The Twitch streamer could pivot from influencer to candidate. But he tells WIRED's podcast he'd rather use his platform to tell Dems "you can't podcast your way out of this problem." Hasan Piker is many things to many people. They don't all feel the same way about Piker or his politics, but most presumably agree on one thing: He is a relentless human being. Most days a week, you can find the 34-year-old Twitch streamer talking to his audience, often for six to nine hours at a stretch. And during President Trump's second term, there's plenty of that to go around. He has nearly 3 million followers on Twitch and has hosted conversations with Senator Bernie Sanders and US representative Alexandria Ocasio-Cortez. He claims his election night stream in 2024 reached a staggering 7.5 million viewers. On this episode of, I talked to Piker about his looks, his love of Italian sandwiches, and any future political aspirations he might (or might not) want to tease. It's great to be here. I heard you were just at the gym. Yeah, I was at the park. Some days I take my dog and I play a little bit of basketball and get to hang out with some people.

Wounded protesters in Nepal have been recounting the moments police opened fire on them as they rallied against alleged corruption and government censorship. More than 19 people were killed and 300 injured. Nepal'Gen Z' protest death toll climbs, parliament stormed

Google's AI Mode to offer Japanese language support Google has said that its AI Mode will soon be available in Japanese, Korean, Hindi, Indonesian and Brazilian Portuguese globally. Google said Monday its AI-powered search engine AI Mode, which launched in May in only English, is set to be available in Japanese and four other languages as it looks to broaden its global reach. Aside from Japanese, the company said it is set to be available in Korean, Hindi, Indonesian and Brazilian Portuguese globally. At the time of writing, it was still unavailable in Japanese. "Building a truly global Search goes far beyond translation -- it requires a nuanced understanding of local information," Hema Budaraju, vice president of Google Search's product management, wrote in a blog post announcing the news. "With ... our custom version of Gemini 2.5 in Search, we've made huge strides in language understanding, so our most advanced AI search capabilities are locally relevant and useful in each new language we support."

Thailand's top court has ruled that former prime minister Thaksin Shinawatra must serve a year in jail, in yet another blow to the influential political dynasty. The decision relates to a previous case where he was sentenced to years in prison for corruption, but ended up spending less than a day in a jail cell as he was moved to a hospital. On Tuesday, the Supreme Court ruled that this transfer was unlawful - and that the 76-year-old would have to serve his sentence in jail. Thaksin and his family have dominated Thai politics since he was first elected PM in 2001. His daughter Paetongtarn previously served as leader but was removed from office last month over a leaked phone call.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Online dating murder suspect lured men into brutal robberies, L.A. County prosecutors allege Rockim Prowell allegedly met his victims online. Above, a person uses a cellphone. Rockim Prowell, 44, fis accused of murder, attempted murder, carjacking and burglary. Prosecutors allege Prowell lured robbery victims using a dating site.

Geedge Networks, a company with ties to the founder of China's mass censorship infrastructure, is selling its censorship and surveillance systems to at least four other countries in Asia and Africa. A leak of more than 100,000 documents shows that a little-known Chinese company has been quietly selling censorship systems seemingly modeled on the Great Firewall to governments around the world. Geedge Networks, a company founded in 2018 that counts the "father" of China's massive censorship infrastructure as one of its investors, styles itself as a network-monitoring provider, offering business-grade cybersecurity tools to "gain comprehensive visibility and minimize security risks" for its customers, the documents show. In fact, researchers found that it has been operating a sophisticated system that allows users to monitor online information, block certain websites and VPN tools, and spy on specific individuals. Researchers who reviewed the leaked material found that the company is able to package advanced surveillance capabilities into what amounts to a commercialized version of the Great Firewall--a wholesale solution with both hardware that can be installed in any telecom data center and software operated by local government officers.

