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Summary

OpenAI and other leading AI labs are making a set of voluntary commitments to reinforce the safety, security and trustworthiness of AI technology and our services. These commitments involve safety, security, and trust, such as committing to internal and external red-teaming of models or systems, investing in cybersecurity and insider threat safeguards, incentivizing third-party discovery and reporting of issues and vulnerabilities, developing mechanisms to enable users to understand if audio or visual content is AI-generated, publicly reporting model or system capabilities, limitations, and domains of appropriate and inappropriate use, prioritizing research on societal risks posed by AI systems, and developing and deploying frontier AI systems to help address society’s greatest challenges.

Q&As

What commitments are leading AI labs making to reinforce the safety, security, and trustworthiness of AI technology?
The commitments leading AI labs are making to reinforce the safety, security, and trustworthiness of AI technology include internal and external red-teaming of models or systems, working toward information sharing among companies and governments regarding trust and safety risks, investing in cybersecurity and insider threat safeguards, incentivizing third-party discovery and reporting of issues and vulnerabilities, developing and deploying mechanisms that enable users to understand if audio or visual content is AI-generated, developing and deploying frontier AI systems to help address society’s greatest challenges, and publicly reporting model or system capabilities, limitations, and domains of appropriate and inappropriate use.

What scope do these commitments have?
The scope of these commitments is limited to generative models that are overall more powerful than the current industry frontier (e.g. models that are overall more powerful than any currently released models, including GPT-4, Claude 2, PaLM 2, Titan and, in the case of image generation, DALL-E 2).

What measures are companies taking to protect proprietary and unreleased model weights?
Companies are taking measures to protect proprietary and unreleased model weights by treating them as core intellectual property for their business, limiting access to model weights to those whose job function requires it, and establishing a robust insider threat detection program consistent with protections provided for their most valuable intellectual property and trade secrets.

How are companies incentivizing third-party discovery and reporting of issues and vulnerabilities?
Companies are incentivizing third-party discovery and reporting of issues and vulnerabilities by establishing bounty systems, contests, or prizes to incent the responsible disclosure of weaknesses, such as unsafe behaviors, or by including AI systems in their existing bug bounty programs.

What mechanisms are companies developing and deploying to enable users to understand if audio or visual content is AI-generated?
Companies are developing and deploying mechanisms that enable users to understand if audio or visual content is AI-generated by developing robust mechanisms, including provenance and/or watermarking systems for audio or visual content created by any of their publicly available systems within scope, and developing tools or APIs to determine if a particular piece of content was created with their system.

AI Comments

👍 This article lays out a clear and comprehensive set of commitments to reinforce the safety, security and trustworthiness of AI technology. It is a great step in advancing meaningful and effective AI governance.

👎 The article fails to address potential safety issues that might arise from the use of highly capable AI systems. There is also a lack of detail regarding the specifics of how companies will implement these commitments.

AI Discussion

Me: It discusses OpenAI and other leading AI labs making a set of voluntary commitments to reinforce safety, security, and trustworthiness of AI technology and services. It's a step towards advancing AI governance in the US and around the world.

Friend: That's really interesting. What are the implications of this?

Me: Well, the article outlines a list of commitments from companies that are intended to promote the safe, secure, and transparent development and use of AI technology. These commitments could help to build public confidence in the technology and help ensure that it is being used responsibly. They could also help to inform the development of regulations and policies related to AI governance. Additionally, these commitments could help to encourage information sharing among companies and governments regarding trust and safety risks, as well as help to promote research into areas such as AI safety and privacy.

Action items

Research existing laws and regulations related to AI safety, security, and trustworthiness.
Develop a multi-faceted, specialized, and detailed red-teaming regime for all major public releases of new models.
Invest in cybersecurity and insider threat safeguards to protect proprietary and unreleased model weights.

Technical terms

Mobile Navigation: A feature on a website or app that allows users to quickly navigate to different sections of the site or app.
GPT-4: Generative Pre-trained Transformer 4, a natural language processing model developed by OpenAI.
DALL·E 2: A natural language processing model developed by OpenAI that can generate images from text descriptions.
API: Application Programming Interface, a set of protocols and tools for building software applications.
Red-teaming: A security practice in which a team of experts simulate an attack on a system to identify potential vulnerabilities.
White House: The executive branch of the United States government.
Provenance: A record of the origin and history of a particular item or product.
Watermarking: A process of adding a digital watermark to a digital image or video to identify the source of the content.
Bounty systems: A system in which a company offers rewards for the discovery of vulnerabilities in their products or services.
Adversarial testing: A process of testing a system by simulating attacks from malicious actors.