Adult content creators are having their performances used without consent. This is just one way that AI now threatens their rights and livelihoods. When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she'd made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she'd never seen before: one of her old videos, but with someone else's face on her body. "At first, I thought it was just a different person," says Jennifer, who is being identified by a pseudonym to protect her privacy. But then she recognized a distinctly garish background from a video she'd shot around 2013, and she realized: "Somebody used me in a deepfake."

SpaceX combining with xAI would bring Elon Musk's rockets, Starlink satellites, the X social media platform and Grok AI chatbot under one roof. NEW YORK - SpaceX is exploring deals with other companies helmed by serial entrepreneur Elon Musk, leaving investors working through permutations between space, autonomous driving and artificial intelligence to analyze which combination makes the most sense. The rocket maker is in discussions to merge with xAI ahead of a blockbuster public offering planned for this year, Reuters reported on Thursday. The combination would bring Musk's rockets, Starlink satellites, X social media platform and Grok chatbot under one roof, according to a person briefed on the matter and two regulatory filings. The deal's value, timing or primary rationale could not be independently determined.

If you want to put people back on the moon, don't gut the agency in charge of getting them there. The senator wanted a promise. For the last six years--or maybe the last decade or quarter century, depending on how you count it--the United States and China had been locked in a space race, a contest to see which nation could put its people on the moon . Senator Ted Cruz wanted President Donald Trump's nominee to run NASA, Jared Isaacman, to pledge that the US would not lose. Cruz brought a little surprise to Isaacman's confirmation hearing last April. It was a poster of the moon. On one side stood three astronauts and a giant Chinese flag. On the other were two more figures in space suits, with the tiniest Stars and Stripes planted in the lunar soil . Cruz apologized for the imbalance. "My team used ChatGPT," explained the senator, who chairs the committee that oversees NASA. Then Cruz, with a bit more seriousness, asked Isaacman, "Do we have your commitment that you will not allow the scenario on the right of this poster to happen? That China will not beat us to the moon?" Isaacman, a billionaire entrepreneur who had paid for his own missions to space, replied, "Senator, I only see the left-hand portion of that poster."

The billionaire's involvement with the Trump administration and DOGE had deep impacts on Tesla's bottom line. But Elon Musk was still able to turn his attention to SpaceX. What a weird time to be Elon Musk. This year opened with the businessman turned political operator throwing what appeared, to Nazis at least, to be a . This spring, activists frequently congregated outside the showrooms of his automaker, Tesla, to protest his foray into the US federal government and cozy relationship with President Trump.

Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all datasets. We revisit the long-standing intuition that different representations capture complementary structure and show that selectively fusing them can yield consistent improvements over Rocket on specific, systematically identifiable kinds of datasets. We introduce Fusion-3 (F3), a lightweight framework that adaptively fuses Rocket, Sax, and Sfa representations. To understand when fusion helps, we cluster UCR datasets into six groups using meta-features capturing series length, spectral structure, roughness, and class imbalance, and treat these clusters as interpretable data-structure regimes. Our analysis shows that fusion typically outperforms strong baselines in regimes with structured variability or rich frequency content, while offering diminishing returns in highly irregular or outlier-heavy settings. To support these findings, we combine three complementary analyses: non-parametric paired statistics across datasets, ablation studies isolating the roles of individual representations, and attribution via SHAP to identify which dataset properties predict fusion gains. Sample-level case studies further reveal the underlying mechanism: fusion primarily improves performance by rescuing specific errors, with adaptive increases in frequency-domain weighting precisely where corrections occur. Using 5-fold cross-validation on the 113 UCR datasets, F3 yields small but consistent average improvements over Rocket, supported by frequentist and Bayesian evidence and accompanied by clearly identifiable failure cases. Our results show that selectively applied fusion provides dependable and interpretable extension to strong kernel-based methods, correcting their weaknesses precisely where the data support it.

