One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and other industries.

AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate.

When a lot of data is available, machine learning is great at automating decisions. But when data is scarce, consider using the data to augment human insight, so people can make better decisions.

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.

Last week, I talked about how best practices for machine learning projects are not one-size-fits-all, and how they vary depending on whether a project uses structured or unstructured data, and whether the dataset is small or big.

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?

Over the last two weeks, I described the importance of clean, consistent labels and how to use human-level performance (HLP) to trigger a review of whether labeling instructions need to be reviewed.

Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.

Beating human-level performance (HLP) has been a goal of academic research in machine learning from speech recognition to X-ray diagnosis. When your model outperforms humans, you can argue that you’ve reached a significant milestone and publish a paper!

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. ????)

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.

There has been a lot of excitement about the idea of using deep learning to diagnose diabetic retinopathy: That is by taking a photo of the retina and using AI to detect signs of disease.

In an earlier letter, I wrote about the challenge of robustness: A learning algorithm that performs well on test data often doesn’t work well in a practical production environment because the real world turns out to be different than the test set.

Last week, I asked readers to tell me what they’re doing to address the Covid-19 pandemic. Many of you wrote to say you’re taking actions such as shopping for neighbors, making masks, and creating posters that promote Covid-safe practices...

Nearly a decade ago, I got excited by self-taught learning and unsupervised feature learning — ways to learn features from unlabeled data that afterward can be used in a supervised task.