Letters from Andrew Ng

As you can read in this issue of The Batch, generative AI companies are being sued over their use of data (specifically images and code) scraped from the web to train their models.

Recent successes with large language models have brought to the surface a long-running debate within the AI community: What kinds of information do learning algorithms need in order to gain intelligence?

Understanding the math behind machine learning algorithms improves your ability to debug algorithms when they aren’t working, tune them so they work better, and perhaps even invent new ones. Today DeepLearning.AI is launching the Mathematics for Machine Learning and Data Science Specialization.

In late December, Google reportedly issued a “code red” to raise the alarm internally to the threat of disruption of its business by large language models like OpenAI’s ChatGPT. Do large language models (LLMs) endanger Google's search engine business?

Will the future of large language models limit users to cutting-edge models from a handful of companies, or will users be able to choose among powerful models from a large number of developers?

In last week’s issue of The Batch, Yoshua Bengio, Alon Halevy, Douwe Kiela, Been Kim, and Reza Zadeh shared their hopes for AI in 2023. I also asked people on LinkedIn and Twitter about their hopes for AI this year. Rather than...

As we enter the new year, let’s view 2023 not as a single year, but as the first of more in which we will accomplish our long-term goals. Some results take a long time to achieve, and even though...

As the winter holiday approaches, it occurs to me that, instead of facing AI winter, we are in a boiling-hot summer of AI.

What should be AI’s role in moderating the millions of messages posted on social media every day? The volume of messages means that automation is required. But the question of what is appropriate moderation versus inappropriate censorship lingers.

One of the dangers of large language models (LLMs) is that they can confidently make assertions that are blatantly false. This raises worries that they will flood the world with misinformation. If they could moderate their degree of confidence appropriately, they would be less likely to mislead.

On Monday, the European Union fined Meta roughly $275 million for breaking its data privacy law. Even though Meta’s violation was not AI specific, the EU’s response is a reminder that we need to build AI systems that preserve user privacy...

Last week, Facebook’s parent company Meta released a demo of Galactica, a large language model trained on 48 million scientific articles. Two days later, amid controversy regarding the model’s potential to generate false or misleading articles, the company withdrew it.

The population of Earth officially reached 8 billion this week. Hooray! It’s hard to imagine what so many people are up to. While I hope that humanity can learn how to leave only gentle footprints on the planet, I’m excited about the creativity and inventiveness that a growing human population...

The economic downturn of the past six months has hit many individuals and companies hard, and I’ve written about the impact of rising interest rates on AI. The effects of high inflation, the Russian war in Ukraine, and an economic slowdown in China are rippling across the globe...

A new report from UN Climate Change says that the world might be on track for 2.5 °C of warming by the end of the century, a potentially catastrophic level of warming that’s far above the 1.5 °C target of the 2015 Paris Agreement.