LLMs and PKM
By Ruthvik Peddawandla
About this collection
Exploring PKMs in the age of AI
Curated Sources
Augmenting Human Intellect: A Conceptual Framework - 1962 (AUGMENT,3906,) - Doug Engelbart Institute
Douglas C. Engelbart's 1962 report 'Augmenting Human Intellect: A Conceptual Framework' explores the concept of enhancing human problem-solving capabilities through the integration of computers and other technologies. The document outlines a systematic approach to improving human intellectual effectiveness by developing new techniques, procedures, and systems. It discusses the H-LAM/T system, comprising a human, language, artifacts, methodology, and training, and identifies four basic classes of augmentation means: artifacts, language, methodology, and training. The report also delves into the concept of process hierarchies and the role of computers in augmenting human intellect, including their ability to manipulate symbols, store information, and display data. Engelbart envisions a future where humans work in close cooperation with computers, leveraging their respective strengths to achieve complex problem-solving. The document concludes by highlighting the potential for significant gains in intellectual effectiveness through the development of improved augmentation means.
Key Takeaways
- The H-LAM/T system represents a synergistic integration of human capabilities and augmentation means, including computers, to enhance intellectual effectiveness.
- The development of automated external symbol manipulation means represents a significant step in the evolution of human intellectual capability, enabling faster and more flexible processing of complex information.
- The use of computers in real-time working association with humans can significantly improve problem-solving capabilities by providing fast and flexible symbol manipulation and display.
- The concept of process structuring and the ability to design and build processes is crucial to the development of effective augmentation systems, allowing humans to capitalize on the strengths of both humans and computers.
- The potential for team cooperation and collaboration is greatly enhanced through the use of computer-based augmentation systems, enabling multiple individuals to work together on complex problems in a highly effective and flexible manner.
Improving Recommendation Systems & Search in the Age of LLMs
This document discusses the evolution of industrial search and recommendation systems in the context of large language models (LLMs). It covers various model architectures, data generation techniques, training paradigms, and unified frameworks that have emerged over the past year. The document highlights how LLMs are being used to augment traditional recommendation systems, improve data quality, and enable cross-domain recommendations. It also explores the application of scaling laws, transfer learning, distillation, and parameter-efficient fine-tuning techniques in recommender systems. Furthermore, the document presents unified architectures that blend search and recommendations, drawing inspiration from foundation models.
Key Takeaways
- LLMs are being increasingly used to improve recommendation systems by augmenting traditional ID-based approaches with multimodal content understanding and behavioral modeling.
- Unified architectures that combine search and recommendations are emerging, enabling shared infrastructure and improved performance across multiple tasks.
- Techniques like transfer learning, distillation, and parameter-efficient fine-tuning are being adopted from LLMs and computer vision to improve recommender systems, including cross-domain recommendations and handling long-tail items.
Voyage AI | Home
Voyage AI offers advanced embedding models and rerankers to enhance search and retrieval for unstructured data, improving RAG retrieval and response quality. Their technology leverages cutting-edge AI research and engineering to provide high accuracy, low dimensionality, low latency, and cost efficiency. The company provides a spectrum of models, including general-purpose, domain-specific, and company-specific models, catering to various industries and use cases. Voyage AI's solutions are deployable through their API, cloud providers, or custom deployments, making it versatile for different organizational needs. The technology is trusted by industry leaders and emphasizes privacy and compliance with standards like SOC 2 and HIPAA.
Key Takeaways
- The use of domain-specific and company-specific embedding models can significantly enhance the relevance and accuracy of search results in specialized industries or organizations.
- Voyage AI's technology achieves a balance between high accuracy and cost efficiency, making it a potentially valuable solution for organizations dealing with large volumes of unstructured data.
- The modularity and deployability of Voyage AI's solutions across different vectorDBs and LLMs, along with various deployment options, suggest a high degree of flexibility and adaptability to different technological ecosystems.