But they certainly wish people were still taking their warnings really seriously. It's a weird time to be an AI doomer. This small but influential community of researchers, scientists, and policy experts believes, in the simplest terms, that AI could get so good it could be bad--very, very bad--for humanity. Though many of these people would be more likely to describe themselves as advocates for AI safety than as literal doomsayers, they warn that AI poses an existential risk to humanity. They argue that absent more regulation, the industry could hurtle toward systems it can't control. They commonly expect such systems to follow the creation of artificial general intelligence (AGI), a slippery concept generally understood as technology that can do whatever humans can do, and better. Though this is far from a universally shared perspective in the AI field, the doomer crowd has had some notable success over the past several years: helping shape AI policy coming from the Biden administration, organizing prominent calls for international "red lines " to prevent AI risks, and getting a bigger (and more influential) megaphone as some of its adherents win science's most prestigious awards. But a number of developments over the past six months have put them on the back foot.

It's a seductive distraction from the advances in AI that are most likely to improve or even save your life On April 28, 2022, at a highly anticipated concert in Spokane, Washington, the musician Paul McCartney astonished his audience with a groundbreaking application of AI: He began to perform with a lifelike depiction of his long-deceased musical partner, John Lennon. Using recent advances in audio and video processing, engineers had taken the pair's final performance (London, 1969), separated Lennon's voice and image from the original mix and restored them with lifelike clarity. For years, researchers like me had taught machines to "see" and "hear" in order to make such a moment possible. As McCartney and Lennon appeared to reunite across time and space, the arena fell silent; many in the crowd began to cry. As an AI scientist and lifelong Beatles fan, I felt profound gratitude that we could experience this truly life-changing moment. Later that year, the world was captivated by another major breakthrough: AI conversation.

After Amazon's acquisition of the Roomba-maker fell apart, it ran out of options. The Massachusetts-based company plans to sell all assets to its primary supplier, a Chinese company known as Picea Robotics. If approved by a bankruptcy court, the move would allow iRobot to continue operating in the ordinary course, pursue its product development roadmap, and maintain its global footprint, iRobot wrote in a press release. The company expects the deal to close in February 2026, but says it will continue to operate with no anticipated disruption to its app functionality, customer programs, global partners, supply chain relationships or ongoing product support. That means your Roomba should continue to clean normally and you'll be able to get consumables and replacement parts.

When you purchase through links in our articles, we may earn a small commission. Save 87% on a ChatPlayground AI lifetime subscription that includes access to every top model and unlimited credits. If you've been experimenting with AI tools, you already know the pain: every platform has its strengths, its weak spots, and its own subscription. ChatPlayground AI solves that by putting GPT-4o, Claude Sonnet 4, Gemini 1.5 Flash, and more into one clean interface. Then, it lets you run the against all of them at once.

The US firm behind the Roomba smart vacuum cleaner, iRobot, has filed for bankruptcy protection after facing competition from Chinese rivals and being hit by tariffs. Under the so-called pre-packaged Chapter 11 process, the main manufacturer of its devices, Shenzhen-based Picea Robotics, will take ownership of the firm. The tough commercial landscape had forced iRobot to cut its prices and make major investments in new technology, according to documents filed on Sunday. US import duties of 46% on goods from Vietnam, where most of iRobot's devices for the American market are made, increased its costs by $23m (£17.2m) this year, the firm said. The loss-making company was valued at $3.56bn in 2021 after the pandemic helped to drive strong demand for its products.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Rob Reiner used his fame to advocate for progressive causes. This is read by an automated voice. Please report any issues or inconsistencies here . Rob Reiner was a Hollywood legend and also a political force, a frequent voice in progressive causes and a Democratic Party activist.

Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . Two people were found dead Sunday afternoon at the Brentwood home of director and actor Rob Reiner, multiple law enforcement sources confirmed. Margaret Stewart, a Los Angeles Fire Department spokesman, said the department was called to the home around 3:30 p.m. for medical aid.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A magnitude 4.0 earthquake was reported Sunday at 3:30 p.m. in the Bay Area. It was followed in the same area by two smaller quakes. This is read by an automated voice. Please report any issues or inconsistencies here .

Softmax attention is a central component of transformer architectures, yet its nonlinear structure poses significant challenges for theoretical analysis. We develop a unified, measure-based framework for studying single-layer softmax attention under both finite and infinite prompts. For i.i.d. Gaussian inputs, we lean on the fact that the softmax operator converges in the infinite-prompt limit to a linear operator acting on the underlying input-token measure. Building on this insight, we establish non-asymptotic concentration bounds for the output and gradient of softmax attention, quantifying how rapidly the finite-prompt model approaches its infinite-prompt counterpart, and prove that this concentration remains stable along the entire training trajectory in general in-context learning settings with sub-Gaussian tokens. In the case of in-context linear regression, we use the tractable infinite-prompt dynamics to analyze training at finite prompt length. Our results allow optimization analyses developed for linear attention to transfer directly to softmax attention when prompts are sufficiently long, showing that large-prompt softmax attention inherits the analytical structure of its linear counterpart. This, in turn, provides a principled and broadly applicable toolkit for studying the training dynamics and statistical behavior of softmax attention layers in large prompt regimes.

Data integration is essential across diverse domains, from historical records to biomedical research, facilitating joint statistical inference. A crucial initial step in this process involves merging multiple data sources based on matching individual records, often in the absence of unique identifiers. When the datasets are networks, this problem is typically addressed through graph matching methodologies. For such cases, auxiliary features or covariates associated with nodes or edges can be instrumental in achieving improved accuracy. However, most existing graph matching techniques do not incorporate this information, limiting their performance against non-identifiable and erroneous matches. To overcome these limitations, we propose two novel covariate-assisted seeded graph matching methods, where a partial alignment for a set of nodes, called seeds, is known. The first one solves a quadratic assignment problem (QAP) over the whole graph, while the second one only leverages the local neighborhood structure of seed nodes for computational scalability. Both methods are grounded in a conditional modeling framework, where elements of one graph's adjacency matrix are modeled using a generalized linear model (GLM), given the other graph and the available covariates. We establish theoretical guarantees for model estimation error and exact recovery of the solution of the QAP. The effectiveness of our methods is demonstrated through numerical experiments and in an application to matching the statistics academic genealogy and the collaboration networks. By leveraging additional covariates, we achieve improved alignment accuracy. Our work highlights the power of integrating covariate information in the classical graph matching setup, offering a practical and improved framework for combining network data with wide-ranging applications.