Remember IBM’s Watson, the AI Jeopardy! champion? A 2010 promotion proclaimed, “Watson understands natural language with all its ambiguity and complexity.” However, as we saw when Watson subsequently failed spectacularly in its quest to “revolutionize medicine with artificial intelligence,” a veneer of linguistic facility is not the same as actually comprehending human language.
Natural language understanding has long been a major goal of AI research. At first, researchers tried to manually program everything a machine would need to make sense of news stories, fiction or anything else humans might write. This approach, as Watson showed, was futile — it’s impossible to write down all the unwritten facts, rules and assumptions required for understanding text. More recently, a new paradigm has been established: Instead of building in explicit knowledge, we let machines learn to understand language on their own, simply by ingesting vast amounts of written text and learning to predict words. The result is what researchers call a language model. When based on large neural networks, like OpenAI’s GPT-3, such models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning.
To read more, click here.