Each year, the public relations agency Edelman produces a report on the online public’s trust in social institutions like government, media, and business. The latest Edelman Trust Barometer contains a worrisome finding...

I have a two-year-old daughter, and am expecting my son to be born later this week. When I think about what we can do to build a brighter future for our children, the most important thing is to create a foundation for education.

Earlier today, I spoke at a DeepLearning.AI event about MLOps, a field that aims to make building and deploying machine learning models more systematic. AI system development will move faster if we can shift from being model-centric to being data-centric.

Over the past weekend, I happened to walk by a homeless encampment and went over to speak with some of the individuals there. I spoke with a homeless man who seemed to be partially speaking with me, and partially speaking with other people that I could not see.

Engineers need strong technical skills to be successful. But many underestimate the importance of developing strong communication skills as well.

One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and other industries.

AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate.

When a lot of data is available, machine learning is great at automating decisions. But when data is scarce, consider using the data to augment human insight, so people can make better decisions.

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.

The price of shares in video game retailer GameStop (NYSE: GME) gyrated wildly last week. Many people viewed the stock’s rapid ascent as a David-versus-Goliath story...

Last week, I talked about how best practices for machine learning projects are not one-size-fits-all, and how they vary depending on whether a project uses structured or unstructured data, and whether the dataset is small or big.

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?

Last Wednesday, the U.S. Capitol building was overrun by insurrectionists at the moment when members of Congress were certifying the results of a national election.

In my letter last week, I alluded to the way AI tends to concentrate power and wealth. This tendency worries me, and I believe it deserves more attention. The U.S. government has been looking into these winner-take-most dynamics at a few leading technology companies...

Happy New Year! As we enter 2021, I want to share with you three wishes I have for AI in the upcoming year. I hope we can: Narrow the gap between proofs-of-concept and production.