AI Articles and Analysis about E-commerce

Episode Summary1: Fraud attacks have become much more sophisticated. Account takeovers are happening more often. Many security attacks involve multiple methods and unexpected attacks can devastate businesses in just a few days, as we saw with Neiman Marcus and Target. False promotion and abuse is seen not only on social media sites but is also targeted at business. To combat these risks, fraud solutions need to be smarter to keep pace with fraudsters to prevent attacks and react quickly when they do happen. This requires a fast-learning solution with the ability to continually evolve - which calls for the application machine learning for fraud detection. In this episode we talk to Kevin Lee from Sift Science and examine the shifts in the info security landscape over the past ten or fifteen year. Lee also highlights what new kinds of fraud are now possible and what machine learning solutions are available.

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle—from assembly to post-sale customer service interactions, but retail players need answers to important questions:

With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using—right now?

Episode Summary: We've interviewed a number of guests on Emerj, but very few who have had a serious part of their career in selling automobiles. But Michael Perry did just that for 5 years before founding Kit, his third startup - an AI-powered Virtual Employee that works in marketing for small businesses and was acquired by Shopify in April 2016. In this episode, Perry speaks about how Kit and Shopify leverage AI on a daily basis, and how a “non-tech” person with no formal background in AI or data science can build a team for an AI project.

Episode Summary: At Facebook headquarters, I learned there are 1 billion active users every month. In a more recent interview at eBay headquarters in San Jose, l learned that the well-known digital store has over 1 billion products for sale. eBay is, without a doubt, the world’s largest marketplace, and there’s enough incoming data to keep a large team of data scientists busy for years. I speak with Zoher Karu, eBay’s chief data officer, about how eBay leverages data and machine learning to create a better experience for its customers and also their sellers, shedding light on important lessons for anyone looking to sell a product online.

In the hundreds of researcher and executive interviews we've been fortunate enough to conduct in the last three years, few artificial intelligence applications are brought up more than marketing and advertising. During talks with execs and researchers from companies ranging from Facebook to Baidu, and IBM to AT&T, marketing has been a perennial theme in conversations of AI's hottest applications.

Episode Summary: While we’ve featured quite a few companies that use and implement AI systems, we’ve more rarely gone behind the scenes with companies or consultants providing AI-related services to companies. In this week’s episode, we talk with Machine Learning Consultant Charles Martin, a data scientist and machine learning expert who has done freelance consulting on machine learning systems at companies including eBay, GoDaddy, and Aardvark. In this interview, Charles talks about the areas in AI that he believes are ripe for implementation in a business context, and where he sees businesses getting AI ‘wrong’ before getting to the hard work of implementing systems that work for them.

Episode Summary: Ever had the perfect book recommended to you by Amazon or gave a pleasantly-surprised thumbs up for a song selected for you by Pandora? Both services are powered by recommendation engines, which are gaining steam int he commercial space. In this episode, we speak with Entrepreneur Raefer Gabriel, who works for Delvv on the commercial applications of recommendation engines. We talk about how this technology works, and how it comes to learn from reviews, ratings, and consumer interactions. Gabriel also gives perspective on how these engines might be enhanced and applied in the future, a good topic for those of you in the startup world.