Beyond NLP: 8 challenges to building a chatbot

“The adoption of language comprehension tools by the broader developer community is the way to get there.” New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers.

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Natural language processing (NLP) is a form of AI that extracts meaning from human language to make decisions based on the information. This technology is still evolving, but there are already many incredible ways natural language processing is used today. Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses.

• As for Newgen KnowledgeWorks, the opportunity for authors and publishers to self-serve (by using various tools on offer) has seen its focus shifting increasingly toward supporting users, deploying tools, and training teams around the world on its solutions.
• “While the market is growing fast, advanced NLP is still used mainly by expert researchers, Big Tech and governments,” Levi told TechCrunch via email.
• “Increasingly, we will be creating our own content to support the impressive technologies—based on AL, ML, NLP, Big Data, or analytics, for instance—that we have developed,” company president Maran Elancheran says.
• If such an evolution is not taken, chatbots will continue to be costlier to develop and maintain than traditional applications.
• New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth.

One AI’s models can also be combined with open source and proprietary models to extend One AI’s capabilities. It’s often difficult to curate open source models, he argues, because they have to be matched both to the right domain and task. For example, a text-generating model trained to classify medical records would be a poor fit for an app designed to create advertisements. Moreover, models need to be constantly retrained with new data — lest they become “stale.” Case in point, OpenAI’s GPT-3 responds to the question “Who’s the president of the U.S.?” with the answer “Donald Trump” because it was trained on data from before the 2020 election. Natural language processing is behind the scenes for several things you may take for granted every day.

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InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Many of the new chatbot vendors are trying to solve these challenges by introducing a richer declarative syntax that enables developers to define the goals of the bot and handle much of the heavy lifting related to system integration, conversation flow, and persistence management within the chatbot framework. If such an evolution is not taken, chatbots will continue to be costlier to develop and maintain than traditional applications. But through AI — specifically natural language processing (NLP) — we are providing machines with language capabilities, opening up a new realm of possibilities for how we’ll work with them. The hurdle One AI will have to overcome is convincing customers that its services are more attractive than what’s already out there.

AI’s Next Great Challenge: Understanding the Nuances of Language

Faris Sweis is senior vice president and general manager of the developer tools business at Progress. With One AI’s language studio, users can experiment with the APIs and generate calls to use in code. In essence, the NLP does not address any of the challenges that you typically face in developing a real-world line of business application. It simply presents the opportunity to deliver a broader and more satisfying experience using a chat interface.

• “We believe that the technology is nearing its maturity point, and after building NLP from scratch several times in the past, we decided it was time to productize it and make it available for every developer.”
• Over at Lumina Datamatics, Big Data and predictive analytics have enabled clients to manage large volumes of structured and unstructured content.
• The objective is to enable participants to make a significant step change in leadership performance based on their individual style within the context of their current or future leadership role.Neuro Linguistic Programming was introduced as a tool for personal development 30 years ago.

When you ask Siri for directions or to send a text, natural language processing enables that functionality. Faris Sweis is senior vice president and general manager of the developer tooling business at Progress. Previously Sweis was the chief technical officer at Telerik, which was acquired by Progress in 2014, and prior to that he spent 10 years at Microsoft.

Meeting the leadership challenge with business NLP

“We believe that the technology is nearing its maturity point, and after building NLP from scratch several times in the past, we decided it was time to productize it and make it available for every developer.” Whether to power translation to document summarization, enterprises are increasing their investments in natural language processing (NLP) technologies. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their NLP budgets grew by at least 10% compared to 2020, while a third said that spending climbed by more than 30%. “With the maturation of Language AI technologies, it is finally time for machines to start adapting to us,” Ben told TechCrunch via email.

“Increasingly, we will be creating our own content to support the impressive technologies—based on AL, ML, NLP, Big Data, or analytics, for instance—that we have developed,” company president Maran Elancheran says. For India’s digital solutions vendors, the application of AI and NLP has been ongoing for years, primarily to accelerate internal production processes and meet the publishing industry’s demands for faster, cheaper, and shorter turnaround time. From sifting through the large volume of incoming content to flagging content and process anomalies, AI/NLP has been indispensable. One of the most challenging and revolutionary things artificial intelligence (AI) can do is speak, write, listen, and understand human language.

As for Levi, he was the VP of online marketing at LivePerson and the head of marketing at WeWork. Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence. The past year has seen MPS working on solutions featuring chatbots for clients in the areas of onboarding, employee self-service, performance support, customer support, and game-based learning. Variable tracking, analytics, and multimedia displays are among the rich array of features developed for these solutions.

Much has been said about cognitive technologies, which include artificial intelligence (AL), machine learning (ML), natural language processing (NLP), robotic process automation, and rule-based or expert systems. Much fear—of robot overlords taking over the world (and our brains)—has also been raised at the same time. Then there is Kashyap Vyas’s “7 Ways AI Will Help Humanity, Not Harm It” (Interesting Engineering, December 3, 2018) providing a completely different line of thought. As for Newgen KnowledgeWorks, the opportunity for authors and publishers to self-serve (by using various tools on offer) has seen its focus shifting increasingly toward supporting users, deploying tools, and training teams around the world on its solutions.

Over at Lumina Datamatics, Big Data and predictive analytics have enabled clients to manage large volumes of structured and unstructured content. “Our range of analytical tools—including predictive modeling, data mining, scorecards, and machine learning—help publishers to automate their existing processes, resulting in increased efficiencies at a lower cost,” says Vidur Bhogilal, vice chairman of Lumina Datamatics. With AI, the reference segment of a STM book or journal can be handled effectively using an online or in-house reference library. “AI can apply and highlight the keywords such as author name, product name, subject, and bibliographic for index process by referring to general master rules and the keyword library.