Building Machine Learning Systems is More Debugging Than Development: Traditional software development requires meticulous planning. With machine learning, it's better to jump right in.
Letters

Building Machine Learning Systems is More Debugging Than Development: Traditional software development requires meticulous planning. With machine learning, it's better to jump right in.

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.
AI at the Speed of Prompting: Prompt-based development enables you to try out ideas quickly and cheaply — no need to scope projects carefully.
Letters

AI at the Speed of Prompting: Prompt-based development enables you to try out ideas quickly and cheaply — no need to scope projects carefully.

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.
What Lawmakers Need to Know About AI: Few people in the world have the information required to regulate AI effectively. Governments need to get it.
Letters

What Lawmakers Need to Know About AI: Few people in the world have the information required to regulate AI effectively. Governments need to get it.

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.
Breakthroughs on the Horizon?: Innovations in computer vision stole the spotlight at this year's CVPR conference.
Letters

Breakthroughs on the Horizon?: Innovations in computer vision stole the spotlight at this year's CVPR conference.

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 Risk and the Resource Curse: Concentration of AI in the hands of a few could undermine human rights. The solution is to make it available to everyone.
Letters

AI Risk and the Resource Curse: Concentration of AI in the hands of a few could undermine human rights. The solution is to make it available to everyone.

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.
Existential Risk? I Don't Get It!: Prominent computer scientists fear that AI could trigger human extinction. It's time to have a real conversation about the realistic risks.
Letters

Existential Risk? I Don't Get It!: Prominent computer scientists fear that AI could trigger human extinction. It's time to have a real conversation about the realistic risks.

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.
Three New Courses!: Check out our short courses on Building Systems with the ChatGPT API, LangChain for LLM Application Development, and How Diffusion Models Work.
Letters

Three New Courses!: Check out our short courses on Building Systems with the ChatGPT API, LangChain for LLM Application Development, and How Diffusion Models Work.

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.
Building AI Systems No Longer Requires Much Data: Pretrained models make it possible to build AI systems using very little additional data.
Letters

Building AI Systems No Longer Requires Much Data: Pretrained models make it possible to build AI systems using very little additional data.

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.
Beyond Test Sets: How prompting is changing machine learning development
Letters

Beyond Test Sets: How prompting is changing machine learning development

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...
Doing Business with Chatbots: Companies might be overestimating how much it costs to run LLM-based applications.
Letters

Doing Business with Chatbots: Companies might be overestimating how much it costs to run LLM-based applications.

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.
New course — ChatGPT Prompt Engineering for Developers: Learn how to use ChatGPT's API to build applications for text processing, robotic process automation, coaching, and more
Letters

New course — ChatGPT Prompt Engineering for Developers: Learn how to use ChatGPT's API to build applications for text processing, robotic process automation, coaching, and more

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.
"Visual Prompting” Builds Vision Models in Seconds: A new approach applies ideas from text prompting to computer vision
Letters

"Visual Prompting” Builds Vision Models in Seconds: A new approach applies ideas from text prompting to computer vision

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.
Opportunities and Pitfalls for Large Language Models: The LLM business landscape is crowded with APIs and short on high-value, hard-to-build applications.
Letters

Opportunities and Pitfalls for Large Language Models: The LLM business landscape is crowded with APIs and short on high-value, hard-to-build applications.

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...
Time to Push Back on AI Pessimism: The recent proposal to pause AI research is a wake-up call.
Letters

Time to Push Back on AI Pessimism: The recent proposal to pause AI research is a wake-up call.

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.
Figure showing how researchers obtained the Alpaca model
Letters

When One Machine Learning Model Learns From Another: Was Google’s Bard trained on output from OpenAI's ChatGPT? The technique is legit, but it raises thorny questions.

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.

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