Only half of performance gains seen after using a more advanced AI model come from the model itself. The other half come from how users adapted their prompts.
From risk management policies to the five stages of AI ethics, here’s how some organizations approach ethical AI.
Machine learning tools only work if people use and trust them. To achieve this, developers and end users should have a back-and-forth conversation.
Henna Karna, EMBA ’18, discusses her journey through academia and her professional career, reflecting on how MIT Sloan's unique mix of quantitative and qualitative learning has influenced her work.
While generative AI is widely accessible and useful, businesses need to know when to use other AI tools, like traditional machine learning.
New MIT Sloan research offers a framework of human-intensive capabilities and a set of metrics to evaluate tasks across all occupations and better understand the effects of AI on the labor market.
AI industry watchers Thomas Davenport and Randy Bean expect the AI hype cycle to slow as organizations focus on infrastructure and strategy.
MIT experts explain how generative AI — and AI generally — could transform enterprises this year, as well as how to set realistic environmental goals.
Leaders are turning to MIT Sloan Executive Education to learn more about AI, including managing humans amid technological change and rethinking their relationships with IT departments.
AI really can pay off. But leaders must take a systematic approach, understand how the technology works, and let their team leaders determine how it’s used.
