynamic programming and you will have great time learning dynamic programming from there right so it is used in chatbots virtual assistants customer support applications and more to provide human-like interaction and assist users with the queries or tasks so this is what chat gpd is going going to do for you it is going to assist you in everything every manner it can and it has got knowledge or information up to till september 2021 if you will ask it what is the score of the match today between sa and england so it's not going to give you that because it's not what it is trained for that's not a dynamic thing that's for google let's leave that for google so google is going to give you the results for the match and whatever dynamic things that you want but here chat gpd is going to give you information only days up to september 2021 that's it this was a little bit about chat yep i think you gave like you picked two ai tools and gave good explanation and people are already appreciating the way you are explaining because in easy way you are explaining the thing so thank you for that so i think maybe we can wrap the session because we are ahead of the time so if you can quickly cover a couple of more slides and i think i think in detail we will be covering everything from day one so that people are going to people can understand the technicality of this ai in those sessions all right all right all right so everyone has google maps on their phones and google maps itself is not a ai thing to do or ai tool to to be played with however it is then ai attached to it ai integrated with the google maps it's going to give you greater results like for example if you had great traffic in a particular area like how to know that you know what is what is the current you know status of that area will i be able to cross that road or will i or should i take some some route which is efficient enough to be crossed and you know it's it's free from all the loopholes and their path holes whatever is there so you know if your tool becomes free or your tool becomes efficient enough to tell you that this is the best way possible without any pathhole without any traffic without any kind of problem and you will be able to get your home in a better stipulated time that is where ai helps google maps to map the way to home right i'm very on to which alexa and siri we are very much aware of of them so siri in your iphones if you're having one or you know my books wherever you are applying maybe ipad so you can just ask siri provided your things are unlocked and you you know you're from a from a dispute only you're going to ask siri hey siri can you play bole churya with me of course it can because it is going to go map up with this any application maybe spotify and then it's going to transact particular song for you and it's going to play for you so what is it it is going to recognize your voice it is going to decode it it is going to have a certain idea about it then map it up with the same thing in the application and then play it for you so all of it looks very easy however it is very tricky to understand every process and go through it because it is a long way however it is done in just a you know second of time so it doesn't give us any hard time to play with it and hence it's so popular right that's about little bit about alexa and siri same with alexa what does alexa do it makes a task easier like you you know you just say it that switch on the lights and it does it for you if your home is a smart home and you know this is alexa which is attached and has got a combined form of you know relativity with every equipment in your house so you know if you're going to kill it that you know can you please switch on the ac so you are sitting here and it is recognizing your voice accepting your voice and then switching on the ac all of these tasks are you know a tabular and they are very much interrelated tasks which has to be explained in a you know very uh fragmented manner one by one but certainly now you will have a different way of understanding these things let's have the last slide of the day which is the difference between the artificial intelligence machine learning and deep learning deep learning though we did not go through it in a very fast track manner the way we did for AI and ML but of course in coming up sessions we are going to have a deep conversation about all of these again so AI refers to the computer systems and algorithms that mimic the human intelligence to perform tasks make decisions learn from the data and solve the complex problems and its subset comes up with the ML ML is that particular thing which is focused on making your computers equipments learn about the various tasks and various data sets and then it's going to produce a desired result finally a subset of ML comes in which is deep learning and is a subfield of machine learning that uses artificial neural networks which is also a great part of AI associate program with multiple layers of modern layers to modern and process complex data enabling breakthroughs in the past like image and speech recognition as i said speech recognition this was done by alexa and city because they recognize my speech and then map it up with the desired applications to play my songs so i think this is it for the day let's call it a day and we'll see you in the next class yep thank you nikita for setting up the stays for this bootcamp so i think those who have yeah so those who have attended attended this bootcamp this overview session they got to know like what we are going to do in upcoming days so guys be ready to understand everything fundamentals of AI in this bootcamp so there will be lots of sessions coming up in future so what we have planned so we will be targeting at least three sessions in a week so that you will be having enough content to watch and if a particular time doesn't suit you so the stream will be recorded and will be available on youtube so anytime whenever you get time so do watch all the sessions so that you can upskill yourself for AI ML and deep learning right so thank you so much for joining the session and thank you nikita for sharing your knowledge because we are yeah because we are doing it for the community totally free of cost so it takes courage to come online share the knowledge to the community without expecting anything back so huge shout out to you and hope this bootcamp will reach out to lots of people and I can see people are attending across the globe not from India people are attending from US as well so after ending the session you can just go in the comment section and you will see the feedbacks so you will have some motivation right so thank you for this so thank you everyone once again so see you in the next session yeah I will be updating the session tracker for upcoming sessions okay thank you bye everyone