Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data

Overview of Artificial Intelligence

AI refers to the technology that enables machines to perform intelligent tasks like humans. It uses reasoning, automatic learning, and scheduling to automate tasks. There are two key points to AI: automated intelligence and artificial intelligence goals. Automated intelligence includes reasoning, automatic learning, and scheduling. Artificial intelligence goals include machine learning, natural language processing, computer vision, and robotics.

Three Stages of Artificial Intelligence

AI has gone through three stages: machine learning, machine intelligence, and machine consciousness. Machine learning involves using a series of algorithms to learn from experience. Machine intelligence involves using advanced algorithms that learn from experience, such as deep neural networks. Machine consciousness involves self-learning from experience without external data.

Types of Artificial Intelligence

There are three types of AI: ANI, AGI, and ASI. ANI refers to basic, role-based tasks completed by chatbots and personal assistants like Siri and Alexa. AGI refers to human-level tasks that involve continuous learning by machines. ASI refers to machines that are smarter than humans.

What Makes a System Intelligent?

A system becomes intelligent through natural language processing, knowledge representation, automatic reasoning, and machine learning. There are differences between NLP, AI, machine learning, deep learning, and neural networks. AI focuses on creating systems that can process things intelligently. NLP focuses on creating systems that can understand language, a branch of AI. Machine learning focuses on creating systems that can learn from experience and is also a branch of AI. Neural networks are artificial neuron networks inspired by biology, and deep learning is a branch of machine learning that uses deep neural networks on large datasets.

What is Natural Language Processing?

NLP is the ability of machines to understand and interpret human language in writing or speech. Its goal is to make computers/machines intelligent in understanding language like humans. Its ultimate goal is to bridge the gap between human communication (natural language) and computer understanding (machine language). NLP processes different aspects of language such as phonetics, morphology, and syntax. It uses distributed, frame-based, theoretical, and interactive methods to understand semantic analysis.

Why Do We Need NLP in Big Data?

NLP helps us complete tasks such as automatic speech and automatic text writing. With NLP, we can use the computer's ability to tirelessly run algorithms on large datasets, saving time. These tasks include other applications of NLP, such as automatic summarization and machine translation.

NLP and Text Mining

NLP and text mining or text analysis are different processes. NLP is used to understand the meaning and structure of given text data, while text mining is used to extract hidden information from text data through pattern recognition. NLP is used to understand the semantics of given text data, and text mining is used to understand the syntax of given text data.

NLP in Log Analysis and Mining

NLP is widely used in log analysis and mining. Different techniques such as word segmentation, stemming, lemmatization, and parsing are used to transform log information into structured data. Once logs are organized, log analysis and mining can extract useful information and knowledge.

Conclusion

NLP is a complex field that lies at the intersection of artificial intelligence, computational linguistics, and computer science. It involves several steps, such as sentence segmentation, tokenization, stemming, part-of-speech tagging, syntactic parsing, named entity recognition, and coreference resolution. NLP has several key applications, including automatic summarization, sentiment analysis, text classification, and information extraction. It is also widely used in big data, log analysis, and mining to extract useful information and knowledge from unstructured data.