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Kate Tolo Says Her Quest to Be'the Female Bryan Johnson' Is'for Women' Like her biohacking boyfriend, Tolo is undergoing a grueling regimen of tests--from tracking nighttime erections to subjecting herself to extreme heat on her period--in service of "half the population." Kate Tolo got her first period at 13--an experience she says left her lying prostrate on the floor, debilitated by cramps and breaking out into a full-body sweat. When she told her dad why she was home from school early, she remembers him saying, "We don't talk about that female stuff ." For most of her teenage years, that was how Tolo thought about her period--as something "you just didn't talk about." When she was in her early twenties and experienced extreme pain while defecating, sending her to the emergency room, a doctor suggested Tolo may have endometriosis, a chronic disease believed to affect an estimated 10 percent of women of reproductive age, according to WHO data.

Of all the threats presented by uncontrollable artificial intelligence, scientists say death by plague ranks low. Concerns about an AI-induced apocalypse are reaching a fever pitch, from Silicon Valley to Washington, DC. One of the top theories about how a rogue AI will wipe us out? Earlier this summer, AI CEOs called for new laws to control the manufacturing of synthetic DNA. Then last month, researchers at Stanford University and the Arc Institute showed that AI can design new viral genomes .

Ella Langley, distractions and Lane Kiffin: Pete Golding calls Ole Miss-LSU'high school homecoming' Mother Jones' WNBA'trans panic' story shows what happens when activism masquerades as journalism'A win is a win' says athlete who didn't let a little diarrhea keep her from finishing in first place Kansas City Chiefs' roster concerns linger as they host the Denver Broncos on Monday Night Football Florida State fires AD Michael Alford, as Mike Norvell's seat reaches a boiling point in Tallahassee Madison Beer's bid to become the NFL's next Taylor Swift hits road bump, Browns are a mess & a Daejon Love fan WWE broadcaster believes'the pieces are all in place' for an upset as Penta challenges Roman Reigns for title Denny Hamlin says forward-facing sonar in professional bass fishing has gone'a little overboard' Ben Shapiro warns Democrats' push for AI regulation is a'coordinated operation' Senate back in session as questions remain over Mitch McConnell's return Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Mullin weighs in on Obama, Biden judges amid Trump's immigration court fights Marc Thiessen warns US is in an'AI arms race' with China Charlie Kirk monument artist'protecting it from evil' after previous statue vandalized twice Army launches'field tested' campaign Vance reveals US has'engaged in direct conversations' with Houthis OutKick Vikings say Kyler Murray feeling'pretty good,' participating in team meetings despite serious concussion Murray was knocked out of Sunday's home opener against the Packers on just his third Vikings series Vikings name Kyler Murray starting QB, Are the Cowboys taking shots at Micah Parsons? | FTF The Minnesota Vikings named Kyler Murray their starting QB over J.J. McCarthy. Nick Wright, Chris Broussard, and Kevin Wildes ask if this is the right decision. Plus, they discuss Jerry Jones potentially taking shots at former Dallas Cowboy Micah Parsons. Kyler Murray's start to his Minnesota Vikings career went awry on Sunday at U.S. Bank Stadium, as he was knocked out of the game with a concussion suffered on just the third offensive possession for the team. The hits he took from two Green Bay Packers players were vicious, and after he officially entered concussion protocol, the question became whether he would be missing game time moving forward.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen AI-enabled identity theft is getting too sophisticated to have predictable tells anymore, so experts recommend answering with a seemingly old-fashioned approach. Deepfakes and AI clones get more sophisticated and harder to detect. Advanced attacks involve long-term infiltration of a company's systems. Experts recommend low-tech security protocols that offer better defenses. In January 2024, an employee at professional services firm Arup joined a video call with someone they believed included the company's CFO.

Benchmarks of machine learning models often include many datasets, making evaluation expensive. For efficiency, it is preferable to perform evaluations on small, representative datasets instead. The selection of such subsets typically relies on heuristics and is rarely analyzed for the robustness of the resulting model rankings. We introduce a framework to perform the task of selecting datasets subsets with an evaluation of how different selection strategies preserve the global model rankings. Our framework includes bootstrap aggregation, which provides valid confidence intervals, allowing a principled comparison of selection strategies. We consider clustering, design criteria (A/D-optimality), random baselines, and greedy farthest-first (FAFI). For the latter, we derive upper bounds on selection quality in terms of ranking errors as a function of the number of selected datasets. Empirically, in time series classification (TSC, 112 datasets) and in a supplementary natural language processing benchmark derived from MTEB (57 tasks), several selection strategies improve rank preservation compared with random subsets, including simple FAFI. In contrast, in recommender systems (30 datasets), the improvement of strategies over random selection is small and typically statistically insignificant. For TSC, our best-performing strategy achieves a Spearman correlation of 0.95 with the full benchmark model rankings using only five selected datasets. Additional experiments indicate that the effectiveness of selection approaches depends on both the quality of dataset representations and the scale of the benchmarking regime.

Data attribution methods aim to answer useful counterfactual questions like "what would a ML model's prediction be if it were trained on a different dataset?" However, estimation of data attribution models through techniques like empirical influence or "datamodeling" remains very computationally expensive. This causes a critical trust issue: if only a few computationally rich parties can obtain data attributions, how can resource-constrained parties trust that the provided attributions are indeed "good," especially when they are used for important downstream applications (e.g., data pricing)? In this paper, we address this trust issue by proposing an interactive verification paradigm for data attribution. An untrusted and computationally powerful Prover learns data attributions, and then engages in an interactive proof with a resource-constrained Verifier.

Monitoring AIs at runtime can help us detect and stop harmful actions. In this paper, we study how to efficiently combine multiple runtime monitors into a single monitoring protocol. The protocol's objective is to maximize the probability of applying a safety intervention on misaligned outputs (i.e., maximize recall). Since running monitors and applying safety interventions are costly, the protocol also needs to adhere to an average-case budget constraint. Taking the monitors' performance and cost as given, we develop an algorithm to find the best protocol. The algorithm exhaustively searches over when and which monitors to call, and allocates safety interventions based on the Neyman-Pearson lemma. By focusing on likelihood ratios and strategically trading off spending on monitors against spending on interventions, we more than double our recall rate compared to a naive baseline in a code review setting. We also show that combining two monitors can Pareto dominate using either monitor alone. Our framework provides a principled methodology for combining existing monitors to detect undesirable behavior in cost-sensitive settings.

Diffusion models are increasingly deployed in real-world text-to-image services. These models, however, encode implicit assumptions about the world based on webscraped image-caption pairs used during training. Over time, such assumptions may become outdated, incorrect, or socially biased-leading to failures where the generated images misalign with users' expectations or evolving societal norms. Identifying and fixing such failures is challenging and, thus, a valuable asset for service providers, as failures often emerge post-deployment and demand specialized expertise and resources to resolve them. In this work, we introduce SURE, the first end-to-end framework that SecUrely REpairs failures flagged by users of diffusionbased services. SURE enables the service provider to securely collaborate with an external third-party specialized in model repairing (i.e., Model Repair Institute) without compromising the confidentiality of user feedback, the service provider's proprietary model, or the Model Repair Institute's proprietary repairing knowledge. To achieve the best possible efficiency, we propose a co-design of a model editing algorithm with a customized two-party cryptographic protocol. Our experiments show that SURE is highly practical: SURE securely and effectively repairs all 32 layers of Stable Diffusion v1.4 in under 17 seconds (four orders of magnitude more efficient than a general baseline). Our results demonstrate that practical, secure model repair is attainable for large-scale, modern diffusion services.

Multimodal Sentiment Analysis (MSA) aims to infer human emotions by integrating complementary signals from diverse modalities. However, in real-world scenarios, missing modalities are common due to data corruption, sensor failure, or privacy concerns, which can significantly degrade model performance. To tackle this challenge, we propose Hyper-Modality Enhancement (HME), a novel framework that avoids explicit modality reconstruction by enriching each observed modality with semantically relevant cues retrieved from other samples. This cross-sample enhancement reduces reliance on fully observed data during training, making the method better suited to scenarios with inherently incomplete inputs. In addition, we introduce an uncertainty-aware fusion mechanism that adaptively balances original and enriched representations to improve robustness. Extensive experiments on three public benchmarks show that HME consistently outperforms state-of-the-art methods under various missing modality conditions, demonstrating its practicality in real-world MSA applications.