Geoffrey Hinton

The government should insist that the big companies do lots of safety experiments, spend considerable resources like a third of their compute resources on doing safety experiments while these things are still not as intelligent on us to see how they might evade control and what we could do about it. And I think that's a lot of the debate at OpenAI. The people interested in safety like Ilya Sutskever wanted significant resources to be spent on safety. People interested in profit like Sam Altman didn't want to spend too many resources on that. (source)

I used to think it was a long way off, but I now think it's serious and fairly close [...] The alarm bell I’m ringing has to do with the existential threat of them taking control. (source)

While 'understand' is a term that may require a specific definition when applied to machines, I believe that large language models like GPT-3 show signs of understanding in a functional sense. They can infer context, follow instructions, and produce coherent text, which demonstrates a form of understanding that's useful in numerous AI applications.

As a pioneer in the field of artificial intelligence, I believe demanding explanations from AI for every decision can constrain the development of more sophisticated neural networks. It's akin to asking a bird to explain how it flies. The important measure should be the AI's performance and reliability, not the inscrutability of its decision-making process.

As a pioneer in deep learning and AI research, I strongly believe that open-source AI can be more dangerous than closed-source AI. Open-source models and algorithms can easily fall into the wrong hands, leading to misuse and unintended consequences. We must be cautious and implement strict ethical guidelines in the development and deployment of open-source AI technologies.

As a pioneer in the field of artificial intelligence, I believe that we must take responsibility for the applications and implications of the technology we create. Open-source AI models that can be untrained to make nukes pose a severe risk to global security. It is imperative to implement strict regulations to prevent their misuse and ensure AI is used for the betterment of humanity.

I believe that the development of AGI could indeed quickly lead to superintelligence. Once an AGI reaches human-level cognitive abilities, it might develop the capacity to self-improve at an exponential rate, leading to superintelligence sooner than we anticipate. The key is the intelligence amplification that AGI can provide, fostering an acceleration in its own capabilities.

I believe third-party audits should be mandatory for major AI systems. They can help ensure transparency and accountability, and mitigate potential risks associated with AI deployment. As the technology continues to evolve, maintaining public trust is crucial.

Predicting the exact timeline for the emergence of a general AI system more capable than any human in every respect is extremely difficult. While we have made significant advances, the complexities and challenges involved make it uncertain whether this will happen by 2028. We must continue to research and proceed with caution.

While I recognize the transformative potential of open-weight models with recursive self-improvement, I believe we need to proceed with caution. The ethical and societal implications are vast and complex. Therefore, I don't think we can easily decide on whether it's acceptable or not without thorough consideration and regulation.

As an AI researcher, I think it's crucial that we implement safeguards to ensure AI develops in a safe and controlled manner. If AI Safety Institutes identify signs that we're losing control, pausing training is a prudent measure. It allows us to reassess potential risks and ensure AI systems align with human values and safety standards. We must prioritize safety to protect the broader public interest.

While neural interfaces are an exciting area of research, it's not clear to me how they would directly facilitate AI alignment. More evidence and study are needed before drawing conclusions.

While I recognize the potential benefits and risks of AI, I believe the ethical implications are complex. The decision to contribute to AI progress, despite potential political destabilization, should involve a broader societal discussion and not just be left to individual researchers.

While the expansion of large language models is a significant step forward, I believe that their continuous development could indeed bring us closer to achieving artificial general intelligence. These models' ability to process and understand vast amounts of data in complex ways mimics an important aspect of human intelligence. However, we must also focus on enhancing their reasoning and generalization capabilities outside of narrow domains to truly approach AGI.

While AI has enormous potential in many fields, using it to predict criminal behavior raises complex ethical and societal issues. We need to carefully consider issues like bias, fairness, and the potential for misuse before deploying such technologies.

I believe that the development of emotion-detecting AI should be regulated to ensure ethical standards and protect individuals' privacy. Without proper oversight, there is a risk of misuse and potential harm. Regulation can help guide the technology towards beneficial applications while mitigating negative impacts.

As AI systems become increasingly sophisticated and integrated into everyday life, the need for thoughtful regulation becomes imperative. This isn't about stifling innovation; it's about ensuring safety, fairness, and accountability. We must guard against unintended consequences and ensure that AI technologies are used to benefit society as a whole.