Sid Sheth, CEO of d-matrix, details their memory-centric approach to AI inference hardware, focusing on their Digital In-Memory Compute (DIMC) architecture. He explains how DIMC, an augmented SRAM technology, minimizes data movement to solve the memory bottleneck, delivering significant gains in latency and energy efficiency, particularly for the 'decode' phase of large language models.
Uma Ramadoss from AWS explains the core concepts of Agentic AI, differentiating it from standard AI workflows. The session covers how to build agentic systems on AWS using services like Bedrock and Step Functions, and open-source frameworks like Strands SDK, emphasizing practical architecture, context enrichment, and the importance of verification.
David Jayatillake, VP of AI at Cube.dev, discusses the critical role of a headless, open-source semantic layer in the modern data stack. He argues against proprietary, BI-tool-specific semantic layers that create vendor lock-in and advocates for a decoupled approach. The conversation explores how AI agents can automate the entire data pipeline—from ingestion and transformation to generating and querying the semantic layer—and compares the functionalities of semantic layers and feature stores, highlighting the crucial difference of temporality.
Explore the shift from reactive to predictive DevSecOps with Akshay Mittal. This discussion covers how AI-Augmented DevSecOps and Agentic Workflows are creating self-healing systems, the critical role of Explainable AI (XAI), and a four-layer architecture for building scalable, enterprise-grade AI solutions.
A guide to understanding the differences between Large Language Models (LLMs), Small Language Models (SLMs), and Frontier Models (FMs). Learn the unique strengths of each model type and see practical use cases for document classification, customer support, and incident response to help you choose the right model for your AI project.
![Fully Connected Tokyo: [Hands-on workshop] Automation of document workflows in financial industry](https://img.youtube.com/vi/3VJZhKEG4ik/maxresdefault.jpg)
This workshop by Upstage demonstrates how to automate financial document workflows using a combination of their specialized Document AI (Document Parse) and Large Language Models (LLMs). The session covers building robust information extraction pipelines, addressing challenges like varied templates and data formatting, and implementing systematic evaluation using Weights & Biases Weave. It also presents real-world case studies from the insurance industry, showcasing significant improvements in efficiency and data utilization.