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Last month, OpenAI unveiled an ambitious new language model capable of working through challenging problems with a simulated kind of step-by-step reasoning. OpenAI says the approach could be crucial for building more capable AI systems in the future.

In the meantime, perhaps a more modest version of this technology could help make AI girlfriends and boyfriends a bit more spontaneous and alluring.

A coalition of human rights groups have today launched legal action against the French government over its use of algorithms to detect miscalculated welfare payments, alleging they discriminate against disabled people and single mothers.

The algorithm, used since the 2010s, violates both European privacy rules and French anti-discrimination laws, argue the 15 groups involved in the case, including digital rights group La Quadrature du Net, Amnesty International, and Collectif Changer de Cap, a French group that campaigns against inequality.

For a while now, companies like OpenAI and Google have been touting advanced “reasoning” capabilities as the next big step in their latest artificial intelligence models. Now, though, a new study from six Apple engineers shows that the mathematical “reasoning” displayed by advanced large language models can be extremely brittle and unreliable in the face of seemingly trivial changes to common benchmark problems.

The fragility highlighted in these new results helps support previous research suggesting that LLMs’ use of probabilistic pattern matching is missing the formal understanding of underlying concepts needed for truly reliable mathematical reasoning capabilities. “Current LLMs are not capable of genuine logical reasoning,” the researchers hypothesize based on these results. “Instead, they attempt to replicate the reasoning steps observed in their training data.”

The European New Car Assessment Program (Euro NCAP) has not yet measured, prodded, weighed, or smashed a Tesla Cybertruck—or checked whether pedestrians could survive a hit from this angular beast—but Elon Musk’s electric pickup truck might potentially score poorly on the tests anyway.

“Based only on the car’s visual appearance, there are several aspects of this vehicle that look like they may be a threat to pedestrians,” claims Euro NCAP’s director of strategic development, Matthew Avery. “You cannot fail Euro NCAP,” he adds, “but you can get a bad score.”

The world’s first assisted suicide pod wraps around the human body like a space capsule, tilting gently toward the sky. The device is designed to look as if the person inside were embarking on a journey, says its inventor, the Australian right-to-die activist Philip Nitschke. “It gives you the idea you’re saying goodbye to the world.”

Last month, the 3D-printed pod was used for the first time. In a forest on the Swiss-German border, an unnamed 64-year-old American woman pressed the pod’s button to release deadly nitrogen gas. She died seven minutes later, estimated the Swiss assisted suicide group The Last Resort, whose president Florian Willet was present at her death and was later detained for “aiding and abetting” the woman’s suicide. Nitschke tried to watch by video link, although he describes the signal as patchy.

In early 2020, deepfake expert Henry Ajder uncovered one of the first Telegram bots built to “undress” photos of women using artificial intelligence. At the time, Ajder recalls, the bot had been used to generate more than 100,000 explicit photos—including those of children—and its development marked a “watershed” moment for the horrors deepfakes could create. Since then, deepfakes have become more prevalent, more damaging, and easier to produce.

Now, a WIRED review of Telegram communities involved with the explicit nonconsensual content has identified at least 50 bots that claim to create explicit photos or videos of people with only a couple of clicks. The bots vary in capabilities, with many suggesting they can “remove clothes” from photos while others claim to create images depicting people in various sexual acts.

Just after 9 pm on an August night in 2020, Kimberly Thompson and Brian James pulled the car into a driveway in Akron, Ohio, and stepped out into a barrage of gunfire. They were shot in the legs, rushed to a hospital, and survived. But Thompson’s 20-month-old grandson, Tyree Halsell, who was still sitting in the car, was shot in the head and mortally wounded.

In the aftermath, Akron police collected video footage from the neighborhood and asked for the public’s help with identifying two men who’d been seen approaching the victims, firing, then fleeing in a truck. Within months, detectives narrowed in on a suspect, Phillip Mendoza, and obtained a search warrant for his cell phone location data from Sprint, according to court records. They also served a geofence warrant on Google, seeking information on devices whose GPS, Wi-Fi, or Bluetooth records placed them near the scene of the shooting. Neither warrant turned up any evidence locating Mendoza or his devices on the 1200 block of Fifth Avenue, where the shooting occurred, that night.

For over a decade, the tech industry has been chasing unicorns—those elusive startups valued at over $1 billion. The obsession began in 2013, when Aileen Lee—a VC based in Palo Alto—coined the term that captured the imaginations first of founders and investors, and then prime ministers and presidents. But these mythical beasts are also rare: only 1 percent of VC-backed startups ever reach this status.

As society enters the age of AI, and financial markets put renewed value on business fundamentals, our understanding of what makes a successful tech company is evolving. Promise alone doesn’t make a national, regional, or global champion. Champions are those companies that combine both the promise of untapped growth and the fundamental metrics that demonstrate strong and sustainable customer demand.

The 2023 Atomico’s State of European Tech Report revealed Netherlands to be a standout success, cementing its position as a star player in the startup ecosystem. In terms of capital invested in its private tech companies, for instance, it’s risen back into the top five countries with a projected $2.1 billion. And while the UK has seen the share of its European capital invested drop by almost 3 percent within the past three years, the Netherlands comes out top, capturing the biggest gains in Europe at almost 2 percent. The hub of the Netherlands’ startup ecosystem is Amsterdam, which hosts around 4,000 startups, including unicorns like Mollie, Mambu, and Backbase. Known for its international focus, collaborative ecosystem, and diverse and skilled workforce, it’s also dedicated to tackling urgent societal issues.

German innovation is not limited to the country’s capital. In fact, some of this year’s most prolific startups are based hundreds of miles away. The AI startup Alpha Alpha hails from Heidelberg. Helsing, which sells AI to Europe’s militaries, was set up in Munich. Yet both companies operate Berlin offices. The city attracts too much talent to ignore. Universities, such as TU Berlin, churn out generative AI founders, and the capital is such a magnet for international talent that many offices operate in English, not German.