From voice-controlled assistants like Siri and Alexa to customer service chatbots, search engines to predictive text applications, the role of natural language processing in real-world applications is increasing significantly. A subset of artificial intelligence, NLP uses linguistics, machine learning, deep learning, and coding to make human languages comprehensible for machines, enabling them to respond to texts or voice inputs as a human would. While NLP is not a new science, the technology is rapidly advancing, owing to enhanced availability of big data, powerful computing, enhanced algorithms, and an increased interest in human-to-machine communications. The increasing amount of unstructured data collected from social media and digital data creates a significant challenge for users to generate useful insights. Hence, companies are leveraging natural language models and machine learning to better understand unstructured voice and text data. Additionally, rising digital transformation and adoption of NLP technology across various industries such as healthcare, automation, etc. are expected to drive the growth of natural language processing market in the coming years.
Microsoft has been making headlines after investing USD10 billion in OpenAI, the new startup that launched ChatGPT and DALL-E2. These two tools have revolutionized the entire landscape of AI and NLP innovations. IBM’s Watson Natural Language Understanding service uses deep learning to extract meaning from unstructured text data, which enables companies to increase their productivity by up to 50%, reducing their time on information gathering tasks. Lemonade, America’s top-rated insurance company interacts with customers through two AI bots, AI Jim and AI Maya, which handle everything about the insurance process, from enrolling customers to filing a claim. Recently, Bain & Company announced global services alliance with OpenAI to leverage its digital capabilities and help global clients identify and implement the value of AI. Hugging Face, an NLP startup has released AutoNLP that can automate training models for standard text analytics tasks.
GPT3: Latest Advancement in Natural Language Processing
GPT3, or the third-generation Generative Pre-trained Transformer is a neural network machine learning model that can produce any kind of text. Developed by OpenAI, GPT-3 has a deep learning neural network with over 175 billion machine learning parameters. To put things in perspective, Microsoft's Turing Natural Language Generation (NLG) model, which included 10 billion parameters, was the biggest trained language model prior to GPT-3. GPT-3 will be the biggest neural network ever created as of early 2021. As a result, GPT-3 is superior to all earlier models in terms of creating text that appears to have been produced by a person.
GPT-3 performs a range of natural language tasks by processing text input. To comprehend and produce natural human language text, it combines natural language processing with natural language production. GPT-3 is trained to produce authentic human writing, which has previously been difficult for machines since they don't comprehend the subtleties and complexity of language. GPT-3 has been used to generate enormous volumes of content from a tiny amount of input text, including articles, poetry, tales, news reports, and conversation.
GPT-3 can undertake rapid repetitive jobs, like with any automation, freeing up people to handle more complicated activities that demand a higher level of critical thinking. There are various circumstances in which hiring a human to produce text output is neither possible or efficient, or when robotic text production that appears human could be required. GPT-3 may be used by customer service departments to assist chatbots or respond to consumer inquiries, while sales teams can utilise it to reach out to new clients. Marketing teams can write copy utilising GPT-3 and the repercussions of a mistake in the copy are likely to be minimal with the AI tool.
The ChatGPT language model is one of the most notable applications of GPT-3. As a variation of the GPT-3 paradigm designed for human conversation, ChatGPT can dispute false premises, ask follow-up questions, and acknowledge when it has made mistakes. To gather user feedback, ChatGPT was made available to the public without charge during its research preview. Dall-E is another popular example. The AI image-generating neural network Dall-E is based on a GPT-3 variant with 12 billion parameters. Dall-E can produce pictures from user-supplied text prompts after being trained on a set of text-image pairs.
Below are the trends expected to dominate the Natural Language Processing market in 2023.
Chatbots Becoming Smarter
Customers are becoming increasingly reliant on chatbots, be it for making inquiries, making purchases, or finding support. In companies that focus on serving customers, applications of NLP have been recognised as a potential substitute to manipulate and express complicated enquiries. NLP applications and use are garnering more attention as technology and the human-computer interface is advancing and leading to a broad adoption in a number of sectors, including banking, supply chains, education, insurance, among others. With developments in NLP technologies, the chatbots can analyse previous conversations, understand intent of the user, and deliver relevant answers. The rise of AI has led to the growing popularity of voice bots, which enables customers to use their voice instead of typing. Using voice bots, customers can now perform everyday activities such as setting up reminders, shopping, cooking, listening to news, etc.
Chatsonic by Writesonic is being recognized as the best AI chatbot for news content creators. The chatbot supported by Google can provide answers and stories related to latest events, which ChatGPT cannot do since its database is limited to 2021. Another notable difference between Chatsonic and ChatGPT is that the former offers footnotes with references to the sources so that you may check the information it is giving you. Besides, the innovative chatbot has intriguing features such as voice dictation, which allows users to give commands through Alexa.
