Digital Alpha Platforms

Successful hedge funds invest in three key components of quantamental platform - ☛ Proprietary datastore: Database built with curated data that focuses on relevant information in a convenient form ☛ Processing Engine: Massively parallel processing with distributed and cloud-native architecture purpose-built for high-performance computing (HPC) - up to 30-50x faster ☛ Analytics Engine: Interface built on an algorithmic approach to data analysis, making it easy to uncover actionable insights #hedgefunds #cloud #architecture #quantitativefinance #quantitativeresearch #quantamental

Developing a #chatbot that can tackle real questions and give appropriate, precise answers is really a hard job. While there has been remarkable progress in large language models, an open challenge is to couple these models with knowledge bases in order to deliver reliable and context-rich responses. The key issues almost always come down to hallucination (the model is creating wrong or non-existing information) and contextual understanding, where the model is unable to understand the nuanced relationships between different pieces of information. Others have tried to build robust Q&A systems without much success, since the models often return shabby answers, though they are connected to comprehensive knowledge bases. https://lnkd.in/eAz8rszW

Large Language Models (#LLMs) are powerful tools for natural language processing tasks, but they come with inherent limitations that make chunking necessary. Here’s why chunking is crucial: LLMs have token limits, and processing large texts in one go can be resource-intensive. Chunking allows for more efficient memory usage and faster computation. To make sense of the text, LLMs need context. Chunking keeps related pieces of text together, so the model understands better and responds more accurately. For big documents, chunking breaks them into smaller parts, making them easier to handle and analyze. https://lnkd.in/gKfdT_bs

What are #knowledge graphs? Knowledge Graphs are comprised of nodes and edges, which respectively represent entities or concepts, as well as the relationships, facts, attributes, or categories between them.This graph describes data in the form of nodes, as well as the associations (relationships) between the nodes. - Nodes are data records - Both nodes and relationships can have properties. - Nodes can be given labels to group them together. - Relationships always have a type and a direction. - A knowledge graph is a database that stores information in nodes and relationships. https://lnkd.in/gRiH9fpd

Retrieval Augmented Generation (#RAG) has emerged as a powerful architectural framework that combines the strengths of Large Language Models (LLMs) with vector databases to overcome the limitations of off-the-shelf LLMs. By leveraging external data sources, RAG systems have the potential to enhance search quality, include proprietary data, and provide more accurate and contextually relevant results. However, despite their promise, RAG systems are not without their challenges and limitations. In this article, we will explore the key limitations of RAG systems across the retrieval, augmentation, and generation phases, and discuss strategies to overcome these challenges for improved AI performance. https://lnkd.in/gB6_tvUg

The world of Generative AI (#GenAI) is evolving rapidly, making it easier and quicker than ever to develop AI-powered applications. In this article, we’ll discuss the GenAI Application Development Stack, a key to creating customized AI solutions. We’ll explore key components like LangChain, Gradio, and Vector Database. Through a practical, step-by-step guide, we’ll build a YouTubeGPT, showing how these technologies work together to create an AI application. By the end of this guide, you’ll not only understand how to use this technology stack for building AI applications but also gain insights into their internal workings. Additionally, we’ll build a functioning prototype of YouTubeGPT. This app will enable you to chat with any YouTube video or local video through a simple user interface. https://lnkd.in/gUMrM-dp

In the everyday life, an #agent is someone who act in a certain way, playing a role in the production of something. In the field of IT, it is a software who can execute certain tasks by its own, in order to answer to the need of a user or a third part software. An AI agent (or LLM, smart or intelligent agent) is an agent that can execute much more complex tasks by doing actions in an autonomous way to achieve pre-defined goal(s). This first article is part of a series of I’m writing about the AI agents. It focuses on the big picture and on the different kind of AI agent. https://lnkd.in/gBdvSbGk

#LLMs have demonstrated impressive capabilities in text generation but struggle with hallucinations — responses that are plausible-sounding yet factually incorrect, particularly when dealing with unfamiliar queries. This issue arises because LLMs tend to fabricate information when their training data lacks the necessary context. To address this, we explore strategies from two recent studies, combining their insights to propose a robust method for steering LLMs away from hallucinations. https://lnkd.in/grRqyxwm

#FineTuning is perhaps one of the most discussed technical aspects when it comes to Large Language Models. Most people understand that training these models is expensive and requires significant capital investment, so it is exciting to see that you can create a model that is somewhat unique by taking a pre-existing model and fine-tuning it with your own data. There are multiple methods to fine-tune a model, but one of the most consistently popular currently is the LoRA method (short for Low Rank Adaptation) discussed in the “LoRA: Low-Rank Adaptation of Large Language Models” paper. Before we dive into the mechanics behind LoRA, we need to understand some Matrix background and some of the basics of fine-tuning a machine learning model. https://lnkd.in/gnkWQYby

#RAGFlow offers open-source data cleansing models and chunking templates to improve data quality and will continue to iterate and evolve these built-in models and tools. Data retrieval: Scenarios with clear question intents require multiple recall to retrieve the relevant context, and the current RAGFlow integrates databases with multiple recall capabilities. Challenges in retrieving answers: In many cases, searching using the question content alone does not necessarily capture the context of the answer. There is clearly a gap to bridge between the semantics of the question and answer. We can approach the final point above from many aspects, including: Implementing an external knowledge graph for query rewriting and understanding of user intentions; improving answer quality by introducing agents, enabling the LLM to better its answers through increased dialogue interactions; retrieving longer contexts for the LLM to find the answer. https://lnkd.in/gWZs4aGS