Will Natural Language Processing Turns AI Robots into Human?

Last Updated on November 13, 2023 by SPN Editor

Natural Language Processing (NLP) is one of the AI technologies that will certainly elevate Human touch in Artificial Intelligence. The human desire for authentic, human-driven communication persists, even with the emergence of AI chatbots and other artificial intelligence tools that enhance efficiency and performance.

While these tools can expedite tasks and provide satisfactory results, there is always an inherent preference for a human touch in content creation, whether it be in blogs, news articles, or conversing in Chatbot. AI detection tools aim to ensure the authenticity of human perspectives in content, and some Chat GPT prompts claim to offer 100 percent human-written content, although this may not fully satisfy the need for genuine human expression.

Even major platforms like Google emphasize the importance of human-created content for human consumption. While AI tools can rapidly generate vast amounts of data, content creators often find that the machine-generated output lacks the human touch that resonates with readers. Despite the time saved by utilizing AI for content creation, the absence of a human tone can be a drawback.

This highlights the growing significance of Natural Language Processing (NLP) algorithms. Learning and implementing NLP are required to empower machines with human-like language capabilities.

With the advancement of conversational AI Chatbot, which aims to replicate human conversations, several companies have started to replace human staff with AI Virtual Agents. Here, let us be very careful that we want to talk to a human, not a robot or some programmed machine.

The continuous improvement in deep learning, machine learning, and Natural Language Processing algorithms enables the development of highly accurate error-free conversational AI models capable of understanding context, mood, and natural human language intricacies. These trends are expected to propel market growth in the future.

Best AI Chatbot Using NLP Technology

Various NLP engines available in the market, such as Google’s Dialogflow, Wit.ai (Facebook), Watson Conversation Service (IBM), and Lex (Amazon), empower chatbots with advanced capabilities. The Royal Bank of Scotland incorporates NLP in its chatbots to enrich customer experience by employing text analysis to interpret trends from customer feedback gathered through surveys, call center discussions, complaints, or emails.

Kommunicate.io features a chatbot utilizing conversational artificial intelligence, infusing a human touch into every interaction with a user. Tidio Chatbot utilizes NLP features, offering one of the most intelligent conversational AIs in the market.

These chatbots employ NLP to comprehend the structure of a customer’s sentence and identify the main topic or keyword. For instance, if a customer expresses, “I want to order a pizza with extra cheese and pepperoni,” the AI chatbot, using NLP, understands that the customer intends to order a pizza with specific toppings.

Future of Natural Language Processing (NLP) Technology

The demands for elevating AI Efficiency while retaining human connection in content creation or conversation are helping to boost the demands for this Natural Language Processing market. It is expected to experience substantial growth, reaching USD 53,505.91 million by 2027 at a CAGR of 30.22%.

This growth is attributed to the increasing demand for Natural Language Processing (NLP) applications, particularly in sectors like healthcare, e-commerce, and customer service. NLP finds widespread use in enhancing user experiences and sales on e-commerce platforms through sentiment analysis, chatbots, and personalized recommendations. Companies recognize the potential of NLP in boosting operational efficiency and customer satisfaction, with its application extending to virtual assistants and automated chat systems for effective and tailored customer interactions.

Again, in terms of job opportunities, proficiency in Natural Language Processing (NLP) can unlock various roles. NLP professionals can explore careers such as an NLP Researcher, conducting research to advance the field, or an NLP Analyst, utilizing NLP techniques to analyze and interpret large volumes of text data. There’s also the role of an NLP Scientist, applying NLP in the development of algorithms and models, and an NLP Engineer, responsible for developing and implementing NLP applications and products.

The salary for Natural Language Processing professionals can vary based on location and experience levels. In the USA, the average salary for an NLP engineer in 2023 is $135,000 per year. Breakdowns indicate that a beginner NLP engineer earns $126,050, an intermediate NLP engineer with 3-6 years of experience earns $160,000, and an advanced NLP engineer with 6-9 years’ experience earns $200,000.

For those seeking opportunities, numerous US companies actively hire Natural Language Processing professionals. Some notable names include Millennial Software, A3 Missions LLC, EpochGeo, Indeed, IBM, OCLC, Bloomberg, Vagaro, Deloitte, Humana, TikTok, and Google.

Why Natural Language Processing (NLP) is necessary in AI?

