AI’s Year in Review: Fifth Annual State of AI Report Details 2022's Trends

2022 was a big year for AI-driven upstarts and scientific discovery, according to a new survey.

What’s new: The fifth annual State of AI Report details the biggest breakthroughs, business impacts, social trends, and safety concerns.

Looking back: Investors Nathan Benaich and Ian Hogarth reviewed research papers, industry surveys, financial reports, funding news, and more. Among their key findings:

  • Small AI had an outsized impact. Startups like Stability AI, which was founded in 2019 and already is valued at $1 billion, and upstart research collaborations like BigScience, the group behind the open source BLOOM large language model, are bucking expectations that tech giants would crowd smaller competitors out of AI innovation. The trend is compounded by an exodus of researchers from places like OpenAI, DeepMind, and Meta, many of whom are joining or launching AI startups.
  • AI drove scientific breakthroughs in biochemistry, materials science, mathematics, and nuclear physics. However, a staggering portion of such research suffers from issues like data leakage (when identical or overly similar examples appear in both training and test sets) and overfitting. Such flaws have prompted fears that AI-driven science is mired in a crisis of reproducibility.
  • Researchers gave serious attention to safety concerns like harmful output, undesirable social impact, and uncontrolled artificial general intelligence. In one survey of researchers, 69 percent believed that safety deserves a higher priority (up from 68 percent in a 2021 survey and 49 percent in a 2016 survey).
  • China’s research community hit the accelerator. U.S. AI researchers published over 10,000 papers in 2022, up 11 percent from the prior year. Their China-based counterparts published less than 7,500 papers, a 24 percent jump from 2021. U.S. researchers led in natural language processing tasks, while Chinese researchers dominated papers on autonomous vehicles, computer vision, and machine translation.

Looking ahead: The authors offer predictions for the year ahead. Among them:

  • DeepMind will train a 10 billion-parameter multimodal reinforcement learning model, an order of magnitude greater than the company’s 1.2 billion parameter Gato.
  • At least one of the five biggest tech companies will invest over $1 billion in startups devoted to building artificial general intelligence.
  • Nvidia’s dominance in AI chips will cause at least one of their high-profile startup competitors to be acquired for less than half its current value or shuttered outright.

Hits and misses: The authors also graded their predictions from last year. A sampling:

  • Correct: DeepMind would make a major breakthrough in the physical sciences. (The Google division helped publish three papers, including work that found materials’ electron configurations and, consequently, their properties.)
  • Incorrect: One or more startups devoted to making specialized AI chips, such as Graphcore, Cerebras, or Mythic, would be acquired by a larger firm.

Why it matters: As investors, the authors earn their bread, butter, and Teslas by developing a keen sense of which tech trends have the greatest commercial value. Their perspective may not be omniscient, but it can be helpful to know what the funders are betting on.

We’re thinking: We’re not enamored of projections in a field that changes as rapidly as AI, but we’re happy when forecasters take a critical look at their own previous predictions.

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