Natural language processing (NLP) has become one of the most important fields in data science. Natural language processing (NLP) is the general term for natural language understanding (NLU), natural language generation (NLG), and natural language interaction (NLI).
Nowadays, natural language technology is increasingly used in enterprises. Many companies have developed numerous text analysis, speech recognition, chatbots, and all imaginable language processing use cases.
With the continuous development and innovation of technology companies such as Google and Microsoft, natural language processing (NLP) has achieved leaps and bounds in accuracy, speed, and methods, which can help computer scientists solve more complex problems. Natural language processing (NLP) has now become one of the most researched fields in the field of artificial intelligence.
Natural language understanding
Human language is very complicated, because language represents human thought. This makes natural language understanding (NLU) one of the so-called “difficult artificial intelligence” problems, because the problem faced by natural language understanding (NLU) is the problem of generalized artificial intelligence.
Therefore, although the natural language understanding (NLU) technology is still immature, the popularity of natural language understanding (NLU) has been proven in enterprise applications.
Natural language understanding (NLU) is used to perform sentiment analysis on customers and understand the questions posed to digital assistants such as Siri and Alexa. It can also perform multilingual text translation through neural machine translation services (such as Google Translate).
What people need to know is that before the emergence of comprehensive generalized artificial intelligence, natural language processing (NLP) needs continuous development.
The development of language models such as GPT is motivating companies to develop many methods and applications of machine intelligence. These applications range from being able to describe network applications in language to imitating language models of public figures, to training through medical literature to provide diagnosis.
Use cases
According to the survey, hundreds of thousands of people around the world are engaged in GPU computing, machine learning and deep learning, especially at the enterprise level. The important thing is that in order to succeed and drive business value, the scope of artificial intelligence projects may be narrow.
Artificial intelligence projects like OpenAI’s GPT-3 have attracted widespread attention because these technologies provide an encouraging vision for people’s future development. However, the scope of enterprise projects and deployments that drive real value today is narrow and can bring specific business value.
One of the most exciting things about natural language understanding (NLU) or natural language processing (NLP) is that the model can be improved over time. In many applications, the accuracy of the model can be increased by a few percentage points, which can bring millions of dollars in value to the basic business of the enterprise. Therefore, it is important to have a predictable deployment system to continue to deliver value over time.