Text Classification in NLP: Techniques, Models, and Applications

Do you know how human brains work? Have you ever wondered if animals also have a brain but can’t make decisions like humans? Why so?

Humans do all this with the help of intelligence, so when a human puts intelligence into a machine, it is called artificial intelligence.

The term artificial intelligence was first coined decades ago in 1956 by John McCarthy at the Dartmouth Conference. he defined artificial intelligence as “Artificial Intelligence is the science and engineering of making intelligent machines”.

AI is the technique of getting machines to work just like humans. These machines are artificially incorporated with human-like intelligence to perform tasks as we do. This intelligence is built using complex algorithms and functions. The practical applications of AI include healthcare, robotics, business analytics, and marketing. AI has become so general that we don’t realize we use it in our daily lives, including smartphones, vehicles, social media, games, banking, and many other aspects of our daily lives. For instance, have you ever noticed how Google search gives you such an accurate result for your query? OR how your social media feeds give you content based on your interests? The answer to this question is artificial intelligence.

There is a misconception that Artificial intelligence, machine learning, and deep learning are the same since they have common applications such as Google Assistant. Let us discuss each one by one: Artificial Intelligence is the science of getting the machine to mimic the behaviour of humans, While Machine Learning is the subset of AI that focuses on getting machines to make decisions by feeding them data and on the other hand Deep Learning is a subset of Machine Learning that uses the concept of the neural network to solve complex problems. They are all interrelated fields. It will be discussed further.

AI allows machines to adapt, reason, and provide solutions. Programming AI systems focuses on cognitive skills such as:

1. Learning: This part of AI programming involves gathering data and creating rules called algorithms to turn it into meaningful information. These algorithms give computing devices direction on how to carry out a particular task step by step.
2. Reasoning: This aspect of AI programming involves choosing the right algorithms to reach a desired result.
3. Self-correction: This part of AI programming involves algorithms continuously learning and tuning themselves to provide accurate results.
4. Creativity: This aspect uses neural networks, statistical systems, and other AI technologies to generate text, images, music, and ideas.

Why is Artificial Intelligence Important?

AI is very important as it can fairly change our everyday lives, our jobs, and our hobbies. It is advancing so fast. It has been of great value to various business processes by offering automation for tasks that were once done by people, such as customer service, lead generation, fraud detection, and quality control.

AI can do certain things better and more efficiently than human beings in a mixture of domains. It is particularly useful for routine tasks, and detail-oriented, for example, analysis of thousands of pages in legal documents to determine the filling of relevant fields. The ability of AI to process huge data sets gives businesses insights into their operations that they may not have noticed. While synthetic AI development is outpacing any other technology in sheer velocity, it has swiftly captured stakeholders' imagination in areas such as education, marketing, and product design.

Advantages of AI:

1. Excellence in detail-intensive careers: AI is well-suited to handle tasks that entail the detection of subtle patterns and relationships in data that may escape human detection. For instance, AI systems are highly accurate in the detection of early cancers, including breast cancer and melanoma, by pointing out areas of concern for further examination by medical professionals.
2. Efficiency in data-heavy tasks: Automation tools and AI systems cut down significantly the time spent on data processing. It is especially effective in industries such as insurance, finance, and healthcare that involve a great deal of routine data entry and analysis, as well as data-driven decision-making.
3. Time-saving and increased productivity: Not only can AI and robots automate tasks, but also enhance efficiency and safety. In production, for instance, AI-based robots are now becoming common to do risky tasks that a human can not do, while increasing productivity.
4. Consistency in the results: Modern analytics tools employ AI and machine learning to analyze vast quantities of data consistently, yet maintain the capacity to evolve with new information through ongoing learning.
5. Round-the-clock availability: The AI program does not need to sleep or take breaks. For example, AI-powered virtual assistants can provide uninterrupted, 24/7 customer service even under high interaction volumes, improving response times and reducing costs.
6. Scalability: AI systems are scalable to manage increasing levels of work and data. This renders AI highly appropriate for applications where data volumes and workloads can increase exponentially.

Disadvantages of AI:

1. High Costs: Creating an AI system can be extremely costly. Creating an AI model can involve a significant initial investment in infrastructure, computational power, and software to train a model and hold its training data. Once trained, there are additional recurring costs involved in model inference and retraining.
2. Technical Complexity: Implementing, running, and debugging AI systems (particularly in live production settings) involves considerable technical expertise.
3. Job Displacement: AI can contribute to the loss of jobs in companies that substitute human employees with machines. An increasing source of worry as the sophistication of AI models improves and businesses increasingly seek to automate processes.
4. Security vulnerabilities: AI systems are prone to a host of cyberattacks, such as data poisoning and adversarial machine learning. Data thieves can tap into sensitive training data from an AI model.
5. Legal issues: AI raises sophisticated issues of privacy and legal responsibility, especially in the context of an unpredictable AI regulatory environment that varies by region. AI raises complex questions around privacy and legal liability, particularly amid an evolving AI regulation landscape that differs across regions.
6. Human laziness: AI’s automation and dependence on technology for answers have the potential to cause laziness among humans by reducing the necessity for effort, critical thinking, and self-sufficiency, which can result in a sedentary lifestyle and deterioration of some skills.