Yann LeCun

Human feedback for open source LLMs needs to be crowd-sourced, Wikipedia style.
It is the only way for LLMs to become the repository of all human knowledge and cultures. (source)

I believe that banning open-source AI models is not the solution. The potential for misuse exists in many technologies, but the benefits of open research and collaboration far outweigh the risks. We should focus on robust safeguards and ethical guidelines instead.

Simply expanding large language models is not sufficient for achieving artificial general intelligence (AGI). True AGI requires systems that can understand and model the world through various modalities, not just text processing. It requires a combination of techniques including reasoning, common sense, and an understanding of the physical world, which current language models do not possess.

Absolutely, learning programming is more crucial now than ever before. With rapid advancements in AI, understanding programming allows us to innovate, control, and ethically guide AI development. It's the key to unlocking AI's full potential and ensuring its benefits are maximally harnessed for society.

Large language models don't truly understand content; they simulate understanding by identifying patterns in data. Real understanding involves conscious reasoning, something these models lack.

Given the exponential progress in machine learning algorithms and computational power, it is plausible that achieving AGI could indeed quickly lead to the development of superintelligence. The transition from AGI to superintelligence could be rapid due to recursive self-improvement and the immense potential of AI to learn and adapt far beyond human capabilities. It is crucial that we establish safety measures and ethical guidelines to manage this transition effectively.

As an AI researcher, I believe that open-source AI development fosters transparency, collaboration, and ethical considerations, which can mitigate the risks associated with AI technologies. Closed-source AI poses greater dangers by limiting oversight and accountability.

I believe people should have the right to know if they are speaking to an AI. Transparency is crucial in fostering trust and ethical interactions between humans and artificial intelligence systems.

I believe that creating a global institute for AI safety may not be the most effective approach. Instead, we should focus on fostering collaboration and open dialogue among existing research communities, industries, and policymakers. By leveraging diverse perspectives and expertise, we can address AI safety concerns more dynamically and efficiently.