By Guest Blogger: Edward Huskin | Within the past 3 or 4 years, natural language processing has made the kind of indelible mark on life that cannot be ignored.
Thanks to its processes, we can now order stuff online without getting up from the bed.
We can understand a Chinese speech as it is being delivered.
Businesses can understand their customers better immediately.
Everything is a lot faster and better because we can now communicate with machines, thanks to natural language processing technology.
10 or 20 years ago, natural language processing technology was still being refined, so it was only in limited use.
What do the next 5 or 10 years hold for this amazing addition to daily life? We explore the possibilities and forthcoming trends below.
The arrival of advanced NLP If NLP-powered tools such as chatbots and smart assistants appear advanced right now, expect to be wowed even more in the future.
As major players in the AI field try to take advantage of the technology’s prospects for the future, natural language processing is going to get even more advanced.
NLP is already being designed to get better at understanding all manner of text using different techniques, so we can expect from the future faster chatbots, whip-smart smart assistants and machines that can start proper conversations with us.
Massive shift from data-driven to intelligence-driven decision making Natural language processing has afforded major companies the ability to be flexible with their decisions thanks to its insights of aspects such as customer sentiment and market shifts.
Smart organizations now make decisions based not on data only, but on the intelligence derived from that data by NLP-powered machines.
As NLP becomes more mainstream in the future, there may be a massive shift toward this intelligence-driven way of decision making across global markets and industries.
Creation of bigger, better NLP platforms like Spark NLP Data scientists dealing with natural language processing and other aspects of AI, rely on established NLP library platforms to build and test their applications.
Today, the platform pool is made up of trusted mainstays such as OpenNMT, Stanford’s CoreNLP, SpaCy and Tensor Flow.
As NLP progresses in the future, bigger and better platforms are going to get built as alternatives to existing ones and their flaw ones.
Some new platforms, such as Spark NLP, have already been released and cited as the future of NLP.
Spark NLP is known for its speed, scalability and its massive library of pipelines, pre-trained neural network models and embeddings.
Some Spark NLP examples of use include the sub-branch Spark NLP for Healthcare, which is being currently employed for the relatively newer area of biomedical text mining.
A major sign of the future is that 16% of registered enterprise companies globally have already signed up for Spark NLP in a 2-year lifetime.
Eradication of human data scientists Data scientists currently have the fancy role of understanding NLP and its attributes on behalf of everyone else.
But their role is likely to be wiped out in the future as NLP, along with machine learning and its attributes such as pattern recognition, advanced analysis and interpretation, improve beyond today’s level.
They’ll become too good that we won’t need human data scientists anymore.
More integration of NLP in aspects of everyday life If there is one thing we can guarantee will happen in the future, it is the integration of natural language processing in almost every aspect of life as we know it.
The past five years have been a slow burn of what NLP can do, thanks to integration across all manner of devices, from computers and fridges to speakers and automobiles.
Humans, for one, have shown more enthusiasm than a dislike for the human-machine interaction process.
NLP-powered tools have also proven their abilities in such a short time.
These factors are going to trigger increased integration of NLP: ever-growing amounts of data generated in business dealings worldwide, increasing smart device use and higher demand for elevated service by customers.
Adoption by many other industries Previously, natural language processing was limited to certain industries across the world, such as customer care, technology and eCommerce.
The last few years have been evidence enough that the technology is making inroads in several other industries, including healthcare (see Spark NLP for Healthcare above for example), risk and compliance, insurance and finance.
Industries such as advertising and digital marketing are expected to take full advantage of NLP in the coming years as its customer segmentation abilities improve.
For businesses, NLP is going to become a major aspect of business intelligence that cannot be ignored.
A shift to natural language understanding The ultimate test for NLP in the future will be whether it can go beyond mere natural language processing and be able to shift to understanding the human language better.
So far, the technology has only had to derive meaningful responses from raw text data and it has proven its worth.
In the future, experts predict that natural language processing will have to evolve in its function to become natural language understanding.
The latter is a more sophisticated level of processing that would allow processors to understand language as it naturally occurs rather than just processing words and text to derive meaning.
That would ideally involve understanding the accents, slang and other nuances that make up natural language.
It would also, to a certain degree, mean that the machines would then be able to generate text for themselves.
This is the ultimate future of natural language processing, but how it plays out in the coming years remains to be seen.
Other trends to look out for in the future of NLP Attempts to help machines learn more from the internet Increased deployment of Enterprise AI Increased deployment across all departments of organizations/businesses Increased adoption by smaller business Conclusion As regards natural language processing, the sky is the limit.
The future is going to see some massive changes as the technology becomes more mainstream and more advancements in the ability are explored.
As a major facet of artificial intelligence, natural language processing is also going to contribute to the proverbial invasion of robots in the workplace, so industries everywhere have to start preparing.
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