Artificial Intelligence in Industry with Dan Faggella
A Close Up of Computer Vision with Shutterstock

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.

Direct download: Nathan_Hurst.mp3
Category:computer vision -- posted at: 7:00pm PDT

Searching for Higher Ground in Rough Seas of Emerging Tech Governance

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.

Direct download: Wendell_Wallach.mp3
Category:Ethics -- posted at: 6:00pm PDT

Predictive Analytics Offers Customized Solutions to Complex Problems

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.

Direct download: Swami_Chandrasekaran.mp3
Category:Big Data -- posted at: 8:09pm PDT

Follow the Data: Deep Learning Leads the Transformation of Enterprise

“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.

Direct download: Naveen_Rao.mp3
Category:Deep learning -- posted at: 7:00pm PDT

Building to Scale: How Yahoo! Turns Machine Learning into Company-Wide Systems

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.

Direct download: Amotz_Maimon.mp3
Category:machine learning -- posted at: 5:01am PDT

Pulling Back the Curtain on Machine Learning Apps in Business

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.

Direct download: Lorien_Pratt.mp3
Category:machine learning -- posted at: 10:40pm PDT

Machine Learning Opening New Doors in Human Resource Industry

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.

Direct download: Ben_Waber.mp3
Category:Emerging Technology -- posted at: 6:00pm PDT

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.

Direct download: Brad_Bailey.mp3
Category:Emerging Technology -- posted at: 6:00pm PDT

NLP Systems Have a Lot to Learn from Humans

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.

Direct download: Catherine_Havasi.mp3
Category:natural language processing -- posted at: 9:21pm PDT

Insights on the Symbiotic Relationship Between Data Science and Industry

When it comes to data science and machine learning, what are the related skills that are getting people jobs and what are the industries that are supplying those in-demand jobs? These are two important questions that we discuss in this week’s episode with CrowdFlower’s CEO Lukas Biewald, whose company is providing a pragmatic perspective of the industry by focusing on assessing job listings and related information in the field of data science. If you’re a company that is interested in finding someone with in-demand data science and related skills, or if you’re in the market to find a position in this field, this episode will likely be very useful!

Direct download: Lukas_Biewald.mp3
Category:general -- posted at: 9:31pm PDT