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...

There are many great applications to be built on top of large language models, and the overhead of doing so may be lower than you think. Sometimes, I've spent all day on a weekend developing ideas only to find that I've spent less than $0.50.

Last week, we released a new course, ChatGPT Prompt Engineering for Developers, created in collaboration with OpenAI. This short, 1.5-hour course is taught by OpenAI’s Isa Fulford and me.

My team at Landing AI just announced a new tool for quickly building computer vision models, using a technique we call Visual Prompting. It’s a lot of fun! I invite you to try it.

The competitive landscape of large language models (LLMs) is evolving quickly. The ultimate winners are yet to be determined, and already the current dynamics are exciting. Let me share a few observations, focusing on direct-to-consumer...

An ill-advised proposal for a 6-month pause in cutting-edge AI research got far more attention than I think it deserved. To me, this is a wake-up call that the AI doomers have done a much better job than the AI optimists at framing the narrative of progress in AI.

Last week, the tech news site The Information reported an internal controversy at Google. Engineers were concerned that Google’s Bard large language model was trained in part on output from OpenAI’s ChatGPT, which would have violated OpenAI’s terms of use.

Generative AI is taking off, and along with it excitement and hype about the technology’s potential. I encourage you to think of it as a general-purpose technology (GPT, not to be confused with the other GPT: generative pretrained transformer).