Sam Eldin AI Project-Presentation Map© Sam Eldin AI Project-Presentation Map: Introduction (Expectation and Patience): Most IT professionals have to keep dynamically learning new thinking, new adaptive leadership-management, new technologies, new and different approaches, new tools and also new attitudes, otherwise they would be left behind and become distinct as dinosaurs. The sad news is that any new technologies and approaches or tools usually starts with vague-unclear goals and approaches. By the time the dust settled and things are based on solid foundations, these IT professionals would have to go through series of learning and relearning with a lot of frustrations. Personally, I have had gone through such experiences since the days of DOS (disk Operating system), C programming, then Object oriented programming, Windows, Internet, cloud, virtualization, automation, Cybersecurity, DevOps, Machine Learning (ML) and then last Artificial Intelligence (AI) and Quantum Computing. Now, our goal is to conquer Artificial Intelligence (AI) Searching the Internet for how to learn AI by IT professionals? To learn AI as an IT professional, start by building a solid foundation in core concepts like machine learning, deep learning, and data analysis through online courses or bootcamps, then focus on hands-on practice by working on projects using popular AI libraries like TensorFlow or PyTorch, and finally, consider specializing in a specific AI area relevant to your current role, like natural language processing or computer vision, by taking advanced courses and actively applying your knowledge to real-world scenarios within your company. What you need to know About AI? Robert Oppenheimer (a Nobel laureate physicist) believed that "There is no better way to learn than to teach." The act of teaching forces us to distill complex ideas into understandable parts, deepening our own understanding in the process. Therefore, we are creating both a training course and architect-design AI platforms since AI is branching into a number of specialties. Our Project-Presentation Map: Our main goal(s) in these webpages is as the heading is stating:        Analysis, Architect-Design and Training Course We need to master-conquer AI and the best way to do that is to teach while goring through the analysis, architect-design. The key points to our Map-Course are:        • How to cut the learning curve and be able to join any team without delay or burden?        • What is already done and how to catchup?        • Using quick Google definitions to learn the jargon and the AI – Project dictionaries Overall System Structure: Image - AI and ML Overall System Structure Diagram Looking at AI – ML Overall System Structure Diagram Image, we are stating that AI and its ML support plus cybersecurity must be architected to handle each level within the overall structure. Each level has its own unique requirement and handling. AI, ML and Cybersecurity would be customized for each of the following level:        1. Internet and Cloud        2. Business within the cloud        3. Cloud Interfaces        4. Data and Social Media        5. Development and testing        6. DevOps Each of the listed levels is an independent system with all its gadgets. Each level must implement its own unique AI, ML and Cybersecurity. AI, ML and Cybersecurity must be able to communicate and share processing powers, tools and data. The communication and data sharing would be performed by Machine Learning Interface tools-soft. Data exchange and storage would be handled by the level own data matrices. Network Attached Storage (NAS): Nas is a fast file transfers (speed depends on interface) Plug and Play (no complicated setup). NAS Uses native file system of the Operating System. Advantage using NAS:        • Inexpensive hardware        • Can be treated as an object or class properties        • NAS can be an independent node and has its own IP addresses        • Programmable        • Easy to install and use        • Easy to move around        • Easy to test        • Reusable        • Fast file transfers (speed depends on interface)        • Plug and Play (no complicated setup)        • Uses native file system of the Operating System        • Multiple users can access the drive at the same time        • Files can be shared among users and devices        • Remote access via Ethernet is possible        • Web-enabled applications provide additional functionality independent of the computer        • Additional storage can be added (depends on NAS function)        • Can be used as Database Visualizer To make our structure clearer, we need to cover the following definitions: Cloud technologies: Cloud technologies, or cloud computing, allow users to access data, files, software, and servers over the internet. Cloud technologies enable users to store and access data and programs without having to store them on a hard drive. Cloud methodologies: Cloud methodologies are a set of processes and frameworks used to develop, test, and migrate applications to the cloud. They can help businesses determine if they are ready for cloud migration and how to best use the cloud to meet their goals. Cloud Interfaces: Cloud interfaces define how applications interact with cloud services. These interfaces can be used to create, retrieve, update, and delete data, manage accounts, and more. The Cloud Data Management Interface (CDMI) is an international standard that defines a functional interface that applications use to create, retrieve, update and delete data elements from cloud storage. A cloud service provider: A cloud service provider is a third-party company offering a cloud-based platform, infrastructure, application, or storage services. Which type of AI that can be implemented within each of the system structure level: Level #1 - Internet and Cloud: Both cloud access and control are very limited and the following AI types can be used within the Interent Cloud Level. 1. Narrow AI 2. Reactive AI 3. Limited memory 4. Generative Adversarial Networks 5. General AI 6. Neural network 7. Computer vision     Level #2 - Business within the cloud: These are services which would be provided by the business. 1. Amazon Alexa 2. Chatbots 3. ChatGPT 4. Computer vision 5. General AI 6. Neural network 7. Face recognition 8. Google Maps Level #3 - Cloud Interfaces: These interfaces can possibly implement all AI types, but we recommend the following list. 1. Narrow AI 2. Reactive AI 3. Limited memory 4. Computer vision 5. General AI 6. Neural network 7. Reinforcement Learning 8. Generative Adversarial Networks Level #4 - Data and Social Media: Data processing AI tools would be the ones mostly used. 1. Narrow AI 2. Evolutionary AI 3. Expert system 4. Natural Language Processing (NLP) 5. General AI 6. Neural network 7. Reinforcement Learning 8. Generative AI Level #5 - Development and Testing: Data processing AI tools would be the ones mostly used. 1. Manufacturing 2. Evolutionary AI 3. Expert system 4. Natural Language Processing (NLP) 5. General AI 6. Neural network 7. Reinforcement Learning 8. Generative Adversarial Networks Level #6 - DevOps: DevOps can use processing AI tools. 1. Narrow AI 2. Evolutionary AI 3. Reactive AI 4. Computer vision 5. General AI 6. Neural network 7. Reinforcement Learning 8. Limited memory Note: See Dictionary and Jargons pages for AI types definitions. Course Syllabus: Our presentation is designed to help IT professionals to grasp the both AI and ML topics with emphasis on performing analysis, architecting and designing AI and ML systems. These systems would be integrated in any of the levels mentioned as well as any existing system. Our presentation would be covering the following topics with sub-topics:        2 - Fundamentals        3 - Structure        4 - Architect-Design        5 - Testing        6 - Services        7 - Existing Tools        8 - Electronics        9 - Security        10 - Ethics        11 - Use and Abuse