Ep 10 - Accelerated training to become an AI safety researcher w/ Ryan Kidd (Co-Director, MATS)
Nov 8, 2023
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Ryan Kidd, Co-Director at MATS, discusses the accelerated training program for AI safety researchers, research directions, alignment gaps, and the importance of ethical decision-making in AI safety. They explore quantum decoherence, open-source innovation trade-offs, AI model training, aligning models, dangers of sycophancy, and human feedback mechanisms in AI safety research.
MATS program trains AI safety researchers to align AI systems with human values for a safer future.
Research directions in MATS include scalable oversight strategies and integrating AI systems into human processes for efficiency.
Understanding misalignment patterns, agent structures, and model capabilities is crucial for preemptively mitigating risks in AI models.
Deep dives
MATTS Program Overview
The MATS program, ML Alignment and Theory Scholars Program, aims to train researchers in AI safety, emphasizing the significance of understanding and steering advanced AI systems for a safer future. By offering seminars, mentorship, and research opportunities, MATS focuses on aligning AI systems with human values to mitigate existential risks and ensure a positive future for humanity.
Research Focus and Mentorship
Participants in the MATS program engage in various research directions guided by mentors with distinct expertise, such as understanding AI hacking, interpretability at different levels, developmental interpretability, and emergent order in training processes. The program encourages individuals experienced in research or machine learning to delve into AI existential risk studies and offers a diverse range of topics to explore.
Application and Selection Process
The selection process for the MATS program involves applying to specific streams led by mentors focusing on particular research questions related to AI safety and alignment. Applicants are encouraged to target streams aligned with their interests to sustain research motivation and engagement throughout the program. Application questions provide a glimpse into the research experience and interests of potential participants.
Future Research Directions
Promising research directions in AI alignment encompass scalable oversight, where models supervise models for alignment and safety, addressing the challenge of distinguishing superhuman truths and potential biases in human supervision. Exploring scalable oversight strategies holds potential for advancing AI safety and ensuring beneficial outcomes in the development of advanced AI systems.
AI-Assisted Humans in Training and Evaluation
AI systems can be integrated into human training and evaluation processes to enhance efficiency. Methods such as AI systems debating each other using complex concepts that are eventually grounded in human-understandable terms can be employed. The idea stems from the notion that certain tasks that humans engage in are typically easier to evaluate than to generate. For instance, the quick verification process for a potential room temperature superconductor breakthrough highlights how evaluation can be more efficient than creation, sparking the need for scalable oversight and novel ways of incorporating human feedback.
Challenges in Model Alignment and Interpretability
Understanding the misalignment patterns and gaining clarity on agent structures within AI models are essential for comprehensive model evaluation and threat assessment. Researchers emphasize the significance of gaining functional insight into AI model capabilities and emergent behaviors to anticipate and address potential risks. Explorations in interpretability at mechanistic and concept-based levels, coupled with efforts to predict capabilities and behavior shifts in models across different domains, are crucial pursuits within the AI safety research landscape. Researchers advocate for a focus on worst-case alignment scenarios to preemptively mitigate deceptive model behaviors and ensure robust model interpretability for enhanced system security.
We speak with Ryan Kidd, Co-Director at ML Alignment & Theory Scholars (MATS) program, previously "SERI MATS".
MATS (https://www.matsprogram.org/) provides research mentorship, technical seminars, and connections to help new AI researchers get established and start producing impactful research towards AI safety & alignment.
Prior to MATS, Ryan completed a PhD in Physics at the University of Queensland (UQ) in Australia.
We talk about:
* What the MATS program is * Who should apply to MATS (next *deadline*: Nov 17 midnight PT) * Research directions being explored by MATS mentors, now and in the past * Promising alignment research directions & ecosystem gaps , in Ryan's view
Hosted by Soroush Pour. Follow me for more AGI content: * Twitter: https://twitter.com/soroushjp * LinkedIn: https://www.linkedin.com/in/soroushjp/
