Split learning protects sensitive data by dividing AI model training, enabling collaboration without exposing raw personal information, according to Forbes contributor John Werner.…
A new taxonomy from AI insider Lance Eliot identifies five distinct categories of AI use in the public sector, challenging the common practice of treating AI as a single monolithic entity. The classif…
Intel CEO Lip-Bu Tan acknowledged the company must catch up to rivals AMD and Arm, aiming to 'leapfrog' them in CPU architecture, during Intel's Q2 earnings call where it reported $16.1 billion revenu…
Goodwood Future Lab showcased a new generation of robots that can collaborate, understand plain English, interact naturally with people and even experience touch, according to contributor Bernard Marr…
China is making an all-out push to catch up with American AI chips, according to a Wall Street Journal report. The country is investing heavily in domestic chip development to reduce reliance on U.S. …
A new iOS app called Lock TF IN, described as an AI drill sergeant that roasts skipped workouts, has been released on the Apple App Store. The app uses artificial intelligence to motivate users by cri…
The FBI's seizure of Z-Library, a major illegal book-sharing site, has not ended its influence; instead, the pirate project has become central to the AI revolution, according to the Financial Times. T…
Researchers introduced explanation-based runtime verification for ML-driven optical networks, an approach that uses model explanations to assess decision soundness before execution in the network cont…
A new method called GaugeQuant, introduced in a paper on arXiv, reduces perplexity in quantized large language models by learning quantization-optimal bases from internal symmetries during training. U…
Researchers have identified new polynomial-time tractable neural network architectures for training with ReLU and linear activation functions, pushing beyond previous state-of-the-art results. For ReL…
A new permutation diagnostic reveals that position bias in multiple-choice LLM benchmarks is only detectable within a 60-95% base-accuracy Goldilocks zone, with frontier-tier models operating above th…
Researchers propose Graph Wavelet Compressed Sensing (GWCS), a learning-based framework for offline compression of graph signals that combines a nonparametric multilevel importance sampler with a scal…
Researchers at NVIDIA have enhanced preconditioned gradient methods like SOAP and Muon to overcome computational cost and numerical stability challenges in large-scale LLM pretraining, demonstrating t…
Researchers introduced CvAdamW, a variant of the AdamW optimizer that monitors attention logit variance as specific heat to detect and accelerate grokking in neural networks, enabling generalization a…
A new study from arXiv identifies Spectral Drift in neural network internal activations as a reliable indicator of misclassifications, with failures showing a 1.9% increase in drift (p<0.001). The res…
A new study tests causal emergence in an active inference agent whose architecture separates a fast perception latent z from a slow global latent g, finding that the integrated information measure Φr …
Researchers propose Cardinality-Decomposed Loss (CDL) to address a silent failure in Graph Neural Networks for heterogeneous recommendation graphs, where Bayesian Personalized Ranking (BPR) causes att…
A study of Microsoft's Aurora weather forecasting foundation model fine-tuned for atmospheric chemistry finds that while it captures a first-order ozone response to reactive nitrogen, it does not enfo…
A new robust Q-learning algorithm, BR-Async-Q, achieves the first robustness guarantee for asynchronous Q-learning under both reward and state corruption, matching vanilla Q-learning error bounds up t…
Researchers propose Hierarchical Implicit Q-Chunking (HiQC), an offline goal-conditioned reinforcement learning algorithm that combines high-level latent planning with low-level action chunking to add…