The author also discusses the role of compute, unsupervised learning, scaling laws, and the rise of the product cycle in the development of AGI. They argue that the current progress in AI is primarily driven by larger compute budgets and scale. The author also suggests that the use of model-generated data could be a significant factor in future AI development. Despite acknowledging several potential obstacles or "bears" to AGI development, such as data provenance, overhangs, scaling difficulties, and the need for physically embodied data, the author maintains that the progress towards AGI is likely to continue.
Key takeaways:
- The author believes that the development of Artificial General Intelligence (AGI) is progressing faster than previously anticipated, primarily due to the scaling of AI models.
- Two hypotheses are presented: one suggesting that scaling is sufficient for AGI, and the other arguing that current scaling methods are not the right approach and that new ideas are needed.
- The author suggests that the use of "self-play" or synthetic data could be a key factor in the continued scaling of AI models, allowing them to generate and learn from their own data.
- Despite various potential obstacles or "bears" such as data provenance, overhangs, the difficulty of scaling, and the need for physical embodiment, the author maintains that the progress towards AGI is likely to continue at a rapid pace.