https://openai.com/blog/our-approach-to-alignment-research/
Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems….
Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems….
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks - June 7, 2025
- Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration - June 5, 2025
- From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents - May 30, 2025