The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem in computer science and artificial intelligence (AI). It involves finding the shortest possible route that allows a salesman to visit N cities exactly once and return to the starting point. The challenge lies in the exponential increase in possible routes as the number of ...
Inference in artificial intelligence (AI) refers to the logical process of deriving conclusions from a given set of premises or facts. It plays a crucial role in automated reasoning, knowledge representation, and decision-making systems, allowing AI to mimic human-like reasoning. Inference mechanisms are widely used in expert systems, natural language processing, and automated theorem proving, ...
The Dempster-Shafer Theory (DST) is a mathematical framework for handling uncertainty and incomplete information in decision-making. Unlike traditional probability theory, which requires prior probabilities, DST allows for degrees of belief based on available evidence. This makes it highly useful in artificial intelligence, expert systems, and data fusion applications. What is Dempster Shafer Theory? The Dempster-Shafer ...
Artificial Intelligence (AI) is transforming the transportation industry, making mobility safer, smarter, and more efficient. AI-powered solutions are enhancing traffic management, predictive maintenance, autonomous driving, and logistics optimization, creating a more sustainable and connected transport ecosystem. One of the key roles of AI in transportation is improving efficiency by analyzing vast amounts of data from ...
Strong AI, also known as Artificial General Intelligence (AGI), refers to an advanced form of artificial intelligence that can think, reason, and learn at a human level or beyond. Unlike current AI models, which are designed for specific tasks, Strong AI aims to possess general intelligence, allowing it to adapt to unfamiliar situations, make independent ...
Artificial Intelligence (AI) algorithms are the core mechanisms that enable machines to process information, learn patterns, and make decisions without human intervention. These algorithms form the foundation of AI systems, allowing computers to recognize speech, classify images, analyze data, and predict outcomes with accuracy and efficiency. AI algorithms play a crucial role in automation, problem-solving, ...
AI ethics refers to the principles and guidelines that govern the responsible development and deployment of artificial intelligence. As AI continues to evolve and impact various industries, ethical considerations ensure that AI systems align with human values, fairness, and accountability. AI ethics helps prevent issues like bias, privacy violations, and harmful automation, ensuring that AI ...
Optical Character Recognition (OCR) is a technology that converts printed or handwritten text into machine-readable digital formats. It enables computers to recognize characters from scanned documents, images, and PDFs, allowing for automated data extraction, editing, and searchability. The evolution of OCR dates back to the early 20th century, with initial developments focused on reading text ...
Large Language Models (LLMs) have become essential in natural language processing (NLP) and AI applications, enabling more human-like text generation and comprehension. Evolving from basic rule-based models to advanced deep learning architectures, LLMs now drive automation, enhance AI communication, and expand capabilities in diverse industries, transforming how AI interacts with language. What is a Large ...
AI automation refers to the integration of artificial intelligence (AI) and automation technologies to create intelligent, self-learning systems that enhance business operations, decision-making, and efficiency. AI automation enables machines to analyze data, learn from patterns, and execute tasks with minimal human intervention. The relationship between AI and automation is crucial to understanding their combined impact. ...
Team Applied AI
Abhimanyu Saxena