AI & Machine Learning

MIT SMR’s fall 2024 issue highlights the need for personal and organizational resilience amid global uncertainty.

Digital nudges can encourage reactive thinking and limit employees’ ethical thinking.

On the Me, Myself, and AI podcast, Wonder Dynamics’s cofounder discusses an AI tool that enables faster, more cost-effective animation.

Learn why humans miss generative AI mistakes and how AI speed bumps help, in this short video.

Business leaders can identify and avoid flawed AI models by employing statistical methods and statistics experts.

On the Me, Myself, and AI podcast, Stitch Fix’s Jeff Cooper explains how generative AI enables employees to gain more efficiencies while still connecting with customers.

New research shows where marketers are seeing gains from AI and how to speed up payoffs.

In the age of artificial intelligence, executives must make maintaining their AI literacy a habit.

Learn three principles for reorganizing work around AI.

Watch this short video to learn more about how AI changes the rules of workforce management.

The nonprofit is delivering technology and services to help its staff build AI applications efficiently and safely.

To reduce the risk of accidentally using bad artificial intelligence, we need regulation — and skepticism.

The spring 2024 issue of MIT SMR looks at opportunities in digital innovation, automation, generative AI, and more.

Analyzing factors behind generative AI’s value can help leaders determine who will benefit most from its growth.

To succeed with machine learning, manage projects as business initiatives, not technology projects.

Many organizations are experimenting with generative AI, and many questions remain about its impact on the workforce.

LLMs have immense capabilities but present practical challenges that require human knowledge workers’ involvement.

This free webinar offers expert insights on effective data-management strategies for AI adoption.

Smart organizations need smarter KPIs. This report outlines how leaders can create and capture value from smart KPIs.

When deciding whether to deploy a machine learning model, focus on business metrics, not technical ones.