Dive into the world of Artificial Intelligence (AI) with this engaging lecture! Learn what AI is—the simulation of human intelligence in machines, powering tools like chatbots and Microsoft Copilot—and explore its real-world impact. key difference: AI is the broad field of creating smart machines, while Machine Learning (ML), a subset of AI, focuses on systems learning from data using algorithms like neural networks. Perfect for mastering AI fundamentals!
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00:00that's why let's start with the first thing what is AI what is artificial intelligence well the
00:13definition says that artificial intelligence is a software that imitates human capabilities now
00:19what kind of human capabilities we all know the same we are using modern AI tools like Alexa or
00:25we are also using modern tools like copilot or maybe chat GPT so we know this thing very well
00:31that these kind of tools are actually building a conversation with the end user and they're trying
00:36to mimic human capabilities like speech or maybe giving answers to certain things or analyzing
00:41certain things now some of the capabilities which I want to highlight is it can predict outcomes and
00:46recognize patterns based on historical data remember we are talking about historical data
00:51it's important thing that AI is always based on some true historical facts which are associated
00:57with that recognizing abnormal events and making decisions it can also help you to interpret visual
01:03input we are talking about visual input we are not talking about generating images visual input means
01:08you can provide an input in a form of images videos or maybe some camera feed and when you do that it's
01:14actually capable to interpret that visual input we will be using understanding language and engaging in
01:20the conversation as well as it can help you to extract information from sources to gain knowledge these
01:26are all some of the capabilities which AI can actually help you to do but basically it's trying
01:30to imitate or mimic human capabilities that's what AI is all about now if I want to focus on AI AI is a big
01:38thing and there are so many capabilities which are provided by that in this short workshop I'm not able to do
01:43that thing so what exactly we are going to focus today is actually one subset of AI which is known as
01:48generative AI yes yes this is that subset which is actually focusing on generation of the new original
01:56content this content is going to be available in the form of natural language text or maybe you can
02:01generate images or you can also generate programming language code now various kind of original content can
02:08be generated with the help of generative AI and that is something which is one of the most useful thing for
02:12us that's the reason we are going to focus on generative AI little in depth in this particular
02:17workshop not on the whole AI actually also with AI and generative AI we need to understand few more
02:24terminologies like there is one thing which is known as machine learning if I focus on machine learning
02:30machine learning is somehow having roots in data science I hope we all know the data science is that
02:35particular field which is focusing on data analysis is trying to understand the relationship between data when you provide a historical data
02:41machine learning is somehow similar kind of field associated with data science field and machine learning is
02:48basically focusing on giving learning or training to the machine that's reason the name is machine learning so
02:54this is going to create computers which are going to be capable to learn new things and in order to do this thing
03:00machine learning create something which we call predictive models machine learning is going to create this
03:06predictive models which are internally going to use algorithms some kind of correlations between data they are going to find out and based on these algorithms is going to find out a relationship between data
03:16this predictive models are actually the root of all the modern AI software which you have heard or which you are using in the recent times now somewhere machine learning is something which is creating this kind of predictive models
03:19but in order to use this with the modern AI software you have to use something which is known as deep learning deep learning is that particular part which is I can say subset of machine learning but this is that particular part which is focusing on mimicking human brains so basically deep learning has created some kind of a structure in which they are trying to
03:49mimic human brain capabilities I hope you all know that in our human brain we have neurons and those neurons are actually going to process the information so when you see something when you hear something or when you just read something you're actually able to understand that thing because your human brain neurons are actually processing that information even right now also whatever I'm saying you're able to understand that because of those neurons only deep learning has created some kind of a mechanism which is actually trying to mimic this kind of
04:19human brain neurons capabilities and that is something which they call neural network now I'm going to show you neural network processing in depth in this particular module but let's focus on this right now that this is something which is trying to process the information with the help of neural network kind of a thing we'll see that what is neural network so this four words which I am just showing you right now first one AI second one generative AI third one machine learning and fourth one deep learning are
04:49actually the most important part is actually the most important part is actually the most important part to understand any kind of modern AI agents now the obvious question people ask me is Maruti do we have any connection between these four words how these four dots are actually connected well the answer is actually available in the next slide if you see right now this slide is giving you answer of two questions how these four things are connected or what is the difference between AI and ML there are many people who are not able
05:19actually basically showing you are actually basically showing you that I know most of you are going to tell me that AI is something which is a very new thing but the facts are different actually artificial intelligence is not a new thing it has invented somewhere in 1950s and all this was the time when we got a definition of artificial intelligence system which was saying that it is a field of computer science that seeks to create intelligent machines that can replicate or exceed human intelligence
05:49the way through online learning and everything but so this is a few different rates in different ways of the way that we have different ways that are faced with from the real mindsets
05:54and all of these things that are different ways that are different than the latest AI system which we have right now but somehow it has to use in 1950s and all
05:56somewhere in 1990s and all we got something called machine learning actually this was that time when training machines is something which become very popular in 1990s and all when we got this machine learning as I said it is a subset of AI which is enabling machines to learn how
06:12to learn. How exactly machines will learn? Machines will learn from the existing
06:17data and improve upon the data to make decisions or predictions. So basically
06:26machine learning is the one which is providing data to your machines and
06:29based on that machines are learning. But then we got something in 2010s and all
06:34which is known as deep learning and deep learning has changed everything. Deep
06:37learning was actually nothing but a specialized machine learning technique. So
06:42it is actually going to be based on machine learning only but in this
06:45specialized technique which is actually going to have layers of neural networks
06:49are going to be used to process the data and make decisions. Obviously till deep
06:55learning kind of a structure which was invented we were not having some kind of
06:58a processing engine which can actually process this data in a much faster way.
