o3 Model by OpenAI TESTED ($1800+ per task)
AI Summary
Video Summary
O3 Model Performance and Limitations
- The new O3 model from OpenAI has been released for early access and safety testing.
- Examples are provided to illustrate where O3 fails, even in high compute mode.
- O3 struggles with tasks that increase in complexity, such as pattern recognition with additional layers or objects.
Performance Data Analysis
- Performance data shows O3 significantly outperforms the 01 model, with 76% accuracy in low compute mode and 88% in high compute mode, compared to 01’s 25-32%.
- The cost per task for O3 is high, with speculation about future pricing models.
Evaluation Data Set and Testing
- A semi-private RC AGI evaluation data set with 100 tasks is used to assess O3’s generalization capabilities.
- The data set is designed to test AI on novel, unseen problems and is kept semi-private to avoid contamination with training data.
- Performance data is available on GitHub, updated by OpenAI.
Technical Insights
- O3 appears to use a natural language program search and execution within the token space, similar to AlphaZero’s Monte Carlo tree search.
- This search is guided by an internal evaluator AI model.
- O3’s inference time is lengthy, suggesting complex computations are taking place.
Cost Implications
- The cost per task in low compute mode is around 1,800 per task.
- These costs reflect the computational resources required for O3 to perform its tasks.
Theoretical Considerations
- O3’s performance suggests that pre-training, fine-tuning, and alignment are not sufficient for high performance on novel tasks.
- Test time adaptation is crucial for dealing with unseen tasks, indicating a shift in AI optimization techniques.
Future Directions and Challenges
- The upcoming R Arc AGI 2 Benchmark, set for 2025, aims to challenge O3 further and focus on its limitations.
- O3’s dependence on high-quality training data and its limitations in out-of-distribution tasks are highlighted.
Conclusion
- O3 represents a significant step in AI’s ability to adapt to arbitrary tasks, but it is not yet AGI.
- The quality of training data remains a critical factor for the performance of large language models.
- Test time compute and adaptation are emerging as key areas for AI performance improvements.
Comments and Engagement
- The video encourages viewers to share their thoughts and engage in the comments section for further discussion.