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!

I just replaced my two-year-old phone with a new one and figured out how to take long-exposure photos of Nova even while she’s asleep and the lights are very low. This piece of technology brought me a surprising amount of joy!

Last week, I saw a lot of social media discussion about a paper using deep learning to generate artificial comments on news articles. I’m not sure why anyone thinks this is a good idea. At best, it adds noise to the media environment. At worst, it’s a tool for con artists and propagandists.

Thinking about the future of machine learning programming frameworks, I recently reread computer scientist Fred Brooks’ classic essay, “No Silver Bullet: Essence and Accidents of Software Engineering.” Three decades after its initial...

I read an interesting paper comparing the results of traditional passive learning (sitting in a lecture) versus active methods like the flipped classroom, where students watch videos at home and work on exercises in class.

Over the weekend, we hosted our first Pie & AI meetup in Kuala Lumpur, Malaysia, in collaboration with the AI Malaysia group, MDEC, and ADAX. The event was part of Malaysia’s AI & Data Week 2019.

When it comes to artificial intelligence, one of the biggest mistakes large companies make is thinking tactically rather than strategically. What’s the difference? Some taxi companies thought they had the internet...

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.