The 2024 Index is our most comprehensive to date and arrives at an important
moment when AI's influence on society has never been more pronounced. This
year, we have broadened our scope to more extensively cover essential trends
such as technical advancements in AI, public perceptions of the technology, and
the geopolitical dynamics surrounding its development. Featuring more original
data than ever before, this edition introduces new estimates on AI training
costs, detailed analyses of the responsible AI landscape, and an entirely new
chapter dedicated to AI's impact on science and medicine. The AI Index report
tracks, collates, distills, and visualizes data related to artificial
intelligence (AI). Our mission is to provide unbiased, rigorously vetted,
broadly sourced data in order for policymakers, researchers, executives,
journalists, and the general public to develop a more thorough and nuanced
understanding of the complex field of AI. The AI Index is recognized globally
as one of the most credible and authoritative sources for data and insights on
artificial intelligence. Previous editions have been cited in major newspapers,
including the The New York Times, Bloomberg, and The Guardian, have amassed
hundreds of academic citations, and been referenced by high-level policymakers
in the United States, the United Kingdom, and the European Union, among other
places. This year's edition surpasses all previous ones in size, scale, and
scope, reflecting the growing significance that AI is coming to hold in all of
our lives.

2024 年度 AI 指数报告是目前为止最全面的，包含技术进步、公众认知、地缘政治动态、负责任的人工智能等重要趋势，并引入了关于 AI 对科学和医药的影响的全新章节。

2024 年人工智能指数报告

Artificial Intelligence Index Report 2024

This paper evaluated the effectiveness of using generative AI to simplify
science communication and enhance public trust in science. By comparing lay
summaries of journal articles from PNAS, yoked to those generated by AI, this
work assessed linguistic simplicity across such summaries and public
perceptions. Study 1a analyzed simplicity features of PNAS abstracts
(scientific summaries) and significance statements (lay summaries), observing
that lay summaries were indeed linguistically simpler, but effect size
differences were small. Study 1b used GPT-4 to create significance statements
based on paper abstracts and this more than doubled the average effect size
without fine-tuning. Finally, Study 2 experimentally demonstrated that
simply-written GPT summaries facilitated more favorable public perceptions of
scientists (their credibility, trustworthiness) than more complexly-written
human PNAS summaries. AI has the potential to engage scientific communities and
the public via a simple language heuristic, advocating for its integration into
scientific dissemination for a more informed society.

使用生成式人工智能来简化科学传播并增强公众对科学的信任的效果进行了评估。通过比较国家科学院期刊（PNAS）的科学摘要与由人工智能生成的摘要，研究评估了这些摘要的语言简明性和公众认知情况。研究结果表明，人工智能生成的简明摘要相较于复杂的人工摘要对科普传播具有更多的优势，有助于提升公众对科学家的可信度和信任度。因此，将人工智能技术引入科学传播领域推广，有助于建立一个更加知情的社会。