Summary Provably Safe Systems Controllable AGI for Humanity arxiv.org
10,030 words - PDF document - View PDF document
One Line
The document outlines a plan for developing safe AGI by focusing on human-specified requirements, advanced AI, and provable safety measures.
Slides
Slide Presentation (10 slides)
Key Points
- Building AGIs to provably satisfy human-specified requirements is crucial for ensuring safe and controlled AGI development.
- Current AI safety efforts are insufficient, with minimal funding and a lack of focus on adversarial AGIs.
- Mathematical proof, provable contracts, and proof-carrying code are powerful tools for controlling AGIs and ensuring their safety.
- The development of provably compliant hardware and operating systems is necessary to guarantee AGI safety.
- Automated software verification, mechanistic interpretability, and provable infrastructure are important areas of research and development in AGI safety efforts.
Summaries
18 word summary
This document proposes a plan for safe AGI development, emphasizing human-specified requirements, advanced AI, and provable safety measures.
141 word summary
This document proposes a plan for the safe development and control of AGIs. The authors emphasize the need to build AGIs that meet human-specified requirements. They suggest using advanced AI for formal verification and mechanistic interpretability. The lack of funding and focus on adversarial AGIs is highlighted. Provable contracts, proof-carrying code, and provably compliant hardware are recommended for safety. Challenges in code generation and proofs are discussed, with the proposal of using large language models and artificial neuroscience. Networks of provable contracts are suggested to protect against risks. The importance of automated software verification, formal verification benchmarks, probabilistic program verification, quantum formal verification, automated mechanistic interpretability, and knowledge extraction from black-box AI systems is emphasized. The authors conclude that provably safe systems are vital for controllable AGI and call for further research and development in automated software verification and provable infrastructure.
203 word summary
This document outlines a plan for the safe development and control of Artificial General Intelligences (AGIs). The authors assert that the only way to guarantee safe and controlled AGI is by building AGIs that provably meet human-specified requirements. They propose using advanced AI for formal verification and mechanistic interpretability to achieve this goal. The lack of funding and focus on adversarial AGIs in current AI safety efforts is highlighted, emphasizing the need for a "security mindset" in AGI design. The authors suggest using provable contracts and proof-carrying code (PCC) to ensure safety, as well as developing provably compliant hardware (PCH) and operating systems. They discuss the challenges of generating code and proofs, and propose using large language models (LLMs) and artificial neuroscience (Mechinterp) to assist. Networks of provable contracts implemented in PCH are proposed to protect against existential risks and promote win-win thinking. The importance of automated software verification, formal verification benchmarks, probabilistic program verification, quantum formal verification, automated mechanistic interpretability, and the extraction of knowledge from black-box AI systems is emphasized. In conclusion, the authors argue that provably safe systems are essential for controllable AGI, and further research and development in areas such as automated software verification and provable infrastructure are needed.
355 word summary
This document presents a plan for ensuring the safe development and control of Artificial General Intelligences (AGIs). The authors argue that the only way to guarantee safe and controlled AGI is by building AGIs that provably satisfy human-specified requirements. They propose using advanced AI for formal verification and mechanistic interpretability to achieve this goal.
The urgency of AGI safety is emphasized, as current AI safety efforts are deemed insufficient. The authors highlight the lack of funding and focus on adversarial AGIs, and stress the need for a "security mindset" in designing AGIs and their infrastructure.
Mathematical proof is presented as humanity's most powerful tool for controlling AGIs. The authors suggest using provable contracts and proof-carrying code (PCC) to ensure safety. They propose the development of provably compliant hardware (PCH) and operating systems that only run PCC meeting risk-commensurate specifications.
The importance of AI discovery of algorithms and knowledge, as well as the creation of formal specifications for generated code, is emphasized. The authors discuss the challenges of generating code that meets desired specifications and generating proofs to verify compliance. They suggest using large language models (LLMs) and artificial neuroscience (Mechinterp) to assist in these tasks.
The authors propose networks of provable contracts implemented in PCH to protect against existential risks. They emphasize the need for precise formal models and suggest that such networks could eliminate social dilemmas and promote win-win thinking and actions.
The significance of automated software verification and fully automated formal verification is discussed. The importance of developing verification benchmarks and databases of correct programs with formal specifications and compliance proofs is highlighted.
