Artificial Intelligence in Industry with Dan Faggella

This week, we're going to be talking about the defense sector. We interview Ryan Welch, CEO of Kyndi, a company working on explainable AI. We focus specifically on the unique data challenges of the defense industry, as well as the general use case of AI in defense writ large. Many of the challenges that the defense sector has to deal with transfer to other spaces and sectors. Business leaders that deal with extremely disjointed text information, what is sometimes called "dark data," and information in various languages or different dialects, will be able to resonate with some of the unique challenges talked about in this episode, and maybe even gain some insights for how to handle them.

 

Read the full interview article on Emerj.com

Direct download: AI_in_Industry-Ryan_Welch-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:30am PST

Whether we're talking about customer service, marketing, or building developer teams, what we try to do on our AI in Industry podcast is bring to bear lessons that are transferable. There are few more transferrable ideas than what makes a company ready to adopt AI. When it comes to the willingness and the ability to integrate AI into a company strategy and to fruitfully adopt the technology to really see an ROI, what do the companies that do so successfully have in common? What do the companies that are not ready or too fearful to do it have in common?

There are probably few companies in the AI vendor space that are aiming to sell AI more ardently into the enterprise than Salesforce, and there are few people that know more about how that process is going than Allison Witherspoon, Senior Director of Product Marketing for Salesforce Einstein, which is their artificial intelligence layer on top of the Salesforce product.

We speak to Witherspoon about the telltale signs of a company that understands the use cases of AI in their industry and that have a good chance of driving value with AI. We also talk about the common qualities of companies that might not ready for I adoption.

Read our full interview article on Sunday at Emerj.com

Direct download: AI_in_Industry-Allison_Witherspoon-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 12:45pm PST

Episode Summary: This week we talk to Alejandro Giacometti, the data science lead at a company called EDITED, based in London. The company claims to help retailers with inventory optimization, and we speak with Alejandro about how artificial intelligence can be used to search the web for the product clusters and individual products of major retailers to help inform other retailers on what products might be popular.

There are two primary takeaways from this episode. The first is the broad capability of monitoring the competition with artificial intelligence, something that can be applied across industries, not just in retail. The second is that EDITED is generating information from what is freely available on the web, and so it would seem their software doesn't require businesses to integrate it into inventory management systems in order to train the algorithm behind it.

I'm not necessarily lauding the company; I haven't used their product nor read all of their case studies. That said, it's worth noting simply because its approach is fundamentally different than most AI vendors.

Read the full interview article on emerj.com

Direct download: AI_in_Industry-Alejandro_Giacometti-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 9:29am PST

Some businesses are going to require a sea change in the way that their computation works and the kinds of computing power that they're leveraging to do what they need to do with artificial intelligence. Others might not need an upgrade in hardware in the near term to do what they want to do with AI.

What's the difference? That's the question that we decided to ask today of Per Nyberg, Vice President of Market Development, Artificial Intelligence at Cray. Cray is known for the Cray-1 supercomputer, built back in 1975. Cray continues to work on hardware and has an entire division now dedicated to artificial intelligence hardware. This week on AI in Industry, we speak to Nyberg about which kinds of business problems require an upgrade in hardware and which don't.

Direct download: AI_in_Industry-Per_Nyberg-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:41am PST

We speak this week with Aneesh Reddy, cofounder and CEO of Capillary Technologies. Capillary is a rather large firm based in Singapore. Aneesh is in Bangalore himself. The firm focuses on machine vision applications in the retail environment.

How do we instrument a physical retail space so that, with cameras, we can pick up on the same kind of metrics that eCommerce stores can? Retail stores, as Reddy talks about in this episode, have to focus on the data that they get from the checkout counter, such as what kind of purchases were made, and potentially some kind of data about how many times the front door was opened or closed. That doesn’t really lay out that much detail about who came in, what percent of them converted, and what the average cart value was for different people.

A lot of that is completely greyed out when looking at the numbers that are accessible to brick and mortar retailers. But some of that is changing. Reddy talks about what’s possible now with machine vision in retail, and what it opens up in terms of possibility spaces for understanding customers better in a physical environment. More importantly, Aneesh paints a bit of a future vision of where he believes retail is going to be when not just computer vision is included, but when audio and other kinds of sensor information are included.

