Seth Meyers on Florida's dengue fever outbreak: 'Man, RFK Jr has only had the job two years and Florida is already turning into an episode of House.' Seth Meyers on Florida's dengue fever outbreak: 'Man, RFK Jr has only had the job two years and Florida is already turning into an episode of House.' Seth Meyers on Trump canceling $1bn in federal funds: 'Is he adding a waterslide to the reflecting pool?' Late-night hosts weighed in on Trump's relationship with tech leaders and the White House's new AI-powered site On Tuesday night, late-night hosts discussed a Maha summit in Washington DC, the White House's new AI -powered website and Donald Trump once again falling asleep on live TV. On NBC, Seth Meyers focused on the president's ill-phrased announcement of the US murder rate declining. "We have the best, uh, murder," Trump said. "What are you talking about, 'We have the best murder'?" On Monday, Trump also took issue with the claim by the US ambassador to China, David Perdue, claim that he offered to sell weapons to China, saying: "I think he was probably talking about Taiwan."

Mashable Selects Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Look Up Creator Playbook Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series Seth Meyers takes'A Closer Look' at Trump's ballroom tour for Xi Jinping Isn't this bullroom of yours supposed to include a bunch of top secret military facilities, and if it does, maybe don't do the tour with the heads of foreign governments? Shannon Connellan is Mashable's Senior Editor, General Assignments, based in London. She has been Mashable's UK Editor (and still manages the illustrious UK team) and Australia Editor, but emotionally, she lives searching for . A Tomatometer-approved critic, Shannon writes about entertainment, tech, social good, science, culture, and Australian horror, and loves to nerd out with movie stars, filmmakers, and TV creators . Seth Meyers launches'A Closer Look' as summer video podcast series Seth Meyers reacts to Trump saying he will'never apologize' for high gas prices Donald Trump's two-day White House hosting of Chinese president Xi Jinping and his wife Peng Liyuan has dominated headlines and late night shows this week, and Seth Meyers took A Closer Look.

Say More Mashable Selects Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Safety Net Versus Gift Ideas For Everyone On Your List All Series These hikers relied on tech and AI. They had to be rescued. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness.

Coders Say They Already Found Workarounds to Claude's Invisible Watermarks Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online. Within four hours of Anthropic confirming that Claude models would globally embed invisible, machine-readable watermarks into any AI-generated content, developer Guillaume Meyer had published his override. His code to remove watermarks from Claude-generated text has since gone viral on GitHub, has been bookmarked more than 20,000 times on X, and has drawn more than 100 contributors, with many more incorporating the technology into their own projects. "Anthropic is embedding watermarks in its Claude texts the issue is practically history just one day later," wrote one AI specialist, accompanied by an image of Meyer breaking out of chains and standing on crumpled EU and Anthropic flags.

A Sneaky Hacking Tool Targeting AI Infrastructure Is Lurking in Victims' Blind Spots A new type of malware can worm deep into AI coding systems to steal data and logins--and can flip a "death switch" to destroy files and keep out real users. As AI tools proliferate and become deeply ingrained in software development around the world, new research from the cybersecurity firm Crowdstrike shows how attackers are actively targeting the AI toolchain to steal access credentials, gain deeper access to a target environment, exfiltrate sensitive data, and even destroy target files and systems--all while finding new ways to cover their tracks. Researchers discovered a worm in the wild while investigating AI software supply chain attacks. Adam Meyers, CrowdStrike's senior vice president of counter adversary work, says that the company has not yet attributed the activity to a specific actor, but that it fits into larger evolutions in how attackers like TeamPCP (which Crowdstrike tracks as "Altered Spider") and North Korean groups are targeting the AI software supply chain. "This is one of the campaigns that we've seen showing that this is an emerging attack class," Meyers tells WIRED.

Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert availability, and shifting expert distribution. We introduce the first online L2D algorithm for multiclass classification with bandit feedback and a dynamically varying pool of experts. Our method achieves regret guarantees of $O((n+n_e)T^{2/3})$ in general and $O((n+n_e)\sqrt{T})$ under a low-noise condition, where $T$ is the time horizon, $n$ is the number of labels, and $n_e$ is the number of distinct experts observed across rounds. The analysis builds on novel $\mathcal{H}$-consistency bounds for the online framework, combined with first-order methods for online convex optimization. Experiments on synthetic and real-world datasets demonstrate that our approach effectively extends standard Learning-to-Defer to settings with varying expert availability and reliability.

Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of exploration and execution. We describe the overall architecture and reasoning behind our design decisions, and also conduct a thorough analysis of the agent behavior to gain insights into future areas for targeted improvements. This work was motivated by the Text2SPARQL challenge, a challenge that was held to facilitate improvements in the Text2SPARQL domain.

Large Language Models (LLMs) are increasingly used for tasks involving Knowledge Graphs (KGs), whose evaluation typically focuses on accuracy and output correctness. We propose a complementary task characterization approach using three complexity frameworks from cognitive psychology. Applying this to the LLM-KG-Bench framework, we highlight value distributions, identify underrepresented demands and motivate richer interpretation and diversity for benchmark evaluation tasks.

The notion of Fr\'echet mean (also known as "barycenter") network is the workhorse of most machine learning algorithms that require the estimation of a "location" parameter to analyse network-valued data. In this context, it is critical that the network barycenter inherits the topological structure of the networks in the training dataset. The metric - which measures the proximity between networks - controls the structural properties of the barycenter. This work is significant because it provides for the first time analytical estimates of the sample Fr\'echet mean for the stochastic blockmodel, which is at the cutting edge of rigorous probabilistic analysis of random networks. We show that the mean network computed with the Hamming distance is unable to capture the topology of the networks in the training sample, whereas the mean network computed using the effective resistance distance recovers the correct partitions and associated edge density. From a practical standpoint, our work informs the choice of metrics in the context where the sample Fr\'echet mean network is used to characterise the topology of networks for network-valued machine learning

Six months ago my son Zac started to play a video game I knew very little about – which, as a games journalist, I found slightly disconcerting. Created by the Canada-based developer Digital Extremes, Warframe is an online sci-fi shooter, originally launched in 2013. Though little discussed outside its fanbase, it is consistently one of the biggest titles on Steam, with 75 million registered users. Set in a distant future version of our solar system, riddled with warring alien factions, the player takes part on the side of the Tenno, an ancient warrior race that employs barely sentient cybernetic fighters – the warframes of the title – as their primary weapons. Each day, Zac spends hours whizzing between planets, carrying out missions or exploring, all the while fighting enemies including a brutish clone army known as the Grineer, and the diseased, monstrous Infested.