![]() ![]() 7: Corporate Investment Dips from 2021 HighsĬorporate investment (mergers/acquisitions, minority stakes, private investment, and public offerings) dipped in 2022 from 2021 highs, but the number has still increased 13-fold in the last decade. California posted the most AI-related jobs by far (142,154), followed by Texas (66,624) and New York (43,899). This year saw an increase in job postings seeking AI skills across all sectors, and the number of AI job postings overall were notably higher in 2022 over the prior year. Academic institutions dominate FAccT, but this past year, industry actors contributed more work than ever before in this space. That demonstrates increased interest in AI ethics and related work. The Conference on Fairness, Accountability, and Transparency, or FAccT, saw a twofold increase in submissions from 2021 to ’22, and a 10x increase since 2018. Some of those reported issues included a deepfake of Ukrainian President Volodymyr Zelenskyy surrendering, face recognition technology to try to track gang members and rate their risk, and surveillance technology to scan and determine emotional states of students in a classroom. Chalk that up to both an increase in AI use and a growing awareness of its misuse. 4: More AI, More ProblemsĪccording to the AI, Algorithmic, and Automation Incidents and Controversies repository, reported issues are 26 times greater in 2021 than in 2012. 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. ( Learn more about benchmark saturation from AI Index steering committee member Vanessa Parli.) 3: The High Environmental Costs of Trainingīig models emit big carbon emissions numbers – through large numbers of parameters in the models, power usage effectiveness of data centers, and even grid efficiency. ![]() This shows us AI systems have become increasingly capable on older benchmarks and will require more difficult tests to be fully challenged. While we saw benchmark saturation last year, this year the trend is much more pronounced. On the technical side, current AI tools keep meeting or beating benchmarks. (And since these are estimates, we've qualified them as mid, high, or low: mid where the estimate is thought to be a mid-level estimate, high where it is thought to be an overestimate, and low where it is thought to be an underestimate.) 2: New Benchmarks Needed It’s not just PaLM: Across the board, large language and multimodal models are becoming larger and pricier. Just three years later, PaLM launched with 540 billion parameters and cost an estimated $8 million. ![]() GPT-2, released in 2019 and considered the first large language model, had 1.5 billion parameters and cost an estimated $50,000 to train. Large language models keep scaling in size and expense. TL DR? Here, learn about the state of AI in 14 charts. It tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind. The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The 2023 AI Index is out, covering the world of artificial intelligence from technical performance achievements, ethics advances, education and policy trends to economic impact, R&D, and the hiring and jobs scene.
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