Day 6 | Salesforce AI Associate Cert | Generative AI & Predictive AI | AI Bootcamp | Sanjay & Nikita

okay going live okay hello everyone uh welcome back to Sanjay Gupta tech school so today we have one more session uh which is uh based on associate AI certification which is uh launched by Salesforce so today's topic is generative Ai and predictive AI so in in last session U like you you got to learn what is generative AI so uh today uh there will be a comparison so I have Nikita with me uh welcome Nikita on the channel so hello sir hello everyone yeah so as you all know like U I'm running this tech school so she's also running her own Venture and you can see the name of that uh Venture silicon Heights so she is also doing some education related stuff there so more she will be talking about that so over to you and uh just give a quick introduction about this and uh you can continue with the topic thank you sir so hi everyone um I I'm Nikita as we have been doing so many sessions on artificial intelligence specifically for associate a programs so we are quite familiar now but I would like to just uh pin one thing which is like I would like to go to the LinkedIn where I am posting the content regularly so I would just take you there where you know we can um just have a look on what I am doing which is here so if you can see see this is a page of artif uh are you sharing your LinkedIn yeah um you need to share that okay one second yeah I have shared it so this is our page this is our LinkedIn page where you can see artificial intelligence by silicon Heights and I constantly posting some content over here can down this chat box yes sorry so if you all can see uh we have um I have posted some content over here which is of predictive AI then we had this uh Gans and uh It Started from the very basic of mathematics and then it went on to you know giving the idea about what all the intricacies of data science and artificial intelligence are so there is some content that is posted nearly every day you all can follow it and if you are looking forward to do the intense AI studies then certainly this is one of the stops that you can you know uh stop on and you can just check out the content and just uh read and embe all the important things that are going to be posted over here so you can see it is all about maths also the one that I taught on day one or day two um all the linear regression things are posted over here then we have some mathematical rules over here so it is not as per the associate program but it is the intense artificial intelligence program that we are going to run in future so you all can just have a look and follow if you like right that's the Page by silicon Heights and the name is artificial intelligence by silicon Heights this is it now we'll go into and uh we'll go and dive into the session and let's check what is there for yeah and uh I already shared the link in the chat so um I I hope everybody will follow this page as well yes so you will have lots of followers soon yes okay so yeah there we are so we have already discussed about generative AI in the previous lecture where I taught you about uh the intrig of how generative AI has made certain advancements in the current scenario in the current technology so let's just go through it and the first slide explains us the V diagram approach for generative AI where actually generative AI stands out so you've got the artificial intelligence as a holistic diagram wherein we have got a subset of machine learning then you have got a subset again which is deep learning and then finally youve got a super subset which is a generative AI artificial intelligence is the broader field which encompasses everything which is in the development or in the you know uh if you have to consider something where you are transferring the skills of human mind into a model or computer then that's what you know you can understand that artificial intelligence holistically is means that is what our Target is that we are able to transmit the human intelligence into the computer or robot or any other model okay this is our whole achievable thing then the super the subset that is through which AI attempts to achieve those targets is machine learning so a subset of AI that focuses on teaching computers to learn from data and make predictions so prediction means predictive AI we are going to dive into it and uh we are going to see how predictive AI actually you know what predictive AI actually is and how much of it is you know lately on the floor that we are going to study then you have a subset of machine learning that utilizes deep neural networks very very important because you are going to see the Gans generative artif adversarial networks and the llms and the other technical things so that's where the neural networks play the major role of uh transmitting and you know understanding all the content and then producing the desired output then you have finally got a subset of deep learning it this generative AI encompasses everything it has to have everything in order to generate a specific output which is in the form of text image music video code anything that you opt for so a subset of deep learning that specifically focuses on teaching computers to generate new and original content is the generative AI I hope this is now pretty clear to you if you have any doubts you may please ask and uh then we can follow to the next so I'll just have a recap of generative AI generative AI is a technology that takes a set of data and uses to create uses it to create something new like poetry now certainly what am I supposed to like what am I supposed to generate that generation has to be based out of something right so that training data set has to be input into the model so that I am now able to