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.

Last week, I described trends that AI Fund, the venture studio I lead, has seen in building AI startups. I'd like to discuss another aspect of building companies that’s unique to AI businesses: the controversial topic of data moats.

AI businesses differ from traditional software startups in important ways. For instance, technical feasibility isn’t always clear, product specification is complex, and data is necessary to train and test the system.

It's official: Elon Musk will buy Twitter, pending approval of the transaction by the company's stockholders and the U.S. government.

Last week, Elon Musk launched a surprise attempt to acquire Twitter. The $43-billion bid was motivated, he said, by his desire to protect free speech endangered by the company’s practice of promoting some tweets while burying others.

With the pandemic easing in the United States and Canada, I’ve been traveling more in the last two weeks. I spoke at TED 2022 in Vancouver and ScaleUp:AI in New York and attended a manufacturing conference in California.

Machine learning engineers routinely use Jupyter Notebooks for developing and experimenting with code. They’re a regular feature in DeepLearning.AI’s courses. But there’s another use of Jupyter Notebooks that I think is under-appreciated.