What is AI

0:07 Hello everyone and welcome to the AI and ML full course. Artificial intelligence and machine learning are transforming the way the world works from voice assistance to self-driving cars and from medical diagnosis to stock market predictions. In this course, we will take you step by step from the fundamentals of AI to hands-on machine learning and deep learning. You will learn the core concepts, explore the most important algorithms, and work with tools to build and train your own models.

0:30 Plus, you will gain insights into the future of AI and ML and prepare for interview questions to boost your career. And by the end of this course, you will not only understand how AI and ML work, but also know how to create intelligent systems that can make predictions, recognize patterns, and solve real world problems. So before we get started, please like, share, and subscribe to Edureka's YouTube channel and hit the bell icon to stay updated on the latest content from Edureka.

0:53 Also check out Edureka's post-graduate program in generative AI and machine learning in collaboration with Illinois Tech. It offers a unique opportunity to explore the cuttingedge world of generative AI and develop advanced AI powered solutions. This program covers in demand topics including machine learning, deep learning, natural language processing from engineering, generative AI, LLM, rag, agentic AI and much more. Learn from industry experts through a curriculum built around real world hands-on use cases designed to equip you with the practical and job ready skills.

1:41 So check out the course link given in the description box below. Now let's get started with our first topic that is what is artificial intelligence. AI or artificial intelligence is the branch of computer science where focused on creating systems that were performed task and that would be normally required by human intelligence. These tasks can range understanding of natural language. Secondly, recognizing patterns then making decisions and lastly learning from the experiences. AI is a collaboration of ideas, methods and knowledge where from the multiple academic disciplines work on a different problem solving and share their knowledge to a better understanding and come up with a good solution.

2:26 But it can also be rule-based and operate under a set of rules and conditions only. So I would like to tell you all guys a small realtime experience or an experiment done by Alan Turing to propagate and to establish the artificial intelligence. Alan Turing was the first person to conduct the sustainal research in the field that he called machine intelligence. The Turing test was conducted to explore whether machines could exhibit humanlike intelligence proposed by Alan Turing in 1950.

2:50 It involves a human evaluator communicating with both a human and a machine through a text interface. If a evaluator cannot distinguish between the two based on their responses, the machine is set to have passed the test. It serves as a benchmark of the assessing the progress of AI and discussions of the nature and intelligence and consciousnesses. So this is to evolve and to establish that even machines can work as humans and that is how it is made to bring up the machine's knowledge and the humans knowledge into machines.

3:39 There are two types of artificial intelligence. First let's talk about weak AI. Before getting into what is weak AI, I would like to tell you with an example that is performed in the real world. I know you all guys will be knowing about Alexa, Apple Siri and also self-driving vehicles. So all of these considered as in weak AI. It is also known as a narrow AI or an AI narrow intelligent that is trained by the AI and focused to perform a specific task.

4:05 Weak AI drives most of the eye that surrounds by us today. Narrow might be a more apt descriptor for this type of AI as it is anything but weak. Next, let us learn about strong AI. Strong AI is made up of artificial general intelligence or artificial super intelligence. Simply it could be told as where a machine would have an intelligence equal to humans where humans will track what AI needs to be done. It would be able to self-aware with the consciousness that would be this ability to solve problems, learn and also plan for the future.

4:30 Have you ever thought what does AI do at its core? I'm here to tell you why AI is essential. It works by analyzing a lot of data to find patterns and a useful information. It even learns from this data to get better at a task over time. With this learning, AI can make decisions, predict future events, and do task automatically that would normally need human intelligence. This helps business and other organizations work more efficiently. So AI is about making computers smarter and more helpful in everyday life.

5:20 So let me tell you some use cases that is happening in daily users or daily real life based. Firstly I have taken is about cyber security as it is a very important and a vital role for many platforms hereafter. Cyber security is a critical concern for individual businesses and also governments. As cyber threats continue to evolve in a complexity and sophistication. It would play a very vital role for argumenting cyber security defenses. It is all based on the false positive effects that is made by the false information given by any criteria leading the alert of fatigue and reduced operational efficiencies.

