Tag: llm

AI, AI_factories, AI_research, Infrastructure

A comprehensive comparison between ‘compute’ and ‘inference’ in AI

Colin Henderson

An integrated analysis of technological, infrastructural, and operational factors including AI Factories, Data Centres, development practices, chips, and overall infrastructure. The following report—structured in APA format—presents a formal, detailed comparative overview, incorporating relevant business, academic, and technical sources.Source: Perplexity.ai Compute and Inference: Definitions and Context Compute generally refers to the computational resources required for both training and running AI models, whereas inference is the process by which a trained model makes predictions or decisions on new data. Compute is foundational for both the intensive process of AI model training and the comparatively lightweight process of model inference. AI inference utilizes … Continue reading A comprehensive comparison between ‘compute’ and ‘inference’ in AI

AI

The difference between using Retrieval Augmented Generation and Agents?

Colin Henderson

A thought provoking piece from Shahab Anbarjafari • Professor, Author, Public Speaker I have been discussing with a few CTOs and CIOs, all sharp, no-nonsense leaders. The question they tossed at me sounded simple: “Shahab, what’s the difference between using Retrieval Augmented Generation (hashtag#RAG) and using hashtag#Agents?” But I could sense their real curiosity—like they were trying to understand a new dimension of AI that could define the future of their organizations. In that moment, I felt like I was back at the dawn of deep learning, where we realized that neural networks were more than just pattern-matchers. They could … Continue reading The difference between using Retrieval Augmented Generation and Agents?