bayesian inference
9 articles about bayesian inference in AI news
BayesBench: LLMs Match Bayesian Posteriors But Fail Downstream Prediction
BayesBench tests 7 LLMs on multi-turn Bayesian reasoning. Scaling improves latent inference but not prediction, exposing a critical gap for agentic deployment.
NVIDIA Research Shows AI Can Optimize Decades-Old EDA Tools Like ABC
New NVIDIA research indicates AI can be used to optimize Electronic Design Automation (EDA) tools, such as the classic ABC system, which have been manually tuned by engineers for decades. This could automate a core, labor-intensive bottleneck in semiconductor design.
Add 197 Bioinformatics Skills to Claude Code with SciAgent-Skills
A ready-to-use plugin that transforms Claude Code into a bioinformatics expert without fine-tuning or RAG setup.
AI Forecasters Revise AGI Timeline: Key Milestones Pulled Forward to 2029-2030 After Recent Model Progress
A significant update from AI forecasters indicates key AGI milestones have been pulled forward, with the median prediction for AGI arrival shifting from 2032 to 2029-2030. This revision follows rapid progress in recent model capabilities, particularly in reasoning and tool use.
Fine-Tuning LLMs While You Sleep: How Autoresearch and Red Hat Training Hub Outperformed the HINT3 Benchmark
Automated fine-tuning tools now let you run hundreds of training experiments overnight for under $50. Here's how Autoresearch and Red Hat's platform outperformed HINT3, and the tools you can use today.
AI Engineer Henry Ndubuaku Releases Open-Source 'Maths, CS & AI Compendium' Textbook
AI engineer Henry Ndubuaku has published a free, open-source textbook compiling mathematics, computer science, and AI concepts. The resource emphasizes intuitive understanding over notation and has reportedly helped users land roles at DeepMind, OpenAI, and Nvidia.
Training-Free Polynomial Graph Filtering: A New Paradigm for Ultra-Fast Multimodal Recommendation
Researchers propose a training-free graph filtering method for multimodal recommendation that fuses text, image, and interaction data without neural network training. It achieves up to 22.25% higher accuracy and runs in under 10 seconds, dramatically reducing computational overhead.
An AI Agent Autonomously Tuned a Model and Beat Grid Search
A developer set up an AI agent to autonomously experiment with and tune a model's hyperparameters. The agent, working unattended, modified code and ran short training cycles, ultimately outperforming a traditional grid search.
Exploration Space Theory: A Formal Framework for Prerequisite-Aware Recommendation Systems
Researchers propose Exploration Space Theory (EST), a lattice-theoretic framework for modeling prerequisite dependencies in location-based recommendations. It provides structural guarantees and validity certificates for next-step suggestions, with potential applications beyond tourism.