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.

I’ve been following with excitement the recent progress in space launches. Earlier this week, Richard Branson and his Virgin Galactic team flew a rocket plane 53 miles up, earning him astronaut wings.

In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification. With traditional software, product managers can specify a product in ways...

Last week, I mentioned that one difference between traditional software and AI products is the problem of unclear technical feasibility. In short, it can be hard to tell whether it’s practical to build a particular AI system.

With the rise of software engineering over several decades, many principles of how to build traditional software products and businesses are clear. But the principles of how to build AI products and businesses are still developing.

I’m thrilled to announce the first data-centric AI competition! I invite you to participate.For decades, model-centric AI competitions, in which the dataset is held fixed while you iterate on the code, have driven our field forward.