CyberCube’s Post

In praise of RDF RDF is a data model, a knowledge representation system, and a data exchange format. It's used to build knowledge graphs and it's a web standard. RDF can even play an increasingly important role in the era of LLM-based applications. This post by Semih Salihoğlu describes RDF, its virtues, vices, history, and applications. It aims to clarify what RDF is, when you need it, and why it's a fundamental data model to know about. It also discusses some fascinating topics in AI that intersect with databases: logic, reasoning, and knowledge representation systems. #KnowledgeGraph #DataModeling #AI #DataScience #GraphDB https://lnkd.in/db7T4NUE

#Knowledgegraphs significantly enhance #AI strategies by providing a foundational understanding necessary for reasoned judgments, training, and validation. They enable AI systems to incorporate essential knowledge into their operations, thereby improving conversational abilities and statistical insights. This integration of organizational intellectual property into AI strategies enhances market differentiators and competitive edge.

LLM Analysis and Evaluation of LangChain and OpenAI RAG using Arize-Phoenix https://lnkd.in/gHTfhVsg #LLMOps #Monitoring #Evaluation #LLM #GenAI #Performance #Metrics #Phoenix #OpenSource #Observability #Analysis #AI #MachineLearning #DataScience #Knowledge LangChain Arize AI OpenAI Towards AI

Came across this interesting article by Vahe Andonians that discussed the possibilities of a superior hybrid intelligence, built by harnessing the powers of Knowledge graphs and Large Language models. I've been reading about the extent of impact RAG models and LLMs have on Data Analytics, and this article explains how all of these methods fit together and work in synergy. It's an interesting read going in-depth with explanations and examples from the world of finance, but here are a bunch of visual notes I made from it- . . . . . . . #knowledgegraphs #llms #Dataanalytics #largelanguagemodels #hybridintelligence #ArtificialIntelligence #ai

Going Beyond the LLM Context Window Limitation: How Alani AI Outperforms Claude 3.5 Sonnet Outright for Research https://lnkd.in/eGb3u2d4

There is a massive amount of unmined data in the world. I’d estimate that less than 1 basis point (0.01%) of all available data has been thoroughly mined. Large Language Model AI #LLM will change this. Within the next decade, most data will have been touched and as much as 10% of historical data will have been thoroughly and exhaustively mined at least in re the known challenges and opportunities of today. Large Action Model AI #LAM has barely begun. While LLM produces actionable insights, LAM will act upon those insights! Cobus Greyling does a great job of explaining the difference between LLM and LAM #artificialintelligence in this article. Worth the 5 minute read. https://lnkd.in/eSVzANVY

#Upstage_News 🤝 Upstage is pleased to announce a partnership with 플리토 to develop an AI language data infrastructure. This collaboration will enable us to build multilingual datasets and run multilingual LLM benchmarks, empowering further innovation in generative AI. 👉 Read More: https://lnkd.in/gCaihtvB #Upstage #UpstageAI #Flitto #LLM #AI #GenAI #Data #SolarLLM #SolarMini 

Vector-enabled databases are an emerging class of databases that are designed to store and process vectors. 🔢 Why the rush to embrace vectors? The answer lies in the fact that when vectors are stored in a database, it opens up a host of usecase scenarios for generative AI, from augmenting large language models (LLMs) to providing semantic search. This NEW report discusses vectors and specifically vector databases and the needs they fill, as well as the drivers, market direction, financial forecast and trends driving vector adoption. Plus #SingleStore is named a key vector database provider! 📖 Download and read it: https://bit.ly/3xQv1QM #vectors #analytics #database #tech #AI #LLMs

Certainly it is necessary, in every single instance, to add the epithet 'so-called' to any use of 'AI.' However, in making out like AI is now a thing, promoters, and uncritical reproducers of promoters (i.e., investors and their Big Consultancies), are causing a bunch inflated expectations to aggregate around 'AI,' something that will ensure that it collapses under all that weight. Being more nuanced about all the different things that are in and around what is getting called AI might just allow some these technologies and their 'profitable' use cases to proceed. So I am wondering whether there might not be an argument for a cynically tactical accelerationism that heaps unsustainable value anticipation onto the AI thing...?

AI and the Future - The essays contained in this post are some of the most informative and thoughtful I've read. Written by Leopold Aschenbrenner. The question is - will we be prepared for AI's potential to do grave harm or as usual will we be playing catch up with trying to address the dangerous geopolitical and closer-to-home impacts which are going to result from AI? AI is the industrial revolution of the 1900s on steroids and then some. https://lnkd.in/eRQH7RXt