Letters from Andrew Ng

The United States proposed limits on exports of AI technology that would dramatically expand previous restrictions, creating a new international hierarchy for access to advanced chips and models.

Harvard University amassed a huge new text corpus for training machine learning models.

Large language models have been shown to be capable of lying when users unintentionally give them an incentive to do so. Further research shows that LLMs with access to tools can be incentivized to use them in deceptive ways.

Anthropic analyzed 1 million anonymized conversations between users and Claude 3.5 Sonnet. The study found that most people used the model for software development and also revealed malfunctions and jailbreaks.

As we approach 2025, my greatest hope for AI is that it will enable prosocial platforms that promote empathy, understanding, and collaboration rather than division.

In 2025, AI will have learned to see, it will be way smarter and more accurate, and it will start to do things on your behalf.

In 2025, I expect progress in training foundation models to slow down as we hit scaling limits and inference costs continue to rise.

Last year, we saw an explosion of models that generate either video or audio outputs in high quality. In the coming year, I look forward to models that produce video clips complete with audio soundtracks including speech, music, and sound effects.

Stability AI’s aim is to liberate artists of all trades from the repetitive, mechanical aspects of their work and help them spend the majority of their time on the creative side. So our highest hope for next year is that generative AI will help people to be more creative and productive.

Is AI progressing rapidly? Yes! But while the progress of underlying AI technology has indeed sped up over the past 2 years, the fastest acceleration is in applications.

The AI community laid the foundation for systems that can act by prompting large language models iteratively, leading to much higher performance across a range of applications.

How do agents based on large language models compare to human experts when it comes to proposing machine learning research? Pretty well, according to one study.

A new model improves on recent progress in generating interactive virtual worlds from still images.

Rapid progress in generative AI comes with a hidden environmental cost: mountains of obsolete hardware.

One of the world’s biggest payment processors is enabling large language models to spend real money.