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AI glossary: 25 artificial intelligence terminologies that you should know

Information Technology

In an era where artificial intelligence (AI) is not simply a buzzword but a fundamental element of progress, it is important to comprehend its language. This blog article offers a comprehensive guide to navigating the complex vocabulary of artificial intelligence. Whether you’re a budding enthusiast, a professional stepping into the AI arena, or simply a curious mind, this glossary aims to demystify the jargon, making the complex world of AI accessible to everyone. 

Clarify your doubt about if AI boon or bane

Artificial intelligence (AI) is a fascinating scientific discipline that focuses on the development of machines capable of doing tasks that typically necessitate human intelligence. These tasks encompass a spectrum of activities, starting with basic problem-solving and extending to more intricate functions such as acquiring knowledge, making decisions, and understanding language. The advancement of artificial intelligence (AI) can revolutionize all aspects of our lives, encompassing healthcare, education, entertainment, and beyond. 

The importance of AI cannot be overstated. It is revolutionizing industries, enhancing human talents, and creating opportunities for invention forms. AI, or artificial intelligence, is increasingly becoming essential to our everyday lives. It ranges from personal assistants like Siri and Alexa to more sophisticated systems capable of predicting natural disasters or diagnosing diseases. This is significant in its technological progress and ability to address worldwide problems. 

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AI-generated  image
Image source: AI-generated

Essential AI glossaries 

Here’s a curated essential AI terms that everyone should know: 

  1. AI ethics and safety: An essential consideration in the creation or development and implementation of AI intelligence, focusing on ensuring that they are used in a manner that is ethical, transparent, and promotes the safety and confidentiality or privacy of individuals.  
  1. Algorithm: An algorithm is a collection of regulations or directions that are created to carry out a certain activity or resolve an issue. 
  1. Artificial General Intelligence (AGI): An abstract form of artificial intelligence that has the capacity to comprehend, acquire knowledge, and employ it in various activities, reaching or surpassing the level of human intelligence. 
  1. Artificial Intelligence (AI): Artificial intelligence refers to imitating human intelligence processes by devices, specifically computer systems. 
  1. Artificial Neural Networks (ANNs): Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s network of neurons. They consist of layers of interconnected nodes or “neurons,” which simulate the neural connections in the brain. Each node in a network act as a small processing unit, performing simple computations and transmitting signals to other nodes across the network. 
  1. Backpropagation: Backpropagation is a technique employed in artificial intelligence (AI) to optimize mathematical weight functions and enhance the precision of the outputs produced by an artificial neural network. 
  1. Big Data: Extremely large, intricate, and rapidly changing data sets that may be studied computationally to uncover patterns, trends, and relationships. 
  1. Classification: A process in machine learning that involves categorizing input data into predefined groups or classes. 
  1. Clustering: A machine learning technique that aims to group a collection of items organised according to their similarities, where objects within the same group exhibit more similarity compared to objects in separate groups. 
  1. Convolutional Neural Network (CNN): A Convolutional Neural Network (CNN) is a sophisticated machine learning system that can process input images, assigning value (via learnable weights and biases) to different elements or objects within the image, enabling it to distinguish between them.  
  1. Data Mining: Data mining is the systematic extraction of patterns, correlations, trends, and valuable insights from extensive collections of data. 
  1. Deep Learning (DL): A subset of machine learning in artificial intelligence involves the use of artificial neural networks with multiple layers (hence the “deep” aspect) to model complex patterns in data capable of learning unsupervised from unstructured or unlabeled data. 
  1. Genetic Algorithm: Search heuristics that imitate the mechanism of natural selection to produce optimal solutions for optimization and search problems. 
  1. Heuristic: An efficient approach is developed to speed up problem-solving when conventional approaches prove to be sluggish or to ascertain an approximate solution when conventional methods cannot identify an exact solution. 
  1. Machine Learning (ML): A subset of AI that involves the creation of algorithms that can modify themselves without human intervention to produce desired outputs by feeding on data. 
  1. Natural Language Processing (NLP): NLP is a subfield of Artificial Intelligence (AI) that focuses on enabling machines to comprehend and interpret human languages. 
  1. Precision: In machine learning, the ratio of relevant instances among the instances selected by the model. 
  1. Quantum Computing: Quantum computing is a sort of computation that use quantum phenomena such as superposition and quantum entanglement to carry out operations on data. 
  1. Recurrent Neural Network (RNN): A Recurrent Neural Network (RNN) is an artificial neural network created to identify sequential data patterns, including numerical time series data, text, genomes, handwriting, and spoken phrases. RNN units differ from standard neural networks because they do not process inputs independently. Instead, prior calculations influence RNN units’ output, allowing them to possess a type of memory. RNNs utilize their internal state (memory) to effectively interpret input sequences, making them well-suited for tasks where the context or the sequential order of data is important. 
  1. Reinforcement Learning: An area of machine learning that focuses on how intelligent agents should take actions to maximize the overall reward they receive in a given environment. 
  1. Robotics and Automation: The use of robots to automate tasks traditionally done by humans, enhancing efficiency and safety across various industries. This involves physical robots in manufacturing environments and software bots conducting automated digital tasks. 
  1. Supervised Learning: Machine learning involves training a computer to make predictions or exhibit certain behaviors by providing it with input and evaluating its accuracy through feedback. 
  1. Transfer Learning: Transfer learning is a research area in machine learning that specifically deals with the retention and application of knowledge acquired from solving one problem to another problem that is closely related. 
  1. Unsupervised Learning: Unsupervised machine learning is a technique that identifies hidden patterns in a dataset without any predefined labels and with minimal human intervention. 
  1. Virtual Reality (VR): Virtual reality is a computer-generated experience that can replicate or diverge from reality, commonly employed for entertainment, education, and training objectives. 

Why it’s important to know these terms 

Understanding AI terminology is essential for several reasons. For one, it enables individuals across different sectors to communicate more effectively, ensuring that ideas and innovations are shared clearly and comprehensively. For businesses, it aids in making informed decisions about investments in AI technologies and how to implement them. For enthusiasts and professionals, it opens a world of learning and exploration in one of today’s most dynamic fields of study. 

Moreover, as AI continues to evolve and integrate into our daily lives, grasping its language will become as basic a requirement as being proficient in digital literacy was at the turn of the century. 

Here is the expert opinion about the future of learning with AI


The language of AI is vast and ever-expanding, reflecting the field’s rapid development and its growing impact on society. By familiarizing ourselves with the terms outlined in this glossary, we enhance our understanding of AI and prepare ourselves for a future where AI plays an even more significant role in our lives. Whether for professional development, academic pursuits, or personal interest, diving into the AI glossary is a step toward demystifying the complexities of this transformative technology. Let this be your springboard into the fascinating world of artificial intelligence, where the potential for innovation is limited only by our collective imagination. 

If you’re interested in learning AI technology, you can pursue online MSc in Data Science from Manipal Academy of Higher Education or online MCA. Please check out the programs on Online Manipal website. 


Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.

Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.

  • TAGS
  • Artificial Intelligence
  • data science
  • Online MSC Data Science

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