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!

[natashacarpcast]

Hi! Thank you for the interesting research.

I wonder if having AI as coaches in competitions (like chess) could create inequality among chess players. I assume not every chess player in the world would have access to AI, so I'm curious about how AI could become another privilege that benefits some people and puts others at a disadvantage.

[MaxwelllzZ]

Thank you for sharing the research with us. In your Maia project, you've explored the intersection of human and AI capabilities in chess. Given the uniqueness of individual cognitive styles and decision-making processes, how does Maia adapt to and learn from the diverse range of human chess-playing styles?

[JerryCG]

Dear Ashton,

This is a very interesting project that focuses on mimicking human behaviors instead of optimizing performance. From my understanding, the project is geered towards helping human learners improve their performance by first identifying their behavioral pattern and limitations, then proposing schemes to make progress. If it is the case, will human learners trained/facilitated by Maia have the potential to outperform optimizing AI agents?

Best, Jerry Cheng (chengguo)

[ksheng-UChicago]

Thanks for sharing. As you mentioned in your paper, this is an empirical proof-of-concept for skill-compatibility in chess. However, this concept seems promising in other human-compatible tasks beyond chess. What are the possible applications beyond chess do you think will be most relevant to explore for the next step?

[KekunH]

Dear Ashton, My questions are how can we ensure that AI tools continue to evolve positively while mitigating issues like increasing wrong responses, and what strategies can be applied to ensure that generative AI models align with human benefit, not just in chess but across other domains where human-AI collaboration is crucial?

[ymuhannah]

Thanks for sharing! Here is my question: considering the methodologies developed for creating skill-compatible AI agents in chess, how might these approaches be adapted or extended to other domains where AI-human or AI-AI interaction is critical? Specifically, what are the challenges and opportunities in applying the concepts of skill compatibility and inter-temporal collaboration, as demonstrated in the 'Stochastic Tag Team' and 'Hand and Brain' frameworks, to areas such as autonomous driving, collaborative robotics in manufacturing, or interactive educational tools?

[fabrice401]

An interesting paper! I learn the principles and methodologies developed in the Maia project, which focuses on creating chess AIs that mimic human playing styles and predict human moves. My question is how can these be applied to other fields where AI could augment human decision-making without overshadowing human expertise, particularly in complex, data-driven environments like healthcare, finance, or urban planning?

[yuzhouw313]

Hello, Professor Anderson, Thank you for sharing your work with us! Given the distinct approaches and capabilities of the Tree, Expector, and Attuned agents in the context of enhancing game strategy through artificial intelligence, how do their respective methodologies—Tree agent's future game state exploration using maia's policies, Expector agent's utilization of models for maximizing win probability, and Attuned agent's self-play reinforcement learning—compare in terms of efficiency and effectiveness in improving strategic decisions in complex games?

[MaoYingrong]

Thank you for sharing this great project! I think this is an innovative way to explore how to facilitate human-AI collaboration. Since a chess player may want to have opponents with different styles and capacities. The latter is easy to reach, but only generative models create the opportunity for a variety of styles. And I believe such style differentiation can be applied to many fields.

[nourabdelbaki]

Thank you for sharing this insightful project, Prof. Anderson! I found this paper super interesting as it demonstrated the effectiveness of skill-compatible AI agents in collaborative chess variants. I wonder, like many of my colleagues, how well would these agents generalize to other complex decision-making settings beyond chess? What are the specific characteristics of chess that make it a good model system for developing skill-compatible AI, or how could the proposed framework be readily adapted to other domains?

[ethanjkoz]

I see the potential in creating AI assistants that know how to deal with these less than ideal decisions in chess, but I am curious as to how these findings might apply to scenarios with much less clearly defined rules? Chess is heavily reliant on rules and taking turns, but how might an AI collaborator navigate situations where there are less clearly defined goals and more chaos (i.e. more scenarios and actors)?

