[00:07] In this course, we are going to explore one of the most transformative technologies shaping the future of how we work, create and solve problem. Artificial intelligence has been around for decades helping machine perform task
[00:22] that usually require human intelligence such as understanding data, recognizing pattern, making prediction and supporting decision making. However, the evolution of AI has taken a major leap with the introduction of Gen AI. Unlike
[00:36] traditional AI system that mainly analyze information or predict outcome, generative AI can create new content such as text, images, videos, audios and even code based on simple instruction. Today, Gen AI tools are becoming a part
[00:50] of everyday workflow across industries. Businesses are using AI to automate repetitive task. Developers are using it to build application faster. Marketers are creating personalized content and professionals across different fields
[01:03] are using AI to improve productivity and creativity. [snorts] In this course, we will start by understanding the foundation of artificial intelligence and how AI has evolved from traditional rule-based system to modern gen AI
[01:16] models. We will explore how generative AI works, understand key concept behind these technologies and learn about practical application in the real world. Moving ahead, we'll also look at popular Genai tools, their capabilities, and how
[01:29] they are being used to create innovative solution across different domains. By the end of this course, you will have a strong understanding of Gen AI fundamentals and how can you use them effectively to enhance your skill and
[01:42] stay prepared for the rapidly changing technology landscape. So without any you're interested in understanding how technologies like multi- aent system are transforming the way we work, there is
[01:55] an opportunity to take your learning to the next level with the advanced AI in collaboration with simply learn. This program is designed to help you build a strong foundation in generative AI concepts. Explore agentic workflow,
[02:09] with frameworks like MCP and multi- aent system. You will also get exposure to popular AI tools, build real world application and learn how AI system can be designed to solve practical business problem. Whether you're a developer
[02:22] professional planning to transition into AI or someone who wants to understand how AI can improve everyday workflow, this program helps you gain practical skill and confidence to work with modern AI technology. Learn more about advanced
[02:36] executive program in applied generative AI in collaboration with SimplyLearn here. So without any further ado, let's get started. course on generative AI for beginners. In this course, we are going to explore
[02:51] technologies shaping the future of how we work, create and solve problem. Artificial intelligence has been around us for decades helping machine perform tasks that usually require human intelligence such as understanding data,
[03:05] recognizing pattern, making prediction and supporting decision making. However, the evolution of AI has taken a major leap with introduction of generative AI. Unlike traditional AI system that mainly analyze information or predict outcome,
[03:20] generative AI can create new content such as text, image, video. Today, generative AI tools are becoming a part of everyday workflow across industries. Businesses are using AI to automate repetitive task. Developers are using it
[03:33] to build application faster. Marketer are creating personalized content and professional across different fields are using AI to improve productivity and In this course, we will start by understanding the foundation of
[03:46] artificial intelligence and how AI has evolved from traditional rule-based system to modern generative AI models. We'll explore how Gen AI works, technology and learn about practical application in real world. Moving ahead,
[04:01] we will also look at popular Gen AI tools, their capabilities and how they are being used to create innovative solution across different domies. So by the end of this course, you will have a clear understanding of Gen AI
[04:13] fundamentals, how these system work and how you can use them effectively to enhance your skill and stay prepared for the rapidly changing landscape. So let us begin this journey and understand the world of generative AI step by step.
[04:31] understanding the fundamental of artificial intelligence and explore how AI has evolved from traditional system to modern gen AI technologies. We will learn the core concept behind generative AI, understand how AI model, create
[04:44] content, explore different type of genai application and discuss popular AI tool used for text, image, video and code generation. Moving ahead, we'll also look at real world use cases and understand how Genai is transforming
[04:56] industry, improving productivity and creating new opportunities. [music] Hello everyone and welcome to this first lesson on introduction to generative AI and AI foundations. Now artificial
[05:09] intelligence has become one of the most transformative technologies of our time. From the recommendation we receive on streaming platform to voice assistant on our smartphone and tools repeat and advanced tools like charge GPT that can
[05:21] generate humanlike conversation. AI is becoming a part of almost every industry and everyday activity. In this lesson, we will build a strong foundation by intelligence means, how AI has evolved over time. What makes generative AI
[05:35] different from traditional AI system and how modern AI models are able to create content such as text, images, audio, video, and code. Now, before jumping into generative AI, it is important to first understand the bigger picture of
[05:48] artificial intelligence because generative AI is a result of decades of research and advancement in the field of AI. Now artificial intelligence commonly known as AI is a branch of computer science that focuses on creating system
[06:01] and machines capable of performing task that normally require human intelligence. In simple terms AI allow computer to learn from information, identify patterns, understanding input, make decisions and
[06:14] generate useful output without requiring humans to manually program every single intelligence, we refer to abilities such as understanding languages, recognizing object, solving problem, learning from experiences and making decision.
