Innovation in IT got us to this point. Graphics processing units (GPUs) that power the computing behind AI have fallen in cost by 99% since 2006. There was similar concern about the energy use of data centers in the early 2010s, with wild projections of growth in electricity demand. But gains in computing power and energy efficiency not only proved these projections wrong but enabled a 550% increase in global computing capability from 2010 to 2018 with only minimal increases in energy use. In the late 2010s, however, the trends that had saved us began to break.

AI's integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. Today, new analysis by MIT Technology Review provides an unprecedented and comprehensive look at how much energy the AI industry uses--down to a single query--to trace where its carbon footprint stands now, and where it's headed, as AI barrels towards billions of daily users. This story is a part of MIT Technology Review's series "Power Hungry: AI and our energy future," on the energy demands and carbon costs of the artificial-intelligence revolution. We spoke to two dozen experts measuring AI's energy demands, evaluated different AI models and prompts, pored over hundreds of pages of projections and reports, and questioned top AI model makers about their plans.

"Dollars are being invested, GPUs are being burned, water is being evaporated--it's just absolutely the wrong direction," says Ali Farhadi, CEO of the Seattle-based nonprofit Allen Institute for AI. But sift through the talk of rocketing costs--and climate impact--and you'll find reasons to be hopeful. There are innovations underway that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock. Here's what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: There are reasons to believe that the underlying business realities will ultimately bend toward more energy-efficient AI. The most obvious place to start is with the models themselves--the way they're created and the way they're run.

Filippos's PhD dissertation focuses on developing a method for recognizing object states without visual training data. By leveraging semantic knowledge from online sources and Large Language Models, structured as Knowledge Graphs, Graph Neural Networks learn representations for accurate state classification. In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we met with Filippos Gouidis, who has recently completed his PhD, and found out more about his research on object state classification.

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Huawei Technologies debuted its first in-house operating system for personal computers, offering an alternative to Microsoft's Windows as China pushes to replace American technologies amid rising geopolitical tensions. The company's HarmonyOS is now ready to run on the MateBook Fold, its new foldable laptop, said the head of its consumer business, Richard Yu, on Monday. Huawei is working to make its operating system compatible with more than 2,000 apps, including the popular WeChat and QQ messaging platforms, he said. The laptop features an 18-inch organic light-emitting diode screen when opened flat. Along with the Huawei MateBook Pro and MateBook Fold Ultimate Design laptops, it will be available from June 6.

The Chinese government has accused U.S. President Donald Trump's administration of undermining recent trade talks in Geneva with its warning that using Huawei Technologies' artificial intelligence chips "anywhere in the world" would violate U.S. export controls. The U.S. Commerce Department had said in a statement last week that it was issuing guidance to make clear that the use of Huawei Ascend chips is a breach of the U.S. government's export controls. The agency said at the time that it would also warn the public about "the potential consequences of allowing U.S. AI chips to be used for training and inference of Chinese AI models." The department's statement has since changed to say that the agency was issuing guidance about "the risks of using PRC advanced computing ICs, including specific Huawei Ascend chips," stripping the "anywhere in the world" reference. PRC is the abbreviation for China's formal name, the People's Republic of China, while ICs refers to integrated circuits, or chips.

Microsoft is adding models from Elon Musk's xAI to its artificial intelligence marketplace. Grok 3, which Musk's AI outfit introduced earlier this year, will be available on Microsoft's cloud computing platform, the company said Monday. Microsoft and its biggest rivals in selling rented computing power, including Amazon and Google, are vying to be the place where AI applications are built and deployed. That has made a battleground out of the competition to host the latest models and build sophisticated controls to manage how they're used.

Character.ai and other bots such as Chat GPT are based on "large language models" of artificial intelligence. These are trained on vast amounts of data – whether that's websites, articles, books or blog posts - to predict the next word in a sequence. From here, they predict and generate human-like text and interactions. The way mental health chatbots are created varies, but they can be trained in practices such as cognitive behavioural therapy, which helps users to explore how to reframe their thoughts and actions. They can also adapt to the end user's preferences and feedback.

Toddlers may swiftly master the meaning of the word "no", but many artificial intelligence models struggle to do so. They show a high fail rate when it comes to understanding commands that contain negation words such as "no" and "not". That could mean medical AI models failing to realise that there is a big difference between an X-ray image labelled as showing "signs of pneumonia" and one labelled as showing "no signs of pneumonia" – with potentially catastrophic consequences if physicians rely on AI…