I just finished reading BJ Fogg’s new book, Tiny Habits: The Small Changes That Change Everything. Fogg explains that the best way to build a new habit is to start small and succeed, rather than starting too big and giving up.

Last week brought reports that the European Union is considering a three- to five-year moratorium on face recognition in public places. Face recognition is a problematic technology with significant potential for misuse, and I celebrate the...

One of the best gifts a friend gave me last year was recommending a book that I subsequently read and loved. She didn’t even have to buy it for me!

Many accomplished students and newly minted AI engineers ask me: How can I advance my career? Companies in many industries are building AI teams, but it may not be obvious how to join one of them.

Happy New Year!Every winter holiday, I pursue a learning goal around a new topic. In between visits with family, I end up reading a lot.

We here at deeplearning.ai wish you a wonderful holiday season. As you consider your New Year’s resolutions and set goals for 2020, consider not just what you want to do, but what you want to learn...

I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on...

I’ve been thinking about AI and ethics. With the techlash and an erosion of trust in technology as a positive force, it’s more important than ever that we make sure the AI community acts ethically.

Recently I wrote about major reasons why AI projects fail, such as small data, robustness, and change management. Given that some AI systems don't work, users and customers sometimes rightly wonder whether they should trust an AI system.

I’ll be spending Thanksgiving with Nova and watching her taste turkey for the first time. To those of you who celebrate Thanksgiving, I hope you spend time with loved ones, reflect on what you are thankful for, and discuss some very important...

My last two letters explored robustness and small data as common reasons why AI projects fail. In the final letter of this three-part series, I’d like to discuss change management.

In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source.

Building AI systems is hard. Despite all the hype, AI engineers struggle with difficult problems every day. For the next few weeks, I’ll explore some of the major challenges. Today’s topic: The challenge of building AI systems that are robust to real-world conditions.

Welcome to the Halloween edition of The Batch! I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.

I’ve heard this conversation in multiple companies: Machine learning engineer: Look how well I did on the test set! Business owner: But your ML system doesn’t work. This sucks! Machine learning engineer: But look how well I did on the test set!