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Natural language processing (NLP) is the relationship between computers and human language, meaning it is the computer understanding, analysis, manipulation, or natural language generation.
Natural language refers to speech analysis in both audible speeches as well as the text of a language. NLP systems capture from an input of words in the form of a structured output. NLP is a fundamental element of artificial intelligence (AI).
MarketstandMarkets predicts that the NLP market size will grow from USD 10.2 billion in 2019 to USD 26.4 billion by 2024. The NLP market's significant growth factors include increased smart device usage, the adoption of cloud-based solutions, and NLP-based applications to enhance customer service and increased adoption in the healthcare industry.
Such advances leave a lot to look forward to improved patient care in medical settings, better customer service, smarter financial and legal decisions, and a slew of other benefits across industry verticals. How will NLP evolve the tech industry? By exploring current practices, challenges, and triumphs of NLP in the enterprise, its potential can be understood. Let's explore which aspects will lead NLP to dominate 2021.
In a Gradient Flow survey, 77% of respondents use at least one of the four NLP cloud services listed in the study (Google, AWS, and IBM), with Google's service topping the list. Google Cloud is particularly popular among respondents who are still in the early stages of adopting NLP. Nevertheless, cloud usage rates slightly drop when looking at companies that have more experience in deploying NLP.
Around 65% of respondents working at companies further along the NLP adoption curve still use at least one of the NLP cloud services. Despite the popularity, respondents cited cost as a critical challenge. There are other concerns like extensibility, as so many NLP applications depend on domain-specific language use, and cloud providers have been slow to service these market needs.
Driverless cars are yet to hit the streets. The Guardian predicts that self-driving vehicles would be on the road from 2020. On the other side, tremendous progress has been made, and with every month, autonomous cars get closer to rolling out.
Volkswagen has developed a reinforcement learning-based model that safely drives a car when transferred from a simulated to a real environment.
There is a significant difference between seeing an autonomous car on busy city streets and in the slightly less demanding highway environment, where automation and semi-automation are expected.
The US Department of Transportation reveals that 63.3% of the USD 1,139 billion goods shipped in 2017 were moved on roads. Autonomous vehicles had been enlisted to do the hauling. Machines can drive for hours without losing concentration. Road freight is the largest producer of emissions globally and consumes more than 70% of all energy used for cargo. Every optimization made to fuel usage and routes will improve both energy and time management.
Today, there are seemingly endless choices for NLP libraries, but around 53% of respondents used at least one of the top two libraries. One is Spark NLP, and the other is spaCy. A third of all respondents stated they use Spark NLP, making it the most popular NLP library in the survey. A quarter of all respondents said they use spaCy and AllenNLP. The most famous libraries varied slightly in several key industry sectors like healthcare (Spark NLP), Technology (spaCy), financial services (nltk).
Even amidst the global COVID-19 pandemic, respondents still indicated that NLP spending went up sharply. 53% of technical leaders pointed out their NLP budget was at least 10% higher than 2019, with 31% saying their account was at least 30% higher than the previous year. It's clear that NLP is poised for another year of growth, and by understanding the current state of the market, it will be exciting to see NLP taking over enterprises in 2021.
