Podcast Queue

Not surprisingly, Joseph Sirosh, has big ambitions for his product portfolio at Microsoft which includes Azure ML, HDInsight and other tools. Chief among them is making it easy for mere mortals to consume these data services from the applications they're familiar with. Take Excel for example.

If a financial analyst can, with a few clicks, send data to a forecast service in the cloud, then get the numbers back, visualized on the same spreadsheet, that's a pretty powerful story, said Sirosh who is corporate VP of machine learning for Microsoft.

But as valuable as those applications and services are, more and more of the value to be derived from computation over time will be the data itself, not all those tech underpinnings.  "In the future a huge part of the value generated from computing will come from the data as opposed to storage and operating systems and basic infrastructure," he noted on this week's podcast. WHich is why one topic under discussion at next month's Structure Data show will be who owns all the data flowing betwixt and betweeen various systems, the internet of things etc.

When it comes to getting corporations running these new systems Microsoft may have an ace in the hole because so many of them already use key Microsoft tools -- Active Directory, SQL Server, Excel. That gives them a pretty good on-ramp to Microsoft Azure and its resident services. Sirosh makes a compelling case and we'll talk to him more on stage at Structure Data next month in New York City.

In the first half of the show, Derrick Harris and I talk about the Hadoop world has returned to its feisty and oh so interesting roots. When Pivotal announced its plan to offload support of Hadoop to Hortonworks and work with that company along with IBM, GE on  the Open Data Platform the response from Cloudera CEO Mike Olsen in a blog post with his take. 

Also on the docket, @WalmartLabs massive OpenStack production private cloud implementation.

Microsoft's machine learning guru on why data matters sooooo much

Peter Gasston (https://twitter.com/stopsatgreen) is a front-end developer and web technologist that works for +rehabstudio (https://www.rehabstudio.com) in London.

Read the blog post about this interview: https://blog.feryn.eu/peter-gasston-commoditization-of-artificial-intelligence. The video is up on Youtube: https://youtu.be/_qremcORl9A

This interview was recorded in Rome at Codemotion 2016 (http://www.codemotionworld.com) where Peter presented his "OK Computer" presentation (https://speakerdeck.com/stopsatgreen/ok-computer-codemotion-rome) about the Commoditization of Artificial Intelligence.

We talk about how improved algorithms and the availability of massive computing power due to maturity of Cloud Computing has boosted Artificial Intelligence in a very profound way.

Both major players (Facebook, Google, Microsoft, IBM) and smaller startups can leverage the Cloud for machine learning and offer added value through easy-to-use customer-facing APIs.

Most implementations focus on natural language understanding and computer vision. Here's a list of tools, services and APIs that were mentioned in the interview and/or Peter's presentation.

https://wit.ai
https://cloud.google.com/vision/
http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html
https://cognitoys.com
http://clarif.ai
https://www.metamind.io
http://www.skymind.io
https://www.projectoxford.ai

Thanks to machine learning and massive data processing, artificial intelligence is now a commodity: recognizing objects, locations and people on pictures. Or even understanding the meaning of a conversation. 

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Peter Gasston - Commoditization of Artificial Intelligence

In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Practical Machine Learning Techniques for Building Intelligent Applications

I'm always a little shocked when I see how much venture investing goes into the healthcare space, which brings me to the subject of this week's episode: just how the healthcare industry&nbsp;is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management&nbsp;talks&nbsp;about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches&nbsp;on which innovations have a better chance of getting blocked and redirected by parties of interest&nbsp;and those that&nbsp;have more promise in being accepted and rolled out sooner. By the end of this episode, listeners will have a&nbsp;more clear picture of practical considerations in&nbsp;healthcare technology adoption, reasons&nbsp;that are&nbsp;often less about quality or potential of the technology and more about&nbsp;clarity on ROI for investors.

AI Healthcare Applications – and Why Doctors Don't Want to Be Replaced

In this episode, discover how chatbots and conversational agents can provide you an advantage in the realms of customer support, product, support, lead engagement, and more, and learn the theory behind creating useful chatbots you can use in your own business. Right now, if we intend to find a piece of information or purchase something on the Internet, we might use a search engine that provides us with a list of sites we can browse in order to find ourselves a resolution for that intent. This week’s guest, Chief Scientist at Conversica, Dr. Sid J Reddy, talks about how AI and ML can usher in the next a new era of search software, one that will bring you a faster, more accurate resolution to your intent. Most importantly, Dr. Reddy discusses how chatbot technology can be integrated into areas such as customer service, product support, and lead engagement. By the end of the episode, listeners will have a better idea of the importance of collecting data and how they can use that data to &nbsp;to build chatbot templates they can use in multiple domains and applications. For more interviews on the business applications of AI, visit: www.TechEmergence.com

What Chatbots Can Do, and Cannot Do

In this episode of AI in Industry, we explore how artificial intelligence can be use to manipulate human behavior - in gaming and in business. We explore how game designers use psychology and machine learning to drive their own desired outcomes, leaving users to "feel" in control. Dr. Charles Isbell teaches machine learning at Georgia Tech. He explores the manipulative elements of game design, and how some of the same AI approaches are likely being used at tech giants like Amazon and Facebook. In this episode you learn how businesses leverage the "illusion of choice" with subtly influential AI techniques. Charles also helps us understand which businesses will be most able to use AI to guide user behavior in the years ahead. For more interviews about the applications of AI in industry, visit: www.TechEmergence.com

AI for Social Influence and Behavior Manipulation with Dr. Charles Isbell

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.&nbsp; For more insights on this topic, go to&nbsp;www.techemergence.com &nbsp;

AI Recommendation Engines for Big Purchases - Will You Buy Your Home or Car Using 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

Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AI

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:&nbsp; techemergence.com/ai-sales-forecasting-works-matters

AI for Sales Forecasting - How it Works and Where it Matters

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

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Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AI

The AI in Business Podcast