AI Literacy

You probably encounter versions of ‘artificial intelligence’ every day in all walks of life – from chat bots to email assistance, from Google maps monitoring traffic to banking fraud detection, from Amazon or Netflix recommendations to new features on smartphones offering to help.  Two of the best-known examples which have been around for a while are the predictive text features on smartphones and Google Translate offering the ability to read (or generate) text with varying degrees of accuracy in a large number of languages.

What is meant in these pages by ‘AI’ is the more limited application of ‘large language models’ or ‘generative AI’ (sometimes generative image AI or text gen AI) which produce text or images in response to user inputs, or prompts.  Some will accept speech input as well as typed text.  These hit the headlines and gained millions of users when ChatGPT 3.5 was released in November 2002.  Others followed, for example Bard and Gemini.  These generative AIs can have the appearance of chatting with you or writing fully fledged answers or essays, but it should always be remembered that LLMs in reality have simply processed a lot of text and have good statistical models on what words are likely to go with other words.  Or, in the case of images, have been trained on billions of images in order to generate pictures which can look photorealistic.

Such generative AI is developing quickly and they have the potential to change how we work, create and even think.  It’s likely that a digital Jeeves (a clever and resourceful butler in P.G. Wodehouse novels) is coming to a device near you very soon if it’s not already there.  Learning how to prompt such AIs to get useful results will become increasingly important.