The AI in Business Podcast

Natural language processing (NLP) sounds cool in theory. We're familiar with Siri and Echo of course, but where does it play a role in other companies? In today's episode, we speak with Samiur Rahman from Mattermark, whose entire business model is predicated on organizing and making findable information about companies, and generating a platform to search by unique criterion. Doing so involves some conceptual work with NLP to make things findable. Samiur talks about what Mattermark is doing with this technology now and where he thinks the future may take the field, and interesting topic for investors and founders alike.

We've spoken in the past about computer vision on the TechEmergence 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.

In addition to focusing on industry applications of artificial intelligence and emerging technology, we also focus on ethical and societal impacts of emerging technology. In this episode, we get back to ethics with Wendell Wallach, a scholar at Yale's Interdisciplinary Center for Bioethics and author of "A Dangerous Master", which addresses tech governance and other emerging technology issues. In this week's episode, Wendell talks about the problems of governing technologies that are developing faster than we can possibly assess all the risks, a topic that Wendell has thought about in-depth through both his extensive consulting, speaking and writing.

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.

"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 Nervanasys, 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.

Many employers (and employees) are familiar with the 'painful' learning curves of using multiple software products or platforms at once, but these may not be gripes you want to share with Amotz Maimon. This week, we feature an interview recorded at Yahoo headquarters with its Chief Architect, Amotz Maimon. He speaks about technology governance and how companies small and large can make faster and better decisions around what technologies to use, how to integrate and streamline the processes, and how to integrate machine learning into the mix (which Yahoo has been using for the past decade). This episode provides important insights for those looking to scale such technologies within their own businesses.

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.

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.

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.

Ten years ago, it would have been difficult to talk into your phone and have anything meaningful happen. AI and natural language processing (NLP) have made large leaps in the last decade, and in this episode Dr. Catherine Havasi articulates why and how. Havasi talks about how NLP used to work, and how a focus on deep learning has helped transform the prevalence and capabilities of NLP in the industry. For the last 17 years, Havasi has been working on a project through the MIT Media Lab called ConceptNet, a common sense lexicon for machines. She is also Founder of Luminoso, which helps businesses make sense of text data and improve their business processes.