As a branch of AI, natural language processing (NLP) has taken over the world by storm. Do you remember Jarvis, the personal AI assistant of Tony Stark from the Iron Man movies or the hospitality robot C3PO from Star Wars? If and when we build such systems, natural language processing would be its foundation. According to IBM CloudOpens a new window , “Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do.†In short, the science of natural language processing allows machines to read, understand, and talk in a way that humans speak.Â
According to Xu Wang, senior manager, data science at LeanTaaS, an AI-driven digital transformation platform, every second, a large amount of text and speech data is generated through in-app messages, social media, forums, and online searches. “How to legally, securely and efficiently collect that data, analyze and make use of it, remains a big challenge and opportunity for the AI industry. In addition, only a fraction of NLP applications, such as chatbots, machine translations, machine-generated meeting summaries, have been identified and applied,†Wang said. It simply means, the applications for natural language processing are endless. As per the report ‘Natural Language Processing (NLP) Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2022-2027Opens a new window ‘ by Research and Markets, the rise in data digitization, internet and connected devices usage has escalated the demand for NLP. In fact, the global NLP market is projected to reach a value of US$61.03 billion by 2027, the report said.
Having said that, can NLP address the air gap between the way humans are and the way technology works? Does NLP matter for the future of AI and enterprises? And, what’s beyond language? To answer these questions, Toolbox rounded up eight AI experts who share their vision and insights on why NLP will steer the AI ship in future. Here’s a peek at what they say:
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1. Natural Language Processing Has Applications in Text Related Areas
Xu WangOpens a new window , senior manager, data science, LeanTaaS
“The future of natural language processing applications that can further help and complement human labor, is very promising. With the initial release of GPT-3 in June 2020 and its ability to write programming codes, people started to realize that NLP technology is able to not only learn natural human languages but also be applied to other text related areas beyond natural languages. Currently machine-generated codes are far from being generally applied or automated, but hopefully we would be much closer to it with the continuous development and improvement of AI.â€
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2. The Next Frontier for Natural Language Processing Is Understanding Images
Vinay Vishnumurthy Adig, manager, software engineering, Jobber
“Natural language generation (NLG – process of automatically generating human understandable text in one or more languages) is a prerequisite for Artificial General Intelligence, which is considered to be the holy grail of AI. The next frontier of NLP research is heading towards understanding common sense which is easy for humans but difficult for machines, inferring and reasoning based on images so that we can generate meaningful sentences in response to any dynamic topics. If we ever were to build Jarvis or C3PO, advances in NLG are essential. The large language models such as GPT-3 from Open AI(trained on 175 billion parameters) or PaLM (trained on 540 billion parameters) from Google are baby steps in that direction, but a big leap in the domain of NLP!â€
3. NLP Can Navigate Employee and Customer Challenges in Future
Raj GuptaOpens a new window , chief engineering officer, Cogito
“Enterprise organizations—especially those in the customer service industry—that lean into NLP can better deduce intent and emotional sentiment from speech or text. If provided in real-time, this analysis can deliver much-needed support to representatives charged with creating quality interactions and customer experiences. As part of the AI ecosystem, NLP provides a way to answer simple questions, help reduce costs, and improve experiences. Ultimately, NLP generates a significant competitive differentiator when labor shortages and employee turnover are at all-time highs, and customers aren’t afraid to take business elsewhere. The future success of enterprises depends on being able to navigate employee and customer challenges—and NLP helps make this a reality.â€
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4. The Next Step for NLP Is Building Empathy Into Conversational InterfaceÂ
Ramprakash RamamoorthyOpens a new window , director of AI research, ManageEngine
“There are a lot of intricacies involved in human language. Today’s plain NLP systems can’t capture them all. For example, things like sarcasm or code-switching (where you combine more than one language in the same sentence) don’t work well with today’s NLP engines. The natural next step is improving NLP systems to have near human-level accuracy in identifying such nuances, thereby building empathy into the conversational interface. Once perfected, the next step is combining insights from all other data points beyond human language input to deliver better value. This would put us one step closer to achieving artificial general intelligence.â€
5. Machine Learning-Powered Decision Making Could Soon Be a Reality
CF SuOpens a new window , VP of machine learning, Hyperscience
“The future could include any number of new content types, like images for example. Machine learning (ML) could help classify and index the images included in insurance claims or other business transactions, and deep learning-powered applications could include caption generation for images, as well. Beyond NLP-powered data extraction and processing, other applications focused on the downstream stages of business processes, such as ML-powered decision making, could soon become a reality. This would not only cause an explosion of machine-readable data in the enterprise world, requiring data science and analytical technologies to process that data, but we could then use natural language processing text generation to convert data insights into a human-readable format easily consumed by human workers.â€
6. Pairing Language Analysis With Video and Contextual Clues Will Strengthen NLP Systems
Loren GoodmanOpens a new window , co-founder and chief technology officer, InRule
“Most experts agree that 70% or more of communication is non-verbal. That means, even with NLP operating at perfection, at least two thirds of what we are trying to say in real time is still up for grabs. Organizations have started pairing language analysis with video and contextual clues to home in on even deeper and usable meaning from non-language communication. We are in endless pursuit of closing the difference between what we think and what technology understands. Natural language processing can handle extracting meaning from the entire spectrum of human created artifacts. Meaning can be found in anything like bad handwriting and crackly audio recordings.â€
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7. Natural Language Processing Can Analyse Genome Sequence and Source Code
Kavita GanesanOpens a new window , author of The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications
“Beyond language, with natural language processing you can also process any type of textual data including source code, genome sequence, data from sensors, and any text based data. Language however, is one of the most important components of AI systems as without the ability for computers to understand human language, true AI systems that can think, act and behave like humans are not possible. Beyond language, the ability for computers to “see†is also crucial. That’s currently being achieved through computer vision applications.â€
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8. Giving Every Business Professional Access to NLP Will Allow Richer Insights To Be Gathered
Hardik ChhedaOpens a new window , chief product officer, Tellius
“Until recently, data used to be considered taboo to anyone who was not in a more data-specific role. The biggest challenge with this outdated mindset is that when business teams solely rely on technical teams for necessary data insights, it creates a significant time lapse between when the data is requested and when results are delivered. Giving every business professional, even those with less technical skill sets, access to NLP increases accessibility to data sources by allowing them to gather their own insights to make better, more informed business decisions that drive revenue, customer loyalty, and bottom lines.â€Â
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