What are Machine Learning and Deep Learning in Artificial Intelligence

Devices connected to the Internet are called smart devices. Almost everything related to the Internet is known as smart device. In this context, the code that makes the devices MORE INTELLIGENT – so that it can operate with minimal or no human intervention – can be said to be based on Artificial intelligence (HAVE). The other two, namely: Machine learning (ML), and Deep learning (DL), are different types of algorithms designed to bring more functionality to smart devices. Let’s see AI vs ML vs DL in detail below to understand what they do and how they are connected to AI.

What is artificial intelligence in ML and DL

AI can be called a superset of Machine Learning (ML) and Deep Learning (DL) processes. AI is generally a generic term used for ML and DL. Deep Learning is again a subset of Machine Learning (see image above).

Some argue that Machine Learning is no longer part of universal AI. They say that ML is a complete science in its own right and therefore it is not necessary to call it with reference to artificial intelligence. AI thrives on data: Big Data. The more data it consumes, the more precise it is. It is not that he will always predict correctly. There will also be false flags. AI trains on these mistakes and becomes better at what it is supposed to do – with or without human supervision.

Artificial intelligence cannot be defined correctly because it has penetrated almost all industries and affects far too many types of (business) processes and algorithms. We can say that artificial intelligence is based on data science (DS: Big Data) and contains machine learning as a separate part. Likewise, Deep Learning is a separate part of Machine Learning.

Given the tilt of the IT market, the future would be dominated by connected smart devices, called the Internet of Things (IoT). Smart devices mean artificial intelligence: directly or indirectly. You already use artificial intelligence (AI) in many tasks in your daily life. For example, typing on a smartphone keyboard that keeps improving on the “word suggestion”. Among other examples where you unknowingly deal with artificial intelligence, you are looking for things on the Internet, online shopping and, of course, the always smart Gmail and Outlook inboxes.

What is machine learning

Machine learning is an area of ​​artificial intelligence where the goal is to learn and train a machine (or a computer or software) without too much programming. These devices require less programming because they apply human methods to perform tasks, including learning to function better. Basically, ML means programming a computer / device / software a bit and allowing it to learn on its own.

There are several methods to facilitate machine learning. Among them, the following three are widely used:

1. supervised,
2. Unattended, and
3. Reinforcement learning.

Supervised learning in machine learning

Supervised in the sense that the programmers first supply the machine with labeled data and responses already processed. Here, labels refer to row or column names in a database or spreadsheet. After providing huge sets of this data to the computer, he is ready to analyze other sets of data and provide results himself. This means that you have taught the computer to analyze the data transmitted to it.

Usually it is confirmed using the 80/20 rule. Huge sets of data are transmitted to a computer which tries and learns the logic behind the answers. 80% of the data of an event is transmitted to the computer with the responses. The remaining 20% ​​is fed unanswered to see if the computer can deliver correct results. These 20% are used for cross-checking in order to see how the computer (machine) learns.

Unsupervised machine learning

Unsupervised learning occurs when the machine is fed with random data sets that are not labeled and are not in order. The machine has to figure out how to produce the results. For example, if you give him softballs of different colors, he should be able to sort them by color. So, in the future, when the machine is presented with a new soft ball, it will be able to identify the ball with labels already present in its database. There is no training data in this method. The machine must learn on its own.

Reinforcement learning

Machines that can make a sequence of decisions fall into this category. Then there is a reward system. If the machine does what the programmer wants, it gets a reward. The machine is programmed so that it aspires to maximum rewards. And to get it, it solves the problems by designing different algorithms in different cases. This means that the AI ​​computer uses trial and error methods to obtain results.

For example, if the machine is an autonomous vehicle, it must create its own scenarios on the road. There is no way for a programmer to program each step because he cannot think of all the possibilities when the machine is on the move. This is where reinforcement learning comes in. You can also call it AI by trial and error.

How is Deep Learning different from Machine Learning

Deep Learning is intended for more complicated tasks. Deep Learning is a subset of Machine Learning. Only, it contains more neural networks that help the machine to learn. Artificial neural networks are not new. Laboratories around the world are trying to create and improve neural networks so that machines can make informed decisions. You must have heard of Sophia, a humanoid in Arabia who obtained regular citizenship. Neural networks are like the human brain, but not as sophisticated as the brain.

There are good networks that allow unsupervised deep learning. You can say that Deep Learning is more made up of neural networks that mimic the human brain. However, with enough sample data, deep learning algorithms can be used to retrieve details from sample data. For example, with an image processor DL ​​machine, it is easier to create human faces whose emotions change according to the questions asked of the machine.

The above explains AI vs MI vs DL in simpler language. AI and ML are vast fields – just opening up and with huge potential. This is the reason why some people are against the use of Machine Learning and Deep Learning in Artificial Intelligence.