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The State of Natural Language Processing in the Sales Process
Sales is a big part of any sort of B2B firm. We speak this week with Micha Breakstone, co-founder of Chorus.ai. He holds a PhD in Cognitive Sciences from the Hebrew University in Jerusalem, and prior to starting his own company, he studied for a few years at MIT and was working on NLP at Intel.
He speaks with us this week about where AI is being applied to sales, answering questions such as:
This is a nascent domain. There are very few companies are actively leveraging artificial intelligence in their sales process, but in the two years ahead we'll likely see more and more firms who are.
For more information on Ai for sales enablement, go to emerj.com
|Jan 17, 2019|
AI for Contract Analysis in the Enterprise
Close to a year ago, we had an interview here on the AI in Industry podcast with Jeremy Barnes of Element AI. We visited their headquarters in Montreal, and we'd interviewed Yoshua Bengio a couple years before that. Jeremy had brought up one point in that interview that I really like and that transfers its way into this conversation, which is that businesses should think not just about being more efficient with artificial intelligence, but places where they can actually make a real difference in the bottom line for the company beyond shaving off some savings.
In this week's episode, we focus on compliance and analyzing contracts. At first, one might think about such an application in terms of cost savings. We speak with Shiv Vaithyanathan, an IBM fellow and Chief Architect of Watson Compare & Comply, about the following:
|Jan 10, 2019|
Computer Vision for Medical Diagnostics in the Chest Area
Episode Summary: Recently, we were called upon by the World Bank to do a good deal of research on the potential of applying artificial intelligence to health data in the developing world. Diagnostics was a very big focus of the information that we presented. It appears as though diagnostics is an area of great promise with regards to AI, and that's what we're focusing on in this episode the podcast.
This week, we speak with Yufeng Deng, Chief Scientist of Infervision, a company that focuses on computer vision for medical diagnostics. We speak with Deng about the expanding capability of machine vision, including what kind of data one needs to collect and what is now possible with the technology.
In addition, Deng also speaks about how Infovision found a business problem to solve using AI, and in that he provides transferable lessons to business leaders in a variety of industries.
|Jan 02, 2019|
How AI Will Become More Accessible to Retailers
Artificial intelligence plays a role in the future of retail in terms of a deeper understanding of customers going beyond intuition. This week, we speak with Pedro Alves, CEO of a company called Ople, based in San Francisco. Alves was previously the Head of Data Science at a number of companies in addition to being Director of Data Science at Sentient Technologies, one of the best known AI firms in the Bay Area. Sentient has raised upwards of $200 million.
|Dec 27, 2018|
Machine Learning for Decision Support in Tax and Accounting
A lot of machine learning applications in business can be boiled down to some form of decision support. There are big decisions like deciding whether or not to merge or acquire another company, and there might be smaller decisions like whether or not a tumor has enough traits that make it seem like it's worth a surgical procedure or if it's worth leaving alone.
In this particular interview, we talk about the domain of decision support, specifically in tax and accounting. There are few firms that know more about tax and accounting than Ernst & Young, and there are few people at Ernst & Young who know more about artificial intelligence than Sharda Cherwoo. Cherwoo is a partner at EY, and she is also the Intelligent Automation Leader for the Americas division of its tax practice.
Cherwoo talks about where decision support is being influenced by machine learning in accounting and tax today, the initial experimentation traction, and results. She also paints a picture of bigger decisions that might be automatable by machine learning software. The focus of this episode may be on tax and accounting, but here are transferable lessons for business leaders in all industries that revolve around how machine learning can help inform decisions made by human experts.
|Dec 20, 2018|
An Overview of AI for Wealth Management - What's Possible Today?