5:42 Cyber attacks can exploit vulnerabilities in AI models by invading detection and compromising security defenses. they have some private data where it is trained on the basis of incomplete data sets may produce the outcomes of private data. This will raise an ethical concern and also regulatory compliances issues having a very complicated problems too. So the next one will be your entertainment. As people know entertainment is taking a very huge part in everyone's life. For example, it would be a social media tool.

6:30 So AI is transforming the entertainment industry by revolutionizing content creation, personalization and audience engagement. So this can be such as television, gaming, music and also digital media platforms. One significant use of AI in entertainment is personalized content recommendation. So personalized entertainment experiences are enhanced through AI driving is recommended through systems. They are even having some platforms like Netflix, Prime, Amazon and also Spotify that is making people engaged in a very hype as of now. These recommendations improve over time.

7:16 So I would like to conclude by telling artificial intelligence can do amazing things like analyzing data and making task easier. But it also brings up huge question about fairness, jobs and who controls it. We need to be careful in how we develop and use the AI. Making sure it helps everyone and doesn't cause harm at all. So this makes easier for people to get into creativity and make rules and guidelines. [Music] So now let's get started with the first topic which is history of artificial intelligence.

7:40 The concept of AI goes back to the classical ages. Under Greek mythology, the concept of machines and mechanical men were well thought of. An example is Talos. Talos was supposedly a giant animated bronze warrior who was programmed to guard the island of Cree. Now, let's get back to the 19th century. In 1950, Alan Turing proposed the Turing test. The Turing test basically determines whether or not a computer can intelligently think like a human being. The Turing test was the first serious proposal in the philosophy of artificial intelligence.

8:28 1951 marked the era for game artificial intelligence. This period was called game AI because here a lot of computer scientists developed programs for checkers and for chess. However, these programs were later rewritten and redone in a better way. 1956 marked the most important year for artificial intelligence. During this year, John McCarthy first coined the term artificial intelligence. This was followed by the first AI laboratory which was set up in 1959. MIT AI lab was the first setup which was basically dedicated to the research of AI.

8:52 In 1960, the first robot was introduced to the General Motors assembly line. In 1961, the first AI chatbot called Eliza was introduced. In 1997, IBM's Deep Blue beats the world champion Gary Casparrow in the game of chess. 2005 marks for the year when an autonomous robotic car called Stanley won the DARPA Grand Challenge. In 2011, IBM's question answering machine Watson defeated the two greatest Jeopardy champions Brad Rutter and Ken Jennings. So, that was a brief history of AI.

9:39 Now guys, since the emergence of artificial intelligence in 1950s, we have seen an exponential growth in its potential. AI covers domains such as machine learning, deep learning, neural networks, natural language processing, knowledge base, expert systems and so on. Now that you know a brief history of artificial intelligence, let's move on and understand what exactly artificial intelligence is. So the term artificial intelligence was first coined by John McCarthy. Like I mentioned earlier, he defined AI as the science and engineering of making intelligent machines.

10:03 In other words, artificial intelligence can also be defined as the development of computer systems that are capable of performing tasks that require human intelligence such as decision making, object detection, solving complex problems and so on. So like I mentioned, artificial intelligence helps in decision making, solving complex problems. It performs highlevel computations and also increases the accuracy of your predictions. Right? These are the main features of AI. So now let's understand the different stages of artificial intelligence.

10:50 So basically when I was doing my research, I found a lot of videos and a lot of articles that stated that artificial general intelligence, artificial narrow intelligence and artificial super intelligence are the different types of AI. If I have to be more precise with you, then artificial intelligence has three different stages, right? The types of AI are completely different from the stages of AI. So under the stages of artificial intelligence, we have artificial narrow intelligence, artificial general intelligence and artificial super intelligence.

