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Cloud Natural Language Processing Market to Reach USD 6 Billion by 2024 and at estimated CAGR of 17% to 2024

Dec 27, 17
Admin
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Exponential growth of the digital data is one of the major factors that is driving the growth of the cloud natural language processing market. Data/information has emerged as one of the most important assets of the organizations. Companies are collecting, analyzing, and reporting vast volume of data for extracting meaningful insights to get competitive edge. Global cloud Natural Language Processing (NLP) market is set to exceed USD 6 billion by 2024, according to this new research report.

Request for a sample copy of this report @ http://bit.ly/2BBTOpA

Growing adoption of the big data technologies such as Hadoop and IoT among organizations along with popularity of cloud deployment is one of the major factors that are fueling the growth of the digital data. Digital data is estimated to grow at a rate of 40% for next 10 years and by the year 2020 data generation is expected to reach 44 zettabytes per year. As the data increases, it leads to the requirement of an effective analytics solution to process the information, therefore, driving the cloud NLP market growth.

Asia Pacific is estimated to be the fastest growing region in the global cloud natural language processing market owing to the increasing adoption of the smart devices. Moreover, increasing investment in AI by Chinese players such as Baidu and Alibaba are also contributing significantly towards the revenue growth.

Some of the vendors offering NLP solutions are Google, Microsoft, IBM, HP, AWS, Baidu, Dolbey Systems, Facebook, Netbase Solutions, Fuji Xerox, Lexalytics, SAS, and Verint Systems. Vendors are trying to capture the market with customized product portfolio, that will help them in gaining more market share. Also, they are collaborating with cloud providers to offer products and services especially, to SMEs. The market is quite attractive as majority of the players are ready to invest in technologies such as AI, deep learning, and machine learning.

Natural Language – Computer Understanding

Apr 17, 16
Admin
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Apple Siri, Amazon Echo, Google Voice, Microsoft Clippy or whatever you call them, the fact of the matter is that voice-control and virtual assistants are coming – and in a big way!

voice collage

  • Why? To make the user experience better to interact with computers just like you would with humans.
  • So what’s the big deal? Having a computer understand human language is extremely difficult.

Every single major IT company including Google, Microsoft, Amazon, Salesforce, Apple (and many others) have invested significant efforts in the area of ‘Artificial Intelligence (AI), Neural Networks or Natural Language Processing (NLP)’. I will group these terms together of the purpose of this article, and I provide some specific definition below, but please know the essence is of machine-learning, where a computer thinks (and reacts) with logic and not an algorithm.

  • Artificial Intelligence definition: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
  • Neural Network definition: a computer system modeled on the human brain and nervous system.
  • Natural Language Processing definition: is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction.

collage_medium

As I mentioned earlier there has been a lot of investment and research in the areas of AI and NLP, so the question begs why are we seeing such a big push in the consumer market of voice-control technology all of a sudden?

That is a reasonable question and I think it’s easily answered by two words: ‘The Cloud’! First, you need unimaginable amounts of data to ‘train’ a computer. For example, IBM Watson, at its core only knows two things which are binary (or 1’s and 0’s, yes or no, true or false). To make IBM Watson, Jeopardy-worthy it had to be pumped with years and years of library data for it to ‘learn’. My point is that without this very specific training, a computer system as powerful as Watson still cannot communicate like humans do naturally every day. Secondly, massive compute-power is required. ‘The Cloud’ offers both access to unimaginable amounts of data plus massive compute-power where, finally, computer systems an achieve machine-learning capabilities in earnest.

I was fortunate enough to work at a company called ABBYY for the past 4+ years. ABBYY is known in the industry as a leader in Optical Character Recognition (OCR), which is true and one of the reasons I decided to join the company, however and what I was pleasantly surprised to quickly learn, was that high-quality OCR was a result of a much bigger vision. This bigger vision was from David Yang, ABBYY founder, and his passion to help people understand each other. It might sound simple but with so many different languages, interpretations of language and structure of language it is impossible to apply scientific logic to understand meaning or intention that a computer might understand.

Eugene

https://www.youtube.com/watch?v=HPlV9mzqeFQ

  • Introduction of ABBYY Machine Translation

https://www.youtube.com/watch?v=_Gb9cgzxPWk (click here to see some great use case examples for NLP)

Eugene describes the use cases for NLP technology including:

  • Keyword vs. Semantic Search
  • Syntactic language parsing
  • Semantic indexing
  • Contextual understanding
  • Document classification
  • Similar documents

It was my great honor to work with some serious Linguistic scientists and learn a little bit about the complications of NLP, and true language understanding. Previously I had thought, probably like most of us reading this article, ‘why can’t Apple Siri understand what I’m saying?’. ‘Why is she so dumb about her answers?’. I do recall those reactions and it still happens to-this-day. The magic of Siri is not the voice recognition itself of word-for-word conversion, but rather the understanding of the complete question or command in a full sentence. I learned that to understand human language, meaning and intent is nearly impossible, even with the greatest scientific-minds and compute-power completely focused on such realization.

 

In summary, and probably in the not so distant future, I can imagine the reality of our everyday lives being controlled by voice-command. It is an undeniable trend. The domain names of NLPIsAFad.com, AIisAFad.comand ArtificialIntellingenceIsAFad.com will become hot comities as collectors gobble-up such forward-thinking treasures. A day where you walk in your front door and say something such as ‘summer-time’ and your stereo immediately starts summer-time music, then the shades automatically turn to a sandy-location and then your television instantly clicks on-to a refreshing beach sunset! Ahhhhh, so tropical. This is all within our grasp now and will soon be a realization for you.

vr living roon