Meeting of the Minds for Machine Intelligence

Meeting of the Minds for Machine Intelligence

Published By: MIT News Office, 11/22/2016

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Industry leaders and computer scientists are pushing for more use of machine intelligence so that machines can aid doctors, business corporations, investors and many more entities in decision making.  The article discusses the potential rewards of using machine intelligence to solve real-world problems, for example, whether machine learning can help to better quantify uncertainty when trying to predict outcomes in various fields.

Extended Discussion Questions

  • Besides what they mention in the article, what are some other benefits that might come from using machine learning algorithms to support decision-making?
    • For example, how might large businesses benefit? Investors? Doctors and other healthcare practitioners?
    • What new kinds of questions can each of those groups ask?
    • What kind of data would they need to answer those questions?
  • Can you think of any potential negative impacts of using machine learning to support decision-making?
  • Can you think of some areas where machines are more reliable than humans? Some areas where humans are more reliable than machines?
  • What are your thoughts on machine intelligence, in general?  What are some pros and cons of having machines that can think more like humans?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.2.1 Explain how computing has impacted innovations in other fields.
  • LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

Global Impact Essential Knowledge:

  • EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.

Other CSP Big Ideas:

  • Idea 2 Abstraction
  • Idea 3 Data and Information
  • Idea 4 Algorithms

Banner Image: “Network Visualization – Violet – Offset Crop”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0

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