[PaulaTepkham]

Thank you for your intriguing paper. As an avid AI user, I feel this paper is really interesting. I see AI as a tool to be able to enhance human ability to solve any kind of problem if we use it in the ethical right way! From the discussion and limitation part, you mentioned that "Our designed frameworks show that in situations where strong engines are required to collaborate with weak engines, playing strength alone is insufficient to achieve the best results; it is necessary to achieve compatibility, even at the cost of pure strength." which spark my curiosity about the level of strength and weakness of the engine that we talk about. Since you also mentioned that there are variety of technique to play chess. Could you please emphasize the strength level of engine?

[QIXIN-ACT]

Considering the advancements in generative AI as described in the exploration of human-AI collaboration through the Maia project in chess, where AI models are developed to replicate human decision-making processes and skills across a wide spectrum, how might such compatible-skill AI systems affect the labor market and employment landscapes?

[hchen0628]

Thank you very much for your insightful sharing. This perspective has opened up new avenues to imagine the relationship between humans and machines. I am also curious about when AI becomes proficient enough to collaborate with human partners and adapt to humans' suboptimal decisions, might it affect the development of human partners' skills and their capacity for independent decision-making due to a potential over-reliance on AI assistance? If it does have an impact, what attitude should we adopt towards this situation?

[nalinbhatt]

Within the paper, it is mentioned that some strong strategies shift to help improve the moves of the weak agents, whereas there are some strong strategies that move to encourage other team's weak agents to make more mistakes/blunders. The former socially compatible strategy that works with the weak agents is more desirable because it can be more conducive to learning. I am curious if there is a way to encourage strong strategies to be more compatible in the former way. Also, since chess is a zero-sum game, how well do you see some of the methodological concepts proposed here to transfer to games with multiple opponents that might have a preference over 1st place 2nd place ... etc or sometimes no opponents at all?

[yuhanwang7]

Thanks for sharing. It is very insightful to see how AI can perform under a well-learned rule. I am curious about how can the development of skill-compatible AI, as exemplified in collaborative chess frameworks, contribute to the design of AI systems that better complement human decision-making in complex problem-solving scenarios.

[anzhichen1999]

"Our paradigm is based on the following idea: skill-compatible agents should still achieve a very high level of performance, but in such a way that if they are interrupted at any point in time and replaced with a much weaker agent, the weaker agent should be able to take over from the current state and still perform well." Does it still work in the scenario where human collaboration is required? (Imagine a chess player and AI (on the same side) play consecutively against a common opponent.) What methodologies or frameworks are employed to test and validate the effectiveness of these skill-compatible agents in real-time strategic adjustments, and how do these methodologies account for the unpredictability of human decision-making in collaborative scenarios

[Weiranz926]

Thank you for your sharing! In your work with Maia, you've explored the alignment of generative AI with human behavior in the context of chess. As we move towards integrating AI more deeply into societal systems, such as in governance or legal decision-making, what challenges do you foresee in ensuring that AI models not only mimic human decision-making but also adhere to ethical and moral standards that are context-dependent and often subjective?

[essicaJ]

Thanks for sharing! Given the vast differences in how humans and AIs approach problem-solving, particularly in complex domains like chess, what methodologies did you employ to effectively integrate human cognitive models with AI? Were there specific aspects of human strategic thinking or decision-making processes that proved particularly challenging to model in Maia?

[yuy123337]

Hi professor Anderson. Thank you for sharing your inspiring work! After reading the discussion, I am wondering how might the development of AI systems that demonstrate compatibility with human creativity and strategic thinking challenge traditional notions of AI replacing human capabilities? Additionally, can AI be trained to enhance its ability to mimic and complement human creativity and strategic decision-making in various domains beyond chess? Does this mean that AI can learn human’s comparatively abstract creativity and standardize it?

[yunfeiavawang]

Thanks for sharing this amazing paper! My question is how can the strategies devised to build AI agents compatible with human skills in chess be transferred to other domains with crucial AI-human or AI-AI interactions? Particularly, what obstacles and advantages exist in implementing skill compatibility and inter-temporal collaboration principles in other fields?

[zhuoqingli526]

Thank you for your insights. This document introduces an intriguing direction for AI research, reminding me of how I always end up losing when playing chess against AI, which negatively affects my gaming experience. I'd like to know how this type of AI, which possesses both high performance and skill compatibility, performs in other scenarios, and whether the characteristics of different settings necessitate adjustments to the model methods.

