Intellectual Wanderings

RAG Time

The world of language models is abuzz with the latest trend: Retrieval-Augmented Generation (RAG). But what exactly is it, and why is it generating such excitement? In a nutshell, RAG models take the traditional approach of language models to a new level by consulting external knowledge sources before generating text. This allows them to produce more factually accurate and nuanced responses, even for complex or open-ended questions and to cite their sources.

Think of it like this: imagine a student preparing for a test. They can memorize facts (like traditional language models), but a truly insightful answer might require looking things up in a textbook or online (like RAG models do). This ability to access and integrate external information is what makes RAG models stand out.

One company leading the charge in this field is Perplexity, a startup boasting a staggering 10 million monthly users. Their RAG-powered platform allows users to ask open ended questions and receive informative, well-researched responses. Whether you’re curious about the latest scientific discoveries or seeking historical insights, Perplexity aims to provide answers that go beyond simple factoids.

But why is RAG causing such a stir? Here are some key reasons:

• Improved Factual Accuracy: By referencing external sources, RAG models can avoid the pitfalls of traditional language models, which can sometimes generate factually incorrect or misleading information.
• Enhanced Creativity: Access to a wider range of information allows RAG models to explore more creative and nuanced responses, making them more engaging and informative.
• Greater Adaptability: The ability to learn from new information sources makes RAG models more adaptable to different contexts and situations, paving the way for personalized and dynamic interactions.

The future of language models is undoubtedly shaped by advancements like RAG. As these models continue to learn and evolve, we can expect even more exciting developments that bridge the gap between human and machine intelligence. So, stay tuned, because the way we interact with language and information is about to undergo a fascinating transformation!

Further Reading:

• Perplexity: [https://perplexity.ai/]

Interesting article mostly about sharing and visualizing ML processes.

Article:

 python.plainenglish.io/5-python-libraries-every-data-scientist-should-know-about-ce04bf19d58d 

This is an article about 5 Python libraries that data scientists should know. It discusses what libraries are useful after a data scientist has chosen a machine learning library and mastered choosing the right architecture for their model. The article lists and explains 5 libraries: MLflow, Streamlit, FastAPI, XGBoost, and ELI5. Each library is introduced with a brief explanation of its purpose and benefits. The author concludes by listing the advantages of knowing these libraries. Some of the important points from this article are that these libraries can make a data scientist more competitive, help them build full-stack projects, and make their models more interpretable.

There is a new edition of the “classic” text for reinforcement learning and it is freely available.

http://www.incompleteideas.net/book/RLbook2020trimmed.pdF