AI Articles and Analysis about Business intelligence and analytics

Episode Summary: We’ve spoken in the past about computer vision on the Emerj show, but we haven’t covered much about it in industry apps. Few businesses have better mastered this technology in the form of an app better than Shutterstock. In today’s episode, we speak with Nathan Hurst, currently a distinguished engineer with Shutterstock and previously with Google, Amazon, and Adobe. Nathan delves into the topic of business apps that can “see”, and touches on what that means for the industry, some of the exciting developments that he’s seen over last the 10 years, and what he sees coming up in the next few years.

Episode Summary: The artificial intelligence field is normally seen as burgeoning and new, populated with lots of small, scrappy companies aiming to become the next de-facto solution, with maybe one exception - “Big Blue”. IBM has been involved since the ‘beginning’ and is perhaps best known for Watson, which has from Jeopardy to a range of applications in small and big businesses, as well as the public sector. Swami Chandrasekaran is chief technologist of industry apps and solutions for IBM, and he speaks in this episode about what he sees as some of the low-hanging fruit for applying predictive models to business data. Swami has seen this technology applied in a variety of contexts, from automotive and shipping to telcos and more, providing an informed perspective for industry executives, data scientists, and anyone else interested in the intersection of predictive analytics and business.

Episode Summary: “Artificial intelligence (AI) can be seen as a progression in our scalability of labor.” This quote comes from this week’s guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervana Systems, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week’s episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil & gas.

Episode Summary: If you’re in the San Francisco Bay area, it’s not all that novel to be trained in or working on some form of AI; however, to be doing so in the 1980s and 1990s was a more rare occurrence. Dr. Lorien Pratt has been working with neural nets and AI applications for many decades, and she does lots of consulting work in implementing these technologies with companies in the Bay area. In this episode, Lorien provides her unique perspective on decades of development and adoption in AI as we ask, where is the traction today in places where it wasn’t 5 or 10 years ago? We also discuss where Lorien thinks machine learning applications in business and government seem to be headed in the near term.

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.