The Ludic Fallacy, and what it means for your data
By David Rolfe Some of you will be aware of the concept of a “Black Swan Event”. The term was coined by Nicholas Taleb, and refers to a rare event with significant consequences. Something so...
Tagged: LLMs
Nikesh Kotecha on Blueprint for Trust: How ChatEHR Establishes a Framework for Responsible AI in Clinical Care
Stanford Health Care, part of Stanford Medicine, was awarded a 2025 InterSystems Impact Award for its AXIOM initiative for fast access to electronic health records. In turn, AXIOM underpins the ChatEHR medical chatbot that Nikesh...
On AI Coding Assistants. Q&A with Ed Charbeneau
“Though AI speeds coding up, Devs still need to Master their craft” Q1. Progress is claiming up to 30% productivity gains with the expanded AI coding assistants across Telerik and Kendo UI libraries. From...
On Ontologies and AI. Q&A with Mattia Ferrini
Q1. You’ve spent two decades working on decision science systems, and ontologies play a critical role in both data management and AI. Can you discuss how you approach the generation and maintenance of ontologies...
On the 4 properties for the data layer of AI applications. Q&A with Andreas Kollegger
Q1. Emil Eifrem identifies ‘extracting structure from unstructured data‘ as a critical property for AI data layers. How does Neo4j’s approach to LLM-driven entity extraction and relationship discovery differ from traditional NLP pipelines? Can...
On Large Reasoning Models (LRMs). Q&A with Vishvesh Bhat
Moderated by Ramesh Chitor. Q1. What are the main limitations of Large Language Models (LLMs) when it comes to solving long-horizon or multi-step reasoning tasks?Can you share specific examples where LLMs tend to fail as...
On Vector Databases and Gen AI. Q&A with Frank Liu
Q1. From a database perspective, what are the main challenges that Generative AI (GenAI) poses? The biggest challenge that Gen AI poses is scale. We have so many sources of data – data used...
On Retrieval Augmented Generation. Q&A with Neil Kanungo
Q1. What is Retrieval Augmented Generation (RAG)? Retrieval Augmented Generation (RAG) is a method that combines the capabilities of generative models, like LLMs, with information retrieval techniques. In a RAG system, when presented with...
WEBINAR: Designing Retrieval Augmentation for Generative Pipelines with Haystack. Aug 10, 2023 09:00 AM Pacific, 12:00 PM Eastern
About this Session In this talk, Tuana Çelik from Deepset will share examples of retrieval augmentation in Haystack and how you might design your own pipelines for specific NLP use-cases. Let’s see how you...
AI: The tool to enhance society – from data analytics to consumer applications
By Madhukar Kumar Artificial Intelligence (AI) has been the topic of much conversation in recent months, and with good reason. AI is transforming everything we know, from how we write emails, plan trips and...