Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

h) Computer Vision Computer vision (CV) 20 1. Learning 3D shapes Controlling operations is a multidisciplinary scientific field that aims to enable computers to understand and automate operations performed by the human visual system. It involves capturing, processing, analyzing, and comprehending digital images to extract meaningful information and make judgments. CV tasks encompass various methods, including image understanding, scene reconstruction, object identi- fication, event detection, video tracking, and more. The field focuses on developing theoretical foundations and computational models to achieve autonomous visual understanding. By leveraging principles from geometry, physics, statistics, and learning theory, computer vision aims to decouple symbolic information from image data. Computer vision finds applications in diverse domains, such as industrial machine vision systems for quality control, research in artificial intelligence, and the development of computers or robots capable of understanding their environment. It overlaps with machine vision, which combines automated image analysis with other technologies for industrial inspection and robot guidance. While traditional computer vision approaches involve pre-programmed tasks, there is an increasing trend toward learning-based methods in the field. This allows systems to adapt and improve their performance through experience and training. A few examples of computer vision applications. 2. Medicine 3. Machine vision. 4. Military 5. Automated Vehicles 6. Tactile feedback 7. Motion Analysis 8. Scene reconstruction 9. Image Restoration 10. System methods 11. Image understanding systems IV. H ow does AI W ork? A vast volume of labeled training data is typically ingested by AI systems, which then examine the data for correlations and patterns before employing these patterns to forecast future states. For the software to learn automatically from patterns or features in the data, artificial intelligence combines massive amounts of data with quick, iterative processing and sophisticated algorithms. Large data sets are combined with clever, 20 V Kishore Ayyadevara and Yeshwanth Reddy, “Modern Computer Vision with PyTorch: Explore Deep Learning Concepts and Implement Over 50 Real-world Image Applications”, 2020 iterative processing algorithms to create AI systems 21 a) Solving Problems by Searching that can learn from patterns and features in the data they analyze. An AI system assesses and evaluates its own performance after each round of data processing, adding to its knowledge base. ML-based financial fraud detection, picture recognition for face unlocks in mobile devices, and voice assistants are a few examples of AI software already being used daily. In most cases, all that is needed is AI software, which can be downloaded from an online retailer. AI refers to a machine's capacity to exhibit traits shared by humans, such as creativity, Learning, planning, and reasoning. AI allows technical systems to comprehend their surroundings, deal with what they see, solve issues, and take action to reach a particular objective. Intelligent agents 22 b) Logical Agents aim to maximize their performance metric, which can be accomplished more quickly if the agent can embrace a goal and work towards achieving it. Imagine that an agent wishes to see Europe while on vacation. The agent's effectiveness is measured by various variables, including how quickly they travel, how many places they visit, how adventurous they are, how well they are accommo- dated, how much variety they sample, etc. An intelligent agent needs information about the outside environment to make wise decisions. Agents know the form of knowledge representation language sentences kept in knowledge bases. A representation language is described by its semantics, which describes the truth of each statement in each conceivable model, and by its syntax, which specifies the structure of sentences. The theory behind logical AI is that an agent may express its knowledge of the world, its objectives, and the current situation using logical phrases and can then decide what to do by assuming that a specific course of action will effectively achieve its objectives. c) Inference in First-Order Logic Declarative and expressive knowledge representation languages for ideal knowledge bases should be compositional, context-independent, and unambiguous. First-order logic 23 , in contrast to propositional logic 24 21 Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill, 3rd Edition, 2018. 22 Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, 3rd Ed., Prentice Hall, 2010. 23 Osondu Oguike, “A First Course in Artificial Intelligence”, 2021 , makes an ontological commitment to the existence of objects and relations, enhancing its expressive power. First-order logic models consist of objects, their connections, and applicable functions. Atomic sentences are formed by applying predicates to 24 Kevin Warwick, “Artificial Intelligence: The Basics”, 2011 © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 16 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview