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.

AI Fund, the venture studio and investment firm that I lead, recently held a summit where CEOs and founders of portfolio companies shared ideas on topics from fundraising to building team culture.

I’ve always thought that how you treat those who are powerless shows your true character. People rarely mistreat others who have power over them -- for example, their boss — because they might suffer adverse consequences.

Last week, I wrote about the grand challenge of artificial general intelligence. Other scientific and engineering grand challenges inspire me as well. For example, fusion energy, extended lifespans, and space colonization have massive...

I’ve always thought that building artificial general intelligence — a system that can learn to perform any mental task that a typical human can — is one of the grandest challenges of our time. In fact, nearly 17 years ago, I co-organized a NeurIPS workshop on building human-level AI.

Last week, DeepLearning.AI invited a group of learners to our Palo Alto office’s courtyard. We had a good time chatting about paths into AI, career trajectories, applications people were working on, and challenges they were facing.