The Nonlinear Library EA - AI safety needs to scale, and here's how you can do it by Esben Kran
Feb 4, 2024
12:46
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: AI safety needs to scale, and here's how you can do it, published by Esben Kran on February 4, 2024 on The Effective Altruism Forum.
AI development attracts more than $67 billion in yearly investments, contrasting sharply with the $250 million allocated to AI safety (see appendix). This gap suggests there's a large opportunity for AI safety to tap into the commercial market. The big question then is, how do you close that gap?
In this post, we aim to outline the for-profit AI safety opportunities within four key domains:
Guaranteeing the public benefit and reliability of AI when deployed.
Enhancing the interpretability and monitoring of AI systems.
Improving the alignment of user intent at the model level.
Developing protections against future AI risk scenarios using AI.
At Apart, we're genuinely excited about what for-profit AI security might achieve. Our experience working alongside EntrepreneurFirst on AI security entrepreneurship hackathons, combined with discussions with founders, advisors, and former venture capitalists, highlights the promise of the field. With that said, let's dive into the details.
Safety in deployment
Problems related to ensuring that deployed AI is reliable, protected against misuse, and safe for users, companies, and nation states. Related fields include dangerous capability evaluation, control, and cybersecurity.
Deployment safety is crucial to ensure AI systems function safely and effectively without misuse. Security also meshes well with commercial opportunities and building capability in this domain can scale strong security teams to solve future safety challenges. If you are interested in non-commercial research, we also suggest looking into governmental research bodies, such as the ones in UK, EU, and US.
Concrete problems for AI deployment
Enhancing AI application reliability and security: Foundation model applications, from user-facing chatbots utilizing software tools for sub-tasks to complex multi-agent systems, require robust security, such as protection against prompt injection, insecure plugins, and data poisoning. For detailed security considerations, refer to the Open Web Application Security Project's top 10 LLM application security considerations.
Mitigating unwanted model output: With increasing regulations on algorithms, preventing illegal outputs may become paramount, potentially requiring stricter constraints than model alignment.
Preventing malicious use:
For AI API providers: Focus on monitoring for malicious or illegal API use, safeguarding models from competitor access, and implementing zero-trust solutions.
For regulators: Scalable legislative auditing, like model card databases, open-source model monitoring, and technical governance solutions will be pivotal in 2024 and 2025. Compliance with new legislation, akin to GDPR, will likely necessitate extensive auditing and monitoring services.
For deployers: Ensuring data protection, access control, and reliability will make AI more useful, private, and secure for users.
For nation-states: Assurances against nation-state misuse of models, possibly through zero-trust structures and treaty-bound compute usage monitoring.
Examples
Apollo Research: "We intend to develop a holistic and far-ranging model evaluation suite that includes behavioral tests, fine-tuning, and interpretability approaches to detect deception and potentially other misaligned behavior."
Lakera.ai: "Lakera Guard empowers organizations to build GenAI applications without worrying about prompt injections, data loss, harmful content, and other LLM risks. Powered by the world's most advanced AI threat intelligence."
Straumli.ai: "Ship safe AI faster through managed auditing. Our comprehensive testing suite allows teams at all scales to focus on the upsides."
Interpretability and oversight
Problems related...
AI development attracts more than $67 billion in yearly investments, contrasting sharply with the $250 million allocated to AI safety (see appendix). This gap suggests there's a large opportunity for AI safety to tap into the commercial market. The big question then is, how do you close that gap?
In this post, we aim to outline the for-profit AI safety opportunities within four key domains:
Guaranteeing the public benefit and reliability of AI when deployed.
Enhancing the interpretability and monitoring of AI systems.
Improving the alignment of user intent at the model level.
Developing protections against future AI risk scenarios using AI.
At Apart, we're genuinely excited about what for-profit AI security might achieve. Our experience working alongside EntrepreneurFirst on AI security entrepreneurship hackathons, combined with discussions with founders, advisors, and former venture capitalists, highlights the promise of the field. With that said, let's dive into the details.
Safety in deployment
Problems related to ensuring that deployed AI is reliable, protected against misuse, and safe for users, companies, and nation states. Related fields include dangerous capability evaluation, control, and cybersecurity.
Deployment safety is crucial to ensure AI systems function safely and effectively without misuse. Security also meshes well with commercial opportunities and building capability in this domain can scale strong security teams to solve future safety challenges. If you are interested in non-commercial research, we also suggest looking into governmental research bodies, such as the ones in UK, EU, and US.
Concrete problems for AI deployment
Enhancing AI application reliability and security: Foundation model applications, from user-facing chatbots utilizing software tools for sub-tasks to complex multi-agent systems, require robust security, such as protection against prompt injection, insecure plugins, and data poisoning. For detailed security considerations, refer to the Open Web Application Security Project's top 10 LLM application security considerations.
Mitigating unwanted model output: With increasing regulations on algorithms, preventing illegal outputs may become paramount, potentially requiring stricter constraints than model alignment.
Preventing malicious use:
For AI API providers: Focus on monitoring for malicious or illegal API use, safeguarding models from competitor access, and implementing zero-trust solutions.
For regulators: Scalable legislative auditing, like model card databases, open-source model monitoring, and technical governance solutions will be pivotal in 2024 and 2025. Compliance with new legislation, akin to GDPR, will likely necessitate extensive auditing and monitoring services.
For deployers: Ensuring data protection, access control, and reliability will make AI more useful, private, and secure for users.
For nation-states: Assurances against nation-state misuse of models, possibly through zero-trust structures and treaty-bound compute usage monitoring.
Examples
Apollo Research: "We intend to develop a holistic and far-ranging model evaluation suite that includes behavioral tests, fine-tuning, and interpretability approaches to detect deception and potentially other misaligned behavior."
Lakera.ai: "Lakera Guard empowers organizations to build GenAI applications without worrying about prompt injections, data loss, harmful content, and other LLM risks. Powered by the world's most advanced AI threat intelligence."
Straumli.ai: "Ship safe AI faster through managed auditing. Our comprehensive testing suite allows teams at all scales to focus on the upsides."
Interpretability and oversight
Problems related...