What are artificial intelligence and machine learning? Let’s learn more!

This year, the market for AI models is getting more and more saturated every week. Only last week was GPT-4.5, the company's "bigger and more compute-intensive than ever," released.

Elon Musk's xAI company unveiled the Grok 3 model a few days before the launch, marketing it as the "world's smartest AI." Claude, a chatbot from Anthropic, had previously introduced a hybrid reasoning technique. And in January, the Chinese startup DeepSeek totally upended the AI business by developing a model at a far cheaper cost and using a very small number of computer processors. The Al model—also referred to as the R1—was heralded as a major breakthrough.

However, the development of new AI models is just one facet of the industry's progress. Furthermore, it can be somewhat daunting to keep track of what's actually happening with every new development. This is because artificial intelligence is notoriously full of technical words like algorithms, neural networks, LLMs, and so on.

The following explainers will explain some of the most used terms in artificial intelligence and their meanings to help you better understand what is going on. In the first installment, we explain two basic ideas: artificial intelligence and machine learning.

Artificial intelligence: what is it?

In order to address complex issues, computer science aims to make computer systems understand, reason, learn, and behave like people. Artificial intelligence (AI) is the term for this.

This field of research began in 1956 in a small workshop at Dartmouth College in New Hampshire, USA. It was organized by John McCarthy, a young mathematician who had developed an interest in the idea of creating a thinking machine. He also persuaded Claude Shannon of Bell Telephone Laboratories, Nathaniel Rochester of IBM, and Marvin Minsky of Harvard University to help with the workshop. Many consider these four men to be among the forerunners of artificial intelligence.

What is machine learning?

Computer systems may imitate human learning and perform tasks autonomously (i.e., without instructions) thanks to machine learning (ML). Machine learning (ML) involves training computers on data to make predictions about new information. Later explainers will also provide clarification on this phrase.

"Machine learning systems discover relationships and patterns within large datasets, allowing them to draw conclusions about new data, through a combination of arithmetic, statistics, and trial-and-error," the tech website Built In explains.