Artificial General Intelligence (AGI) Demystified
Published by Pearson
Explore reasoning, multimodal AI, benchmarking, and agents
- AGI Evolution Demystified: Explore the historical context and current advancements in AGI research, including the development of reasoning models and agent systems.
- Benchmark Analysis: Learn to critically evaluate AGI benchmarks, understand their limitations, and identify where current models excel or struggle in reasoning tasks.
- Practical Implementation: Gain hands-on experience with cutting-edge reasoning models like Claude 3.7, DeepSeek-R1, and OpenAI's O1/4.5, with real-world applications and case studies.
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 GPT 4.5. 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.
As AI systems grow more sophisticated, understanding the frontier of AGI becomes crucial for professionals working with these technologies. This course bridges theoretical knowledge with practical application, providing insights into how today's most advanced models are pushing the boundaries of machine intelligence and how they can be leveraged for solving real-world problems.
What you’ll learn and how you can apply it
- Critically evaluate AGI benchmarks and understand which reasoning capabilities they effectively measure.
- Implement and configure advanced reasoning models and agents for complex problem-solving tasks.
- Design prompting strategies that maximize the reasoning capabilities of today's most sophisticated AI systems.
- Apply knowledge of model limitations to create more robust AI applications that combine reasoning and agency.
This live event is for you because...
- You're an AI Engineer or Developer looking to understand and implement cutting-edge reasoning models in your applications.
- You're a Data Scientist or Researcher interested in the frontier of AGI research and how to evaluate model capabilities beyond standard benchmarks.
- You're a Technical Product Manager needing to understand the realistic capabilities and limitations of advanced AI systems for product development.
Prerequisites
- Intermediate Understanding of Large Language Models: Familiarity with how LLMs work, basic prompt engineering concepts, and their general capabilities.
- Basic Python Programming Skills: Ability to read and modify Python code for implementing and testing AI models.
- General AI/ML Knowledge: Understanding of fundamental AI and machine learning concepts and terminology.
- Exposure to AI Applications: Some experience working with or developing applications that incorporate AI components.
Course Set-up
- Python Environment: Python 3.8+ installed, preferably through Anaconda.
- GitHub Repository: Course materials will be available in a GitHub repository that will be shared before the class. https://github.com/sinanuozdemir/oreilly-agi
- API Keys: While not required, having API access to systems like OpenAI, Anthropic, and DeepSeek will enhance the hands-on experience.
- Required Libraries: Instructions for installing necessary libraries will be provided in the GitHub repository.
Recommended Preparation
- Read: Introduction to Transformers for NLP by Shashank Jain
- Attend: Hands-on NLP with Transformers by Sinan Ozdemir
- Explore: Expert Playlist AI Unveiled by Sinan Ozdemir
Recommended Follow-up
- Read: Quick Start Guide to Large Language Models by Sinan Ozdemir
- Watch: Modern Automated AI Agents by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: The AGI Landscape: History and Context (30 min)
- Evolution of AGI research and key milestones
- Definitions and frameworks for measuring general intelligence
- Current state of reasoning models and agents
Segment 2: Benchmarks and Evaluation (30 min)
- Critical analysis of current AGI benchmarks
- Where benchmarks succeed and fail in measuring reasoning
- Practical approaches to evaluating model capabilities
- Exercise: Evaluating model responses on reasoning tasks
- Q/A + Break
Segment 3: Advanced Reasoning Models in Depth (60 min)
- Architecture and capabilities of models like Claude 3.7, DeepSeek-R1, and GPT 4.5
- Comparing reasoning strategies across different models
- Hands-on: A walkthrough of the training process for reasoning models
- Hands-on: Crafting prompts for reasoning tasks
- Exercise: Testing reasoning capabilities across models
Segment 4: Multimodal AI Capabilities (30 min)
- Evolution of multimodal systems: vision, audio, and beyond
- Benchmarks for evaluating multimodal understanding
- Practical applications of multimodal AI in AGI Development
- Hands-on: Testing different Multimodal AIs
- Q/A + Break
Segment 5: Practical Applications of Reasoning (45 min)
- Case studies: Real-world applications of reasoning models
- Implementing agent-based systems in LangGraph and a customized framework
- Hands-on: Designing a reasoning-based solution for computer use / operator
Segment 6: Limitations, Ethics, and Future Directions (25 min)
- Current limitations of reasoning models and agents
- Ethical considerations in deploying advanced AI systems
- Future research directions and developments
Segment 7: Conclusion and Next Steps (10 min)
- Key takeaways and practical implementation strategies
- Resources for continued learning and experimentation
- Final Q/A
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.