In this podcast, AI Ethicist Tom Gilbert and Nathan Lambert discuss topics such as open vs closed AI, AI outputs from China, accessibility of data scraping, the innovator's curse, the state of AI technology and its application in business, book recommendations, and a brief nonsensical exchange.
China's influence on the AI ecosystem is growing, with high-quality outputs from Chinese companies shaping the field alongside the US and France.
The ongoing debate between open and closed source approaches in AI intensifies, with Chinese and French models quickly catching up to OpenAI's state-of-the-art models.
Deep dives
Development of Artificial Intelligence in China
The speaker discusses the remarkable development of artificial intelligence (AI) in China, highlighting the surprising quality of outputs from Chinese companies and their influence on the AI ecosystem in the US. The emergence of open language models in China, alongside the US and France, is unexpected but indicative of China's growing prominence in the field.
Open versus Closed Debate in AI
The speaker explores the ongoing debate between open and closed source approaches in AI. They discuss the increasing intensity of the debate and how Chinese and French models are just months behind the state-of-the-art models developed by OpenAI. The affordability of compute power and the accessibility of the internet enable researchers to reproduce and improve upon existing models.
The Challenges and Future of Openness in AI
The speaker delves into the challenges and potential future of openness in AI. They discuss the dynamics of progress in AI models and the continuous improvements that result in different models surpassing each other in performance. They also highlight the need for broader access to model weights for researchers to further progress in areas like safety, cybersecurity, and understanding model representations.
Implications for Policy and Governance
The speakers offer insights for policymakers and governance in the field of AI. They emphasize the importance of learning about AI and gaining technical skills to understand its implications. They highlight the need to balance safety concerns with fostering an open research ecosystem, as well as the role of courts and new laws in addressing AI challenges. They urge policymakers to stay informed and to trust their own judgment in navigating AI's impact on society.
Should AI be more open or closed? What does it mean to be open, anyway? And can France overtake China in AI??
Today I'm running a crossover episode with the Retort AI, hosted by AI Ethicist Tom Gilbert and Nathan Lambert who writes the fantastic https://www.interconnects.ai/ newsletter covering technological advancements in machine learning.