Search algorithms form the backbone of problem-solving in artificial intelligence (AI). Whether it’s navigating a maze, planning a robot’s movement, or strategizing in a game, AI systems often need to explore various possibilities to reach a goal. This exploration is enabled by search algorithms. These algorithms simulate intelligent behavior by systematically examining sequences of decisions ...
In today’s data-driven world, businesses and organizations generate an overwhelming volume of unstructured documents—ranging from invoices and contracts to healthcare forms and insurance claims. Traditionally, extracting useful information from these documents required manual labor or basic optical character recognition (OCR) tools. Document AI is transforming this landscape by bringing the power of artificial intelligence to ...
Probability theory plays a foundational role in artificial intelligence (AI) by helping systems reason, make predictions, and handle uncertainty. In AI, especially in real-world scenarios, outcomes are rarely deterministic. Agents must often make decisions with incomplete or noisy information, requiring a framework to measure, update, and infer probabilities dynamically. One of the most important tools ...
In the field of artificial intelligence and machine learning, learning algorithms are essential for developing systems that can adapt, predict, and improve over time. Among the foundational learning techniques is inductive learning, which focuses on drawing general conclusions from specific examples. It mimics the way humans learn from experience—by observing patterns and extrapolating rules. Inductive ...
Artificial Intelligence (AI) is reshaping the retail industry by driving smarter operations, hyper-personalized experiences, and data-informed decision-making. In an era where customer expectations are rapidly evolving, retailers face intense competition and growing pressure to deliver seamless, personalized, and responsive shopping journeys—both online and in-store. Shoppers today demand real-time service, tailored recommendations, and frictionless checkout. To ...
Uncertainty in Artificial Intelligence (AI) refers to the lack of complete certainty in decision-making due to incomplete, ambiguous, or noisy data. AI models handle uncertainty by using probabilistic methods, fuzzy logic, and Bayesian inference. Proper uncertainty representation enables AI systems to make informed predictions and improve reliability in real-world applications. What is Uncertainty in Artificial ...
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 ...
Team Applied AI
Abhimanyu Saxena
