12 Graphs That Explain the State of AI in 2022

1. Investment is Off the Hook

The amount of money pouring into AI is mind-boggling. The most noteworthy gain came in global private investment, which soared from US $46 billion in 2020 to $93.5 billion in 2021. That jump came from an increase in big funding rounds; in 2020 there were four funding rounds that topped $500 million, in 2021 there were 15. The report also notes that all that money is being funneled to fewer companies, since the number of newly funded startups has been dropping since 2018. It’s a great time to join an AI startup, but maybe not to found one yourself.

2. The U.S. vs. China Narrative Is Complicated

There’s a fair bit of talk about an AI race between China and the United States these days. “When you see all the news about geopolitical tensions, you would think that the number of collaborations would decrease between those two countries,” says Daniel Zhang, a policy researcher at Stanford’s HAI and editor in chief of this year’s AI Index. Instead, he tells IEEE Spectrum, “the past 10 years have been an upward trend.” When it comes to cross-country collaborations on publications, China and the United States produce more than twice as many as the next pairing, China and the United Kingdom.

3. Applying for a Patent and Getting It Are Two Separate Things

China dominates the world on number of patents applied for; the report states that China accounted for 52 percent of global patent filings in 2021. However, many of those filings may have been somewhat aspirational. The United States dominates on number of patents granted, coming in at 40 percent of the global total. Zhang notes that having patents granted “certifies that your patents are actually credible and useful,” and says the situation is somewhat analogous to what’s been happening with publications and citations. While China leads on number of publications, publication citations, and conference publications, the United States still leads on citations of conference publications, showing that prestigious papers from U.S. researchers are still having an outsized impact.

4. A Plateau in Computer Vision?

The field of computer vision has been advancing so rapidly, it’s been hard to keep up with news of the latest accomplishments. The AI Index shows that computer vision systems are tremendously good at tasks involving static images such as object classification and facial recognition, and they’re getting better at video tasks such as classifying activities.

But a relatively new benchmark shows the limits of what computer vision systems can do: They’re great at identifying things, not so great at reasoning about what they see. The visual commonsense reasoning challenge, introduced in 2018, asks AI systems to answer questions about images and also explain their reasoning. For example, one image shows people seated at a restaurant table and a server approaching with plates; the test asks why one of the seated people is pointing to the person across the table. The report notes that performance improvements have become increasingly marginal in recent years, “suggesting that new techniques may need to be invented to significantly improve performance.”

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