Introduction, history and types of AI

0:29 Hello and welcome to this generative AI full course by Intellipaat. Last week, Google introduced Gemini 3.6 Flash, making AI faster, more efficient, and more affordable to use as well. Around the same time, Anthropic released Cloud Opus 5 model, delivering performance close to its flagship Cloud 3 5 model at nearly half of the cost. And this has become the new normal. Every few weeks, a new AI model is launched that's smarter, faster, and more capable than the last.

0:52 This tells us the AI race is moving at a pace we have never seen before. But this isn't just about competition between tech companies. It's changing the way the entire world works. What once required teams of engineers, months of development, and millions of dollars can now be accomplished in just few hours using these AI models. And as businesses continue to adopt AI, the demand for skilled professionals is growing just as quickly. Today, nearly 80% of organizations use AI in at least one part of their business.

1:16 As a result, companies are actively hiring professionals who understand large language models, AI agents, AI-powered automation, and the architecture behind these systems. With the roles like AI engineer, LLM engineer, AI architect, and AI automation specialist becoming some of the fastest-growing careers in technology. But here's the catch. Most people knew how to use AI, while very few understand how to build with it. And that's what we call the AI skill gap. And that's exactly where Intellipaat's generative AI course in.

1:40 Throughout this course, you will start understanding the fundamentals of AI and ML before exploring how neural networks evolved into CNNs, RNNs, LSTMs, and finally transformers. The architecture behind ChatGPT and today's most powerful AI models. So, by the end of this course, you will have have the skills needed to work with generative AI confidently. So, without any further ado, let's get started. All right. Now that we have seen AI systems interacting with each other, let's step back and understand the basics first.

2:27 That's where our first question comes in. That is, what exactly is artificial intelligence? So, let's study that. So, in simple terms, artificial intelligence or AI is the ability of machines to perform the tasks that require human intelligence. For example, these tasks include recognizing images, understanding language, making predictions, or even helping in the decision-making. All right. However, there's a key difference between human intelligence and machine intelligence. As humans can learn through experiences, emotions, and understanding, machines don't think or feel like humans.

2:49 Instead, they learn from data, where AI systems analyze a large amounts of information and find patterns within that data. For understanding this in much more clearer way, you can think of AI as a digital brain that learns from patterns and processing the data. The more data it processes, the better it becomes at recognizing those patterns and producing more precise and useful results. Now, the question comes is, why does AI matter so much today? Like you are hearing about it almost everywhere in almost every field.

3:12 So, the modern world today generates huge amounts of data through searches, photos, videos, and all the online activities. And humans alone cannot analyze this huge information by themselves. But, AI can help us process this information and data quickly by turning it into useful and information, which is why it is becoming most important across almost every domain. So, most of the AI systems today focus on three core capabilities. The first is production, where AI analyzes past data to estimate what might happen next.

4:00 And the second is recognition, such as identifying faces, voices, or objects in images. And the third is decision making, where AI evaluates different options and selects the best one based on the data. What's more interesting is that we are already using AI in almost everything. For example, the smartphones uses AI for face unlock and camera improvements, while YouTube recommends videos using AI. Google Translate converts different languages instantly. And chatbots help answer questions online as well. However, AI has limits too.

4:22 It does not truly understand the world like humans do, because it simply learns from the data. Like for example, it has no real understanding where AI detects patterns, but doesn't truly know what they mean the way humans can understand it. The second is dependent on data. It is completely AI dependent, which is why a poor or biased training data leads to inaccurate or unreliable outputs. The third is no emotions or creativity like humans does. AI cannot feel empathy, experience joy, or generate truly original thought or something that is based on the real human creativity.

5:10 Lastly, AI can feel unexpectedly too. Edge cases and novel situations can trip up even the best AI models to fail, right? So, before we continue to the next module, let me know which is your favorite AI tool that you use almost every day in your daily routine. And that brings us to the module two, that is history and evolution of AI, which will help us understand how AI evolved over the time and how it came from basics to the agentic AI that we live every day today.

5:34 So, the idea of artificial intelligence started in 1940s and 1950s when scientists began asking the simple question, can machines really think? And one of the key pioneers in this field was Alan Turing. He proposed the famous Turing test which suggested that if a machine could have conversation indistinguishable from a human, it could be considered as an intelligent machine. So, let's see the timeline now. So, it all began in 1950s where the Turing test started to test early logic of the machines.

5:58 After that, it went to 1970s to 1980s which was the era of expert systems which was mostly rule-based. After that came 1990s which led to machine learning era takeover. After that, it came to 2010 where the deep learning revolution actually began. And lastly, into the 2020s where generative AI and agentic AI started exploding slowly but steadily. So, let's understand the timelines now. So, in between 1970s to 1980s, AI research focused on the expert systems. So, these systems tried to mimic the decision-making abilities of human experts that were using large set of rules.

6:47 They were pretty useful in the fields like medicine and engineering but were difficult to scale at large levels. After that, later, the focus shifted to the machine learning era where computers started learning through patterns and from data instead of following only fixed kind of rules that was based on expert systems. So, this approach soon become much more powerful than that. And in the recent years, major breakthroughs happened with deep learning powering powerful GPUs and availability of big data.

7:11 These advances allowed AI systems to process complex information and learn much faster than before. And this progress led to the generative AI era where machines can create new content. For example, the tools that we use today are effect of generative AI which includes ChatGPT which is conversational AI for everyone. After that came DALL-E, which can generate images from the prompts. And after that, he also gave us tools like Stable Diffusion and Midjourney, Copilot, and even Sora, that can generate videos just by a simple prompt.

7:35 And today, we are moving towards the agentic AI era, where AI systems can now plan multi-step tasks, coordinate workflows, and act proactively. Where they don't just respond to prompts, but they work autonomously towards goals and take actions as well. So, now if we visualize this as a timeline, AI has moved from simple experiments to powerful intelligent machines, and the future is still unfolding. >> So far, we have covered two important things. First, we understood what AI is, what it can do, and why it matters in today's data-driven world.

8:22 Then, we explored how AI evolved over time, from early idea and rule-based system to modern AI powered by data, deep learning, and massive computing power. All right, enough of theory for now. Let's make it a bit interesting. So, here's the deal. There will be three questions presented in this video, and yeah, this is a challenge. If you can get all three right, and drop your