Gradient Flow presents a rich array of high quality content on data, technology and business, with a focus on machine learning and AI. Named by Coursera as one of the Top 10 Sites for Data Scientists, Gradient Flow helps you stay ahead on the latest technology trends and tools with in-depth coverage, analysis and insights.
Learn the latest trends and best practices in data, technology and business, with a focus on machine learning and AI.
I think Paco makes a very interesting and crucial point at the 30:35 mark. These LLM-powered graph builders are creating graphs from unstructured data, but how much domain knowledge do they possess to build truly sensible and accurate graphs in specific areas? For example, if I work with medical record systems and want to enhance them with data from medical guidelines, how confident can I be that the LLM understands the proper relationships between diseases, symptoms, and medical encodings like ICD-10 to generate a sensible and accurate graph? I know that some SciSpacy models have been trained on biomedical data and could theoretically do a better job of extracting relevant medical entities and relationships. How can this be incorporated into current GraphRAG workflows? I was hoping Paco would discuss this more and possibly explain ways to improve the resultant knowledge graph, either using existing approaches (Microsoft GraphRAG or Neo4J Graph Builder) or other alternative methods.
That's a fantastic question from Ben: We can accomplish everything you've outlined using a straightforward software class, without the need for an agent.
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I hope : in oxide or somebody else, ansure all compute, network and storage get its own purposed very specific functions , maybe integrated FPGA alike ??
Really enjoy listening when Dmitriy explains how A.I. is being applied at Ginkgo. He breaks it down into bite-sized pieces that I am able to understand.
"Nothing is better than showing". I could not agree more! I worked two months on an MVP just to be able to show my target audience what I wanted to sell them. I found out someone was already doing what my app did but better. Oh well.
Very good conversation. Like every other technology, AI requires efforts to make it work. One can’t simply assume that it is like flicking a switch. This realisation is important for us to begin to realise the value at scale. Else there will be disappointment.
I really like that Sudhir showed open-minded opinion about the market trends, not just pitching Neo4j - where in fact Neo4j can be very important player for this wave on AI revolution because of mixing graphs and VBD capabilities.
Would love more podcasts on navigating, defending against, and combatting next generation identity theft, social engineering, misinformation/disinformation, etc. That’s honestly what keeps me up most at night regarding these rapidly developing technologies.
Bought the book after just a few minutes, in the chat about statistics. Just great! I wish Chris had been my prof at uni! Thank you Ben. Adding real value.
It's fascinating to see how tools like Dagster and Ray are being used to solve infrastructure issues and optimize workloads. Technology solutions were critical.😍
