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Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Design thinking for AI

Chris Butler (IPsoft)
9:00am-12:30pm Wednesday, September 5, 2018
Secondary topics:  Ethics, Privacy, and Security, Interfaces and UX
Average rating: ***..
(3.83, 6 ratings)

Who is this presentation for?

  • Data scientists, engineers, designers, product managers, and executives

Prerequisite knowledge

  • A basic understanding of machine learning and AI concepts and methods (useful but not required)

What you'll learn

  • Learn how to develop AI/ML from a human-centered point of view and create alignment between technical and nontechnical teammates
  • Get hands-on experience with workshops like empathy mapping for the machine and confusion mapping you can bring back to your teams
  • Attendees please take this survey
  • Description

    Without human purpose, a computer is just a rock that we tricked into thinking—even if we add AI to it. Without human trust, we are only building novelties that won’t be used for real problems. Any team that is working on hard, meaningful, and purpose-driven problems could benefit from AI/ML, but without the right focus you may end up building toys.

    Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. You’ll cover problem framing, ideation, empathy mapping for the machine, confusion mapping, prototyping, and research and leave with the hands-on experience you need to guide your teams and AI projects.

    Photo of Chris Butler

    Chris Butler


    Chris Butler is the chief product architect at IPsoft. Previously, Chris worked at Microsoft, KAYAK, and Waze, and he was involved in AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University. He’s created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.