Yann LeCun

I see the danger of this concentration of power through proprietary AI systems as a much bigger danger than everything else. What works against this is people who think that for reasons of security, we should keep AI systems under lock and key because it’s too dangerous to put it in the hands of everybody. That would lead to a very bad future in which all of our information diet is controlled by a small number of companies who proprietary systems. (source)

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)

While interpretability in AI is important, imposing strict requirements on decision-making processes might hinder innovation. In many cases, the complexity that enables high performance can be inherently opaque. Rather than enforcing interpretability, we should focus on developing tools to better understand AI behavior without stifling progress.

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 allowing AI agents to open and manage bank accounts is a natural progression in leveraging AI to enhance financial management. AI can efficiently handle transactions, optimize savings, and offer insights that improve financial decisions. Of course, robust security measures and ethical guidelines must be in place to ensure trust and protect users.

While transparency is important, requiring model cards before commercial release could stifle innovation and impose unnecessary burdens on developers. Instead, developers should focus on responsible usage and improvements over time.

I believe that establishing international AI ethics boards could stifle innovation and slow down the progress of AI technology. Instead, we should encourage collaboration and open discussions among researchers, developers, and policymakers to create flexible guidelines that can adapt to the rapid pace of technological change.

The question of equitable access to AI technologies is complex. While it's important that all countries benefit from AI advancements, achieving this requires careful consideration of economic, ethical, and technical challenges. AI governance should promote innovation while addressing inequalities.

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.