We spoke with Robert Golladay, General Manager, Europe at CognitiveScale, which offers AI software that helps both wealth advisors personalize insights and identify new opportunities for clients. According to Golladay, AI is being applied to wealth management services in two areas today:
|Dec 18, 2018|
Data Challenges in the Defense Sector
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
|Dec 16, 2018|
What It Looks Like to Be Ready for AI Adoption in the Enterprise
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
|Dec 07, 2018|
How AI Can Help Retailers With Inventory Optimization
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
|Dec 02, 2018|
When to Upgrade Your Hardware for Artificial Intelligence
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.
|Nov 25, 2018|
Setting Up Retail Stores for Machine Learning - Cameras, Microphones, and More
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.
|Nov 18, 2018|
How to Use AI to Hire and Recruit Talent
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
|Nov 11, 2018|
How to Get a Chatbot to do What One Wants in Business
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/
|Nov 04, 2018|
Balancing Machines and Human Employees When Adopting AI in the Enterprise
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?
|Oct 26, 2018|
How IT Services Firms Can Adapt to Artifical Intelligence
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.
|Oct 21, 2018|
Predicting Sales Propensity with Artificial Intelligence - Opportunities and Challenges
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.
|Oct 14, 2018|
Bridging the Data Science Gap - Why Subject-Matter Experts Matter
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.
|Oct 12, 2018|
How Machine Learning Could Help CPG Companies Beat Out Their Competitors
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.
|Oct 12, 2018|
AI for Enterprise Search - Challenges and Opportunities
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.
|Oct 07, 2018|
How to Determine the Data Needs of an AI Project or Initiative
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.
|Sep 30, 2018|
Data Collection and Enhancement Strategies for AI Initiatives in Business
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.
|Sep 27, 2018|
The State of AI for Sales Enablement, and the Evolution of the CRM
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.
|Sep 23, 2018|
AI for Retail and eCommerce in India - Challenges and Opportunities
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
|Sep 14, 2018|
The Future of Drug Discovery and AI - The Role of Man and Machine
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:
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
|Sep 09, 2018|
AI for Government and NGO Social Good Initiatives - an Interview with the Wadhwani Institute
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
|Sep 02, 2018|
Machine Learning for Video Search and Video Education - How it Works
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/
|Aug 26, 2018|
AI in Industry: How AI Ethics Impacts the Bottom Line - An Overview of Practical Concerns
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
|Aug 20, 2018|
How Recommendation Engines Actually Work - Strategies and Principles
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
|Aug 19, 2018|
What Executives Should be Asking about AI Use-Cases in Business
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.
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
|Aug 15, 2018|
NLP for Text Summarization and Team Communication
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
|Aug 12, 2018|
How to Determine the Best Artificial Intelligence Application Areas in Your Business
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
|Aug 03, 2018|
The Financial ROI of AI Hardware - Top-Line and Bottom-Line Impact
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:
|Jul 30, 2018|
The Future of Advertising and Machine Learning - Audience Targeting, Reach, and More
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:
|Jul 29, 2018|
How Existing Businesses Should Organize Their Data Assets for AI
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:
|Jul 22, 2018|
White Collar Automation in Healthcare - What's Possible Today?
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:
|Jul 15, 2018|
Using NLP for Customer Feedback in Automotive, Banking, and More
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:
|Jul 08, 2018|
Can Businesses Use "Emotional" Artificial Intelligence?
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
|Jun 30, 2018|
Improving Customer Experience with AI, Gaining Quantifiable Insight at Scale
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:
|Jun 28, 2018|
Better Than Elasticsearch? How Machine Learning is Improving Online Search
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:
|Jun 24, 2018|
AI Use-Cases for the Future of Real Estate
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:
|Jun 15, 2018|
High Performance Computing in Artificial Intelligence Applications with Paul Martino from Bullpen Capital
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
|Jun 11, 2018|
Machine Learning for Credit Risk - What's Changing, and What Does it Mean?
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/
|Jun 03, 2018|
Applications of Machine Vision in Heavy Industry
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/
|May 18, 2018|
Artificial Intelligence for Personalization in Marketing - Current and Future Possibilities
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:
|May 13, 2018|
Will Artificial Intelligence Become Easier to Use?
