Course Description
In these lessons on AI and Machine Learning’s Unique Challenges, you’ll explore the types of machine learning, including supervised, which uses labeled data to make predictions; unsupervised, a series of techniques to understand the structure of data without an outcome variable; and reinforcement learning, one of the oldest styles of machine learning that uses a goal-based method of learning. You’ll also understand ethical considerations around neural networks and generative AI.
Machine learning underlies many AI systems built today. Artificial neural networks are among the most successful machine learning methods. They solve many of the field's hardest problems, including those that spent decades unsolved. But using neural networks has also added some new challenges to AI, including how to test for edge cases and for robustness of the outputs. The complexity of the testing systems might make them particularly difficult to analyze. We will review the specific considerations that neural networks have in terms of ethics.
Generative AI is a newer and emerging area relevant to AI ethics and accountability.
We’ll discuss the Lovelace test that aims to test whether a computer could generate something surprising and original.
What You'll Learn
- Differentiate between the main types of machine learning: supervised, unsupervised, and reinforcement learning
- Recognize the unique ethical challenges posed by artificial neural networks
- Explain the ethical challenges around generative AI
- Examine why neural networks make testing for edge cases and output robustness difficult
- Describe the Lovelace test for whether a computer can generate something surprising and original
Key Takeaways
- Supervised learning uses labeled data to make predictions, unsupervised learning uses techniques to understand the structure of data without an outcome variable, and reinforcement learning is a goal-based method of learning.
- Artificial neural networks are among the most successful machine learning methods and have solved problems that remained unsolved for decades.
- Neural networks introduce new challenges to AI, including how to test for edge cases and for robustness of outputs, and their testing systems can be particularly difficult to analyze.
- Generative AI is a newer, emerging area relevant to AI ethics and accountability.
- The Lovelace test aims to assess whether a computer could generate something surprising and original.
Frequently Asked Questions
What does this course cover?
It covers the types of machine learning (supervised, unsupervised, and reinforcement learning), the ethical considerations around artificial neural networks, and the ethical challenges of generative AI, including a discussion of the Lovelace test.
What are the main learning objectives?
To differentiate between the main types of machine learning, recognize the unique challenges with artificial neural networks, and explain the ethical challenges around generative AI.
Which lessons are included?
The course includes lessons on Supervised Learning, Unsupervised Learning, Reinforcement Learning, Neural Networks, and Generative AI.
What skills will I build?
You will build skills in Artificial Intelligence, Automated Machine Learning, Machine Learning, Machine Learning Methods, Supervised Learning, and Unsupervised Learning.
Why are neural networks given special ethical attention in this course?
Because, while among the most successful machine learning methods, neural networks add new challenges such as testing for edge cases and output robustness, and their complexity can make the testing systems particularly difficult to analyze.
