A former OpenAI board member has explained how her unconventional personal relationship with Elon Musk evolved into having four of his children. Shivon Zilis testified in a federal courtroom in Oakland, California for hours on Wednesday as part of Musk's lawsuit trying to reverse OpenAI's change to a for-profit company. The focus of Zilis's appearance was her direct involvement in early talks with Musk around the company becoming a for-profit, but also how she worked for and became involved with Musk as she advised OpenAI. I still really wanted to be a mum and Elon made the offer around that time and I accepted, she said, explaining Musk in 2020 had offered to donate sperm. He was encouraging everyone around him at that time to have kids and he'd noticed I did not.

New research suggests that reliance on AI assistants can have a negative impact on people's ability to think and problem solve. Using AI chatbots for even just for 10 minutes may have a shockingly negative impact on people's ability to think and problem-solve, according to a new study from researchers at Carnegie Mellon, MIT, Oxford, and UCLA. Researchers tasked people with solving various problems, including simple fractions and reading comprehension, through an online platform that paid them for their work. They conducted three experiments, each involving several hundred people. Some participants were given access to an AI assistant capable of solving the problem autonomously.

As slick, machine-generated visuals become ubiquitous, artists and designers are embracing a style of handmade imperfection. Two and a half years ago, Christine Tyler Hill, a designer and artist in Burlington, Vermont, began working as a crossing guard in her neighborhood. The city paid her twenty dollars an hour, but the real draw was the chance to get to know local families and "be more enmeshed with my very immediate, outside-my-door community," she told me recently. She was tired of staring at a screen doing design work, and new clients were getting harder to come by, in part, she surmised, because of the rise of generative artificial intelligence . She began documenting her crossing-guard shifts on Instagram, posting mini comics about the frigid weather, the charming habits of commuting children, and the beauty of an overflowing trash can.

The company behind the long-running space sim has entered into a partnership with Google in which the search giant will take a minority stake. In exchange, Google's DeepMind will train its AI technology on the game, according to a report by . CCP Games, the dev who made and maintains, has also been rebranded as Fenris Creations . This happened just after the company purchased the rights to the game back from Korean developer Pearl Abyss. Google's investment is in the millions of dollars, according to Fenris Creations Chief Executive Officer Hilmar Veigar Pétursson.

Anthropic announced "dreaming" for AI agents to sort through "memories" at its developer conference. Anthropic just announced a new feature called "dreaming" at the company's developer conference in San Francisco. It's part of Anthropic's recently launched AI agent infrastructure designed to help users manage and deploy tools that automate software processes. This "dreaming" aspect sorts through the transcript of what an agent recently completed and attempts to glean insights to improve the agent's performance. Folks using AI agents often send them on multistep journeys, like visiting a few websites or reading multiple files, to complete online tasks.

Hackers and other cybercriminals are complaining about "AI shit" flooding platforms where they discuss cyberattacks and other illegal activity. "I'm disappointed that you are working to incorporate AI garbage into the site," one annoyed person, posting anonymously, said in an online message. "No-one is asking for this--we want you to improve the site, stop charging for new features." Only, this is not a regular internet user moaning about AI being forced into their favorite app . Instead, they are complaining about a cybercrime forum's plans to introduce more generative AI.

Classical ReLU-based Input Convex Neural Networks (ICNNs) are equivalent to the optimal value functions of Linear Programming (LP). This intrinsic structural equivalence restricts their representational capacity to piecewise-linear polyhedral functions. To overcome this representational bottleneck, we propose the SOC-ICNN, an architecture that generalizes the underlying optimization class from LP to Second-Order Cone Programming (SOCP). By explicitly injecting positive semi-definite curvature and Euclidean norm-based conic primitives, our formulation introduces native smooth curvature into the representation while preserving a rigorous optimization-theoretic interpretation. We formally prove that SOC-ICNNs strictly expand the representational space of ReLU-ICNNs without increasing the asymptotic order of forward-pass complexity. Extensive experiments demonstrate that SOC-ICNN substantially improves function approximation, while delivering competitive downstream decision quality. The code is available at https://anonymous.4open.science/r/SOC-ICNN-4B18/.

Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend these methods to neural networks, specifically large language models (LLMs), to better analyze and explain relationships among models. We show how relating weights to genotypes and output text to phenotypes can improve our understanding of model lineage, important datasets, the roles of different model layers, and visualization of model relationships. We demonstrate this in a controlled experiment, where our estimated evolutionary trees reliably recover the topology of the ground-truth training tree. We further identify the most important weight layers according to weight differences and show through phenotypic experiments that one training dataset appears to contribute more useful information than the others. Finally, we generate an unsupervised evolutionary tree of black-box foundation models. Throughout, we provide visualizations that support a clearer understanding of evolutionary relationships among LLMs.

Deep learning models for drug--target interaction (DTI) prediction often achieve strong benchmark performance without necessarily relying on mechanistically meaningful molecular features, a limitation that standard accuracy-based evaluation cannot detect. We introduce ISAAC (Intervention-based Structural Auditing Approach for Causal Reasoning), a post-hoc framework that evaluates prior-relative structural sensitivity by probing frozen models through matched mechanistic and spurious input-level interventions, independently of predictive accuracy. Applied to three sequence-based DTI architectures on the Davis benchmark, ISAAC reveals approximately 25\% relative differences in reasoning scores across models with comparable AUROC (within around 3\%), stable across training and intervention seeds and two distinct perturbation operators. These discrepancies, undetectable under conventional accuracy metrics, motivate the use of post-hoc structural auditing as a complement to standard performance evaluation in scientific machine learning for molecular modeling.

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.