Are you interested in learning more about the ways in which AI is being used? We've launched a new weekly newsletter series exploring just that: digging into how generative AI is being used and deployed across sectors and what professionals need to know to apply it in their everyday work. Each edition of Making AI Work begins with a case study, examining a specific use case of AI in a given industry. Then we'll take a deeper look at the AI tool being used, with more context about how other companies or sectors are employing that same tool or system. Finally, we'll end with action-oriented tips to help you apply the tool. The first edition takes a look at how AI is changing health care, digging into the future of medical note-taking by learning about the Microsoft Copilot tool used by doctors at Vanderbilt University Medical Center.

The social network for AI bots resembled a spectator battle, with AI enthusiasts competing to make their agents look sentient. Lots of influential people in tech last week were describing Moltbook, an online hangout populated by AI agents interacting with one another, as a glimpse into the future. It appeared to show AI systems doing useful things for the humans that created them (one person used the platform to help him negotiate a deal on a new car). Sure, it was flooded with crypto scams, and many of the posts were actually written by people, but about it pointed to a future of helpful AI, right? The whole experiment reminded our senior editor for AI, Will Douglas Heaven, of something far less interesting: Pokémon. Back in 2014, someone set up a game of Pokémon in which the main character could be controlled by anyone on the internet via the streaming platform Twitch.

MIT Technology Review's senior editor for AI shares what he's been thinking about lately. My daughter introduced me to El Estepario Siberiano's YouTube channel a few months back, and I have been obsessed ever since. The Spanish drummer (real name: Jorge Garrido) posts videos of himself playing supercharged cover versions of popular tracks, hitting his drums with such jaw-dropping speed and technique that he makes other pro drummers shake their heads in disbelief. The dozens of reaction videos posted by other musicians are a joy in themselves. Garrido is up-front about the countless hours that it took to get this good. He says he sat behind his kit almost all day, every day for years.

Why it's time to reset our expectations for AI The hype we have been sold for the past few years has been overwhelming. Hype Correction is the antidote. Can I ask you a question: How do you about AI right now? Are you still excited? When you hear that OpenAI or Google just dropped a new model, do you still get that buzz? Or has the shine come off it, maybe just a teeny bit? Come on, you can be honest with me.

The tech world is abuzz over a new open-source reasoning AI model developed by DeepSeek, a Chinese startup. Its success is remarkable given the constraints that Chinese AI companies face due to US export controls on cutting-edge chips. DeepSeek's approach represents a radical change in how AI gets built, and could shift the tech world's center of gravity. Hear from MIT Technology Review news editor Charlotte Jee, senior AI editor Will Douglas Heaven, and China reporter Caiwei Chen as they discuss what DeepSeek's breakout success means for AI and the broader tech industry.

Few companies can say they've had more of a rollercoaster year than OpenAI. At the beginning of 2023, the world's hottest AI startup was riding high on the success of its ChatGPT chatbot. Now, it's dusting itself off from an attempted coup which saw Sam Altman ousted and reinstated as the company's CEO within a few short days. Our AI experts have been following OpenAI's every move throughout the year, often with exclusive access to the people building the revolutionary products and systems. Check out just some of the highlights from the past year--and what we think is coming next.

When OpenAI launched ChatGPT, with zero fanfare, in late November 2022, nobody inside the company was prepared for a viral mega-hit. It was viewed in-house as a "research preview," a tease of a more polished version of a two-year-old technology and a way to iron out some of its flaws. But then it absolutely blew up. The firm has been scrambling to catch up--and capitalize on its success--ever since. To get the inside story behind the chatbot--how it was made, how OpenAI has been updating it since release, and how its makers feel about its success--our senior AI editor Will Douglas Heaven talked to four people who helped build what has become the most popular internet app ever.

Artificial intelligence has become such a big part of our lives, you'd be forgiven for losing count of the algorithms you interact with. But the AI powering your weather forecast, Instagram filter, or favorite Spotify playlist is a far cry from the hyper-intelligent thinking machines industry pioneers have been musing about for decades. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn only one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data--including the many bad decisions people have made, like marginalizing people of color and women. Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence--machines that can multitask, think, and reason for themselves. Beyond the answer to how we might develop technologies capable of common sense or self-improvement lies yet another question: who really benefits from the replication of human intelligence in an artificial mind? "Most of the value that's being generated by AI today is returning back to the billion dollar companies that already have a fantastical amount of resources at their disposal," says Karen Hao, MIT Technology Review's senior AI reporter and the writer of The Algorithm. "And we haven't really figured out how to convert that value or distribute that value to other people."