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/
|May 06, 2018|
How to Apply AI to an Existing Business with Larry Lafferty
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/
|Apr 29, 2018|
Will McGinnis (Predikto) - Predictive Maintenance for Trains and Mobile Heavy Industry
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
|Apr 21, 2018|
Improving Robot Safety and Capability with Artificial Intelligence - with Rodney Brooks
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/
|Apr 18, 2018|
What's the Value of AI Events and Consulting?
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/
|Apr 14, 2018|
Spoken Voice AI Applications in the Smart Home - with Peter Cahill from Voysis
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/
|Apr 09, 2018|
What Industries Will Adopt Voice-Related AI Applications First?
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:
|Apr 01, 2018|
Reducing the Friction of AI Adoption in the Enterprise - with Rudina Seseri
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:
|Mar 25, 2018|
NLP for eCommerce Search - Current Challenges and Future Potential
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:
|Mar 18, 2018|
Robbie Allen from Automated Insights - The Use-Cases of Natural Language Generation
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:
|Mar 11, 2018|
Applying AI to Legal Contracts - What's Possible Now
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:
|Mar 03, 2018|
Artificial Intelligence for Team Communication
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.
|Feb 24, 2018|
Artificial Intelligence for Content Marketing and Content Creation
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:
|Feb 16, 2018|
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:
|Feb 10, 2018|
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:
|Feb 03, 2018|
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:
|Feb 01, 2018|
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:
|Jan 29, 2018|
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:
|Jan 24, 2018|
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 21, 2018|
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 14, 2018|
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:
|Jan 07, 2018|
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:
|Dec 30, 2017|
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:
|Dec 24, 2017|
AI for Social Influence and Behavior Manipulation with Dr. Charles Isbell
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:
|Dec 16, 2017|
Ben Goertzel on How Blockchain Might Make AI More Accessible
If you combine the hype-factor of both "blockchain" and "artificial intelligence" you often get a supernova of jargon. This week on the AI in Industry podcast, we aim to get beyond the hype to discuss how blockchain might make AI more accessible for small and mid-sized businesses in the years ahead. Dr. Ben Goertzel - CEO of SingularityNET - is our guest this week.
For more expert interviews about the business applications of AI, visit:
|Dec 09, 2017|
Machine Learning with Less Training Data - Approaches and Trends
Expert systems and machine learning are two ends of a spectrum working to solve similar problems quite differently. One one hand you have if-then scenarios and a logical approach, and on the other you have vast neural networks and a big data approach. Some companies exist to try and bridge the gap between the if-then rule systems and the massive piles of data. They hope to find a middle ground of sorts, one that mitigates their individual disadvantages. One such company is Montreal’s fuzzy.ai.
In this episode, we interview its founder, Evan Prodromou about the state of the middle ground, so-called hybrid systems. The middle ground is an elusive, still mostly theoretical concept, but businesses can take steps to prepare for when it becomes accessible to them. What exactly would a hybrid system provide to businesses in terms of automation? How accessible are they now, and what can businesses do to best integrate them when they’re ready? Find out in this episode of the podcast.
For more interviews about the business applications of AI, visit:
|Dec 03, 2017|
How Chatbots Work, and How They Evolve
There’s a lot of hype out there about conversational AI. Although according to our guest, we’re nowhere near the day when AI can generate accurate conversations for the average business to integrate into their customer service, chatbots still have practical applications. In this episode, we interview the head of research at Digital Genius, Yoram Bachrach. Yoram succinctly outlines the current applications of chatbots—what they can and can’t do—and details how business can best prepare to automate their customer service.
For more interviews about the applications of AI in industry, visit us online:
|Nov 27, 2017|
Machine Vision for Advertising - Possibilities in Social and Online Media
How can machine learning help us advertise through social media? In this episode, Thomas Jelonek, CEO of Envision.ai, talks to us about how in the next five years, machine learning might automate the laborious guess-and-check process of finding visual content with which users can engage. Right now, finding images and videos that will best generate engagement is a task reserved for a human. He or she shifts through images and video clips that may work for an audience based on anecdotal evidence and perception of past post success. Learn how, according to Thomas, machine learning could help you save time and money, generate you a better ROI, and build you a larger list with more accurate targeting on social media.
