This post was originally published on the Shoprunner Engineering blog here feel free to check it out and at some of the other work our teams are doing. Our ShopRunner Data Science team allows all members to have a quarterly hack week. It is important for data science teams to keep innovating so once per quarter team members are allowed to spend a week working on more speculative projects of their choice. For my 2019 Q3 hack week I decided to build a series of generator models to attempt to create fake products. Generator models are models commonly trained to create realistic images or text based on real world examples. This project may seem fairly outlandish, which it is, but my general idea is that if we can create strong generator models that can capture the diversity of our product catalog then we could use these generators to augment low frequency classes within our catalog for other deep learning projects such as taxonomy classification or attribute tagging.

Amazon is taking additional steps to stop counterfeit sellers. Dharmesh Mehta, Amazon's vice president of consumer trust and partner support, told CBS News that the e-commerce giant has made'progress in reducing the amount of counterfeit' on the site. But Mehta admits the firm is still a long ways off from eradicating the issue. So far, Amazon has added 500 brands to its invite-only Project Zero initiative, which was rolled out in February and aims to give sellers greater controls over fake goods. Amazon has announced new steps to combat fake listings on the site.

That's the rough amount of money that counterfeiters displaced last year by selling phony products. Some 2.5% of all trade is for fake goods. The United States is hit hardest by the scourge of counterfeit products -- U.S. brands accounted in 2013 for 20% of the world's infringed intellectual property. When most people think about counterfeiting, they think of knock-off Louis Vuitton handbags sold on the sidewalk. But fake products also include business and enterprise products, as well as everyday consumer goods.

The arms race is on: Counterfeiters will use AI to create convincing fakes; smart enterprises will use AI to fight back. Half a trillion dollars.That's the rough amount of money that counterfeiters displaced last year by selling phony products. Some 2.5% of all trade is for fake goods. The United States is hit hardest by the scourge of counterfeit products -- U.S. brands accounted in 2013 for 20% of the world's infringed intellectual property.

Amazon is every online retailer's forbidding nightmare. Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales. One Click Retail, an eCommerce analysis provider, explains its dominance with the fact that millennials, Amazon's core demographic, are getting older and starting to spend more. Moreover, advanced marketing capabilities for sellers, developments in Alexa, and pioneering in applications of the hottest technologies make it impossibly hard for smaller competitors to actually… well, compete. Amazon is not only a simple and familiar platform selling everything you can think of -- it's also one of the most innovative players on the market.

Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The system works by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. "The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," said Lakshminarayanan Subramanian, Professor at New York University. The system is scheduled to be presented on 14 August at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia, Canada. The system described in the presentation is commercialised by Entrupy Inc., an New York University start-up founded by Ashlesh Sharma, Vidyuth Srinivasan, and Subramanian.

Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The system works by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. "The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," said Lakshminarayanan Subramanian, Professor at New York University. The system is scheduled to be presented on 14 August at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia, Canada. The system described in the presentation is commercialised by Entrupy Inc., an New York University start-up founded by Ashlesh Sharma, Vidyuth Srinivasan, and Subramanian.

Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The system works by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. "The classification accuracy is more than 98 per cent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," said Lakshminarayanan Subramanian, Professor at New York University. The system is scheduled to be presented on August 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia, Canada. The system described in the presentation is commercialised by Entrupy Inc., an New York University start-up founded by Ashlesh Sharma, Vidyuth Srinivasan, and Subramanian.