Questions for Ashton Anderston concerning his talk about "Generative AI for Human Benefit"

[jamesallenevans]

Pose your (and uprank 5 others') questions here for Ashton Anderson about his 2024 ICLR paper "Designing Skill-Compatible AI: Methodologies and Frameworks in Chess", Karim Hamade, Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, and Ashton Anderson. and associated talk on Generative AI for Human Benefit: Lessons from Chess: Artificial intelligence is becoming increasingly intelligent, kicking off a Cambrian explosion of AI models filling thousands of niches. Although these tools may replace human effort in some domains, many other areas will foster a combination of human and AI participation. A central challenge in realizing the full potential of human-AI collaboration is that algorithms often act very differently than people, and thus may be uninterpretable, hard to learn from, or even dangerous for humans to follow. For the past six years, my group has been exploring how to align generative AI for human benefit in an ideal model system, chess, in which AI has been superhuman for over two decades, a massive amount of fine-grained data on human actions is available, and a wide spectrum of skill levels exist. We developed Maia, a generative AI model that captures human style and ability in chess across the spectrum of human skill, and predicts likely next human actions analogously to how large language models predict likely next tokens. The Maia project started with these aggregated population models, which have now played millions of games against human opponents online, and has grown to encompass individual models that act like specific people, embedding models that can identify a person by a small sample of their actions alone, an ethical framework for issues that arise with individual models in any domain, various types of partner agents designed from combining human-like and superhuman AI, and algorithmic teaching systems. In this talk, I will share our approaches to designing generative AI for human benefit and the broadly applicable lessons we have learned about human-AI interaction.

[bhavyapan]

Thank you for sharing your paper! Considering the vast scope of your work with the Maia project, focusing on creating generative AI models that closely mimic human style and skill in chess, a key aspect appears to be the development of AI that is not only superhuman in capability but also possesses the nuance to interact with humans in a way that is interpretable and beneficial. This duality presents a significant challenge in AI design, particularly in ensuring that such models can effectively teach or collaborate without overwhelming or misleading users. Do you anticipate this as a challenge for the use-case of the technology? Where do you see future research adapting to the skill aspect you mentioned in the limitations of the paper, and in which direction should innovation be directed?

[Jessieliao2001]

Thanks for your generous sharing! My curious question is: How does the Maia project address the challenge of aligning generative AI with human cognitive styles and decision-making processes, particularly in the context of chess, and what implications does this have for broader applications of human-AI collaboration in other fields?

[shaangao]

Really cool research! Over the recent months, an increasing amount of research effort has been devoted to the interaction between superhuman models and (relatively weaker) humans. One line follows from the proposal of weak-to-strong generalization problem by OpenAI, investigating how humans can effectively supervise superhuman models; another line focuses on augmenting human capabilities with strong model capabilities and enabling humans to learn from superhuman models. This paper focuses on the "teaching" field, but I wonder if the insights are also applicable to the weak-to-strong generalization realm -- by enabling the strong model (finetuned with weak decisions/labels in the first iteration) to effectively teach & assist the weak model in (re-)generating its decisions/labels, we might be able to iteratively improve the quality of decisions/labels made by the weak model, and subsequent finetuning of the strong model based on these refined weak labels might then elicit better performance from the strong model than naively finetuning on the original weak decisions/labels alone.

[saniazeb8]

Hi,

Thank you for sharing dynamic and intriguing research. I am interested to know more about your view on development of AI as we are also observing that some AI tools are deteriorating in their abilities considering the amount of increasing wrong responses. How can we optimize benefits for learning from AI in such circumstances.

[Anmin-Yang]

This is really an interesting topic. I wonder how would the skill-compatible AI introduced in your paper in the broader AI alignment context?

[oliang2000]

Thank you for sharing your paper! This work explores skill-compatible AI in team settings, particularly in Chess, where the weaker player collaborates with a stronger AI. I'm curious about the current state of research regarding AIs being compatible with opponent players to facilitate learning, such as the MAIA engines mentioned in your paper, and I'd like to know how your work relates to this research landscape.

[XiaotongCui]

Thanks for sharing! What strategies and considerations have you found most effective in ensuring that generative AI models, such as Maia in the realm of chess, align with human benefit? And how can these insights be translated to other domains where human-AI collaboration is essential?

[HamsterradYC]

Thanks for sharing this paper! While the paper indeed discusses the effectiveness of using low-skill AI, such as maia 1100, as training and evaluation partners for developing and testing skill-compatible AI, it primarily focuses on the interactions between AIs. I'm curious about the issue of designing adaptive AI models, especially in contexts involving more decision-makers, particularly with dynamically evolving human player skills and the variability of psychological factors.

[Kevin2330]

Your research on the Maia project and the development of AI systems that can adapt to and mimic human behavior in chess involves complex interactions between the AI models and human players. Given the emphasis on creating AI that not only predicts but also understands and adapts to human actions, to what extent did causal inference play a role in your methodologies? In the context of enhancing human-AI collaboration, how do you balance the importance of prediction accuracy with the need to understand the underlying causal mechanisms of decision-making differences across skill levels?

[volt-1]

Thanks for sharing this insightful paper. In what ways might the principles of designing AI to capture human style and ability, as seen in the Maia project, be applied to enhance text-to-image AI models to better align with human creative processes?

[zhian21]

This paper explores the development of skill-compatible AI agents in chess, demonstrating their ability to collaborate effectively with lower-skilled partners through novel frameworks and strategies. The study evaluates three agents (TREE, EXP, and ATT) against the AI chess engine LEELA, highlighting mechanisms like tricking and helping that enhance junior partner performance. It underscores skill compatibility as a distinct, measurable attribute, achieved through methodologies that hold potential for broader applications in human-AI interaction across various domains.

Given the study's insights on enhancing skill compatibility in AI agents for chess, how could these approaches be adapted to improve collaborative human-AI interactions in fields requiring complex decision-making, such as autonomous driving or personalized education?

[Yuxin-Ji]

Thanks for sharing your work! It is interesting to learn that a weaker but more skill-compatible agent could beat stronger superhuman agents, in a sense that they are better collaborators. My question is: how generalizable is this type of skill-compatible agent to other human-AI decision-making scenarios? For example, in healthcare or education?

[Hai1218]

How can the principles of skill-compatibility, as demonstrated in the collaboration between chess engines of differing strengths, be applied to the design of AI-based decision aids in critical domains (such as healthcare, finance, and disaster response) to enhance human-AI collaboration, ensuring that AI systems not only complement but elevate human decision-making capabilities across varying levels of expertise?

[secorey]

Hi Prof. Anderson, thanks for presenting your work. In your paper, you lay out the STT and HB frameworks for chess interactivity. How well do you think these frameworks map onto other domains? For example, in the context of self-driving cars, the STT framework is more intuitive to me—would you agree, or do you think both could be implemented?

[ecg1331]

I thought the analogy you made comparing the AI to a coach was really interesting.

After you made this comparison, I began to wonder if the AI mentioned in your paper is a specific type of AI ( one that is more compatible with lesser-skilled counterparts) or if you recommending that all AI should become adaptable to different skill levels. And if you are, what would that look like?

Thank you!