Understanding AI jargon: Artificial intelligence vocabulary

Listen to the author reading this blog post:

the title "2023 Word of the Year" on an illustrated background showing a modern city, a flying robot and a plane with bird-like wingsby Kate Woodford

Today, the Cambridge Dictionary announces its Word of the Year for 2023: hallucinate. You might already be familiar with this word, which we use to talk about seeing, hearing, or feeling things that don’t really exist. But did you know that it has a new meaning when it’s used in the context of artificial intelligence?

To celebrate the Word of the Year, this post is dedicated to AI terms that have recently come into the English language. AI, as you probably know, is short for artificial intelligence – the use of computer systems with qualities similar to the human brain that allow them to ‘learn’ and ‘think’. It’s a subject that arouses a great deal of interest and excitement and, it must be said, a degree of anxiety. Let’s have a look at some of these new words and phrases and see what they mean and how we’re using them to talk about AI.

A word that we hear all the time now is chatbot. A chatbot is a computer program designed to have conversations with people, usually over the internet. Most chatbots have learned from, or are trained on, a small amount of language that is based around problems that a company’s customers most commonly have, such as returning a purchase or cancelling a contract. But recently a new breed of chatbot has appeared that uses a kind of AI called generative AI (or GenAI) in which computer models can create text and images. Text-based generative AI is based on huge amounts of language data in the form of a large language model (LLM), and can apparently have a natural conversation with you about any subject you choose, as well as writing poems, stories or even essays.  These models are becoming increasingly impressive, with some even able to pass exams in law and medicine! But don’t expect them to get everything right when you ask them a question – there have been some notable cases when AI models have simply ‘made something up’ that wasn’t true at all. We call these mistakes hallucinations or confabulations. In general, the accuracy and quality of the results you get from AI models are based on the amount and quality of the data that they are trained on.

It’s hard to predict exactly what the effects of AI will be for the world. Some people are convinced that it will make certain professions redundant. However, it is possible that the new skill of prompt engineering – knowing how to phrase questions and requests to AI tools to get exactly the responses that you want – will help workers be more efficient and make many jobs easier to do.

As the field of AI continues to develop quickly, so does the language we use to talk about it. In a recent New Words post, we shared some words about AI that are being considered for addition to the Cambridge Dictionary. Find out what they mean and vote on whether you think they should be added to the dictionary.

Have you tried to use any generative AI tools? Have you come across any strange, funny or worrisome AI hallucinations? Share your thoughts in the comments below!

Find out more about the Cambridge Dictionary Word of the Year, or read about previous Words of the Year on this blog.

25 thoughts on “Understanding AI jargon: Artificial intelligence vocabulary

  1. Em

    I have! I used generative AI to write an advert about my house. My son used it to write covering letters for job applications. I’ve also used it to summarize the contents of some economics articles. It’s amazing! It did a really good job and we could ask it to make modifications, too.

  2. Murozel

    I have seen a GenAI confabulate when it just mixed up historical facts upon a question I asked. When I gave feedback on this, it simply apologised and came up with a better version of its answer, which was impressive.

  3. GUI

    I using chatbots to make applications with languages I don´t really full understand or with project patterns, algorithmical structures such vectors and maps…

  4. Kristin

    Thanks for the post. I’m wondering if the voiceover was recorded by the author herself, or was it made with some kind of voice AI tool? Forgive me – as in this AI generation, I could barely believe anything I heard or saw…

  5. doctoribrahmuswati

    Having spent a significant part of my life as a love astrologer, I find the evolving language around artificial intelligence fascinating. The concept of ‘hallucinations’ or ‘confabulations’ in AI models underscores the need for a nuanced understanding of their capabilities and limitations. In my profession, effective communication is key, and it’s intriguing to see how this skill aligns with the emerging field of prompt engineering in AI.

    As someone who helps people through love healing astrology, I appreciate the importance of accurate communication. While AI, especially generative models, has shown remarkable advancements, the notion of ‘making something up’ raises interesting questions about the reliability of these tools. It’s a reminder that, like any tool, the accuracy of AI results depends on the quality of the data it’s trained on.

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