mLogica’s Post

I really enjoyed this 25 minute video discussing artificial intelligence. It gave me some new perspectives in a very grounded delivery. #artificial_intelligence #ai #general_artificial_intelligence https://lnkd.in/gQg9D3Wv

Successfully fine-tuned the LLaMA2 model on the mlabonne/guanaco-llama2-1k dataset, yielding impressive results. As an AI professional, I leveraged my expertise in model optimization to achieve this outcome. Below is the link to the model https://lnkd.in/e7P7CXz3 #AI #LLaMA2 #FineTuning #ModelOptimization #Achievement"

What is the true power behind AI's performance? Today's #AIQuoteoftheDay challenges common assumptions.    Peter Norvig offers a nuanced perspective on the interplay between data and algorithms. His insight highlights the critical importance of data quality in AI systems.    #AIXC #AIQuoteoftheDay #AI #PowerofData 

Trust is crucial, as data quality and integrity are essential for viable AI systems. In data science, trust is a precarious balance between reliance and skepticism. Thierry Kellerhals and Isabel Piljek share their insights on how to potentially define and cultivate trust in AI systems in our newest blog. Find out more here: https://lnkd.in/eE7ixB4T #DataScience #AI #KPMGBlog

A great principle in systems and strategies, the Knuth's optimization principle: Premature optimization is the root of all evil. #data #ai #dataproduct #datastrategy

What is the difference between fine-tuning an LLM and augmented generation? Discover the advantages of providing an LLM the answer via your data and systems vs. fine-tuning. #LLMs #AI #GenAI

In this Fed Gov Today interview, Farid Moussa, VP of Strategy at Babel Street, covers the importance of the ever-evolving landscape of #opensource intelligence, #AI filtering, collaboration between sectors and more. Gain valuable insights from the full interview here: https://ow.ly/9jtF30sEqtB

Combining Graph and Semantic Search in RAG: A New Frontier Traditional RAG and pure graph approaches are great but each have limitations: → RAG: Can miss additional context available in complex, interconnected documents → Graphs: Time-consuming entity extraction, rigid schemas RAG graphs offer the best of both worlds: → Combines similarity-based retrieval with graph traversal Why Is This Important: → Most importantly, it provides rich context for AI models → Gain access to previously unseen datasets → Ability to report on the semantic structure of the data prior to user queries ____________ Follow For More 👇 https://lnkd.in/e5idANnw #ai #genai #rag

Get a quick overview of vectors, vector search, and why #Couchbase is an ideal database platform for building AI-powered adaptive applications in this video 📲⚡️ https://bit.ly/3V3mjIe #AI #vectorsearch #GenAI #RAG

For anyone interested in organizational culture - this important session will go beyond the hype and hyperbole and address not just how AI will affect what we do and how we do it, but how we work together.