Eye-tracking technology can aid in understanding neurodevelopmental disorders and tracing a person's identity. However, this technology poses a significant risk to privacy, as it captures sensitive information about individuals and increases the likelihood that data can be traced back to them. This paper proposes a human-centered framework designed to prevent identity backtracking while preserving the pedagogical benefits of AI-powered eye tracking in interactive learning environments. We explore how real-time data anonymization, ethical design principles, and regulatory compliance (such as GDPR) can be integrated to build trust and transparency. We first demonstrate the potential for backtracking student IDs and diagnoses in various scenarios using serious game-based eye-tracking data. We then provide a two-stage privacy-preserving framework that prevents participants from being tracked while still enabling diagnostic classification. The first phase covers four scenarios: I) Predicting disorder diagnoses based on different game levels. II) Predicting student IDs based on different game levels. III) Predicting student IDs based on randomized data. IV) Utilizing K-Means for out-of-sample data. In the second phase, we present a two-stage framework that preserves privacy. We also employ Federated Learning (FL) across multiple clients, incorporating a secure identity management system with dummy IDs and administrator-only access controls. In the first phase, the proposed framework achieved 99.3% accuracy for scenario 1, 63% accuracy for scenario 2, and 99.7% accuracy for scenario 3, successfully identifying and assigning a new student ID in scenario 4. In phase 2, we effectively prevented backtracking and established a secure identity management system with dummy IDs and administrator-only access controls, achieving an overall accuracy of 99.40%.

Beginning with text and images, generative AI has expanded to audio, video, computer code, and molecules. Yet, if generative AI is the answer, what is the question? We explore the foundations of generation as a distinct machine learning task with connections to prediction, compression, and decision-making. We survey five major generative model families: autoregressive models, variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models. We then introduce a probabilistic framework that emphasizes the distinction between density estimation and generation. We review a game-theoretic framework with a two-player adversary-learner setup to study generation. We discuss post-training modifications that prepare generative models for deployment. We end by highlighting some important topics in socially responsible generation such as privacy, detection of AI-generated content, and copyright and IP. We adopt a task-first framing of generation, focusing on what generation is as a machine learning problem, rather than only on how models implement it.

AI audits play a critical role in AI accountability and safety. One branch of the law for which AI audits are particularly salient is anti-discrimination law. Several areas of anti-discrimination law implicate the "less discriminatory alternative" (LDA) requirement, in which a protocol (e.g., model) is defensible if no less discriminatory protocol that achieves comparable performance can be found with a reasonable amount of effort. Notably, the burden of proving an LDA exists typically falls on the claimant (the party alleging discrimination). This creates a significant hurdle in AI cases, as the claimant would seemingly need to train a less discriminatory yet high-performing model, a task requiring resources and expertise beyond most litigants. Moreover, developers often shield information about and access to their model and training data as trade secrets, making it difficult to reproduce a similar model from scratch. In this work, we present a procedure enabling claimants to determine if an LDA exists, even when they have limited compute, data, information, and model access. We focus on the setting in which fairness is given by demographic parity and performance by binary cross-entropy loss. As our main result, we provide a novel closed-form upper bound for the loss-fairness Pareto frontier (PF). We show how the claimant can use it to fit a PF in the "low-resource regime," then extrapolate the PF that applies to the (large) model being contested, all without training a single large model. The expression thus serves as a scaling law for loss-fairness PFs. To use this scaling law, the claimant would require a small subsample of the train/test data. Then, the claimant can fit the context-specific PF by training as few as 7 (small) models. We stress test our main result in simulations, finding that our scaling law holds even when the exact conditions of our theory do not.

In supply chain management, decision-making often involves balancing multiple conflicting objectives, such as cost reduction, service level improvement, and environmental sustainability. Traditional multi-objective optimization methods, such as linear programming and evolutionary algorithms, struggle to adapt in real-time to the dynamic nature of supply chains. In this paper, we propose an approach that combines Reinforcement Learning (RL) and Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges for dynamic multi-objective optimization under uncertainty. Our method leverages MOEAs to search the parameter space of policy neural networks, generating a Pareto front of policies. This provides decision-makers with a diverse population of policies that can be dynamically switched based on the current system objectives, ensuring flexibility and adaptability in real-time decision-making. We also introduce Conditional Value-at-Risk (CVaR) to incorporate risk-sensitive decision-making, enhancing resilience in uncertain environments. We demonstrate the effectiveness of our approach through case studies, showcasing its ability to respond to supply chain dynamics and outperforming state-of-the-art methods in an inventory management case study. The proposed strategy not only improves decision-making efficiency but also offers a more robust framework for managing uncertainty and optimizing performance in supply chains.