generate a text or generate a video which is uh new in the sense but it is based on the training data set it is not as new as you know coming out of totally raw content but it's like it is based on something however new we can say so that's what generative way technology is it takes a set of data and uses it to create something new out of it that is the different difference also from the predictive Ai and other traditional AIS so unlike the traditional AI models generative AI doesn't classify or predicts but it creates the content of its own so your llms and other models are creating the content of your own once you have input the desired training data into the models there you go with the generative AI tools now so as I mentioned that since you have generative AI uh get gotten into the nerves so we need to classify the generative AI on the basis of the functions it does so whether it is creating audio or it is creating video or it is creating images or it is creating code Voice or music anything that you opt for right so it has to give you these Generations these uh Generations are based out of the trained data set so you can see that the generative AI tools are visual text generators code generators audio generators these are all the types of generators that you can have and then visual generators will generate image generators video generators design generators so you will have um different different text to image text to video text to design text to voice text to music generators and also image to from image to another image you can you know transition from so this is what um it looks like right so I mean this is how the functional classification of generative AI Tools looks like so that then you have uh how does generative AI work so let me explain it to you like um in the previous session also and today also I'm giving a quite recap for it that how generative a models perform beyond the machine learning so you know uh the first sorry yeah go ahead go ahead yeah so the first thing is to uh have a chunk of data that you want to train your model with and then by utilizing multiple forms of machine Learning Systems models and algorithms and neural networks this gen is going to give you the outputs so these models are typically trained on large data sets containing a wide range of information such as text images and videos and by analyzing these sorts of patterns now my llm is ready to analyze the patterns and then create its own predict prediction or generation whatever it is uh referred to whatever it is made for so by analyzing patterns and relationships within the DAT can understand the underlying structure and generate new content similar in the style and context so your contextual Beauty has to be typically gotten into this model and now this is going to generate different uh you know uh outputs for you so I can also say that you do not have to have billions of billions or you know millions of content or data set for your llms what you can just do is be domain specific organization specific and whichever uh you know company specific you are you can just train the model on behalf of whatever your company requirements are you do not have to have some extra because you're not creating something Universal something Universal has already been created in the form of chat gbd so you can restrict yourself into a domain and let's say your organization is you know uh something like fmcg so you can just have a uh training or or the train data that is based on your fmcg context or any other context which you know you think it is referable to so this is all about the generative AI I just hope that you know watching the previous video and this one is getting you the nerve of generative AI yeah last yeah this this one very helpful like with the diagrams um those who attended the previous session and if we uh like you are explaining things through diagram so um like those who have attended the previous session they can easily relate uh what what are the function functions of generative Ai and um what are different components available there so it is I think really helpful correct so now we are going to hop on to one very common type of generative a model which is also uh a very very important part of your associate a program because there have been several quiz questions on this part which is generative adversarial Network so be very much attentive while you read out of it so you can see one common type of generative a model is generative adversarial Network which is marked over here now I'm going to go onto the different slide to explain you what it actually does okay so when you have got a generator over here like there are two components of this GM one of them is generator one of them is the discriminator these are the two components that you have to be very much you know you means you should learn that these are two two components now what does this do is the generator is going on with the uh synthetic data like you are actually wanting some synthetic output so what are we doing is we are granting some input we are just doing some input of raw data which is not very real it is synthetic it is also very much deflected from the uh real data that you know that is the input from here so there are two types of data one is uh very much deflected from the real data however this is artificial and raw data which is being fed into the generator and then you have certain samples of real data and artificial data now what are we doing with the discriminator is we are going to seclude that uh okay if the discriminator is able to figure out that you know this is fake data and this is real data that means the work is yet not done the the work is going to be done only when the discriminator is not able to discriminate further between the