The author also highlights the consistent trend of algorithmic progress, despite perceived obstacles, and the potential for further improvements in the future. The article suggests that the pace of deep learning progress has been extraordinary, with models rapidly reaching or exceeding human-level performance in many domains. The author concludes by stating that if these AI systems could automate AI research itself, it would set in motion intense feedback loops, potentially leading to even more rapid advancements in the field.
Key takeaways:
- By 2027, it is highly plausible that AI models will be able to perform the work of an AI researcher or engineer, due to consistent trends in scaling up deep learning.
- The progress from GPT-2 to GPT-4 in the last four years has been significant, with models now being able to write code, solve complex math problems, and perform well on college exams.
- There are three main drivers of this progress: increased compute power, algorithmic efficiencies, and "unhobbling" gains, which involve fixing obvious ways in which models are limited.
- Despite claims of stagnation, the pace of deep learning progress has been extraordinary, with models rapidly reaching or exceeding human-level performance in many domains.