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).

Here’s a quiz for you. Which company said this? “It’s always been a challenge to create computers that can actually communicate with and operate at anything like the level of a human mind...

Last week, Silicon Valley Bank (SVB), Signature Bank, and Silvergate Bank suddenly collapsed. If it passed uneventfully from your point of view, good for you! Many companies worked nonstop through the weekend scrambling to preserve funds so they could pay their employees.

ChatGPT has raised fears that students will harm their learning by using it to complete assignments. Voice cloning, another generative AI technology, has fooled people into giving large sums of money to scammers, as you can read in this issue of The Batch.

Landing AI, a sister company of DeepLearning.AI, just released its computer vision platform, LandingLens, for everyone to start using for free. You can try it now.

As you can read in this issue of The Batch, Microsoft’s effort to reinvent web search by adding a large language model snagged when its chatbot went off the rails. Both Bing chat and Google’s Bard...

AI has an Instagram problem. Just as Instagram’s parade of perfect physiques makes many people feel they don’t measure up, AI’s parade of exciting projects makes many people feel their own projects are lacking.

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...