fake data and the real data or fake images and the real images that is when our process is finally done so the outputs are fed into the generator until and unless the discriminator forgets or the discriminator is not able to differentiate between the fake and the real image or fake data and the real data that's how the Gans would work so now we will have the study of it before that I have made these diagrams to make you understand that Gans have got generator and discriminator as two major important components which are generator neural networks and discriminator neural network this is how the Gans are made up of these are the components of Gans and how are they working I have also put these working into the LinkedIn page where you can refer to and I've also tried to put and compile it over here here also in a very concise way wherever whichever way is going to be okay with you you all can do it like that so Gans or generative a adversary networks are types of AI architecture that have revolutionized the field of synthetic data generation since their Inception very very important the synthetic data generation is a bvp thing because you know you are creating avatars you are creating fake images uh you know the de the Deep fakes whatever you are creating the different different pictures you must be also been aware that you know uh there have been some reals and uh uh the picture outputs of different different celebrity that how would their future look like how how will they look like when they are old you know this sort of content is pretty popular on Instagram these days so this is all out of this Gans thing right so when you play with the images you create different images uh which are fake take images so you should see that you know this is the important technology that you are working on so Gans consist of two distinct neural network that engage in Dynamic rivalry one is generator and one is a discriminator dynamic rivalry means so until and unless the discriminator is able to figure out the difference that means the Gan is not properly working as soon as the discriminator uh stops to differentiate between the real and the fake data that means our Gan is now working fine and this is how the model should be looking like and working like so generator is Ted with generating synthetic data such as images based on random noise or some other input its goal is to produce data that is indistinguishable from the real data and the discriminator what does it do the discriminator is trained to distinguish between the real data and fake data and its objective is to correctly classify whether a given input is real or fake when it stops doing so the Gan model is perfectly working fine so here I have also deep dived into the processing of the Gans there were I remember some questions based on it so that is why I have taken it into so much depth during training the generator aims to produce increasingly realistic data to fool the discriminator while the discriminator aims to become more Adept to distinguish between the real and the fake data this adversarial process encourages both networks to improve their performance relative uh iteratively what do you mean by iteration so iteration if you must have heard about this term into different programming languages if you are aware of or if you are somebody who is new so I'll repeat that itation means a regular continuous process until and unless a specified condition is fulfilled right I I think because uh you know sir only made this uh iteration process very much familiar to us when I was in the btech first year so I think he would agree with this that my definition is working fine so once trained the generator can be used to produce new synthetic data that resembles the training data so GS have been widely used in various applications including image Generation image to image translation super resolution and data augmentation so in future you are going to see very clear images highly high definition images uh which will have high pixel for high pixels and you know the dimensions also will be very wide So This Is How They are going to work in the future and this was about Gans that's the major important study for the day the Gans now if you have any problem you all can please um ask or comment whatever is good for you and if if if you think that you know there's any something that uh I have not explained you you can also um let me know yep the ne the second architecture that we are going to see today will be large language models now what are L large language models these are again based on uh certain neural networks only uh because those are the I should say the foundation of any um you know um llms so Chad GPT one of the major uh thing that was a hype in 2022 I guess uh and since then it has been Hypes so Chad chatbot generative pre-train Transformer there are three terms that are over here which is one is gener creative second is pre-trained third is Transformer now pre-trained means as you know it has been trained on a lot of data that was surfaced on the on the Google and the Sam Sam already who is the creator of this chat GPD the open AI He also mentioned that you know uh all the data that has been fed into for this chat GPD model has been taken from various sources not only one but there has been there have been various sources for the training data that is fed to this model of chat GPD and it's not only this model openai has got so many models the lately developed models is Sora which is text to image model where you can plug in the prompt and you will have a video generated out of it so the chatboard foundation is Chad GP large language Model llm A a computer algorithm that processes natural language inputs and pred the text the next word based on what it's already seen so here