Hello, everyone.

This is Swaira welcome to today's topic of discussion on AI vs Machine Learning vs Deep Learning. These are the term that has confused many people, and if you too 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 subsets of each other. So let's move on and understand how exactly they differ from each other. 


Artificial Intelligence

The term artificial intelligence was first coined in the year 1956. The concept is pretty old, but it has gained popularity recently. But why well, the reason is earlier we had a 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 number of data statistics suggest that by 2020 the  accumulated volume of data will increase from 4.4 zettabyte stew roughly around 44 zettabytes or 44 trillion GBs of data along with such an enormous amount of data.

Now, we have more advanced algorithms and high-end computing power and storage that can deal with such a large amount of data as a result. It is expected that 70% of Enterprise will implement AI over the next 12 months, up from 40 percent in 2016 and 51 percent in 2017. Just for your understanding what does AI well, it's nothing but a technique that enables the machine to act like humans by replicating the behavior and nature with AI a machine can learn from the experience.

The machines are just the responses based on new input thereby performing human-like tasks. Artificial intelligence can be trained to accomplish specific tasks by processing a large amount of data and recognizing a pattern in them. You can consider that building artificial intelligence is like Building a Church, the first church took generations to finish. So most of the workers were working in it never saw the final outcome those working on it took pride in their craft building bricks and chiseling stone that was going to be placed into the great structure.

Machine learning 

Machine learning came into existence in the late 80s and the early 90s, but what were the issues with the people which made machine learning come into existence? Let us discuss them one by one in the field of Statistics. The problem was how to efficiently train large complex models in the field of computer science and artificial intelligence. The problem was how to train a more robust version of the AI system while in the case of Neuroscience problem faced by the researchers was how to design an operating model of the brain. So these are some of the issues which had the largest influence and led to the existence of machine learning. Now this machine learning shifted its focus from the symbolic approaches. It had inherited from the AI and move towards the methods and model. It had borrowed from statistics and probability theory. So let's proceed and see what exactly machine learning is. Well Machine learning is a subset of AI which the computer to act and make data-driven decisions to carry out a certain task. These programs are 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 that tells the expected weight of a person based on its side.

The first thing you do is collect the data. Let's see there is how your data looks like now each point on the graph represents 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 W equal x minus hundred where W is waiting for kgs and edges hide and centimeter 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. So 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 production lines for them. Once the line is created. So the next time, if we feed new data, for example, the height of a person to the model, it would easily predict the data for you and it will tell you what has predicted weight could be. I hope you got a clear understanding of machine learning.


Deep learning

Let's learn about deep learning. Now, what is deep learning? You can consider the deep learning model as a rocket engine and its fuel is the huge amount of data that we feed to these algorithms 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 neurons which led to the concept of artificial neural network. It simply takes the data connection between all the artificial neurons and adjusts them according to the data pattern more neurons are added at the size of the data is large 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, no one actually knows what happens inside a neural network and why it works so well, so currently, you can call it a black box. 

Let us discuss some of the examples of deep learning and understand it in a better way. Let me start with a simple example and explain to you how things happen at a conceptual level. Let us try and understand how you recognize a square from other shapes. The first thing you do is check whether there are four lines associated with a figure or not a simple concept, right? If yes, we further check if they are connected and closed again a few years. We finally check whether it is perpendicular and all its sides are equal, correct if it Fulfills. Yes, it is a square. Well, it is nothing but a nested hierarchy of Concepts what we did here we took a complex task of identifying a square and this case and broken it into simpler tasks. Now this deep learning also does the same thing but at a larger scale.


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