Artificial Intelligence (1)— What is it?

Kasun Dissanayake
6 min readJan 17, 2023


Artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. This concept is introduced first in 1956. From that stage, this concept is grown with many more improvements.

You may hear about Machine Learning and Deep Learning words with this Artificial Intelligence subject. Artificial Intelligence has a wide range and Machine Learning and Deep Learning are subsets of Artificial Intelligence. Artificial Intelligence is creating machines that are intelligent enough like the human brain. As an example, you can identify dogs and cats separately. How you can do that? For sure you may have seen a dog or a cat and you know how the dog is and how the cat is and the appearance of these animals. But how we can teach a thing like this to a machine?

If you have a programming background you can implement simple programs. As an example identify positive and negative numbers.

But writing a code to identify Dogs and Cats separately is not a simple task. How we can do that? We can learn machines to identify Dogs and Cats. Here we are training the Machine Learning model by giving a large dataset of Cats and Dogs. The dataset is comprised of photos of dogs and cats provided as a subset of photos from a much larger dataset of 3 million manually annotated photos.

The machine will create a pattern according to the features of these millions of photos for DOG and CAT. Now the machine can identify Cat or a Dog separately. This is a very basic level of Artificial Intelligence.

Applications that are using Artificial Intelligence

Virtual Assistants — A virtual assistant, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes tasks for the user. Most companies have started investing in developing AI virtual assistants. The AI virtual Assistants can create solutions that can efficiently and quickly process large amounts of data and provide insights and intelligent recommendations. With voice and speech recognition capabilities, AI assistants can perform daily tasks like adding events to your calendar, setting up a reminder, or even tracking expenses. Statista estimates that by 2024, there will be more than 8 billion digital voice assistants in use globally, approximately equal to the world’s population.

Types of Virtual Assistants

Self-Driving Cars — As technology advances, the car industry has used new developments to develop new ways to ease the user (driver). One of them includes using artificial intelligence to make cars self-driving. A self-driving car (also known as an autonomous car or driverless car) is a vehicle that uses a different number of sensors, radars, cameras, and artificial intelligence to travel to destinations without needing a human driver. Many companies have started to manufacture self-driving cars, such as Tesla, Audi, BMW, Ford, and many more. These companies put their vehicles through many tests to ensure they are eligible to be on the road without making any errors. A car must navigate routes to the predetermined destination without any human intervention to qualify as a fully autonomous car. Artificial intelligence powers self-driving vehicle frameworks. Engineers of self-driving vehicles utilize immense information from image recognition systems, alongside AI and neural networks, to assemble frameworks that can drive self-sufficiently. The neural networks distinguish patterns in the data, which is fed to the AI calculations. That data includes images from cameras for self-driving vehicles. The neural networks figure out how to recognize traffic signals, trees, checks, people on foot, road signs, and different pieces of any random driving environment.

Recommendation Systems — A recommendation system is an artificial intelligence or AI algorithm, usually associated with machine learning, that uses Big Data to suggest or recommend additional products to consumers. These can be based on various criteria, including past purchases, search history, demographic information, and other factors. Recommender systems are highly useful as they help users discover products and services they might otherwise have not found on their own.

Recommender systems are trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions. These include impressions, clicks, likes, and purchases. Because of their capability to predict consumer interests and desires on a highly personalized level, recommender systems are a favorite with content and product providers. They can drive consumers to just about any product or service that interests them, from books to videos to health classes to clothing. Ex: Youtube, Facebook, Netflix, Amazon….

Facial-Recognition — Facial recognition is one of the front-runner applications of AI. It is one of the advanced forms of biometric authentication capable of identifying and verifying a person using facial features in an image or video from a database. Face recognition uses AI algorithms and ML to detect human faces from the background. The algorithm typically starts by searching for human eyes, followed by eyebrows, nose, mouth, nostrils, and iris. Once all the facial features are captured, additional validations using large datasets containing both positive and negative images confirm that it is a human face. Some of the common techniques used for facial recognition are feature-based, appearance-based, knowledge-based, and template matching. Each of these methods has its advantages and disadvantages.

Feature-based methods rely on features such as eyes or nose to detect a face. The outcomes of this method could vary based on noise and light. Further, appearance-based methods use statistical analysis and machine learning to match the characteristics of face images.

In a knowledge-based approach, a face is recognized based on predefined rules. This could be challenging considering the efforts needed to define well-defined rules. Whereas template-matching methods compare images with previously stored face patterns or features and correlate the results to detect a face. However, this method fails to address variations in scale, pose, and shape.

Artificial Intelligence offers a multitude of opportunities and endless possibilities to work for the betterment of the world. However, it is essential to pay attention to the ethics and privacy of people while dealing with data. Data storage, management, and security are the other aspects that will play an important role in making these technologies invasive.

Thank You! See you in another tutorial.



Kasun Dissanayake

Senior Software Engineer at IFS R & D International || Former Software Engineer at Pearson Lanka || Former Associate Software Engineer at hSenid Mobile