2022斯坦福AI指数报告（英文）-HAI (1).pdf

Artificial IntelligenceIndex Report 20222Artificial IntelligenceIndex Report 2022INTRODUCTION TO THE AI INDEX REPORT 2022Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries, and a new chapter with an in-depth analysis of technical AI ethics metrics. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world’s most credible and authoritative source for data and insights about AI.FROM THE CO-DIRECTORSThis year’s report shows that AI systems are starting to be deployed widely into the economy, but at the same time they are being deployed, the ethical issues associated with AI are becoming magnified. Some of this is natural—after all, we tend to care more about the ethical aspects of a given technology when it is being rolled out into the world. But some of it is bound up in the peculiar traits of contemporary AI—larger and more complex and capable AI systems can generally do better on a broad range of tasks while also displaying a greater potential for ethical concerns. This is bound up with the broad globalization and industrialization of AI—a larger range of countries are developing, deploying, and regulating AI systems than ever before, and the combined outcome of these activities is the creation of a broader set of AI systems available for people to use, and reductions in their prices. Some parts of AI are not very globalized, though, and our ethics analysis reveals that many AI ethics publications tend to concentrate on English-language systems and datasets, despite AI being deployed globally. If anything, we expect the above trends to continue: 103% more money was invested in the private investment of AI and AI-related startups in 2021 than in 2020 ($96.5 billion versus $46 billion). Jack Clark and Ray Perrault3Artificial IntelligenceIndex Report 2022Private investment in AI soared while investment concentration intensified: • The private investment in AI in 2021 totaled around $93.5 billion—more than double the total private investment in 2020, while the number of newly funded AI companies continues to drop, from 1051 companies in 2019 and 762 companies in 2020 to 746 companies in 2021. In 2020, there were 4 funding rounds worth $500 million or more; in 2021, there were 15. U.S. and China dominated cross-country collaborations on AI: • Despite rising geopolitical tensions, the United States and China had the greatest number of cross-country collaborations in AI publications from 2010 to 2021, increasing five times since 2010. The collaboration between the two countries produced 2.7 times more publications than between the United Kingdom and China—the second highest on the list.Language models are more capable than ever, but also more biased: • Large language models are setting new records on technical benchmarks, but new data shows that larger models are also more capable of reflecting biases from their training data. A 280 billion parameter model developed in 2021 shows a 29% increase in elicited toxicity over a 117 million parameter model considered the state of the art as of 2018. The systems are growing significantly more capable over time, though as they increase in capabilities, so does the potential severity of their biases.The rise of AI ethics everywhere: • Research on fairness and transparency in AI has exploded since 2014, with a fivefold increase in related publications at ethics-related conferences. Algorithmic fairness and bias has shifted from being primarily an academic pursuit to becoming firmly embedded as a mainstream research topic with wide-ranging implications. Researchers with industry affiliations contributed 71% more publications year over year at ethics-focused conferences in recent years.AI becomes more affordable and higher performing: • Since 2018, the cost to train an image classification system has decreased by 63.6%, while training times have improved by 94.4%. The trend of lower training cost but faster training time appears across other MLPerf task categories such as recommendation, object detection and language processing, and favors the more widespread commercial adoption of AI technologies. Data, data, data: • Top results across technical benchmarks have increasingly relied on the use of extra training data to set new state-of-the-art results. As of 2021, 9 state-of-the-art AI systems out of the 10 benchmarks in ...