[06:29] Artificial intelligence attempts to replicate some of these abilities using computer algorithm data and over an advanced mathematical model. Unlike traditional software program that simply follow predefined instructions, AI
[06:41] system can analyze large amount of information, identify relationship between different data points and use those data pattern to produce meaningful results. Now to understand this better, let us consider some example from our
[06:54] everyday lives. When you unlock your smartphone using facial recognition, AI analyzes different feature of your face and compare them with previously stored [snorts] Similarly, when an online shopping platform recommends product
[07:09] based on your previous searches and purchases, AI system analyzes your behavior pattern and predict what product you may be interested in. Another common example is navigation application. When Google map suggests
[07:23] the fastest route during heavy traffic, AI analyzes multiple factors such as location, data, traffic condition, and historical pattern to provide recommendations. Now these example shows that AI is not limited to robots or
[07:37] futuristic technologies. Instead it is already integrated into many application and services that we interact with every day. Now let us understand how artificial intelligence actually work. At a basic level AI system depends on
[07:51] three important components. Data algorithm and computing power. Data acts as a learning material for AI system. Just like human learn by observing example and experiences. AI model learn by analyzing large amount of
[08:04] information. Now algorithm provide the instruction and mathematical processes that help AI system identify pattern, understand relationships and make prediction. Computing power allow these system to process enormous amount of
[08:18] information quickly and efficiently. For example, imagine training an AI system to identify different type of animal. A human can easily recognize a dog because we have seen many example throughout our lives and understand
[08:31] characteristic such as shape, eyes, eel. Similarly, an AI model is trained using thousands or even millions of images of dog. The system analyzes these image, identify common pattern and learn which features are usually associated with
[08:44] dogs. Later when the AI receives a new image, it uses everything it has, learn to predict whether the image contains a dog or another object. The important thing to remember is that AI does not think exactly like humans. Instead, it
[08:58] identify patterns from data and uses those pattern to generate results, prediction or decisions. Moving forward, let us understand the different levels of artificial intelligence. AI is generally discussed across three
[09:10] artificial general intelligence and artificial super intelligence. Narrow AI also known as weak AI refers to system designed to perform a specific task. Most AI system that we use today fall under this category. Example include
[09:25] voice assistant, recommendation system, fraud detection system and image recognition software. Now these system can perform their specific task extremely well but they do not possess general humanlike intelligence. For
[09:40] example, a recommendation system can suggest movie based on your interest but it cannot independently perform task outside its specific purpose. Now the next category is artificial general intelligence or AGI. AGI refers to
[09:54] future form of AI that would have human level intelligence and the ability to perform a wide variety of intellectual task. Now unlike narrow AI, AGI would not be restricted to a single purpose. A human can learn programming, understand
[10:10] science, create art, communicate and solve different type of programs. and AGI aims to achieve similar level of flexibility. However, true artificial general intelligence has not been achieved yet and remains an active area
[10:24] of research. Now, the third category is artificial super intelligence which refers to a theoretical future stage where AI system become more intelligent than human across almost every area. This concept explores a future where AI
[10:40] capabilities significantly surpass human abilities. However, current AI system are still far away from achieving this level of intelligence. Now, let us look at how AI is already being used across different industries. Now, search engine
[10:55] uses AI to understand user query, identify relevant information and rank better results. Email platform use AI to identify spam messages by analyzing pattern from previous spam email. In healthcare, AI help doctor analyze
[11:10] medical images, identify patterns and support research activities. In finance, AI help detect fraudulent transaction by analyzing unusual spending behavior. Businesses uses AI to automate customer support, analyze data and improve
[11:25] decision making and personalize customer experience. The important part is that AI has moved beyond being only a research concept and has become a practical technology that organization used to improve efficiency and solve
[11:40] understand the difference between automation and artificial intelligence because these two terms are often confused. Automation refers to system that follow predefined instruction to perform repetitive task. For example, a
[11:55] software system that automatically sends an email after someone completes a form is an automated process because it follows fixed rule. Artificial intelligence goes a step further because AI system can analyze information,
