Artificial Intelligence in Industry with Dan Faggella
Artificial intelligence is more interesting when it comes from the source. Each week, Dan Faggella interviews top AI and machine learning executives, investors and researchers from companies like Facebook, eBay, Google DeepMind and more - with one single focus: Gaining insight on the applications and implications of AI in industry. Follow our Silicon Valley adventures and hear straight from AI's best and brightest.
Starting with AI the Right Way - With Monika Wilczak of EYDec 3, 2019 24:03
This month's theme is on using AI for a competitive advantage. We speak first with Monika Wilczak, Managing Director of AI at EY. Monika speaks to us about how large companies can start to get an edge over the competition by leveraging AI, emphasizing how companies can get started with gaining that advantage.
If you're looking for areas of AI opportunity, be sure to download our "3 Ways to Discover AI Trends in Any Sector" report by going to emerj.com/t3.
The Phases of Building an AI Strategy - With Shane Zabel of RaytheonNov 26, 2019 21:13
It's the final week of our month-long series on planning your corporate AI strategy. This week we speak with Shane Zabel, Head of AI at Raytheon. Shane talks to us about the phases of building an AI strategy. What are the steps? He discusses the importance of finding an AI pioneer at a company who can build some initial ideas of what AI use-cases could be viable at the company.
If you're in the process of analyzing AI use-cases for your company or clients, we created a guide for this exact topic. Learn more about it at emerj.com/t3.
Bonus Episode: The Critical AI Capabilities for Nontechnical Professionals - With Scott Nowson of PWCNov 21, 2019 26:58
This week, we speak with Scott Nowson, AI Lead at PWC Middle East, about the critical capabilities nontechnical business people need to understand to be able to advance their career and apply AI in their industry even if their company hasn't started with AI yet.
Scott has an uncanny ability to convey business lessons on AI, and he's one of the few people who got our full Getting Started with AI report before today's formal launch.
The report is finally open, and in it, listeners can find the must-know knowledge that will allow you to take your AI interest and turn it into real career opportunity without learning any code. Listeners can learn more about it at emerj.com/a1
Assess Your Data to Find AI ROI Opportunity - With Adam Bonnifield of AirbusNov 19, 2019 24:00
In this episode, we speak with Adam Bonnifield, VP of AI at Airbus, one of the youngest executives at the firm. He talks about how to think about starting a corporate AI strategy, which for him entails beginning with the data assets.
Adam thinks through how to take account of those assets and what kinds of people need to be part of the conversation to unlock the most fruitful AI applications in an established company.
Bonus Episode: How to Level Up Your AI Skill Set Without Learning to CodeNov 14, 2019 20:55
This is a special bonus episode of AI in Industry about advancing your career in the era of AI, specifically for non-technical professionals.
If you don't want to learn to code but still make yourself tremendously valuable in the era of AI, this episode is for you. We put together a report on this topic that will be coming out this month, all about getting started with AI in business for nontechnical professionals. Interested listeners can go to emerj.com/c1 to learn more.
This week, we have Germán Sanchis-Trilles on the podcast. He's one of our technical advisors, well-schooled in natural language processing, and extremely experienced in applying AI in business. In this episode, he reviews some of the key themes from our upcoming report, including critical ideas about how nontechnical professionals can involve themselves in AI while at work.
A Pragmatists Approach to Finding AI Opportunities - With Carlos Escapa of Amazon Web ServicesNov 12, 2019 20:20
This week, we speak with Carlos Escapa, Global AI and ML Practice Leader for Amazon Web Services. Carlos speaks with us this week about starting an AI strategy with a more practical approach. Instead of thinking about how to radically reshape a key part of your business with AI or use AI for AI's sake, Carlos talks about instead thinking about where AI fits in with what your business is already doing.
He provides some thought experiments to run through for thinking through this and how to get started with AI.
How to Begin Planning an AI Strategy - With Ian WilsonNov 5, 2019 28:18
Last month, we focused on advancing your career in the age of AI, and this month we have a new theme: building your corporate AI strategy. At Emerj, much of our work in the public and private sector is in building an AI strategy and giving organizations data on where the ROI is in the AI world.
This week, we speak with Ian Wilson, former Head of AI at HSBC and a research advisor for our banking work. He has rare experience applying AI strategically at one of the largest banks in the world, and I think he is just the person to start off this month's theme. Ian talks about beginning to plan your AI strategy.
Three AI-Related Career Roles That Involve No Coding - With Emerj CEO Daniel FaggellaOct 29, 2019 16:13
This is our final episode in our series on advancing your career in the era of AI this month. We had more Linkedin messages on this theme than any we've done before, and it got me excited to think about what we could do with this kind of series in the future.
In this episode, we distill the insights from this month's series with insights from our broad catalog of interviews with AI-minded executives throughout the many years doing this podcast.
We also cover three AI-related career roles that do not involve coding.
How to Think About and Lead AI Projects in Business - With Bret Greenstein of CognizantOct 24, 2019 27:45
We continue our theme on advancing your career in the era of AI. This week, we speak with another AI lead from a gigantic IT services firm: Cognizant. Bret Greenstein is Head of AI at Cognizant, and he talks about what folks who think about AI in terms of strategic direction, project management, etc., have in common.
Brett also discusses how non-technical folks can think about AI in order to take on leadership roles in AI projects, including having a firm understanding of what is possible with AI in their industry.
The Important Nontechnical Roles in Making AI Work in the Enterprise - With Sriram Ramanathan, CTO at GenpactOct 16, 2019 20:34
This week, we speak with Sriram Ramanathan, CTO at Genpact, about what the important non-technical roles exist for making AI work in the enterprise. Everything from project management to quality control and beyond, Sriram lists out areas where nontechnical experts play a critical role in bringing AI to life.
How to Turn an AI Interest Into a Career Path (Without Learning How to Code) - With Muriël Serrurier SchepperOct 9, 2019 21:52
If you're listening to this podcast, you at least have an interest in leveraging AI in the enterprise. But how do you take that interest and use it to move up in your company and advance your career?
In this week's episode, we speak with Muriël Serrurier Schepper, who worked with AI at Rabobank and Shell managing advanced analytics projects. She now has her own AI consulting firm.
Muriël speaks with us about her experience using her prior skillset to enter the world of AI, take the reigns of exciting AI projects, and open up more career opportunities for herself.
The Strengths Non-Technical Employees Bring to AI Projects - With Wijay Wijayakumaran of IBMOct 1, 2019 24:57
In October, we're focusing on how non-technical employees can still gain an edge in the era of AI even if they've never learned any code. I can't think of a better guest off the bat than our quest this week: Wijay Wijayakumaran, Chief Architect of Machine Learning and AI at IBM Australia.
Wijay emphasizes how much stock he places in the critical importance of subject-matter experts and business leaders with domain knowledge. He also runs through possible career opportunities that non-technical employees can look for in the era of AI and questions they can ask to get more involved with AI projects at their organization.
AI's Strategic Value is the Anchor for ROI - With Daniel Faggella of EmerjSep 23, 2019 16:16
This is the final episode of our series on the ROI of AI. This week is the monthly analyst call, in which Emerj CEO Daniel Faggella breaks down some of the key themes from this month's interviews. In particular, Daniel puts a large emphasis on connecting the dots between near-term and long-term ROI.
A lot of these themes and core questions are discussed and answered for clients of our AI Product Development Roadmap services.
A Framework for Long-Term and Near-Term AI ROI - With David Carmona of MicrosoftSep 17, 2019 27:41
This week, we spoke with David Carmona, the GM of Artificial Intelligence at Microsoft, about his approach to AI ROI with the enterprise clients of Microsoft. The biggest takeaway from this episode comes right at the beginning. David talks about how to think about artificial intelligence ROI in the long-term and the near-term.
That is to say, how are we going to see a relatively near-term return with AI that might be able to improve our condition while keeping in mind the longer-term disruption in our industry?
Bonus Episode: Electrical Considerations for Artificial Intelligence Solutions - With Robert Gendron of VicorSep 12, 2019 16:12
It's clear that there's a revolution in how artificial intelligence is done with neural networks as opposed to the old school systems of the '80s and the '90s. It's clear that hardware is beginning to evolve, and it's also quite clear that the way that we power these hardware systems is going to have to change.
GPUs and AI hardware are tremendously power-intensive, and this week we speak with Robert Gendron of Vicor Corporation, a company focused on powering AI systems. Vicor is in partnership with Kisaco Research, which is putting on the 2019 AI Hardware Summit September 17 and 18 in Mountain View, California.
Robert speaks about why the way that they are powered needs to be different than traditional manufacturing equipment. He also discusses how the powering of these systems need to work if businesses want to reduce energy costs and be as efficient as they can when it comes to AI.