Direct download: Copy_of_AI_in_Industry-Aneesh_Reddy-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:14am PST

In this episode of AI In Industry, we interview Nick Possley, the CTO of a company called AllyO, based in the San Francisco Bay area. We speak with Nick about where artificial intelligence and machine learning are playing a role in recruiting today and how picking the right candidates from a pool is in some way being informed by artificial intelligence. Whether a business leader is hiring dozens and dozens of people or whether they ’re just interested in understanding how AI can engage with individuals on more of a one-to-one basis, this should be a fruitful episode. In addition, the fundamentals of what we discuss in this episode, in terms of taking in data from profiles and responding and engaging with applicants, could be applied to all sorts of cases, such as customer service and marketing.

Read the full interview article here: https://www.techemergence.com/how-to-use-ai-to-hire-and-recruit-talent

Direct download: Copy_of_AI_in_Industry-Nick_Possley-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:25am PST

What makes a chatbot or a conversational interface actually work? What kind of work does one need to do to get a chatbot to do what one wants it to do? These are pivotal questions and questions that for most business leaders are still somewhat mysterious, but that’s exactly what we’re aiming to answer on this episode of the AI in Industry Podcast.

This week we speak with Madhu Mathihalli, CTO and co-founder of Passage AI. We speak specifically about what kinds of tasks conversational interfaces are best at, what kinds of word tracks, what kind of questions and answer are they suited for and which are a bit beyond their grasp right now. In addition, we speak about what it takes to train these machines. In other words, how do we define the particular word tracks that we want to be able to automate and determine which of them might be lower hanging fruit for applying a chatbot or which of them might not?

Read or listen to the full podcast here: https://www.techemergence.com/how-to-get-a-chatbot-to-do-what-one-wants-in-business/

Direct download: AI_in_Industry-Madhusudan_Mathihalli-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:56pm PST

Episode Summary: In this episode of the AI in Industry podcast, we interview Rajat Mishra, VP of Customer Experience at Cisco, about the best practices for adopting AI in the enterprise and how business leaders should think about the man-machine balance at their companies. Mishra talks with us about how the executive team should be able to imagine the future of specific work roles that might integrate AI technology or envision how those roles will shift in the short-term. In other words, how will AI affect workflows?

Direct download: AI_in_Industry-Rajat_Mishra-Mixdown.mp3
Category:general -- posted at: 3:51pm PST

In this episode of the AI in Industry podcast, we interview Nikhil Malhotra, Creator and Head of Maker's Lab at Tech Mahindra, about how artificial intelligence changed the nature of IT services and business services in general. Malhotra talks about what businesses should consider to make themselves relevant for the future. In addition, he discusses the philosophy shift that has to happen for people to be appreciative of the process of problem-solving, and to see profit and growth from AI. We hope business leaders in the IT services industry will take from this interview the low-hanging fruit applications in the IT services industry.

Direct download: AI_in_Industry-Nikhil_Malhotra-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:08am PST

Episode Summary: Prominent technology companies like Google and Amazon lead the way in the B2C world, having access to streams of searches, clicks, and online purchases. They have access to large volumes of consumer data pointss numbering in the billions that can be used to train machine learning algorithms.

B2B companies operate under a different model: "propensity to buy," as it's called. A typical B2B company might at most make a couple hundred sales per year, and many B2B companies make only dozens. In other words, every sale matters.

In this episode of the AI in Industry podcast, we interview Kiran Rama, Director of Data Sciences Center of Excellence at VMWare, about purchasing external data and to leveraging internal data. Rama also talks about using data to determine how likely certain leads are to turn into high-value customers. In addition, he discusses with us the "propensity to buy."

We hope that this interview can help business leaders determine if and how AI can help their organizations identify which leads could yield the highest ROI and which customers are the most primed for reselling.

Direct download: AI_in_Industry-Kiran_Rama-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:52am PST

For business leaders who are thinking about integrating AI into their company or who are just in the very beginning of that journey, this may be a useful episode of the podcast.

Many times, people think that finding the right talent is the biggest challenge when it comes to integrating AI into the enterprise. Much of our own research and  conversations with machine learning vendors and the consultants trying to sell AI into the enterprise actually think there's another, bigger problem: combing the expertise of subject matter experts and that of data scientists to leverage information for future initiatives in business.

This week, we interview Grant Wernick, CEO of Insight Engines in San Francisco. We speak with Grant about the initial challenges of organizing data and setting up a data infrastructure a business can use to leverage AI. We also talk about using data in leveraging normal workflows so that non-technical personnel can use it to drive better product innovation to help the company.

