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
    • Subscribe!
    • 3D Cad Models
      • PARTsolutions
      • TraceParts
    • Digital Issues
      • Design World
      • EE World
    • Women in Engineering
  • Supplier Listings

Waterloo University Researchers Take AI To Next Level

By Michael Luciano | November 16, 2017

Share

Technology developed by researchers at the University of Waterloo has gotten artificial intelligence (AI) another step closer toward relinquishment from internet and cloud computing. Software produced for AI deep learning is compact enough to fit on mobile computer chips that can be used in a broad array of technologies from smartphones to industrial machinery. These breakthroughs would enable the independent operation of devices from the internet, while utilizing AI that performs at levels equivalent to tethered and neural networks.

Waterloo University researchers feel the breakthrough’s potential is astronomical, and could help ease people’s struggles of getting deep learning AI operational in many fields. Stand-alone deep learning AI can prompt significantly lower data processing, transmission costs, greater privacy, along with utilization in areas where existing technology is currently impractical due to factors like expenses. Deep learning AI emulates the human brain by processing data through several artificial neuron layers, which typically require considerable computational power, memory, and energy in order to function.

Researchers turned to evolutionary forces in nature to increase AI efficiency by inserting the software into a virtual environment, before continually (yet progressively) cutting the technology off of resources. The response from the deep learning AI was an ample adaptation to continue functionality whenever computational power and memory were withdrawn. These networks self-evolve themselves over generations, over which they make themselves smaller in order to better sustain these environmental conditions.

Some results that were recently presented at the International Conference on Computer Vision in Venice, Italy, attained a 200-fold reduction in deep learning AI software size that was used to particular tasks pertaining to object recognition. Upon being applied to a chip embedded in a smartphone, AI with this degree of compaction ably ran its speech-activated virtual assistant, along with other intelligent qualities.

This greatly reduces data usage, while eliminating the need for internet services to operate. In addition, other potential applications ranging from low-cost drones and smart grids to surveillance cameras and manufacturing plants also endured issues regarding streaming sensitive or proprietary data to the cloud that continue to persist.


Filed Under: 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

  • Industrial disc pack couplings
  • Pushing performance: Adding functionality to terminal blocks
  • Get to Know Würth Industrial Division
  • Renishaw next-generation FORTiS™ enclosed linear encoders offer enhanced metrology and reliability for machine tools
  • WAGO’s smartDESIGNER Online Provides Seamless Progression for Projects
  • Epoxy Certified for UL 1203 Standard

Design World Podcasts

July 26, 2022
Tech Tuesdays: Sorbothane marks 40 years of shock and vibration innovation
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
    • Subscribe!
    • 3D Cad Models
      • PARTsolutions
      • TraceParts
    • Digital Issues
      • Design World
      • EE World
    • Women in Engineering
  • Supplier Listings