mlsanityData to 14 Oct 2026

Unlocking the Potential of Global Human Expertise

arXiv 2411.00156 ↗First posted 31 Oct 2024cs.AI cs.CY cs.NEcited by 0

Not on the map. Why?

Abstract (arXiv)

Solving societal problems on a global scale requires the collection and processing of ideas and methods from diverse sets of international experts. As the number and diversity of human experts increase, so does the likelihood that elements in this collective knowledge can be combined and refined to discover novel and better solutions. However, it is difficult to identify, combine, and refine complementary information in an increasingly large and diverse knowledge base. This paper argues that artificial intelligence (AI) can play a crucial role in this process. An evolutionary AI framework, termed RHEA, fills this role by distilling knowledge from diverse models created by human experts into equivalent neural networks, which are then recombined and refined in a population-based search. The framework was implemented in a formal synthetic domain, demonstrating that it is transparent and systematic. It was then applied to the results of the XPRIZE Pandemic Response Challenge, in which over 100 teams of experts across 23 countries submitted models based on diverse methodologies to predict COVID-19 cases and suggest non-pharmaceutical intervention policies for 235 nations, states, and regions across the globe. Building upon this expert knowledge, by recombining and refining the 169 resulting policy suggestion models, RHEA discovered a broader and more effective set of policies than either AI or human experts alone, as evaluated based on real-world data. The results thus suggest that AI can play a crucial role in realizing the potential of human expertise in global problem-solving.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 79 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

23 of 79 references.

References · 23

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2017A Survey on Multi-Task Learningcited by 840
  4. 2022A Generalist Agentcited by 808
  5. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  6. 2013Do Deep Nets Really Need to be Deep?cited by 474
  7. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
  8. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  9. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 177
  10. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
  11. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 150
  12. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
11 more
  1. 2023Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritizationcited by 48
  2. 2023Inverse Reinforcement Learning without Reinforcement Learningcited by 36
  3. 2018Multi-objective Contextual Bandit Problem with Similarity Informationcited by 21
  4. 2019Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernelcited by 19
  5. 2022Runtime Analysis for the NSGA-II: Provable Speed-Ups From Crossovercited by 14
  6. 2022From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Boundscited by 13
  7. 2021The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciencescited by 12
  8. 2020Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescriptioncited by 7
  9. 2020The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddingscited by 7
  10. 2019Creative AI Through Evolutionary Computationcited by 3
  11. 2022Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)cited by 2

Built on by

Not cited yet.