Episode Summary: Facebook and Google’s advertising complex is founded on machine learning, allowing people to self-serve their data needs across a broad audience. India-based InMobi is a company in the advertising technology space that delivers 10 billion ad requests daily. Today, we speak with Avi Patchava, Vice-President of Data Sciences and Machine Learning at InMobi, which operates in China, Europe, India, and the US. Patchava explains how machine learning plays a role in appropriately matching advertising requests to the right audience at scale,  whether on mobile, desktop or different devices and media. Patchava paints a robust picture of what this technology will look like moving forward and how it will change the game for marketers and advertisers, especially with the emphasis on data and machine learning. See the full interview article here: www.techemergence.com/future-advertising-machine-learning-audience-targeting-reach

Companies with wells of data at their disposal may find themselves asking how they can use them in meaningful ways. Generally speaking, a clean set of data is the foundation for AI applications, but business owners may not know how exactly to organize their data in a way that allows them to best leverage AI. How exactly does a business transition from having data with the potential for usefulness to having data that’s going to allow for an accurate, helpful machine learning tool—one that can actually help solve business problems? In this episode of the podcast, we speak with Bryon Jacob, Co-founder and Chief Technology Officer at data.world, a company that offers products and services that help enterprises manage their data. In our conversation, Bryon walks us through the common errors companies make when creating and organizing data sets, and how these companies can transition to a more organized and meaningful data management system. The details in this interview should provide business leaders with a better understanding of some of the processes involved in getting started with AI initiatives, and how to hire data science-related roles into a company. See the full interview article with Bryon Jacob live at:  https://www.techemergence.com/how-existing-bus…ta-assets-for-ai/

Episode summary: In this episode of Ai in industry, we speak with Manoj Saxena, the Executive Chairman of CognitiveScale, about how AI and automation are being applied to white-collar processes in the healthcare sector. In simple business language, Manoj summarizes key healthcare applications such as invoicing handling, bad debt reduction, claims combat, and the patient experience, and explains how AI and automation can make these processes more efficient to improve the patient experience in healthcare organizations. Interested readers can listen to the full interview with Manoj here:  https://www.techemergence.com/white-collar-automation-in-healthcare/  

Episode Summary: Natural language processing (NLP) has become popular in the past two years as more businesses processes implement this technology in different niches. In inviting our guest today, we want to know specifically which industries, businesses or processes NLP could be leveraged to learn from activity logs. For instance, we aim to understand how car companies can extract insights from the incident reports they receive from individual users or dealerships, whether it is a report related to manufacturing, service or weather. In the same manner, how can insights be gleaned from the banking or insurance industries based on activity logs? We speak with the University of Texas’s Dr. Bruce Porter to discover the current and future use-cases of NLP in customer feedback.  Interested readers can listen to the full interview with Bruce here: https://www.techemergence.com/using-nlp-customer-feedback-automotive-banking  

Episode summary: This week on AI in Industry, we speak to Rana el Kaliouby, Co-founder and CEO of Affectiva about how machine vision can be applied to detecting human emotion - and the business value of emotionally aware machines. Enterprises leveraging cameras today to gain an understanding of customer engagement and emotions will find Rana’s thoughts quite engaging, particularly her predictions about the future of marketing and automotive. We’ve had guests on our podcast say that the cameras of the future will most likely be set up for their outputs to be interpreted by AI, rather than by humans. Increasingly machine vision technology is being used in sectors like automotive, security, marketing, and heavy industry - machines making sense of data and relaying information to people. Emotional intelligence is an inevitable next step in our symbiotic relationship with machines, an in this interview we explore the trend in depth. Interested readers can listen to the full interview with Rana here: https://www.techemergence.com/can-businesses-use-emotional-intelligence  

A myriad of customer service channels exist today, such as social media, email, chat services, call centers, and voice mail. There are so many ways that a customer can interact with a business and it is important to take them all into account. Customers or prospects who interact via chat may represent just one segment of the audience, while the people that engage via the call center represent another segment of the audience. The same might be said of social media channels like Twitter and Facebook. Each channel may offer a unique perspective from customers – and may provide unique value for business leaders eager to improve their customer experience. Understanding and addressing all channels of unstructured text feedback is a major focus for natural language processing applications in business – and it’s a major focus for Luminoso. Luminoso founder Catherine Havasi received her Master’s degree in natural language processing from MIT in 2004, and went on to graduate with a PhD in computer science from Brandeis before returning to MIT as a Research Scientist and Research Affiliate. She founded Luminoso in 2011. In this article, we ask Catherine about the use cases of NLP for understanding customer voice – and the circumstances where this technology can be most valuable for companies. Read the full article: techemergence.com/improving-customer-experience-with-ai-gaining-quantifiable-insight-at-scale

Episode summary: In this episode of AI in Industry, we speak with Khalifeh Al Jadda, Lead Data Scientist at CareerBuilder, about the applications of machine learning in improving a user’s search experience. Khalifeh also talks about what the future of search might look like and how AI will continue to make the search experience more intuitive (for search engines, platforms, eCommerce stores, and more). Business leaders listening in will get a sneak peak into the future of online search - and an understanding of how and where improvements in search features could impact their business. Interested readers can listen to the full interview with Khalifeh here: https://www.techemergence.com/better-than-elasticsearch-machine-learning-search/

Episode summary: In this episode of AI in Industry, we speak with Andy Terrel, the Chief Data Scientist at REX - Real Estate Exchange Inc., about how AI is being used in the real estate sector today. Looking ahead ten years into the future, Andy paints a picture of the areas where he believes AI will change the real estate business. Andy explores how marketing in real estate might change in the future with chatbots and conversational interfaces in real estate which are high value per ticket interactions - a process that will likely vary greatly from the chatbot applications we see for smaller B2C purchases (in the fashion sector, eCommerce, etc). Interested readers can listen to the full interview with Andy here: https://www.techemergence.com/ai-use-cases-future-real-estate/

Episode summary: Here on the AI in Industry podcast, we’ve heard AI experts explain how high-performance computing (HPC) has enabled everything from machine vision to fraud detection. In this week’s episode, we speak with Paul Martino, Managing Partner at Bullpen Capital, about which industries and AI applications will require high-performance computing most. Paul also adds some useful tips for business leaders on how to prepare for the coming AI-related developments in hardware and software. Interested readers can listen to our full interview with Paul here: https://www.techemergence.com/?p=12779&preview=true    

Episode summary: In this episode of AI in Industry, we speak with Dr. Sanmay Das from the Washington University in St. Louis about risk prediction and management in industries like banking, insurance and finance. Sanmay explores how are banks and other financial institutions are improving risk and fraud prevention measures with machine learning. In addition, he explores the ramifications of improved fraud detection in the coming 5 years ahead. Interested readers can listen to the full interview with Sanmay here: https://www.techemergence.com/machine-learning-for-credit-risk/