Use Cases For Natural Language Processing
Posted: Sat Feb 08, 2025 8:11 am
How NLP is Used Today
Natural language processing has come a long way since the creation of the Turing test. And while machines have yet to pass it, there are many new use cases for natural language processing that have developed especially in recent years.
With the rise of deep learning and neural network technology, the machine learning possibilities of NLP have increased exponentially, and the kind of language work it can do has become increasingly complex.
Google’s LaMDA represents the most sophisticated use case for NLP yet. It’s definitely a far cry from the more limited applications of ELIZA, and there have been many successive developments already in between.
Already, NLP tech can be used to generate fiction in uruguay mobile database the style of certain authors, and more advanced programs such as DALL-E that generate images based on text prompts. Some NLP programs are even able to generate code and create simple video games!
The tech’s still far from perfect, but recent developments truly have begun to fall into an uncanny valley more than ever before.
At the very least, natural language processing has begun to find much greater use outside of research circles already. NLP is all around us in the digital sphere. It’s in the autocomplete features when we text on our phones, in the speech recognition of virtual assistants like Siri and Alexa, and of course, on machine translation engines like Google Translate.
There is now a diverse range of industrial uses to which NLP has been put from customer service and marketing to finance. It’s difficult to exhaust the possibilities of what NLP can do, but here are some examples.
Natural language processing has come a long way since the creation of the Turing test. And while machines have yet to pass it, there are many new use cases for natural language processing that have developed especially in recent years.
With the rise of deep learning and neural network technology, the machine learning possibilities of NLP have increased exponentially, and the kind of language work it can do has become increasingly complex.
Google’s LaMDA represents the most sophisticated use case for NLP yet. It’s definitely a far cry from the more limited applications of ELIZA, and there have been many successive developments already in between.
Already, NLP tech can be used to generate fiction in uruguay mobile database the style of certain authors, and more advanced programs such as DALL-E that generate images based on text prompts. Some NLP programs are even able to generate code and create simple video games!
The tech’s still far from perfect, but recent developments truly have begun to fall into an uncanny valley more than ever before.
At the very least, natural language processing has begun to find much greater use outside of research circles already. NLP is all around us in the digital sphere. It’s in the autocomplete features when we text on our phones, in the speech recognition of virtual assistants like Siri and Alexa, and of course, on machine translation engines like Google Translate.
There is now a diverse range of industrial uses to which NLP has been put from customer service and marketing to finance. It’s difficult to exhaust the possibilities of what NLP can do, but here are some examples.