Direct download: AI_in_Industry-Grant_Wernick-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:55pm PST

One of most fun parts about doing our geolocation pieces at TechEmeergence is that we are able to interview so many people within a given country or city. Recently we did a huge piece on AI in India. We got to interview folks from the government and the bigger existing businesses, as well as a handful of people at the unicorns in Bangalore.

One of those companies is Fractal Analytics. Fractal Analytics works in a number of spaces. One of them, consumer packaged goods, is an area on which we haven’t done much coverage. Many of our readers are in the retail space, but CPG has some pretty curious AI use cases.

This week, we interview Prashant Joshi, Head of AI and Machine Learning at Fractal Analytics, about the different applications of machine learning in the CPG sector: doing chemical tests or finding new buyer segments within existing groups of consumers to determine who is buying from a company and who is buying from competitors.

Hopefully, for those in retail, this interview will not only highlight some of the interesting use cases of AI in the CPG world but also provide some ideas about winning market share from what some of the bigger CPG firms are doing with Fractal Analytics.

Direct download: AI_in_Industry-Prashant_Joshi-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:27pm PST

In this episode of the AI in Industry podcast, we interview Grant Ingersoll at Lucidworks, about enterprise search. Ingersoll talks about how companies have massive amounts of siloed data, making it difficult to find within enterprise systems.

We hope businesses might take away from this interview what is required and what is involved in building search applications to make corporate data more accessible and structured. Ingersoll will also discuss how data strategies are going to evolve and how scientists and data experts might come together to build an enterprise search application.

Direct download: AI_in_Industry-Grant_Ingersoll-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:12am PST

We receive a lot of interest from business leaders in the domain of data enrichment, and we've executed on a few campaigns for these businesses. At the same time, our audience seems particularly interested in the collection of data to train a bespoke machine learning algorithm for business, asking questions related to how to get started on data collection and from where that data could come.

This week on AI in Industry, we seek to answer those questions. We are joined by Daniela Braga, CEO and founder of DefinedCrowd, a data enrichment and crowdsourcing firm, who discusses with us how a business might determine what kind of data it might need for its AI initiative.

We hope the insights garnered from this interview will help business leaders get a better idea of how they could go about starting an AI initiative and seeing it through from data collection or enhancement to solving its business problem.

Direct download: AI_in_Industry-Daniela_Braga-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 10:14am PST

There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data.

For this episode, we spoke with Mark Brayan, CEO of Appen, a firm that offers crowdsourced training data for machine learning applications. We discuss how developing a sound data strategy is essential for using AI to solve business problems. Brayan also helped us detail how and when a business can make use of certain data collection and enrichment methods depending on their business goals.

Direct download: AI_in_Industry-Mark_Brayan-Mixdown_v2.mp3
Category:Artificial Intelligence -- posted at: 12:55pm PST

Over the last year, we've covered a lot of marketing applications. Many people know of our deep marketing research we've done on the landscape of machine learning in marketing applications and which industries will be affected first. But marketing doesn't tell the whole story when it comes to B2B sales. At some point, we need to take these clicks and turn them into appointments, for example. In this episode of AI in Industry, we are joined by Vitaly Gordon, VP of Data Science and Engineering at Einstein, Salesforce’s customer relationship management application driven by artificial intelligence.

We speak with Vitaly about where AI is serving a role in sales enablement today and how the CRM and sales tool ecosystem might be different in the near-term future; how will salespeople be able to leverage AI to make themselves more productive? Vitaly paints an interesting picture of where he sees the low hanging fruit and the unique challenges with sales data and B2B data that are quite different from the challenges those in the B2C world might deal with.

Direct download: AI_in_Industry-Vitaly_Gordon-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:02am PST

In this episode of the AI in Industry podcast, we interview Sumit Borar, Senior Director of Data Sciences and Engineering at Myntra, an eCommerce site for fashion, about the current and future state of eCommerce personalization and how the way customers in India purchase products online affect that personalization. Myntra talks about the challenges of bringing dialed-in personalized recommendations to the physical world and the challenges of bringing eCommerce into the developing world.

In addition, he discusses with us the different ways that eCommerce is being experienced in rural parts of India and some of the unique hurdles that they’ve had to overcome. Business leaders looking to apply machine learning and data science to the eCommerce world in developing markets and business leaders aiming to bring data science to the physical retail world should tune into this episode.

