ai podcasts

Podcast Queue

In this episode of the Data Show, I spoke with Bruno Fernandez-Ruiz, co-founder and CTO of Nexar. We first met when he was leading Yahoo! technical teams charged with delivering a variety of large-scale, real-time data products. His new company is helping build out critical infrastructure for the emerging transportation sector.

Vehicle-to-Vehicle Communication Networks Can Help Fuel Smart Cities

In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.

Machine Learning Needs Machine Teaching

Episode Summary: Machine learning (ML) can be used to identify objects and pictures or help steer vehicles, but is not best suited for text-based AI applications says Robbie Allen, founder of Automated Insights. In this episode of AI in Industry, we speak with Robbie about what is possible in generating text with AI and why rules based processes are a big part of natural language generation (NLG). We also explore which industries are likely to adopt such NLG techniques and in what ways can NLG help in business intelligence applications in the near future. You can listen to the full interview with Robbie here:    https://www.techemergence.com/robbie-allen-from-automated-insights-the-use-cases-of-natural-language-generation   &nbsp;

Robbie Allen from Automated Insights - The Use-Cases of Natural Language Generation

Episode summary: There are many challenges to bringing AI into an enterprise for example the lack of skilled AI talent, or issues around data organization. In this week's episode, we focus on AI adoption in the enterprise from an investor’s perspective. We expect that founders looking to sell B2B enterprise AI-products and people in enterprises who are looking for the right qualities in an AI firm which would ease integration, would find this episode relatable. We speak with Rudina Seseri from Glasswing Ventures about what are the pain points for AI integration in the enterprise and at the other end of the spectrum, some factors that are aiding AI adoption. Interested readers can see the full interview with Rudina here:  https://www.techemergence.com/reducing-friction-ai-adoption-enterprise-rudina-seseri/ &nbsp;

Reducing the Friction of AI Adoption in the Enterprise - with Rudina Seseri

In this episode of the SuperDataScience Podcast, I chat with Serial Entrepreneur, Josey Parks. You will learn how a brick and mortar entrepreneur is leveraging data to build a business empire, know how Josey is training his salesmen as data analysts and get valuable tips on how to spread the power of data in the contractor industry. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/145

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 145: How to Use Data Science In Offline Business

Episode summary: One of the key challenges that enterprises face in adopting artificial intelligence is finding skilled data science talent; ). Business leaders want to know when it's best to hire AI talent, to "upskill" existing workers, or simply to bring in AI consultants - and the answers aren't always obvious. In this episode of AI in Industry we speak with Nikolaos Vasiloglou from MLTrain about how AI consulting and AI training events can be used to upgrade an existing team’s skills. Nikolaos also distinguishes the right and wrong circumstances to bring on AI consultants, and shares his tips on how training, upskilling, and consulting can level up an existing company’s AI capabilities. Listeners can find out &nbsp;how to set realistic goals for re-training existing teams for new AI skill sets. Lastly, we also explore how AI consultants can support developer and engineering teams to produce fruitful real-world AI applications (without developing unhealthy reliance on outside experts). Interested readers can also listen to our previous episode of AI in Industry&nbsp;(here) where we look at overcoming the data and talent challenges of AI in life sciences Interested readers can listen to the full interview with Nikolaos here:https://www.techemergence.com/whats-the-value-of-ai-events-and-consulting/

What's the Value of AI Events and Consulting?

Episode summary: In this week’s episode of AI in Industry, we speak with Larry Lafferty, the President and CEO of Veloxiti. Larry has been building large AI projects for DARPA and other large private companies for the last 30 years. In this interview, Larry explains three critical factors to applying artificial intelligence in the enterprise (with insights especially relevant for companies who aren’t very familiar with AI and data science). AI vendors and business leaders should find the “how to” insights in this interview useful – particularly Larry’s details on organizing data and defining an AI-applicable business problem. Interested readers can listen to the full interview with Larry here:&nbsp;https://www.techemergence.com/how-to-apply-ai-…h-larry-lafferty/

How to Apply AI to an Existing Business with Larry Lafferty

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/

AI Use-Cases for the Future of Real Estate

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:&nbsp; https://www.techemergence.com/how-existing-bus…ta-assets-for-ai/

How Existing Businesses Should Organize Their Data Assets for AI

This week’s episode of the AI in Industry podcast focuses on two main questions. First, how should business leaders determine the most fruitful, potential applications of AI in their business? Second, how do they choose the right one into which to invest resources? This week, we interview someone who has spoken with a number of CTOs and CIOs about early adoption strategies for machine learning for customer service, marketing, manufacturing and other applications. He is Madhusudan Shekar, Principal Evangelist at Amazon Internet Services. See the full interview article here: www.techemergence.com/how-to-determine-the-best-artificial-intelligence-application-areas-in-your-business

How to Determine the Best Artificial Intelligence Application Areas in Your Business

AI has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. Searching for content within videos is more challenging because video is not just voice and sound, it is also a collection of moving and still images on screen. How could AI work to overcome that challenge? In this episode of the AI in Industry podcast, we interview Manish Gupta, CEO and co-founder of VideoKen, about the future of video search as machine learning is increasingly integrated into the process. Dr. Gupta talks about how video is becoming more searchable and discusses his own forecasts about what that will look like in the future. He also predicts what machine learning will allow Youtube to do as people continue to search for more specific video content. Our Content Lead, Raghav Bharadwaj, joins us for this interview. See the full video article here: www.techemergence.com/artificial-intelligence-video-search-youtube-future

Artificial Intelligence for Video Search and the YouTube of the Future

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Why It's Hard to Design Fair Machine Learning Models

Emerj.com is where business leaders turn to understand how AI is impacting their business and industry - and what to do about it.

Our objective, jargon-free research and industry overviews are designed to give executives and decision-makers exactly what they need for competitive insight, informed AI technology procurement and strategic planning around AI.

AI disruption will open up new vistas for opportunity, creating new business paradigms and making others extinct. 

Emerj market research helps leaders survive and thrive in an era of AI disruption.

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How to Apply AI to an Existing Business with Larry Lafferty

The AI in Business Podcast