Want to Know the AI Lingo? Learn the Basics, From NLP to Neural Networks

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A bewildering array of new terms has accompanied the rise of artificial intelligence, a technology that aims to mimic human thinking. From generative AI to machine learning, neural nets and hallucinations, we’ve gained a whole new vocabulary. Here’s a guide to some of the most important concepts behind AI to help demystify one of the most impactful technology revolutions of our lifetime: Algorithm: Today’s algorithms are typically a set of instructions for a computer to follow.

Those designed to search and sort data are examples of computer algorithms that work to retrieve information and put it in a particular order. They can consist of words, numbers, or code and symbols, as long as they spell out finite steps for completing a task. But algorithms have their roots in antiquity, going at least as far back as clay tablets in Babylonian times.

A Euclidean algorithm for division is still in use today, and brushing your teeth could even be distilled into an algorithm, albeit a remarkably complex one, considering the orchestration of fine movements that go into that daily ritual. Machine Learning: a branch of AI that relies on techniques that let computers learn from the data they process. Scientists had previously tried to create artificial intelligence by programming knowledge directly into a computer.

You can give an ML system millions of animal pictures from the web, each labeled as a cat or a dog. This process of feeding information is known as “training." Without knowing anything else about animals, the system can identify statistical patterns in the pictures and then use those patterns to recognize and classify new examples of cats and dogs. While ML systems are very good at recognizing patterns in data, they are less