This week, I’m speaking at the World Economic Forum (WEF) and Asia-Pacific Economic Cooperation (APEC) meetings in San Francisco, where leaders in business and government have convened to discuss AI and other topics.

I’ve always believed in democratizing access to the latest advances in artificial intelligence. As a step in this direction, we just launched “Generative AI for Everyone” on Coursera.

Welcome to the Halloween special issue of The Batch, where we take a look at fears associated with AI. In that spirit, I’d like to address a fear of mine: Sensationalist claims that AI could bring about human extinction will cause serious harm.

I wrote earlier about how my team at AI Fund saw that GPT-3 set a new direction for building language applications, two years before ChatGPT was released. I’ll go out on a limb to make another prediction

Over the weekend, Hamas launched a surprise terrorist attack on Israel, slaughtering and kidnapping civilians. The images in the media are horrifying, and over 1,000 people...

Andrej Karpathy, one of the Heroes of Deep Learning who currently works at OpenAI, quipped, “The hottest programming language is English.” While I appreciate the sentiment...

Improvements in chatbots have opened a market for bots integrated with dating apps. I’m concerned that AI romantic partners create fake relationships that displace, rather than strengthen, meaningful human relationships.

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.