Why?
Natural language processing (NLP) is a set of techniques for teaching computers to understand human language. It’s a subfield of artificial intelligence (AI) that has been gaining popularity in recent years as more and more companies adopt chatbots and other language-based technologies.
However, NLP has its limits. In particular, it cannot teach computers to understand the context in which language is used. This is because context is often implicit in human communication and cannot be expressed in the symbols and rules used by computers.
As a result, NLP systems will never be able to approximate human understanding. They will always fall short in their ability to interpret the nuances of language and to understand the intentions of the speaker.
This limitation is not just theoretical. It has been demonstrated in practice by a number of studies that have shown that NLP systems perform significantly worse than humans when it comes to tasks that require understanding context.
One such study was conducted by researchers at the University of Washington. They found that a state-of-the-art NLP system was only able to answer 55% of questions about a short story when the questions required understanding context. In contrast, humans answered 87% of the same questions correctly.
Another study, conducted by researchers at Stanford University, found that NLP systems performed worse than humans on a task that required understanding the sentiment of a sentence. The NLP system only got the sentiment right 50% of the time, while humans were accurate 70% of the time.
These studies show that NLP systems are far from perfect and that there is still a long way to go before they can match human performance on tasks that require understanding context.
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