The expeditious advancements in AI & cloud tech, in tandem with the rising demand for deriving powerful insights is certain to push cloud NLP market trends in the many years to come. With globalization ruling the charts, and the growing business applications of AI, cloud natural language processing industry has indeed taken on a new dimension, driven by the technology’s ability to interpret the massive amount of information stored in free text files and make it worthy of a detailed analysis. In a quest to simplify the perception of human speech and flawlessly analyze data, companies have been persistently investing in artificial intelligence technology to expand its product and application landscapes, which has had a tremendous impact on the revenue graph of cloud NLP market. Facebook, one of the most prominent cloud NLP industry players, recently declared the development of an innovative neural network that apparently translates languages more accurately than most other systems that used standardized machine translation, and in addition, conducts the translation at a speed that is nine time faster than the conventional speed. Undeniably, researchers at the Facebook AI Research (FAIR) have gone one step ahead of the expected output, by tapping the CNN (convolutional neural network), a technique that reads words in groups and enables the system to conduct parallel operations with itself. As rumors fly afloat, in appreciation of the company’s efforts to extend its internal support for translating more than 45 different languages, analysts affirm that this would catapult Facebook’s position in cloud natural language processing market in the years ahead.
U.S. Cloud NLP Market Size, By Product, 2016 & 2024 (USD Million)
A glimpse of the competitive landscape of cloud NLP market:
The competitive spectrum of cloud natural language processing industry is indeed enviable, as it encompasses a group of some of the highly established tech shots that have the resources and the zeal to further unearth the application scope of top-grade technologies. As the cloud paves its way amidst practically every end-use domain, companies in cloud NLP market undoubtedly have had to realign their strategies to bring about novel product development. As per reliable estimates, cloud computing industry is likely to cross a massive valuation in the next couple of years. More than 70% of tech CFOs claim that the cloud will have a highly profitable impact on most business verticals, NLP being one of the foremost ones.
Alternatively, global AI industry has been a witness to a plethora of lucrative ventures, the impact of which has been depicted on the revenue potential of cloud NLP market – numerous technology giants, since the last half a decade, have been involved in rather enormous deals riding on the base of strongly developing tech based machine learning, image processing, and translation. For instance, DNNresearch, a deep-learning startup had been purchased by Google in 2013 from the University of Toronto, and enabled Google to bring about an improvisation in its image search features. A leading survey reports that nearly 30 AI startups have been purchased by accomplished players such as Twitter, Facebook, Google, IBM, AOL, Intel, Apple, Yahoo, and Salesforce since 2012 – which has no doubt, had quite some impact on cloud NLP market share. Google, in 2014, also acquired the DeepMind Technologies, while Twitter, in 2016, paid up USD 150 million for Magic Pony, an image-processing start-up based in UK.
The year 2017 also witnessed a slew of M&As and partnerships in cloud NLP industry, that have helped bring about a change in the commercialization scope of this business space. In October, New Tranx, a Beijing-based translation startup secured USD 7.5 million to invest in AI technologies that would contribute eventually toward enhancing machine translation. H-Farm, an Italy-based venture incubator, in June, took over CELI, a language tech service provider for USD 2.69 million, in order to expand CELI’s service portfolio inclusive of proprietary chatbots, NLP, semantic search products, and data analytics across the Asian markets. With considerable advancements in the AI and cloud domains, cloud natural language processing market does not stand far distant as far as sales and revenue are concerned. In 2016, say estimates, cloud NLP market size stood at a modest USD 1.5 billion.
It is vitally imperative to mention that in the current era of extensive globalization, the necessity for machine translation stands as one of most pivotal driving forces for the growth of cloud NLP market. Renowned giants such as Google, Facebook, and Amazon have been significantly investing in machine learning technologies, which has indeed come a long way from inaccurate output processed by humans to the neural networks of today. In 2016 for instance, Microsoft provided an exhibition of how four computers helped translate Leo Tolstoy’s novel - War and Peace, comprising 1,440 pages, from Russian into English. The same year witnessed the Harvard NLP Group declare their OpenNMT project – an open-source software for machine translation that accepts aligned sentences in the source language and translates them to the target language through neural networks, and is equipped with additional features such as entity extraction, document filtering, and language detection. The involvement of numerous companies and academic institutions to bolster the application of AI in NLP through various product innovations is certain to chart out a profitable growth path for cloud NLP market.
It is indeed irrefutable that companies partaking in cloud natural language processing market share have spared no expense as far as product innovation and R&D activities are concerned. Given the vast expanse of the cloud sphere, it is overt that companies pour in enormous investments in order to bring forth a plethora of innovative NLP products based on distinguished technologies and deployment modes. As the years roll on, cloud natural language processing industry share is forecast to grow at a CAGR of 17% over 2017-2024, driven by the fact that this technology successfully closes the gap between insights and action across numerous businesses and assists in quicker decision-making in marketing and product management.