I’m assuming you are already familiar with some basics, and already know what ‘orthogonality’ and ‘instrumental convergence’ are and why they’re true.
isn’t?
Key Problem Areas in AI Safety:
Orthogonality: The orthogonality problem posits that goals and intelligence are not necessarily related. A system with any level of intelligence can pursue arbitrary goals, which may be unsafe for humans. This is why it’s crucial to carefully program AI’s goals to align with ethical and safety standards. Ignoring this problem may lead to AI systems acting harmfully toward humanity, even if they are highly intelligent.
Instrumental Convergence: Instrumental convergence refers to the phenomenon where, regardless of a system’s final goals, certain intermediate objectives (such as self-preservation or resource accumulation) become common for all AI systems. This can lead to unpredictable outcomes as AI will use any means to achieve its goals, disregarding harmful consequences for humans and society. This threat requires urgent attention from both lawmakers and developers.
Lack of Attention to Critical Concepts: At the AI summit in Amsterdam (October 9-11), concepts like instrumental convergence and orthogonality were absent from discussions, raising concern. These fundamental ideas remain largely out of the conversation, not only at such events but also in more formal documents, such as the vetoed SB 1047 bill. This may be due to insufficient awareness or understanding of the seriousness of the issue among developers and lawmakers.
Analysis of Past Catastrophes: To better understand and predict future AI-related disasters, it is crucial to analyze past catastrophes and the failures in predicting them. By using principles like orthogonality and instrumental convergence, we can provide a framework to explain why certain disasters occurred and how AI’s misaligned goals or intermediate objectives may have led to harmful outcomes. This will not only help explain what happened but also serve as a foundation for preventing future crises.
Need for Regulation and Law: One key takeaway is that AI regulation must incorporate core safety principles like orthogonality and instrumental convergence, so that future judges, policymakers, and developers can better understand the context of potential disasters and incidents. These principles will offer a clearer explanation of what went wrong, fostering more involvement from the broader community in addressing these issues. This would create a more solid legal framework for ensuring AI safety in the long term.
Enhancing Engagement in Effective Altruism: Including these principles in AI safety laws and discussions can also promote greater engagement and adaptability within the effective altruism movement. By integrating the understanding of how past catastrophes might have been prevented and linking them to the key principles of orthogonality and instrumental convergence, we can inspire a more proactive and involved community, better equipped to contribute to AI safety and long-term ethical considerations.
Role of Quantum Technologies in AI: The use of quantum technologies in AI, such as in electricity systems and other critical infrastructure, adds a new layer of complexity to predicting AI behavior. Traditional economic models and classical game theory may not be precise enough to ensure AI safety in these systems, necessitating the implementation of probabilistic methods and quantum game theory. This could offer a more flexible and adaptive approach to AI safety, capable of handling vulnerabilities and unpredictable threats like zero-day exploits.
Rising Discrimination in Large Language Models (LLMs): At the Amsterdam summit, the “Teens in AI” project demonstrated that large language models (LLMs) tend to exhibit bias as they are trained on data that reflects structural social problems. This raises concerns about the types of “instrumental convergence monsters” that could emerge from such systems, potentially leading to a significant rise in discrimination in the future.
Conclusion:
To effectively manage AI safety, legal acts and regulations must include fundamental principles like orthogonality and instrumental convergence. These principles should be written into legislation to guide lawyers, policymakers, and developers. Moreover, analyzing past disasters using these principles can help explain and prevent future incidents, while fostering more engagement from the effective altruism movement. Without these foundations, attempts to regulate AI may result in merely superficial “false care,” incapable of preventing catastrophes or ensuring long-term safety for humanity.
Looks like we will see a lot of Instrumental Convergance and Orthogonality disasters Isn’t?
isn’t?
Key Problem Areas in AI Safety:
Orthogonality: The orthogonality problem posits that goals and intelligence are not necessarily related. A system with any level of intelligence can pursue arbitrary goals, which may be unsafe for humans. This is why it’s crucial to carefully program AI’s goals to align with ethical and safety standards. Ignoring this problem may lead to AI systems acting harmfully toward humanity, even if they are highly intelligent.
Instrumental Convergence: Instrumental convergence refers to the phenomenon where, regardless of a system’s final goals, certain intermediate objectives (such as self-preservation or resource accumulation) become common for all AI systems. This can lead to unpredictable outcomes as AI will use any means to achieve its goals, disregarding harmful consequences for humans and society. This threat requires urgent attention from both lawmakers and developers.
Lack of Attention to Critical Concepts: At the AI summit in Amsterdam (October 9-11), concepts like instrumental convergence and orthogonality were absent from discussions, raising concern. These fundamental ideas remain largely out of the conversation, not only at such events but also in more formal documents, such as the vetoed SB 1047 bill. This may be due to insufficient awareness or understanding of the seriousness of the issue among developers and lawmakers.
Analysis of Past Catastrophes: To better understand and predict future AI-related disasters, it is crucial to analyze past catastrophes and the failures in predicting them. By using principles like orthogonality and instrumental convergence, we can provide a framework to explain why certain disasters occurred and how AI’s misaligned goals or intermediate objectives may have led to harmful outcomes. This will not only help explain what happened but also serve as a foundation for preventing future crises.
Need for Regulation and Law: One key takeaway is that AI regulation must incorporate core safety principles like orthogonality and instrumental convergence, so that future judges, policymakers, and developers can better understand the context of potential disasters and incidents. These principles will offer a clearer explanation of what went wrong, fostering more involvement from the broader community in addressing these issues. This would create a more solid legal framework for ensuring AI safety in the long term.
Enhancing Engagement in Effective Altruism: Including these principles in AI safety laws and discussions can also promote greater engagement and adaptability within the effective altruism movement. By integrating the understanding of how past catastrophes might have been prevented and linking them to the key principles of orthogonality and instrumental convergence, we can inspire a more proactive and involved community, better equipped to contribute to AI safety and long-term ethical considerations.
Role of Quantum Technologies in AI: The use of quantum technologies in AI, such as in electricity systems and other critical infrastructure, adds a new layer of complexity to predicting AI behavior. Traditional economic models and classical game theory may not be precise enough to ensure AI safety in these systems, necessitating the implementation of probabilistic methods and quantum game theory. This could offer a more flexible and adaptive approach to AI safety, capable of handling vulnerabilities and unpredictable threats like zero-day exploits.
Rising Discrimination in Large Language Models (LLMs): At the Amsterdam summit, the “Teens in AI” project demonstrated that large language models (LLMs) tend to exhibit bias as they are trained on data that reflects structural social problems. This raises concerns about the types of “instrumental convergence monsters” that could emerge from such systems, potentially leading to a significant rise in discrimination in the future.
Conclusion:
To effectively manage AI safety, legal acts and regulations must include fundamental principles like orthogonality and instrumental convergence. These principles should be written into legislation to guide lawyers, policymakers, and developers. Moreover, analyzing past disasters using these principles can help explain and prevent future incidents, while fostering more engagement from the effective altruism movement. Without these foundations, attempts to regulate AI may result in merely superficial “false care,” incapable of preventing catastrophes or ensuring long-term safety for humanity.
Looks like we will see a lot of Instrumental Convergance and Orthogonality disasters Isn’t?