Weekly Highlights Issue 4: The Q* Hypothesis, Problem Solving with LLMs, Apple OpenAI deal and more...

Welcome to this week’s roundup of highlights and resources!

Here’s what caught my eye this week ??

?? Weekly Highlights

?? The Q* Hypothesis: A New Dawn in AI Development

?? Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. (research paper) ??

?? Artificial Intelligence Expertise

(new LinkedIn group for AI research papers and news)

Within the intersection of artificial intelligence and business models, the mission is to create a community that encourages the exchange of insights, fosters a better understanding of AI's role in various sectors, and keeps you updated on the latest in AI applications.

Artificial Intelligence Expertise is a one-stop destination for all things AI.

Here, we bring together project managers, business leaders, data scientists, AI enthusiasts, and tech-savvy individuals who share a common interest in artificial intelligence's transformative power.

Join us today, and be a part of the revolution in AI.

?? Interesting Articles

?? Apple's OpenAI deal reportedly fixed – and Microsoft is worried

At WWDC, Apple will showcase its approach to AI, focusing on everyday applications. And ChatGPT is coming to iOS. At its WWDC developer event in June, Apple will focus on tools normal people can use in their daily lives.

?? Google adds AI-powered features to Chromebook

Google announced new AI-powered features today for its Chromebook Plus line of devices, such as a writing assistant, a wallpaper creator and easy access to Google’s Gemini chatbot. As a reminder, Chromebook Plus laptops are productivity-focused devices with minimum spec requirements.

?? Video Highlights

?? Python RAG Tutorial (with Local LLMs): AI For Your PDFs

This project contains some more advanced topics, like how to run RAG apps locally (with Ollama), how to update a vector DB with new items, how to use RAG with PDFs (or any other files), and how to test the quality of AI generated responses.

?? Learning Resources

?? NVIDIA: Building RAG Agents with LLMs (free course)

The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap. These models are not just tools for automation; they are partners in enhancing productivity, capable of holding informed conversations by interacting with a vast array of tools and documents.

If you find this Weekly Newsletter interesting you can subscribe here.

See you in the next one! ??

Stan