SF Unstructured Data Meetup January 16 2024

🎥 Once a month, we’ll meet, socialize, and hear speakers present topics on unstructured data and generative AI.

Timeline:
0:54 – Speaker Christy Bergman, OpenAI RAG vs Custom Rag Workflow
30:54 – Speaker George Williams, NeurIPS Benchmarks 2021 and 2023
1:00:45 – Speaker Alexy Khrabrov, IBM, LinuxFoundation, and thealliance.ai

~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~

🎥 Playlist https://www.youtube.com/playlist?list=PLPg7_faNDlT7SC3HxWShxKT-t-u7uKr–
🖥️ Website: https://www.meetup.com/unstructured-data-bay-area/events/
X Twitter – https://twitter.com/milvusio
🔗 Linkedin: https://www.linkedin.com/company/zilliz
😺 GitHub: https://github.com/milvus-io/milvus
🦾 Invitation to join discord: https://discord.gg/FjCMmaJng6

~~~~~~~~~~~~~~ MEETUP VIDEO CONTENTS ~~~~~~~~~~~~~~

1. Host: Christy Bergman
Linkedin: https://www.linkedin.com/in/christybergman/

2. Speaker: Christy Bergman, Developer Advocate, Zilliz
Title: Vector Search in the Age of OpenAI Assistants using Milvus
Abstract: The OpenAI Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. I will talk about and show a few examples what you can and cannot do just invoking the OpenAI assistant directly to get final answers, versus using custom execution loops for Retrieval.
Slides: https://docs.google.com/presentation/d/1hpiaiVMHm4oQr5P86NhcrL0qXwBIdWhHEZOlWKySHyM/edit?usp=sharing
Demo code: https://github.com/milvus-io/bootcamp/blob/master/bootcamp/OpenAIAssistants/custom_RAG_workflow.ipynb

3. Speaker: George Wiliams
Title: Are CPUs Enough? A Review Of Vector Search Running On Novel Hardware
Abstract: The Cambrian explosion of artificial intelligence has ushered in a new Golden Age for computer hardware. While Nvidia steals the lion share of headlines due to their dominance running deep learning workloads, several other hardware alternatives are starting to appear in the market. AI-adjacent technologies like vector search will also reap the benefit from this hardware renaissance. In this talk, I’ll talk about the intersection of vector search and advanced hardware, hardware that goes far beyond traditional CPU design which has dominated the computing landscape for over 60 years. I’ll review the results from the first NeurIPS BigANN hardware competition, we’ll discuss the benchmarks that are key to differentiate hardware alternatives, and we will look at new chip architectures that will fuse vector search and transformer inference in one device. No prior knowledge about hardware is necessary for this talk. I will describe at a high level the fundamental differences among CPU alternatives such as GPUs, Vector Processors, Compute-In-Memory microprocessors, AI Accelerators, Neuromorphic Chips, and Field Programmable Arrays.
Slides: https://docs.google.com/presentation/d/12J5X7w0SWagC2wM6Dzd-sq54tAApuyftI1faToyaWpI
Linkedin: https://www.linkedin.com/in/george-williams-8130902/

4. Speaker: Alexy Khrabrov
Title: theAlliance.ai: Defining AI Adoption as a Community
Abstract: With more than 60 members, including Zilliz, the mission is to ensure and evaluate high-quality and safe AI through benchmarks, tools, and methodologies. The Alliance encompasses leading AI organizations, including HuggingFace, LangChain, LlamaIndex, top research universities, hardware giants such as Dell, Intel and AMD, foundations such as NumFOCUS and Linux Foundation, and more. Work streams cover AI Safety, cross-platform AI acceleration, models and frameworks, and applications for enterprise adoption.
Linkedin: https://www.linkedin.com/in/chiefscientist/

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