[12:09] identify pattern and make decision based on what they have learned. For example, an AI customer support system can understand customer questions, analyze the context and generate appropriate responses. So while automation follows
[12:23] predefined instruction, AI has the ability to learn from data and make intelligent predictions. As we move into an increasingly AIdriven world, understanding artificial intelligence is becoming an important skill for
[12:37] professional across different industries. You do not need to become an AI researcher or machine learning engineer to benefit from AI. marketing, finance, healthcare, education, software development and
[12:50] management can use AI tools to improve productivity, automate task and solve problem more efficiently. Now the future will not simply be about human competing with AI. Instead, it will be about humans learning how to collaborate with
[13:04] AI and use these technologies to achieve better outcomes. Now professional who understand how AI work and how to apply AI effectively will have a significant advantage in the evolving workplace. Now that we understand what artificial
[13:18] intelligence is and how AI system learn from data, recognize pattern and make decision. Let us move forward and understand how AI has evolved over the years. The AI we see today including advanced gen AI tools like chart GPT,
[13:32] Claude, Gemini, Dale and other AI application is the results of decades of research, innovation and technological advancement. AI did not become powerful overnight. Instead, it evolved through multiple stages where each stage
[13:46] introduced new capabilities and solved the limitation of the previous ones. The journey of AI can be broadly understood through four major stages which is traditional AI, machine learning, deep learning and generative AI. Now the
[14:01] first stage was traditional AI also known as rule-based AI system. In this approach, computers were programmed with predefined instructions created by humans. The system followed specific rules to make decision based on given
[14:14] conditions. For example, a banking system could be programmed with rules such as identifying a transaction as suspicious if it happens from an unusual location or exceeds a certain amount. These system were useful for solving
[14:29] specific problem and automating repetitive task. However, traditional AI had a major limitation. It could only perform task based on the rule provided by the humans. It could not learn from new experiences, adapt to new situations
[14:45] or improve automatically. As real world problem became more complex, creating rule for every possible scenario became very difficult which created the need for more flexible approach. This led to development of machine learning which
[14:59] changed the way computers learned. Instead of manually programming every possible rule, machine learning allowed system to learn patterns from data. The core idea behind machine learning was that computers could analyze examples,
[15:14] identify relationship and improve their performance over time. For example, instead of creating thousands of rule to detect spam, email, a machine learning model could be trained using example of spam and normal emails. The system would
[15:29] analyze pattern such as suspicious words, unusual links and sender behavior to identify whether a new email was likely to be spam. Now this ability to learn from data made AI system more adaptable and powerful. Machine learning
[15:44] is commonly divided into supervised learning where models learn from labelled data, unsupervised learning where system discover hidden pattern from unlabelled data and reinforcement learning where AI learn through action,
[15:59] feedback, rewards and penalties. The next major advancement was deep learning, a specialized area of machine learning that uses artificial neural network inspired by the human brain. These network contains multiple layer
[16:12] that help AI system understand increasingly complex pattern. Deep learning became powerful because of three major factors. The availability of massive amount of data, increased computing power through advanced
[16:26] processes and GPUs and improvement in neural network algorithm. Now these advancement enabled breakthrough in areas such as image recognition, speech recognition, neural language processing and autonomous system. For example, deep
[16:42] learning allow computer to recognize face, understand spoken languages, analyze medical images and process large amount of information with high accuracy. Moving forward, the next major evolution of AI was Genai which
[16:57] introduced a completely new capability, the ability to create new content. Earlier AI system were mainly focused on analyzing information, identifying pattern and making prediction. For example, traditional AI could identify
[17:11] whether an image contained a cat or a dog or predict whether a transaction was fraudulent. Gen AI moved beyond prediction and introduce content creation. It can generate humanlike text, creates images, produce audio,
[17:24] generate video, and even write computer code. For example, instead of simply identifying an image, a Gen AI model can create a completely new image based on text description such as a futuristic city with flying cars at sunset. This
[17:40] represent a major shift from recognition to creation. Now the success of genai has been driven by development of foundation models which are AI model trained on massive amount of data and capable of performing multiple task.
