Listen "Ai Pin Device 💻 // OpenAI's Spider Problem 🕷️ // Adapting Transformer to Vision 🧠"
Episode Synopsis
The Ai Pin, a new device that offloads smartphone tasks, is discussed, funded by OpenAI's Sam Altman and other companies.
A Twitter thread about OpenAI's spider problem is shared, raising questions about the consequences of AI technology.
The paper "Adapting LLaMA Decoder to Vision Transformer" explores adapting decoder-only Transformers to computer vision, resulting in the creation of iLLaMA.
The paper "Exploring Concept Depth" studies how large language models acquire knowledge at different depths, with implications for understanding learning processes and designing models.
Contact: [email protected]
Timestamps:
00:34 Introduction
01:33 This Artificially Intelligent Pin Wants to Free You From Your Phone
03:32 Anyone got a contact at OpenAI. They have a spider problem.
04:47 STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking
06:28 Fake sponsor
08:23 Adapting LLaMA Decoder to Vision Transformer
10:11 RULER: What's the Real Context Size of Your Long-Context Language Models?
12:05 Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
13:42 Outro
A Twitter thread about OpenAI's spider problem is shared, raising questions about the consequences of AI technology.
The paper "Adapting LLaMA Decoder to Vision Transformer" explores adapting decoder-only Transformers to computer vision, resulting in the creation of iLLaMA.
The paper "Exploring Concept Depth" studies how large language models acquire knowledge at different depths, with implications for understanding learning processes and designing models.
Contact: [email protected]
Timestamps:
00:34 Introduction
01:33 This Artificially Intelligent Pin Wants to Free You From Your Phone
03:32 Anyone got a contact at OpenAI. They have a spider problem.
04:47 STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking
06:28 Fake sponsor
08:23 Adapting LLaMA Decoder to Vision Transformer
10:11 RULER: What's the Real Context Size of Your Long-Context Language Models?
12:05 Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
13:42 Outro
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