Artificial Intelligence Explained: What is AI, how it works, where it is used, can it replace human beings?

When Microsoft co-founder Bill Gates said last week that Artificial Intelligence or AI would replace humans for most things, he shared his insights on what he thinks will be future-proof in the upcoming years. He also predicted that AI can not replace only three types of employees. This is enough to coax all of us to understand what AI is and how it works. 

What is Artificial Intelligence?

AI is the capability of computational systems to perform tasks that are generally performed by humans using their intelligence. These may involve learning, reasoning, problem-solving, perception, and decision-making. The AI tries to mimic human intelligence by perceiving the environment and using the learning and intelligence of human beings to achieve the goal. To do so, it develops and studies methods and software. These machines are called the AIs.

Use of AI

The AI is used in a wide range of applications like the web search engines (Google Search); recommendation systems (YouTube, Amazon, and Netflix); virtual assistants (Google Assistant, Siri, and Alexa); autonomous vehicles (Waymo); generative and creative tools (ChatGPT and AI art).

How does AI work?

Artificial Intelligence works using algorithms, data, and computational power. An algorithm is a set of instructions that are followed in an attempt to solve a problem or accomplish a task. Machin Learning is a subset of AI where systems learn patterns from data without being explicitly programmed. It is used in the email's spam filter. 

Algorithm

Inspired by the human brain, neural networks are layers of interconnected "nodes" that process data. Natural Language Processing uses an algorithm that helps AI understand and generate human language, powering tools like chatbots. The most important part of an AI is the training phase when it is fed labeled data and adjusts its internal parameters to minimise errors. 

Training of AI

After getting trained, the AI applies its learned model to new, unseen data. It is used in the facial recognition system to compare pixel patterns in a photo to its trained database. Similarly, a language model predicts the next word in a sentence based on patterns in prior text.