When I wrote recently about how to build a career in AI, several readers wrote to ask specifically about AI product management: the art and science of designing compelling AI products. I’ll share lessons I’ve learned about this here and in future letters.

In this letter, I’d like to address the serious matter of newcomers to AI sometimes experiencing imposter syndrome, where someone — regardless of their success in the field — wonders if they’re a fraud and really belong in the AI community.

Activities such as writing code and solving math problems are often perceived as purely intellectual pursuits. But this ignores the fact that they involve the mental equivalent of muscle memory.

Stable Diffusion, an image generation model that takes a text prompt and produces an image, was released a few weeks ago in a landmark event for AI. While similar programs can be used via API calls or a web user interface, Stable Diffusion can be freely downloaded and run on the user’s hardware.

I’m thrilled! Paywalled journals that block free access to scientific research are the bane of the academic community.

Every job search is different. Here are some tips from Andrew Ng to increase the odds you’ll find the right position.

Last week, I wrote about switching roles, industries, or both as a framework for considering a job search. If you’re preparing to switch roles or industries, there’s a lot about your target job that you probably don’t know.

Andrew Ng presents a framework for job seekers in AI, especially those entering from a different field.

Andrew Ng considers how inconsistent human decisions are, and how AI can reduce that inconsistency.

While working on Course 3 of the Machine Learning Specialization, which covers reinforcement learning, I was reflecting on how reinforcement learning algorithms are still quite finicky.

Many AI systems have been built using data scraped from the internet. Indeed, even the cornerstone dataset for computer vision research, ImageNet, was built using images taken from the public internet.

Last week’s letter focused on coming up with AI project ideas, part of a series on how to build a career in the field. This letter describes how a sequence of projects might fit into your career path.

In the last two letters, I wrote about developing a career in AI and shared tips for gaining technical skills. This time, I’d like to discuss an important step in building a career: project work.

Last week, I wrote about key steps for building a career in AI: learning technical skills, doing project work, and searching for a job, all of which is supported by being part of a community. In this letter, I’d like to dive more deeply into the first...

The rapid rise of AI has led to a rapid rise in AI jobs, and many people are building exciting careers in this field. A career is a decades-long journey, and the path is not always straightforward.