The need for probabilistic program verification, quantum formal verification, and automated mechanistic interpretability is mentioned. The authors emphasize the importance of automating the extraction of knowledge and algorithms from black-box AI systems and the development of mechanistic interpretability benchmarks.
In conclusion, the authors argue that provably safe systems are the only path to controllable AGI. They stress the importance of mathematical proof, provable contracts, and proof-carrying code in ensuring AGI safety. Further research and development in areas such as automated software verification, mechanistic interpretability, and provable infrastructure are called for.
650 word summary
This paper presents a path to ensuring the safe development and control of Artificial General Intelligences (AGIs). The authors argue that building AGIs to provably satisfy human-specified requirements is the only way to guarantee safe and controlled AGI. They propose using advanced AI for formal verification and mechanistic interpretability to achieve this goal.
The urgency of AGI safety is emphasized, as corporations and research labs are racing to develop AGI without adequate consideration for the potential risks. The authors point out that current AI safety efforts are insufficient, with minimal funding and a lack of focus on adversarial AGIs. They highlight the need for a “security mindset” in designing AGIs and the infrastructure they interact with.
The authors argue that mathematical proof is humanity's most powerful tool for controlling AGIs. They propose the use of provable contracts and proof-carrying code (PCC) to ensure safety. Proofs of safety can be precisely validated, independent of the alignment status of the AGIs. They suggest the development of provably compliant hardware (PCH) and operating systems that only run PCC meeting risk-commensurate specifications.
The paper emphasizes the importance of AI discovery of algorithms and knowledge, as well as the creation of formal specifications for generated code. The authors discuss the challenges of generating code that meets desired specifications and generating proofs to verify compliance. They suggest using large language models (LLMs) and artificial neuroscience (Mechinterp) to assist in these tasks.
The authors propose networks of provable contracts implemented in PCH to protect against existential risks. They emphasize the need for precise formal models of hardware designs, adversary capabilities, and desirable outcomes. They suggest that such networks could eliminate social dilemmas and promote win-win thinking and actions.
The paper discusses the significance of automated software verification and the need for fully automated formal verification and automatic theorem proving. It also highlights the importance of developing verification benchmarks and databases of correct programs with formal specifications and compliance proofs.
The authors mention the need for probabilistic program verification, quantum formal verification, and automated mechanistic interpretability. They emphasize the importance of automating the extraction of knowledge and algorithms from black-box AI systems and the development of mechanistic interpretability benchmarks.
In conclusion, the authors argue that provably safe systems are the only path to controllable AGI. They stress the importance of mathematical proof, provable contracts, and proof-carrying code in ensuring AGI safety. They call for further research and development in areas such as automated software verification, mechanistic interpretability, and provable infrastructure.
The document discusses the need for provably safe systems and controllable artificial general intelligence (AGI) for the benefit of humanity. It highlights several key areas that need to be addressed in order to achieve this goal. One area is the development of a framework for provably compliant hardware, which involves quantifying and bounding errors in approximating physical systems as classical or quantum computations. This framework should also extend to all relevant hardware, such as locks and motors.
Another important aspect is the development of a framework for provably compliant governance, which involves aligning incentives for humans, corporations, and organizations with the well-being of others. Mechanism design and collaboration mechanisms, such as empathy, love, gossip, and legal systems, are important tools in achieving this alignment. However, as adversaries become smarter, adversarial attacks against these mechanisms become more sophisticated, making it crucial to have an adversarial security mindset.
The document also emphasizes the need to create provable formal models of tamper detection. This includes developing hardware that is provably safe against specified levels of attack and implementing sensors and actuators that can detect and respond to intrusion. Additionally, the reliability of sensors is crucial in ensuring the accuracy of information in hardware security.
Transparency in design is another important aspect discussed in the document. Research is needed on how to design hardware that is easily verifiable and inspectable, with provable high confidence in detecting dangerous flaws. This includes designing sensors
893 word summary
This paper presents a path to ensuring the safe development and control of Artificial General Intelligences (AGIs). The authors argue that building AGIs to provably satisfy human-specified requirements is the only way to guarantee safe and controlled AGI. They propose using advanced AI for formal verification and mechanistic interpretability to achieve this goal.
