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Stanford’s annual AI Index aims to pin down a complex subject—the current status of the machine learning industry—and this year, researchers spent 385 pages painting that picture, from economic impact to policy and governance.
We combed through the report to highlight top business takeaways. Here’s our look at 2022’s report. Now let’s dive into 2023.
AI advancement goes corporate: Industry has taken the reins from academia when it comes to AI development. In 2022, just three significant machine learning models were developed in the world of academia, compared to 32 industry-produced models, researchers wrote.
- More context: The amount of money and energy needed to build and train large-scale AI models seems to be ever-increasing, and—for better or worse—large companies typically have more resources to meet those requirements. For instance, Google’s PaLM model cost $8 million to train, and OpenAI’s GPT-3 training phase emitted 502 metric tons of carbon, according to the report.
In-demand AI skills: Although many companies have frozen or paused hiring for tech roles amid the industry’s rising layoffs, AI hiring is on an upward trend. Many industrial sectors have displayed increased demand for machine learning-related roles, with related job postings rising from an average of 1.7% in 2021 to 1.9% last year.
- More context: “One area where [tech companies] plan to do a lot of hiring, still, is when it comes to AI and machine learning,” Julia Pollak, chief economist at ZipRecruiter, told Tech Brew in January.
Stagnated investment: VC funding for AI has decreased year over year for the first time in a decade, with a decline in both the number of “AI-related funding events” and “newly funded AI companies,” according to the report.
- More context: The slowdown in private AI investment was apparent even in August, as Tech Brew wrote, when AI funding was already falling faster than the market. It’s important to note that this decline comes after a decade of heightened investment: “In 2022 the amount of private investment in AI was 18 times greater than it was in 2013,” researchers wrote.—HF