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

Level 4: Bots will be permitted to enter a conference in real-time at this level because they can discern human intent with sufficient accuracy: Here is the most recent research from Gartner on market estimates for the next three years in case we curious.AI may be able to comprehend nonverbal interpersonal interactions at this level as well. In a meeting, if we turn to someone and ask, "So we will follow up?" The bot will comprehend who and what we are speaking with and whether the other person has given their consent to be followed up with. After a meeting, it will be able to assist participants in keeping their commitments. Teams that use Level 4 meeting bots will succeed because they will keep participants on task following a meeting. A Level 4 meeting AI is fully extended when it can perceive and comprehend the complete spectrum of human communication, the majority of which is non-verbal. Nevertheless, it will be a fascinating challenge for engineers to figure out how AI would use human speech that can vary from what humans say. Level 5: Level 4 bots will support special teams, whereas Level 5 bots will unite disparate teams. According to Andy Payne, Senior Director of Cisco Emerge, one of our research arms, "The meeting intelligence is not just in one meeting, it is in every meeting" at this utopian (or dystopian, depending on your perspective) level. Based on information gathered from meeting material and social network analysis, which includes chat and email data mining, this level of the bot is aware of overlapping meeting subjects, employees' particular skill sets, and the projects individuals are working on across the firm. A Level 5 bot might be aware of the overall business objectives, suggest team members for projects, and introduce people based on objectives, project requirements, and compatibility. A Level 5 bot might affect how well a company performs by enhancing team and interpersonal relationships. No one is ready for a robot boss, and we may never be. However, if we follow the trends in artificial intelligence, machine learning, and social data mining, we will inevitably be able to develop this capability. a) Probabilistic Language Processing Assuming a probabilistic model of the language, probabilistic language processing employs that model to infer things like how sentences should be broken down or how to understand unclear words. Applying statistical analysis codes to data analysis is known as probabilistic models in machine learning. It was one of the earliest approaches to artificial intelligence, and even now, it has still used quite a bit. The Naive Bayes algorithm 33 33 Giuseppe Bonaccorso, “Machine Learning Algorithms”, 2nd Edition, Packt, 2018 is one of the group's most well-known algorithms. In addition to creating data distributions in latent space representations, ML models are probabilistic in assigning probabilities to predictions in a supervised learning setting. These models can be entirely random, partially deterministic, or both. b) Image Formation and Designing The study of image formation covers the radiometric and geometric processes by which 2D images of 3D objects are created. Analog-to-digital conversion and sampling are also a part of the image generation process for digital images. Image processing is modifying an image to make it larger and produce information. There are two ways to process images: • Photographs, prints, and other tangible copies of images are processed using analog image processing. • Digital image processing, which uses intricate algorithms to manipulate digital images. Businesses are scrambling to show off how AI is used in their products and services as interest in AI has surged. Often, they refer to AI as merely one element of AI, like machine learning, and AI needs specialized hardware and software to create and refine machine learning algorithms. No one computer language is exclusively associated with AI, but a few stand out, including Python, R, and Java. Large amounts of labeled training data are typically consumed by AI systems, which then analyze the data for correlations and patterns before using these patterns to predict future states. A chatbot given samples of text chats may learn to have realistic conversations with people by looking at millions of instances. On the other hand, an image recognition program may be taught to identify and describe objects in pictures. AI programming focuses on three cognitive processes 34 : Learning, reasoning, and self-correction. 34 Adelyn Zhou, Mariya Yao, and Marlene Jia, “Applied Artificial Intelligence: A Handbook for Business Leaders”, 2018 © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 19 ( )D Year 2023 Journey of Artificial Intelligence Frontier: A Comprehensive Overview 1. Learning Processes: This part of AI programming focuses on acquiring data and creating rules for turning the data into information that can be used. The guidelines, sometimes called algorithms, instruct computer equipment on carrying out a specific activity step-by-step. 2. Reasoning Processes: The best way to accomplish a goal is what this field of AI programming is all about. 3. Self-Correction Procedures: This aspect of AI programming aims to continuously improve algorithms and make sure they deliver the most accurate results.