AI Articles and Analysis about Business intelligence and analytics

Episode Summary: When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn’t fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML  to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.

Episode Summary: There are hedge funds and financial institutions that already use real-time data and sentiment analysis from social media, articles and videos in real-time to potentially make better trading decisions - but what does it mean when those same companies can use real-time satellite information to detect company activities and make trades based on that data? In this episode, Research Director of Capital Markets at Celent Securities discusses the focus on emerging technologies in trading and finance. He talks about the way that analytics and machine learning have affected the ways banks operate, the kinds of data that hedge funds and individual investors now have at their fingertips, and what that means for the future implications of AI-related technology in the finance world.

[This story has been revised and updated.]

Big data has turned out to be a key ingredient in turning machine learning from an abstract technology into a potentially invaluable tool of insight and foresight for businesses across industries. The burgeoning cognitive technologies of predictive analytics and data visualization are opening new windows of opportunity to companies trying to solve complex problems with multiple moving parts. From finding ways to retain new customers to more efficiently monitoring multiple performance metrics and easing performance volatility, more companies are gravitating towards machine learning-based data analysis tools in an effort to optimize operations and find innovative solutions and opportunities that were once too obscure for only the human eye.

Episode Summary: Tad Slaff is the founder of Inovance Financial Technologies, the creator of TRAIDE - a strategy creation platform that uses machine learning algorithms to help traders uncover patterns in assets and indicators and build more reliable trading strategies. In this episode, Tad speaks about the state of machine learning in finance today, and touches on how future applications of machine learning and trends may alter what gives an edge to one hedge fund or institutional investor over another.

Episode Summary: It’s more common to ask what AI can to do to win at games, but it’s less common to ask what games can do to help develop AI. This is a particularly fitting topic after Google’s DeepMind’s defeat of Go, and in this episode we talk with New York University’s Julian Togelius about his research in how games can help us develop AI. We discuss how simple AI has been used in more common video games; the ‘smoke and mirrors’ effect that is often used to mimic AI; and the more innovative ways that AI are being used in gaming at present, setting precedents for the future role of AI in gaming.

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.

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.)