AIGP Training | Domain 1 - Understanding the foundations of AI governance - Sachin Hissaria X TechEd

Dear friends/ colleagues, please do not skip any part of the video. Key topics discussed are mentioned below - Domain 1 were discussed, including the definition of AI, the turing test, the limitations of AI as a socio-technical system, and the types of AI (ANI, AGI, ASI, rule-based, fuzzy logic), topics such as data privacy and incorrect labeling risks. The meeting extensively covered various machine learning models—supervised, unsupervised, semi-supervised, and reinforcement learning—along with specific algorithms like regression, decision trees, SVM, and KNN, concluding with an introduction to neural networks, key terminologies, deep learning, the historical Perceptron model, and the risks of overfitting/underfitting, including Generative Adversarial Networks (GANs). For full training connect us on - https://www.techedacademy.com/ Please comment "Part-2" to get an access to next Video. We are aiming for 100 comments and likes (: Thanks a lot for watching the video. If you like the video do share with your friends and subscribe to my channel. Thank you. LinkedIn -   / sachin-hissaria   Instagram -   / sachin_hissaria   Twitter -   / sachin_hissaria   Here are the key timestamps and topics from the video: 00:04: Introduction and Welcome 04:36: Overview of AIGP 06:16: AIGP Syllabus Update (Effective February 2, 2026) 07:48: AIGP Exam Pattern (MCQ-based, Case Studies, Multiple-Option Selection) 11:24: Course Structure and Resources (PPTs, Q&A, Mock Tests) 17:44: Domain 1: Understanding the Foundations of AI Governance 18:28: Discussion on Artificial Intelligence (AI) 32:32: Sociotechnical Nature of AI Systems 35:12: Types of AI Systems by Capabilities 35:32: Artificial Narrow Intelligence (ANI) 38:56: Artificial General Intelligence (AGI) 48:24: Artificial Super Intelligence (ASI) 50:52: Types of AI Systems Based on Technical Approach/Functions 51:32: Rule-Based or Knowledge-Based AI 54:16: Fuzzy Logic System (Dealing with Uncertainty and Degree of Truth) 01:00:52: Machine Learning as a Subset of AI 01:01:48: Defining Machine Learning (Learning Patterns from Data) 01:05:12: Supervised Learning (Learning through Labeled Data) 01:21:12: Unsupervised Learning 01:25:00 ; Re-enforcement Learning