The Stanford 2023 AI Index
🤖 The 2023 #AI#Index is out, covering the world of #artificialintelligence from #technical, #ethics, #education and policy trends to #economic impact, R&D, and the #hiring and jobs scene.
A great snapshot of what happened this past year in AI:
👉 1: LLMs #ScaleUp: PaLM launched with 540 billion parameters and cost an estimated $8 million
👉 2: New #Benchmarks Needed: AI systems have become increasingly capable on older benchmarks and will require more difficult tests to be fully challenged
👉 3: The High #Environmental Costs of Training: The heaviest carbon emitter by far was GPT-3, but even the relatively more efficient BLOOM took 433 MWh of power to train, which would be enough to power the average American home for 41 years.
👉 4: More AI, More #Problems: Reported issues are 26 times greater in 2021 than in 2012
👉 5: More #Ethics-Related Papers: FAccT, saw a twofold increase in submissions from 2021 to ’22, and a 10x increase since 2018
👉6: Increasing AI #Labor Demand: increase in job postings seeking AI skills across all sectors
👉 7: Corporate #Investment Dips from 2021 Highs:Corporate investment dipped in 2022 from 2021 highs, but the number has still increased 13-fold in the last decade
👉 8: China’s #Robot Rush: In 2021, China installed more of these robots than the rest of the world combined, and today, the country represents 51.8% of all industrial robotic installations.
👉 9: Industry Draws #Talent, Government Lags: When newly minted AI PhDs leave school, most head into industry jobs.
👉 10: More Countries Pass AI-Related #Legislation: Since 2016, countries have passed 123 AI-related bills, the majority in recent years.
👉 11: And Broader Ethnic #Diversity as Well: Meanwhile, CS bachelors are growing more ethnically diverse. While white students still make up the majority of new grads, the proportion of new graduates who are Asian, Hispanic, or multiracial has steadily increased over the past decade.
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