e-Commerce Services

GOAT uses machine learning, computer vision to verify your top dollar sneakers are authentic


This ebook, based on the latest ZDNet/TechRepublic special feature, looks at the rise of e-commerce and the digital transformation of retail companies. It takes a lot of machine learning and computer vision to ensure that a pair of high-end sneakers is authentic. GOAT is the largest sneaker marketplace and specializes in selling authentic goods. Specifically, GOAT provides buyers and sellers of sneakers an authenticity guarantee with a "ship to verify" model. GOAT, which has both e-commerce and physical retail locations, has 400 employees and 60 of them are engineers with 7 data scientists.

Gartner Identifies Three Megatrends That Will Drive Digital Business Into the Next Decade


The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems. The Hype Cycle for Emerging Technologies report is the longest-running annual Gartner Hype Cycle, providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios. The Emerging Technologies Hype Cycle is unique among most Gartner Hype Cycles because it garners insights from more than 2,000 technologies into a succinct set of compelling emerging technologies and trends. This Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years (see Figure 1).

Machine learning and big data know it wasn't you who just swiped your credit card


You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card – in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Credit card companies have a vested interest in identifying financial transactions that are illegitimate and criminal in nature. According to the Federal Reserve Payments Study, Americans used credit cards to pay for 26.2 billion purchases in 2012.

How PayPal beats the bad guys with machine learning


When Amazon Web Services announced a new machine learning service for its cloud last week, it was a sort of mini-milestone. Now all four of the top clouds -- Amazon, Microsoft, Google, and IBM -- will offer developers the means to build machine learning into their cloud applications. As InfoWorld's Andrew Oliver has observed, both machine learning and big data will eventually disappear as separate technology categories and insinuate themselves into many, many different aspects of computing. Fraud detection is first among them, because it addresses an urgent problem that would be impractical to solve if machine learning didn't exist. To get a sense of how machine learning is combating fraud, I interviewed Dr. Hui Wang, senior director of risk sciences for PayPal.