How to spot real value in AI — and avoid the snake oil
A new book aims to help business leaders separate real AI value from overhyped claims.
A new book aims to help business leaders separate real AI value from overhyped claims.
This collection of prompt templates functions like cognitive scaffolding, providing structure without limiting options as you innovate.
When stakeholders become more involved in generative AI design and implementation, it’s more likely that such tools will augment work rather than displace workers.
Health care, and helping hands for those with limited resources.
Open-source and open-weight AI models perform well and cost less — but users opt for closed models 80% of the time, according to new research.
Highlights from Gary Gensler and Lily Bailey’s “Deep Learning and Financial Stability” outline five ways AI could lead to future financial crises.
Adding images to predictive models can help retailers estimate return rates as they decide what to feature on their websites.
Machine learning can drive climate action initiatives, but its widespread use could have negative implications, according to Climate Change AI’s Priya Donti.
The work tasks that AI is least likely to replace are those that depend on uniquely human capacities, such as empathy, judgment, ethics, and hope.
Leaders must rethink the way they manage people and projects to ensure that everyone reaps the efficiency and innovation benefits of generative AI.