[17:53] Instead of building separate AI system for every individual problem, organization can use these powerful model for various application such as content creation, question answering, document summarization, language
[18:06] translation, coding assistant and research support. Large language models as a GPT, Claude, Gemini and Llama are example of foundation model that have transformed how people interact with AI. To summarize, the evolution of AI has
[18:20] moved from traditional rule-based system that followed human instruction to machine learning system that learned from data to deep learning models capable of understanding complex pattern and finally to generative AI system that
[18:33] can create entirely new content. Now that we have understood the evolution of AI, let us move forward and understand the most important advancement in the AI field today which is generative AI. Now generative AI is a type of artificial
[18:48] intelligence that enables machine to create new content by learning patterns from existing data. The word generative refers to the ability of AI system to generate something new rather than simply analyzing or classifying existing
[19:03] simply analyzing or classifying existing information. generative AI journey. In the previous lesson we explored the fundamental of
[19:16] artificial intelligence, the evolution of AI, how genai works and how large language models power modern AI application. Now that we understand the concept behind generative AI, let us move forward and explore the practical
[19:29] side of this technology by understanding the different gen AI tools available today. generative AI has moved beyond being a technology used by only researcher and AI specialist. Today million of people across different
[19:41] industry use AI tool to improve productivity, automate repetitive tasks, create content, analyze information and solve real world problem. So whether you're a student preparing for exam, a marketer creating campaign, a developer
[19:54] writing code, a designer creating visuals or a business professional analyzing information, geni tool can help you work faster and more efficiently. The ji tool are software application powered by advanced AI model
[20:07] that can understand user instructions and create new content based on those Now these tools are built using different type of AI models including large language model for text generation, image generation model for
[20:21] creating visuals, speech model for generating audio, video generation model for creating videos and coding model for assisting developers. The important thing to understand is that different generative AI tools are designed for
[20:34] different purposes. Some tools specialize in generating text. Some focus on creating images. Some generate realistic voices while other help developers write and improve code. Now let us explore these category one by
[20:46] one. Now the first one of the most widely used of category of generative AI tool is text generation. Now text generation tools are designed to understand human language and generate written content based on the user
[20:59] different type of content such as articles, email reports, summaries, social media post and stories, business document and even computer code. The biggest advantage of text generation tool is that they help users save time
[21:14] by assisting with writing, brainstorming, research and information. Now instead of starting from a blank page, user can provide a simple instructions and allow AI to generate a first draft that can later be reviewed
[21:27] and improved. For example, instead of spending hours writing a product announcement email, a user can provide a prompt such as write a professional email announcing the launch of a new smartphone. For example, if I go on any
[21:41] browser and click chat GPT and if I write write me a mail. Now here you can see it has created a first draft of the announcement.
[21:53] Here we have to add the recipants. Here the subject and this has the body of the email which it has created. Now if we want to uh improve this mail, Now if we want to uh improve this mail, we can give our more follow-up prompts.
[22:08] Now the AI can generate a complete email draft as you have seen here including a subject line, introduction, product benefit and a CTA. Now one of the most popular example of generative AI tools is chart GPT which you can see here
[22:21] powered by large language model that can understand natural language instruction and generate humanlike responses. Now user can interact with chart GPT for a wide range of task including answering question, learning new concept, writing
[22:35] content, generating ideas, summarizing information, preparing document and information, preparing document and assisting with programming task. For example, a student like you or me can ask chart GPT to explain difficult
[22:48] topic into simple language. Uh for example, explain example, explain LLM into
[23:07] now here you can see it has given the simplest example of LLM and how you can understand it. Now a developer can use to understand programming errors or generate code. A marketing profession can use to create campaign ideas, social
[23:19] media content or customer communication strategies. Now if we talk about the key strength of chart GPT is ability to handle multiple task is its ability to natural language conversation. The second tool is claude. Now it is another
[23:33] powerful generative AI assistant is claude developed by enthropic. Claude is designed for task that requires strong reasoning, writing ability and analysis. This is the claude interface. You will see here I can give the prompts and it
[23:48] has got one more feature which is known as claude code. Now it is completely used for working with large document, summarizing information, analyzing report, generating written content and assisting
[24:00] with complex problem solving. So here I can just attach my folder or sheet or anything which I like and I can ask question or I can add my Google docs, Google drive or anything and I can ask question from that. Now key insights or
[24:16] explain complicated section in simpler term. Now cloud is particularly useful when user need AI assistance with detailed analysis and long form content. For example, I can ask claude to generate
[24:42] will give a long detailed analysis for this content. share you can share your script from here.
[24:59] See this is the script which it has given. Now now Gemini developed by Google is another important generative AI model. Gemini focuses on multimodal capabilities which means it can work with multiple layer of information
[25:12] including text, images and other data formats. Now this allow user to perform tasks such as analyzing image, understanding documents, generating content, answering questions and solving complex problems. For example, go to any
[25:26] browser and search for Gemini. So this is the interface of a Gemini. So a user can upload an email and ask Gemini to explain what it is shown in the picture. Say for example, I can upload any image from here. Upload files. I'll go to
[25:44] suppose this image. I'll open it and I'll just ask it to analyze this image and the AI can analyze the visual information and provide a meaningful response. So the image is standard educational slide with title and what is
[26:01] the missing content it is also given. So it is a it has given a thorough description of the image. Now coming to some AI generation tools. So moving forward let us explore another exciting category of generative AI which is
[26:14] images generation tool. Now image generation AI tool allow user to create visual by simply describing what they want using natural language. So instead of manually designing every element, user can provide a text description
[26:26] known as AI prompt and AI generates an image based on that description. Now for example, a user can enter a prompt such as uh let us I'll show you myself. Let as uh let us I'll show you myself. Let us go to GPT again. I'll just ask it to
[26:40] create a futuristic city with flying cars.
