I traveled to Taiwan last week, where I met many CEOs interested in AI transformation of traditional companies. I also visited Taiwan AI Labs which, similar to OpenAI, started as a nonprofit AI research institute.

We ended a busy week in Colombia hosting a Pie & AI meetup in Medellín. I met hundreds of engineers and business professionals excited to take the next step in their AI careers. I was energized by the enthusiasm the Colombia community had for collaborating...

I am writing to you from Colombia today, and am excited to announce the opening of our office in Medellín. The office will serve as the Latin American headquarters for three of the companies in our AI ecosystem: Landing AI, deeplearning.ai, and AI Fund.

Shortly after Pi day (3/14), I announced our Pie & AI series of meetups. We held them in Seattle in March and London in April. We just hosted our third Pie & AI meetup to celebrate the launch of the final course in the deeplearning.ai TensorFlow Specialization.

I recently met an engineer at a large travel agency who had built a fun ML project for sending guests greetings. This was one of the company’s first forays into ML. The project did not generate any revenue, and their managers discouraged them from doing more work on ML.

My stash of new blank notebooks just arrived in the mail. I have a weakness for stationery. I always feel that if only I had the perfect pen and notebook, I might have better ideas.

If you wonder how often I take online courses myself, the answer is: Quite often. I have a longstanding interest in AI for healthcare.

As we were working on the latest course of the deeplearning.ai TensorFlow Specialization, instructor Laurence Moroney messaged me his LSTM-generated poetry, created by learning from a database of roughly 100 Irish song lyrics.

AI is still compute-hungry. With supervised learning algorithms and emerging approaches to self-supervised and unsupervised learning, we are nowhere near satisfying this hunger.

Last Friday, I attended the International Conference on Machine Learning. I spoke at the AI and Climate Change workshop on projects we’re doing to model methane emissions and on wind turbines. John Platt gave an overview of climate issues.

I spoke last week at re:MARS, Amazon's conference focusing on machine learning, automation, robotics, and space. I heard great talks from Jeff Bezos, Kate Darling, Ken Goldberg, Marc Raibert, and others.

Healthcare is one of many sectors being transformed by AI. I have a personal interest in it, since my father worked on machine learning for diagnosis of liver diseases almost 40 years ago. It’s thanks partly to this work that I learned about AI from an early age.

In March, I announced our Pie & AI series of meetups. Since then, we've held events in Seattle and London, two growing centers of AI talent. It’s inspiring to see AI develop around the world and not just in Silicon Valley and Beijing.

So many people who are just starting out in machine learning are doing amazing work. With online education, open source software, and open publications, it takes less time than ever to go from 0 to 100 in ML.

I’ve been thinking a lot about "small data." If you have an image classification problem and 1,000,000 images, then dozens of teams around the world can build a good classifier.