Thing is, I've been trying to find a moment to write down what happened to Bennett and me for a while now, but the demands of my audience rarely abate. I've hardly time to jot down a grocery list, let alone compose a personal chronicle. Bennett says I'm practically the Charles (as in Dickens) of scribblers devoted to mining the rich vein of a certain underappreciated sitcom of the nineteen-eighties, but I will leave that for history to judge. Besides, what does Bennett know? Just before he got that way, I was in Amok Mocha, where I like to sip cold brew and do my "C: FB" conjuring, and I struck up a conversation with a young woman who confessed to being a creative-writing student. She told me that in her workshop they talk about the "occasion" of the story. Why is the narrator telling this tale now? What pressures or conditions have coalesced to move a person to speak? I feigned ignorance of the concept, though I'd heard it often in my own writing classes long ago. Instead, I told her that, if the installment I was presently crafting flowed from any occasion, it was this: Charles is anxious about the imminent disintegration of the universe via the ever-increasing tug of dark matter. Moreover, he's ticked off that his best buddy, Buddy, doesn't seem perturbed by the prospect. "How imminent?" the woman said, and sipped her Balkan, a new offering at Amok. When I informed her that he was the titular hero of "Charles in Charge," the most criminally uncelebrated television program of the Reagan era, the woman pursed her lips. "We all write fan fiction," I told her. "Some of us are just more honest about it." The young woman gathered up her belongings, moved to another table. Did she think I was being facetious? Still, if there is an occasion for the story I'm relating now, it's a bit nearer on the space-time continuum. My best buddy, Bennett, is in a vegetative state induced by an anoxic brain injury, and, if he doesn't wake up soon and vouch for me, I could be kicked out of our apartment.

Multi-omics data capture complex biomolecular interactions and provide insights into metabolism and disease. However, missing modalities hinder integrative analysis across heterogeneous omics. To address this, we present MOIRA (Multi-Omics Integration with Robustness to Absent modalities), an early integration method enabling robust learning from incomplete omics data via representation alignment and adaptive aggregation. MOIRA leverages all samples, including those with missing modalities, by projecting each omics dataset onto a shared embedding space where a learnable weighting mechanism fuses them. Evaluated on the Religious Order Study and Memory and Aging Project (ROSMAP) dataset for Alzheimer's Disease (AD), MOIRA outperformed existing approaches, and further ablation studies confirmed modality-wise contributions. Feature importance analysis revealed AD-related biomarkers consistent with prior literature, highlighting the biological relevance of our approach.

NVIDIA CEO and co-founder Jensen Huang commends President Donald Trump's A.I. agenda and outlines what the country's job future will look like on'Special Report.' Classical and character-based education may seem to some antiquated concepts in the new AI-driven world. However, two recent and prominent AI developments definitively prove the opposite to be true. Going back to our nation's founding, great minds were universal in the belief that the survival of the Republic depended on an educated and virtuous public. Now, if AI experts are to be believed, classical and character education is fundamental to the very survival of humanity.

Background and Objective: High-throughput multi-omics technologies have proven invaluable for elucidating disease mechanisms and enabling early diagnosis. However, the high cost of multi-omics profiling imposes a significant economic burden, with over reliance on full omics data potentially leading to unnecessary resource consumption. To address these issues, we propose an uncertainty-aware, multi-view dynamic decision framework for omics data classification that aims to achieve high diagnostic accuracy while minimizing testing costs. Methodology: At the single-omics level, we refine the activation functions of neural networks to generate Dirichlet distribution parameters, utilizing subjective logic to quantify both the belief masses and uncertainty mass of classification results. Belief mass reflects the support of a specific omics modality for a disease class, while the uncertainty parameter captures limitations in data quality and model discriminability, providing a more trustworthy basis for decision-making. At the multi omics level, we employ a fusion strategy based on Dempster-Shafer theory to integrate heterogeneous modalities, leveraging their complementarity to boost diagnostic accuracy and robustness. A dynamic decision mechanism is then applied that omics data are incrementally introduced for each patient until either all data sources are utilized or the model confidence exceeds a predefined threshold, potentially before all data sources are utilized. Results and Conclusion: We evaluate our approach on four benchmark multi-omics datasets, ROSMAP, LGG, BRCA, and KIPAN. In three datasets, over 50% of cases achieved accurate classification using a single omics modality, effectively reducing redundant testing. Meanwhile, our method maintains diagnostic performance comparable to full-omics models and preserves essential biological insights.

