Understanding Artificial Intelligence

What is Artificial intelligence? It is a machine’s ability to replicate human intelligence. It can learn, interact with the environment, and solve problems. I would argue that it is the next biggest technological advancement after the internet. But, how exactly does a machine - that sees everything in 0’s and 1’s - carry out these complicated actions?

Machine Learning

Machine learning is one of the significant components that separate AI from ordinary systems. With machine learning, computers can learn without having to be manually programmed. It makes use of data to recognise patterns and create predictions. Here’s an example. Imagine a red balloon and a tree. The computer can recognise that the balloon is a balloon because it is red, and it can recognise the tree because it’s green. But what if you make the balloon green? Well, the computer will choose another variable to compare, like the shape. And if you find a balloon-shaped tree to compare with the balloon, it will create even more variables.

Simply put, machine learning allows a computer to find patterns in data. Additionally, the more data that the computer has, the more accurately it can predict patterns as it has more and more variables to compare. This is the essence of what allows artificial intelligence to learn like a human.

Algorithms

But, how exactly does a computer with machine learning… learn? The answer is algorithms. An algorithm is a complex set of rules the computer follows to compare variables and recognise patterns. It enables the computer to run independently. Machine learning algorithms have four types: supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning makes use of examples. An operator - the programmer - gives a known dataset to the algorithm, and the algorithm attempts to make predictions while the operator corrects any mistakes. Similarly, unsupervised learning also uses a known dataset and an operator. But unlike supervised learning, some of that data lacks information, which allows the algorithm to create its own “label” for the data.

Unsupervised learning is similar to my description of machine learning. It studies data to recognise patterns, decides the relationship between groups of data and stores these relationships as variables to consider. Moving on, reinforcement learning uses trial and error. Based on its parameters, it will consider multiple possibilities and decide the most optimal option.

Big Data

Naturally, artificial intelligence requires big data to run. Big data refers to large and complex datasets that no human can traverse. However, AI systems require big data to run because they base their predictions on this big data. For example, hospitals have millions of patients every day and consequently generate a lot of data. Based on this data, an AI system can identify patterns and predict the diagnosis of particular patients. Without this data, the AI cannot make decisions as it has no reference.

Artificial intelligence is a spectacular creation. With its machine learning, big data, and algorithms, it may seem omnipotent. However, we must realise that AI runs on data that was generated by us, and you won’t ever have to worry about AI replacing us as it simply doesn’t have our creativity. So, don’t you find AI fascinating?