Introduction to AI News and Updates
Good morning, AI enthusiasts, this week’s issue dives into where the field is heading — beyond generation, toward autonomy and better error awareness. We’re starting with a breakdown of the increasingly fuzzy but important distinctions between Generative AI, Agentic AI, and AI Agents. Then we move into applied innovation: Microsoft’s GraphRAG, multimodal RAG systems using Cohere and Gemini, and a practical framework for predicting when your model is about to get something wrong.
Community Section
Featured Community Post
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AI Poll of the Week
Most of you are doing vibe checks, and of course, for general tasks, the entire idea is for the AI to not feel like AI. But would you also rely on “vibes” for more quantitative tasks, where output accuracy matters more than output feel? Share in the thread and let’s decide together.
Meme of the Week
Meme shared by rucha8062.
TAI Curated Section
Article of the Week
How GraphRAG Works Step-by-Step by Mariana Avelino. This blog explains Microsoft’s GraphRAG, a method that uses knowledge graphs for retrieval-augmented generation. Key detailed processes were graph creation, involving entity extraction, community partitioning, and querying, with distinct Local and Global Search functions.
Must-Read Articles
- Distill-then-Detect: A Practical Framework for Error-Aware Machine Learning by Shenggang Li. The author presented a framework to address prediction errors in machine learning models, particularly the “big misses” on critical data slices.
- Beyond Text: Building Multimodal RAG Systems with Cohere and Gemini by Sridhar Sampath. This article details a multimodal RAG system designed to overcome the limitation of traditional RAG systems by understanding both text and images within documents.
- Generative AI vs. Agentic AI vs. AI Agents: What Everyone Needs to Know by Poojan Vig. The article clarified the distinct roles of Generative AI, Agentic AI, and AI Agents, explaining how they can operate independently or collaboratively to perform complex tasks.
- DNNs vs Traditional Tree-Based Models for E-Commerce Ranking by Nikhilesh Pandey. The author discusses the evolution of e-commerce ad ranking systems, detailing the shift from traditional tree-based models to Deep Neural Networks (DNNs).
Conclusion
The field of AI is rapidly evolving, with advancements in areas such as Generative AI, Agentic AI, and AI Agents. With innovations like Microsoft’s GraphRAG and multimodal RAG systems, the potential for AI to improve various aspects of our lives is vast. Understanding the distinctions between these AI types and keeping up with the latest developments is crucial for those interested in the field.
FAQs
- Q: What is the main difference between Generative AI and Agentic AI?
- A: Generative AI produces new content based on learned patterns, while Agentic AI focuses on strategy, planning, and iteration towards a goal without continuous human intervention.
- Q: How do multimodal RAG systems improve over traditional RAG systems?
- A: Multimodal RAG systems can process both text and images, enhancing their ability to extract information from documents that include visuals like charts and tables.
- Q: What is the purpose of the "Distill-then-Detect" framework?
- A: The framework is designed to address prediction errors in machine learning models by distilling a compact model from a larger one and predicting where the larger model is likely to err.