[wenyizhaomacss]

Thank you for sharing this insightful study. I'm curious about the potential broader applications of the methodologies explored for developing AI chess agents that are compatible with human skill levels. How do you think these techniques could potentially be adapted or generalized to other domains where the interaction between AI and humans, or between multiple AI agents, is of critical importance? I'd be very interested to hear your thoughts on the extensibility of these approaches.

[Caojie2001]

Thank you for sharing your interesting work. I think the idea of creating AI agents that combine strong performance with skill compatibility is really attractive, especially at this time when the definition of high performance in AI has been developing quickly. Actually, I am curious about the methods that online chess applications, such as Chess, use to train their agents.

[Zhuojun1]

Thanks for sharing!In your research on human-AI collaboration in chess, you've developed models like Maia that mimic human play and predict human actions. Given the increasing importance of AI in various real-world applications, how do you envision the principles of your research being applied to enhance human-AI collaboration in more dynamic and unpredictable environments, such as disaster response or real-time medical decision-making, where the stakes are higher and the consequences of actions are more critical?

[Brian-W00]

Your research describes how the Maia model adapts to various human chess skill levels. What mechanisms does Maia employ to modify its complexity and decision-making processes to suit different skill levels? Additionally, how might these methodologies apply to other fields beyond chess?

[lguo7]

I am glad to know Maia model. Given the advancements in AI capabilities, especially in domains like chess where AI has surpassed human performance levels, Here is my question: In the context of chess, where AI models like Maia are designed to align closely with human thinking and capabilities across a spectrum of skill levels, does the interaction with or observation of superhuman AI performance influence human players to experience feelings of frustration or fear due to their comparative limitations? Moreover, do these AI models analyze and adapt to the potential changes in human behavior that might result from such emotional responses?

[C-y22]

Thank you for sharing your research! The paper presents a fascinating approach to addressing the challenge of compatibility between AI agents of varying skill levels in collaborative chess variants. This research opens up new avenues for exploring the interplay between AI systems of different skill levels and provides valuable insights into creating more effective and harmonious human-AI collaborations in complex decision-making scenarios. How do the three methodologies proposed in the paper contribute to creating skill-compatible AI agents in complex decision-making settings, particularly in the context of collaborative chess variants?

[zihua-uc]

Hi Prof. Anderson, thanks for sharing your work! You mentioned that Maia has had to play millions of games against human opponents to learn individual characteristics to act like specific people. What are your suggestions for research in the social sciences where often we do not have access to so much data?

[jialeCharloote]

Thanks for sharing! Given the potential for generative AI models like Maia to replicate and even surpass human abilities in specific domains such as chess, how do you address concerns about the potential loss of human creativity and agency in those areas, and what measures do you propose to ensure that human-AI collaboration remains beneficial and empowering for individuals? Thanks!

[jinyz1220]

Thank you for sharing your work! My question is: How does the development of AI models like Maia impact the future of skill-based professions, where both expertise and human judgment are critical?

[Pritam0705]

Thank you for sharing your work! I am curious about, what are the key challenges in designing AI systems that can successfully interact with less-skilled agents or humans in complex collaborative environments.

[kunkunz111]

Thanks for sharing! Fascinating exploration into the integration of AI with human capabilities, especially through your work on the Maia project in chess. Your paper delves into creating AI systems that not only surpass human skills but also emulate human decision-making processes. This approach seems particularly beneficial for educational and collaborative applications, where AI's understanding of human strategies and errors could greatly enhance learning and skill development. Could you discuss how this balance between AI superiority and human-like decision-making influences the design and implementation of such AI systems? Additionally, how do you see this impacting the broader field of AI, especially in terms of developing models that are more accessible and beneficial to users with varying levels of expertise?

[kangyic]

Thank you for sharing your work! What are the potential applications of this coach-student AI? Can it actually serve as a coach? Is it generalizable to other games other than chess? How are you planning to make it more natural (human-like)?

[lbitsiko]

Taking into account some of the challenges of GAI (e.g. understanding or mimicking humans), what are the most promising areas for applying your insights? In addition, how do they impact ethical frameworks for GAI?