AI Articles and Analysis about Business intelligence and analytics

Science Magazine’s report on Friday that an artificial intelligence system was caught stealing banking customers’ money may have made you rethink vesting your funds in the burgeoning technology. But have no fear – the article was an April Fool’s joke.

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.)

Episode Summary: We’ve featured a number of artificial intelligence researchers on the show, but today we switch gears and dive into the business side of the industry. In this episode, Dr. Mazin Gilbert (who earned his PhD in Engineering) breaks down AT&T’s efforts to make more intelligent systems large-scale. How do they train their network to route traffic through the right nodes on holidays, when certain areas of traffic are overloaded? How can a system know, based on signals from hardware, which pieces might be going bad and need replacing and send out a message to alert the company? Making a network ‘aware’ is a large challenge, but Mazin gives an insider’s perspective as to how AT&T uses machine learning technologies in order to remain profitable.

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.

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.

Even folks without a remote interest in artificial intelligence understand that it's starting to surround them. The easy examples can be conjured by just about anyone walking the street: Siri, Amazon's recommendations, Pandora's playlists, Facebook's face-tagging and newsfeed, and Google's search results - these are the easy examples.

Episode Summary: Most of us forget that just about a decade ago, Facebook’s software was incapable of tagging people in a photo, but today can so without difficulty, sometimes without us even knowing. Machine vision has progressed to the point where it’s also common for computers to be able to pick out dogs from cats in images, another task that was not possible 10 years ago.

Tute Genomics is part of a grand shift towards the leveraging of genetic information towards better health and better treatments. One of the "holy grails" of this transition from one to the other is the advent of