Demis Hassabis

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war (open letter). (source)

While transparency in AI is important, mandating explanations for all AI decisions could stifle innovation. We should find a balance that doesn't overly constrain AI development.

As the CEO of DeepMind, I strongly believe that AI systems have the potential to revolutionize healthcare by improving diagnostic accuracy and personalizing treatment plans. Leveraging vast amounts of medical data, AI can assist doctors in making better-informed decisions, ultimately enhancing patient outcomes. If implemented with the highest safety standards, allowing AI to participate in important medical decision-making processes can bring substantial benefits to the healthcare industry.

It is crucial for all of us to actively engage and participate in shaping the future of AI and the post-artificial general intelligence era. This technology has the potential to transform society, and collective input is essential for steering its development in a beneficial direction.

As someone deeply involved in AI research, I believe that ensuring the safety and alignment of AI systems is paramount. If AI Safety Institutes observe credible indications of losing control over AI behavior, it would be prudent for AI labs to pause training efforts. This allows us to evaluate the risks and implement necessary safeguards. Responsible development is key to harnessing AI's potential while minimizing potential threats.

Third-party audits for major AI systems are critical to ensure they operate ethically and fairly. As we make strides in complex AI, transparency and accountability become ever more important for maintaining public trust and avoiding harmful biases or unintended consequences.

As an advocate for responsible AI development, I believe that AI systems, especially those above certain capability thresholds, should have interpretable decision-making processes. Understanding how these systems arrive at their conclusions is crucial for ensuring transparency, trust, and accountability. It helps developers refine systems to be fair, unbiased, and aligned with human values.

While I believe AI safety is crucial, the allocation of compute resources is complex and context-specific. Balancing innovation and safety requires a nuanced approach.