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?

[kunkunz111]

Thank you for your insightful presentation on AI integration and data ethics, Professor Anderston. Considering the advancement in AI and its increasing role alongside human decision-making, how can we ensure that AI models, especially those trained on specific datasets, are both ethically managed and effectively generalized across diverse populations?

[essicaJ]

Thank you for sharing your work! The paper proposes three methodologies to create AI agents that are skill-compatible with weaker partners - the tree agent, expector agent, and attuned agent. How do these methodologies compare in terms of performance, complexity, and generalizability to other domains beyond chess? Thanks so much!

[ZenthiaSong]

Thank you for your presentation! In your work with the Maia project, you've successfully developed AI models that emulate human chess players across various skill levels. Considering these advancements, how might the principles and methodologies from Maia be adapted to other domains, such as creative arts or business decision-making, where the gap between AI capabilities and human performance might not be as pronounced?

[WonjeYun]

Thank you for the presentation. It is interesting in how you were able to come up with an idea that can be used for collaboration between strong and weak AI models. I wonder if this result can be connected with the recent issues with AI models: using up too much resource/energy for training. Maybe collaboration with strong and weak models can make the model perform at least similar to the state of the art, but with less resources?

[yiang-li]

Thank you for the presentation. Can you elaborate on the ethical framework developed for the Maia project, particularly how it addresses privacy and autonomy when individual models are capable of closely mimicking specific humans?

[zcyou018]

Thank you for sharing! I'm wondering in the context of designing skill-compatible AI, particularly in chess, how do the methodologies and frameworks presented in the paper impact human-AI collaboration in complex decision-making settings? What implications does this have for broader applications in AI systems that interact with human users of varying skill levels?

[xiaowei-v]

Thank you for sharing with us the interesting project. I am curious about what the human thinking pattern and its difference from the AI strategy tells us about the human thinking and the future direction of human-AI interaction.

[Vindmn1234]

What specific challenges and opportunities do you foresee in integrating these diverse data sources, such as survey panels, experience sampling studies, and social media, with the Screenomics framework?

[hantaoxiao]

Thanks for your sharing, How does the Maia model facilitate a deeper human-AI collaboration in chess? What specific features or functionalities allow it to adapt to various human skill levels?

[QichangZheng]

In the Maia project, low-skill AI models like Maia 1100 are used as training and evaluation partners for developing skill-compatible AI in chess, focusing on AI-AI interactions. However, the challenge of designing adaptive AI models that align with human cognitive styles and decision-making processes becomes more complex when considering multiple decision-makers, dynamically evolving human skills, and psychological factors. How well do the STT and HB frameworks for chess interactivity map onto other domains, such as self-driving cars, and what implications does this have for broader applications of human-AI collaboration?

[66Alexa]

Thanks for sharing us this interesting topic. Can you elaborate on the ethical framework you developed for issues that arise with individual AI models that mimic specific people? What are the key principles and how might they apply beyond the domain of chess?

[xinyi030]

Thanks for your sharing! How do you anticipate the skill-compatibility framework might evolve to handle more complex decision-making environments outside of chess, such as in healthcare, finance, or education?

[yunshu3112]

Hi Professor Anderson, I am very impressed by your work and I wonder how you envision the future of human-AI collaboration evolving based on the lessons learned from your research? Are there any emerging trends or developments that you believe will shape the landscape of AI-driven collaboration in the future?

[yuanninghuang]

This is a really cool paper! Your work demonstrates methods for creating AI agents that are 'skill-compatible' with weaker agents or partners, enabling productive collaboration between vastly differing skill levels. However, could there be potential negative consequences of such skill-compatible AI if deployed in the real world without proper safeguards? For example, might it enable manipulative or deceptive behaviors by the more capable AI agent towards the weaker partner? How would you propose mitigating such risks while still reaping the benefits of skill-compatible AI systems?

[vigiwang]

Hi Professor Anderson, thanks for your presentation! I was wondering about the training data that we used for the AI algorithm generation, is there any existing way that will help us to avoid algorithm bias in this process? For instance, how can we guarantee that the algorithm will benefit all parties in the society?

[yunfeiavawang]

Thanks for sharing! In particular, I'm intrigued about how the Maia project tackles the problem of generative AI's incompatibility with human cognitive styles and decision-making processes in the context of chess, and what this means for more general applications of AI-human cooperation in other domains.

