Neural Networks news and analysis on Web Pulse: 759 curated articles tracking the latest Neural Networks developments, tools, and research, updated continuously from vetted sources.
Mixture-of-Experts (MoE) architecture allows large language models to use only a fraction of their total parameters for each token, enabling models with 671 billion total parameters to run at speeds comparable to a 37-bi…
Researchers have developed a new global dataset of annual migration flows across 230 countries from 1990 to the present, using deep learning to integrate diverse data sources. The model, which outperforms existing five-y…
Two people connected via high-bandwidth brain-computer interfaces could share deep understanding in minutes to days, according to a new analysis of telepathic communication technology. The approach uses existing neural d…
Researchers trained a small transformer encoder to detect reward hacking in reinforcement learning trajectories by mapping them onto a unit sphere where embedding distance approximates reward-metadata signal differences.…
Cosmologists trained an AI neural network on simulations of the standard model of cosmology, then tested whether that pre-training would help or hinder the AI's investigations into new physics. The AI developed biases th…
A developer has created a fully playable, single-file browser-based tactical bomb-defusal first-person shooter using Three.js, complete with procedural textures, audio, and enemy AI. The game features a compact desert-to…
Unconventional AI uses digital twins to simulate brain-inspired hardware operating at the edge of chaos, a highly sensitive state where accurate modeling is critical. The company's system modeling team must account for n…
Harold Hotelling's 1936 Canonical Correlation Analysis (CCA) is the conceptual foundation for modern Joint Embedding Predictive Architecture (JEPA) models, as JEPA implicitly performs a nonlinear generalization of CCA. T…
A Cambridge Digital Minds course participant argues that if large language models (LLMs) are conscious under predictive processing theory, their consciousness would only emerge during training, not inference. The theory …
PyTorch's `nn.Linear` module transposes its weight tensor before performing matrix multiplication and addition, as revealed by profiler traces showing an `aten::t` operation that only modifies tensor metadata on the CPU …
A large international research team led by Harvard Medical School and Princeton University has published the first complete map of every neural connection in an adult fruit fly's central nervous system, including both th…
Neuroscientists restored lost olfactory memories in a mouse model of Alzheimer's disease by using light to stimulate neural pathways between the piriform and infralimbic cortices. The study, published in Molecular Psychi…
NVIDIA released a workflow for converting FP8-quantized CLIP model checkpoints into TensorRT engines, enabling faster inference and higher GPU throughput for production deployment. The process involves exporting the quan…
Tesla's full self-driving software, the first large-scale deployment of end-to-end AI in a physical system, has demonstrated that the probabilistic, data-driven engineering approach carries inherent risks of catastrophic…
Neuronpedia launched HeadVis, a tool for exploring attention heads in collaboration with Anthropic, now available for 37 models with over 36,000 attention head dashboards. The platform also added new SAEs and NLAs, inclu…
AI engineers must master five critical Python concepts to build scalable, secure, and robust production systems, including PyTorch's autograd for automatic gradient computation and the `__call__` method for clean model i…
A perceptron, the simplest neural network, was introduced by Frank Rosenblatt in 1958. This article demonstrates building a perceptron from scratch in Python to classify numbers as positive or negative, showing how it le…
Ernest Chan presented Conditional Portfolio Optimization (CPO), a machine-learning method that trains a neural network to predict portfolio performance based on market features and then solves a constrained optimization …
OpenAI’s GPT models and Meta’s LLaMA-family use transformer architectures that convert text into token IDs, then map each ID to a dense vector via an embedding matrix. These vectors are combined with positional encodings…
A new interactive simulation allows users to ask questions to a digital recreation of Albert Einstein, with the AI model's neural network visually lighting up in real-time to show which parts of the "brain" are processin…