oreilly-agi

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# Artificial General Intelligence (AGI) Demystified


This repository contains code for live session and video for the [O'Reilly Course on Artificial General Intelligence (AGI) Demystified](https://www.oreilly.com/live-events/artificial-general-intelligence-agi-demystified/0642572174033)

This course offers an exploration of the current approaches toward Artificial General Intelligence (AGI), focusing on state-of-the-art reasoning models and agent architectures. Participants will learn about the evolution of AGI research, understand key benchmarks used to measure progress, and gain practical knowledge in working with advanced models like Claude 3.7, DeepSeek-R1, and OpenAI's o3. Through hands-on exercises and case studies, attendees will develop the skills needed to evaluate these models' capabilities, understand their limitations, and apply them effectively to complex tasks.

## Notebooks

- **Using Reasoning Models**

	- [An Introduction to Reasoning Models](notebooks/intro_to_reasoning_models.ipynb) - Using 6 reasoning models across 5 providers
	
	- [Short Term Memory with OpenAI Agents](notebooks/OpenAI%20Agents.ipynb) - OpenAI Agents doesn't implement short term memory by default, this code will add the concept to the agent

	- [Computer Use with reasoning models](notebooks/computer_use_reasoning.ipynb) - Letting a reasoning model control our laptop (caution advised when running this code)

	
- **Benchmarking**

	- [Benchmarking Reasoning Models](notebooks/benchmarking_reasoning_models.ipynb) - Running questions from [Humanity's Last Exam](https://huggingface.co/datasets/cais/hle/discussions) on reasoning models

	- [Benchmarking Llama 3.2 Instruct on MMLU and Embedders on MTEB](https://colab.research.google.com/drive/1zDCqXc7vHoZilHVe3y2lYyTmSUSe6bh3?usp=sharingb) 
	
	
		- [Follow-up Evaluating Llama 3.2 non-instruct on MMLU](https://colab.research.google.com/drive/1aMy19Ikyody9CGyn42K3E_DQwLScL0Ek?usp=sharing)

		- [Evalua