On Halloween, the veil lifts between the spirit and AI worlds, allowing the two to pass through one another. The resulting paranormal — or, as AI practitioners call it, paragaussian — phenomena raise questions...

In June, I announced the first Data-centric AI Competition. The deadline for submissions was in early September, and today I’m thrilled to announce the winners!

I’ve seen many friends transition from an academic or research role to a corporate role. The most successful ones adjusted to corporate work by shifting their mindset in a few crucial ways.

The image below shows two photos of the same gear taken under different conditions. From the point of view of a computer-vision algorithm — as well as the human eye — the imaging setup that produced the picture on the right makes

In my experience, the most sophisticated decision makers tend to be hypothesis-driven thinkers. They may be engineers solving a technical problem, product designers fulfilling a customer need, or entrepreneurs growing a business. They

In my experience, the most sophisticated decision makers tend to be hypothesis-driven thinkers. They may be engineers solving a technical problem, product designers fulfilling a customer need, or entrepreneurs growing a business.

I invite you to be part of Pie & AI, a series of meetups that bring together members of the global AI community for education and conversation. Pie & AI is a place where you can network with peers, learn best practices from industry leaders...

I’m thrilled to announce the NeurIPS Data-Centric AI Workshop, which will be held on December 14, 2021. You may have heard me speak about data-centric AI, in which we systematically engineer the data that feeds learning algorithms. This workshop is a chance to delve more deeply into the subject.

Building AI products and businesses requires making tough choices about what to build and how to go about it. I’ve heard of two styles...

Recently I attended an online celebration of my late grandfather’s life. He had passed away quietly in his sleep in March. Two days later, Coursera was publicly listed on the New York Stock Exchange. And two days after that, my son Neo Atlas Ng was born.

Say you’ve trained a learning algorithm and found that it works well on many examples but performs poorly on a particular subset, or slice, of the data. What can you do?...

How much math do you need to know to be a machine learning engineer? It’s always nice to know more math! But there’s so much to learn that, realistically, it’s necessary to prioritize.

Since the pandemic started, several friends and teammates have shared with me privately that they were not doing well emotionally. I’m grateful to each person who trusted me enough to tell me this. How about you — are you doing okay?

In earlier letters, I discussed some differences between developing traditional software and AI products, including the challenges of unclear technical feasibility, complex product specification, and need for data to start development.

In a recent letter, I mentioned some challenges to building AI products. These problems are distinct from the issues that arise in building traditional software. They include unclear technical feasibility and complex product specification.