Pieter Abbeel, Zsolt Katona and Matthew Stepka - The Business of AI

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Syllabus - The Business of AI (EWMBA267-11 - Spring 2021)

Note: this document may be updated in the future. Please check back for the latest version.
Last Update: January 16, 2021

Dates, Times, Location

2/7: 9am-3pm, Zoom (Link on bCourses)

2/14: 9am-3pm, Zoom (Link on bCourses)

Instructors - Pieter Abeel, Zsolt Katona and Matthew Stepka

Email: pabbeel@berkeley.edu, zskatona@berkeley.edu and mstepka@berkeley.edu - please use [EWMBA267] in the subject.
Office hours: online by appointment

Introduction

From self driving cars to humanoid robots, Artificial Intelligence (AI) is here and changing the way we live, work and do business. The class is designed to introduce future managers to AI technology and its many business applications. Students will walk away with a foundational understanding of AI and its near and long term applications, explore the myths and realities surrounding the technology, and delve into the legal, social and policy implications of AI.

Pieter Abbeel is Professor in Electrical Engineering and Computer Sciences and Co-Director of the Berkeley AI Research (BAIR) Lab. His research focuses on making robots learn from people (apprenticeship learning) and on how to make robots learn through their own trial and error (reinforcement learning). He also brings AI into practice as founder of Gradescope and of Covariant.

Zsolt Katona is the Cheryl and Christian Valentine Associate Professor of Marketing and has been at Haas since 2008. Zsolt has a Phd in Marketing and Computer Science. His research focuses on the several topics related to business analytics such complex networks, search advertising, network economics, social media and digital marketing.

Matthew Stepka is Executive-in-Residence and Lecturer at Haas. He is Managing Partner of Machina Ventures, an investment firm focused on early stage, artificial intelligence and data science enabled companies. Previously, Matthew was VP, Special Projects at Google, where he led and incubated strategic initiatives, especially mission-driven projects with high social impact.

Class Format

All class sessions will be conducted remotely either in the Barco Virtual Classroom or via Zoom. To reduce the amount of time you need to spend in front of your computer on Sundays, we have some pre-recorded video modules that you can watch any time before class. Please watch the assigned videos before each meeting. Videos will be numbered as VL.A.x.y and VL.B.x.y . The VL.A segment will be available on 1/24, please watch these videos before our first meeting on 2/7. The VL.B segment will be available on 2/7, please watch these videos before our second meeting on 2/14. The videos will total to just above 3 hours each week.

With most of the lectures pre-recorded, we will shorten our live meetings from 7.5 hours to 4 hours. We will add generous breaks so that we can all stay engaged during those live session. Hence the live sessions are scheduled 9am-3pm, but these will include about 2 hours worth of breaks.

Readings and Videos

Required Readings

For each day, please read the case study(ies) listed under sessions before class. These are required readings. You can access the case studies via study.net.

Required Video Lectures

You also need to complete the assigned video lectures as desribed in the session outline. These are available on bCourses.

Textbooks

We do not use a textbook in the class. If you want to learn more, we recommend these two:

Assignments

Deep Learning Homework (Individual)

You will learn to code deep learning yourself! Don't worry you don't have to write code, rather you will be given a step by step guide and the homework will involve changing bits of the code to experiment with a classification task. Details will be available on bCourses after the 2/9 session and you will have until Saturday night, 2/15 to complete the assignment.

The assignment will be run on Haas' Jupyterhub platform. The coding language will be Python and we will use the Tensorflow and Keras libraries. Learning/knowing python is not a prerequisite, but it is highly recommended that you complete the Jupyterhub Python tutorial before our first session.

To access the tutorial, go to https://jupyterhub.haas.berkeley.edu/ and click on R & Python Tutorials. After you log in with your calnet account, click on "Start Server" and then click on the Python-Tutorials folder. Then click on the 1_Start_Here folder and the mp4 video file will walk you through the rest. The tutorial contains problem sets from the Data and Decisions class and goes through the solutions in Python. Completing the tutorial will give you a basic understanding of Python and the Jupyterhub platform, both of which are widely used by data scientist.

Due date: 2/13 11pm: Submit deliverable on bCourses

Online Quiz (Individual)

There will be a short multiple-choice quiz that you need to complete after our second meeting. The quiz will assess your knowledge of the most important points we learn in class. The quiz will be open book, but timed. You can start any time before the due date, but obviously do not discuss with anyone else.

Due date: 3/6 11pm: Take on bCourses

AI Application Business Plan (Group)

In this project, you will develop a plan for an AI application. Your final report should be a business plan much like you would make a pitch as the founders of a company to a venture capital investor. In addition to the instructors, a group of VC representatives who will attend our first session will provide feedback on your final submission. To facilitate their review, you will be asked to create and submit a 2-3-minute video pitching your idea. The format of the final report should be: PowerPoint deck with 15-20 slides including Appendix. Write out speaker notes as if you were presenting.

Click here for a detailed description of the project

Due date: 2/13 11pm: Submit a document containing

  1. Names of the members of your group
  2. The Title of your project (you can change this later if needed)
  3. A brief, one-paragraph description of the AI application idea that your group wishes to pursue

Due date: 3/6 11pm: Submit the final report.

Submission of Documents

Submit all the assignments on bcourses in .pptx, .pdf format or as a link (to your video or a Google slides presentation), no paper submission is necessary. If there is a problem with bCourses, email me a copy before the deadline. Your slides should be self-explanatory as much as possible, but you can add presentation notes if you need to explain something in more detail. Once project groups are assigned bCourses will have a feature for groups to submit assignments. Until then, one submission per group is enough. If multiple members submit from a group, the last submission will be graded.

Grading

Your grade will be based on the number of points you get from the following assignments. The maximum total score is 100 points.
  • Deep Learning Homework: 25 points
  • Online Quiz: 15 points
  • Group Project: 60 points
Note that Haas policies require the average GPA in elective classes to not exceed 3.5.

Session Details

2/7 - AI and what's behind it. AI Applications

Prework

  • Watch Video Lectures: VL.A.1, VL.A.2, VL.A.3, VL.A.4
  • Read Case Study: "Zebra Medical Vision"

Topics:

  • What is AI? How does AI work?
  • Supervised Learning and Neural Networks
  • Deep Learning
  • Computer Vision Basics
  • Natural Language Processing Basics
  • AI Apprlications: Where is it useful? What are the limits, limiting factors?
  • From idea to application

Case Discussion: "Zebra Medical Vision"

VC Panel

  • James Hardiman, DCVC
  • Minnie Ingersoll, TentoOne
  • Sandy Li, Baidu Ventures
  • Stephanie Zhan, Sequoia

Optional Readings:

2/14 AI Strategy, AI and the Future of Humans

Prework

  • Watch Video Lectures: VL.B.1, VL.B.2, VL.B.3
  • Read Case Study: "Osaro: Picking the Best Path"

Topics:

  • Deep Learning Homework Debrief
  • Robotics and Reinforcement Learning
  • AI and Automation
  • AI and Business Strategy, Competitive Advantage
  • Future Applications of AI and the Startup Space>
  • Artifical and Human Intelligence Working Together
  • Policy Considerations: Algorithmic Bias

Case Discussion: "Osaro: Picking the Best Path"

Optional Readings: