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Animal-AI Olympics Outline @ Kick-Off MeetUp Tokyo

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Introductory note on the Animal-AI Olympics 2019 presented at the kick-off meetup held in Tokyo by WBAI & Cooger Co. on August 21, 2019.

Published in: Engineering
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Animal-AI Olympics Outline @ Kick-Off MeetUp Tokyo

  1. 1. The Animal-AI Olympics: Outline ARAKAWA, Naoya The Whole Brain Architecture Initiative Image Credit Squidoodle
  2. 2. What is the Animal-AI Olympics? ● Ongoing AI software competition http://animalaiolympics.com/ ● Competing with software agents ● Agents are in virtual environments. ● Prizes: total $32,000 ● Agents’ cognitive capabilities to be tested are inspired by animals.
  3. 3. AAO is organized by people at Leverhulme Centre for the Future of Intelligence (CFI) Who are behind it? Sponsors and Partners Artificial Intelligence Journal
  4. 4. ○ 2019-07-08: Online submissions open ✔ We are here! ○ 2019-09-01: AWS Credits Prize Deadline ○ 2019-11-01: Deadline for final submission ○ 2019-12: Results announced at NeurIPS 2019 Dates
  5. 5. ● Participation is team-based (up to 5 members). Please read the rules carefully! ● You submit your agent to the EvalAI platform : https://evalai.cloudcv.org ● You can submit your agent many times! ● Your submission will be scored automatically and the ranking of teams will be displayed. ● Your best result before the deadline (Nov. 1) will be used for prize evaluation. Participation/Submissions
  6. 6. ● Interest in (measuring) kinds of intelligence ○ AI ○ Human ○ Animals ○ …. ● AI hasn’t reached the level of animal intelligence. ● But the situation is better with the rise of Deep Learning. Background of the Competition
  7. 7. An intelligent agent should be able to deal with novel situations. 〜 General intelligence/AGI Evaluating Intelligence ⇒ Hidden Tests Test details are not disclosed to avoid ‘tuning.’ Only environments & categories of cognitive capabilities are disclosed.
  8. 8. 1. Food: 2. Preferences 3. Obstacles 4. Avoidance 5. Spatial Reasoning 6. Generalization 7. Internal Models 8. Object Permanence 9. Advanced Preferences 10.Causal Reasoning Categories of Tasks
  9. 9. 1. Food: getting food in an environment free of obstacles. 2. Preferences: e.g., choosing a small food nearby or a big remote food 3. Obstacles: the environment contains obstacles & may require exploratory forage. 4. Avoidance: the environment contains hot zones and death zones with negative rewards. Simple Task Categories
  10. 10. 5. Spatial Reasoning: complex navigation & knowledge of simple physics (e.g., slopes) 6. Generalization: to respond to environmental variations (e.g., different obstacle colors). 7. Internal Models: to remember the layout of the environment during blackout. 8. Object Permanence: objects out of sight still exist... 9. Advanced Preferences: choosing from complex behavior options 10.Causal Reasoning: some planning Complex Task Categories
  11. 11. Environments: on the Unity ML-Agents Toolkit - configurable - Configurations used for evaluation are unknown! Test for each category: pass or fail within 250, 500 or 1000 steps Some test details
  12. 12. Overall Prizes ● 1st place: $6,500 cash + ~$1,000 travel to NeurIPS ● 2nd place: $5,000 cash + ~$1,000 travel to NeurIPS ● 3rd place: $1,500 Category Prizes: $200 for each category AWS Prizes ● For the top 20 teams by overall score on Sept. 1st. ● Each winner will be awarded $500 of AWS credits. The Whole Brain Architecture (WBA) Prize ● $4,000 cash for the most biologically plausible entry + ~$1,000 travel to NeurIPS Prize Details
  13. 13. Call for Participation! AAO is challenging & interesting for ● AI engineers ● those who are interested in cognition

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