Special projects

OpenAI Blog News

Summary

OpenAI announces special project areas for machine learning experts to work on, including detecting covert AI breakthroughs, building competitive programming agents, cybersecurity defense, and complex multi-agent simulations.

Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
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# Special projects Source: [https://openai.com/index/special-projects/](https://openai.com/index/special-projects/) OpenAIImpactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter\. Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter\. In this post, we list several problem areas likely to be important both for advancing AI and for its long\-run impact on society\. We see these problems as having either very broad implications, or addressing important emerging consequences of AI development\. If you are a strong machine learning expert and wish to start an effort on one of these problems at OpenAI, please[submit an application⁠](https://openai.com/careers/)\. 1. **Detect if someone is using a covert breakthrough AI system in the world\.**As the number of organizations and resources allocated to AI research increases, the probability increases that an organization will make an undisclosed AI breakthrough and use the system for potentially malicious ends\. It seems important to detect this\. We can imagine a lot of ways to do this—looking at the news, financial markets, online games, etc\. 2. **Build an agent to win online programming competitions\.**A program that can write other programs would be, for obvious reasons, very powerful\. 3. **Cyber\-security defense\.**An early use of AI will be to break into computer systems\. We’d like AI techniques to defend against sophisticated hackers making heavy use of AI methods\. 4. **A complex simulation with many long\-lived agents\.**We’re interested in building a very large simulation with lots of different agents in it that can interact with each other, learn over a long period of time, discover language, and accomplish a rich variety of goals\.

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