[26:56] representation based on the given instruction. Now these tools are becoming increasingly popular in industries such as marketing, advertising, graphic design, entertainment, education and
[27:09] product development. So here you can see it has created of course a image with flying cars. It needs more improvement. So, Dalai is another uh AI tool developed by OpenAI. One of the most popular AI image generation tool. It
[27:24] description. description. So, if I'll go here,
[27:38] this is the interface. You can login from here. You can give your prompt. Suppose I'm signing up signing up with Suppose I'm signing up signing up with the Google. I'll use my account.
[27:53] the Google. I'll use my account. I can go here create image and I can describe whatever image I want to generate here. So I can write a prompt such as create an information of a robot teaching
[28:06] student in a modern classroom and the AI will generate a visual based on the idea will generate a visual based on the idea here.
[28:21] existing image create can create create a concept and visuals. Now businesses marketing material. Designer can use use them to create visual learning
[28:34] content. So you'll see here it is generating after this. So after 100% it popular AI image generation platform known for creating highly detailed and known for creating highly detailed and artistic visuals. So, you can see here
[28:49] it has created the image. I can recreate or I can edit the image from here. So, and next is MJI. So, it is widely used for designer, artist, content creator and marketing professional for creating concept and
[29:04] visual ideas. So, you can log in, sign up, explore. For example, a designer working on product advertisement can use midjourney to generate multiple creative design. Now these tools are changing the
[29:18] creative process by allowing people to convert ideas into visual quickly without requiring advanced design skills. Now coming to our next category of
[29:30] rapidly growing area of generative AI which is audio and voice generation. The generative AI can now create realistic humanlike voices, convert text into speech, generate audio content and support applications such as podcast,
[29:43] audiobooks, virtual assistant and training material. Now these technologies are making content creation faster and more accessible. Uh for example, we have 11 laps. 11 laps is one of the most popular AI voice generation
[29:56] platform that can create realistic humanlike voices from text input. I'll humanlike voices from text input. I'll search
[30:14] can see that this is the interface of 11 laps. It is taking time. Yeah. So I'll just hit here. Continue.
[30:31] Next. Say for example I'm a creator text to speech or audio books or music sound effect I can create anything. So skip we
[30:43] have to take multi subscription for the same. So a content creator can provide a written script and AI can convert that script into natural sounding voiceovers. So here I'll type my
[30:58] like prompt hello. This is a kanga. This is a kanga. I can add a speaker.
[31:13] Say for example anyone I'll add. It's paid. It is free. hands before us. >> Okay, I'll just add it.
[31:33] >> Okay. So, businesses uses these tool for creating training material, educational videos, marketing content and digital assistant. Now, the advancements of AI voice technology is allowing creators and organization to produce highquality
[31:47] audio content with fewer resources. Now, traditional video production often requires camera, actors, editing team, and significant time and resources. AI making it possible to create videos faster by using simple text
[32:02] instructions. Now AI video tool can help create training videos, marketing content, animation, AI avatars and short form videos. Now some of the most popular tools are Rang. So Runway is a popular
[32:15] AI video generation and editing platform that allow user to create and modify videos using artificial intelligence. Now user can generate videos from text description, apply AI powered editing effect and experiment with creative
[32:28] concepts. For example, a filmmaker can describe a scene and use AI to generate visual ideas before production. Businesses can also use AI tools to create promotional content and educational video more efficiently. So
[32:43] the final category we have is coding assistant. Now, generative AI has significantly transformed software development by helping developers write, understand and improve code. Now, AI coding assistant can help with
[32:57] generating code, explaining the code, debugging the programming concepts, finding errors, suggesting improvement and creating documentation. Now these tools are becoming valuable productivity partner for developers because they
[33:11] reduce repetitive work and allow developer to focus more on solving complex problems. Now one of the most famous tool is GitHub copilot which is most popular AI coding assistant. It helps developer by suggesting code while
[33:25] they work, generating functions, explaining existing code and assisting with debugging. For example, if I go here, I'll search GitHub copilot. So, this is GitHub copilot. I'll go here. This is how it looks. We have to
[33:41] download it for our Windows and then we can use it in our uh VS code or any uh coding platform and then it can debug code for us. So here by here you can go
[33:53] and download your the Windows and then you can install it in VS code. [snorts] So create a function to calculate customer uh discount. GitHub copilot can implementation. Now the goal of AI
[34:06] developer but to help them write better code faster and improve productivity. Now to summarize generative AI tools are available across multiple categories each designed to solve different problems. Text generation tools such as
[34:21] chart GPT cla and Gemini help user create and analyze information. Image generation tools such as Dale and Medjourney help user create a visual from text and audio generation tools like 11 laps help generate realistic
[34:33] particular module. See you in the next one. geni tools and learned how to communicate effectively with AI through
[34:46] prompts engineering, let us move forward and understand the next stage of AI journey. moving from simply using AI tool to building AI powered solutions. Now today most people interact with AI as users. They open tools like charg