Read the full interview article here: www.techemergence.com/ai-retail-ecommerce-india-challenges-opportunities

Direct download: AI_in_Industry-Sumit_Borar-Mixdown.mp3
Category:general -- posted at: 3:23pm PST

This week on AI in Industry, we speak with Amir Saffari, Senior Vice President of AI at BenevolentAI, a London-based pharmaceutical company that uses machine learning to find new uses for existing drugs and new treatments for diseases.

In speaking with him, we aim to learn two things:

  • How will machine learning play a role in the phases of drug discovery, from generating hypotheses to clinical trials?
  • In the future, what are the roles of man and machine in drug discovery? What processes will machines automate and potentially do better than humans in this field?

We hope the insights in this episode provide business leaders in the pharma industry with an understanding of the current state of AI in their space and where it might play a role in their industry in the next two to three years.

See the full interview article here: www.techemergence.com/future-drug-discovery-ai-role-man-machine

Direct download: AI_in_Industry-Amir_Saffari-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:17am PST

We usually discuss the impact of artificial intelligence on a business's bottom line, but governments and NGOs are also considering AI as a mechanism for improving society.

This week on the AI in Industry podcast, Anandan Padmanabhan, CEO of the Wadhwani Institute for Artificial Intelligence in India, speaks to us about where and how the public sector should consider leveraging AI.

Padmanabhan discusses the challenges that the Indian government faces in providing education and healthcare to its citizens. Although AI might help overcome these challenges, those who need these services most may not have access to the technologies necessary to work with it.

See the full interview article here: www.techemergence.com/ai-government-ngo-social-good-initiatives-interview-wadhwani-institute

Direct download: AI_in_Industry-Anadan_Padmanabhan-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:44am PST

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/machine-learning-video-search-video-education-how-it-works/

Direct download: AI_in_Industry-Manish_Gupta-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:38am PST

This week on AI in Industry, we are talking about the ethical consequences of AI in business. If a system were to train itself to act in unethical or legally reprehensible ways, it could take actions such as filtering or making decisions about people in regards to race or gender.

When machine learning is integrated into technology products, could a misbehaving system put the company at financial and legal risk?

Our guest this week, Otto Berkes, Chief Technology Officer of New York-based CA Technologies, speaks to us about realistic changes in the technology planning and testing process that leaders need to consider. We discussed how businesses could integrate machine learning into the products and services, while still protecting themselves from potential legal downsides.

See the full interview article featuring Otto Berkes live at: https://www.techemergence.com/?p=13752&preview=true

Direct download: AI_in_Industry-Otto_Berkes-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 10:05am PST

When we think of recommendation engines, we might think of Amazon or Netflix, but while consumer goods and entertainment might be the most prominent domains for recommendation engines, there are others. This week, we speak with Madhu Gopinathan of MakeMyTrip.com, one of the few Indian unicorn companies, about recommendation engines for travel companies.

According to Madhu, MakeMyTrip’s recommendation engine has to figure out the best hotels for customer given their destination, but recommending hotels to first-time users and those who don’t frequent the site can prove challenging. How does a travel company’s AI-based recommendation engine start the process of making well-informed recommendations?

Madhu talks to us about how a recommendation engine might match people immediately with their preferred product or service when the on-site data does not exist to inform the AI-driven recommendations.

See the full interview article here: www.techemergence.com/recommendation-engines-actually-work-strategies-principles

Direct download: AI_in_Industry-Madhu_Gopinathan-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:35am PST

When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar.

With AI and machine learning news increasing in tech media, a business leader may find it challenging to cut through the hype and identify valid, useful case studies.

We talked to Ben Lorica, the Chief Data Scientist at O’Reilly Media, to get his insights on what key details executives should be looking for within a case study.

To see the our interview article, visit https://www.techemergence.com/what-executives-should-be-asking-about-ai-use-cases-in-business

Direct download: AI_in_Industry-Ben_Lorica-Mixdown_1.mp3
Category:general -- posted at: 12:37pm PST

Episode Summary: In this episode of the podcast, we interview AIG’s Chief Data Science Officer, Dr. Nishant Chandra, about natural language processing (NLP) for internal and team communication. Dr. Chandra talks about how NLP can help with sharing documents with specific team members whose roles warrant viewing those documents.

Instead of a broad memo that would go out across the company, a document could be transformed to a tailored message depending on the individual receiving it. For instance, a document could be presented in a digestible way to the executive team, but be distilled to contain fewer details for the technology team to make it relevant to them. How might NLP serve this summarization role for internal communications in the next 5 years?

