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.
Highlights
You’ve probably heard that we’re in the midst of an AI revolution
The mathematician and philosopher Gian-Carlo Rota famously asked, “I wonder whether or when AI will ever crash the barrier of meaning.”
The lack of humanlike understanding in machines is underscored by recent cracks that have appeared in the foundations of modern AI
Summary
You’ve probably heard that we’re in the midst of an AI revolution. We’re told that machine intelligence is progressing at an astounding rate, powered by “deep learning” algorithms that use vast amounts of data to train complicated programs known as “neural networks.” Today’s AI programs can recognize faces and transcribe spoken sentences. Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning.
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