Bonus Episode: Software Defined Compute - Possibilities and Advantages in Machine Learning - With Jonathan Ross, CEO and Founder at GroqSep 11, 2019 22:51
This week, we have a bonus episode.
We spoke with Jonathan Ross, CEO and founder of Groq, an AI hardware company, about software defined compute. Groq is in partnership with the AI Hardware Summit happening n Mountain View, California on September 17 and 18.
Software defined compute is a way of thinking about how compute can be optimized for machine learning functions. Ross talks about some of the pros and cons of GPUs and where software defined computer might make its way into future machine learning applications.
Pitfalls to Avoid for the ROI of AI - With Dr. Charles Martin of Calculation ConsultingSep 10, 2019 45:33
This week, we speak with Dr. Charles Martin of Calculation Consulting. He's a bit of a mentor of mine when it comes to AI knowledge. Charles speaks to us about the pitfalls in getting to ROI, particularly the cultural elements within enterprises that make it so hard to get a return from AI projects.
Charles and I tend to go off in a variety of directions when we talk—he's an animated guy—so be prepared for that. But I think this is an awfully fun episode of the podcast.
For more on the fundamentals of getting started with AI in business, learn more about our newest report: Getting Started with AI: Proven Best Practices of Adoption.
Bonus Episode: Processing AI at the Edge - Use-Cases and AI Hardware Considerations - With Moe Tanabian of MicrosoftSep 6, 2019 24:55
We have a bonus episode this week. We spoke to Moe Tanabian, General Manager of Intelligent Devices at Microsoft, who is speaking at the AI Hardware Summit in Mountain View, California on September 17 and 18.
Tanabian discusses how to think about and reframe business problems to make them more accessible for AI, as well as AI at the edge, which involves doing AI processing on individual devices rather than in the cloud.
The edge could open up new potential for business problems to be solved with AI. Tanabian also provides representative use cases of intelligent devices.
How to Measure the ROI of AI - With Sankar Narayanan of Fractal AnalyticsSep 3, 2019 28:45
This month, we focus on the ROI of AI, and our guest this week is Sankar Narayanan, Chief Practice Officer at Fractal Analytics, a unicorn company in Bangalore.
In this episode, Narayanan discusses how to measure the ROI of AI in ways that aren't just financial return. In addition, he provides examples from his hands-on experience implementing AI to provide business leaders with ways of thinking about success when it comes to AI projects.
For more on measuring the ROI of AI, learn about our newest report Getting Started With AI: Proven Best Practices of AI Adoption.
Getting Started with AI, Best-Practices - With Daniel Faggella of EmerjAug 29, 2019 22:51
This is the final episode in the month-long series on getting started with AI. In this episode, Emerj CEO Daniel Faggella breaks down the key insights from all four of this month's interviews, distilling them into core best-practices for getting started with artificial intelligence in business. In addition, Daniel discusses insights from our newest report:
Getting Started with AI: Proven Best-Practices for AI Adoption
Misconceptions About AI Adoption, and How to Overcome Them - with Jan Kautz of NVIDIAAug 26, 2019 25:02
This week we interview Jan Kautz, Vice President of Learning and Perception Research at NVIDIA. Kautz talks about what people underestimate when they start an AI initiative. In addition, he emphasizes the critical value of data storage.
Kautz dives into the importance of getting started with an AI project when you already have a barometer of success. Essentially, he talks about why it's important to select a first AI project in an area where you already have a way of measuring success.
Learn more about AI adoption in our full report, Getting Started With AI: Proven Best Practices for AI Adoption.
Scaling AI Best-Practices in the Enterprise - with Jan Neumann of ComcastAug 20, 2019 34:03
This week, we speak with Jan Neumann, Senior Director of Applied AI Research at Comcast. Comcast is an enormous company; it has lots of data, lots of application areas for AI, and a lot of opportunity for confusion about AI. As such, Neumann speaks with us about scaling AI expertise in the enterprise.
Neumann talks about a very strong distinction between software and AI and how to think through problems to determine whether or not it's a software problem or an AI problem.
He also talks about scaling the problem-solving abilities of business experts in the organization. Lastly, Neumann talks about his ideas for how to determine a first AI initiative.
Critical Questions to Ask Before Adopting Artificial Intelligence - With David Carmona of MicrosoftAug 13, 2019 27:55
This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal.
He discusses how to attack both of these scenarios, which to handle first, and why.