See the full interview article here: www.techemergence.com/nlp-text-summarization-team-communication

Direct download: AI_in_Industry-Nishant_Chandra-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:36am PST

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

Direct download: AI_in_Industry-Madhusudan_Shekar_-Mixdown.mp3
Category:general -- posted at: 12:40pm PST

At TechEmergence, we often talk about the software capabilities of AI and the tangible return on investment (ROI) of recommendation engines, fraud detection, and different kinds of AI applications. We rarely talk about the hardware side of the equation, and that will be our focus today. For hardware companies like Nvidia, stock prices have soared thanks to the popularity of new kinds of AI hardware being needed not only in academia but also among the technology giants. Increasingly, AI hardware is about more than just graphics processing units (GPUs).

Today we interview Mike Henry, CEO of Mythic AI. Mike speaks about the different kinds of AI-specific hardware, where they are used, and how they differ depending on their function. More specifically, Mike talks about the business value of AI hardware. Can specific hardware save money on energy, time, and resources? Where can it drive value? Where is AI hardware necessary to open new capabilities for AI systems that may not have been possible with older hardware? What is the right business approach to AI hardware?

This interview was brought to us by Kisaco Research, which partnered with TechEmergence to help promote their AI hardware summit on September 18 and 19 at the Computer History Museum in Mountain View California.

See the full interview article here:

www.techemergence.com/financial-roi-ai-hardware-top-line-bottom-line-impact

Direct download: AI_in_Industry-Mike_Henry-Mixdown.mp3
Category:general -- posted at: 1:24pm PST

Episode Summary: Facebook and Google’s advertising complex is founded on machine learning, allowing people to self-serve their data needs across a broad audience. India-based InMobi is a company in the advertising technology space that delivers 10 billion ad requests daily.

Today, we speak with Avi Patchava, Vice-President of Data Sciences and Machine Learning at InMobi, which operates in China, Europe, India, and the US. Patchava explains how machine learning plays a role in appropriately matching advertising requests to the right audience at scale,  whether on mobile, desktop or different devices and media. Patchava paints a robust picture of what this technology will look like moving forward and how it will change the game for marketers and advertisers, especially with the emphasis on data and machine learning.

See the full interview article here:

www.techemergence.com/future-advertising-machine-learning-audience-targeting-reach

Direct download: AI_in_Industry-Avi_Patchava-Mixdown.mp3
Category:general -- posted at: 4:22pm PST

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: 

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

Direct download: AI_in_Industry-Bryon_Jacobs-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 4:51pm PST

Episode summary: In this episode of Ai in industry, we speak with Manoj Saxena, the Executive Chairman of CognitiveScale, about how AI and automation are being applied to white-collar processes in the healthcare sector.

In simple business language, Manoj summarizes key healthcare applications such as invoicing handling, bad debt reduction, claims combat, and the patient experience, and explains how AI and automation can make these processes more efficient to improve the patient experience in healthcare organizations.

Interested readers can listen to the full interview with Manoj here: 

https://www.techemergence.com/white-collar-automation-in-healthcare/

 

Direct download: AI_in_Industry-Manoj_Saxena-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:00pm PST

Episode Summary: Natural language processing (NLP) has become popular in the past two years as more businesses processes implement this technology in different niches. In inviting our guest today, we want to know specifically which industries, businesses or processes NLP could be leveraged to learn from activity logs.

For instance, we aim to understand how car companies can extract insights from the incident reports they receive from individual users or dealerships, whether it is a report related to manufacturing, service or weather.

In the same manner, how can insights be gleaned from the banking or insurance industries based on activity logs? We speak with the University of Texas’s Dr. Bruce Porter to discover the current and future use-cases of NLP in customer feedback.

 Interested readers can listen to the full interview with Bruce here:

https://www.techemergence.com/using-nlp-customer-feedback-automotive-banking

 

Direct download: AI_in_Industry-_Bruce_Porter-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:50am PST

Episode summary: This week on AI in Industry, we speak to Rana el Kaliouby, Co-founder and CEO of Affectiva about how machine vision can be applied to detecting human emotion - and the business value of emotionally aware machines.

Enterprises leveraging cameras today to gain an understanding of customer engagement and emotions will find Rana’s thoughts quite engaging, particularly her predictions about the future of marketing and automotive.

