AI then vs now

0:00 this is a non-technical intro to generative AI you'll learn about the evolution of AI capabilities and analyzing the key technological breakthroughs that have enabled modern generative AI models to achieve remarkable performance you learn about the different levels of llm applications like Q&A systems chatbots rag Solutions and how large language models can be leveraged for Downstream natural language processing tasks and the development of intelligent AI agents you'll also learn about the potential of large language model operating systems Abdul created this [Music] course we call as generative AI is not too old in fact it has been just last couple of years so first let's take a look at what has changed how have we reached here in this particular place and what kind of challenges lie ahead of us this is very important before you actually start using the tool in itself imagine this is like the manual that tells you what you should do with the tool and what the tool is not good for without this knowledge it's particularly

1:12 very scary to have a tool which you believe could be great but maybe it is not I'm not saying that generative AI is bad but I'm just saying that you need to know how did we get here and that is the whole point of this particular section first of all why the name generative AI few years back whenever we said something as AI it was mostly a niche Within mission learning or deep learning for example if you take text you would have noticed that few years back we had something called ner that stands for named entity recognition if you have got a bunch of text like this named entity recognition would help you find out the named entities like like Wall Street is a location $15 is price 2011 is a date Amarin Corp is an org Visa is an org so this was possible with NLP natural language processing using a very popular technique called Neer named entity recognition while today you can go ask a

2:27 question and then it would give you an answer so even though previously you had text you were using AI or AI let's say for fathers like EML and deep learning to get something out of the text so analyze the text process the text find out something from the text but you're not necessarily using it to create new text in itself and that is where the generative part comes in you are now generating text rather than just processing processing text let's take another example the next one is few years back you were just trying to F figure out whether a given image is a cat or a dog this example can go multi-level this is a typical classification problem in machine learning it could be like cancer not a cancer looking at the image you can say whether the patient has got pneumonia or not a pneumonia so it could be at any level but imagine you have got two sets of input or you've got like let's say unlabeled input and you want to figure out whether the output is cat

3:38 or not cat or cat or dog so it could be of any type so even in this case the AI or the Deep learning model was purely used to classify an input image but what has changed now now you can actually artificially generate the image of a cat flying from the sky falling from the sky you can generate a cat as a president you can do basically anything that you want you can generate images as much as you want and that's exactly why it is generative agent a new powerful class of large language models is making it possible for machines to write code write text draw something or create something with credible and sometimes superum results now let's break this down first of all we say large language models but let's let's say large models language is only one aspect of it we have got multimodel models these days that can do multimodality that can create text

4:51 understand images generate audio like for example Google Gemini is a great example of a multimodal model in the open source world you have got something like lava which is a great example of a multimodal model so either way we have got a new class of large models really really large models and these models enable or let humans use these models use this AI systems to write English or multilingual text code which is a computer program that successfully runs draw create images and like create bunch of other things like audio video 3D Point Cloud a lot of other these things the main thing here is that one when something like this happens the result is credible like you can see this and then believe it could have come from a human being and that is very important I mean you could have used AI to create something maybe like 5 years back I have created let's say tweet BS before like long back before all these generative AI using a technique called Mark of chain Mark of chain used to use the underlying

6:04 patterns within the text and then try to create the next word very similarly like that Mark of chain is a very popular technique that uses States and transition to understand the underlying pattern from the previous pattern the previous state but those were not as good as what it is today even though we had those things those were not like actual human text but now we have models that can create text exactly like how Shakespeare would write a new book or a new play we have models that can code like a proper programmer we have models that can create art like let's say van go or some other famous artist so we have all these possibilities that are credible when you see it you have to take a second look just to believe that this is either a human or an AI and also it can make super human result what do I mean by superhuman result a human being would take x amount of time to create something you asked me to create a stream lit application I would take a given amount of time to create that

7:20 streamlit application given that I'm a human being I mean given that I am a human being I would need an x amount of time to create it or let's say you are a human being and you need to create a computer program you have to take a certain amount of time you want to write a book you would take time you want to create an art you would take time but these models are scalable that means you can write a book in maybe one day you can create a powerful application like a desktop application maybe a python GUI graphical user interface in just a couple of prompts and all these things are possible thanks to this large models that are mostly mostly mostly at this point Transformer based architectures I'm not going to get into the details but if you are attending a course about generative AI you should know that most of these models are based on something called Transformers which is a very popular architecture that was popularized or that was at least released by Google which uses a very important technique called attention so the attention based Transformer model is at the core of all these things and now we have got language models that can

