What Is AI?

AI systems work by being fed or ingesting vast amounts of data and using human-like cognitive processes to analyse and assess that data. In doing this, AI systems identify and categorise patterns and use them to carry out tasks or make predictions about future outcomes without direct human oversight or instructions.

For example, an image-generating AI program like Midjourney that’s fed huge numbers of photographs can learn how to create “original” images based on prompts entered by a user. Similarly, a customer service AI chatbot trained on large volumes of text can learn how to interact with customers in ways that mimic human customer service agents.

While each system is different, AI models are usually programmed following the same five-step process:

1. Training—the AI model is fed huge amounts of data and uses a series of algorithms to analyse and assess the data.
   
   
2. Reasoning—the AI model categorises the data it has received and identifies any patterns in it.
   
   
3. Fine-tuning—as the AI model tries different algorithms, it learns which ones tend to be most successful and fine-tunes its actions accordingly.
   
   
4. Creating—the AI model uses what it’s learned to carry out assigned tasks, make decisions, or create music, text, or images.
   
   
5. Improving—finally, the AI model makes continuous ongoing adjustments to improve its accuracy, effectively “learning” from its experience.

Most modern AI systems use a variety of techniques and technologies to simulate the processes of human intelligence. The most important of these are deep learning and machine learning (ML). While the terms machine learning and deep learning are sometimes used interchangeably, they’re actually very distinct processes in the context of AI training.

Machine learning uses algorithms to analyse, categorise, sort, learn, and make sense of massive quantities of data to produce precise models and predict outcomes without having to be told exactly how to do it.

Deep learning is a sub-category of machine learning that accomplishes the same objectives by using neural networks to mimic the structure and function of the human brain. We’ll explore both of these concepts in greater detail below.

There are four main different types of machine learning:

1. Supervised machine learning—the AI uses datasets that are known, established, and classified to discover patterns before feeding them to the algorithm.
   
   
2. Semi-supervised machine learning—AI is trained on a small known and labeled dataset, which it then applies to larger, unlabeled, and unknown datasets.
   
   
3. Unsupervised machine learning—the AI learns from datasets that are unknown, unlabelled, and unclassified.
   
   
4. Reinforced machine learning—the AI model isn’t trained initially on any datasets, so it learns from trial and error instead and alters its behaviors until it is successful.

Machine learning is what enables self-driving cars to respond to changes in the environment well enough to be able to deliver their passengers safely to their chosen destination. Other applications of machine learning run the gamut from image and speech recognition programs, language translation apps, persona-driven AI agents, and data mining to credit card fraud detection, health care diagnoses, and social media, product, or brand recommendations.

Deep learning is a form of machine learning that’s based on the use of advanced neural networks—machine learning algorithms that emulate how the neurons in a human brain function in order to identify complex patterns in large sets of data.

For example, even very young children can instantly tell their parents apart from other adults because their brains can analyse and compare hundreds of unique or distinguishing details in the blink of an eye, from the color of their eyes and hair to distinctive expressions or facial features.

Neural networks replicate the way human brains work by similarly analysing thousands or millions of tiny details in the data they’re fed to detect and recognise larger patterns between them. Generative AI (or “GenAI”) systems like OpenAI’s ChatGTP or the Midjourney image generator, for example, use deep learning to ingest and analyse large numbers of images or text, and then use that data to create new text or images that are similar to—but different from—the original data.