“SHOULD WE AUTOMATE away all the jobs, including the fulfilling ones? Should we develop non-human minds that might eventually outnumber, outsmart...and replace us? Should we risk loss of control of our civilisation?” These questions were asked last month in an open letter from the Future of Life Institute, an NGO. It called for a six-month “pause” in the creation of the most advanced forms of artificial intelligence(AI), and was signed by tech luminaries including Elon Musk. It is the most prominent example yet of how rapid progress in AI has sparked anxiety about the potential dangers of the technology.
In particular, new “large language models” (LLMs)—the sort that powers ChatGPT, a chatbot made by OpenAI, a startup—have surprised even their creators with their unexpected talents as they have been scaled up. Such “emergent” abilities include everything from solving logic puzzles and writing computer code to identifying films from plot summaries written in emoji.
These models stand to transform humans’ relationship with computers, knowledge and even with themselves. Proponents of AI argue for its potential to solve big problems by developing new drugs, designing new materials to help fight climate change, or untangling the complexities of fusion power. To others, the fact that AIs’ capabilities are already outrunning their creators’ understanding risks bringing to life the science-fiction disaster scenario of the machine that outsmarts its inventor, often with fatal consequences.
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.
节选自《经济学人》：How to worry wisely about AI
1. fulfilling 表示让人感觉有意义的；令人满足的
例：a fulfilling experience 有成就感的经历
2. luminary 泰斗；权威
例：...the political opinions of such luminaries as Sartre or de Beauvoir.
3. start-up 新兴公司
例： Gold gave an example — an energy startup company called Scottish Bioenergy.
4. scale up 增大；增加；提高（规模或数量）
例：Since then, Wellcome has been scaling up production to prepare for clinical trials. 从那以后，威康公司一直在增加产量，为临床试验作准备。