About Artificial Intelligence 101:
Artificial intelligence 101 course aims at building the base knowledge, and hints for artificial intelligence knowledge seekers where it is divided into five themes with general information for each so that learners can choose their interest in AI after completion their journey. Artificial intelligence 101 themes include AI history, AI types, the science behind AI, data science, and AI emerging technologies. After completing the course learners should be able to distinguish between AI types based on their abilities and functionalities, recognize the turning points in AI development, realizing the impact of data science on AI systems, and exploring some AI emerging technologies.
1- Basic knowledge about AI history, definition, and use cases.
2- Types of artificial intelligence systems based on their abilities and functionalities.
3- Basic knowledge about the science behind artificial intelligence.
4- Data science concept and its impact on artificial intelligence systems.
5- Emerging artificial intelligence technologies like Emotion AI and Edge AI.
Identifying artificial intelligence from different aspects and its relation with human intelligence and exploring a set of fields that are boosted due to the deployment of artificial intelligence like healthcare, agriculture, traffic and effective jobs.
Exploring artificial intelligence turning points with respect to their time extent and recalling the pioneers of these stages as companies, organizations, and individuals.
Identifying artificial intelligence components and their impact on building effective AI systems which include autonomy, depth, breadth, training, and competencies.
Identifying the main skills and attributes that are needed for developers and designers of the AI systems like creative thinking, modeling, integrity, and resilience.
Identifying the learning methodology, course expectations, and outcomes in addition to author’s recommendations for optimal learning results.
Exploring the two types of artificial intelligence systems that are based on ability and functionality with use cases for each.
The artificial intelligence systems are classified into three types based on their ability to perform certain tasks that are Narrow, General, and Super AI.
Explaining the reactive machines and limited memory systems and Identifying the difference between both with use cases for each.
Identifying two advanced types of artificial intelligence systems that are considered science fiction with potential future creation these are theory of mind and self-awareness.
Illustrating the difference between vertical and horizontal artificial intelligence systems and their impact on companies’ strategies of market penetration.
Recognizing the concept of knowledge in general and in artificial intelligence systems by focusing on its two main parts that are truth and belief.
Illustrating the concept and components of expert systems that are knowledge base, inference engine and user interface and their impact on handling tasks.
Identifying the main mathematical fields that are needed for optimal understanding and development of artificial intelligence techniques.
Stating the main fields that contribute to artificial intelligence systems like cognitive computing, computer vision, machine learning, natural language processing, deep learning, and artificial neural networks.
Clarifying machine learning and deep learning concepts including their three underlying operations that are representation, evaluation, and optimization.
Illustrating data science concept, data types, the difference between data analyst and data scientist, and why we need data science.
Clarifying data science life cycle which includes discovery, data preparation, model planning, model building, operationalize, and communicate results.
Exploring the impact of artificial intelligence on data science and how data science penetrates the market to become one of the leading fields in this era.
Differentiating between the required technical and non-technical competencies for optimal understanding and implementation of data science in artificial intelligence systems like mathematical modeling and machine learning.
Identifying the concept of big data, when and who articulates it, the need and impact of big data on artificial intelligence systems workflow.
Stating the meaning of artificial intelligence code of ethics and its emerging need, common rules, basic ethics and how to deploy it.
Exploring augmented intelligence concepts, its emerging need, use cases, and the main difference between artificial intelligence development mindsets and augmented intelligence ones.
Recognizing the difference between edge artificial intelligence and explainable artificial intelligence systems, their processes, workflows, use cases and components.
Stating natural language processing and cognitive computing concepts, components and impact on artificial intelligence machines and humans’ lives.
Identifying artificial emotional intelligence technology (Emotion AI) concept, importance, complications, and use cases like healthcare, gaming, education, and recruiting.