Published By: Venture Beat, 9/22/2016

>> View the Article <<

Summary

With the help of crowdsourced data, Google AI’s image recognition algorithms are achieving greater accuracy. Objects in photographs are now more accurately described, and are interrelated with other objects in auto-generated captions. With the increase in photo-captioning accuracy, more questions arise about privacy online and on social media.

Extended Discussion Questions

• How might this increase in accuracy help you, for example with your assignments?
• Who else might benefit from having images captioned in this descriptive way? Prompt: Who needs to have images described for them?
• Why might Google be investing so much in this AI ability? How might making the models open source benefit them?
• How might this technology intrude on someone’s privacy online and on social media? Specifically, what risks do captioned photos pose?

Related Article

“Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge”, by Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan

Published By: IEEE Transactions on Pattern Analysis and Machine Intelligence, 9/21/2016 || View the Article

Technical research paper with abstract that explains the Google AI data algorithms and the reasons for this research.

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

• LO 7.1.2 Explain how people participate in a problem-solving process that scales.
• LO 7.3.1 Analyze the beneficial and harmful effects of computing.

Global Impact Essential Knowledge:

• EK 7.1.1L Computing contributes to many assistive technologies that enhance human capabilities.
• EK 7.1.2F Crowdsourcing offers new models for collaboration, such as connecting people with jobs and businesses with funding.
• EK 7.1.2C Human computation harnesses contributions from many humans to solve problems related to digital data and the Web.
• EK 7.3.1G Privacy and security concerns arise in the development and use of computational systems and artifacts.

Banner Image: “Network Visualization – Violet – Crop 2”, 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