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 ai so much more capable? How scared should you be? And what should governments do?
In a special Science section, we explore the workings of llms and their future direction. The first wave of modern ai systems, 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’s systems do not require pre-labelling, and as a result can be trained using much larger data sets taken from online sources. llms can, in effect, be trained on the entire internet—which explains their capabilities, good and bad.
Those capabilities became apparent to a wider public when Chatgpt was 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’s popularity, and Microsoft’s move to incorporate it into Bing, its search engine, prompted rival firms to release chatbots too.
Rapid progress in AI is arousing fear as well as excitement. How worried should you be? https://t.co/YM85dCBxu3 pic.twitter.com/hWMRvB4C62
— The Economist (@TheEconomist) April 20, 2023