The role of language in everything we do is apparent: human beings are unique in the diversity and use of their communication and linguistics. Decoding and reverse-engineering all elements of human languages and applying these to technical tools is a fundamental part of technology. Making these computational technology tools better has, for the majority of the last 50 years, predominantly centred on the robotic branch of tech that enables this: Artificial Intelligence (AI).
In this blog article, we are discussing how the Natural Language Processing (NLP) niche of AI is a booming area of tech that has major significance for mobile app development. While the industry of AI was worth $387.45 billion USD in 2022, reports Fortune Business Insights, the NLP niche alone is projected to exceed a value of $161 billion USD by 2029 growing at a CAGR of 18.1 percent from 2022-2029. Both on a technical level and on a business level, the existing functionalities and long-term potential of NLP is astounding; in fact, NLP can help with writing, coding, and even, reasoning in the abstract and then interactively with a user as it processes the existing and new information inputs. This blog is intended to help illuminate how NLP benefits organizations who are investing in a custom app development and for industry professionals to gain a deeper understanding of the invaluable ways mobile app development is elevated with this AI niche.
NLP, also sometimes referred to as ‘computational linguistics’, is one of the major niches of AI that has been pivotal in the progression of realistic, humanoid technology. In a scholarly article for ScienceDirect, Venkat N. Gudivada and Kamyar Arbabifard, explain the high-level technology undertaking of creating NLP. Gudivada and Arbabifard begin to outline the history leading up to this technological advancement, “Natural languages are inherently complex and many NLP tasks are ill-posed for mathematically precise algorithmic solutions.” They continue explaining the fusion of linguistics and AI that enables computers to comprehend human or robotic inputs, “With the advent of big data, data-driven approaches to NLP problems ushered in a new paradigm, where the complexity of the problem domain is effectively managed by using large datasets to build simple but high quality models.” As a field of data science and AI, these systems can analyze text-based data in a range of small to large volumes without fatigue and are developed to sustain analysis without fatigue and bias. On Thomson Reuters, they explore the technical utility of this AI niche further saying, “Natural language processing focuses on designing algorithms to parse, analyze, mine, and ultimately understand and generate human language.”
AI has long been considered the key to widespread automation and, in essence, it is the key to all computer learning. Today, whenever we are using any computer device and any smart device, we are using some kind of AI. The centrality of AI is such that Ashley Stahl writing for Forbes said nearly two years ago that, “AI and machine learning are at the top of many lists of the most important skills in today’s job market.” For software and mobile app developers, AI plays a crucial role in everything they do. Before delving into the role of AI and NLP in software and mobile app development, let’s first review the building blocks of AI that facilitate NLP:
In general, NLP is central in the following areas of AI that work to enable computers to understand written and spoken human communication. NLP technology applied to programs and software, web applications and websites, and mobile apps encompasses:
As an AI niche, natural language processing is extraordinarily valuable in mobile app development. In the outline above we’ve explained the numerous needs NLP fulfils across software and mobile applications. Technically speaking, there are a myriad of areas that make up AI though NLP has been amongst the most prevalent in recent times with the announcement of OpenAI’s ChatGPT tool. This is due to how astounding the development of ChatGPT is in accuracy, robustness, and versatility. To emphasize, when we talk about utilizing NLP in mobile app development, this AI technology is a fulcrum of how the back-end of the application is devised and engineered during the development services process to specifically enable the commonly-used features of:
NLP is ultimately what’s making mobile applications smarter based on how data-centric all technology is.
For mobile app developers, the realistic humanoid characteristics of good NLP technology promises a revolution in integrating these new features into a development design. The way that cutting-edge AI can be used in the analytical components of the app code is groundbreaking in its ability to make the application itself smarter than the existing development algorithms used to fulfil this. Using NLP in development, they’re able to: create a refined interface; a simplified yet elevated design that’s responsive and accurate; produce products that are scaleable; perfect the user flow; eliminate any clunky features that are slowing down the application; and, free a client to reallocate their customer service labor resources. In totality, NLP offers a win for mobile app developers tasked with producing an application that’s slick yet able to drive conversions through a positive UX that meets dynamic user needs.