8:31 write and code we have got like diffusion models that can create images we have got like multitude of other models in fact multimodel models where we have got text and images in the same space within the same model in itself and that is what makes generative AI quite interesting look at these images these are images created by AI I mean somehow you could say today that these are AI created images but if you had shown the same images to Me 3 4 years back I wouldn't have even guessed even like with enough clue that these are AI generated images because not even in my dream I've thought that AI could create something like this and these things get better and better every single day thanks to the powerful class of models that we have God and thanks to the research advancement that is happening every day and in in fact if you see AI today the AI is not affecting the blue colar workers I mean back in the day people used to talk about automation people used to always say automation is going to take the jobs of let's say

9:43 Factory workers automation used to take the jobs of people who are working in factories and Manufacturing units it did in fact there are a lot of information about how Amazon has made most of its packaging and shipping and Logistics automated these things happened like Tesla's Factory if you see there are a lot of robots in there so definitely there was a bit of Automation and robots taking the jobs of let's say blue color workers but as your Barber gone out of job as your hairstylist gone out of job not it but if you see the current the world of generative AI it primarily focuses on knowledge workers and creative workers knowledge workers and creative workers are mostly people like you and I who are part of this course we use our knowledge and create something and that's how we get paid either we write English some other language we write code computer program we create something using an image or we create an image in itself we produce an audio or video or we listen to an audio or video

10:57 and produce something so some how if you see these knowledge workers or creative workers either their input or their output are one of these either text or code or image or video audio 3D or Point Cloud you can just go on and on now if you see the current state of generative AI models you can pretty much say that it's four out of five like I could have given five out of five but I still want to say that it's not still there so almost like four out of five you can say that these large language models are pretty pretty pretty good at writing and not multilingual yet I mean there are multilingual models but you can pretty much tell the difference like if you for example if you ask a model to create something in my language which is South Indian language Tam you know that okay maybe this is not necessarily human because these models are really not that good yet the next one is code these models can do pretty much good code they can create create GUI applications but not to the text level if you can say the model is good four out of five in text the code part is let's say three out of

12:10 five then images the way the model understands images the way the model can create images creating images is really good but still there are certain aspects you can look at the eyeballs you can look at the fingers I mean fingers have almost got sorted out at this point but you can still look at things like that the skin tone lot of other things to tell that maybe this is an AI generated image the same thing goes with image understanding then you have got the video and audio which maybe you know at this point is like one out of five cuz it's still improving the video interlacing the change of uh the frames all frame transition it's there is still an improvement then there are like other modalities that we don't discussed about at all like 3D Nerf Point cloud and all these things exist but one thing that is very sure that if you are talking about generative eii you have to say that the particular set of people that it impacts whether it positively impacts or negatively impacts as knowledge worker and creative worker previously it might have taken me let's say 3 to 4 hours to

13:22 create a YouTube thumbnail but now it takes much lesser time thanks to generative AI previously it might have taken me let's say a lot more time to summarize a document but thanks to generative AI it takes me much lesser time now so it positively impacts and also negatively impacts primarily knowledge workers who have to use their brain and either take the output which is somewhat this so it has to use all of these modalities like text code video image audio etc etc why is that now we have a huge flux in growth of generative VII I mean what has happened we already discussed about Transformers very briefly that the paper and the new neural network architecture Transformers gave way for all the models that we are using now or most of the models that we are using now but there is something else that we need to pay attention to now it is the time we have a lot of other other things combining together at the same time so if you see now we have

14:35 got better models we have got different architecture which is what like I said Transformers architecture and in fact there are Transformer Alternatives that are coming up called like for example Mamba these are like State space models SSM that solves the problems Transformers face in terms of model scaling and time complexity we have got more compute computer has never been cheaper like this it is completely possible for you to rent a very huge amount of computer on AWS the accessibility is there compute in itself is there NV has released almost like a super computer level GPU or accelerated Computing device that you or I can probably own maybe expensive but still somebody can own and we have got more amount of data historically and all the humans have been always creating data we create data volunteer we create data without being asked to create data you go to a supermarket you try to pick something there is a CCTV capturing your data there is video data you go to the

15:46 same Supermarket you buy something now that is going to be part of a POS like Point of Sales system that is a data you come out of the supermarket maybe you're going to Tweet about it that is the data that you are generating maybe there is going to be an Instagram post that is a data that you generating maybe you have got a loyalty points card that is a data that you are generating maybe you're going to return it that is a data that you are generating so there is a lot more amount of data from humans but also we have more sensors and other equipments that can collect data there is data from uh let's say electricity like sensors there is data from Air planes and there is data everywhere else the amount of images that we have digitized huge the amount of books that we have digitized huge the amount of unstructured information to structured information that we have moved huge so we have now tons and tons and tons of data and one thing that you can also see is that the models have become better with the model size also when you have

16:57 got more data more compute the models have started becoming better for example Palm which is a Google model has got 540 billion parameter not saying that you always need a lot of data for a good model that's not the outcome that you should take but a lot of data will help you build a good model and finally at least for me personally one of the most important reason why things have gone is open source open research open models open techniques open tools few years back you did not have let's say a place like hugging face where you can go share