John Schulman (OpenAI Cofounder) - Reasoning, RLHF, & Plan for 2027 AGI
May 15, 2024
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Join John Schulman, OpenAI co-founder and ChatGPT architect, as he dives deep into the future of AI. He discusses how post-training enhances model capabilities and the roadmap to achieving AGI by 2027. Schulman highlights the importance of reasoning in AI, the evolution of language models, and the delicate balance between human oversight and automation. He also shares insights on the role of memory in AI systems and how new training methods can reshape interactions, making AI assistants more proactive and effective.
Post-training refines AI behavior for specific tasks like chat assistance, enhancing versatility and content generation.
AI models progressing to handle complex tasks over multiple files, improving task efficiency and error recovery.
Caution and planning crucial for safe deployment of AGI, requiring collaboration on guidelines and regulations in AI research.
Advancements in data quality and model scaling boosting AI capabilities, prompting rapid evolution in AI systems.
Deep dives
The Shift Towards AI-Driven Coding Projects
In the near future, models could potentially carry out whole coding projects, transitioning from being search engines to collaborative project partners. While AI models may possess the ability to successfully manage businesses, caution is advised against immediate adoption for running entire firms. The progression towards artificial general intelligence (AGI) raises questions about future strategies and the implications of its arrival. John Schulman, co-founder of OpenAI, highlights the distinctions between pre-training and post-training AI models and their respective contributions to creating versatile personas for generating content and assisting with specific tasks.
The Evolution of Pre-Training and Post-Training in AI Models
Pre-training involves training models to imitate web content, including websites and code, to generate content similar to web pages. Post-training refines model behavior to cater to specific tasks, such as chat assistance, focusing on usefulness and human-liked outputs. The calibration and adaptability of these models enable them to generate varied content and personas, indicating a move towards more task-oriented functionalities.
Enhanced Capabilities in AI Models for Complex Tasks
AI models are expected to improve significantly in the next one to two years, enabling them to handle more complex tasks beyond basic suggestions. The progression towards longer project-based tasks will involve training models to operate cohesively over numerous files of code, enhancing sample efficiency and error recovery capabilities. By expanding training to encompass longer projects, models are anticipated to demonstrate marked advancements in task efficiency.
Anticipated Growth and Challenges in AI Development
AI development is expected to continue rapidly, potentially leading to a new phase of scientific advancement and augmented productivity. However, the advent of AGI would necessitate caution and planning to ensure safe deployment and management. Coordination among entities in the AI research field may be essential to establish guidelines and regulations for responsible AI advancement and deployment.
The Role of Data and Model Scaling in AI Progression
The ongoing progression in AI models indicates a rapid evolution powered by advancements in data quality, model scaling, and training methodologies. The development of larger models has shown promise in boosting model intelligence and sample efficiency, contributing to significant improvements in AI capabilities. The continuous refinement of data sources and training regimes is anticipated to further enhance AI systems and their applications across various domains.
Evolution of User Assistants
User assistants like Command Bar are evolving to provide a more personalized and interactive experience on websites and applications. These assistants can analyze user history, utilize APIs for actions, and even proactively guide users to explore new features. A key feature is the ability to show users actions instead of just text responses, making interactions more dynamic and engaging.
Future of AI in Running Firms
The discussion revolves around the future implications of AI potentially running entire firms autonomously. While the idea of AI-led firms raises questions about processes and oversight, there is a consideration for human involvement in key decisions even if AI can run businesses efficiently. Concerns arise regarding economic equilibrium, regulatory frameworks to ensure human oversight, and the need for global collaboration on AI governance to align with user expectations and values.
Chatted with John Schulman (cofounded OpenAI and led ChatGPT creation) on how posttraining tames the shoggoth, and the nature of the progress to come...
(00:00:00) - Pre-training, post-training, and future capabilities
(00:16:57) - Plan for AGI 2025
(00:29:19) - Teaching models to reason
(00:40:50) - The Road to ChatGPT
(00:52:13) - What makes for a good RL researcher?
(01:00:58) - Keeping humans in the loop
(01:15:15) - State of research, plateaus, and moats
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