For more interviews with AI experts, visit:
|Nov 19, 2017|
Modeling Biology with Machine Learning - with Turbine.ai's CEO Kristóf Zsolt Szalay
This episode explores the ways in which artificial intelligence has the potential to revolutionize the field of medicine. This week's guest, Dr. Kristóf Zsolt Szalay speaks to this topic, discussing research that hopes to create automated learning networks and algorithms designed to predict the development of human cells in response to drugs. This technological innovation would make it possible for near-instantaneous simulations to be run, allowing optimal combinations and optimal doses of drugs to be pinpointed and distributed to patients.
For more interviews on the applications and implications of AI in business, visit:
|Nov 12, 2017|
What Chatbots Can Do, and Cannot Do
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 to build chatbot templates they can use in multiple domains and applications.
For more interviews on the business applications of AI, visit:
|Nov 05, 2017|
How Can Businesses Get NLP to Work?
This week on AI in Industry, we speak to Paul Barba (Chief Scientist at Lexalytics) about what how companies are using natural language processing, and what it takes (in terms of expertise, time, and training) to get these systems working. From sentiment analysis to categorization, Paul walks us through interesting and fruitful use-cases and sheds light on the back-end "tweaking" required to keep NLP productive in a changing business environment.
For more interviews on the applications of AI in business, visit:
|Oct 29, 2017|
AI for Theft Prevention and Process Adherence - with Alan O'Herlihy from Everseen
In this episode, we speak with Alan O'Herlihy, Founder and CEO of Ireland-based Everseen. Alan speaks to us about how machine vision systems can be used to detect theft or mistakes at a checkout counter (including forgetting to scan items, customers intentionally hiding items, and more). Alan not only explains where these technologies are in use today, but he also breaks down some of his own predictions about what these computer vision systems might make possible in the workplace of tomorrow.
For more interviews and use-cases of AI in industry, visit:
|Oct 22, 2017|
Qrativ's Murali Aravamudan on "What's Possible" for AI in Drug Discovery
In this episode, we talk to Murali Aravamudan, Founder and CEO of AI-driven drug discovery startup Qrativ, a joint venture by the Mayo Clinic and biotech/data science firm nference. Murali and I discuss the surge of medical information and data in the medical industry, the role of artificial intelligence in developing drugs for treatments to various diseases, and the future of AI in drug discovery.
For more in-depth interviews on the business applications of artificial intelligence, find us online at:
|Oct 14, 2017|
AI in Healthcare IT Security - Why Hospitals are Targets
In this episode, we talk to Daniel Nigrin, MD, Senior Vice President and CIO at Boston Children’s Hospital. Daniel and I discuss why hackers have come to prey on the healthcare industry, how these hackers benefit from their illicit activities, and what healthcare IT security precautions can be taken to prevent such attacks.
For more interviews on AI applications in business, visit:
|Oct 08, 2017|
NLP for Customer Service - How Does it Work?
Natural language processing has gained more and more attention with the raise of (or rather, the "fad" of) chatbots. Despite the flurry of press releases from companies about their conversational agents (only a few of which seem to be delivering real business value), few business leaders understand the value of NLP for customer service, sales enablement, or eCommerce.
In this week's episode of AI in Industry we interview Narjes Boufaden, computational linguistics PhD and CEO of Keatext, an NLP company based in Montreal. Narjes explores the possible business applications of NLP - specifically for customer service and customer experience - and she also explains (in layman's terms) how NLP systems are trained and integrated into businesses today.
The ROI on this episode (in my opinion), is a firm understanding of what NLP can and cannot do, and what business applications it can realistically solve today. I was fortunate to meet Narjes in person during my Montreal trip, and I'm glad we were able to bring her on the program shortly thereafter.
