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.

Every year for the past decade, I flew to Singapore or Hong Kong to celebrate my mother’s birthday with her on December 22. This year, for the first time, we did it via Zoom.

When a researcher works for a company, what rights should they have to publish their work, and what rights should the company that sponsored the work have? This issue has come up many times in the AI community across many companies...

Like many people in the AI community, I am saddened by the sudden departure from Google of ethical AI researcher Timnit Gebru. Timnit is a tireless champion of diversity and fairness in AI.

The rise of AI creates opportunities for new startups that can move humanity forward. In the 1990s, the internet was embraced successfully by incumbent companies including Apple and Microsoft, but it also inspired hugely impactful...

Over the last two weeks, I described the importance of clean, consistent labels and how to use human-level performance (HLP) to trigger a review of whether labeling instructions need to be reviewed.

Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.

Beating human-level performance (HLP) has been a goal of academic research in machine learning from speech recognition to X-ray diagnosis. When your model outperforms humans, you can argue that you’ve reached a significant milestone and publish a paper!

As I write this letter, the vote count is underway in yesterday’s U.S. presidential election. The race has turned out to be tight. In their final forecast last night, the political analysts at fivethirtyeight.com suggested an 89 percent chance that Joe Biden would win.

Welcome to this special Halloween issue of The Batch! In AI, we use many challenging technical terms. To help you keep things straight, I would like to offer some definitions that I definitely would not use.

Today Landing AI, where I am CEO, launched LandingLens, an AI-powered platform that helps manufacturers develop computer vision solutions that can identify defective products.

My father recently celebrated a milestone: He has completed 146 online courses since 2012. His studies have spanned topics from creative writing to complexity theory. Ronald Ng is a great example of lifelong learning.