A researcher shares progress on the PRISM project, an MSCA-PF grant focused on validating spatial patterns in machine learning for remote sensing.
A review of a free LinkedIn Learning course on AI and Machine Learning fundamentals tailored for Java developers, covering predictive and generative AI.
Explores the evolution of AI from symbolic systems to modern Large Language Models (LLMs), detailing their capabilities and limitations.
A technical analysis using R to classify iris images from a dataset, applying PCA and LDA for machine learning classification.
Using an LLM to label Hacker News titles and train a Ridge regression model for personalized article ranking based on user preferences.
An introduction to reasoning in Large Language Models, covering concepts like chain-of-thought and methods to improve LLM reasoning abilities.
An introduction to reasoning in Large Language Models, covering key concepts like chain-of-thought and methods to improve LLM reasoning abilities.
A guide to building neural networks using JavaScript and the Brain.js library, covering fundamental concepts for web developers.
A clear explanation of the attention mechanism in Large Language Models, focusing on how words derive meaning from context using vector embeddings.
Argues that AI can improve beyond current transformer models by examining biological examples of superior sample efficiency and planning.
Explores the concept of 'generality' in AI models, using examples of ML failures and LLM inconsistencies to question how we assess their capabilities.
An analysis of the ethical debate around LLMs, contrasting their use in creative fields with their potential for scientific advancement.
Explores the critical challenge of bias in health AI data, why unbiased data is impossible, and the ethical implications for medical algorithms.
Distinguishes between Functional AGI (replacing knowledge workers) and Technical AGI (true generalization), arguing Functional AGI's societal impact matters most.
A technical guide exploring IBM's Granite 3.1 AI models, covering their reasoning and vision capabilities with a demo and local setup instructions.
Summary of key concepts for optimizing AI inference performance, covering bottlenecks, metrics, and deployment patterns from Chip Huyen's book.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
A guide to the best newsletters, blogs, and resources for staying updated on the fast-moving field of Artificial Intelligence in 2025.
A summary and discussion of Chapter 1 of Chip Huyen's book, exploring the definition of AI Engineering, its distinction from ML, and the AI Engineering stack.
Interview with Dr. Nick Feamster on network measurement, machine learning, and the Internet Equity Initiative's work on broadband access.