For more expert interviews on the business applications of AI, visit:
|Oct 01, 2017|
Computer Vision for Body Language - How it Works and How it Could be Used
As a human, we can often understand the mood, intention, and future action of another person just by looking at them. We see their posture, their facial expression, where their eyes are focused, and we can get a decent understanding of what they might do next. The problem of computer vision for body language is a much harder problem to solve, but we are indeed making progress.
Our guest this week is Paul Kruszewski, an computer science PhD who's spent nearly the last 20 years focused on 3D modeling and artificial intelligence. Today, he's CEO of Wrnch, a Montreal-based AI company focused on reading and understanding human body language.
Paul explains how advances in 3D modeling and computer vision have allowed researchers to get machines to "understand" the posture, movements, and intentions of human being - and he also helps explore the future applications that this technology might have in security, retail, sports, and more.
For more interviews on the applications of AI in business, visit:
|Sep 23, 2017|
AI for Cameras and Computer Vision - with Algolux's Allan Benchetrit
In the future, the vast majority of photos and videos recorded won't be seen and used by humans - they'll be seen and used by machines. This week we interview Allan Benchetrit, CEO at Algolux - a Montreal-based AI company focusing on computational imaging.
If you take an image for a human being in a consumer application (maybe an iPhone app or a recreational DSLR camera), you probably want it to be visually appealing and clear to the human eye.
As it turns out, machines don't need pretty images, they need to do their jobs. If a computer vision system needs to detect road signs, or suspicious people in an airport, or the presence of weeds in a cornfield - it may create images that are ugly to the human eye, but perfectly calibrated for being interpreted by machines for their jobs. As it turns out, this is a complicated AI-related problem itself, and Allan walks us through it.
If your business uses cameras heavily - or may do so in the future - this interview will provide an around-the-corner look at what it takes to create effective computer vision applications.
For more expert interviews about the business applications of artificial intelligence, visit:
|Sep 17, 2017|
Tamr's Eliot Knudsen on the Automation of Procurement
Procurement isn't usually seen as a "sexy" aspect of a business's operations. Procurement personnel are responsible for sourcing suppliers or vendors, determining criterion of success, negotiating deal terms, and tracking results and deliverables - all of which could be considered "under appreciated" work. This week, Tamr's Eliot Knudsen walks us through the ways that AI is making it's way into the procurement process, and what it means for the future of this job function.
For more executive interviews about the applications and implications of AI, visit:
|Sep 09, 2017|
AI Use-Cases in the CRM - with Bastiaan Janmaat of DataFox
This week we speak with Bastiaan Janmaat (CEO and co-Founder of DataFox) about the current and future applications of artificial intelligence in the CRM.
No matter what business you're in, there's a high likelihood that managing relationships with customers, wholesalers, suppliers, or affiliates is important to your daily operations. Artificial intelligence is currently being employed to help with automating data entry, automating email and phone reminders, and even prompting salespeople with the right phone scripts in real time.
In addition to covering "what's being done now" - spend the end of the interview asking Bastiaan about his predictions of the most likely AI-for-CRM capabilities that will become commonplace in the next 5 years.
For more AI executive interviews, and insights into current and future AI trends that are shaking up industries, visit:
|Sep 02, 2017|
Surviving the Machine Age - Technological Job Loss with Kevin LaGrandeur
Artificial intelligence is coming - should be worried about our jobs? Well, it depends. Our guest Dr. Kevin LaGrandeur spent the last two years researching the impacts of automation and artificial intelligence on society and the job market. In this interview on AI in Industry, we explore the near future of AI's impact on the world of work, and I ask Kevin some important questions, including:
For more interviews with AI executives and researchers (and more insight on applying AI in your organization) - visit us online at:
|Aug 26, 2017|
Might AI Need Standards to Scale? - with Konstantinos Karachalios of the IEEE
Though we don't think about it on a daily basis - the technologies around us often "work" because of an underlying standard that they depend on. These technologies include: Wifi, ethernet, fax, and much of the internet itself. Do certain AI applications need their own set of standards in order to scale?
