Investigating the possibilities for bi-directional knowledge transfer between biological and artificial intelligence.

AI has made rapid progress in recent years, overtaking human performance on complex tasks. However, there remain many areas where AI cannot compete with even simple biological organisms. Such areas include understanding object permanence and intuitive physics, one-shot learning, intelligent exploration and generalising to unexpected situations. This project explores these areas looking towards understanding and then closing the gap between biological and artificial intelligence.

The Animal-AI Olympics

The Animal-AI Olympics is an AI competition using experiments translated from the animal cognition literature. Animal cognition has a long history of creating ingenious experiments for empirically investigating intelligent behaviour across biological species. A standard experimental paradigm begins by familiarising an animal with a new apparatus and training it until it can successfully complete a simple task. Once the animal achieves a certain level of performance, a new dimension is introduced to the task, constructed to test whether the animal has learnt generalisable properties to solve the problem or was just relying on blindly repeating previously successful actions. This approach is in stark contrast to most modern AI testbeds, where the training dataset is often drawn from exactly the same source as the test set. It is only recently that progress in deep learning has made it possible to even consider such a competition. We hope that it will illuminate the recent successes in AI, and also identify areas in which there is still a long way to go to meet animal-level general intelligence.

Resources

• Visit the Animal-AI Testbed website where you can play the tests yourself and watch the top agents perform. (You can also download the environment at Github)

• Read media coverage of the first Animal-AI Olympics held in 2019: 
  Digital Trends, MIT Technology Review, IEEE Spectrum and New Scientist.

• Read our article in Nature Machine Intelligence.
• Read our paper in  Proceedings of Machine Learning Research.

• Watch a video of Matt Crosby's presentation on the project at the London Machine Learning Meetup (July 2019).

External Collaborators from GoodAI 

• Olga Afanasjeva, COO of GoodAI 
• Marek Havrda, Strategy Advisor GoodAI
• Marek Rosa, CTO and CEO of GoodAI