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Building on the success of its first edition in 2023, the 2024 summit was hosted again by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the Stanford Accelerator for Learning. The summit brought together Stanford faculty and students along with leaders and participants from academia, industry, civic society, PK-12 education, and government.
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.
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We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
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The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
As AI technologies continue to rapidly evolve, and new opportunities and challenges emerge in education, the AI+Education research community showcased the latest AI research and solutions for key education challenges, examining how AI technologies (e.g., large language models, computer vision, and AR/VR) can advance learning through new pedagogies and assessments, new in-person or virtual classroom designs, new support mechanisms for teachers and students, and more. The summit addressed how AI can transform teaching and learning in an ethical, equitable, and safe manner; what metrics of assessment, fair evaluation, and overall regulatory framework are required to deliver these qualities; and how to build AI for education and with educators.
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