VOX

Veritas Alta Copilot has been designed using the latest large language models to reduce the complexity of enterprise data management and is now available for users of Veritas Alta View. It can create sophisticated reports to monitor data protection infrastructure through simple conversation in natural language, quickly identify cyber vulnerabilities and operational inefficiencies, proactively assist with troubleshooting and guide users through complex data management tasks. Alta Copilot has been trained with Veritas best practices and product documentation. It empowers multi-tasking IT generalists to drive the level of optimal performance from their technology investment typically only achieved with management and configuration by highly specialized experts. Learn more about Veritas Alta Copilot: https://www.veritas.com/blogs/introducing-veritas-alta-copilot-simplifying-data-management-with-ai  ... View more

There’s a lot to sift through when starting your AI journey. Join me in unpacking the basics of Generative AI starting with an overview of Large Language Models (LLMs). Watch now: https://vrt.as/3IBOC9v  ... View more

As the AI hype continues to build, it's crucial to separate fact from fiction, especially when it comes to cybersecurity. Renowned technologist and thought leader, Sally Eaves recently joined me to dive deep into the evolving landscape of cyber resilience in the age of generative AI. Watch our conversation here: https://vrt.as/4bzFXBo Learn more about how Veritas helps you navigate your Generative AI journey: Navigate Cyber Resilience in the Age of Generative AI  ... View more

RAG explained in 60 seconds: https://youtu.be/bN9oGxZCIU8?si=1f9Ax3wbr3sFFyxq Over the past few weeks, a number of people have asked me about  Retrieval Augmented Generation (RAG). Here is a brief overview of RAG and how it enhances LLMs. TL;DR: RAG is a framework that upgrades Large Language Models (LLMs) by integrating real-time, enterprise-specific data. This approach keeps AI outputs up-to-date, relevant, and more aligned with business needs, addressing the typical knowledge cutoff of LLMs. Deep Dive: RAG represents a significant shift in LLM application, enabling these models to dynamically access and utilize updated external data. This method not only keeps the AI's responses current but also tailors them to specific business contexts. Key Advantages of RAG: 1. Addresses Knowledge Cutoff: RAG allows LLMs to access fresh data beyond their training set, ensuring timely and relevant outputs. 2. Customizable AI Responses: By connecting to specific enterprise databases or documents, RAG delivers insights that are directly applicable to the business. 3. Efficiency in Updating Models: It offers a practical alternative to frequent model retraining, saving time and resources. How RAG Works: RAG combines the generative power of LLMs with a retrieval function. This function searches connected data sources for information relevant to a given query, which the LLM then uses to generate informed and current responses. Applications in Business: Practical uses of RAG include enhancing customer service bots with the latest product data or equipping financial analysis tools with real-time market information, making AI systems more responsive and effective in a business setting. Image Source: Generative AI with Large Language Models (DeepLearning.AI) ... View more

Generative AI Fundamentals Part 5 - Fine-tuning: 60 second version: https://youtu.be/oEbOJiYIRxE?si=7u0PTFrWKEAiHgvH What is Fine-tuning? Fine-tuning involves tailoring a pre-trained model to perform specific tasks. While you could do unsupervised fine-tuning, in most cases it is a supervised learning process where you use a dataset of labeled examples with prompts and responses to update the weights of an LLM & make the model improve its ability for specific tasks such as translation, summarization, writing articles etc. Once a model has been fine-tuned, you won't need to provide as many examples in the prompt. This saves costs and enables lower-latency requests. How does Fine-tuning work? At a high level, fine-tuning involves the following steps: - Prepare and upload training data - Train a new fine-tuned model - Evaluate results and go back to step 1 if needed - Use your fine-tuned model Detailed explanation: https://youtu.be/7Qkzn6r5H1M?si=UNSbK-fFeCSlO6zd In the next video, we will dive into the hot topic of RAG or Retrieval-Augmented Generation. Thanks for tuning in ... View more