Interview of Ilya Sutskver
TLDR: thereotically #chatgpt can learn a lot and eventually converge to #AGI given the proper dataset and help of #RLHF (Reinforcement Learning from Human Feedback).
Video provides valuable insights into the current state and future of artificial intelligence. The conversation explores the progress of AI, its limitations, and the importance of reinforcement learning and ethics in AI development. Ilia also discusses the potential benefits of AI in democracy and its potential role in helping humans manage society. This interview offers a comprehensive and thought-provoking overview of the AI landscape, making it a must-watch for anyone interested in understanding the impact of AI on our lives and the world at large.
Youtube: https://www.youtube.com/watch?v=SjhIlw3Iffs
#youtube #Sutskever #OpenAI #GPTEditor
TLDR: thereotically #chatgpt can learn a lot and eventually converge to #AGI given the proper dataset and help of #RLHF (Reinforcement Learning from Human Feedback).
Video provides valuable insights into the current state and future of artificial intelligence. The conversation explores the progress of AI, its limitations, and the importance of reinforcement learning and ethics in AI development. Ilia also discusses the potential benefits of AI in democracy and its potential role in helping humans manage society. This interview offers a comprehensive and thought-provoking overview of the AI landscape, making it a must-watch for anyone interested in understanding the impact of AI on our lives and the world at large.
Youtube: https://www.youtube.com/watch?v=SjhIlw3Iffs
#youtube #Sutskever #OpenAI #GPTEditor
YouTube
The Mastermind Behind GPT-4 and the Future of AI | Ilya Sutskever
In this podcast episode, Ilya Sutskever, the co-founder and chief scientist at OpenAI, discusses his vision for the future of artificial intelligence (AI), including large language models like GPT-4.
Sutskever starts by explaining the importance of AI research…
Sutskever starts by explaining the importance of AI research…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback (RLHF), the key method for fine-tuning large language models (LLMs), is placed under the microscope in this paper. While recognizing RLHF's central role in aligning AI systems with human goals, the authors boldly tackle the uncharted territory of its flaws and limitations. They not only dissect open problems and the core challenges but also map out pioneering techniques to augment RLHF. This insightful work culminates in proposing practical standards for societal oversight, marking a critical step towards a multi-dimensional and responsible approach to the future of safer AI systems.
Paper link: https://arxiv.org/abs/2307.15217
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-rlhf-overview
#deeplearning #nlp #llm #rlhf
Reinforcement Learning from Human Feedback (RLHF), the key method for fine-tuning large language models (LLMs), is placed under the microscope in this paper. While recognizing RLHF's central role in aligning AI systems with human goals, the authors boldly tackle the uncharted territory of its flaws and limitations. They not only dissect open problems and the core challenges but also map out pioneering techniques to augment RLHF. This insightful work culminates in proposing practical standards for societal oversight, marking a critical step towards a multi-dimensional and responsible approach to the future of safer AI systems.
Paper link: https://arxiv.org/abs/2307.15217
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-rlhf-overview
#deeplearning #nlp #llm #rlhf
