Despite the fact that AI is becoming increasingly common in enterprises, interest in natural language processing (NLP) has recently increased. This is at least partially due to the rise of chatbots and intelligent assistants in call centers, help desks, kiosks, and other customer support apps.
NLP isn’t only utilized in these scenarios, though. When people can simply say what they want rather than type it in or click their way through a long menu, back-office functions such as software development and data analytics, as well as systems management and risk assessment, become significantly more efficient and successful. Getting there won’t happen overnight, though. While NLP’s accuracy and effectiveness have improved recently, it has a long way to go before it becomes an essential member of the team.
NLP is on the trail of money
However, despite these investments, the company has demonstrated a growing inclination to open its wallet wider to finance various NLP projects in recent months. According to new research from NLP developer Jon Snow Labs and data analysis firm Gradient Flow, 60% of IT executives said that NLP funding had at least doubled over the previous year, with about a third seeing increases of 30%.
Health care, technology, education, and financial services were among the most prominent beneficiaries of this curve, while name identity recognition and document classification were two key application use cases.
The appeal of NLP is in its ability to handle vast quantities of unstructured data, which has long been suspected of containing vital information and hidden data patterns that might be utilized to promote business growth, productivity, and competitiveness if properly exploited.
Jim Carson, a Data Center Frontier recent guest and data science manager for Service Express, claims that NLP fills the gap between computer understanding and human understanding. This may result in substantial gains across a range of business procedures, such as email management and contract analysis, as well as equipment logging and data center infrastructure monitoring.
NLP can also have a significant impact on companies when used in conjunction with other types of AI, such as machine learning. The Computational Story Lab at the University of Vermont’s work in sentiment analysis builds on the integration of NLP, ML, and other techniques to extract the emotional context of communications, according to CIO.com. The Leonard D.
Shron Lab’s Hedonometer project analyzes 50,000 tweets each day to generate a daily “happiness score.” While the current approach is based on a simple plus-minus method to reach its conclusions, more sophisticated algorithms may one day be able to create more complex analyses and target certain data in order to track things like brand popularity and consumer trends.
New insights into NLP
Despite being deposed as Jeopardy champion 10 years ago, IBM’s Watson is still one of the most well-known conversational variants of NLP.
Since winning Jeopardy, IBM has added several new features to the platform, including extracting more sophisticated meaning from leading document formats like PDFs and developing multi-language communication and subject-matter experts with data analysis and knowledge development. It also offers enhanced customization options that simplify AI training processes.
We’re still in the early phases of this shift, but it appears to be heading in the same direction: toward a completely conversational user interface that allows users to access massive computing power just as easily as talking with a coworker. We aren’t there yet, but we’ll soon see another member of the business team—essentially the corporation itself—chatting at meetings, reacting to user queries, and maybe even joking at the water cooler.
As a beginner, it will have a lot to learn, but it has already demonstrated great promise.
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