The Paradox of Simplicity in AI Architecture and Naming Challenges | 2min snip from "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Teaching AI to See: A Technical Deep-Dive on Vision Language Models with Will Hardman of Veratai

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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The Paradox of Simplicity in AI Architecture and Naming Challenges

This chapter explores the effectiveness of transformer architectures in AI, contrasting their simplicity with the complexities of human cognition. The conversation also humorously critiques the naming conventions of models, emphasizing their impact on visibility and market appeal.

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In this episode of The Cognitive Revolution, Nathan hosts Will Hardman, founder of AI advisory firm Veritai, for a comprehensive technical survey of vision language models (VLMs). We explore the evolution of VLMs from early vision transformers to state-of-the-art architectures like InternVL and Llama3V, examining key innovations and architectural decisions. Join us for an in-depth discussion covering multimodality in AI systems, evaluation frameworks, and practical applications with one of the field's leading experts.

Here's to the link to one of the most comprehensive reference documents for VLMs prepared by Will Hardman: https://dust-mailbox-c73.notion.site/Vision-Language-Models-11b675d75dd480af994cc474a754bb26

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CHAPTERS:

(00:00:00) Teaser

(00:00:55) About the Episode

(00:05:45) Introduction

(00:09:16) VLM Use Cases

(00:13:47) Vision Transformers (Part 1)

(00:17:48) Sponsors: Oracle Cloud Infrastructure (OCI)

(00:19:00) Vision Transformers (Part 2)

(00:24:58) OpenAI's CLIP Model

(00:33:44) DeepMind's Flamingo (Part 1)

(00:33:44) Sponsors: 80,000 Hours

(00:35:17) DeepMind's Flamingo (Part 2)