AI encounters numerous challenges from the intricate and variable nature of human language. A few prominent challenges include the contextual nuances of words and phrases, as well as the presence of homonyms—words that share the same pronunciation but possess different meanings, posing difficulties for applications like question answering and speech-to-text.

Synonyms further compound the complexity, as various words may express identical ideas or vary in levels of complexity. Irony and sarcasm present hurdles for machine learning models due to their use of words and phrases that may, by strict definition, appear positive or negative but convey the opposite.

Again, ambiguity is pervasive in human language, posing a challenge for AI machines to interpret accurately. Errors in text or speech, such as misspellings, are more easily resolved by humans who can quickly associate misspelled words with their correct equivalents.

Colloquialisms, slang, and informal expressions introduce additional complexities for Artificial Intelligence. Domain-specific language limitations are notable, as most of the AI are tailored for specific tasks and struggle to adapt to new domains or expand their functionality.

Besides, low-resource languages pose a significant challenge, lacking the necessary annotated datasets and resources for developing effective AI chatbot

Role of NLP in Balancing AI Efficiency and the Human Element

Natural Language Processing (NLP) plays a pivotal role in striking a balance between AI efficiency and the human element. It constitutes a branch of artificial intelligence focused on enabling machines to read, understand, and derive meaning from human languages.

Combining principles from linguistics and computer science, NLP delves into language structure and rules, creating models capable of comprehending, deciphering, and extracting pertinent details from both text and speech1.

NLP holds particular significance in the advancement of Conversational AI Chatbots, a technology facilitating human-like interactions between computers or machines and users. Harnessing a blend of AI, machine learning, and NLP techniques, Conversational AI is designed to comprehend and generate human language in a conversational context.

Natural Language Processing empowers these systems to understand user inputs and respond in a manner reminiscent of human conversation, thereby delivering more precise, context-aware, and natural interactions.

The applications of NLP and Conversational AI are reshaping how we interact with machines and each other. They find utility across various domains, including chatbots, virtual assistants, customer support platforms, and voice-activated devices.

How to Become an NLP Professional?

Becoming an NLP (Natural Language Processing) professional involves a combination of education, skills development, and practical experience.

Education: Obtain a bachelor’s degree in a related field such as computer science, data science, or engineering. Some positions may prefer or require a master’s degree in a related subject such as data science or artificial intelligence, or a Ph.D. with a focus in Natural Language Processing.

Skills Development: Develop skills in systems architecture, computer logic, data structures, artificial intelligence, and other useful fields. Understanding of machine learning, linguistics, and cognitive science can also be beneficial.

Certification: Consider obtaining an NLP certification. There are three primary credentials you could seek if you want to certify your knowledge of NLP: NLP Practitioner Certification, NLP Master Practitioner Certification, and NLP Trainer.

Practical Experience: Gain practical experience by working on NLP projects. This could be through your job, internships, or personal projects. Practical experience will help you understand the real-world applications of NLP and give you a chance to apply what you’ve learned.

Stay Updated: The field of NLP is constantly evolving, so it’s important to stay updated with the latest research and developments.

Important Courses Available for NLP Programs

ICF Coach Certification Training

This program provides an extensive Natural Language Processing training and certification course, covering NLP Diploma, Certified NLP Associate Practitioner, Certified Practitioner of Hypnotherapy, Certified Life Coach, Certified Associate Leadership and Executive Coach, and Certified Organizational Development Coach.

iGNLP™ – Certified NLP Assoc and Master Practitioner

This fully certified NLP training program combines NLP Practitioner and NLP Master Practitioner Training at the level recognized by international NLP institutes.

NLP India

Offering a range of NLP professional training courses, NLP India provides NLP coaching training and NLP therapy with reputed trainers in India.

Coursera

Known for its diverse offerings, Coursera provides various NLP courses tailored to individual learners seeking personal growth and corporate teams looking to upskill.

DigitalDefynd

Compiling a comprehensive list of the best NLP Training, courses, certifications, and classes available online, DigitalDefynd serves as a valuable resource for those seeking NLP training options.

Thus, with the advancement in Natural Language Processing (NLP) platform, the AI will certainly understand what we want from them. Despite its capacity to make AI systems more interactive and user-friendly, it is very clear that NLP doesn’t imbue these systems with true “human” qualities. AI, even when utilizing NLP, operates based on programmed algorithms and learned patterns, lacking human attributes like consciousness, emotions, or subjective experiences. Just wait for the Technology Singularity when AI outshines humans!