EP36: ChatGPT Vision Road Tested, AutoGen Cheese Test & Anthropic's Break Through
whatshot 17 snips
Oct 13, 2023
The podcast explores the capabilities of GPT-4 vision, including its ability to identify locations from photos. They discuss the use of multiple AI agents with AutoGen and Anthropic's research on neural superposition. The podcast also delves into Meta's lobotomized AI chatbots. They discuss the challenges of using GPT for image understanding and the cost of AI. They also explore the concept of augmented intelligence and its potential in coding. Additionally, they discuss the benefits and criticism of using different prompts and personas with GPT-4.
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question_answer ANECDOTE
GPT-4 Vision Road Test
Mike tested GPT-4 Vision by uploading a photo from his car.
It correctly identified his Tesla Model 3 and even his location in Newcastle, Australia.
insights INSIGHT
Augmented Intelligence
GPT-4 Vision's ability to interpret images and infer context is impressive.
It goes beyond object recognition by predicting potential outcomes.
question_answer ANECDOTE
Stolen Mail
Mike's mailbox keeps getting robbed despite a security camera.
He wants AI to alert him when mail theft occurs, not just motion.
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This mind-blowing episode explores the shocking capabilities of GPT-4 vision, including how it identified Mike's exact location just from a traffic photo. We dive into the cheese-filled insanity of using multiple AI agents together with AutoGen, and discuss Anthropic's groundbreaking research into neural superposition. Don't miss our dramatic exposé of Meta's new lobotomized AI chatbots - this episode takes you on a wild ride through the cutting edge of AI!
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CHAPTERS ===== 00:00 - Shakespearean AutoGen DRs 00:29 - ChatGPT Vision Road Tested, Augmented Intelligence & Comparison to LLaVA Vision 31:25 - The Cost of AI: Is There A Business Model With Margin? 36:52 - The Value of AI is in Productivity: Discussion of Business Models 43:19 - AutoGen Agents Cheese Test: Do Multi-Agents Perform Better? 58:09 - Anthropic's AI Break Through: Decomposing Language Models Into Understandable Components