AI Articles and Analysis in Consumer goods

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.

It's been over a month since our last major artificial intelligence consensus (which covered 33 AI researcher perspectives on the 20-year risks of AI), and we decided that this time around, we'd speak with AI executives directly about the future of artificial intelligence and machine learning in consumer tech.
The media is awash with buzz-stories about autonomous vehicles, speech recognition, robotics, and more, but it seems difficult to glean a perspective on which consumer AI tech trends are likely to make the biggest impact in the coming 5 years.
While there's certainly no crystal ball, our preference as a market research firm is to combine research and news analysis with a strong consensus from dozens of experts in the field. When it comes to AI for consumer tech, we decided to ask executives and founders of artificial intelligence companies what they believe to be the most important AI consumer tech trends in the next half decade.
You can see a full list of the answers to our "AI Consumer Tech Trends" below in our large infographic.

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: This week's in-person interview is with Dr. Adam Coates, who spent 12 years at Stanford studying artificial intelligence before accepting his current position of Director of Baidu's Silicon-Valley based artificial intelligence lab. We speak about his ideas around consumer artificial intelligence applications and impact and what he's excited about, as well as what he thinks may be more 'hype' than reality. He gives a an idea about applications that Baidu is working, to potentially influence billions of mobile and computer users worldwide. If you're interested in the developments of speech recognition and natural language processing, this is an episode you won't want to miss.

In the first week of 2016, Facebook’s Mark Zuckerberg announced in a post that his goal for the year was to “build a simple AI to run my home and help me with my work.” He clarified, "You can think of it kind of like Jarvis in Iron Man.” Zuckerberg went on to describe his plan to explore presently available smart home technologies, implement them into his home, and train the system to coordinate with his family life and workaday. (Interestingly, Zuckerberg’s AI may utilize a number of devices, but he refers to the technology as a singular system, implying that he intends to develop a unified AI to oversee the many individual devices.)

How emotions influence consumer buying habits has long intrigued and evaded the business sector. Face recognition technology, once limited to security and surveillance systems, has made it possible to gauge more specific metrics to allow companies to predict consumer behavior and accelerate revenue growth.

The Internet of Things (IoT) has the potential to fall into the general pit of buzzword-vagueness. Artificial intelligence (AI) often falls into the same trap, particularly with the advent of new terms such as "machine learning," "deep learning," "genetic algorithms," and more.

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.