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.

Last week, safe.org asserted that “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.

In April, DeepLearning.AI launched a short course, “ChatGPT Prompt Engineering for Developers,” taught by OpenAI’s Isa Fulford and me. I’m thrilled to announce three more short courses, available today.

It’s time to move beyond the stereotype that machine learning systems need a lot of data. While having more data is helpful, large pretrained models make it practical to build viable systems using a very small labeled training set — perhaps just a handful of examples specific to your application.

A few weeks ago, I wrote about my team at Landing AI’s work on visual prompting. With the speed of building machine learning applications through text prompting and visual prompting, I’m...