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If you combine the hype-factor of both "blockchain" and "artificial intelligence" you often get a supernova of jargon. This week on the AI in Industry podcast, we aim to get beyond the hype to discuss how blockchain might make AI more accessible for small and mid-sized businesses in the years ahead. Dr. Ben Goertzel - CEO of SingularityNET - is our guest this week.

The U.S. music industry is an economic staple generating an estimated $7.7 billion in retail revenue in 2016, according to the Recording Industry Association of America. The year 2016 also marked the first time that music industry revenue was generated primarily from “streaming music platforms.”

Expert systems and machine learning are two ends of a spectrum working to solve similar problems quite differently. One one hand you have if-then scenarios and a logical approach, and on the other you have vast neural networks and a big data approach. Some companies exist to try and bridge the gap between the if-then rule systems and the massive piles of data. They hope to find a middle ground of sorts, one that mitigates their individual disadvantages. One such company is Montreal’s fuzzy.ai.

This article was written in partnership and collaboration with the IEEE. For more information about content partnerships visit the Emerj Partnerships page.

Artificial intelligence companies continue to find ways of developing technology that will manage laborious tasks in different industries for better speed and accuracy. In the legal profession, AI has already found its way into supporting lawyers and clients alike.

Introduction: There’s a lot of hype out there about conversational AI. Although according to our guest, we’re nowhere near the day when AI can generate accurate conversations for the average business to integrate into their customer service, chatbots still have practical applications. In this episode, we interview the head of research at Digital Genius, Yoram Bachrach. Yoram succinctly outlines the current applications of chatbots—what they can and can’t do—and details how business can best prepare to automate their customer service.

The entertainment and media (E&M) industry is a diverse sector composed of multiple segments including film, television and media streamed online. By 2021, the U.S. E&M industry is projected to reach $759 billion in revenue,  increasing at a compound annual growth rate (CAGR) of 3.6 percent.

Episode Summary: How can machine learning help us advertise through social media? In this episode, Thomas Jelonek, CEO of Envision.ai, talks to us about how in the next five years, machine learning might automate the laborious guess-and-check process of finding visual content with which users can engage. Right now, finding images and videos that will best generate engagement is a task reserved for a human. He or she shifts through images and video clips that may work for an audience based on anecdotal evidence and perception of past post success. Learn how, according to Thomas, machine learning could help you save time and money, generate you a better ROI, and build you a larger list with more accurate targeting on social media.

With internet penetration on the rise, the e-commerce sector is booming in India. According to research by Boston Consulting Group, the number of internet users in  India will jump upwards of 550 million in 2018 from 190 million as of June 2014. India has over 300 million smartphone users which has surpassed the US to become the second largest smartphone market in the world. As per estimates, the Gross Merchandise Value (GMV) sold by eCommerce companies in India is expected to grow to around $80 billion by 2020 up from around $4 billion in 2009.

Companies and cities all over world are experimenting with using artificial intelligence to reduce and prevent crime, and to more quickly respond to crimes in progress. The ideas behind many of these projects is that crimes are relatively predictable; it just requires being able to sort through a massive volume of data to find patterns that are useful to law enforcement. This kind of data analysis was technologically impossible a few decades ago, but the hope is that recent developments in machine learning are up to the task.