07:02That's the reason I always found deep learning as a super duper innovation
07:07which has actually made the modern AI possible in the recent time. After deep
07:11learning and all everything become easy because processing the data using this
07:15kind of a neural network becomes super easy and that's where we got something
07:19which is generative AI. Now this is a recent buzzword or it came up just before
07:24few years and that is what people are actually seeing now AI. Generative AI is one
07:29particular part of AI which is focusing on creating new written visual and auditory
07:35content given based on the prompt or existing data. Remember this is the first
07:39time I'm introducing this term called prompt. Basically right now if you want to understand
07:44what is a prompt I will say prompt is nothing but your input text which you are providing
07:48to this kind of AI agents. So whatever you're going to type or say is what you're going
07:54to provide as an input in the natural language format. That is your prompt and based on that
08:00your generative AI systems are going to return and based on that your generative AI systems are going
08:05to return with a specific response. So now once we know these four things we know this that
08:11generative AI is actually going to focus on generating new content which is actually
08:15internally going to use deep learning because ultimately processing is going to happen using
08:19deep learning only. Then deep learning is nothing but one specialized technique of machine learning.
08:24So it is also going to use machine learning model which are predictive models allowing you to
08:29process and predict certain things. And then machine learning is nothing but somehow connected
08:34with artificial intelligence because ultimately it's nothing but a subset of AI. These four dots
08:40which we are connecting right now is something which is very important for you to understand.
08:43And that is exactly what we are doing right now. Now once you understand these four things
08:47generative AI, deep learning, machine learning and artificial intelligence this is a time we have
08:53to understand. So what kind of common AI workloads which are available as of this year 2025.
08:59So let's say the first thing which we have here is again machine learning only.
09:04So if you ask me what kind of AI workloads we have today Maruti, the first one is still machine
09:08learning. And I always say this thing that machine learning is going to help you to create
09:13predictive models. And that's why I say this thing that this is nothing but a foundation for AI.
09:18This is basically a foundation for AI. So this is the one which is running in the background for
09:23every modern AI software. Remember that. All other things which I'm showing you here other than machine
09:29learning all the below things which I'm showing you. You can see I'm highlighting one word here
09:33which is capabilities. All other four things are nothing but capabilities only which we are
09:39actually using in our modern AI systems. Like we have a computer vision capability. This is that
09:44specific capability which is focusing on interpreting world visually. Basically you're going to provide
09:49input through images, videos or camera and it's going to help you to interpret that. That's what computer vision
09:55is doing. Then you have natural language processing capability which is focusing on interpreting written
10:01or spoken languages and is going to respond appropriately. Almost all the modern chatbots
10:06are actually doing something which is nothing but natural language processing. Then you have document
10:11intelligence capability. Maybe individuals are not going to use document intelligence but organizations
10:17are using this extensively. Because this is a specific capability which is focusing on managing and processing
10:23using high volumes of data which is found in the forms and documents. Digital forms, physical forms kind
10:30of documents you can use and associate with document intelligence systems. Then we have knowledge mining
10:36which is actually a concept but in the background we have a service associated with that called AI
10:41search. Knowledge mining capability is helping you to extract information from the large volume of unstructured data
10:48and then it's going to make that data much more meaningful, searchable using something called knowledge
10:53store. This is also one of the concepts which organizations are using extensively and you will be having unstructured data
11:00also in the searchable format using this. And then last but not the least we have generative AI which is a
11:06specialized capability which is focusing on creating original content. Remember this is going to create original content.
11:13original content is not copying content from somewhere internet sources. It's going to create an
11:18original content in the variety of formats like natural language text, images, code, videos and many more.
11:25In this particular video today, I'm not able to focus on each and other capability which is available here.
11:31If you are interested in a specific capability like computer vision or document intelligence, you can check out our
11:37skill tech club YouTube channel and we have videos on every capability available here.
11:42But in this particular video, because this is just a beginning, I'm going to focus on machine learning concepts only conceptually
11:48and I'm going to focus on generative AI conceptually and practically. So this is where our focus is.
