Creator Playbook Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Mamdani's AI ban may not be enough to protect NYC students Coalitions backing AI moratoriums remain skeptical of New York City's new AI school policies. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. New York City Mayor Zohran Mamdani stood at a podium today (Sep. To those fighting to ban artificial intelligence in schools across the country, this moment was . Mamdani, speaking alongside NYC Chancellor Kamar Samuels, announced the new comprehensive, human-first AI policy for the nation's largest school district.

New York City mayor Zohran Mamdani just announced a one-year ban of generative AI tools in public schools for students up to eighth grade. The legislation takes effect during the 2026-2027 school year and will impact around 600,000 students. "This moratorium is a commitment to getting the future right," Mayor Mamdani said. "We will embrace new technology, but only when it serves our students." New York Governor Kathy Hochul added that the state "has been leading the way in our efforts to keep kids focused on learning and growing -- not clicking and scrolling."

The Cabinet has adopted revised guidelines for artificial intelligence-related policies to bolster cybersecurity measures. The government has revised its guidelines for artificial intelligence-related policies, calling for constantly strengthening measures against cyberattacks in light of serious risks posed by cutting-edge AI models. The original AI policy guidelines were compiled only last December. The revision comes amid rapid technological innovation, including the launch of U.S. startup Anthropic's Claude Mythos. The revised guidelines, adopted at a Cabinet meeting on Tuesday, note the growing threat of cyberattacks against the backdrop of advancing AI capabilities, and call for collaborating with foreign government agencies and AI development companies to significantly strengthen the capabilities of Japan's AI Safety Institute. The guidelines also highlight the need to avoid excessive reliance on specific countries or companies for AI, and express the government's intention to develop domestic AI that addresses challenges facing Japan.

Such AI-generated content attracted approximately 335 million views on social media, resulting in financial losses estimated at ¥2 billion to ¥4.5 billion for celebrities and artists, according to the study. The estimated losses were calculated based on licensing fees related to using a person's likeness or voice, as well as the advertising value of view counts. However, the nonprofit added that the "actual financial losses might be significantly larger than the estimate," as the calculation only covered cases they were able to find. Only 1.1% of companies said they had guidelines on how to deal with these violations. Some 52% said they were "currently considering" options, while the rest had no plans as of date.

Chief Cabinet Secretary Minoru Kihara (third from left) speaks at a meeting of the digital administrative and fiscal reform council, held at the Prime Minister's Office in Tokyo on Tuesday. The government decided Tuesday to establish a new council to drastically overhaul legal frameworks governing the development and use of artificial intelligence. The plan was included in the government's 2026 basic policy guidelines adopted at a meeting of the digital administrative and fiscal reform council, held at the Prime Minister's Office in Tokyo. This council, launched under the administration of former Prime Minister Fumio Kishida, will be reorganized into the new body. The guidelines stressed the urgency of advancing what is described as AI transformation, or a fundamental review of work using AI, to cope with population decline.

Touted as a less-hookup-focused Grindr, Goose is an invite-only space for gay men. The problem is the people promoting it don't seem real. "You're receiving this because you're exactly the type of person we're building this for," the caption reads, accompanied by a code for an invite to a "members only community." The link leads to a login for Goose, a dating and friendship app for gay men with the slogan "for the boys," which allows users to "meet guys through the life you already have," according to its website. Neither does @danielmmulugeta, the cute dark-haired influencer who shared the above caption, with the exact same verbiage, on Close Friends' Stories.

The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections. Standard estimators of the SW distance rely on Monte Carlo averages of one-dimensional Wasserstein distances computed via quantile functions, which require sorting projected samples and access to full datasets. In this work, we introduce a new class of estimators for the Sliced Wasserstein distance based on cumulative distribution functions (CDFs) of projected measures, that avoid sorting and scale via massive dataset parallelism. This class includes several estimators, some of them being indexed by hyperparameters controlling their variance or smoothness. We show that they are especially well suited to scenarios in which CDFs are more tractable than quantile functions, such as mixtures of Gaussians, and moreover that they are also naturally compatible with federated learning, since CDFs of projected data can be computed and aggregated locally without requiring the exchange of raw samples.

The essence of 3DGaussian Splatting (3DGS) training is to smartly allocate Gaussian primitives, expressing complex regions with more primitives and vice versa. Prior researches typically mark out under-reconstructed regions in a renderingloss-driven manner. However, such a loss-driven strategy is often dominated by low-frequency regions, which leads to insufficient modeling of high-frequency details in texture-rich regions. As a result, it yields a suboptimal spatial allocation of Gaussian primitives. This inspires us to excavate the loss-agnostic visual prior in training views to identify complex regions that need more primitives to model.

Diffusion models have demonstrated remarkable capabilities in generating highquality images. Recent advancements in Layout-to-Image (L2I) generation have leveraged positional conditions and textual descriptions to facilitate precise and controllable image synthesis.

Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data.