[35:01] Claude or Gemini, ask question, generate content or use AI to complete specific task. Now this is the first level of AI adoption where individual use existing AI application to improve their productivity and complete their work
[35:15] faster. For example, a marketing professional may use chat GPD to create social media ideas. A student may use AI to understand difficult concept or a developer may use GitHub copilot to generate code suggestions. In these
[35:28] cases, users are benefiting from AI capabilities, but they are not building their own AI solution. Moving forward, organizations are shifting from simply using AI tools to creating customized AI powered application that solve specific
[35:44] business problem. This is where the role of an AI builder becomes important. An solution by combining AI model, businesses, data, workflow and application to solve real world challenges. Instead of only asking an AI
[35:58] tool question, AI builder create system that can automatically perform task, assist users analyze information and improve business processes. For example, a company may not want employees to manually search through thousands of
[36:11] internal documents to find information. Instead, they can build an AI powered knowledge assistant that understands company document and provide instant answers. Similarly, a customer support team may create an AI assistant that can
[36:23] understand customer queries, access product information, and provide personalized responses. The transition from AI user to AI builder represents a major change in how businesses are adopting artificial intelligence.
[36:37] Initially, organization experimented with AI tool to understand their potential. Today companies are moving toward implementing AI application that are integrated into their daily operations. This shift is happening
[36:49] because businesses are realizing that the true value of AI comes not only from generating content but from creating intelligent system that can automate process, improve decision making and deliver better experiences.
[37:02] The next important thing to remember is that using AI help individual become more productive. But building AI solution always allow organization to transform entire workflow. So now that we understand the difference between AI
[37:14] user and AI builders, let us move forward and explore some real world application where companies are using Gen AI today. Now generative AI is transforming industries by helping organization automate task, improve
[37:26] productivity, enhance customer experiences and create new ways of application that follow fixed instruction, generative AI system can understand natural language, analyze information, generate content and assist
[37:40] human in completing complex task. Now let us explore some of the common application of genai across businesses. Now traditional chatbot were mainly use rulebased system. They could answer only predefined question and struggle when
[37:53] user ask something outside their programmed responses. Now, generative AI has changed this way by enabling chatbot to understand context, interpret user intent and generate more natural conversations. For example, instead of
[38:06] customer can simply explain their problem in their own words. An AI chatbot can understand, identify user intention, provide troubleshooting step and guide them through the solution. Now companies are using AI chatbot for uh
[38:22] customer support, employee assistance, product recommendations, frequently product recommendations, frequently asked questions, service automation. These AI assistant help businesses provide faster support while reducing
[38:34] the workload on human teams. Then we have AI coding research and marketing application. In AI coding assistance, it help developers generate code, understand programming concept, identify errors, create documentation. For
[38:47] example, a developer can describe requirement to AI. AI suggests the possible implementation in research assistant. It help professional analyze document, summarize report, identify trend, extract insight
[39:01] and it is used for marketing research, competitor analysis and knowledge discovery. In marketing automation, AI helps with content creation, social media post, email campaign, advertising ideas, customer personalization and it
[39:15] is used for brand awareness, audience engagement and targeted marketing. Now how businesses are getting productive with AI is they can help write emails, create presentation, summarizing meeting, analyzing document and
[39:29] generating reports. So how AI agent work is understands the goal, creates the plan, uses the tool and make decisions and therefore complete multiple task. Now let us move forward and understand another important concept which is
[39:44] agentic AI. Agentic AI refers to AI system that have the ability to operate reasoning, making decision and completing task with minimal human involvement. While a single AI agent can perform specific task, agentic AI
[39:59] focuses on creating system where multiple AI agent can work together to achieve larger goals. You can think about a real world organization. Different employees have different responsibilities. One person handle
[40:12] marketing, another handles finance, another manages customer support. Similarly, in an agentic AI system, different AI agent can specialize in different task and collaborate with each other. For example, imagine an AI
[40:25] powered business system. One agent can analyze customer data. Another agent can create marketing strategy. Another AI agent manages customer communication. Together, this agent can collaborate to complete complex business workflow. So
[40:39] why is agentic AI important? So agentic AI is becoming an important trend because businesses are moving beyond simple AI assistant towards intelligent system that can automate complete processes. In the future, AI system may
[40:52] not just help human complete individual task. They may manage entire workflow, coordinate different activities and support complex decision making. So that support complex decision making. So that is all for this particular module.
