Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7292384944075522048
How To Advance Beyond STATIC GenAI-LLMs-RAG to DYNAMIC Quantum-GenAI: Packt Invited Editorial Book Review: Unlocking Data with Generative AI and RAG: https://lnkd.in/gFGvjcGD:
https://lnkd.in/eXmSXGkY : https://lnkd.in/e_AYYdya:
Google AI Podcast: https://lnkd.in/gQhAq_ZZ:
Inspired by #AI-#GA pioneer, we published first #AI paper on #DataIsProfoundlyDumb pioneering #MeaningAwareHumanCenteredAI in 2000, followed by first paper on #RealTimeEnterprise #Models in 2005. In context of #AI-#GenAI, #RAG is #Augmentation of Data-driven Decision-Making by using #RealTime Data #Generation & #Retrieval of #RightData given specific Chosen #Context, beyond #BigData and #SmallData.
You will find the specific Hands-On Applied Workbook on RAG useful in developing applied intuitive understanding for real-world practical #AI-#GenAI applications. As a comprehensive guide to harness the power of RAG, I find this book a crucial resource for practitioners, researchers, and academics eager to implement RAG effectively… It provides several complete code labs including one that walks through the implementation of a RAG pipeline using Python, LangChain, and Chroma. It also goes into more advanced techniques like prompt engineering, query expansion, and multi-modal RAG (MM-RAG). The author also discusses the importance of evaluating RAG pipelines, and provides guidance on how to do so effectively. This is a practical, hands-on guide that will be valuable to anyone looking to build RAG applications. It emphasizes real-world coding examples, case studies, and strategies for effective implementation. The book also includes information on setting up a development environment with Jupyter notebooks, and using tools like #LangChain, #Chroma DB, and #OpenAI’s APIs…
RAG enables AI systems to go beyond their original training data by incorporating real-time information from selected sources, thus improving the contextual relevance and accuracy of their outputs. This process is crucial for making effective decisions in #dynamic #environments that are the focus of our 30-year R&D leading #Dynamic and #Adversarial #Uncertainty, #Time-#Space #Complexity and #RiskManagement Practices recognized for global impact among #ArtificialIntelligence and #Quant-#Finance Nobel laureates.
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30-Years Building Quantum Minds for Quantum Uncertainty: Decades Ahead of ‘Backward’ GenAI-LLMs-RLHF