Like so many things in Elon Musk's orbit, a lot of it may come down to AI. SpaceX is planning to raise tens of billions of dollars through an initial public offering next year, multiple outlets have reported, and Ars can confirm. This represents a major change in thinking from the world's leading space company and its founder, Elon Musk . The Wall Street Journal and The Information first reported about a possible IPO last Friday, and Bloomberg followed that up on Tuesday evening with a report suggesting the company would target a $1.5 trillion valuation. This would allow SpaceX to raise in excess of $30 billion. This is an enormous amount of funding.

Modern work relies on an assortment of digital collaboration tools, yet routine processes continue to suffer from human error and delay. To address this gap, this dissertation extends TheAgentCompany with a finance-focused environment and investigates whether a general purpose LLM agent can complete representative wealth-management tasks both accurately and economically. This study introduces synthetic domain data, enriches colleague simulations, and prototypes an automatic task-generation pipeline. The study aims to create and assess an evaluation set that can meaningfully measure an agent's fitness for assistant-level wealth management work. We construct a benchmark of 12 task-pairs for wealth management assistants spanning retrieval, analysis, and synthesis/communication, with explicit acceptance criteria and deterministic graders. We seeded a set of new finance-specific data and introduced a high vs. low-autonomy variant of every task. The paper concluded that agents are limited less by mathematical reasoning and more so by end-to-end workflow reliability, and meaningfully affected by autonomy level, and that incorrect evaluation of models have hindered benchmarking.

This technical report presents a comprehensive formal verification approach for probabilistic agent systems modeling ballistic rocket flight trajectories using Probabilistic Alternating-Time Temporal Logic (PATL). We describe an innovative verification framework specifically designed for analyzing critical safety properties of ballistic rockets engineered to achieve microgravity conditions for scientific experimentation. Our model integrates authentic flight telemetry data encompassing velocity vectors, pitch angles, attitude parameters, and GPS coordinates to construct probabilistic state transition systems that rigorously account for environmental stochasticity, particularly meteorological variability. We formalize mission-critical safety properties through PATL specifications to systematically identify trajectory deviation states where the rocket risks landing in prohibited or hazardous zones. The verification framework facilitates real-time safety monitoring and enables automated intervention mechanisms, including emergency engine disengagement protocols, when predefined safety thresholds are exceeded. Experimental validation demonstrates the practical effectiveness and reliability of our approach in ensuring mission safety while maintaining scientific mission objectives.

As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (TinyML), for example, due to privacy and latency concerns. However, CPS often comprise ultra-low-power microcontrollers, whose limited compute resources make training challenging. This paper presents RockNet, a new TinyML method tailored for ultra-low-power hardware that achieves state-of-the-art accuracy in timeseries classification, such as fault or malware detection, without requiring offline pretraining. By leveraging that CPS consist of multiple devices, we design a distributed learning method that integrates ML and wireless communication. RockNet leverages all devices for distributed training of specialized compute efficient classifiers that need minimal communication overhead for parallelization. Combined with tailored and efficient wireless multi-hop communication protocols, our approach overcomes the communication bottleneck that often occurs in distributed learning. Hardware experiments on a testbed with 20 ultra-low-power devices demonstrate RockNet's effectiveness. It successfully learns timeseries classification tasks from scratch, surpassing the accuracy of the latest approach for neural network microcontroller training by up to 2x. RockNet's distributed ML architecture reduces memory, latency and energy consumption per device by up to 90 % when scaling from one central device to 20 devices. Our results show that a tight integration of distributed ML, distributed computing, and communication enables, for the first time, training on ultra-low-power hardware with state-of-the-art accuracy.

This was the last flight of SpaceX's V2 Starship design. Version 3 arrives next year. SpaceX closed a troubled but instructive chapter in its Starship rocket program Monday with a near-perfect test flight that carried the stainless steel spacecraft halfway around the world from South Texas to the Indian Ocean. The rocket's 33 methane-fueled Raptor engines roared to life at 6:23 pm CDT (7:23 pm EDT; 23:23 UTC), throttling up to generate some 16.7 million pounds of thrust, by a large measure more powerful than any rocket before Starship. Moments later, the 404-foot-tall (123-meter) rocket began a vertical climb away from SpaceX's test site in Starbase, Texas, near the US-Mexico border.