[41:08] explored the foundation of artificial intelligence, the evolution from traditional AI to Gen AI, how large language model works, popular Gen AI tools, prompt engineering techniques, AI application, AI agents, and how
[41:23] businesses are building AI powered solution. So now that we understand the capabilities of Gen AI, it is actually important to understand its limitation. How to use AI responsibly? What is the future of this technology looks like?
[41:37] and the career opportunities emerging in this rapidly growing field. Generative AI is one of the most powerful technologies of our time. But like any technology, it comes with challenges. Understanding these challenges helps us
[41:49] use AI more effectively and responsibly. So let us begin by understanding the limitations of genai. So while generative AI has transformed the way people create, learn and work, it is important to understand that AI system
[42:02] are not perfect. These model are powerful pattern learning system but they do not think, understand or verify information in the same way humans do. Now one of the biggest challenges with genai is AI hallucination. AI
[42:15] hallucination occurs when an AI model generates information that appears accurate and confident but is actually incorrect or completely made up. Since Gen AI model generate responses by predicting patterns from their training
[42:28] data, they may sometime produce answer that sound realistic even when they do not have reliable information. We create a fake research paper, incorrect statistic or a fictional source while responding to a user's question. This
[42:42] does not happen because the AI intentionally provide false information. Rather, it happens because the model is designed to generate likely responses based on the pattern. And this is why human validation is extremely important
[42:55] when using AI especially for critical areas such as healthcare, finance, legal decision and your business strategy. Now another important limitation is bias in AI system. Now AI model learn from large amount of data created by humans. If the
[43:11] training data contains certain biases, the AI model may unintentionally reproduce those biases in its response. Now for example, if an AI system is trained on data that does not represent diverse perspective, it may generate
[43:26] output that are unfair or less accurate for certain group of people. Now reducing biases require careful data selection, continuous testing, monitoring and output and ensuring that AI system are designed with fairness in
[43:39] the mind. Now moving ahead, another major concern is privacy and data security. Now GI system often process large amount of information and users need to be careful about the type of data they provide to AI tool such as
[43:53] personal identification details, confidential business document or financial information or private customer data should not be shared with AI system unless proper security measures are in place. Now organization
[44:07] here need strong data protection policies to ensure that AI is used safely. Another important area is copyright and intellectual property concerns. Now, Gen AI models can create text, images, music, videos and code by
[44:22] learning pattern from large amount of existing data. This raises question around ownership, originality and how AI generated content should be used. For example, businesses need to understand whether AI generated content can be
[44:36] commercially used, whether proper permission are required and how creators right should be protected. And finally, one of the most important limitation of generative AI is the need for human judgment and validation. Now AI can
[44:50] generate ideas, analyze information and automate task. But humans are still responsible for making final decision. The most effective approach is not replacing human intelligence with AI but combining human expertise with AI
[45:02] capabilities. AI can provide speed, scale and assistance while human provide critical thinking, creativity, ethical judgment and real world understanding.
[45:14] Now the future of AI will be built around human and AI collaboration, responsible AI practices. Now that we understand the limitation of generative AI, let us move forward and discuss how can we use AI responsibly. Now
[45:27] responsible AI means designing and using AI system in a way that is ethical, transparent, safe and beneficial for the people. The first and foremost important practice is verifying AI output. Even though modern AI models are highly
[45:42] advanced, user should not blindly trust every response generated by AI. And before using AI generated information especially in professional situation users should verify important fact check sources and confirm accuracy. For
[45:56] example if AI generates a business report a financial analysis or a medical information human expert should review and validate the content before making the decision. The second important practice is protecting sensitive
[46:09] information. Users should always be careful about the data they share with AI system. Now before entering information into AI tools, organizations should consider is this information confidential enough? Does it contain
[46:22] customer data? Does it include company secret? Is the AI platform secure? So protecting privacy is essential for building trust in AI systems. Now another important principle is maintaining human oversight. AI should
[46:36] support human decision making not completely replace human responsibility. For example, an AI system can help doctor analyze medical information with the final healthcare decision should involve medical professionals.
