Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
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Raw Feed is chronological evidence. Signals ranks and interprets material change.
Way Security, a startup, is developing enterprise identity solutions, arguing that AI changes security pace but not fundamental trust and identity principles.
Microsoft introduces 'Project Perception' for AI-native cybersecurity, arguing for a new cyber stack to counter autonomous threat capabilities.
Microsoft launched the External Red Team Alliance (EXTRA), a global initiative partnering with researchers to advance AI safety and red teaming.
Amazon seeks FCC approval to launch 5,000 satellites for direct-to-device mobile services, intensifying competition with SpaceX.
ASML shares declined after reports that a Chinese state-backed company initiated mass production of DUV chipmaking tools, potentially impacting ASML's sales.
Insight Partners details how four companies deploy Forward Deployed Engineers (FDEs) to rapidly convert customer problems into production value.
Nvidia made a "substantial" investment in Safe Superintelligence Inc., the new AI startup co-founded by former OpenAI chief scientist Ilya Sutskever.
A new project demonstrates using Cursor AI with a Raspberry Pi to enable non-coders to build hardware integrations for personal use.
Truist Financial Corp. appointed Jefferies veteran Craig Mineard as co-head of its Technology Media and Telecom (TMT) investment banking group.
China warns of retaliation if US sanctions Chinese AI firms over alleged improper use of American models for training.
OpenAI is expanding its European headquarters in Dublin, creating 250 new jobs, citing growing AI demand.
Bloomberg reports on how companies communicate AI's impact on employment to both investors and employees, balancing efficiency and workforce concerns.
Nvidia is reportedly orchestrating over $750 billion in new AI deals, sparking concerns about inflated demand and valuations.
HSBC plans to hire over 100 AI specialists and establish a global AI center in Singapore, deepening investment in AI capabilities.
CXMT founder Zhu Yiming pledged $5.6 billion in worker bonuses after China IPO, highlighting wealth creation in US-China tech race.
NVIDIA introduced Cosmos-H-Dreams, a framework for real-time generative simulation, specifically for surgical robotics applications.
Chinese chip manufacturer CXMT's market debut saw its stock soar 466%, briefly becoming China's most valuable listed company.
Multiverse Computing SL is raising $570 million at a $1.7 billion valuation to fund efforts in reducing AI operational costs.
OpenAI, Anthropic, Google, and Microsoft significantly increased lobbying expenditure in Washington D.C., signaling growing policy influence efforts.
France questions UK's participation in the EU’s €5bn tech start-up fund amid strained UK-EU relations during 'reset' negotiations.
The Financial Times reports some publishers believe AI will replace authors, creating a premium market for human-written books.
TeamSystem's private equity owners are exploring a stake sale at an €8bn valuation amidst market disruption from AI.
Research introduces Math Education Digital Shadows (MEDS), a dataset to evaluate 14 LLMs' mathematical performance and biases across personifications.
Research finds the pretraining domain, not the training objective, is the primary factor in differentially private medical imaging models' utility-privacy trade-off.
DriftXpress introduces an accelerated formulation for 'drifting models,' a new paradigm for one-step generative modeling that reduces inference costs.
Research introduces 'alternation metrics' for multi-agent systems to evaluate temporal fairness in resource access beyond aggregate payoffs, using the Honey-Jar Game.
Research paper explores statistical mechanics to better understand extensive-width Bayesian neural networks near interpolation, bridging theory-practice gap.
Researchers introduced Atlas 2, Atlas 2-B, and Atlas 2-S, three pathology vision foundation models addressing performance, robustness, and computational requirements for clinical deployment.
Research explored multi-agent Q-learning coordination in a tabular predator-prey gridworld, isolating coordination structure from approximation.
Re-FORC proposes an adaptive reward prediction method for Chain-of-Thought reasoning to enable early stopping and reduce compute costs by up to 26%.
Research introduces Hierarchical Online Learning of Multiscale (HOLM) models, combining online latent-cause inference with hierarchical Bayesian models.
Research proposes a layer-wise LoRA fine-tuning method using a similarity metric to improve LLM predictive performance efficiently.
Research introduces Heavy-Tailed Principal Component Analysis (HTPCA) for robust dimensionality reduction in heavy-tailed data and impulsive noise.
Research explores safety in In-Context Reinforcement Learning (ICRL), addressing unexamined test-time behavior for real-world deployments.
Research benchmarks federated learning strategies for in-hospital mortality prediction using heterogeneous and imbalanced clinical data.
Research characterizes self-training (ST) in linear classifiers using pseudo-labels on Gaussian mixture data to understand generalization improvement.
Research explores differentially private federated learning for imbalanced clinical data using SMOTETomek and FedProx to balance privacy and utility.
Research explores Minimum Norm Interpolation (MNI) framework in overparameterized models, focusing on generalization under 2-uniform convexity.
A research paper introduces Predictive Query Language (PQL), a domain-specific language for predictive modeling directly on relational databases.
Research finds Transformer models' high performance in intrusion detection is often due to flawed evaluation, not true temporal gains.
Research on two-layer neural networks explores optimal generalization and learning transitions near the interpolation threshold for large models.
Research introduces a supervisory runtime stability framework for neural network training to detect and recover from severe destabilizing updates.
Research proposes a theory of indecisions for selective hypothesis testing to minimize abstention rates while maintaining target accuracy in high-risk scenarios.
Research identifies numerical fragility in Transformer models due to low-precision execution, proposing a layer-wise risk estimator and controller.
Research paper introduces a framework to analyze representation costs from parameter-space regularizers in data-fitting methods, including DNNs.
Research introduces Wasserstein Gradient Flows for scalable and regularized barycenter computation, improving aggregation of probability measures.
PCS-UQ introduces a framework for robust uncertainty quantification in high-stakes ML domains, integrating prediction-checks and bootstrapping.
Research proposes a Decentralized Multi-Agent Swarm (DMAS) architecture using autonomous agents for security in Industrial IoT (IIoT) environments.
Research uses reinforcement learning to replace fixed parameters with state-dependent functions in weather and climate models, improving adaptation to physics.
Research paper gp2Scale proposes a method for exact Gaussian Processes on up to 10 million data points, improving scalability without approximations.
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