ΑΙhub.org
 

AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity


by
14 April 2020



share this:

AAAI squirrel AI award
The AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity is a new prize, to be awarded for the first time at the Association for the Advancement of Artificial Intelligence (AAAI) conference in February 2021. It recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways.

The award will be an annual event and is accompanied by a prize of $1,000,000 plus travel expenses to the conference. The award is administered by AAAI, with support from the European Artificial Intelligence Association (EurAI) and the Chinese Association for Artificial Intelligence (CAAI). Financial support for the award is provided by Squirrel AI.

The deadline for nominations is May 24, 2020 and you can find the online nomination form here.

Candidates may be individuals, groups, or organizations that are directly connected with the main contribution stated in the nomination. Qualifications or technical knowledge in artificial intelligence are not requirements for nominations. The emphasis is on the significance and impact of the work.

The award will be judged by a prestigious committee:

  • Yoshua Bengio is professor in the Department of Computer Science and Operations Research at the Universite de Montreal. He was a joint winner of the 2018 ACM A.M. Turing Award.
  • Tara Chklovski is CEO and founder of global tech education non-profit Technovation (formerly Iridescent).
  • Edward A Feigenbaum is Kumagai Professor of Computer Science Emeritus at Stanford University. He won the 1994 ACM Turing Award
  • Yolanda Gil (Award Committee Chair) is Director of Knowledge Technologies at the Information Sciences Institute of the University of Southern California, and Research Professor in Computer Science and in Spatial Sciences. She is the current President of the AAAI.
  • Xue Lan is Cheung Kong Chair Distinguished Professor and Dean of Schwarzman College, and Dean Emeritus, School of Public Policy and Management in Tsinghua University.
  • Robin Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M and directs the Center for Robot-Assisted Search and Rescue.
  • Barry O’Sullivan holds the Chair in Constraint Programming at University College Cork in Ireland. He is the current President of the European AI Association

To find out more visit the AAAI website.



tags: ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

A multi-armed robot for assisting with agricultural tasks

and   27 Mar 2026
How can a robot safely manipulate branches to reveal hidden flowers while remaining aware of interaction forces and minimizing damage?

Resource-constrained image generation and visual understanding: an interview with Aniket Roy

  26 Mar 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.

RWDS Big Questions: how do we highlight the role of statistics in AI?

  25 Mar 2026
Next in our series, the panel explores the statistical underpinning of AI.

A history of RoboCup with Manuela Veloso

  24 Mar 2026
Find out how RoboCup got started and how the competition has evolved, from one of the co-founders.

Information-driven design of imaging systems

  23 Mar 2026
Framework that enables direct evaluation and optimization of imaging systems based on their information content.

Machine learning framework to predict global imperilment status of freshwater fish

  20 Mar 2026
“With our model, decision makers can deploy resources in advance before a species becomes imperiled.”

Interview with AAAI Fellow Yan Liu: machine learning for time series

  19 Mar 2026
Hear from 2026 AAAI Fellow Yan Liu about her research into time series, the associated applications, and the promise of physics-informed models.

A principled approach for data bias mitigation

  18 Mar 2026
Find out more about work presented at AIES 2025 which proposes a new way to measure data bias, along with a mitigation algorithm with mathematical guarantees.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence