Li emphasizes the growing focus among consulting firms on the legal and ethical issues surrounding AI, stating, 'We need some principles for AI safety, in terms of regulatory compliance and ordinary usage.' He highlights how AI models are now evaluated not on intelligence alone but on their problematic behavior, which has become crucial in today's regulatory environment.
The research led by Li and his colleagues resulted in the creation of AIR-Bench 2024, a benchmark designed to assess the safety features of language models. It demonstrates that different AI models, such as Anthropic's Claude 3 Opus and Google's Gemini 1.5 Pro, excel at avoiding specific risks like generating harmful content, while others like Databricks' DBRX Instruct displayed significant vulnerabilities across various metrics.
The analysis of AI policies across several regions reveals a pressing need for a standardized approach to managing AI risks. Li's work illustrates that businesses need to understand not only the capabilities of AI but also the specific risks they present, especially as AI technology continues to permeate different sectors.
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