Course Overview
Take your first steps in AI training to understand the principles of AI, its benefits and risks, and the processes behind Machine Learning.
Examples of AI have been in the news a lot lately, it started with chatbots like Google Assistant and now ChatGPT. Of course, AI is much more than just chatbots, yet we will also discuss this to learn the do’s and don’t of this.
This course will teach you the potential benefits of AI; types of Artificial Intelligence (AI); the basic process of Machine Learning (ML); the challenges and risks associated with an AI project, and the future of AI and Humans in work
Course Objectives
- Recall the general definition of human and Artificial Intelligence (AI).
- Describe ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
- Understand that ML is a significant contribution to the growth of Artificial Intelligence.
- Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
- Describe the challenges of Artificial Intelligence, and give general examples of the limitations of AI compared to human systems, general ethical challenges AI raises.
- Demonstrate understanding of the risks of Artificial Intelligence, identify a typical funding source for AI projects and list opportunities for AI.
- Demonstrate an understanding that Artificial Intelligence (in particular, Machine Learning) will drive humans and machines to work together;
- List future directions of humans and machines working together.
Course Content
1. Artificial and Human Intelligence: An Introduction and History
- general definition of human and Artificial Intelligence (AI).
- ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
- ML is a significant contribution to the growth of Artificial Intelligence.
- AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
2. Examples of AI: Benefits, Challenges and Risks
- benefits of Artificial Intelligence
- advantages of machine and human and machine systems;
- challenges of Artificial Intelligence
- the limitations of AI compared to human systems
- general ethical challenges AI raises
- understanding of the risks of Artificial Intelligence
- funding source for AI projects
- opportunities for AI
3. An introduction to Machine Learning
- AI intelligent agent
- differences with Machine Learning (ML)
- the four rational agent dependencies,
- performance measure, environment, actuators and sensors,
- reflex, model-based reflex, goal-based and utility-based.
- examples of Machine Learning in the following contexts:
– business
– social (media, entertainment)
– science - which typical, narrow AI capability is useful in ML and AI agents’ functionality;
- forms of ML:
– supervised
– unsupervised
– reinforcement - the basic schematic of a neutral network.
4. The Future of Artificial Intelligence – Human and Machine Together
- Artificial Intelligence (in particular Machine Learning) will drive humans and machines to work together
- list future directions of humans and machines working together