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29 Jul 2026

29 Jul 2026 11:44am GMT

Ars Technica

Google's SynthID watermark is hard to break, but it doesn't solve AI misinformation

Deciding what's real on the Internet won't be easy in the future.

29 Jul 2026 11:00am GMT

Elon Musk finally launches X Money. What could possibly go wrong?

Expect bumps with X Money rollout as major US financial markets are excluded.

29 Jul 2026 10:00am GMT

alternative_right shares a report from ScienceAlert: [I]n a new study published in the Journal of Cognitive Neuroscience, researchers from Georgetown University Medical Center in the U.S. have revealed that we can put certain tasks on autopilot in a way that enables something closer to true multitasking. Driving is the perfect example: When you take your test, your entire mental and physical energy is concentrated on executing the right combinations of movements and thoughts. After a decade behind the wheel, the brain is no longer consciously thinking everything through in great detail. In the new study, the researchers found that after extensive practice, the brain can effectively reroute some tasks around the brain's main computing center -- like a bypass road around a city -- to reduce mental overload and improve multitasking capability. "We have another stepping stone in our understanding of how the brain learns," says neuroscientist Maximilian Riesenhuber, senior author of the study. "The encouraging part is that you really can learn to multitask. There is actually a way to remodel your brain architecture and use other parts of your brain." [...] "Previous studies have shown that parts of the temporal cortex can be activated by particular object categories in experienced observers -- birds, cars, even Pokemon -- but a limitation of all of those studies is that they only looked after people became experts," says psychologist Patrick Cox, first author of the study. "We measure before and after training, so we can see that extensive training essentially put a category-selective area in the temporal lobe that was not there before." The study culminated in a dual-task experiment, in which the participants were asked to identify cars and take on another challenge. The individuals whose brains had offloaded more car-sorting work to the temporal cortex did better at the second task.

Trump Administration Bans New Chinese Humanoid Robots

The Trump administration has banned newly authorized foreign-made humanoid and four-legged robots, along with power inverters, citing "unacceptable risks" to the country's national security. FCC chairman Brendan Carr said the agency was doing its part "to secure America's critical supply chains." The BBC reports: The FCC has added the items to its Covered List -- a register of goods and services that are deemed a risk to US national security. The ban applies to new foreign-produced advanced robotic devices and power inverters and does not prevent the sale or import of any existing models that had been previously authorized by the FCC. The FCC cited concerns that the use of foreign-made inverters could allow overseas firms to turn them off, steal data, facilitate remote access and surveillance by "foreign government actors, or be otherwise exploited through a cyberattack." It added that the use of robots made outside the US could allow "malign actors to surveil Americans, enhance the capabilities of foreign intelligence services, or to remotely commandeer the robots."

Workplaces Look For Cheaper AI As 'Tokenmaxxing' Fades As a Corporate Fad

An anonymous reader quotes a report from the Associated Press: A corporate fad of "tokenmaxxing" on artificial intelligence technology is hitting its limits as workplaces throwing AI at everything are seeing the costs rise without a similar spike in productivity. What started as tech industry-fueled springtime hype over squeezing as much AI-generated work as possible out of products like OpenAI's ChatGPT and Anthropic's Claude has shifted to a summertime backlash. [...] Just a few months ago, Silicon Valley executives were promoting high token consumption as a signal of high-performing employees. The stereotypical tokenmaxxer was staying up late -- perhaps ignoring their significant other -- while orchestrating an army of 24-hour AI agents performing work on their behalf. [...] The trend boosted revenue for leading AI large language model developers like Anthropic and OpenAI, but it fizzled as it became apparent it wasn't necessarily the best strategy for everyone else. [...] Bain & Company management consultant Jue Wang said many of the big businesses her firm advises have been taking a closer look at returns on their AI investments. "The token cost for them has been doubling, almost every other month," she said. "Let's say $200 per developer per month. Multiply that by 20,000 developers, which is often what we're dealing with at these companies, and that quickly gets you to a number that is not a line item that any general manager has planned for." Sometimes that just means not using the AI equivalent of a sledgehammer to crack a nut. "Not everything needs a Claude Opus 4.6," she said of one of Anthropic's more capable models suited to software engineering or deep research. "And yet you see so many companies, so many users, default to using Opus for everything, including generating emails." That's led to a search for tools that do AI "model routing" -- in which easier queries get automatically sent to cheaper and more efficient AI systems and more complex tasks go to more powerful models. [...] At the same time, those who favor racking up as many tokens as possible are having a field day with new open-source models from Chinese startups like Moonshot's Kimi or Zhipu's GLM, which nearly match the capabilities of top U.S. models at a fraction of the price. "There is some validity to the theory that this could push tokenmaxxing a little bit further," said Raffi Krikorian, the chief technology officer at Mozilla. "But if we look at the industry overall, I think it's realizing that tokenmaxxing is a dumb thing." It's similar, Krikorian said, to how software companies once considered how many lines of code a programmer wrote to be a good metric of productivity. That later fell out of favor. "I think tokenmaxxing is moving through the exact same pattern," he said. "I think this is going to be an interesting blip that we're all going to look back to laugh at in a year."