11:14 So what is artificial narrow intelligence? Artificial narrow intelligence, also known as weak AI, is a stage of artificial intelligence that involves machines that can perform only a narrowly defined set of specific tasks. Right? At this stage, the machines don't possess any thinking ability. They just perform a set of predefined functions. Examples of weak AI include Siri, Alexa, Alph Go, Sophia, the self-driving cars and so on. Almost all the AI based systems that are built till this date fall under the category of weak AI or artificial narrow intelligence.

11:59 Next we have something known as artificial general intelligence. Artificial general intelligence is also known as strong AI. This stage is the evolution of artificial intelligence wherein machines will possess the ability to think and make decisions just like human beings. There are currently no existing examples of strong AI, but it's believed that we will soon be able to create machines that are as smart as human beings. Strong AI is actually considered a threat to human existence by many scientists.

12:22 This includes Stephen Hawkings. Stephen Hawkings quoted that the development of full artificial intelligence could spell the end of human race. Moving on to our last stage, which is artificial super intelligence. Artificial super intelligence is that stage of AI when the capability of computers will surpass human beings. Artificial super intelligence is currently seen as a hypothetical situation as depicted in movies and science fiction books. You see a lot of movies which show that machines are taking over the world.

12:45 All of that is artificial super intelligence. Now I believe that machines are not very far from reaching the stage taking into consideration our current pace. However, such systems don't currently exist, right? We don't have any machine that is capable of thinking better than a human being or reasoning in a better way than a human. Artificial super intelligence basically any robot that is much smarter than humans. Now moving on to the different types of artificial intelligence. Based on the functionality of AI based systems, artificial intelligence can be categorized into four types.

13:32 The first type is reactive machines AI. This type of AI includes machines that operate solely based on the present data and take into consideration only the current situation. Reactive AI machines cannot form inferences from the data to evaluate any future actions. They can perform a narrowed range of predefined tasks. An example of reactive AI is the famous IBM chess program that beat the world champion Gary Caspro. This is one of the most impressive AI machines built so far.

13:55 Next, we have limited memory AI. Now, like the name suggests, limited memory AI can make informed and improved decisions by studying the past data from its memory. So, such an AI has a short-lived or you can say a temporary memory that can be used to store past experiences and hence evaluate your future actions. Self-driving cars are limited memory AI that use the data collected in the recent past to make immediate decisions. For example, self-driving cars use sensors to identify civilians that are crossing the road.

14:42 They identify any steep roads or traffic signals and they use this to make better driving decisions. This also helps in preventing any future accidents. Next, we have something known as theory of mind artificial intelligence. The theory of mind AI is a more advanced type of artificial intelligence. This category is speculated to play a very important role in psychology. This type of AI will mainly focus on emotional intelligence so that human beliefs and thoughts can be better comprehended.

15:04 The theory of mind AI has not been fully developed yet, but rigorous research is happening in this area. Moving on to our last type of artificial intelligence is the self-aware artificial intelligence. So guys, let's just fold hands and pray that we don't reach the state of AI where machines have their own consciousness and become self-aware. This type of AI is a little far-fetched, but in the future, achieving a stage of super intelligence might be possible. Geniuses like Elon Musk and Stephen Hawkings have constantly warned us about evolution of AI.

15:50 So guys, let me know your thoughts in the comment section. Do you ever think we'll reach the stage of artificial super intelligence? Moving on to the last topic of today's session is the different domains or the different branches of artificial intelligence. So artificial intelligence can be used to solve real world problems by implementing machine learning, deep learning, natural language processing, robotics, expert systems and fuzzy logic. Now guys, these are the different domains or you can say the different branches that AI uses in order to solve any problem.

16:13 Recently, AI has also been used as an application in computer vision and image processing. Right? For now, let me tell you briefly about each of these domains. Machine learning is basically the science of getting machines to interpret, process, and analyze data in order to solve real world problems. Right? Under machine learning, there's supervised, unsupervised, and reinforcement learning. If any of you are interested in learning about these technologies, I'll leave a link in the description box. You all can go through that content.

