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.

Many things in life have a positive side and a negative side. For instance, a new AI system might help democratize access, and at the same time it might be more accessible to people who have internet access than those who don’t.

A Google Engineer recently announced he believes that a language model is sentient. I’m highly skeptical that any of today’s AI models are sentient. Some reporters, to their credit, also expressed skepticism.

Last week, I wrote about how rising interest rates are likely to lead investors and other finance professionals to focus on short-term returns rather than longer-term investments.

The United States Federal Reserve Bank has signaled that it will continue to raise interest rates. As one consequence, the stock market is significantly down, particularly tech stocks, relative to the beginning of the year.

Years ago, I had to choose between a neural network and a decision tree learning algorithm. It was necessary to pick an efficient one, because we planned to apply the algorithm to a very large set of users on a limited compute budget.

One of the challenges of building an AI startup is setting customer expectations. Machine learning is a highly experiment-driven field. Until you’ve built something, it’s hard to predict how well it will work.