We’ve had guests on our podcast say that the cameras of the future will most likely be set up for their outputs to be interpreted by AI, rather than by humans. Increasingly machine vision technology is being used in sectors like automotive, security, marketing, and heavy industry - machines making sense of data and relaying information to people. Emotional intelligence is an inevitable next step in our symbiotic relationship with machines, an in this interview we explore the trend in depth.

Interested readers can listen to the full interview with Rana here: https://www.techemergence.com/can-businesses-use-emotional-intelligence

 

Direct download: AI_in_Industry-Rana_el_Kaliouby-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

A myriad of customer service channels exist today, such as social media, email, chat services, call centers, and voice mail. There are so many ways that a customer can interact with a business and it is important to take them all into account.

Customers or prospects who interact via chat may represent just one segment of the audience, while the people that engage via the call center represent another segment of the audience. The same might be said of social media channels like Twitter and Facebook.

Each channel may offer a unique perspective from customers – and may provide unique value for business leaders eager to improve their customer experience. Understanding and addressing all channels of unstructured text feedback is a major focus for natural language processing applications in business – and it’s a major focus for Luminoso.

Luminoso founder Catherine Havasi received her Master’s degree in natural language processing from MIT in 2004, and went on to graduate with a PhD in computer science from Brandeis before returning to MIT as a Research Scientist and Research Affiliate. She founded Luminoso in 2011.

In this article, we ask Catherine about the use cases of NLP for understanding customer voice – and the circumstances where this technology can be most valuable for companies.

Read the full article:

techemergence.com/improving-customer-experience-with-ai-gaining-quantifiable-insight-at-scale

Direct download: AI_in_Industry-Catherine_Havasi-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:55pm PST

Episode summary: In this episode of AI in Industry, we speak with Khalifeh Al Jadda, Lead Data Scientist at CareerBuilder, about the applications of machine learning in improving a user’s search experience.

Khalifeh also talks about what the future of search might look like and how AI will continue to make the search experience more intuitive (for search engines, platforms, eCommerce stores, and more).

Business leaders listening in will get a sneak peak into the future of online search - and an understanding of how and where improvements in search features could impact their business.

Interested readers can listen to the full interview with Khalifeh here:

https://www.techemergence.com/better-than-elasticsearch-machine-learning-search/

Direct download: AI_in_Industry-Khalifeh_Al_Jadda-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:48am PST

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/

Direct download: AI_in_Industry-Andy_Terrel-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 5:14am PST

Episode summary: Here on the AI in Industry podcast, we’ve heard AI experts explain how high-performance computing (HPC) has enabled everything from machine vision to fraud detection. In this week’s episode, we speak with Paul Martino, Managing Partner at Bullpen Capital, about which industries and AI applications will require high-performance computing most.

Paul also adds some useful tips for business leaders on how to prepare for the coming AI-related developments in hardware and software.

Interested readers can listen to our full interview with Paul here: https://www.techemergence.com/?p=12779&preview=true

 

 

Direct download: AI_in_Industry-Paul_Martino-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:40am PST

Episode summary: In this episode of AI in Industry, we speak with Dr. Sanmay Das from the Washington University in St. Louis about risk prediction and management in industries like banking, insurance and finance.

Sanmay explores how are banks and other financial institutions are improving risk and fraud prevention measures with machine learning. In addition, he explores the ramifications of improved fraud detection in the coming 5 years ahead.

Interested readers can listen to the full interview with Sanmay here: https://www.techemergence.com/machine-learning-for-credit-risk/

Direct download: AI_in_Industry-Sanmay_Das-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:30am PST

Episode summary: In the last two or three years we at TechEmergence have witnessed a definite uptick in AI applications like predictive maintenance and heavy industry. Many exciting business intelligence and sensor data applications are making their way into “stodgy” industries like transportation, oil and gas, and telecom - where machine vision has countless applications.

We had caught up with Massimiliano Versace, CEO of Neurala over 4 years ago in an interview about the ethical implications of AI. In this week’s episode of AI in Industry, Max speaks with us about how machine vision and drones can be used together to automate the process of facilities and heavy asset upkeep. Max walks us through potential applications in telecom and rail transportation and explains where he thinks machine vision has the strongest potential to impact the bottom line.

Business leaders who manage heavy assets or physical infrastructure should find this interview insightful, as Max explains both current and near-future applications for machine vision for maintenance and upkeep.

