In an increasingly divided world, one thing that everyone seems to agree on is that artificial intelligence is a hugely disruptive—and sometimes downright destructive—phenomenon.

At WIRED’s AI Power Summit in New York on Monday, leaders from the worlds of tech, politics, and the media came together to discuss how AI is transforming their intertwined worlds. The Summit included voices from the AI industry, a current US senator and a former Trump administration official, and publishers including WIRED’s parent company, Condé Nast. You can view a livestream of the event in full below.

“In journalism, many of us have been excited and worried about AI in equal measure,” said Anna Wintour, Condé Nast’s chief content officer and the global editorial director of Vogue, in her opening remarks. “We worry about it replacing our work, and the work of those we write about.”

Leaders from the world of politics offered contrasting visions for ensuring AI has a positive impact overall. Richard Blumenthal, the Democratic senator from Connecticut, said policymakers should learn from social media and figure out suitable guardrails around copyright infringement and other key issues before AI causes too much damage. “We want to deal with the perfect storm that is engulfing journalism,” he said in conversation with WIRED global editorial director Katie Drummond.

In a separate conversation, Dean Ball, a senior fellow at the Foundation for American Innovation and one of the authors of the Trump Administration’s AI Action Plan, defended that policy blueprint’s vision for AI regulation. He claimed that it introduced more rules around AI risks than any other government has produced.

Figures from within the AI industry painted a rosy picture of AI’s impact, too, arguing that it will be a boon for economic growth and would not be deployed unchecked.

One of the most prestigious honors we award products is inclusion on our Buy It for Life gear roundup. This list represents products that WIRED writers have personally used for years, and as the name implies, they should last you for the rest of your life with proper care and warranty support. There’s only one coffee maker on that list, the Moccamaster KBGV Select, and you can currently pick it up from Amazon for up to $40 off its list price, depending on the color.

These drip coffee makers are seriously built to last, handmade in the Netherlands with solid steel and copper components. They’re fully repairable, which means they’ll keep churning out hot mugs of perfect coffee even after the five-year warranty ends. There are a variety of models, but we like the KBGV Select because it can also brew a half carafe instead of a full carafe, a useful trick for smaller households or an afternoon energy burst.

Extremely precise temperature control means you get excellent coffee every time, managing to consistently heat within a range of 4 degree Celsius. Technivorm is one of less than a dozen companies producing SCA-certified coffee makers for home use, and the Moccamaster models take up a noticeable chunk of that list.

It has all the features you’d expect from a drip coffee maker, like a hot plate for the carafe that has an automatic shut off, which automatically adjusts temperature based on whether you brewed a full or half carafe. The reservoir is 1.25 liters, so you can brew up to 10 cups of coffee at once, and it takes just four to six minutes from start to finish.

This model is available in a huge variety of colors, and your discount will vary based on which you think will match your kitchen best. I found the best price of $317 on the Turquoise, with the Apricot and Matte Black right behind at $320, as well as lesser discounts on the Off-White, Polished silver, and Juniper varieties. While we think it’s worth spending the extra cash for something that will last you years to come, you can always peruse our other favorite coffee makers if you’re looking for something more wallet-friendly.

The rise of e-commerce has brought unprecedented convenience to consumers, but it has also created fertile ground for deceptive practices in online marketplaces. A growing body of research is now focusing on the detection of fake or misleading product reviews, often referred to as spam reviews. These are deliberately written to either unfairly promote a product or damage a competitor’s reputation.

These reviews frequently use fabricated profiles or carefully crafted language, making them difficult to distinguish from genuine customer feedback. Moreover, the use of large language models, colloquially known as generative AI, are now being used to generate authentic-seeming spam reviews.

The impact of spam reviews is significant. Consumers may be persuaded to purchase low-quality goods, while legitimate businesses suffer reputational harm. Ultimately, this might erode trust in digital marketplaces. However, distinguishing between authentic opinions and deceptive ones is difficult.

For their article published in the International Journal of Services, Economics and Management researchers have turned to computational opinion mining, which involves analyzing text to extract sentiment and meaning, to detect patterns indicative of fraudulent activity.

Traditional techniques include filtering for suspicious keywords, monitoring abnormal posting patterns, assessing reviewer credibility, and employing verification tools such as anti-spam CAPTCHAs.

More recently, advances in machine learning (ML) and natural language processing (NLP), which allows a computer to interpret human language, have enabled automated systems to detect the subtle linguistic and contextual cues that often reveal fabricated content.

The researchers explain that central to their approach is the creation of ground truth datasets. These are curated examples of real and fake reviews. These datasets provide a reference for training machine learning models to recognize subtle indicators of deception, including unusual writing styles, sentiment inconsistencies, or anomalies in sentence structure.

The new approach then combines multiple algorithms into a hybrid classifier. A deep learning framework, such as a convolutional neural network (CNN), which is adept at identifying complex patterns, is paired with a traditional statistical classifier. The accuracy rate of this hybrid is between 96% and 99% when tested on standard datasets.

As global e-commerce continues to expand, accurate spam detection systems will become increasingly important in maintaining the reliability of digital marketplaces, reinforcing transparency and trustworthiness.