Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Programming your way to explainable AI

Mark Hammond (Microsoft)
1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Sutton Center/North Level: Intermediate
Secondary topics:  IoT and its applications, Machine Learning
Average rating: ****.
(4.00, 1 rating)

Prerequisite Knowledge

  • General programming experience

What you'll learn

  • Understand the challenges of programming explainability using current approaches
  • Explore the latest techniques being used to build explainability into intelligent systems
  • Learn how easy and effective it is to build explainability using conceptual hierarchies

Description

Greater interpretability is crucial to greater adoption of applied AI, yet today’s most popular approaches to building AI models don’t allow for this. Explainability of intelligent systems has run the gamut from traditional expert systems, which are totally explainable but inflexible and hard to use, to deep neural networks, which are effective but virtually impossible to see inside. Developing trust between consumers of AI applications and the algorithms that power them will require the ability to understand how intelligent systems reach conclusions.

Mark Hammond explores the latest techniques and cutting-edge research currently underway to build explainability into AI models. Mark dives into two approaches—learning deep explanations and model induction—and discusses the effectiveness of each in explaining classification tasks. Mark then explains how a third category—learning more interpretable models with recomposability—uses building blocks to build explainability into control tasks. To keep it fun and engaging, Mark then demonstrates these approaches by more effectively solving the game Lunar Lander in the Bonsai platform.

Photo of Mark Hammond

Mark Hammond

Microsoft

Mark Hammond is cofounder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works and has been thinking about AI throughout his career. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.