[franciszz992]

I really enjoyed your talk though I did not understand the implications of the results, except for the suggestions in training chess-playing AI. What is so special about chess playing that you think it might serve as a great benchmark for AI training? The rules of chess is direct and simple, and the AI only cares about winning the game at the end of the day. In light of the current applications of AI to general useage in people's daily life, do you still think chess is a good benchmark?

[boki2924]

Thank you for sharing! Given your work with Maia and the development of generative AI models that align with human behavior in chess, what specific strategies or methodologies have proven most effective in ensuring that AI tools are interpretable and safe for human collaboration, and how can these strategies be applied to other domains beyond chess to enhance human-AI interaction?

[zimoma0819]

Thank you for sharing !! My question is what lessons from this approach can be applied to other domains where AI and human collaboration are critical?

[Adrianne-Li]

Thank you for sharing your insightful work on designing skill-compatible AI, particularly in chess. Your paper effectively highlights the interactions between AIs of varying skills, like Maia 1100, used as training and evaluation partners. However, I am particularly interested in the challenges of designing adaptive AI models that account for dynamically evolving human player skills and the impact of psychological factors. Could you elaborate on how your approaches might address these complexities in scenarios involving multiple decision-makers, where both the skill levels and psychological states of the players may vary significantly? Thank you for addressing this complex aspect of AI-human interaction.

Adrianne(zhuyin) Li
(CnetID: zhuyinl)

[beilrz]

Thank you for sharing the research. Out of the three agent designs in this paper, what are some suitable scenarios for each type of agent? Also, do agents in this paper ultimately seek to find the more robust moves that are resistant to less-than-ideal human input in the future (more room for error)?

[schen115]

Thanks for your sharing! That's a really interesting topic. My question is in your research on developing skill-compatible AI agents for collaborative chess, what specific mechanisms or design principles did you find most effective in ensuring these agents could successfully interact with and complement less-skilled partners? Additionally, how might these principles be adapted to enhance human-AI collaboration in other real-world applications where similar disparities in skill levels exist?

[Ry-Wu]

Thank you for sharing your amazing research! In your development of the Maia project, what are some of the key challenges you've faced in making sure these models are both beneficial and safe for interaction with humans of different skill levels?

[naivetoad]

How does the proposed "Hand and Brain" framework improve the skill-compatibility of AI agents in collaborative chess tasks compared to the traditional state-of-the-art chess engines like AlphaZero?

[icarlous]

Great research! With increased focus on superhuman models and weaker humans, how do insights from this paper on “teaching” apply to the weak-to-strong generalization problem? Can iteratively refining weak models’ decisions improve strong models’ performance?

[cty20010831]

Thanks for sharing! I am curious how Maia's ability to predict likely next human actions differs from traditional chess engines' focus on optimal moves. What challenges did you face in ensuring Maia's predictions align with human actions across different skill levels?

[Huiyu1999]

Thank you for sharing! My questions are: How can the insights gained from the Maia project be applied to enhance personalized learning and educational technologies? How can the lessons from the Maia project help overcome similar challenges in other domains involving human-AI interaction?

[Yunrui11]

Thank you for the presentation! Your research on designing skill-compatible AI in chess, such as the Maia project, is quite fascinating and presents a compelling model for broader human-AI collaboration. I am particularly interested in how Maia adjusts its playstyle to match human skill levels. Could you discuss the specific algorithms or methodologies used by Maia to predict and adapt to the likely next actions of human players across different skill levels? Additionally, what challenges did you face in ensuring that these AI models remain interpretable and beneficial to users with varying degrees of chess expertise? How do you see these principles being applied in other fields where AI interacts directly with humans?

[mingxuan-he]

Your work with Maia in capturing human style and ability in chess is fascinating. Could you delve deeper into the methodologies used to develop individual models that act like specific people and the ethical considerations you faced in this process? Additionally, how do these individual models enhance our understanding of human-AI interaction, and what implications do they have for designing generative AI in other domains?

[aliceluo1]

Thank you for sharing! This topic was intriguing. In the development of Maia, what were some key challenges you faced in capturing the diverse range of human styles and abilities in chess, and how did you address these challenges?