ACM Special Interest Group on Artificial Intelligence

We promote and support the growth and application of AI principles and techniques throughout computing

Call for Nominations: The 2023 ACM SIGAI Industry Award for Excellence in Artificial Intelligence

The ACM SIGAI Industry Award for Excellence in Artificial Intelligence (AI) will be given annually to individuals or teams who have transferred original academic research into AI applications in recent years in ways that demonstrate the power of AI techniques via a combination of the following features: originality of the research novelty and technical excellence of the approach; importance of AI techniques to the approach; and actual or predicted societal impact of the application. Awardees receive a plaque accompanied by a prize of $5,000, and will be recognized at the International Joint Conference on Artificial Intelligence through an agreement with the IJCAI Board of Trustees.

After decades of progress in the theory, research and development of AI, AI applications are increasingly moving into the commercial sector. A great deal of pioneering application-level work is being done by those transferring research results into industry—from startups to large corporations—and this is influencing commerce and the broad public in a wide variety of ways. This award complements the numerous academic, best-paper and related awards, in that it focuses on innovators of fielded AI applications. It is intended especially to recognize those who are not only active in the academic community, but also playing key roles in AI commercialization. The award honors these innovators and highlights their achievements (and thus the benefit of AI techniques) to computing professionals and the public at large. The award committee will consider applications that are open-source or proprietary and that may or may not involve hardware.

Evaluation Criteria

The criteria include the following, but there is no fixed weighting of them:

  • Novelty of application area
  • Novelty and technical excellence of the approach
  • Importance of AI techniques for the approach
  • Actual and predicted societal benefits of the fielded application
Eligibility Criteria

Any individual or team, worldwide, is eligible for the award.

Nomination Procedure

One nomination and three endorsements must be submitted. The nomination must identify the individual or team members, describe their fielded AI system, and explain how it addresses the award criteria. The nomination document itself should not be longer than 4000 words (10 pages) including all tables, references, and figures.

The nomination must be written by a member of ACM SIGAI. Two of the endorsements must be from members of ACM or ACM SIGAI. Endorsements are intended to be brief statements of support (typically 1-2 paragraphs and should not exceed 1000 words) that provide additional perspective on the nomination itself.

If you are not a member of ACM SIGAI, please join here:

Please submit the nomination and endorsements through our Google form:

For any questions please contact Craig Boutilier (Award Chair, or Nicholas Mattei (SIGAI Vice Chair,

  • Nominations Due: May 31, 2023
  • Award Announcement: June 30, 2023
  • Award Presentation: Aug 19th – 25th at IJCAI 2023,


Past editions

Winner of 2022 ACM SIGAI Industry Award


The Industry Award for Excellence in Artificial Intelligence is awarded by ACM SIGAI in 2022 to Sony’s Gran Turismo Sophy(TM), a project developed by Sony AI, Sony Interactive Entertainment and Polyphony Digital. Gran Turismo (GT) Sophy is a collection of agents trained using reinforcement learning (RL) techniques to race in Gran Turismo, a hyper-realistic, physics-based automotive racing simulator. The GT Sophy team developed novel, state-of-the-art RL methods for this purpose. Racing against some of the world’s best e-sports drivers, GT Sophy has not only performed at world-class levels, it also won a team event in October 2022 by an impressive margin. GT Sophy has demonstrated that SOTA RL can be applied effectively to continuous control problems requiring performance at the edge of human capabilities, while respecting the informal norms and protocols associated with racing. Apart from the impact on gaming, the technology offers potential societal benefits in areas such as simulation-based training and autonomous vehicle development, among others.

More information: Nature Paper, Project Homepage, Keynote from Peter Wurman, Director of Sony AI America

Project leads: Peter R. Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Peter Stone, Michael Spranger

Full list of team members: Peter R. Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, HaoChih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Winner of 2021 ACM SIGAI Industry Award

The Industry Award for Excellence in Artificial Intelligence is awarded by ACM SIGAI in 2021 to DrAid, an intelligent assistant for radiologists developed by VinBrain, a subsidiary of Vingroup in Vietnam. DrAid assists in the interpretation of chest x-rays for diagnosis of clinical syndromes, and the end-to-end support of clinical management tasks. DrAid brings together and enhances state-of-the-art AI techniques-multi-class classification and structured prediction, active learning, adversarial training, convolutional networks, speech recognition, Text2Speech and among others into a comprehensive cognitive service. The system has been a major breakthrough valued by practitioners for its initial (and growing) impacts via deployment in many hospitals. Therefore, VinBrain exemplifies social values based upon AI to drive changes to healthcare and patient outcomes.

The award will be presented at IJCAI 2021.

More information: ProductAlgorithm, Applications for Covid-19TuberculosisUser Guide

Team Leads: Steven Quoc Hung Truong, Mudit Jain, and Trung Bui

Team Members: Nguyen Minh Man, Nguyen Do Trung Chanh, Nguyen Van Duong, Hoang Vu, Nguyen Hoang Nam, Phan Hoan Vu, Huynh Minh Thanh, Ta Duc Huy, Nguyen Ngoc Hoang, Dang Tran Kien, Nguyen Thi Phuong Anh, Duong Quy Giap, Tran Anh Duc, Do Nam Phuong, Nguyen Van Thang, Bui Duc Thai Tan, Nguyen Nhat Linh, Dinh Thi Hong Duyen, Nguyen Duong Du, Nguyen Thi Thai Ha, Le Thi Thuy Ha, Vo Tan Loc, Huynh Thai, Hoang Minh Truong, Pham Duc Tuyen, Nguyen Manh Hung, Tran Minh Quan

2020 ACM Industry Award

Due to the pandemic, the 2020 Industry Award was unable to go forward.


Winner of 2019 ACM SIGAI Industry Award

The selection committee for the ACM SIGAI Industry Award for Excellence in Artificial Intelligence (AI) is pleased to announce that the Decision Service created by the Real World Reinforcement Learning Team from Microsoft, has been chosen as the winner of the inaugural 2019 award. The committee was impressed with the identification and development of cutting-edge research on contextual-bandit learning, the manifest cooperation between research and development efforts, the applicability of the decision support throughout the broad range of Microsoft products, and the quality of the final systems. All these aspects made the Microsoft team well worthy of this award.

 The award was presented at IJCAI 2019

More information: ProductAlgorithmWorkshopPaper

Learn more about the Real World Reinforcement Learning team

 Team Leads: John Langford and Rafah Hosn

Team Members: Alekh Agarwal, Jacob Alber, Sarah Bird, Rajan Chari, Roger Chen, Tyler Clintworth, Markus Cozowicz, Miro Dudik, Jack Gerrits, Luong Hoang, Edward Jezierski, Akshay Krishnamurthy, Rodrigo Kumpera, Sheetal Lahabar, Stephen Lee, Jiaji Li, Sharath Malladi, Dan Melamed, Dwaipayan Mukherjee, Gal Oshri, Oswaldo Ribas, Marco Rossi, Siddhartha Sen, Mandy Shieh, Alex Slivkins, Pavithra Srinath, Yann Stadnicki, Adith Swaminathan, Cheng Tan, Alexey Taymanov, Chenxi Zhao