While AI is a general-purpose technology that’s useful for many things, it isn’t good for every task under the sun. How can we decide which concrete use cases to build? If you’re helping a business figure out where to apply AI, I’ve found the following recipe useful as a brainstorming aid…

I recently spoke about “Opportunities in AI” at Stanford’s Graduate School of Business. I'd like to share a few observations from that presentation, and I invite you to watch the video (37 minutes).

Amidst rising worry about AI harms both realistic (like job loss) and unrealistic (like human extinction), It’s critical to understand AI’s potential to do tremendous good. Our new specialization is designed to empower people to identify, scope, and build impactful AI projects.

I’d like to share a part of the origin story of large language models that isn’t widely known. A lot of early work in natural language processing (NLP) was funded by U.S. military intelligence agencies that needed machine translation and speech recognition capabilities.

Machine learning development is an empirical process. It’s hard to know in advance the result of a hyperparameter choice, dataset, or prompt to a large language model (LLM).

An increasing variety of large language models (LLMs) are open source, or close to it. The proliferation of models with relatively permissive licenses gives developers more options for building applications.

Do large language models understand the world? As a scientist and engineer, I’ve avoided asking whether an AI system “understands” anything. There’s no widely agreed-upon, scientific test for whether a system really understands — as opposed to appearing to understand —

Last week, I returned home from Asia, where I spoke at Seoul National University in Korea, the National University of Singapore, and the University of Tokyo in Japan and visited many businesses.

Last week, the White House announced voluntary commitments by seven AI companies. Most of the points were sufficiently vague that it seems easy for the White House...

Many laws will need to be updated to encourage beneficial AI innovations while mitigating potential harms. One example: Copyright law as it relates to generative AI is a mess!

Internalizing this mental framework has made me a more efficient machine learning engineer: Most of the work of building a machine learning system is debugging rather than development.

Prompt-based development is making the machine learning development cycle much faster: Projects that used to take months now may take days. I wrote in an earlier letter that this rapid development is causing developers to do away with test sets.

Suddenly it seems like everyone wants to regulate AI. The European Union is on the verge of enacting a comprehensive AI Act that’s intended to mitigate risks and protect individual rights. In the United States, Senate Majority leader Chuck Schumer foresees legislation possibly within months.

I spent Sunday through Tuesday at the CVPR computer vision conference in Vancouver, Canada, along with over 4,000 other attendees. With the easing of the pandemic, it’s fantastic that large conferences are being held in person again!

AI risks are in the air — from speculation that AI, decades or centuries from now, could bring about human extinction to ongoing problems like bias and fairness.