NLP research notes
The Robotic Future of Artificial Intelligence and Natural Language Processing (NLP)
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The main goal for sharing AI research is to program machines and software to mirror the ability and improvisation of the human mind. When it comes to business processes, the underlying tasks for achieving human-like cognition is to automate repetitive tasks and lower priority work. This allows employees to focus on more critical tasks and implement long-term strategies and management. Due to the time allotted for more important work, organizations have been utilizing machines powered with artificial intelligence to accomplish routine jobs.
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What Can Come From the Future of AI In NLP
As artificial intelligence becomes more equipped to comprehend human communication, more businesses will adopt this technology for areas that require communication where Natural Language Processing (NLP) would make a difference. AI technology is already being used in these areas:
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Customer Service
The expansion of customer service to include emotionally intelligent chatbots has been growing exponentially for some time. Chatbots are capable of understanding text written in natural language and responding with answers to basic questions and problem resolutions. Some bots are equipped with NLP so intelligent that humans can’t distinguish if they are humans or robots. With further advancements, natural language processing will allow AI empowered virtual customer service representatives and voice assistants to vocally communicate to solve complex problems. Potentially, the bots could be used for technical support, providing responses and services and recording notes for field staff.
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Smart Home and Office Assistants
We’ve all had experience interacting with virtual assistants via web and mobile devices. They're becoming more and more intelligent with completing basic operations by listening and understanding common voice commands. As AI technology continues to advance, soon we will have in-vehicle voice assistants with the power to perform tasks for various vehicle operations and other complex commands.
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Homes furnished with smart amenities have in-home assistants that work with natural language processing to recognize commands. This technology and the advancement of the technology in voice assistants like Alexa will be capable of understanding young children who have not developed perfect speech, and people from different regions of the world who may have accents or speak multiple languages. They will not only be able to listen to voice commands but will respond in an innate manner.
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Healthcare Filling and Recording
Healthcare physicians spend more time filling health record documents than they they do consulting with patients. The medical industry serves serves billions of people a year. To prioritize time, prevent burnout for physicians and provide patients with better healthcare, AI technology powered by natural language processing can assist with dictating observations and details that will automate filling in the EHR.
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Human Robotics
Just a few years ago, robots that can move, think and speak like humans seemed out of reach, but they will become familiar soon. Humanoid robots that can function like humans are being developed to assist organizations with tasks that are time consuming and predominately unsafe for employees, including manufacturing. To achieve this, robots will need the ability to perfectly comprehend human speech, making natural language processing more important than ever. Without NLP being perfected, misinterpreted commands can lead to the robot performing an unwanted action.
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There are many aspects of artificial intelligence and natural language processing that can be implemented in various areas of our everyday lives and everyday processes within organizations. Considering the current levels of competency within AI and machine learning-with continuous advancements in AI paired with NLP- the possibility of having machines that can listen and comprehend written and spoken language like humans make the future of AI exciting.
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Source:
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What is Artificial Intelligence?
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Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
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The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
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Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency.
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Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge.
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On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
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https://www.britannica.com/technology/artificial-intelligence
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One-Hot Encoding vs Word Embedding
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Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric.
This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model.
The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods.
In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras.
After completing this tutorial, you will know:
(1) The challenge of working with categorical data when using machine learning and deep learning models.
(2) How to integer encode and one hot encode categorical variables for modeling.
(3) How to learn an embedding distributed representation as part of a neural network for categorical variables.
https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/
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Top 4 Sentence Embedding Techniques using Python!
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- What is Word Embedding?
- Introduction to Sentence Embedding
- Doc2Vec
- SentenceBERT
- InferSent
- Universal Sentence Encoder
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https://www.analyticsvidhya.com/blog/2020/08/top-4-sentence-embedding-techniques-using-python/
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