Infrastructure & Hardware

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Showing 151–180 of 210
  • IEEE Spectrum (AI section) · EN Safety & Evaluation
    Your Robot’s Safety Functions Already Work. What If the Input Lies?
    IEEE Spectrum asks what happens if a robot's safety inputs are falsified
    Neural Network
    Robot safety functions such as slowing near people depend on inputs like distance readings, position estimates or stop signals. This IEEE Spectrum piece, sponsored by VicOne, asks what happens when those inputs are false.
    Read original (IEEE Spectrum (AI section)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    From Interaction to Capability: Task Advancement as State and Supervision for GUI Agents
    AI Agents Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    Sponsored: Ground control: Structuring land deals for data center development
    Sponsored: structuring land deals for data center development
    A sponsored piece arguing that AI and unprecedented demand for compute have fundamentally changed how investors, developers and operators evaluate data center opportunities.
    Read original (Data Center Dynamics) ↗
  • NVIDIA Developer Blog · EN Agents & Tool Use
    Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages
    Fine-tuning NVIDIA Nemotron to know local rules, not just languages
    AI Agents Fine-tuning Generative AI Machine Learning NVIDIA
    As AI use spreads across languages and cultures, general-purpose models serve many needs. This NVIDIA post covers fine-tuning Nemotron models so they understand local rules as well as local languages, going beyond language support alone.
    Read original (NVIDIA Developer Blog) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    M4Bench: Evaluating Procedural Specification for Clinical EHR Derivation Agents
    AI Agents Claude GPT Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    An Input Skip Can Reparameterize a VAE Without Improving the Generator
    Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Test-Time Communication in Trading Agent Swarms
    AI Agents
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers
    DeepSeek Inference Mistral Mixture of Experts (MoE) Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Beyond the Readout: A Latent Numerical Frame Shared Across Modality and Operation
    Neural Network Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    Displacement Moments and Path-Terminal Separation in Brownian Interpolant Sampling
    Neural Network Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    SoleCrypt ups size of planned data center campus in Bizerte, Tunisia
    SoleCrypt enlarges planned Tunisia data center campus
    Neural Network Reinforcement Learning
    SoleCrypt has increased the size of its planned data center campus in Bizerte, Tunisia. The first 7MW phase is due live in early 2028 and will host Vera Rubin GPUs.
    Read original (Data Center Dynamics) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    JEPA-Inspired Latent Field Prediction for Sparse-Pilot MIMO-OFDM Channel Reconstruction
    Embeddings Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
    Mixture of Experts (MoE) Neural Network Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model
    Fine-tuning GPT Mixture of Experts (MoE)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    The Geometry of Manifold Learning: Singular foliations via Deep Learning models
    Algorithms & Theory Deep Learning Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Industry Adoption
    TRIDENT-Seq: Profile-Matched Prefix-Future Contrast for Label-Efficient Payment Risk Modeling
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    STTGDC secures $590m green loan for Tokyo data centers
    STTGDC secures $590m green loan for Tokyo data centers
    STTGDC has secured a $590m green loan to finance the fit-out and operation of its 70MW campus in Tokyo.
    Read original (Data Center Dynamics) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    Broadcom organizes $63bn deal with Bank of America, Blackstone, Citigroup, and Morgan Stanley to fund Anthropic chip purchases
    Broadcom arranges $63bn financing for Anthropic chip purchases
    Anthropic Google
    Broadcom organized a $63bn deal with Bank of America, Blackstone, Citigroup and Morgan Stanley to fund Anthropic's chip purchases. Project North Star will finance the purchase of Broadcom-developed Google TPUs.
    Read original (Data Center Dynamics) ↗
  • NVIDIA Developer Blog · EN Inference & Efficiency
    3 Ways to Boost MoE Inference on NVIDIA DGX Spark
    NVIDIA outlines three ways to speed up MoE inference on DGX Spark
    Generative AI Inference Mixture of Experts (MoE) NVIDIA
    An NVIDIA developer blog post describes three ways to boost mixture-of-experts (MoE) inference on DGX Spark. It argues that faster token generation starts with identifying what limits inference performance, using a batch-size-1 evaluation of Inclusion AI's Ling-3.0-flash as its example.
    Read original (NVIDIA Developer Blog) ↗
  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    Online Interval Selection with Binary Predictions: Optimal Consistency and a Continuous Robustness Tradeoff
    Algorithms & Theory Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    AirTrunk secures AU$4.25 billion green loan for Sydney data center campus
    AirTrunk secures AU$4.25bn green loan for Sydney campus
    AirTrunk has secured a AU$4.25 billion green loan for its Sydney data center campus. The current expansion phase at the SYD3 site is due live in 2028.
    Read original (Data Center Dynamics) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware
    First building at Project Jupiter data center campus in New Mexico topped out
    First building at Project Jupiter in New Mexico topped out
    The first building at the Project Jupiter data center campus in New Mexico has been topped out. Structural work on it is complete, with three more buildings to go.
    Read original (Data Center Dynamics) ↗
  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Your Policy Is Secretly a Harness: The Decision Geometry of Agent Interfaces
    AI Agents
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Safety & Evaluation
    Decision Models as Judges for Hallucination Detection
    GPT
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Mechanistic Failure Modes of Weight Pruning in Transformer Language Models
    GPT Machine Learning Transformer
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Prospector: learning a compact graph traversal policy for scientific reasoning
    Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Self-Regularized Flow Language Models
    Inference Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • The Register (Data Centre) · EN Infrastructure & Hardware
    UK ministers had plenty of time for AI datacenter bosses, but none for environmental groups
    UK ministers met AI datacenter bosses, not environmental groups
    An investigation found that UK ministers had plenty of time for AI datacenter bosses but none for environmental groups. Businesses dominated government meetings on AI Growth Zones while communities were left out of decisions.
    Read original (The Register (Data Centre)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    JevRec: Linguistic Encoding with Jev-like Decision Modeling for LLM-based Generative Recommendation
    Deep Learning Inference
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Do We Need an LLM to Know Why Sources Conflict? Decision Models for Knowledge-Conflict Typing in RAG
    Gemini Inference Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