the intelligence is working the neural network is working which is observing certain patterns out of the language that we have uh put in so whatever prompt you put in it's it's not a language which is Chinese or Japanese or you know uh like uh you know what you are putting in so this chat G has been made familiar with lot of languages out of which English language is one of the language that it works on so when you are putting some content in it like you know create something for me create a PPD for me uh which is based on uh you know um let's say conservation of animals right conservation of wildlife sanctuary so you can put in any sort of data so this data is uh you know in the form of prompts now what are prompts there are various um I should say that uh designations also related to The Prompt engineering one of the designation that is related to this prompts generation is prompt engineering where you create subtle prompts to get out most of from the U AI model whatever you looking for so prompts are generally the orders that you give to an llm in order to generate some output or generate some required data that you are looking for okay so some major uh important prompts that are going on lately I have told you one is you know how do you um like send me a PPD or create a PPD sorry create a presentation for uh Wildlife Conservation send or create a PP that is U related to the conservation of animals which are you know uh relatively in the endangered zone so these sort of prompts would generally work and get you the results so next is then it predicts the next word and the next word and so on until the answer is complete so you can even ask it like you know what is artificial intelligence it will tell you and you will ask the chat GPD like who are you you know what what is chat GPT you if you want to learn more about it so it is going to get you the answers from various uh you know websites collectively and then give you the desired output so this is how it works it works on word to word and then you know until and unless your paragraph is complete your answer is complete uh with the rational that it has had so you know that's how llms would work some llms that you must have heard would be open AI gpt3 first and the foremost then you have um 3 and 4 and GPD 5 3.5 also these are all the large language models that we are familiar with then you have Google Lambda and Pam llm that is the base for Bard Bard is another um llm that uh is there then you have hugging faces Bloom and xlm Roberta which is another llm that you can you know just work on and see how does it go Nvidia Nemo llm Excel net these are another set of llms that we have and finally we have meta's large language model meta AI That's Lama in the sh so these are the some of the large language models that we are we have been served as a study or as a you know model to um work on so this is all about the the large language models we might conclude a little bit to the to make it um you know a holistic study for you so in the conclusion what we have is an llm is a machine learning neuron Network trained through one second yeah so an LM is a machine learning neural network trained through data input output sets frequently the text is unlabeled or uncategorized and the model is using self-supervised or semi-supervised learning methodology both these terms have been explained to you kindly go to the um previous lectures where you can see the supervised unsupervised machine learning I think this you covered in the very first session I remember yes so that's about machine learning and then information is ingested or content is entered into this llm and the output is what the algorithm predicts the next word will be so the input can be propior corporate data you can see over here as I said it can either be organizational data that you can fed feed to it and you know it can be fed with or if you want to make another GPT chat GPT sort of thing which is you know worldwide for worldwide so that is uh you will have to train again you will have to putting lot of energy into the training process of your model so or as in case of chat GPT whatever data it's sped and scrap directly from the internet the last is training llms to use the right data requires the use of massive expensive servers Farms that act as superc computers this is very very important like what is the actual requirement for the server of a large language model that is State over stated over here which is superc computers right so we started from a recap of generative AI where uh we discussed how gen AI works and what are the future um you know outcomes out of it and how you can learn it so that's all for the presentation up till now now we are going to study about the predictive artificial intelligence and I'll also take you to the Salesforce content that is there for the generative a and predictive AI because they have given two parts for it and that's only content there's no quiz out of it so you all can just you know click on the site and just have a visit though everything has been covered over here but there might be a possibility that we might have left something that you can cover from there in order to have a substantial amount of data that you have to have for your associate program correct yep yes so let's read the predictive a also and then I'll go there okay the predictive artificial intelligence is another type of AI so fundamentally if I have to iterate it I will say that it will be an astrologer for the business and why I say that it's because um predictions are made by the astrologers and you know that's something that they think that you know can happen in the future and then they restrain you to do some things in order to be uh a little careful about what might happen in the future same