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.
A researcher reflects on 2024 highlights in AI, covering societal impacts, software tools like Scikit-learn, and technical research on tabular data and language models.
An overview of Microsoft Azure's suite of AI services, including prebuilt APIs, machine learning, and cognitive tools for building intelligent applications.
A curated list of notable LLM and AI research papers published in 2024, providing a resource for those interested in the latest developments.
Introduces Label-Studio, an open-source tool for annotating text, image, audio, and video data for AI/ML projects, highlighting its ease of use and features.
A guide on transitioning into AI careers, distinguishing between working 'on' AI models and 'in' AI infrastructure, products, and engineering processes.
![7 New Books added to Big Book of R [7/12/2024]](https://alldevblogs.blob.core.windows.net/thumbs/article-74a3437fb78e-full-9ff738a4.webp)
Announces 7 new free R programming books added to the Big Book of R collection, covering topics like machine learning, data science, and software engineering.
A guide on starting and running a weekly paper club for learning about AI/ML research papers and building a technical community.
Author shares detailed experience and study tips for passing both AWS Machine Learning Engineer Associate and Machine Learning Specialty certification exams.
Key lessons from 2024 ML conferences on building effective machine learning systems, covering reward functions, trade-offs, and practical engineering advice.
