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5 Different Types of Artificial Intelligence

In the past few years, artificial intelligence has revolutionized the company’s present approach to acquiring insights from data. The majority of people feel it is the next revolutionary technology. According to PwC, artificial intelligence will generate $15.7 trillion for the global economy by 2030.

These are the five types of AI

1. Machine learning (ML)

Machine learning is a subset of AI that includes algorithms interpreting data sets and learning from them to make educated judgements. It is commonly employed in industry and society to create solutions for addressing real-world concerns like streaming music services and data security. Machine learning algorithms can handle any data scale and perform efficiently on typical low-end devices. They divide issues into several parts, solving each one independently and integrating the results. With machine learning, simpler and more transparent principles, such as decision trees, are employed, making them easier to grasp and extensively utilized by businesses.

2. Deep learning

Deep learning is a subset of machine learning that aims to model the environment in a conceptual hierarchy equivalent to the human brain. It evaluates continuous data using Artificial Neural Networks (ANN), which are similar to biological neural networks. Deep learning algorithms, which conduct multiple operations, require the use of specialized high-end processors with GPU capability. They need a significant quantity of data and complicated parameters, which results in a lengthy training period. An end-to-end problem-solving strategy is adopted, and deep neural network internal workings make interpretation challenging. Deep learning algorithms, despite their complexity, are still considered Artificial Intelligence.

3. Natural language processing (NLP)

NLP allows computers to understand, interpret, and shape human language. Natural language processing (NLP) highlights the importance of natural language processing in AI for voice-activated systems or chatbots which translate languages. Natural language processing in AI enhances communication technology by combining AI, deep learning, and machine learning, showcasing its potential in a new era.

Natural Language Processing (NLP) is an aspect of artificial intelligence which combines AI and languages to permit machines to communicate with humans using natural language.

Natural Language Understanding (NLU) draws meaning from confusing texts by analyzing the following:

  • Lexical Ambiguity – Evaluate whether the terms have multiple meanings.
  • Syntactic Ambiguity – Analyzes multiple vocabulary and grammar in sentences.
  • Semantic Ambiguity – Evaluate whether the sentences have multiple meanings.
  • Anaphoric Ambiguity – A phrase or term mentioned earlier but now has a different meaning.

At this stage, the meaning of each word is determined using lexicons (vocabularies) and a collection of grammatical rules.

4. Computer vision

Computer vision is a rapidly evolving technology that enhances user experiences while cutting costs and strengthening security. By 2025, the market is predicted to increase at a 30% annual pace to $26.2 billion. Computer Vision is the study of teaching computers to observe and analyze the visual world via the integration of theory and technology to construct artificial systems for extracting information from images or multi-dimensional data. It has a wide range of uses, including guiding robots across areas and giving them vision sensors and environmental data. The concept of computer vision stretches back to its initial use in optical character recognition for highlighting handwritten texts.

Computer vision assists machines in identifying and classifying things, and then reacting to what they “see.”

In artificial intelligence, computer vision consists of three sequential operations that run one after the other.

  • Image acquisition
  • Image processing
  • Image Processing and Interpretation

5. Explainable AI (XAI)

Explainable artificial intelligence (XAI) is a collection of procedures and methods that enable human users to interpret and accept the results of data created by machine learning algorithms. Explainable AI is used to discuss an AI model, its predicted impact, and any biases. It aids in defining model accuracy, fairness, and accessibility, which results in AI-powered decision-making. Explainable AI is critical for a business to develop trust and confidence when using AI models. AI explainability also assists the organization in adopting a reasonable stance towards AI development.