AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017

AI is when machines :

• Exhibit Intelligence
• Perceive their environment
• Take actions/ make decision to maximize chance of success at a goal

Elon Musk was wrong The AI Singularity Won't Kill Us All.
Most people working in AI have a healthy skepticism for the idea of singularity. We know how hard it is to get even a little intelligence into a machine, let alone enough to achieve recursive self improvement.
Don't fear super intelligent AI remember we could unplug the machine.

Cognitive computers are:

• Made with algorithms
• Knowledgeable ONLY about what taught
• Control ONLY what we give them control of
• Aware of nuances and can continue to learn more

Cognitive computer (Algorithm) Can

• Do very boring work for you
• Often make better more consistent decisions than humans
• Be efficient won't get tired

Exhibit intelligence

• Transfer human concept and relationship

Dependent on experts

• Subject Matter Experts (SME) Availabilty
• Lawyers
• Machinishts
• Physicians
• Insurance Adjusters

Usually not EXPERIENCED in machine learning.

• Need close collaboration with those making algorithms
  It is only as good as data and time spent improving it

Creating an AI requires

• Algorithms
• Documents
• Teaching
• Iteration
• Ground Truth
• Repeat
  
  

The goal is saving time, machine learning creates a more highly trained specialist not an ''All Knowing Being''

Examples of AI and Cognitive Computers
Consider for each example:

• What intelligence does the system need?
• What the AI perceiving in their environment?
• What actions are taken to maximize success of goals?

• Watson developed for quiz show jeopardy , won against champions in 2011 for 1million $

Humans teach what we feel is important , teach them to share our values.
Super knowings not super doing.

Can we trust machines as much as well trained human?