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Inside Orca 2: Microsoft's Small Language Model that Outperforms Models 10x Larger in Reasoning Capabilities
The model innovating in the training procedures to improve reasoning abilities in small language models.
Edge 361: LLM Reasoning with Graph of Thoughts
Not chains or trees but graph structures for LLM reasoning.
Edge 357: Understanding Chain-of-Thought Prompting
A deep dive into the most popular LLM reasoning technique.
Edge 359: Understanding Tree-Of-Thoughts in LLM Reasoning
A variation of chain-of-thought for evaluating different reasoning paths.
Edge 355: A Taxonomy to Understand LLM Reasoning Methods
Not all LLM reasoning methods are created equal. Here are the main categories to understand the different types of LLM reasoning techniques.
Edge 360: Meet Ghostbuster: An AI Technique for Detecting LLM-Generated Content
Created by Berkeley University, the new method uses a probability distribution method to detect the likelihood of AI-generated tokens within a document.
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Edge 358: Inside AGENTS: An Open Source Framework for Autonomous Language Agents
The framework includes the core building blocks to enable autonomous agents based applications.
The LLama Effect: How an Accidental Leak Sparked a Series of Impressive Open Source Alternatives to ChatGPT
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial…
Edge 264: Inside Muse: Google’s New Text-to-Image Super Model
The new generative AI model shows significant efficiency improvements over models like Stable Diffusion, Imagen and Parti.
Inside FunSearch: Google DeepMind’s LLM that Discovered New Math and Computer Science Algorithms
Discovering new science is one of the ultimate frontiers for AI.
Edge 256: The Architecture and Methods Powering ChatGPT
An overview of the AI techniques behind OpenAI's new supermodel
📢 Event: ML practitioners from Affirm, Block, Remitly, Tide & more share their learnings from building risk & fraud detection systems
Want to connect with the ML engineering community and learn best practices from ML practitioners on how to build risk and fraud detection systems?
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