One of the dominant trends of artificial intelligence in the past decade has been to solve problems by creating ever-larger deep learning models. And nowhere is this trend more evident than in natural language processing, one of the most challenging areas of AI.
In recent years, researchers have shown that adding parameters to neural networks improves their performance on language tasks. However, the fundamental problem of understanding language—the iceberg lying under words and sentences—remains unsolved.
Linguistics for the Age of AI, a book by two scientists at Rensselaer Polytechnic Institute, discusses the shortcomings of current approaches to natural language understanding (NLU) and explores future pathways for developing intelligent agents that can interact with humans without causing frustration or making dumb mistakes.
Marjorie McShane and Sergei Nirenburg, the authors of Linguistics for the Age of AI, argue that AI systems must go beyond manipulating words. In their book, they make the case for NLU systems can understand the world, explain their knowledge to humans, and learn as they explore the world.