This special issue of The Batch celebrates the launch of our new Generative Adversarial Networks Specialization! GANs are among the most exciting technologies to emerge from deep learning. These networks learn in a very different way than typical supervised methods...

AI researchers keep coming up with impressive innovations: transformer-based language models, self-supervised learning, deep reinforcement learning, small data. All of these developments hold great promise.

I’d like to share a programming tip that I’ve used for years. A large part of programming involves googling for code snippets you need on Stack Overflow and other websites. (Shh. Don’t tell the nondevelopers. ????)

Today we take it for granted that many people know how to read and write. Someday, I hope, it will be just as common that people know how to write code. Several hundred years ago, society didn’t view language literacy as a necessary skill.

Did you ever spend days obsessing over a technical problem? If so, I applaud you. Determined pursuit of solutions to hard problems is an important step toward building deep expertise.

I’ve been trying to teach my toddler the alphabet. Despite having some educational experience, when she mispronounces a vowel for the nth time, I can’t help but feel like I’m doing it wrong.

Last week, I asked what values the AI community stands for. Thank you to everyone who replied! The email responses in aggregate ran to 55 pages of text, and I enjoyed reading all of them.

Earlier this week, I asked a question on social media: What is the most important problem that the AI community should work on? Thousands of you responded. The most frequently mentioned themes included...

I spoke last week at the National Intergovernmental Audit Forum, a meeting attended by U.S. federal, state, and local government auditors. (Apparently some of the organizers had taken AI for Everyone.)

Over the weekend, my family celebrated my grandfather’s 102nd birthday on Zoom. We dialed in from Hong Kong (my grandfather), the U.S. (myself), the UK, Singapore, and New Zealand.

I received a copy of Why We Sleep: Unlocking the Power of Sleep and Dreams as a Christmas gift — back in the pre-Covid era — and finished it last weekend. This book by Matthew Walker, director of UC Berkeley’s sleep and neuroimaging lab...

Last week, I wrote about the U.S. Immigration and Customs Enforcement (ICE) policy that would have forced international students to leave the country if their university went fully online to manage the risk of Covid-19.

I am appalled by the policy, announced on Monday by U.S. Immigrations and Customs Enforcement (ICE), that international students in the country on an F-1 visa must leave if their school goes fully online to cope with Covid-19.

We know that biased data leads to biased machine learning. But does the problem go beyond that? A few colleagues asked about this after a heated exchange on Twitter between Yann LeCun and Timnit Gebru (see “Image Resolution in Black and White” below).

I was dismayed on Monday to read that the U.S. is suspending the H1-B visa program at least through the end of the year. This effort to discourage immigration can only bring distress to workers from other countries and harm to the U.S.