Imagine if you needed a new type of cable or input every time you wanted to jack your computer into the wall? Imagine if you needed different hardware to pick up wifi in every location you moved around to? Imagine if all websites had totally different protocols for how they were loaded or served to your computer? If this were the case, it would be extremely challenging for a robust "ecosystem" of internet companies and technologies to emerge, because the technology wouldn't scale or work well at all.
This week we interview Konstantinos Karachalios, Managing Director of the Standards Association at the Institute of Electrical and Electronics Engineers (IEEE). Konstantinos holds a PhD in Physical and previously worked for 25 years at the European Patent Office. He speaks with us this week about the kinds of AI standards that may need to arise in order for AI to be safe and trusted enough to support a business ecosystem.
Konstantinos also speaks to us about some of the current AI standards that IEEE is working on developing currently, and the implications they might have businesses everywhere.
|Aug 19, 2017|
Predictive Maintenance for Equipment and Machinery - with Predii's Tilak Kasturi
It would be great if instead of having our car break down - could have them fixed as soon as the underlying problem began. It would be great if instead of having to diagnose a malfunctioning piece of mechanical equipment - would could have the right "fix" presented to us immediately. As it turns out, artificial intelligence may be working its way to accomplish both of those goals in the not-so-distance future.
This week we interview Tilak Katsuri, CEO of Predii, a predictive maintenance AI company based on Palo Alto. Predii focuses on helping service people by using AI and sensor data to prescribe proper repairs. In this episode, Tilak speaks with us about what's currently possible within the world of "predictive maintenance," as well as the possible ramifications of industrial IoT and AI in the next 5 years.
For more interviews about the real-world applications of artificial intelligence in business, visit:
|Aug 13, 2017|
Artificial Intelligence and the Future of Programmatic Advertising
A huge percentage of digital advertising dollars today go to Google and Facebook, who dominate that sector - and are inevitably central for the future of programmatic advertising. There’s a lot of evidence to suggest that the growth in digital advertising in the last two to three years has gone almost entirely into their coffers. At least for the foreseeable future, Facebook and Google will retain the ability to dominate that space.
The ability to be able to bid for the attention of particular target audiences, whether they’re searching for a specific term, live in a specific place or they like a specific sports team, is something that doesn’t seem to be going away, and seems to be rather efficient, thanks in the large part to Artificial Intelligence.
In this episode we talk to Lior Tasman who is the CEO of PredictiveBid, an Israeli-based predictive advertising optimization start-up. The team focuses on applying AI to some of the bigger issues in programmatic advertising to help draw out more ROI from ads. We discuss some of the challenges of programmatic advertising and what the future of programmatic advertising may look like from an advertiser’s perspective.
For more executive interviews on the applications of AI in Industry, visit:
|Aug 06, 2017|
AI for Real-Time Personalization - with LiftIgniter's Adam Spector
The big tech giants, such as Amazon, Google and Netflix, tend to set the stage in a lot of different domains and set public expectations to raise the aggregate tide of consumer experience. Our online experience is somewhat different each time we use these and other sites. This is because many of these tech giants alter their experience user per user in a real time iterative fashion in order to create sticky experiences and to beat their competitors.
In this episode we talk to Adam Spector, the Co-Founder & Chief Business Officer at LiftIgniter, a company which provide a service which modulates website experience per users, for an array of different businesses. Adam and I discuss what the tech giants are doing to customize their business experiences, what data they’re using to continually alter user experience and what industries and sectors might be impacted by this aggregate trend as it moves forward.
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|Jul 28, 2017|
Bringing AI into an Old, Large, Existing Business - with Muriel Serrurier Schepper of Rabobank
Imagine you work in a large organization with tens of thousands of employees across multiple countries, a business that’s been around for over a hundred years, and all of a sudden you have people in one department who are interested in applying chatbots, colleagues in another department who wish to implement sentiment analysis and still another department that wants to begin using AI for fraud and risk analysis. How do you manage to put all these pieces together?
