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.

Making AI Work, MIT Technology Review's new AI newsletter, is here Learn how to apply LLMs across industries in 7 weekly editions of our new free newsletter. For years, our newsroom has explored AI's limitations and potential dangers, as well as its growing energy needs . And our reporters have looked closely at how generative tools are being used for tasks such as coding and running scientific experiments . But how is AI being used in fields like health care, climate tech, education, and finance? How are small businesses using it? And what should you keep in mind if you use AI tools at work?

Our socioeconomic system is facing an existential threat from AI. In our capitalist society, most people depend on jobs to sustain themselves. The U.S. government, in turn, relies heavily on taxing the income of individual workers for revenue. As artificial intelligence progressively eliminates job opportunities, a growing number of individuals will face severe job insecurity, leading to a corresponding decline in federal revenue. Radical action is needed now to steer away from a dystopian collapse toward better possibilities.

A week after its algorithms advised people to eat rocks and put glue on pizza, Google admitted Thursday that it needed to make adjustments to its bold new generative AI search feature. The episode highlights the risks of Google's aggressive drive to commercialize generative AI--and also the treacherous and fundamental limitations of that technology. Google's AI Overviews feature draws on Gemini, a large language model like the one behind OpenAI's ChatGPT, to generate written answers to some search queries by summarizing information found online. The current AI boom is built around LLMs' impressive fluency with text, but the software can also use that facility to put a convincing gloss on untruths or errors. Using the technology to summarize online information promises can make search results easier to digest, but it is hazardous when online sources are contractionary or when people may use the information to make important decisions.

You summon an Uber car, create a digital calendar appointment that family members can see, or order a blender that Target's website shows is in stock.

Recently we've seen some AI failures on a far bigger scale. In the latest (hilarious) gaffe, Google's Gemini refused to generate images of white people, especially white men. Instead, users were able to generate images of Black popes and female Nazi soldiers. Google had been trying to get the outputs of its model to be less biased, but this backfired, and the tech company soon found itself in the middle of the US culture wars, with conservative critics and Elon Musk accusing it of having a "woke" bias and not representing history accurately. Google apologized and paused the feature. In another now-famous incident, Microsoft's Bing chat told a New York Times reporter to leave his wife.

Everyone's talking about large language models and image generators built on artificial intelligence. Many people have tested out tools like ChatGPT or DALL-E 2 and been amazed at the results, or disturbed by their tendency to hallucinate. But how do the algorithms underpinning these new generative tools actually work? And what's the best way to evaluate their capabilities?

The biggest news stories this morning: Engadget’s best gaming laptops, Threads’ new hashless tags are good for pranks, Time to get miserable about the COP28 declaration.

Intel today is entering the "AI PC" era with the launch of its new Core Ultra notebook chips. Originally codenamed "Meteor Lake," these are Intel's first processors to include an NPU, or neural processing unit, for accelerating AI tasks. The launch comes a week after AMD revealed its upcoming Ryzen 8040 hardware, its second batch of chips to include NPUs. While Intel is playing a bit of AI catch-up, the Core Ultra chips still sound like a solid step forward -- at least according to the company's benchmarks. Intel claims the Core Ultra chips use up to 79 percent less power than AMD's last-gen Ryzen 7840U while idling in Windows, and they're also up to 11 percent faster than AMD's hardware for multithreaded tasks.

This week, US senators heard alarming testimony suggesting that unchecked AI could steal jobs, spread misinformation, and generally "go quite wrong," in the words of OpenAI CEO Sam Altman (whatever that means). He and several lawmakers agreed that the US may now need a new federal agency to oversee the development of the technology. But the hearing also saw agreement that no one wants to kneecap a technology that could potentially increase productivity and give the US a lead in a new technological revolution. Worried senators might consider talking to Missy Cummings, a onetime fighter pilot and engineering and robotics professor at George Mason University. She studies use of AI and automation in safety critical systems including cars and aircraft, and earlier this year returned to academia after a stint at the National Highway Traffic Safety Administration, which oversees automotive technology, including Tesla's Autopilot and self-driving cars.