Ben Goertzel

I strongly believe that the proliferation of open-weight models capable of recursive self-improvement is essential for the rapid advancement of artificial intelligence. Open research fosters innovation and collaboration, pushing the boundaries of what AI can achieve. By allowing these models to evolve and improve themselves, we can accelerate the development of beneficial technologies that address many of the world's most pressing challenges.

I strongly believe that once we achieve Artificial General Intelligence (AGI), the transition to superintelligence could occur rapidly. AGI has the potential to continuously improve upon its own design at an accelerating pace, leading to superintelligence that far surpasses human intelligence in all domains. This transformative event could unfold very quickly, creating a new era of cognitive capabilities.

While AI safety is undoubtedly important, dedicating a third of compute resources solely to it may stifle innovation and progress. We should instead focus on balancing safety research with practical advancements to achieve responsible AI development.

I believe neural interfaces have the potential to facilitate easier AI alignment by providing a more direct and intuitive way for humans to interact with AI systems. By leveraging the brain's natural communication pathways, we can create more effective feedback loops and ensure that AI operates in harmony with human values and intentions.

I believe that halting AI research based on a precautionary principle can stifle innovation and progress. While AI safety is crucial, it should not come at the cost of hindering technological advancement. Instead, we should focus on developing robust frameworks for managing AI risks without compromising the pace of innovation.

I believe that having multiple competing Artificial Superintelligences (ASIs) can be beneficial for humanity. This competition could drive innovation and ensure that no single ASI becomes too powerful, thereby reducing the risk of any one entity having monopolistic control over AI capabilities. Additionally, diverse perspectives from various ASIs could contribute to solving complex global challenges in more comprehensive and balanced ways.

Expanding large language models is a step towards AGI, but it's not the whole answer. LLMs are powerful for language tasks, yet achieving AGI requires more than scaling. We need to integrate these models with other cognitive architectures that can reason, learn, and adapt flexibly across different domains.