State of AI Report 2022

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    Now in its fifth year, the State of AI Report 2022 is reviewed by leading AI practioners in industry and research. It considers the following key dimensions, including a new Safety section:
  1. Research: Technology breakthroughs and their capabilities.
  2. Industry: Areas of commercial application for AI and its business impact.
  3. Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
  4. Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
  5. Predictions: What we believe will happen and a performance review to keep us honest.

    Key themes in the 2022 Report include:
  1. New independent research labs are rapidly open sourcing the closed source output of major labs. Despite the dogma that AI research would be increasingly centralised among a few large players, the lowered cost of and access to compute has led to state-of-the-art research coming out of much smaller, previously unknown labs. Meanwhile, AI hardware remains strongly consolidated to NVIDIA.
  2. Safety is gaining awareness among major AI research entities, with an estimated 300 safety researchers working at large AI labs, compared to under 100 in last year's report, and the increased recognition of major AI safety academics is a promising sign when it comes to AI safety becoming a mainstream discipline.
  3. The China-US AI research gap has continued to widen, with Chinese institutions producing 4.5 times as many papers than American institutions since 2010, and significantly more than the US, India, UK, and Germany combined. Moreover, China is significantly leading in areas with implications for security and geopolitics, such as surveillance, autonomy, scene understanding, and object detection.
  4. AI-driven scientific research continues to lead to breakthroughs, but major methodological errors like data leakage need to be interrogated further. Even though AI breakthroughs in science continue, researchers warn that methodological errors in AI can leak to these disciplines, leading to a growing reproducibility crisis in AI-based science driven in part by data leakage.

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