Intro to Artificial Intelligence and Machine Learning

Artificial Intelligence (or AI) has already become prevalent in our daily lives – from recommendation engines that suggest the next movie to stream, to predictive text that can finish your sentence in emails or text messages. A lot of the technology behind some of these applications are available as open source products, and can be modified to perform research-based or administrative tasks that are specific to the unique types of work that Smithsonian employees do.

However, in order to make sure that AI is adopted ethically, responsibly, and effectively, we want to give you the literacy you will need to critically evaluate AI technologies.

Target Audience

This workshop is open to any Smithsonian badge holders at any Smithsonian Unit. We think that any Smithsonian staff member, intern or fellow can benefit from participating in this workshop.

Schedule

00:00	1. Introduction	What will be covered in this lesson? What will not be covered in this lesson?
00:15	2. What are AI and Machine Learning?	What is the difference between Artificial Intelligence and Machine Learning? What is the data science life cycle? What are some of the ethical considerations with using machine learning?
01:00	3. What is Machine Learning good at?	What kinds of tasks do machine learning models excel at? What machine learning can be done on tabular data? What tasks are common in computer vision? What tasks are common in Natural Language Processing?
01:40	4. What are weaknesses of Machine Learning?	What types of tasks are hard for machine learning? How does dataset construction affect models? How can machine learning make biased decisions?
02:20	5. Applying machine learning	How can I test out machine learning on a handful of samples? What are ways of scaling up in machine learning?
02:45	6. Next steps	How can I learn how to build custom machine learning models myself? Where can I learn more about the ethical considerations of machine learning?
03:00	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.