- The Role Of AI In Cybersecurity – Boon Or Bane?
- Less Than Half Of Cybersecurity Professionals Have A Plan In Place To Deal With IoT Attacks: Study
- Cyberattacks Go Up For Small Businesses Over The Past Year: Study
- Phishing And Credential Stuffing Attacks Remain Top Threat To Financial Services Organizations And Customers: Study
- IT-Based Attacks Increasingly Impacting OT Systems: Study
Google open-sources mobile-first computer vision models for TensorFlow
By Nisheeth Bhakuni | June 15th, 2017
Google is helping smartphones better recognize images without requiring
massive power consumption, thanks to a new set of models the company
released today.
Called MobileNets, the pre-trained image recognition models let developers pick between a set of models that vary in size and accuracy to best suit what their application needs.
Right now, a lot of the machine learning inside mobile apps works by passing data off to cloud services for processing and then providing the resulting insights to users once they return over the network. That means it’s possible to use very powerful computers in a data center and alleviate the burden for processing information on a smartphone. The drawback to that approach is that latency and privacy suffer.
By processing data on a user’s smartphone, it’s possible to return results a lot faster, and data never has to leave the phone. However, optimizing a machine learning model for use on mobile is a tall order. Eating up a bunch of battery with computationally intensive machine learning operations is no good.
Nisheeth Bhakuni
