The Nonlinear Library: LessWrong LW - Provably Safe AI: Worldview and Projects by bgold
Aug 10, 2024
13:12
Link to original article
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Provably Safe AI: Worldview and Projects, published by bgold on August 10, 2024 on LessWrong.
In September 2023, Max Tegmark and Steve Omohundro proposed "Provably Safe AI" as a strategy for AI Safety. In May 2024, a larger group delineated the broader concept of "Guaranteed Safe AI" which includes Provably Safe AI and other related strategies. In July, 2024, Ben Goldhaber and Steve discussed Provably Safe AI and its future possibilities, as summarized in this document.
Background
In June 2024, ex-OpenAI AI Safety Researcher Leopold Aschenbrenner wrote a 165-page document entitled "Situational Awareness, The Decade Ahead" summarizing AI timeline evidence and beliefs which are shared by many frontier AI researchers. He argued that human-level AI is likely by 2027 and will likely lead to superhuman AI in 2028 or 2029.
"Transformative AI" was coined by Open Philanthropy to describe AI which can "precipitate a transition comparable to the agricultural or industrial revolution". There appears to be a significant probability that Transformative AI may be created by 2030. If this probability is, say, greater than 10%, then humanity must immediately begin to prepare for it.
The social changes and upheaval caused by Transformative AI are likely to be enormous. There will likely be many benefits but also many risks and dangers, perhaps even existential risks for humanity. Today's technological infrastructure is riddled with flaws and security holes. Power grids, cell service, and internet services have all been very vulnerable to accidents and attacks. Terrorists have attacked critical infrastructure as a political statement.
Today's cybersecurity and physical security barely keeps human attackers at bay. When these groups obtain access to powerful cyberattack AI's, they will likely be able to cause enormous social damage and upheaval.
Humanity has known how to write provably correct and secure software since Alan Turing's 1949 paper. Unfortunately, proving program correctness requires mathematical sophistication and it is rare in current software development practice. Fortunately, modern deep learning systems are becoming proficient at proving mathematical theorems and generating provably correct code.
When combined with techniques like "autoformalization," this should enable powerful AI to rapidly replace today's flawed and insecure codebase with optimized, secure, and provably correct replacements. Many researchers working in these areas believe that AI theorem-proving at the level of human PhD's is likely about two years away.
Similar issues plague hardware correctness and security, and it will be a much larger project to replace today's flawed and insecure hardware. Max and Steve propose formal methods grounded in mathematical physics to produce provably safe physical designs. The same AI techniques which are revolutionizing theorem proving and provable software synthesis are also applicable to provable hardware design.
Finally, today's social mechanisms like money, contracts, voting, and the structures of governance, will also need to be updated for the new realities of an AI-driven society. Here too, the underlying rules of social interaction can be formalized, provably effective social protocols can be designed, and secure hardware implementing the new rules synthesized using powerful theorem proving AIs.
What's next?
Given the huge potential risk of uncontrolled powerful AI, many have argued for a pause in Frontier AI development. Unfortunately, that does not appear to be a stable solution. Even if the US paused its AI development, China or other countries could gain an advantage by accelerating their own work.
There have been similar calls to limit the power of open source AI models. But, again, any group anywhere in the world can release their powerful AI model weig...
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Provably Safe AI: Worldview and Projects, published by bgold on August 10, 2024 on LessWrong.
In September 2023, Max Tegmark and Steve Omohundro proposed "Provably Safe AI" as a strategy for AI Safety. In May 2024, a larger group delineated the broader concept of "Guaranteed Safe AI" which includes Provably Safe AI and other related strategies. In July, 2024, Ben Goldhaber and Steve discussed Provably Safe AI and its future possibilities, as summarized in this document.
Background
In June 2024, ex-OpenAI AI Safety Researcher Leopold Aschenbrenner wrote a 165-page document entitled "Situational Awareness, The Decade Ahead" summarizing AI timeline evidence and beliefs which are shared by many frontier AI researchers. He argued that human-level AI is likely by 2027 and will likely lead to superhuman AI in 2028 or 2029.
"Transformative AI" was coined by Open Philanthropy to describe AI which can "precipitate a transition comparable to the agricultural or industrial revolution". There appears to be a significant probability that Transformative AI may be created by 2030. If this probability is, say, greater than 10%, then humanity must immediately begin to prepare for it.
The social changes and upheaval caused by Transformative AI are likely to be enormous. There will likely be many benefits but also many risks and dangers, perhaps even existential risks for humanity. Today's technological infrastructure is riddled with flaws and security holes. Power grids, cell service, and internet services have all been very vulnerable to accidents and attacks. Terrorists have attacked critical infrastructure as a political statement.
Today's cybersecurity and physical security barely keeps human attackers at bay. When these groups obtain access to powerful cyberattack AI's, they will likely be able to cause enormous social damage and upheaval.
Humanity has known how to write provably correct and secure software since Alan Turing's 1949 paper. Unfortunately, proving program correctness requires mathematical sophistication and it is rare in current software development practice. Fortunately, modern deep learning systems are becoming proficient at proving mathematical theorems and generating provably correct code.
When combined with techniques like "autoformalization," this should enable powerful AI to rapidly replace today's flawed and insecure codebase with optimized, secure, and provably correct replacements. Many researchers working in these areas believe that AI theorem-proving at the level of human PhD's is likely about two years away.
Similar issues plague hardware correctness and security, and it will be a much larger project to replace today's flawed and insecure hardware. Max and Steve propose formal methods grounded in mathematical physics to produce provably safe physical designs. The same AI techniques which are revolutionizing theorem proving and provable software synthesis are also applicable to provable hardware design.
Finally, today's social mechanisms like money, contracts, voting, and the structures of governance, will also need to be updated for the new realities of an AI-driven society. Here too, the underlying rules of social interaction can be formalized, provably effective social protocols can be designed, and secure hardware implementing the new rules synthesized using powerful theorem proving AIs.
What's next?
Given the huge potential risk of uncontrolled powerful AI, many have argued for a pause in Frontier AI development. Unfortunately, that does not appear to be a stable solution. Even if the US paused its AI development, China or other countries could gain an advantage by accelerating their own work.
There have been similar calls to limit the power of open source AI models. But, again, any group anywhere in the world can release their powerful AI model weig...