The urgency of AGI safety is emphasized, as corporations and research labs are racing to develop AGI without adequate consideration for the potential risks. The authors point out that current AI safety efforts are insufficient, with minimal funding and a lack of focus on adversarial AGIs. They highlight the need for a "security mindset" in designing AGIs and the infrastructure they interact with.
The authors argue that mathematical proof is humanity's most powerful tool for controlling AGIs. They propose the use of provable contracts and proof-carrying code (PCC) to ensure safety. Proofs of safety can be precisely validated, independent of the alignment status of the AGIs. They suggest the development of provably compliant hardware (PCH) and operating systems that only run PCC meeting risk-commensurate specifications.
The paper emphasizes the importance of AI discovery of algorithms and knowledge, as well as the creation of formal specifications for generated code. The authors discuss the challenges of generating code that meets desired specifications and generating proofs to verify compliance. They suggest using large language models (LLMs) and artificial neuroscience (Mechinterp) to assist in these tasks.
The authors propose networks of provable contracts implemented in PCH to protect against existential risks. They emphasize the need for precise formal models of hardware designs, adversary capabilities, and desirable outcomes. They suggest that such networks could eliminate social dilemmas and promote win-win thinking and actions.
The paper discusses the significance of automated software verification and the need for fully automated formal verification and automatic theorem proving. It also highlights the importance of developing verification benchmarks and databases of correct programs with formal specifications and compliance proofs.
The authors mention the need for probabilistic program verification, quantum formal verification, and automated mechanistic interpretability. They emphasize the importance of automating the extraction of knowledge and algorithms from black-box AI systems and the development of mechanistic interpretability benchmarks.
In conclusion, the authors argue that provably safe systems are the only path to controllable AGI. They stress the importance of mathematical proof, provable contracts, and proof-carrying code in ensuring AGI safety. They call for further research and development in areas such as automated software verification, mechanistic interpretability, and provable infrastructure.
Overall, this paper presents a comprehensive argument for the importance of provably safe systems in ensuring the safe development and control of AGIs. It highlights the need for a security mindset, mathematical proof, and automated verification techniques in AGI safety efforts. The proposed approach offers a path towards controllable AGI that is supported by advanced AI technologies.
The document discusses the need for provably safe systems and controllable artificial general intelligence (AGI) for the benefit of humanity. It highlights several key areas that need to be addressed in order to achieve this goal. One area is the development of a framework for provably compliant hardware, which involves quantifying and bounding errors in approximating physical systems as classical or quantum computations. This framework should also extend to all relevant hardware, such as locks and motors.
Another important aspect is the development of a framework for provably compliant governance, which involves aligning incentives for humans, corporations, and organizations with the well-being of others. Mechanism design and collaboration mechanisms, such as empathy, love, gossip, and legal systems, are important tools in achieving this alignment. However, as adversaries become smarter, adversarial attacks against these mechanisms become more sophisticated, making it crucial to have an adversarial security mindset.
The document also emphasizes the need to create provable formal models of tamper detection. This includes developing hardware that is provably safe against specified levels of attack and implementing sensors and actuators that can detect and respond to intrusion. Additionally, the reliability of sensors is crucial in ensuring the accuracy of information in hardware security.
Transparency in design is another important aspect discussed in the document. Research is needed on how to design hardware that is easily verifiable and inspectable, with provable high confidence in detecting dangerous flaws. This includes designing sensors that can detect a certain level of attack on the physical structure.
Network robustness in the face of attacks is also highlighted as a key area. A formal model of a network of provable contracts with proven resilience to physical and software attacks is needed. This model should specify and prove the resilience of the network to ensure its security.
The document also discusses the development of useful applications of provably compliant systems. Examples include implementing expiration dates for AI systems, geofencing AI to prevent misuse, using crypto-tokens to throttle or shut down AI systems, implementing AI kill switches, and formalizing guiding principles for AI systems.
The document addresses frequently asked questions (FAQs) related to AGI safety. It clarifies that debugging and evaluations are necessary but not sufficient conditions for safety. It also explains that humans do not need to understand or verify the proofs themselves, but rather trust the proof-carrying AI to obey the specifications. The document also addresses concerns about performance impact, automation of program synthesis and verification, and the need for formal specifications.
The document concludes with acknowledgements and references to relevant sources.