The seventh-annual report on the global state of artificial intelligence from Stanford University’s Institute for Human-Centered Artificial Intelligence offers some concerning thoughts for society: the technology’s spiraling costs and poor measurement of its risks.
According to the report, “The AI Index 2024 Annual Report,” published Monday by HAI, the cost of training large language models such as OpenAI’s GPT-4 — the so-called foundation models used to develop other programs — is soaring.
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“The training costs of state-of-the-art AI models have reached unprecedented levels,” the report’s authors write. “For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.”
(An “AI model” is the part of an AI program that contains numerous neural net parameters and activation functions that are the key elements for how an AI program functions.)
At the same time, the report states, there is too little in the way of standard measures of the risks of such large models because measures of “responsible AI” are fractured.
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There is “significant lack of standardization in responsible AI reporting,” the report states. “Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models.”
Both issues, cost and safety, are part of a burgeoning industrial market for AI, especially Gen AI, where commercial interests, and real-world deployments, are taking over from what has for many decades been mostly a research community of AI scholars.
“Investment in generative AI skyrocketed” in 2023, the report notes, as the industry produced 51 “notable” machine learning models – vastly more than the 15 that came out of academia last year. “More Fortune 500 earnings calls mentioned AI than ever before.”
The 502-page report goes into substantial detail on each point. On the first point – training cost – the report’s authors teamed up with research institute Epoch AI to estimate the training cost of foundation models. “AI Index estimates validate suspicions that in recent years model training costs have significantly increased,” the report states.
For example, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900 to train. RoBERTa Large, released in 2019, which achieved state-of-the-art results on many canonical comprehension benchmarks like SQuAD and GLUE, cost around $160,000 to train. Fast-forward to 2023, and training costs for OpenAI’s GPT-4 and Google’s Gemini Ultra are estimated to be around $78 million and $191 million, respectively.
The report notes that training costs are rising with the increasing size of computation required for the increasingly large AI models. The original Google Transfomer, the deep learning model that sparked the race for GPTs and other large language models, required about 10,000 petaFLOPs, or 10,000 trillion floating point operations. Gemini Ultra approaches a hundred billion petaFLOPs.