Interested readers can listen to the full interview with Max here: https://www.techemergence.com/applications-of-machine-vision-in-heavy-industry/

Direct download: AI_in_Industry-Massimilano_Versace-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:49am PST

Episode summary:  In this episode of AI in Industry we speak with Abhi Yadav, the CEO of ZyloTech, a Boston-based customer analytics platform for omni-channel marketing operations. Abhi talks about what's possible now with AI for marketing personalization, and what will be possible in the next 5 years.

Business leaders with an increasing focus on narrower customer targeting will be interested in Abhi’s insights on how technology allows for businesses to reach an “audience of one”.

Interested readers can listen to the full interview with Abhi here:

https://www.techemergence.com/artificial-intelligence-personalization-marketing-current-future-possibilities/

Direct download: AI_in_Industry-_Abhi_Yadav-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 3:18am PST

Episode summary: In this week’s episode of AI in Industry we speak with DataRobot CEO Jeremy Achin about the future of AI applications for people without a data science background. We specifically discuss how future AI tools might bypass the complexity of machine learning programming and make intuitive interfaces that function more like today’s everyday software. Our business leader listeners will be interested in Jeremy’s predictions about how the UX for AI-related tools might become more simplified and code-less in the coming 5 years.

Interested readers can listen to the full interview with Jermy here: https://www.techemergence.com/will-artificial-intelligence-become-easier-use/

Direct download: AI_in_Industry-Jeremy_Achin-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 4:33am PST

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: https://www.techemergence.com/how-to-apply-ai-…h-larry-lafferty/

Direct download: AI_in_Industry-Larry_Lafferty-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:56am PST

Episode summary: In the heavy industry sector, the cost of unpredicted repairs or machine failures can be very expensive. For example: A cargo train with an engine failure in will incur costs from it’s own repairs, from the transit required to reach the broken down engine, and with holding up other trains and cargo in the process.

Predictive maintenance has the potential to help businesses assess the condition of vehicles, equipment and parts in order to predict when maintenance should be performed. Using data collected by sensors on machines (including vibration, temperature, and more) heavy industry companies can potentially predict which machines or parts need imminent maintenance and which machines are least likely to breakdown.

In this week’s episode, we speak with Will McGinnis, Chief Scientist of Predikto, a predictive maintenance software provider based in Atlanta. Will speaks with us about predictive maintenance applied for the improvement railways and trains equipment, and how companies in the railway sector can use predictive maintenance to coax out patterns in maintenance schedules and heavy equipment data.

Interested readers can listen to the full interview with Will here:https://www.techemergence.com/will-mcginnis-predikto-predictive-maintenance-trains-mobile-heavy-industry

 

 

Direct download: AI_in_Industry-Will_McGinnis-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

Episode summary: In this week’s episode of AI in Industry we speak with Rodney Brooks, Founder and CTO of Rethink Robotics, a collaborative robot manufacturers founded in Boston in 2008. Rodney explores robotic safety an regulations and he also paints a picture of what robots might be capable of in the next five years.

Executives in the logistics and manufacturing sectors considering adopting robots will find Rodney’s insights most valuable.  Rodney explores what applications will move into the realm of robotics and what application won't in the near future and delves into what business executives need to know about human robot collaboration before considering their adoption.

Interested readers can see the full interview with Rodney Brooks from Rethink Robotics here: https://www.techemergence.com/improving-robot-safety-capability-artificial-intelligence-rodney-brooks/

Direct download: AI_in_Industry-Rodney_Brooks-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:44am PST

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  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 (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/

Direct download: AI_in_Industry-Nikolaos_Vasiloglou-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

Episode Summary: Over the last couple of years there has been a definite but small shift from mobile as the primary interface focus for businesses to voice. With home assistant devices like the Amazon Echo and the Google Home becoming more commonplace, we aim to focus on how voice based AI applications are being used by businesses today and what this adoption will look like in the future.  

In this week’s episode of AI in Industry, we speak with Peter Cahill, the founder and CEO of Voysis, a voice AI platform that enables voice-based natural language instruction, search, and discovery. Peter explores areas where voice related AI applications will be used by businesses in B2B and B2C spaces today and what this might look like in five years.  

 Interested readers can see the full interview with Peter Cahill from Voysis here: https://www.techemergence.com/spoken-voice-ai-applications-smart-home-peter-cahill-voysis/

Direct download: AI_in_Industry-Peter_Cahill-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:15am PST

In this week’s episode we focus on AI application in the customer service business function, - specifically in the context of call centers. We speak with Ali Azarbayejani, CTO of Cogito based in the Boston area, which works on coaching and providing feedback for call center agents in real time.

