#186 Introduction to GenAI RAG
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on 2024-02-15 08:00:00 +0000
with Eduardo Alverez, Darren W Pulsipher,
In a rapidly evolving digital sphere, generative Artificial Intelligence (GenAI) is capturing the attention of technophiles across the globe. Regarded as the future of AI technology, GenAI is broadening boundaries with its potential for accurate simulations and data modeling. A prominent figure in this arena, Eduardo Alveraz, an AI Solution Architect at Intel and former geophysicist, holds invaluable insights into this fascinating world of GenAI.
Keywords
#generativeai #geophysics #machinelearning #datasecurity #dataanalysis #dataprivacy #knowledgesharing #cloud #cybersecurity
An Intersection of Geophysics and AI
Eduardo’s journey from geophysics to artificial intelligence provides an exciting backdrop to the emergence of GenAI. As he transitioned from a hands-on role in the field to an office-based role interpreting geophysics data, Eduardo was introduced to the ever-intriguing world of machine learning and AI. His first-hand experience collecting and processing data played a pivotal role as he explored the tech-saturated realm of AI. This journey underscores how disciplines often perceived as separate can contribute significantly to the development and application of AI technology.
Bridging the Gap between Data Scientists and Users
Generative AI presents several promising benefits, a key being its potential to act as the bridge between data scientists and end-users. In traditional setups, a significant gap often exists between data scientists who process and analyze data and the users who leverage the results of these actions. GenAI attempts to close this gap by providing more refined and user-friendly solutions. However, it’s crucial to acknowledge that GenAI, like any technology, has limitations. The thought of storing sensitive data on public cloud platforms is indeed a daunting prospect for many businesses.
Enhancing Interaction with Proprietary Data
Despite concerns around data security, mechanisms exist to securely enhance models’ interaction with private or institutional data. For instance, businesses can train their models on proprietary data. Still, this approach raises questions about resource allocation and costs. These interactions emphasize the significance of selectively augmenting data access to improve results while maintaining data security.
The Exciting Potential of GenAI
The conversations around GenAI hold promise for the future of AI. This period of rapid advancement brings countless opportunities for innovation, growth, and transformation. As more industries adopt this revolutionary technology, it’s clear that Generative AI empowers the world by sculpting the landscape of artificial intelligence and machine learning. This exploration instigates a more profound interest in GenAI and its potential possibilities. Our journey into the AI landscape continues as we unravel the mysteries of this exciting technological frontier.
Extending GenAI with Retrieval Augmented Generation (RAG)
GenAI has some limitations that include data privacy, long training times, and accuracy of results. This is because large language models require extensive data for training. Context becomes crucial, particularly in language processing, where a single word can have multiple meanings. RAG architectures help in augmenting user prompts with context from a vector database, which reduces the training time, enhances data privacy, and limits the wide out-of-the-box context of LLMs.