How AI is different from Machine Learning?

We often come up with the terms like AI and ML in our daily life and they are used alternatively for the same topic. Most of the article writers and bloggers don’t really understand these terms and categorize these in the same category. Actually, they are different and they have a different scope of the field. This use of these terms has caused confusion in the minds of readers. These terms are very closely related but are different.

What Is Artificial intelligence (AI)?

In the early days, AI was defined as the machine doing a set of sequential instructions embedded by the Human. Now the definition is changed and it includes the machine’s own ability to make decisions on its own. AI system consists of features like learning, reasoning, planning, perception, motion, and problem-solving.

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In general, AI can be divided into Narrow AI and General AI. Narrow AI systems usually perform single tasks. Some of these tasks include email spam filtering, recommendation systems, and autonomous vehicle.

The other category is General AI. These systems have the ability to think and function like humans do to make decisions. General AI is also known as strong AI. Strong AI uses AI framework theory which is based on the ability to recognize other entities like needs, emotions, and thoughts

Machine learning

Machine learning (ML) is part of a greater whole of AI. ML depends on defining behavioral rules learned from a set of data through patterns recognitions. The main focus of ML is to make machines learn themselves through reasoning mechanisms close to human learning processes like eyes, for computers cameras, sonar, radars and other, sense of feelings, for computers temperature meters, smoke meters, and others.

In the early days, AI was just rule-based systems, and hardcoded algorithms developed to do single tasks. Machine learning was introduced when the objective of AI was shifted towards making systems which mimics human behavior and how human learn.

Machine learning is mainly divided into three categories:

Supervised learning:

It consists of a set of labeled data. That labeled data is used to train the system by giving instructions of particular case outcomes and tons of data.  to train the model on. This algorithm predicts the action to be taken on the training data, and if the resulting outcome is not correct then is tuned for expected performance. Once the system achieves its goals on training data it is deployed in the real environment.

Unsupervised learning:

In this category, the system is given just the algorithm and unlabeled data to the system. The algorithm allows the system to find and identify patterns. The goal of the system is to model the underlying structure to learn more about the data. Algorithms allow systems to discover interesting patterns in the data on their own.

Reinforcement learning.

A set of rules is given to the system and lets the system find the solution for the goal. This type involves a reward system. The system maximizes the rewards within the set of rules. The most known system of this kind is the Ant Colony.

AI refers to a machine that behaves just like a human and thinks like humans. There are different techniques involved in it and ML is one of them. ML system just allows the system to learn and take action by getting input from different techniques in AI like one of them involves computer vision. ML alone cannot make a whole system that mimics human learning and human reasoning.

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