That is exactly the situation that Muriel Serrurier Schepper found herself in. Muriel is the Business Consultant Advanced Data Analytics & Artificial Intelligence at Rabobank Digital Bank in Naarden, Netherlands. In this episode, Muriel and I discuss the Artificial Intelligence Center of Excellence at Rabobank, where she manages projects and has connected ad virtual and physical team across the company which is comprised of over 60,000 employees spread across the world.
For more interviews on the applications of AI in industry, visit:
|Jul 22, 2017|
Where is AI Making it's Way into Hospitals? - with Sangeeta Chakraborty of AYASDI
If you work in healthcare, or in an established business that is looking to implement AI for the first time - then this won't be an interview you'll want to miss.
AYASDI is one of those rare AI startups that has raised over $100MM since it's inception in 2008. This week on the "AI in Industry" podcast, Sangeeta Chakraborty of AYASDI breaks down some of AI's important recent applications in the healthcare field. She also explores how hospitals are "modernizing" their processes and systems to include data science and AI applications - and we pick apart those "modernizing" strategies in a way that makes them applicable to nearly any "stodgy" business or industry that is just beginning to implement AI.
For more interviews, research, and case studies on AI in industry, visit:
|Jul 15, 2017|
Marshall Brain on Technological Unemployment and the Role of Man and Machine
Marshall Brain discusses how wetware (the human brain) is increasingly becoming a part of a bigger system which may in itself be managed by software systems. The roles and relationships of humans and machines are rapidly changing. With the increasing advances in technology, there are fewer and fewer skills or activities that an enterprise needs from human beings, and they only need those until they can be replaced by software or hardware.
For example, computer vision systems are often still not as effective as the human eye, so we still need human vision systems to recognize text or to recognize object placement, and take action accordingly (in a store, warehouse, or other setting). A human can fill that role as a piece of wetware until the software or the hardware catches up. How will man and machine collaborate in the future? We explore these dynamics in depth in this week's interview.
For more interviews and insights from leading thinkers in AI and automation, visit:
|Jul 08, 2017|
Obstacles to Progress in Machine Learning - for NLP, Autonomous Vehicles, and More
Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles. We also touch on some of the impact that might be made if machine learning is able to overcome its own boundaries in terms of computational research, in terms of certain algorithms, and what kind of impact that might have in the arena of autonomous driving and in the realm of natural language processing (NLP).
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|Jul 03, 2017|
Machine Learning for Fraud Detection - Modern Applications and Risks
Fraud attacks have become much more sophisticated. Account takeovers are happening more often. Many security attacks involve multiple methods and unexpected attacks can devastate businesses in just a few days, as we saw with Neiman Marcus and Target. False promotion and abuse is seen not only on social media sites but is also targeted at business. To combat these risks, fraud solutions need to be smarter to keep pace with fraudsters to prevent attacks and react quickly when they do happen. This requires a fast-learning solution with the ability to continually evolve. In this episode we talk to Kevin Lee from Sift Science and examine the shifts in the info security landscape over the past ten or fifteen year. Lee also highlights what new kinds of fraud are now possible and what machine learning solutions are available.
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|Jun 25, 2017|
The Future of AI in Heavy Industry
Unlike the field of self-driving cars, the fields of construction, mining, agriculture, and other classes of “heavy industry” involve a huge variety of equipment and use-cases that go beyond traveling from A to B. The heavy industry leaders of today are no farther behind automakers in their understanding that AI and automation will be essential for the future of their companies. In this episode, guest Dr. Sam Kherat discusses the areas in heavy industry where AI is currently playing a role in heavy industry, what type of capabilities and functions are automatable, and at what level. He also shines a light on how AI might affect the future of the industry within the next 2-3 years, and in what ways we can expect large equipment to become more autonomous.