[46:49] Similarly, AI can help recruiters analyze rumés. But human judgment is required to make fair hiring decisions. Now, human oversight ensures that AI system are used responsibly. Another important aspect is ethical AI usage. AI
[47:04] should be developed and used in the way that benefit society while reducing potential harm. Organization need to consider questions such as is the AI system fair? Is it transparent? Could it negatively impact people? Are users
[47:18] aware that they are interacting with AI? Finally, reducing bias is an important part of responsible AI. Organization should continuously test AI system, evaluate output, improve training data and monitor performance to ensure fair
[47:33] and reliable result. Responsible AI is not only a technical challenge, it is also a human responsibility. Now let us look ahead and understand the future trends that are shaping the generative AI industry. Gen AI is evolving rapidly
[47:47] and the next generation of AI system will become more intelligent, more interactive and more integrated into our daily lives. One major trend is multimodal AI. Earlier AI model mainly focused on single type of data such as
[48:00] text. So however modern AI system are becoming multimodel meaning they can understand and work with multiple form of information together. For example a multimodel AI system can analyze your text, images, audio, video code. A user
[48:16] can upload an information ask question about it and receive a detailed explanation. This ability allow AI system to understand the world in a more humanlike way. Another major trend is AI agent. As we discussed earlier, AI agent
[48:30] go beyond answering question. They can understand goal, plan task, use tool and complete workflow. In the future, AI agent may help people manage complex activities such as planning project, conducting research, managing business
[48:45] processes and automating repetitive task. The next evolution is agentic AI workflow. Agentic AI focuses on creating system where multiple AI agent can example, one AI agent could analyze data, another could create report and
[49:01] another could communicate insight to the user. Now these collaborative AI system could transform how organization operate. Another important trend is AI powered workplaces. AI will increasingly become a productive partner for
[49:14] professional across industries. Now employee may use AI assistant for writing document, analyzing information, automating workflow, generating ideas, making decisions and instead of replacing employees, AI will help people
[49:29] work faster and focus on higher value activities. Another emerging trend is personal AI assistant. In the future, individual may have personalized AI assistant that understand their preferences, work pattern and goals. Now
[49:43] these assistant could help manage their day-to-day activity, their schedules, their learning plans, their communication and personal productivity. Finally, AI automation will continue to grow. Businesses will increasingly use
[49:57] AI to automate a repetitive process, improve efficiency, and create smart workflow. The future of generative AI is not just about creating content. It's about creating intelligent system that can understand, assist, and collaborate
[50:11] with human. Now talking about career opportunities in Genai. As Genai continues to grow, it creates many new career opportunities across technology and business domain. The AI industry is not limited only to researcher or
[50:24] machine learning expert. Professional from different background can build career by developing skills related to AI tools, application and workflow. Now one emerging role is an AI engineer. Now AI engineer build and deploy AI powered
[50:38] application using technology such as large language model APIs, machine learning frameworks and AI platform. Now their key responsibility includes integrating AI model into applications, building AI powered systems, working
[50:54] with data, developing intelligent solution. Now important skill for this AI engineer includes Python, APIs, machine learning fundamentals and gen AI concepts. Now another emerging role is a prompt engineer. Now prompt engineers
[51:08] specialize in designing effective instruction that help AI model generate better outputs. Now here they understand how AI model works, how to structure prompts, how to improve AI responses, how to optimize AI workflow. Prompt
[51:23] engineering is becoming valuable because organization need professional who can effectively communicate with AI. Another career is AI product manager. AI product manager combine business understanding with AI knowledge. They focus on
[51:36] identifying user problem, defining AI product strategies, working with engineering team, ensuring AI solution create best business value. Another goal is AI automation specialist. These professional use AI tool and automation
[51:50] platform to improve business. They help organization automate tasks such as customer support, marketing workflow, data analysis, business operation. Another important role is AI application developer. AI application developer
[52:03] builds software application powered by AI mod. They work with technology such as APIs, LLM frameworks, databases, AI development frameworks. To start a focus on building a combination of technical and practical skill. Now
[52:19] important skill here includes understanding AI fundamentals, learning how large language model works, developing prompt engineering skills, understanding APIs and AI tool, learning programming basics, building real world
[52:33] AI project, understanding responsible AI practices and the most important skill is not just knowing AI tool but how to apply in real world scenarios. So that apply in real world scenarios. So that is all for this particular lesson.
[52:49] intelligence and generative AI fundamentals. How generative AI models work and how they differ from traditional AI system. You will learn about various generative AI application, explore commonly used AI tools and
[53:02] professional are leveraging these technologies to automate task, enhance creativity and improve decision making. This course will help you build a strong fundamental to start your journey in the world of generative AI.