Will computers ever become as skilled as humans when it comes to communicating? Science suggests we may be getting close.
Researchers from Tulane University, the MIT-IBM Watson AI Lab, and the University of Illinois recently revealed their research that, in theory, allows a computer to closely replicate human-like reading comprehension and inference.
The research symbolizes a breakthrough in AI communication that transforms knowledge into natural language processing, especially as many AI technologies are struggling to understand content and context in human language.
Here’s what we might expect moving forward:
Making a Business Case for NPL
Live chat will continue to grow in use as NPL advances.
Natural language processing, or NPL, is a subcategory of data science that focuses on teaching computers how to read and interpret text in the same manner as a human brain. This requires not only a knowledge of the words themselves, but also the context in which they are used.
NPL is believed to be the missing link between computer reading and analyzing, which is being explored in use cases where there are large volumes of data.
In addition, many businesses are interested in exploring the technology for use in customer service and customer analytics, particularly concerning preferences, sentiments, and mood. Chatbots are an obvious use case for NPL technologies, as they’re becoming the first line of defense for customers to connect with businesses online, 24/7. And given that 42% of customers say they prefer live chat over another option when available, making these tools as sophisticated as possible will continue to be a top priority among developers and businesses alike.
Many businesses are already using tools to collect and measure customer data sets, but NPL may soon render those tools obsolete. Using NPL, early adopters may gain a better understanding of their audience, along with a competitive edge over other companies who choose not to invest in NPL tools.
What’s in Store for Natural Language Processing?
One goal of NPL is to teach robots to read and interpret data like humans.
If natural language processing continues to progress, it could forever change the way we gain, analyze, and use the copious amounts of data we collect. In fact, data scientists claim that we generate twice the volume of data every two years, of which about 79% is textual data. NPL serves as a potential tool for text mining to allow companies and agencies a better, faster, less expensive way to mine data for relevant insights.
Google’s BERT can take into account the contextual relevance of words, differentiating between things like “river bank” and “bank account.” Another emerging tool, ERNIE, takes all of BERT’s best elements and includes a process of matching semantically related elements from other sources, including news articles, encyclopedia entries, and online forums. Another solution developed by Google, called XLNet, is also applying predictive analytics to natural language processing and expand its learning capacity.
Right now, NPL is undergoing a massive learning experience and making great strides along the way. Companies can already use NPL tools like BERT, ERNIE, and XLNet, and as these technologies grow in sophistication and ease of use, the barriers to entry will be lowered to allow for more widespread use.