|Jun 18, 2017|
Rebellion Research's Alexander Fleiss - How AI is Eating Finance
Although machine learning in finance is far from new, it is merely at the cusp of a much wider set of applications (in all segments of finance, from insurance to bookkeeping and beyond). Already machine learning has overhauled so many aspects of the financial landscape, from accounting to trading, and it is destined to have more and more impact as it develops further. Guest Alexander Fleiss and his team at Rebellion Research are developing and using AI which uses quantitative analysis to pick investments. Fleiss discusses the current status of machine learning in the world of finance as well as lesser-known niche applications that don’t make headlines - but do make a big impact on how businesses are run. He then goes on to explore the effects of future innovative applications of AI in the financial domain.
|Jun 12, 2017|
The Challenges and Opportunities of Healthcare Data - with Remedy Health
Guests Will Jack and Nikhil Buduma co-founders of Remedy Health Inc discuss the challenges involved in collecting, setting up and structuring data in order to implement AI in healthcare. By the end of this episode, listeners will have gained insight into the challenges of healthcare data systems, and the potential solutions to cleaning and organizing this data for healthcare AI applications.
|Jun 05, 2017|
How Innovative Healthcare Companies Use AI to Put Patients First
If there's any industry ripe for disruption by AI and ML applications, it's healthcare. This week, we speak with ElevenTwo Capital's Founder and Managing Partner Shelley Zhuang, whose investment focus (among other spaces) is on innovative healthcare services. In addition to discussion how AI is helping propel genomics, diagnostics, therapeutic treatment, and other innovations, she touches on what the healthcare space might look like in the next 10 years. For healthcare startups looking to break into the healthcare market, Zhuang doesn't pretend to have simple answers; however, she identifies commonalities among companies that have been successful in smart preparation for meeting regulatory and other industry considerations. This interview was recorded live in San Francisco at Re-Work's Machine Intelligence in Autonomous Vehicles Summit in March 2017.
|May 28, 2017|
Prescriptive Analytics Driving the Smart Enterprise with Ann Miura-Ko
In the last few months, we've had a string of fantastic interviews with investors and have gained a cross-industry picture of what's important for start-ups and emerging trends in the AI and ML space. This week's interview is no exception. Ann Miura-Ko, co-founder and partner at Floodgate, starts with an explanation of the "self-driving enterprise" concept, her functioning idea about AI investing and the future of software in general. Her high-level insights embody an interesting emphasis on the dynamic of human-machine interactions and relationships cross industries, including the constant workflows and interactions of people using software and bolstering the predictive and prescriptive analytics capabilities of that software. While forward-thinking, Miura-Ko also paints a picture of how these synergistic relationships between humans and machines are happening with companies today.
|May 21, 2017|
Gary Swart on Defensibility and Scale for AI Companies
Getting an investor's perspective in AI is always a good idea for companies looking to raise money, in terms of understanding of excites VC's, but even more broadly an investor's perspective can point to emerging factors in how AI is going to impact a particular industry, shining a light on industry developments, including the commonalities that matter for any company, in any industry, leveraging these tools that are increasingly embedded with AI. In this episode we interview Polaris Partners' Gary Swart, who speaks about elements of companies that are laying the right foundations for using AI optimally and making a more defensible, durable company in an increasingly competitive landscape.
|May 14, 2017|
Deep Learning on Front Line Against New Malware Attacks
The upsurge of malware and sophisticated attacks continue to keep cybersecurity in the spotlight, but new developments in AI and deep learning offer more advanced solutions to combat security threats. This week, we catch up with Eli David, CTO of Deep Instinct—a company founded in Israel with US headquarters in San Francisco—that applies deep learning to information security. David spoke with us about why and how the deep-learning approach to AI is relevant to the future of cybersecurity.
Companies that are actively building their own security infrastructure, or are in growth mode and know they will eventually need to, should find this interview particularly relevant. David shares his perspective on how and where potential cyberthreats focus their attacks and the resulting ramifications for industries as they look for best ways to respond and prevent attacks.
|May 07, 2017|