

The AI in Business Podcast
Daniel Faggella
The AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups - uncovering trends, use-cases, and best practices for practical AI adoption.
Visit our advertising page to learn more about reaching our executive audience of Fortune 2000 AI adopters: https://emerj.com/advertise
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups - uncovering trends, use-cases, and best practices for practical AI adoption.
Visit our advertising page to learn more about reaching our executive audience of Fortune 2000 AI adopters: https://emerj.com/advertise
Episodes
Mentioned books

Feb 10, 2018 • 22min
Overcoming Challenges in Spoken Voice based Natural Language Processing (NLP) for business use
In this episode of AI in industry, we speak with Michael Johnson, the director of research and innovation for Interactions llc, in Boston MA. Michael explores the inbound (human to machine) and outbound (machine to human) applications of voice based natural language processing (NLP) and also talks about attaching a timeframe to how soon small and medium enterprises (SMEs) would have access to this technology in a financially sensible manner. Although NLP is often associated with chat or text interfaces, voice is important for applications in call centers, mobile phones, smart home devices, and more. In addition, Michael explains that voice involves unique challenges that text does not have to deal with - including background noise and accents, which need to be overcome to deliver a good user experience. See the full interview article with Michael Johnston live at: www.techemergence.com/overcoming-challenges-spoken-voice-based-natural-language-processing-nlp-business-use

Feb 3, 2018 • 48min
Natural Language Processing - Current Applications and Future Possibilities
In order to shed more light on the growing applications of natural language processing, we speak with Vlad Sejnoha (CTO of Nuance Communications) about the current and near-term applications of NLP for voice and text across industries. In this podcast interview, Vlad breaks down real-world NLP use-cases in industries like banking, healthcare, automotive, and customer service. For the full article of this episode, visit: TechEmergence.com/natural-language-processing-current-applications-and-future-possibilities

Feb 1, 2018 • 26min
How Microtasking Helps Optimize AI-Based Search - in Media, eCommerce and More
This week on AI in Industry we interview Vito Vishnepolsky of Clickworker. Clickworker is a large microtasking marketplace that crowdsources the search optimization work for many of the world's leading search engines. So how does crowdsourced human work play a role in making sure eCommerce and media searches give users what they want? That's exactly what we explore this week. Vito's perspective is valuable because he has a finger on the pulse of crowdsourced demand, handing business development for various crowdsourced AI support services - both for tech giants and startups. Read the full article online at TechEmergence: TechEmergence.com/how-microtasking-helps-optimize-ai-based-search

Jan 29, 2018 • 26min
AI for Sales Forecasting - How it Works and Where it Matters
Sales forecasting is big business. If you can better predict how much of a certain product or service you will sell in a given day, you can better stock inventory, better staff your facilities, and ultimately keep more margin in your business's accounts. This week on AI in Industry we interview Dr. John-Paul B Clarke, professor at Georgia Tech and co-founder / Chief Scientist at Pace (previously called "Prix"). Dr. Clarke shares details about how sales predictions are done today, and what AI advancements may allow for in helping businesses sell everything from groceries to hotel rooms. Read the full interview article online at: techemergence.com/ai-sales-forecasting-works-matters

Jan 24, 2018 • 26min
Overcoming the Data and Talent Challenges of AI in Life Sciences
In this episode of AI in industry, Innoplexus CEO Gunjan Bhardwaj explores how pharma giants are working to overcome two critical challenges with AI: Data, and talent. Pharmaceutical data is challenging because the same term (say "EGFR") might be referred to as a "protein", a "biomarker", or a "target". Gunjan explores how this kind of relevance and context for data - and how pharma companies may need to hire the talent issues involved with making life sciences and computer sciences teams work together productively. See the full interview article online at: techemergence.com/overcoming-data-talent-challenges-ai-life-sciences

Jan 21, 2018 • 24min
Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AI
This week, AI in Industry features Jeremy Barnes, Chief Architect at Element AI. Jeremy talks about the common mistakes some businesses might make while adopting AI to solve broad business problems. He also sheds light on the problem areas that could raise the market value of businesses through AI adoption, hiring the right talent with the right combination of subject matter expertise and business experience, and the business and technical aspects executives should consider before contemplating the adoption of AI. For more insights on the B2B applications of AI, go to techemergence.com

Jan 14, 2018 • 22min
AI Recommendation Engines for Big Purchases - Will You Buy Your Home or Car Using AI?
This week, AI in Industry features Dr. David Franke, Chief Scientist at Vast. David talks about how AI can work with scarce transaction data to derive meaningful analytics for big purchases, such as cars and houses. He elaborates on how the AI can glean information from user interaction and marketplace data to provide customers with the relevant product fit, deals and recommendations on big purchases. He also discusses the future trends and business benefits for early adopters of AI for purchase recommendations of high-cost items. For more insights on this topic, go to www.techemergence.com

Jan 7, 2018 • 28min
The Future of Medical Machine Vision - Possibilities for Diagnostics and More
This week's episode covers the medical applications of machine vision for the diagnosis and treatment of cancer. Medical science has integrated AI since the late 90s, and it's been useful in the fight against cancer. This week's guest is Dr. Alexandre Le Bouthillier, founder of Imagia. Imagia is a medical imaging company which specializes in using AI and machine learning to detect cancer in its early stages so that oncologists can make quicker, more accurate diagnoses for patients. AI is a useful tool in the detection of breast cancer, colon cancer, and lung cancer. It can even detect genetic mutations, something humans certainly cannot. Learn just how important AI has been over the last two decades in developing the medical infrastructure necessary for patients to have a chance at surviving and even curing their cancer. See the full interview article - with images and audio included - on TechEmergence: TechEmergence.com/the-future-of-medical-machine-vision-possibilities-for-diagnostics-and-more

Dec 30, 2017 • 29min
Building and Retaining a Data Science Team
This week on AI in Industry, we speak with Equifax's Dr. Rajkumar Bondugula about how the dynamics, composition and requirements of the data science team have evolved over the years. Raj also shares valuable insights on how to build a robust data science and machine learning team, use its collective intelligence to solve problems, and retain the team by engaging them with the right problems they expect to solve. For more insights from AI executives, visit: TechEmergence.com

Dec 24, 2017 • 29min
AI for IoT Security - with Dr. Bob Baxley of Bastille
This week on AI in Industry, we explore IoT security with Bob Baxley (Chief Engineer at Bastille). This includes information on how different IoT security is compared to infosec, the unique challenges IoT security presents (for detecting and scanning wireless network traffic that runs on various protocols and for classifying types of cyberthreats), what the future of IoT security might look like, and how deep learning and machine learning tools can be used to better classify and detect threats and attacks in the cyberspace. For more insight on the applications of AI in industry, visit: TechEmergence.com


