People and computers do not speak the same language, but progress in natural language processing is closing the gap. It’s possible to ask the same questions in different ways: “How long before the American Widget order we placed last month for 10 widgets gets here?” versus “When will we receive the 10 widgets that we ordered last month from American Widget?”
Computers, on the other hand, parse inquiries in a specific form. For example, the algorithm would understand the inquiry only if it were stated as follows: When will (time) American Widget (vendor) deliver (action) the 10 (unit) widgets (product) ordered (action) 22 February 2021 (format)?
While the IT department might know how to frame queries in precise computer-speak, everyone else in the organization who needs data to perform their roles will probably not. Graduates with an advanced degree, such as a Master of Science (M.S.) in Applied Digital Technology with a concentration in Artificial Intelligence (AI) and Machine Learning (ML), can gain critical technology, AI and ML skills that allow professionals to use natural language processing (NLP).
“These unique aspects of communication, which make human-to-human conversations more varied and interesting, only serve to muddy the waters for a computer built to think in black and white,” according to Forbes.
How Is Natural Language Processing Closing the Human/Computer Language Gap?
As a branch of ML in AI, natural language processing (especially deep learning-enabled NLP) is a process that enables computers to find patterns or features in new, unstructured data without human intervention and match them to patterns in models of historical data.
“The goal is to create a system where the model continuously improves at the task you’ve set for it,” Lexalytics explains.
NLP is a field in machine learning that seeks to understand, analyze, manipulate and potentially generate human language. According to IBM, NLP is a “driving force” in everyday life that can:
- Scan email for spam, phishing and other cyberattack strategies
- Translate any human language to any other
- Perform customer service functions through chatbots and smart devices
- Discover consumer insights through analysis of social media interactions
- Summarize large texts and provide content synopses
“But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes,” the company notes.
What Are Some Trends in Natural Language Processing in Business?
MonkeyLearn reports that over half of technology executives and managers polled in late 2020 predicted 10% budget growth for implementing or building out NLP capabilities. According to the AI services provider, future trends in the field included the following:
- Repurposing ML models to perform secondary, related tasks, which will cut costs and time to enable new ways to use NLP
- Adopting click-and-build tools that allow non-technical users to design, train and integrate ML models
- Implementing NLP training algorithms that accelerate learning by providing feedback on previous interactions
What Are the Career Tracks in Natural Language Processing and Artificial Intelligence?
Demand for professionals with expertise and knowledge for careers in fields related to artificial intelligence is running well ahead of the supply. Graduates of the Arkansas State University (A-State) M.S. in Applied Technology with a concentration in AI and ML online program are ready for roles like project manager, director of process improvement, implementation analyst, automation implementation manager, product manager and digital application manager.
The online curriculum offered by the A-State includes courses that emphasize skills in the following applied technology topics:
- Project management and business fundamentals
- Management information systems that inform business production, finance, marketing and more
- Analysis, design and deployment of machine learning and AI tools and strategies
Professionals with an advanced degree in applied technology will be well equipped to succeed in a workforce that increasingly values ML and NLP strategies.