16:35 Next we have deep learning or neural networks. So deep learning is a process of implementing neural networks on highdimensional data to gain insights and form solutions. It is basically the logic behind the face verification algorithm on Facebook. It is the logic behind the self-driving cars virtual assistants like Siri and Alexa. Then we have natural language processing. Natural language processing refers to the science of drawing insights from natural human language in order to communicate with machines and grow businesses.

17:20 So an example of NLP is Twitter and Amazon. Twitter uses NLP to filter out terroristic language in their tweets. Amazon uses NLP to understand customer reviews and improve user experience. Then we have robotics. Robotics is a branch of artificial intelligence which focuses on the different branches and applications of robots. AI robots are artificial agents which act in the real world environment to produce results by taking some accountable actions. So I'm sure all of you have heard of Sophia.

17:42 Sophia the humanoid is a very good example of AI in robotics. Then we have fuzzy logic. So fuzzy logic is a computing approach that is based on the principle of degree of truth instead of the usual modern logic that we use which is basically the boolean logic. Fuzzy logic is used in medical fields to solve complex problems which involve decision making. It is also used in automating gear systems in your cars and all of that. Then we have expert systems.

18:07 An expert system is an AI based computer system that learns and reciprocates the decision-making ability of a human expert. Expert systems use if then logic notions in order to solve any complex problem. They do not rely on conventional procedural programming. Expert systems are mainly used in information management. They are seen to be used in fraud detection, virus detection, also in managing medical and hospital records and so on. So guys to sum it up these were the different branches of artificial intelligence.

18:55 [Music] These are the term which have confused a lot of people and if you two are one among them let me resolve it for you. Well artificial intelligence is a broader umbrella under which machine learning and deep learning come. You can also see in the diagram that even deep learning is a subset of machine learning. So you can say that all three of them the AI the machine learning and deep learning are just the subset of each other.

19:18 So let's move on and understand how exactly they differ from each other. So let's start with artificial intelligence. The term artificial intelligence was first coined in the year 1956. The concept is pretty old but it has gained its popularity recently. But why? Well, the reason is earlier we had very small amount of data. The data we had was not enough to predict the accurate result. But now there's a tremendous increase in the amount of data. Statistics suggest that by 2020 the accumulated volume of data will increase from 4.4 zetabytes to roughly around 44 zetabytes or 44 trillion GBs of data.

20:07 Along with such enormous amount of data, now we have more advanced algorithm and high-end computing power and storage that can deal with such large amount of data. As a result, it is expected that 70% of enterprise will implement AI over the next 12 months, which is up from 40% in 2016 and 51% in 2017. Just for your understanding, what is AI? Well, it's nothing but a technique that enables the machine to act like humans by replicating the behavior and nature.

20:32 With AI, it is possible for machine to learn from the experience. The machines adjust their responses based on new input, thereby performing humanlike tasks. Artificial intelligence can be trained to accomplish specific tasks by processing large amount of data and recognizing pattern in them. You can consider that building an artificial intelligence is like building a church. The first church took generations to finish. So most of the workers who were working in it never saw the final outcome.

20:54 Those working on it took pride in their crafts, building bricks and chiseling stone that was going to be placed into the great structure. So as AI researchers we should think of ourself as humble brick makers whose job is to study how to build components example parses planners or learning algorithm or etc anything that someday someone and somewhere will integrate into the intelligent systems. Some of the examples of artificial intelligence from our day-to-day life are Apple series chess playing computer Tesla's self-driving car and many more.

21:41 These examples are based on deep learning and natural language processing. Well, this was about what is AI and how it gained its hype. So, moving on ahead, let's discuss about machine learning and see what it is and why it was even introduced. Well, machine learning came into existence in the late 80s and the early '90s. But what were the issues with the people which made the machine learning come into existence? Let us discuss them one by one.

