The Future of NLP: 5 Critical Area and Work

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NLP is hope that the natural language processing is less advanced than other artificial intelligence areas. What’s going to be in this field, let’s always look like...

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What is NLP?

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NLP is the name of a technology created from the head letters of words "Natural Language Processing". With Turkish "Natural Language Processing" is a artificial intelligence field. Natural language is a tool that people use to communicate in everyday life. There are 7,000 languages in the world. It is quite difficult to process human language. What is the reason? Because human language is not only a tool with certain rules, but it is an asset that has cultured. Understanding the presence of these different cultures is not easy for the machine. Google Translate can be given as an example. Translate was previously created with rules-based methods. So, it was not very good to translate. However, the strengthened state with NLP translates as good as a person who knows almost the target language.

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If you want to learn more about NLP, What is Natural Language Processing (NLP)? read our article.

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Future Studies with NLP

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NLP denilinceklakla chatbots, translation apps, etc. if it comes to actually the work area is quite wide. Emotional AI is often used in important areas such as multiple artificial intelligence, sound biometry, text generation and robotics. It has a critical location in projects for the future. Let’s examine some work done and done together.

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Emotionally Intelligent AI: Artificial Emotional Intelligence

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Have you heard about Artificial Emotional Intelligence before? Artificial intelligence aims to learn the cognitive parts of human intelligence, artificial emotional intelligence target human emotional intelligence. It is easy to train artificial intelligence with analytical data; but it is quite difficult to install feelings that belong to a living just like human. Emotional AI is a technology in development phase. As we express our feelings with the most words, the place of NLP in emotional AI is large. In a study conducted at Stanford University, artificial emotional intelligence is aimed at communicating more meaningful and empathy with people.

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Multimodal AI Systems (Multimode Artificial Intelligence)

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Multi-mode artificial intelligence is a machine learning method that can process different types of data. Works multiple data such as text, picture, audio simultaneously. For example, when a picture is given, it can write a text about this picture. You’ll guess one of the important technologies here, NLP is the Natural Language Processing. Gives strong ability to model because it works in multiple fashions.

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One of the most popular examples of natural language processing is ChatGPT. This application was an AI with only one mode that gives text input and text output only with NLP method. For example, DALL-E was the first multimode application of the GPT model of OpenAI, but GPT-4 also introduced very modulated capabilities to ChatGPT. Jina AI CEO Han Xiao says that communication between people is very modulated, so many modulated artificial intelligence will communicate better with people. The GPT-5 model of OpenAI, which is in the development stage, also contains very modluity. Is OpenAI really remarkable?

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Natural Language Production (NLG)

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Carnegie Mellon University is known for projects in the field of natural language processing. There are still natural language processing projects that are used by multi-modeness. As an example, the "Multimode Live Review Production and Evaluation" project is the live review project used by NLG technology. This project enhances live comment generation technology using multi-mode input and evaluation methodology. There are also interesting projects related to natural language processing in this university. For example, projects such as rebuilding ancient languages, text mining and question-cevap information contest reveal the potential of natural language processing.

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Voice Biometrics (Ses Biometrics)

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Ses biometric is an application that includes artificial intelligence-based NLP technology that allows the user to create audio signature by analyzing the biometric and physical characterist of the voice. Face, fingers and sound biometry began to be widespread in various industries. It was started to be used especially by government apartments and law enforcement forces. Data privacy breach has also increased with the use of biometric data. This is one of the best known laws that regulate biometric information in the Law of the State Biometric Information Privacy Act (BIPA). Especially face recognition is used for many years but the accuracy is discussed. For this, it is necessary to take more ways.

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Sample projects related to sound biometry are:

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• VoiceSens Project: Python is a project developed in the language. It aims to make authentication by analyzing users’ audio data.
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• Speaker Diagnostic Labs for ITMO University: This project focuses on the development of audio biometric technologies, voice recognition, sound verification and audio recognition systems. Python was written in language and passed five different laboratory tests.
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Humanoid Robotics Project

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Humanoid robotic projects are robotic systems, such as human body. These robots mimic human kinematic movements and interactions. Within the scope of the RobotX Research Program in ETH Zurich, Advanced Humanoid Locomotion (AHL) project uses NLP techniques to communicate more natural and fluently with the robots. Robots can understand and implement natural language commands. Using NLP technology, the natural language of robots is better understood and the application is expected.

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In summary; NLP offers innovations from many areas of hope by developing interaction between human language and machines. Google Translate, ChatGPT, projects such as artificial emotional intelligence, multi-mode artificial intelligence systems and sound biometry reveal the potential of NLP. Humanoid robotic projects in ETH Zurich also aims to communicate with people more natural with robots using NLP techniques. The future of NLP promises to provide more effective solutions in everyday life and professional areas by moving human-machine interaction further. We are waiting for the future of natural language processing. What do you think about it?

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Source

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