This year, the market for AI models is becoming more and more cluttered with each passing week. Just last week, OpenAI released GPT-4.5. This was to be “larger and more compute-intensive than ever before” for the company.
The announcement comes just days after the introduction of xAI, which is owned by Elon Musk. groku 3 model — the company touted it as “the world’s smartest AI.” Before that, Anthropic released a hybrid inference model for the Claude chatbot. And in January, Chinese startup DeepSeek transformed the AI industry by building models using a modest number of computer chips at a much lower cost. The Al model, known as R1, was called a major advance.
However, when it comes to the evolution of the AI industry, the release of new AI models is only part of the picture. And it can be very difficult to track what’s actually happening with each new development. That’s because the field of AI is full of technical terms like LLM, neural networks, and algorithms.
So, to help you better understand what’s going on, here’s a series of explanations explaining some of the most common terms used in AI and why they’re important. In Part 1, we will clarify two basic terms: artificial intelligence and machine learning.
What is artificial intelligence?
Artificial intelligence (AI) refers to the field of computer science that aims to make computer systems think, reason, learn, and behave in ways similar to humans to solve complex systems.
This field of study was established in 1956 in a small workshop at Dartmouth College (New Hampshire, USA). It was organized by a young mathematician named John McCarthy, who was interested in the idea of creating thinking machines. He also convinced Marvin Minsky of Harvard University, Nathaniel Rochester of IBM, and Claude Shannon of Bell Telephone Laboratories to collaborate on the workshop. These four people are considered some of the founders of AI.
The term artificial intelligence was coined by McCarthy. “McCarthy later admitted that no one liked the name – after all, the goal was real, not ‘artificial’ intelligence – but ‘it had to be called something, so we called it ‘artificial intelligence,'” writes Melanie Mitchell in her book, Artificial Intelligence: A Guide for Thinking Humans.
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However, recently, the word AI has been heard more often as a technology or as an entity. For example, Google says it uses artificial intelligence to improve many of its products, making them smarter. Then there are AI models (more on that later) that power AI tools like OpenAI’s ChatGPT.
What is machine learning?
Machine learning (ML) is used to enable computer systems to mimic the way humans learn and perform tasks autonomously (that is, without instructions). ML is implemented by training a computer (this term is also explained below) on data so that it can make predictions about new information.
In other words, according to the technology website Built In, “Through a combination of arithmetic, statistics, and trial and error, machine learning systems can identify relationships and patterns within large datasets and draw conclusions about new data.”
As computer systems are exposed to more data, they can learn how to perform new tasks without being explicitly programmed.
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One of the best examples of real-life applications of machine learning is recommendation systems. Companies like Spotify and Netflix use machine learning models to track your behavior, recognize patterns in your viewing history, and use this data collection to accurately predict which artists and movies you’ll enjoy.
Next in the series: Deep learning and neural networks