22:05 In the field of statistics, the problem was how to efficiently train large complex model. In the field of computer science and artificial intelligence, the problem was how to train more robust version of AI system. While in the case of neuroscience, problem faced by the researchers was how to design operational model of the brain. So these were some of the issues which had the largest influence and led to the existence of the machine learning. Now this machine learning shifted its focus from the symbolic approaches it had inherited from the AI and moved towards the methods and model it had borrowed from statistics and probability theory.

22:27 So let's proceed and see what exactly is machine learning. Well, machine learning is a subset of AI which enables the computer to act and make datadriven decisions to carry out a certain task. These programs or algorithms are designed in a way that they can learn and improve over time when exposed to new data. Let's see an example of machine learning. Let's say you want to create a system which tells the expected weight of a person based on its height.

22:51 The first thing you do is you collect the data. Let's see this is how your data looks like. Now each point on the graph represent one data point. To start with we can draw a simple line to predict the weight based on the height. For example, a simple line walus 100 where w is weight in kgs and h is height in cm. This line can help us to make the prediction. Our main goal is to reduce the difference between the estimated value and the actual value.

23:35 So in order to achieve it, we try to draw a straight line that fits through all these different points and minimize the error. So our main goal is to minimize the error and make them as small as possible. Decreasing the error or the difference between the actual value and estimated value increases the performance of the model. Further on, the more data points we collect, the better our model will become. We can also improve our model by adding more variables and creating different prediction lines for them once the line is created.

23:57 So from the next time if we feed a new data for example height of a person to the model it would easily predict the data for you and it will tell you what his predicted weight could be. I hope you got a clear understanding of machine learning. So moving on ahead let's learn about deep learning. Now what is deep learning? You can consider deep learning model as a rocket engine and its fuel is its huge amount of data that we feed to these algorithms.

24:21 The concept of deep learning is not new but recently its hype has increased and deep learning is getting more attention. This field is a particular kind of machine learning that is inspired by the functionality of our brain cells called neuron which led to the concept of artificial neural network. It simply takes the data connection between all the artificial neurons and adjust them according to the data pattern. More neurons are added if the size of the data is large.

24:44 It automatically features learning at multiple levels of abstraction thereby allowing a system to learn complex function mapping without depending on any specific algorithm. You know what no one actually knows what happens inside a neural network and void works so well. So currently you can call it as a black box. Let us discuss some of the example of deep learning and understand it in a better way. Let me start with an simple example and explain you how things happen at a conceptual level.

25:08 Let us try and understand how you recognize a square from other shapes. The first thing you do is you check whether there are four lines associated with a figure or not. Simple concept, right? If yes, we further check if they are connected and closed. Again if yes we finally check whether it is perpendicular and all its sides are equal. Correct? If everything fulfills yes it is a square. Well it is nothing but a nested hierarchy of concepts.

25:31 What we did here we took a complex task of identifying a square in this case and broke it into simpler task. Now this deep learning also does the same thing but at a larger scale. Let's take an example of machine which recognizes the animal. The task of the machine is to recognize whether the given image is of a cat or of a dog. What if we were asked to resolve the same issue using the concept of machine learning?

25:55 What we would do? First, we would define the features such as check whether the animal has whiskers or not or check if the animal has pointed ears or not or whether its tail is straight or curved. In short, we will define the facial features and let the system identify which features are more important in classifying a particular animal. Now when it comes to deep learning, it takes this to one step ahead. Deep learning automatically finds out the feature which are most important for classification.

26:42 Comparing to machine learning where we had to manually give out that features. By now I guess you have understood that AI is the bigger picture and machine learning and deep learning are its subpart. So let's move on and focus our discussion on machine learning and deep learning. The easiest way to understand the difference between the machine learning and deep learning is to know that deep learning is machine learning. More specifically, it is the next evolution of machine learning.

27:05 Let's take few important parameter and compare machine learning with deep learning. So starting with data dependencies. The most important difference between deep learning and machine learning is its performance as the volume of the data gets increased. From the below graph you can see that when the size of the data is small, deep learning algorithm doesn't perform that well. But why? Well, this is because deep learning algorithm needs a large amount of data to understand it perfectly.