According to TechSci Research report on “Artificial Intelligence (AI) Market - Global Industry Size, Share, Trends, Opportunities, and Forecast, 2018-2028, Segmented By Type (Strong AI and Weak AI), By Technology (Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Others) By Deployment (Cloud and On-premises), By Industry (Healthcare, Retail & E-Commerce, Logistics and Transportation, Manufacturing, Consumer Electronics, BFSI, and Others), By Region, Competition”, the global artificial intelligence market is expected to grow at a formidable rate. The market growth can be attributed to the increasing penetration of digital technologies and rising automotive industry. Additionally, increasing adoption of hyper-personalized services and emergence of new AI tools and technologies are expected to fuel their market growth in coming years,
Growing Role of Social Media Sentiment Analysis
As our world becomes more digital, it produces exponential volumes of text, audio, and video data. Natural language processors can analyse huge amounts of data, but they cannot distinguish between speech that is positive, negative, or neutral. Additionally, unlike standard NLP models, support agents are able to modify their dialogue with consumers based on their emotional state. As a result, entrepreneurs are developing NLP models that recognise the sentimental or emotional content of text data in addition to its context. Through the provision of improved services and customer experiences, such NLP models increase client loyalty and retention. Sentimental analysis is a branch of natural language processing (NLP) that aims to find and extract opinions from texts across blogs, reviews, social media, forums, news, etc. Sentiment analysis is often referred to as opinion mining or emotion AI. With the use of open-source software and NLP, sentiment analysis can transform the unstructured text into structured data.
For instance, US-based startup, Spiky has created an AI-based analytics tools to enhance coaching, training, and sales calls. The startup's automated coaching tool for revenue teams creates engagement measurements from video recordings of sessions. Additionally, it produces context- and behaviour-driven analytics and offers a variety of special communication- and content-related metrics from spoken and non-verbal sources. The platform enhances sales teams' abilities to engage customers and perform better in sales.
Multilingual Language Models Take Over
Models that enable communication using natural language are now being widely used. While the most recent generation of language and multi-modal models are displaying enhanced sophisticated capabilities, research models like BERT and T5 have become considerably more accessible. At the same time, a flurry of NLP firms has begun to put this technology to work in real world applications. Although this type of language technology may have a significant influence, previous models have mostly concentrated on English and a small number of other languages with abundant resources. There are several reasons why it is crucial to create models that work for additional languages, including bridging the language gap already present and protecting speakers of non-English languages from being left behind.
The creation of NLP models that effectively comprehend unstructured input in several languages is made possible by the availability of sizable training datasets in various languages. This increases data accessibility, enables businesses to streamline their translation processes, and broadens the reach of their brands.
Finnish startup Lingoes has developed a one-click method for developing and deploying multilingual NLP models. It includes automated setup of all technical NLP model-setting processes and intelligent text analytics in 109 languages. The solution also offers an application programming interface (API) for customised integrations in addition to integrating with a wide range of programmes and procedures. This enables developers to produce multilingual NLP classifiers that are production-ready, product teams to analyse customer input, and marketing teams to track consumer attitudes.
Advancements in Semantic Search
Search engines have come a long way since their conception in the early 1990s. With the integration of artificial intelligence (AI) technologies like machine learning (ML) and natural language processing, search engines have recently made enormous leaps towards offering far more complex and important answers. AI skills are added to search engines to develop semantic search, which aims to not only interpret keywords within the search bar but also determines the intent and contextual meaning behind a search query. For years, Google has trained various language models through natural language processing. Currently, BERT is the most critical advancement in Google search based on NLP. It is designed to improve search query interpretation, rank and compile featured snippets, and interpret text questionnaires in documents. Google also rolled out MUM update, which understands images, videos, and audio files. To extract information from unstructured data, Google will rely most heavily on natural language processing to identify entities and their meanings.
Way Ahead
Natural language processing has advanced significantly in recent years, despite its historically sluggish rate of progress. However, NLP has also drawn a lot of interest due to its potential to change how we conduct our business. NLP is expected to remain a dominant trend in analytics and continue to gain momentum in following years. NLP will continue to expand in the next years with the growing availability of low-code, no-code, and ready-to-use pre-trained models. Businesses, in particular, will benefit from NLP since it helps them improve operations, increase customer satisfaction, save expenses, and make better choices.
According to TechSci Research report on “Machine Learning (ML) Market – Global Industry Size, Share, Trends, Opportunity, and Forecast. 2018-2028 Segmented By Component (Services & Solutions), By Enterprises Size (SMEs and Large Enterprises), By Deployment (Cloud and On-premises), By End-User (Healthcare, Retailer, IT & Telecom, Automotive and Transports, Advertising & Media, BFSI, Government and Defense and Others), By Region”, the global machine learning market is anticipated to grow at a robust pace. The market growth can be attributed to the rising technological innovation and increasing applications of machine learning in various industries.