“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.
“我們是否應該將所有的工作都自動化，包括有意義的工作？我們是否應該開發那些最終超過、勝過...并取代我們的非人類大腦？我們是否有失去人類文明的風險？”上個月，非政府組織“未來生命研究所”在一封公開信中提出了這些問題。它呼吁在創造最先進的人工智能方面“暫停”6個月，公開信由埃隆·馬斯克等多位科技名人簽署。這是迄今為止最突出的例子，說明人工智能的快速發展已經引發了對該技術潛在危險的擔憂。
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.
特別是，新的“大型語言模型”(LLM)——為初創公司OpenAI開發的聊天機器人 ChatGPT提供動力的那種——在規模擴大時以其意想不到的才能讓其創造者感到驚訝。這種“新興”能力包括解決邏輯難題的能力、編寫計算機代碼的能力、從表情包的情節摘要中識別電影的能力。
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.
這些模型將改變人類與計算機、知識、甚至與人類自身的關系。人工智能的支持者認為它有解決重大問題的潛力，如開發新藥、設計新材料以幫助應對氣候變化、解開核聚變發電的復雜問題。反對者認為，人工智能的能力已經超過了其創造者的理解力，這有可能使科幻片中機器勝過其發明者的災難場景成為現實，通常會帶來致命的后果。
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?
這種興奮和恐懼的混合體使我們難以權衡機會和風險。但是可以從其他行業及曾經的技術變革中吸取教訓。那么是什么樣的改變讓AI變得如此強大？你應該有多恐懼呢？政府應該做什么呢？
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.
在特別的科學章節中，我們研究了大型語言模型的工作原理及其未來方向。10年前出現的第一波現代AI系統，依靠的是精心標記的訓練數據。一旦接觸到足夠數量的標記實例，它們就能學會做一些事情，比如識別圖像或轉錄語音。如今的AI系統不需要預先標記，因此可以使用在線資源里的大規模數據集進行訓練。實際上，大型語言模型可以在整個互聯網上進行訓練——這就是他們為什么如此強大的原因，有好有壞。
節選自《經濟學人》：How to worry wisely about AI
1. fulfilling 表示讓人感覺有意義的；令人滿足的
例：a fulfilling experience 有成就感的經歷
2. luminary 泰斗；權威
例：...the political opinions of such luminaries as Sartre or de Beauvoir.
...諸如薩特、波伏娃等大家的政見。
3. start-up 新興公司
例： Gold gave an example — an energy startup company called Scottish Bioenergy.
以一家新興能源公司——蘇格蘭生物能源公司為例。
4. scale up 增大；增加；提高（規?；驍盗浚?/p>
例：Since then, Wellcome has been scaling up production to prepare for clinical trials. 從那以后，威康公司一直在增加產量，為臨床試驗作準備。
溫馨提示：文章由作者233網校-chenjing獨立創作完成，未經著作權人同意禁止轉載。