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Complete your application – Enter the rest of the information required for the IC Postdoc Program Research Opportunity. The application itself contains detailed instructions for each one of these components: availability, citizenship, transcripts, dissertation abstract, publication and presentation plan, and information about your Research Advisor co-applicant.
Additional information about the IC Postdoctoral Research Fellowship Program is available on the program website located at: https://orise.orau.gov/icpostdoc/index.html.
If you have questions, send an email to ICPostdoc@orau.org. Please include the reference code for this opportunity in your email.
Research Topic Description, including Problem Statement:
Artificial Intelligence (AI) represents a new paradigm for the Intelligence Community. While AI promises to automate many collection and analytic processes, it does not come without risk, particularly as AI systems develop layers of optimizers to improve results and/or automate solutions. As this occurs there will be a misalignment from the developer's perspective and the AI model. Specifically, AI systems are mesa optimizers, meaning that their internal optimization functions (e.g., reward functions) will not necessarily converge with the learned model (generally speaking, the programmer’s goal for the AI system).1 This leads to misalignment between the objectives of the AI system and those of their human programmers. Furthermore, as more capable AI systems are given meaningful control over critical systems, the ability to control or influence AI decision making diminishes (termed the “Control Problem”). As the IC continues to operationalize AI, this understanding will improve models in everything from enterprise image detection and natural language processing to better understanding and interdiction of control risks before they manifest in critical national security systems
1see Hubinger et al.'s "Risks from Learned Optimization in Advanced Machine Learning Systems.”
Example Approaches:
Develop mathematical models of decision-making in deep learning neural networks; Explore statistical methods of minimizing bias in training data sets; Examine potential game-theoretic solutions to the Control Problem; Develop mathematical models of reinforcement learning systems; Examine mathematical approaches to understanding mesa optimization.
Relevance to the Intelligence Community (IC):
As the IC continues to operationalize AI, this understanding will improve models in everything from enterprise image detection and natural language processing to better understanding and interdiction of control risks before they manifest in critical national security systems.
Key Words: #Artificial Intelligence, #Control Problem, #Machine Learning, #Deep Learning, #Explainability, #Alignment, #Critical Systems, #Mesa Optimization, #Complexity, #Game Theory
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