ARTIFICIAL INTELLIGENCE
CAN IT THINK?
In the breathless response to the rise of powerful new artificial intelligence, we may be overlooking the most fundamental issue of all: what it actually means to have a mind
by PHILIP BALL
CREATED BY PROSPECT WITH MIDJOURNEY
Artificial intelligence has turned a corner, and no one is sure how worried we should be. After years of hype, stilted chatbots and indifferent language translation, suddenly AI can engage us in spookily convincing conversation—enough so to convince at least one tech engineer that the machine is sentient. We have seen reports of AI technology doing astonishing things, from professing its love for its interlocutor and attempting to wreck his marriage, to allegedly persuading a Belgian man with mental health problems to commit suicide. Teachers despair of setting homework essays now that pupils can use AI to generate well-crafted answers; journalists and even novelists and artists worry that their jobs are on the line.
All this stems from the advent of large language models (LLMs)—AI algorithms capable of scanning vast banks of online data, such as text or images, in order to generate convincing responses to almost any query: “Paint me a view of Bradford in the style of Vermeer”; “write me a funny limerick about the robot apocalypse”. LLMs such as ChatGPT and its successor GPT-4, created by the San Francisco-based OpenAI (and interviewed for last month’sProspect “Brief Encounter”...), supercharge methods that have been developed over years of AI research and can produce an eerie simulacrum of human discourse.
The risks of misuse are real. When GPT-4 was released in March, prominent figures in industry, policy and academia—including Elon Musk, Apple cofounder Steve Wozniak, futurist Yuval Noah Harari, and AI specialists Stuart Russell, John J Hopfield (who devised some of the key theory behind today’s computational “neural networks”) and Gary Marcus—signed an open letter organised by the nonprofit Future of Life Institute in Pennsylvania, calling for an immediate moratorium on making AI any more powerful until we can implement schemes for independent oversight and safety protocols. On recently retiring from his AI role at Google, computer scientist Geoffrey Hinton—another influential pioneer in the field—told the BBC that the potential dangers posed by AI chatbots are “quite scary”. Hinton has since ramped up the catastrophism, saying “My intuition is: we’re toast. This is the actual end of history.”
Others are more relaxed. “Calm down people,” wrote AI veteran Rodney Brooks of the Massachusetts Institute of Technology. “We neither have super powerful AI around the corner, nor the end of the world caused by AI about to come down upon us.”
But while the pressing issue is ensuring that these systems are used safely and ethically, the deeper challenge is to figure out what kinds of cognitive systems they are. Few believe that LLMs are truly sentient, but some argue that they show signs of genuine intelligence and of having a conceptual understanding of the world. These claims, whether right or not, are forcing us to revise ideas about what intelligence and understanding actually are. Might it be time to abandon the notion that humanlike capability demands human-like cognition, and to consider whether we are inventing an entirely new kind of mind?
Birth of the thinking machine
The term “artificial intelligence” was coined in 1955 by mathematician John McCarthy for a workshop to be held at Dartmouth College in New Hampshire the following year, on the potential to create machines that “think”. Early efforts in the field in the 1960s and 1970s focused on trying to find the rules of human thinking and implementing them in the form of computer algorithms. But gradually the emphasis shifted towards so-called neural networks: webs of interconnected logic devices (the nodes of the network) whose links are tweaked until they can reliably produce the right outputs for a given set of inputs—for example, to correctly identify images.