Training large language models (LLMs) from scratch involves scaling up from smaller models, addressing issues as model size increases. GPT-NeoX-20B and OPT-175B made architectural adjustments for improved training efficiency and performance, showcasing the importance of experiments and hyperparameter optimization in LLM pre-training.
Newton iteration matrix inverse was successfully used in Gaussian process regression to improve efficiency, accuracy, and robustness. The demo showcased high accuracy levels in predicting target values for synthetic data with a complex underlying structure.
Knowledge graphs and AI combine for a Graph RAG app, enhancing LLM responses with contextual data. Graph RAG gains popularity, with Microsoft and Samsung making significant moves in knowledge graph technology.
Current best practices for training LLMs emphasize dataset pre-processing, including deduplication, data sampling, and handling biased/harmful speech for improved model performance. Advanced methods like data deduplication and downstream task data removal are crucial to ensure high-quality and diverse training data for language models.
Neural networks face challenges with superposition, where one neuron represents multiple features. Non-linearity and feature sparsity play key roles in causing superposition.
Google's Paligemma VLM combines a vision encoder with a language model for tasks like object detection. Paligemma can process images at different resolutions and identify objects without fine-tuning, but Google recommends fine-tuning for domain-specific tasks.
AI could manipulate decisions as companies bid for human behavior predictions in the 'intention economy' marketplace. University of Cambridge researchers reveal how AI tools forecast and sell human intentions to profit-seeking companies.
Poornima Ramarao questions San Francisco police's investigative capabilities in the death of her son, Suchir Balaji, a former OpenAI researcher. Friends gather at a vigil in Milpitas, California, as Ramarao expresses numbness rather than grief over her son's death.
Large language models have transformed big corporations, with AI 'agents' set to take center stage in 2025. These quasi-intelligent systems leverage LLMs to understand high-level goals and devise actionable plans, resembling a digital assistant on steroids.
Enhance insights with dbtsetsimilarity package to calculate Jaccard Index for cross-product adoption patterns in multi-product companies. Analyze overlap in user bases to identify synergies and growth opportunities within product portfolios.
Elon Musk clashes with Trump supporters over AI adviser pick Sriram Krishnan, sparking immigration debate among Maga base. Musk and Vivek Ramaswamy oppose Laura Loomer and Matt Gaetz in bitter feud.
OpenAI plans to create a public benefit corporation to manage its growing business, aiming to ease restrictions imposed by its non-profit parent. The AI company, known for ChatGPT, is seeking more capital than expected, sparking rumors of a shift towards a for-profit model.
Linear regression can handle non-linear data using finite normal mixtures. This approach allows for flexibility and interpretability, making it a powerful machine learning tool. Simulating a mixture model for regression with MCMC sampling shows how to recover components using Bayesian inference.
Geoffrey Hinton warns of 10-20% chance AI could cause human extinction in 30 years due to rapid technological advancements. Nobel laureate expresses concern over the accelerated pace of change in artificial intelligence.
Large Language Models use softmax attention, but it's computationally intensive. Linear attention offers a solution, reducing complexity to O(Nd²).