We aim to focus on what our readers and business executives can do today with AI in the context of call center applications, and how they can go about seeing measurable impacts over a predetermined period of time.

We speak with Ali about what is possible with analyzing voice in real-time today and what kind of ROI can businesses expect for this application. Lastly we touch-base on what factors will make AI inevitable for some companies in the next two to three years.

Interested readers can see the full interview with Ail here:

https://www.techemergence.com/what-industries-will-adopt-voice-related-ai-applications-first/

 

Direct download: AI_in_Industry-Ali_Azarbayejani-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 4:09am PST

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/

 

Direct download: AI_in_Industry-Rudina_Seseri-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:30am PST

Episode summary: In this week's interview on the AI in Industry podcast, we speak with Amir Konigsberg, the CEO of Twiggle, about the future of product search - and how eCommerce and retail brands can use natural language processing (NLP) to improve their user experience.

Amir explains some of the factors that make eCommerce product search challenging, and the artificial intelligence approaches that can improve it today and within the next five years.

Interested readers can learn more about present and future use-cases for artificial intelligence applications in retail in our full article on that topic.

You can listen to the full interview with Amir Konigsberg from Twiggle here:

https://www.techemergence.com/nlp-for-ecommerce-search-current-challenges-and-future-potential

Direct download: AI_in_Industry-Amir_Konigsberg-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 3:46am PST

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:

 

Direct download: AI_in_Industry-Robbie_Allen-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:14am PST

Episode summary: This week’s episode explores the current possibilities in applying natural language processing for legal contract review. We speak with Andrew Antos and Nischal Nadhamuni from Klaritylaw, a Boston-based startup focused on using natural language processing (NLP) based information extraction, from non-disclosure agreements (NDAs), in a live setting.

We delve into the current and future roles of AI and lawyers with respect to legal contracts. AI is currently being applied in applications like retroactive analysis and information identification in legal documents. According to Andrew and Nishchal, in the future we will see on-the-fly legal content creation from AI tools and NLP being applied to most commercial contracting. Although, one restraint that AI companies presently face in the legal domain is the lack of access to huge amounts of publicly available data.

You can listen to the full interview with Andrew and Nischal here:

Direct download: AI_in_Industry-_Klarity_Law-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00am PST

Episode summary: Most NLP applications we hear about involve marketing, customer service, and other customer-facing functions - but that there are NLP-related opportunities in other back-end functions as well.  

In this episode of AI in industry, we speak with Talla's Chief Data Scientist, Byron Galbraith, about how businesses can leverage chatbots or other NLP applications for improving document search for internal company communication. Byron explores what is currently possible using AI to improve search operations using contextual awareness. Byron also paints a vision of what AI-enabled "knowledge sharing" and "knowledge discovery" might look like in the future.

For the full article of this episode, visit: TechEmergence.com/artificial-intelligence-team-communication/

Direct download: AI_in_Industry-Byron_Gilbreath-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

When we talk about natural language processing (NLP), applications like handling customer service or chatbots which can aid with questions, come to mind. Yet, in recent years, NLP platforms have been increasingly used in content marketing and content production applications.

In this episode of AI in industry, we talk to Tomás Ratia García-Oliveros, the co-founder and CEO founder of Frase.io, a Boston based startup which focuses on NLP problems around content marketing and content creation. Tomas explores how NLP platforms are now able to summarise resources on the web, perform contextual search and language understanding applications related to this domain.

See the full interview article with Tomás Ratia García-Oliveros live at:

www.techemergence.com/artificial-intelligence-content-marketing-content-creation

Direct download: AI_in_Industry-Tomas_Fraseio-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

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

Direct download: AI_in_Industry-Michael_Johnson-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:00pm PST

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

Direct download: AI_in_Industry-Vlad_Sejnoha-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:16am PST

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

Direct download: AI_in_Industry-Vito_Vishnepolsky-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 9:29pm PST

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

Direct download: AI_in_Industry-JP_Clarke-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 6:27pm PST

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

 

Direct download: AI_in_Industry-Gunjan_Bhardwaj-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 8:31pm PST

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

Direct download: AI_in_Industry-Jeremy_Barnes-Mixdown.mp3
Category:general -- posted at: 12:00am PST

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

 

Direct download: AI_in_Industry-David_Franke-Mixdown.mp3
Category:general -- posted at: 11:30pm PST

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

Direct download: AI_in_Industry-Alexandre_Le_Bouthillier-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 5:13pm PST

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