Neural networks (Computer science), Meaning (Philosophy), Understanding
Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.
Mitchell, M. (2019). Artificial Intelligence Hits the Barrier of Meaning. Information, 10(2), 51.