Discover a whole new take on Artificial Intelligence with Squirro's educational podcast! Join host Lauren Hawker Zafer, a top voice in Artificial Intelligence on LinkedIn, for insightful chats that unravel the fascinating world of tech innovation, use case exploration and AI knowledge. Dive into candid discussions with accomplished industry experts and established academics. With each episode, you'll expand your grasp of cutting-edge technologies and their incredible impact on society, and y ...
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Game up: AI models, GPT, and Cyberpunk 2077, with former AI teacher, Virgil Ilian, E22
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Manage episode 282780402 series 2624980
Content provided by Alexandra Petrus. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexandra Petrus or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
- 2:00 - Hottest AI trends for 2021
- 5:35 - Open source for AI - paradigm shift
- 11:30 - AI model supremacy
- 21:10 - Authorship rights when AI contributes
- 31:00 - GPT encapsulating knowledge?
- 34:00 - Human consciousness replicable as computation
- 41:50 - Are we in a matrix?
- 42:30 - Cyberpunk 2077
- 50:50 - Can AI create emotion the way we cannot tell it is AI?
Conversation references:
- Book: "You look like a thing and I love you" - Janelle Shane
- Book: "Shadows of the Mind", Roger Penrose
- Chinese Room argument
- Manhattan project
- Art Breeder project
- The Origin of Circuits - re FPGA topic
Host's notes:
- Gartner Top Strategic Technology Trends for 2021
- Jukebox - music-making tool by OpenAI. While the achievement is significant from a technological perspective, the results are unlikely to threaten the livelihoods of human musicians.
- DALL·E generates images in response to written inputs, and (whose name honours both Salvador Dalí and Pixar’s WALL·E) is a decoder-only transformer model. From Andrew Ng's 'The Batch' newsletter: OpenAI trained it on images with text captions taken from the internet. Given a sequence of tokens that represent a text and/or image, it predicts the next token. Then it predicts the next token given its previous prediction and all previous tokens. This allows DALL·E to generate images from a wide range of text prompts and to generate fanciful images that aren’t represented in its training data, such as “an armchair in the shape of an avocado.” WHY it matters? As Ilya Sutskever puts it ‘combining language and vision techniques could overcome computer vision’s need for large, well labeled datasets’.
33 episodes
MP3•Episode home
Manage episode 282780402 series 2624980
Content provided by Alexandra Petrus. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexandra Petrus or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
- 2:00 - Hottest AI trends for 2021
- 5:35 - Open source for AI - paradigm shift
- 11:30 - AI model supremacy
- 21:10 - Authorship rights when AI contributes
- 31:00 - GPT encapsulating knowledge?
- 34:00 - Human consciousness replicable as computation
- 41:50 - Are we in a matrix?
- 42:30 - Cyberpunk 2077
- 50:50 - Can AI create emotion the way we cannot tell it is AI?
Conversation references:
- Book: "You look like a thing and I love you" - Janelle Shane
- Book: "Shadows of the Mind", Roger Penrose
- Chinese Room argument
- Manhattan project
- Art Breeder project
- The Origin of Circuits - re FPGA topic
Host's notes:
- Gartner Top Strategic Technology Trends for 2021
- Jukebox - music-making tool by OpenAI. While the achievement is significant from a technological perspective, the results are unlikely to threaten the livelihoods of human musicians.
- DALL·E generates images in response to written inputs, and (whose name honours both Salvador Dalí and Pixar’s WALL·E) is a decoder-only transformer model. From Andrew Ng's 'The Batch' newsletter: OpenAI trained it on images with text captions taken from the internet. Given a sequence of tokens that represent a text and/or image, it predicts the next token. Then it predicts the next token given its previous prediction and all previous tokens. This allows DALL·E to generate images from a wide range of text prompts and to generate fanciful images that aren’t represented in its training data, such as “an armchair in the shape of an avocado.” WHY it matters? As Ilya Sutskever puts it ‘combining language and vision techniques could overcome computer vision’s need for large, well labeled datasets’.
33 episodes
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