In the same area of discussion, there is a paramount need to address the key value of chatbots and problem-solving as this NLP functionality is synonymous with 21st century customer service. The value proposition of 24-hour service with rapid, consistent, on-demand, and detailed responses is now the baseline of customer expectations for their app UX. During use, the data analysis capabilities can additionally speed up the problem-solving process with regards to if escalation is necessary or if there is a technical issue affecting the UX and the in-app technical or experiential problems the user is facing. Apps are designed to pare back the busyness of the UX in whatever a user is doing — shopping, streaming, administration, banking, or travelling — so that they can complete their activity at their own pace without distraction, clutters, and lags. NLP features support this without their knowledge while simultaneously being available when they need to access interactive app components.
Late 2022 ended with some of the biggest technology news ever as OpenAI launched their free ChatGPT prototype to the open market. This prototype, which is still web-based, has just hit 10 million users less than two months after launch (note that Instagram took nearly a year to reach 10 million users), is still garnering headlines for a range of reasons and it’s not just tied up in the disruptive impact of this AI product. Up until now, NLP technology has been fairly omnipresent for a little over a decade with Apple’s Siri launched in 2011, Amazon’s Alexa first used as part of the Echo smart speaker released in 2014, and the Google Assistant launched in 2016 amongst the three most widely-used products and services. For mobile app developers and organizations using custom mobile app development services, incorporating NLP technology makes sense for a range of reasons. From standardized customer service and chatbot functionalities to securing accounts and customizing UX, NLP features are part of delivering a memorable customer experience in every industry.
When we refer to the ChatGPT release at the end of last year, what we can hypothesize for 2023 is that what the NLP AI has been shown to do and how this is incorporated will only become more sophisticated. The accuracy, speed, scale, and flexibility of this latest evolution in AI is virtually unparalleled. There is nothing quiescent about this era of AI development and how we are seeing the NLP subset expand in its capabilities, understanding, and scope of reach. Alongside OpenAI, there are numerous other companies that are developing their own comparable NLP-centric products, for example Google’s LaMDA, that indicates the technology will have high-level, normalized integration in mobile app development sooner rather than later though the problems around accuracy and server overwhelm with ChatGPT and concerns around Google’s AI launch conservatism may inhibit success with this. Overall, it does look likely that the future of this technology in 2023 and onwards is in the hands of both start-ups and the big tech companies with former Google employee Timnit Gebru telling The Washington Post that, “After the release of ChatGPT, however, perhaps Google sees a change to its ability to make money from these models [LaMDA and other AI in development] as a consumer product, not just to power search or online ads.”
Even with the boom in NLP-leveraging programs, like ChatGPT, the full scale of this state-of-the-art AI technology is still not quite there — hence why users accessing the free version of ChatGPT are experiencing ‘At Capacity’ server errors. Academics Bahar Sateli, Gina Cook, and René Witte refer to NLP as “resource-intensive” in a standalone app form despite the use of personal assistant-style features using NLP functionalities in many apps from voice assist to predictive text. They attest in their conference paper Smarter Mobile Apps through Integrated Natural Language Processing Services that, “This major limitation prompts the need for novel approaches that can bring NLP power to smartphones, taking into account their limited input and processing capabilities.”
Even with how close the technology development industry is to delivering widespread and reliable NLP and machine learning solutions, challenges remain meaning that anyone looking to invest in mobile app development with this AI should approach the application-building process with their eyes open. Experienced, knowledgeable app developers can advise the best course of action for the desired outcomes however the high-level utilization of a functional NLP in all components of a mobile app does still require powerful servers and a robust, agile back-end to deliver a reliable product to users.
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