Instrumental variables (IV) analysis is an important applied tool for areas such as healthcare and consumer economics. For IV analysis in high-dimensional settings, the Generalized Method of Moments (GMM) using deep neural networks offers an efficient approach. With non-i.i.d. data sourced from scattered decentralized clients, federated learning is a popular paradigm for training the models while promising data privacy. However, to our knowledge, no federated algorithm for either GMM or IV analysis exists to date. In this work, we introduce federated instrumental variables analysis (FedIV) via federated generalized method of moments (FedGMM). We formulate FedGMM as a federated zero-sum game defined by a federated non-convex non-concave minimax optimization problem, which is solved using federated gradient descent ascent (FedGDA) algorithm. One key challenge arises in theoretically characterizing the federated local optimality. To address this, we present properties and existence results of clients' local equilibria via FedGDA limit points. Thereby, we show that the federated solution consistently estimates the local moment conditions of every participating client. The proposed algorithm is backed by extensive experiments to demonstrate the efficacy of our approach.

The two workers say their employment was terminated over the protests. Fox News' "Antisemitism Exposed" newsletter brings you stories on the rising anti-Jewish prejudice across the U.S. and the world. TOP STORY: Microsoft fired an employee who disrupted the company's 50th anniversary celebration event to voice their opposition to its work supplying artificial intelligence technology to Israel. As Microsoft AI CEO Mustafa Suleyman spoke at the event, Ibtihal Aboussad began shouting at him, accusing him of being "a war profiteer." She demanded that Suleyman "stop using AI for genocide."

Artificial general intelligence (AGI) is an established field of research. Yet Melanie Mitchell and others have questioned if the term still has meaning. AGI has been subject to so much hype and speculation it has become something of a Rorschach test. Mitchell points out that the debate will only be settled through long term, scientific investigation. To that end here is a short, accessible and provocative overview of AGI. I compare definitions of intelligence, settling on intelligence in terms of adaptation and AGI as an artificial scientist. Taking my queue from Sutton's Bitter Lesson I describe two foundational tools used to build adaptive systems: search and approximation. I compare pros, cons, hybrids and architectures like o3, AlphaGo, AERA, NARS and Hyperon. I then discuss overall meta-approaches to making systems behave more intelligently. I divide them into scale-maxing, simp-maxing, w-maxing based on the Bitter Lesson, Ockham's and Bennett's Razors. These maximise resources, simplicity of form, and the weakness of constraints on functionality. I discuss examples including AIXI, the free energy principle and The Embiggening of language models. I conclude that though scale-maxed approximation dominates, AGI will be a fusion of tools and meta-approaches. The Embiggening was enabled by improvements in hardware. Now the bottlenecks are sample and energy efficiency.

Ahead of the 21st Bafta games awards this April, the institution is running a public survey asking people to nominate the most influential video game of all time. As the survey points out, this is an open-ended question: early, groundbreaking titles such as Space Invaders and Pong regularly crop up as answers because they helped write the rules of the form, but on a personal level, the right game at the right time can be exceptionally influential, too. For players, it's often the games that made us feel differently about what games could do that feel the most influential. For a game designer, a film director, a writer or a musician, one particular game might inspire a whole creative era. Inspired by Bafta's survey, we asked people from across games and culture for their most influential game – and not one name cropped up twice.

Bennett's tree kangaroos, one of Australia's most mysterious marsupials, have long eluded researchers. Our new study, published in Australian Mammalogy today, has achieved a breakthrough: using thermal drones to detect these rare animals with unprecedented efficiency. Tree kangaroos are found only in the tropical rainforests of Australia and New Guinea. Unlike their ground-dwelling relatives, they spend their lives in treetops, feeding on leaves and vines. Their dependence on rainforest trees makes them vulnerable to deforestation and climate change.

Language Model Agents (LMAs) are increasingly treated as capable of autonomously navigating interactions with humans and tools. Their design and deployment tends to presume they are normal agents capable of sustaining coherent goals, adapting across contexts and acting with a measure of intentionality. These assumptions are critical to prospective use cases in industrial, social and governmental settings. But LMAs are not normal agents. They inherit the structural problems of the large language models (LLMs) around which they are built: hallucinations, jailbreaking, misalignment and unpredictability. In this Position paper we argue LMAs should not be treated as normal agents, because doing so leads to problems that undermine their utility and trustworthiness. We enumerate pathologies of agency intrinsic to LMAs. Despite scaffolding such as external memory and tools, they remain ontologically stateless, stochastic, semantically sensitive, and linguistically intermediated. These pathologies destabilise the ontological properties of LMAs including identifiability, continuity, persistence and and consistency, problematising their claim to agency. In response, we argue LMA ontological properties should be measured before, during and after deployment so that the negative effects of pathologies can be mitigated.