AI for IA’s: Machine Learning Demystified

I had the pleasure of presenting “AI for IA’s: Machine Learning Demystified” at the 2017 Information Architecture Summit last week in Vancouver, BC, Canada.

I provided the basics of artificial intelligence (AI) and how machine learning works as well as how it is relevant to information architecture (IA).

The presentation is on Slideshare.

Overview

The presentation includes discussions on:

  • How training is conducted: the general work cycle and a comparison of training methods.
  • Watson Knowledge Studio — an example of supervised Machine Learning
  • Process and dependence on experts
  • Relative importance of knowledge and accuracy
  • Goal of artificial intelligence to save time and augment human effort.
  • Results of machine learning are more highly trained specialists without additional training. One physician can train a system and that system can then support as many medical staff as needed.

Examples of AI and Cognitive Computing

  • 1997 Chess, IBM
  • 2016 Go, Google

Self Driving (autonomous) vehicles

Watson wins Jeopardy!

Automating labeling of birdsongs

Image Recognition — Google Photos

Automating Repetitive Work — Watson examples:

  • Radiology imaging
  • Retina imaging
  • Customer service representative support

H&R Block

Staples Easy App

Chatbots

Watson Personality Insights

Watson Tone Analyzer

Ethics in AI

• build systems that have ethical and moral foundation?’

• that are transparent to users?

• teach mercy and justice of law?

• extend and advance healthcare?

• increase safety in dangerous work?

So that we trust machines just as much as a well-trained human?

AI will “extend human experience in profound ways” — Grady Booch, Scientist, philosopher, IBM’er

Explore the technology

1) Heavily referenced in this presentation is Grady Booch’s inspiring Ted Talk.

UX Leader, speaker and community organizer. My thoughts on user research, design, AI and more. Provoking human values in AI.

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