thing the predictive artificial intelligence does it is going to predict the outcomes of your business and if you see that the outcomes are not in compliance with whatever you know you have expected that means either there's a problem in the model or if there's no problem in the model that means there's a problem in the business so two things you can short list from uh its predictions so predictive AI is a computer computer programs ability to use the statistical analysis to identify the patterns and anticipate what do we mean by anticipate is to realize if or not uh you know these uh behaviors would eventually come out and give us some Essence out of it so to anticipate behaviors and forecast the future events so we are fundamentally wanting to know what is the future holding for us so your weather data that you know it will rain today and your data related to whether it will be snow in you know in the hill station or not when whether it will be delayed so these all are the predictions that are having a certain advancement today uh previously they were not as apt as they are today so this is all the essence of predictive AI also if I would say any example of a person of a human being so let's say I go to the park and I generally see um some orange clouds orange Dusty clouds and you know which is a followed by a storm but this is just one experience and twice and Thrice I see the same things happening constantly so when I do when I go to the park again and I see that you know there is some dusty clouds and there's some wind that is happening so this has happened for twice and then again I go third time and then fourth time so I can now predict enough that whenever I see Dusty clouds and a little wind it is generally followed by it is generally followed by the storm Ms that are there right so this is how I can predict from certain now obviously predictive takes a lot of information I am predicting is it just on the basis of single um what should I say um single you know data or single entity this is what I'm counting that okay red sky and blue a little bit of dustiness that means uh I am predicting that there will be storms out there so I'm just counting one thing however predictive would require a lot of things to be entered and then you can uh have a desired output from the predictive a so they are not creative however generative artificial intelligence if I talk about they are creative in nature now it's not a person that I'm saying that but the model generally is creative when we talk about the difference in the creativity so they are just outcome based they would predict something and then they'll give you this desired or not so desired output so predictive AI makes statistical analysis faster and more accurate via ML and access to vast amounts of data again everything wants data so data is a new o That's again Justified and then you have while its predictions are not guaranteed to be correct predictive AI can help businesses prepare for the future and personalize experiences for their customers so this is a you know one part of uh what should I say this we have completed about 30 to 40% of this uh content that is there in the Salesforce AI associate program then we will move forward so this is the I should say the essense has been covered and now I'll take you to the different so uh is that mean like generative and predictive AI uh is it complete or we have some more content left it is it is done done it is done it is done okay so as of yeah so for AI associate certification whatever knowledge one should have so those things are covered in this session and in the previous session right yes okay so now what's next should should I should I just show the um what is left and what is you know what are we going to do next and a little bit about the yep yep sure yeah just give me a moment I'll I'll just yeah we have we have few viewers who regularly watch your sessions so I'm receiving their comments so it it would be better if you can showcase what was what will be the next and I think uh guys I'm sharing the session tracker already with you so if you go to the description of this video uh this live stream you will find a session tracker so there we are also maintaining like what also sessions we have already done so you can just go through with that also uh I need to change one yeah you can stop share and then I think you need to reshare the link I guess um has manag from my side but I'll just okay no problems so maybe in the next session you can start with if okay it is not not a big deal yeah so yeah or maybe you can stop share and reshare then I think it will work I had marked it over here but I think it has okay no problem so we can wrap the session here only so uh guys uh thank you for joining the session so I hope you understood the generative and predictive AI uh differences and um I hope with the help of these two sessions uh the previous one which we did uh in this week and this one you will be able to uh prepare these concepts for the Salesforce AI ass associate certification exam and uh next week also we'll be having two more sessions on two different topics right so keep following all the sessions um she's doing lots of efforts uh I I I just know like she's preparing content from everywhere doing some research so so if you follow all those sessions so I'm sure you will be able to know everything uh basic things those are needed one to know about the AI okay so do follow all the sessions and and do follow the LinkedIn page link is already shared in the chat so so that you will be like updated with whatever is happening in the field of AI okay so thank you for joining the session and thank you Nikita for sparing some time and sharing your knowledge thank you're welcome see you in the next session bye everyone