4 min readNew DelhiMar 5, 2025 12:09 PM IST
First published on: Mar 4, 2025 at 06:39 PM IST
The market of AI models is getting increasingly cluttered with each passing week this year. Just last week, OpenAI released GPT-4.5, which was supposed to be the company’s “bigger and more compute-intensive than ever”.
The launch came days after Elon Musk-owned xAI introduced the Grok 3 model — the company touted it as the “world’s smartest AI”. Before that, Anthropic released a hybrid reasoning model for its Claude chatbot. And in January, Chinese startup DeepSeek upended the AI industry by building a model at a much cheaper cost and using a modest number of computer chips. Known as the R1, the Al model was called a major breakthrough.
But the release of new AI models is just a part of the picture when it comes to developments in the AI industry. And it can be quite daunting to follow what’s actually happening with each new development. That’s because the field of AI is filled with jargon such as LLMs, neural networks, algorithms, etc.
So, to better understand what is going on, here is a series of explainers, which will break down some of the most common terms used in AI, and why they are important. In the first instalment, we declutter two basic terms: artificial intelligence and machine learning.
What is artificial intelligence?
Artificial intelligence (AI) refers to the field of computer science which aims to make computer systems think, reason, learn, and act to solve a complex system like humans.
This field of research was established in 1956 at a small workshop at Dartmouth College (New Hampshire, United States). It was organised by a young mathematician named John McCarthy, who had become intrigued with the idea of creating a thinking machine. He also persuaded Marvin Minsky of Harvard University, Nathaniel Rochester from IBM, and Claude Shannon from Bell Telephone Laboratories to help with the workshop. These four men are considered some of the founding fathers of AI.
The term artificial intelligence was coined by McCarthy. “McCarthy later admitted that no one really liked the name — after all, the goal was genuine, not ‘artificial’, intelligence — but ‘I had to call it something, so I called it “Artificial Intelligence”,’” wrote Melanie Mitchell in her book, ‘Artificial Intelligence: A Guide for Thinking Humans’.
However, these days the term AI is often heard about as a technology, or even as an entity. For instance, Google says it has used artificial intelligence to improve many of its products, making them smarter. Then, there are AI models (which will be explained in detail in subsequent explainers) that power AI tools such as OpenAI’s ChatGPT.
What is machine learning?
To enable computer systems to imitate the way that humans learn, and perform tasks autonomously (meaning, without instructions), machine learning (ML) is used. ML is implemented by training (this term will also be explained in subsequent explainers) computers on data so that they can make predictions about new information.
In other words, “Through a combination of arithmetic, statistics and trial-and-error, machine learning systems identify relationships and patterns within large datasets, enabling them to draw conclusions about new data,” according to Built In, a tech website.
As the computer systems get exposed to more data, they get better at learning to perform new tasks without the need for being explicitly programmed to do so.
One of the best examples of real-life applications of machine learning is recommendation systems. Companies such as Spotify or Netflix use machine learning models to track the user’s behaviour to recognise patterns in their listening and viewing history, and then use this data collection to accurately predict which artists or films they may enjoy.
NEXT IN SERIES: Deep learning and neural networks
AI as a field of research was established in 1956 at a small workshop at Dartmouth College (New Hampshire, United States). (Photo: Pixabay) 
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