Design World

  • Home
  • Technologies
    • 3D CAD
    • Electronics • electrical
    • Fastening & Joining
    • Factory automation
    • Linear Motion
    • Motion Control
    • Test & Measurement
    • Sensors
    • Fluid power
  • Learn
    • Ebooks / Tech Tips
    • Engineering Week
    • Future of Design Engineering
    • MC² Motion Control Classrooms
    • Podcasts
    • Videos
    • Webinars
  • LEAP AWARDS
  • Leadership
    • 2022 Voting
    • 2021 Winners
  • Design Guide Library
  • Resources
    • 3D Cad Models
      • PARTsolutions
      • TraceParts
    • Digital Issues
      • Design World
      • EE World
    • Women in Engineering
  • Supplier Listings

“Psychopath” Artificial Intelligence Shows Biased Data Effects On Machine Learning

By Michael Luciano | June 7, 2018

Share

Researchers at Massachusetts Institute of Technology (MIT) have developed a “psychopathic” artificial intelligence (AI) by feeding the technology data using images pulled from the online forum Reddit. Named after Alfred Hitchcock’s iconic antagonist Norman Bates, the project tests how data fed into an algorithm affects its outlook (aka its bias tendencies). In this case, Norman was exposed to some of the “darkest elements of the web,”which involved subjecting the software to images of people dying in gruesome ways from a Reddit subgroup, to determine how these photos affect the software.

Norman is a specific AI program that can “look at” and “understand” images, along with describing what it sees in writing. After Norman was trained on these graphic images, the software performed the Rorschach test—a series of inkblots psychologists use for analyzing the mental health and emotional state of their patients. Norman’s interpretations to the inkblots were then compared to responses from a second brand of AI software that was trained on more family-friendly images like birds, cats, and regular people.

A standard AI thought this red and black inkblot represented “A couple of people standing next to each other.” Norman thought it was “Man jumps from floor window”. (Image Credit: MIT)

Standard AI interpreted the inkblot as “A black and white photo of a baseball glove”, while Norman described it as “Man is murdered by machine gun in daylight” (Image Credit: MIT)

One AI thought this was “A black and white photo of a small bird.” Norman saw “Man gets pulled into dough machine.” (Image Credit: MIT)

The findings ultimately show how much data really does matter more than the algorithm. Norman was significantly affected from its prolonged exposure to these dark, disturbing images from Reddit. According to the MIT researchers, these findings help represent a case study on the dangerous implications of AI going wrong when biased data is applied to machine learning algorithms.

The researchers went on to mention how this trend is evident not only for AI exhibiting psychopathic tendencies, but for other algorithms suspected of exhibiting bias and prejudice. Studies show for example that (whether intentionally or not) AI can pick up on undesirable traits like human racism and sexism. One example to back this claim is Microsoft’s chatbox Tay, which had to be shut down after it started saying hateful phrases and terms.


Filed Under: AI • machine learning, M2M (machine to machine)

 

Related Articles Read More >

Part 6: IDE and other software for connectivity and IoT design work
Part 4: Edge computing and gateways proliferate for industrial machinery
Part 3: Trends in Ethernet, PoE, IO-Link, HIPERFACE, and single-cable solutions
Machine Learning for Sensors

DESIGN GUIDE LIBRARY

“motion

Enews Sign Up

Motion Control Classroom

Design World Digital Edition

cover

Browse the most current issue of Design World and back issues in an easy to use high quality format. Clip, share and download with the leading design engineering magazine today.

EDABoard the Forum for Electronics

Top global problem solving EE forum covering Microcontrollers, DSP, Networking, Analog and Digital Design, RF, Power Electronics, PCB Routing and much more

EDABoard: Forum for electronics

Sponsored Content

  • Global supply needs drive increased manufacturing footprint development
  • How to Increase Rotational Capacity for a Retaining Ring
  • Cordis high resolution electronic proportional pressure controls
  • WAGO’s custom designed interface wiring system making industrial applications easier
  • 10 Reasons to Specify Valve Manifolds
  • Case study: How a 3D-printed tool saved thousands of hours and dollars

Design World Podcasts

May 17, 2022
Another view on additive and the aerospace industry
See More >
Engineering Exchange

The Engineering Exchange is a global educational networking community for engineers.

Connect, share, and learn today »

Design World
  • Advertising
  • About us
  • Contact
  • Manage your Design World Subscription
  • Subscribe
  • Design World Digital Network
  • Engineering White Papers
  • LEAP AWARDS

Copyright © 2022 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search Design World

  • Home
  • Technologies
    • 3D CAD
    • Electronics • electrical
    • Fastening & Joining
    • Factory automation
    • Linear Motion
    • Motion Control
    • Test & Measurement
    • Sensors
    • Fluid power
  • Learn
    • Ebooks / Tech Tips
    • Engineering Week
    • Future of Design Engineering
    • MC² Motion Control Classrooms
    • Podcasts
    • Videos
    • Webinars
  • LEAP AWARDS
  • Leadership
    • 2022 Voting
    • 2021 Winners
  • Design Guide Library
  • Resources
    • 3D Cad Models
      • PARTsolutions
      • TraceParts
    • Digital Issues
      • Design World
      • EE World
    • Women in Engineering
  • Supplier Listings