Artificial Intelligence Hits the Barrier of Meaning

Abstract

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.

Keywords

• deep neural networks
• meaning
• understanding