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...

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.