Artificial intelligence
There is no doubt that the latest advances in artificial intelligence are more impressive than what came before, but are we in just another bubble of AI hype? Jeremy Hsu reports
THIS moment for artificial intelligence is unlike any that has come before. Powerful languagebased AIs have lurched forward in ability and can now produce reams of plausible prose that often can’t be distinguished from text written by humans. They can answer tricky technical questions, such as those posed to lawyers and computer programmers. They can even help better train other AIs.
However, they have also raised serious concerns. Prominent AI researchers and tech industry leaders have called for research labs to pause the largest ongoing experiments in AI for at least six months in order to allow time for the development and implementation of safety guidelines. Italy’s regulators have gone further, temporarily banning a leading AI chatbot.
At the centre of it all are large language models and other types of generative AI that can create text and images in response to human prompts. Start-ups backed by the world’s most powerful tech firms have been accelerating the deployment of these generative AIs since 2022 – giving millions of people access to convincing but often inaccurate chatbots, while flooding the internet with AI-generated writing and imagery in ways that could reshape society.
AI research has long been accompanied by hype. But those working on pushing the boundaries of what is possible and those calling for restraint all seem to agree on one thing: generative AIs could have much broader societal impacts than the AIs that came before.
Boom and bust
The story of AI is one of repeating cycles involving surges of interest and funding followed by lulls after people’s great expectations fall short. In the 1950s, there was a huge amount of enthusiasm around creating machines that would display human-level intelligence (see “What actually is artificial intelligence?”, below left). But that lofty goal didn’t materialise because computer hardware and software quickly ran into technical limitations. The result was so-called AI winters in the 1970s and in the late 1980s, when research funding and corporate interest evaporated.
The past decade has represented something of an AI summer both for researchers looking to improve AI learning
