The physical world is full of unique details that differ from place to place, person to person, and item to item. In contrast, the world of software is built on abstractions that make for relatively uniform coding environments and user...

On Monday, Landing AI (where I’m CEO) announced the close of a $57 million Series A funding round. The investment enables the company to continue building its data-centric MLOps platform for computer vision, with a focus on manufacturing visual inspection.

Years ago, whenever I had to do something boring or unpleasant — such as drive to work or go for a run — I used to listen to music to provide a distraction. Although I still appreciate music, as I got older I decided to cut out distractions.

On Halloween, the veil lifts between the spirit and AI worlds, allowing the two to pass through one another. The resulting paranormal — or, as AI practitioners call it, paragaussian — phenomena raise questions like these...

In June, I announced the first Data-centric AI Competition. The deadline for submissions was in early September, and today I’m thrilled to announce the winners!A total of 489 individuals and teams submitted 2,458 unique datasets.

The image below shows two photos of the same gear taken under different conditions. From the point of view of a computer-vision algorithm — as well as the human eye — the imaging setup that produced the picture on the right makes...

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: Ready, Aim, Fire and Ready, Fire, Aim

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.

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.