Letters from Andrew Ng

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM

Much has been said about many companies’ desire for more compute (as well as data) to train larger foundation models.

Multi-agent collaboration is the last of the four key AI agentic design patterns that I’ve described in recent letters.

Planning is a key agentic AI design pattern in which we use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a larger task.

Tool use, in which an LLM is given functions it can request to call for gathering information, taking action, or manipulating data, is a key design pattern of AI agentic workflows.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress this year: Reflection, Tool use, Planning and Multi-agent collaboration.

I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it.

I’ve noticed a trend in how generative AI applications are built that might affect both big companies and developers: The gravity of data is decreasing.

Progress on LLM-based agents that can autonomously plan out and execute sequences of actions has been rapid, and I continue to see month-over-month improvements.

I think the complexity of Python package management holds down AI application development more than is widely appreciated. AI faces multiple bottlenecks — we need more GPUs, better algorithms, cleaner data in large quantities.

The rise of cloud-hosted AI software has brought much discussion about the privacy implications of using it. But I find that users, including both consumers and developers building on such software

It is only rarely that, after reading a research paper, I feel like giving the authors a standing ovation. But I felt that way after finishing Direct Preference Optimization (DPO) by...

Large language models, or LLMs, have transformed how we process text. Large vision models, or LVMs, are starting to change how we process images as well. But there is an important difference between LLMs and LVMs.

One year since the launch of ChatGPT on November 30, 2022, it’s amazing how many large language models are available.  A year ago, ChatGPT was pretty much the only game in town for consumers (using a web user interface) who wanted to use a large language model (LLM)...

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.