27:29 On the other hand, the machine learning algorithm can easily work with smaller data set. Fine. Next comes the hardware dependencies. Deep learning algorithms are heavily dependent on high-end machines. While the machine learning algorithm can work on low-end machines as well. This is because the requirement of deep learning algorithm include GPUs which is an integral part of its working. The deep learning algorithm require GPUs as they do a large amount of matrix multiplication operations and these operations can only be efficiently optimized using a GPU as it is built for this purpose only.

27:51 Our third parameter will be feature engineering. Well, feature engineering is a process of putting the domain knowledge to reduce the complexity of the data and make patterns more visible to learning algorithms. This process is difficult and expensive in terms of time and expertise. In case of machine learning, most of the features are needed to be identified by an expert and then handcoded as per the domain and the data type. For example, the features can be a pixel value, shapes, texture, position, orientation or anything.

28:38 Fine. The performance of most of the machine learning algorithm depends on how accurately the features are identified and extracted. Whereas in case of deep learning algorithms, it try to learn highle features from the data. This is a very distinctive part of deep learning which makes it way ahead of traditional machine learning. Deep learning reduces the task of developing new feature extractor for every problem. Like in the case of CNN algorithm, it first try to learn the low-level features of the image such as edges and lines and then it proceeds to the parts of faces of people and then finally to the highle representation of the face.

29:00 I hope the things are getting clear to you. So let's move on ahead and see the next parameter. So our next parameter is problem solving approach. When we are solving a problem using traditional machine learning algorithm, it is generally recommended that we first break down the problem into different subp parts, solve them individually and then finally combine them to get the desired result. This is how the machine learning algorithm handles the problem. On the other hand, the deep learning algorithm solves the problem from end to end.

29:47 Let's take an example to understand this. Suppose you have a task of multiple object detection and your task is to identify what is the object and where it is present in the image. So let's see and compare how will you tackle this issue using the concept of machine learning and deep learning. Starting with machine learning. In a typical machine learning approach, you would first divide the problem into two step. First object detection and then object recognization.

30:09 First of all, you would use a bounding box detection algorithm like grab cut for example. to scan through the image and find out all the possible objects. Now once the objects are recognized you would use object recognization algorithm like SVM with hog to recognize relevant objects. Now finally when you combine the result you would be able to identify what is the object and where it is present in the image. On the other hand in deep learning approach you would do the process from end to end.

30:33 For example, in a yoloet, which is a type of deep learning algorithm, you would pass an image and it would give out the location along with the name of the object. Now, let's move on to a fifth comparison parameter. It's execution time. Usually, a deep learning algorithm takes a long time to train. This is because there are so many parameter in a deep learning algorithm that makes the training longer than usual. The training might even last for 2 weeks or more than that if you're training completely from the scratch.

30:57 Whereas in the case of machine learning, it relatively takes much less time to train ranging from a few weeks to few hours. Now the execution time is completely reversed when it comes to the testing of data. During testing the deep learning algorithm takes much less time to run. Whereas if you compare it with a KN&N algorithm which is a type of machine learning algorithm, the test time increases as the size of the data increase. Last but not the least, we have interpretability as a factor for comparison of machine learning and deep learning.

31:44 This factor is the main reason why deep learning is still thought 10 times before anyone uses it in the industry. Let's take an example. Suppose we use deep learning to give automated scoring to essays. The performance it gives in scoring is quite excellent and is near to the human performance. But there's an issue with it. It does not reveal why it has given that score. Indeed, mathematically it is possible to find out that which node of a deep neural network were activated.

32:07 But we don't know what the neurons were supposed to model and what these layers of neuron were doing collectively. So we failed to interpret the result. On the other hand, machine learning algorithm like decision tree gives us a crisp rule for what it chose and what it chose. So it is particularly easy to interpret the reasoning behind it. Therefore the algorithms like decision tree and linear or logistic regression are primary used in industry for interpretability. [Music] So why exactly are we using Python for artificial intelligence?

32:31 Why aren't we using any other language? Right now