Direct Human-AI Comparison in the Animal-AI Environment

Abstract

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing (such as Chess and Go) towardsability-oriented testing, in which AI systems are tested on their capacity to solve certainkindsof novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10 (n = 52). We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.

Is Artificial Intelligence Viz M and D Learning Could Be the Solution Promising Alternative to Animals: A Learning-Based Toxicity Recitation?

“…This fact is problematic and leads to further discussion of the ethical consequences of suffering in these animals, with many initiatives against animal experimentation and helping others. In addition to the complex ethical issues introduced, there are also concerns about the accuracy of the animal paradigm for mimicking what typical drugs can do in the human body 6,8 especially when it comes to emulating animal toxicology.…”

“…It bodes well if we accept the interpretation that there is a balance between our evolution and commitment to our planetary stewardship agenda. One answer is to use existing data using computer models, a field that has grown rapidly in recent years, including the perspectives 3,6 of pharmacologists, neuroscientists, toxicologists, statisticians, biologists, mathematicians and engineers to view and make accurate predictions.…”