“All right, so today we are going to dive deep into some cutting-edge tech,” a chatty American male voice says.

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NotebookLM, which was originally marketed as a study tool, has taken a life of its own among users. The company is now working on adding more customization options, such as changing the length, format, voices, and languages, Martin said. Currently it’s supposed to generate podcasts only in English, but some users on Reddit managed to get the tool to create audio in French and Hungarian.

Yes, it’s cool—bordering on delightful, even—but it is also not immune from the problems that plague generative AI, such as hallucinations and bias.

Here are some of the main ways people are using NotebookLM so far.

It is morally wrong to use AI detectors when they produce false positives that smear students in ways that hurt them and where they can never prove their innocence.

While some educators…

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About two-thirds of teachers report regularly using tools for detecting AI-generated content. At that scale, even tiny error rates can add up quickly.

Boston Dynamics and Toyota Research Institute (TRI) Wednesday revealed plans to bring AI-based robotic intelligence to the electric Atlas humanoid robot. The collaboration will leverage the work that TRI has done around large behavior models (LBMs), which operate along similar lines as the more familiar large language models (LLMs) behind platforms like ChatGPT.

Last September, TechCrunch paid a visit to TRI’s Bay Area campus for a closer look at the institute’s work on robot learning. In research revealed at last year’s Disrupt conference, institute head Gill Pratt explained how the lab has been able to get robots to 90% accuracy when performing household tasks like flipping pancakes through overnight training.

“In machine learning, up until quite recently there was a tradeoff, where it works, but you need millions of training cases,” Pratt explained at the time. “When you’re doing physical things, you don’t have time for that many, and the machine will break down before you get to 10,000. Now it seems that we need dozens. The reason for the dozens is that we need to have some diversity in the training cases. But in some cases, it’s less.”