This bubbling mixture of excitement and fear makes it hard to weigh the opportunities and risks. But lessons can be learned from other industries, and from past technological shifts. So what has changed to make\u00a0ai<\/small>\u00a0so much more capable? How scared should you be? And what should governments do?<\/p>\n
In a\u00a0special Science section<\/a>, we explore the workings of\u00a0llm<\/small>s and their future direction. The first wave of modern\u00a0ai<\/small>\u00a0systems, which emerged a decade ago, relied on carefully labelled training data. Once exposed to a sufficient number of labelled examples, they could learn to do things like recognise images or transcribe speech. Today\u2019s systems do not require pre-labelling, and as a result can be trained using much larger data sets taken from online sources.\u00a0llm<\/small>s can, in effect, be trained on the entire internet\u2014which explains their capabilities, good and bad.<\/p>\n Those capabilities became apparent to a wider public when Chatgpt<\/small>\u00a0was released in November. A million people had used it within a week; 100m within two months. It was soon being used to generate school essays and wedding speeches. Chatgpt<\/small>\u2019s popularity, and Microsoft\u2019s move to incorporate it into Bing, its search engine, prompted rival firms to release chatbots too.<\/p>\n Read it all<\/a>.<\/p>\n Rapid progress in AI is arousing fear as well as excitement. How worried should you be? https:\/\/t.co\/YM85dCBxu3<\/a> pic.twitter.com\/hWMRvB4C62<\/a><\/p>\n — The Economist (@TheEconomist) April 20, 2023<\/a><\/p><\/blockquote>\n \n