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Innehåll tillhandahållet av Michael Sharkey and Chris Sharkey. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Michael Sharkey and Chris Sharkey eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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EP36: ChatGPT Vision Road Tested, AutoGen Cheese Test & Anthropic's Break Through

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Manage episode 379640658 series 3446506
Innehåll tillhandahållet av Michael Sharkey and Chris Sharkey. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Michael Sharkey and Chris Sharkey eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Join the discord: https://discord.gg/bb6VZHks

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!

If you like this episode please consider subscribing and leaving a comment or review.

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

SOURCES:
=====
https://llava.hliu.cc/
https://www.wsj.com/tech/ai/ais-costly-buildup-could-make-early-products-a-hard-sell-bdd29b9f
https://techcrunch.com/2023/10/09/chatgpts-mobile-app-hit-record-4-58m-in-revenue-last-month-but-growth-is-slowing/
https://www.wired.com/story/generative-ai-chatgpt-is-coming-for-sales-jobs/
https://twitter.com/amasad/status/1712276988403294610?s=46
https://www.anthropic.com/index/decomposing-language-models-into-understandable-components
https://twitter.com/julesterpak/status/1711766882934534631?s=20

PAPERS:
=====
https://transformer-circuits.pub/2023/monosemantic-features/index.html
https://arxiv.org/pdf/2304.08485.pdf

  continue reading

86 episoder

Artwork
iconDela
 
Manage episode 379640658 series 3446506
Innehåll tillhandahållet av Michael Sharkey and Chris Sharkey. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Michael Sharkey and Chris Sharkey eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Join the discord: https://discord.gg/bb6VZHks

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!

If you like this episode please consider subscribing and leaving a comment or review.

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

SOURCES:
=====
https://llava.hliu.cc/
https://www.wsj.com/tech/ai/ais-costly-buildup-could-make-early-products-a-hard-sell-bdd29b9f
https://techcrunch.com/2023/10/09/chatgpts-mobile-app-hit-record-4-58m-in-revenue-last-month-but-growth-is-slowing/
https://www.wired.com/story/generative-ai-chatgpt-is-coming-for-sales-jobs/
https://twitter.com/amasad/status/1712276988403294610?s=46
https://www.anthropic.com/index/decomposing-language-models-into-understandable-components
https://twitter.com/julesterpak/status/1711766882934534631?s=20

PAPERS:
=====
https://transformer-circuits.pub/2023/monosemantic-features/index.html
https://arxiv.org/pdf/2304.08485.pdf

  continue reading

86 episoder

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