What does a machine need to know to understand language? In this video, learn about finding words and their relationships.
- Symbolic reasoning and planning AI worked great…for machines whose matching patterns were limitive.…You could easily use this approach for creating…a program to help you with your taxes.…The IRS only has a limited number of rules,…and you can go through the paperwork step by step.…You could even create many if-then statements.…If you have a dependent, then fill out x form.…If you live in this state, then fill out the z form.…The challenge with this approach…is that if the list of possibilities gets too long,…then the system is difficult to manage.…
Let's say you wanted to create an AI program…that identified animals from a database of images.…You wouldn't want to create a list…of these matching patterns,…that would have too many variables.…Imagine all the different lists of size, eye shapes,…number of legs, or other distinguishing features.…You also wouldn't want to create something like…if it has fur, then check to see if it has whiskers,…if it has whiskers, then check the shape of its ears.…A list like this would become way too long…
This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
- The history of AI
- Machine learning
- Technical approaches to AI
- AI in robotics
- Integrating AI with big data
- Avoiding pitfalls