Google I/O 2019 featured two Indian companies for excellent use of Machine Learning
Machine Learning was one of the most widely covered topic and the buzz word at the event.
The much awaited Google I/O 2019 was held in Mountain View, California this year. Inaugurated in 2008, this annual developer conference by Google for a product, tech launches and announces research projects, is one of the most prominent and highly awaited tech events globally. "I/O" stands for input/output, as well as the slogan "Innovation in the Open".
‘Security’, ‘Privacy’ & ‘Inclusive AI’ defined the themes of the keynote this year and Machine Learning was one of the most widely covered topics and the buzz word at the event. In this context, there were two Indian companies that were featured at Google I/O 2019 for excellent use of Machine Learning:
Gradeup School App:
The Gradeup School app was featured in the Developer keynote address in Google I/O 2019 for using Machine Learning on mobile devices. Gradeup School is an exclusive app for the K12 market, helping students with their homework, assignments, exams, competitions, Olympiads etc. Students can simply take a picture of a question and post it in the app to get the answer. They can also share the question in the community to get answers from fellow students. Owing to the ML models and algorithms, Gradeup is able to deliver answers to user queries with very high accuracy. ML on mobile allows Gradeup School to work with low latency on low network bandwidth. Featuring at Google I/O 2019 is recognition of their pioneering use of ML for online education purpose and making it highly interactive, instantaneous & fun.
Air Cognizer:
Air Cognizer was another Indian company that was featured at in Google I/O 2019 for the use of ML. Air Cognizer is an Air Quality Analytics Application which uses smartphone camera images to estimate the Air Quality Index (AQI) levels. The user is required to click an image in an open environment with half of the image covering the sky region. Using image processing techniques, features are extracted and the Machine Learning Model estimates the AQI for your location.