AI Papers by Henri Nguembi – Details, episodes & analysis

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AI Papers by Henri Nguembi

Claude Henri Nguembi

News

Frequency: 1 episode/10d. Total Eps: 3

We use Notebook LM to explain latest and important AI papers. Our two hosts explain complex matters in a simple and fun way.

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🇬🇧 Great Britain - techNews
26/07/2026
#100
🇬🇧 Great Britain - techNews
25/07/2026
#83
🇬🇧 Great Britain - techNews
24/07/2026
#66
🇬🇧 Great Britain - techNews
23/07/2026
#50
🇩🇪 Germany - techNews
10/06/2026
#96
🇩🇪 Germany - techNews
09/06/2026
#76
🇩🇪 Germany - techNews
08/06/2026
#63
🇩🇪 Germany - techNews
07/06/2026
#43
🇬🇧 Great Britain - techNews
14/12/2025
#95
🇬🇧 Great Britain - techNews
13/12/2025
#72

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https://transformer-circuits.pub/2025/attribution-graphs/biology.html
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Score global : 42%

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Introduction to Reinforcement Learning

Sunday, April 20, 2025 • Duration 21:47

In this episode we explore Reinforcement Learning, an AI framework used in systems such as ChatGPT. Reinforcement Learning, a subfield of Artificial Intelligence, is a method for machines to learn optimal decision-making through trial and error by receiving rewards or penalties for their actions. This beginner-friendly introduction covers fundamental aspects, such as basic terminology like agents, environments, and rewards, alongside core concepts like the Markov Decision Process. The text further explains the workflow of reinforcement learning, outlines its key characteristics including sequential decision-making and delayed feedback, and categorizes common algorithms and types like positive and negative reinforcement. Finally, it showcases practical applications of this technology across diverse fields, including robotics, autonomous vehicles, and game playing.

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Biology of a Large Language Model

Monday, March 31, 2025 • Duration 26:47

In this first episode we dive into this paper from AnthropicAI called Biology of a Large Langage Model where the autors present a detailed investigation into the inner workings of the large language model Claude 3.5 Haiku, employing a methodology centered around attribution graphs to understand how it processes information and generates responses. Through various case studies, the authors explore phenomena such as multi-step reasoning, planning in poetry generation, and multilingual understanding, uncovering specific circuit components and their functions. The research also examines the model's ability to handle harmful requests, its tendencies toward hallucination, and the faithfulness of its chain-of-thought reasoning. Ultimately, this work aims to reverse engineer the mechanisms within advanced language models to improve our understanding and assess their capabilities, while also acknowledging the limitations of current interpretability methods.

Here is the full paper:

https://transformer-circuits.pub/2025/attribution-graphs/biology.html

AI Papers by Henri Nguembi – Details, episodes & analysis

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AI Papers by Henri Nguembi – Details, episodes & analysis

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