Home addresses in India pose a uniquely Indian problem- lack of standardization. This poses a challenge to e-commerce players whose success relies on efficiencies in last-mile logistics. This post talks about how Flipkart, an Indian e-commerce major is using Machine Learning(ML) to make sense of complex Indian addresses to iron out associated inefficiencies. In addition, we also look at other key areas of ML application for e-commerce companies.
Uncategorized
The Regulatory Challenge of Autonomous Vehicles
The rapid rise of Autonomous Vehicles will require a rapid and wise regulatory approach to foster innovation and protect safety.
How Stryker Hopes to Win with Additive Manufacturing
While Stryker has launched 3D-printed medical implants in the past, it recently made significant investments in additive manufacturing technology that it hopes will make it a leader in the medical technology industry.
Partnering with AI: Optum Labs’ Efforts to Improve U.S. Health Care
Through industry partnerships, a 200 million life database, and the use of artificial intelligence (AI), Optum Labs is confronting the daunting cost and quality challenges facing the U.S. health care system
GE Additive: Additive Manufacturing and Aerospace
GE unleashes the power of Additive Manufacturing
Adidas and Additive Manufacturing
Explores Adidas’s push to mass-produce 3D-printed shoes
Is machine learning in education the new textbook?
Education technology company Knewton strives to deliver personalized learning experience for college students using machine learning technology. Can Knewton's product really replace textbooks as the company aspires to?
Training Robots to Curate Individualized News
Social news provider Newstag is addressing issues of user engagement and content aggregation through deployment of machine learning algorithms
LEGO: Leveraging the Building Blocks of Open Innovation
How LEGO's legion of global fans helped the company drive value from open innovation.
Applying Machine Learning for the Common Good – Is it Always Worthwhile?
How do we thoughtfully use data to increase efficiency for the greatest amount of people in a sector that has historically and exclusively been driven by human judgement? Where is human oversight non-negotiable?