In addition, Carmona talks about proprietary data and things that are close to your own IP. How do you take advantage of the real strategic data value within your own organization? How should you be thinking about that differently? Carmona poses three different questions to determine where those valuable opportunities are for you.
Table Stakes AI Insights for the Enterprise - with Vlad Sejnoha of Glasswing VenturesAug 6, 2019 29:04
It's the first episode of the new style of AI in Industry, in which we spend a month at a time on a specific theme. This month is AI adoption.
This week we speak with Vlad Sejnoha at Glasswing Ventures, an AI-focused VC firm. Sejnoha spent many years as the CTO at Nuance Communications. He talks to us about the table stakes AI insights the C-suite have to know and the dangers of relying entirely on consulting firms and vendor companies for these insights.
In addition, Sejnoha discusses the need for a "BS-o-meter" for when someone is making a claim about AI to determine if it's real or hype.
Lastly, Sejnoha discusses how he would go about choosing a first AI project.
Where AI is Driving Value in Insurance (and Where It's Not) - With Jerry Overton, Head of AI and Fellow at DXC TechnologyAug 2, 2019 29:57
This episode of the AI in industry podcast is all about where the rubber meets the road for AI in Insurance. We interview Jerry Overton, Head of AI and a Fellow at DXC Technology. He speaks to us about his experience implementing AI in insurance, about where there's real traction with AI in insurance, and where there's only hype. In particular, Overton discusses how anomaly detection technology is a natural fit for AI in the insurance sector.
This is the last episode of its kind on AI in Industry. Starting next Tuesday, we'll be kicking off a new format for the show. Each month, we'll focus on a specific theme, and in August, we're focusing on AI adoption in the enterprise. We hope you'll join us.
What's the Difference Between Business Intelligence and Artificial Intelligence? - With Elif Tutuk, Senior Director at Qlik ResearchJul 25, 2019 21:06
When we polled our audience about what they were interested in, the most selected response was "business intelligence." As a follow-up, we asked them what business intelligence meant to them, and their responses boiled down to anything about understanding the data businesses are already collecting.
That kind of broad definition gets to the heart of the confusion surrounding the differences between business intelligence and artificial intelligence. The line is starting to get blurry.
Our guest this week is Elif Tutuk, Senior Director at Qlik. Tutuk talks about how business intelligence is evolving and how we might define it now that a lot of BI is becoming AI. Tutuk discusses where AI is making its way into business intelligence and what that might enable for businesses.
Read our comprehensive definition of machine learning for business leaders here: https://bit.ly/2Ya2NxK
How Lenders Can Win More Business with Machine LearningJul 18, 2019 26:41
This week, we interview Jay Budzik, CTO at ZestFinance, about where AI applies to the world of auto-lending. We speak with Budzik about how underwriting and credit scoring is evolving as a result of advances in machine learning.
In addition, we talk about how companies might solve the "black box" of machine learning in finance, particularly how ZestFinance is focusing on transparent models. The financial sector has to contend with complex regulations that prevent certain information from being leveraged in credit models. It can be near impossible to determine how machine learning comes to the conclusions it does, but ZestFinance claims their software in part solves this problem.
China's AI Education Initiatives and What It Means for the USJul 12, 2019 21:11
Some say that the competitive dynamics between the US and China in terms of AI are overblown, but there's a lot of truth to them. The US has access to more of the base research, but China can orchestrate various organizations (corporations, government bodies) and secure government funding.
That said, very few people talk about K-12 education and what countries are doing to prepare their future workforce for AI. David Touretzky talks to us about just that. He is a research professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He's heading up an initiative for K-12 education, and he discusses what countries should be doing to secure their positions and technological leadership in the 21st century.
How to Maximize ROI From AI in Finance: Banking, Investing, and InsuranceJul 5, 2019 30:44
While AI is certainly finding its footing in finance, we still find most of our subscribers are in a phase where they're trying to catch up in terms of data and data infrastructure and figure out where there's real traction with AI in finance: in banking, investing, or insurance.
In this episode, we explore AI use-cases in a number of these areas of the financial industry. We interview Carlos Pazos and Anwar Ghauche at Spark Cognition about how to maximize a smaller data science team at a financial institution, how AI and alternative data is being used for quantamental investing, and how AI is automating some financing and underwriting processes.
How to Get Started With an Effective AI StrategyJun 28, 2019 01:02:41
Building an AI strategy - there's hardly anything more vague and open-ended than that. Business leaders have probably gotten the idea that they should develop one, but where should they start? That's what we talk about this week with Charles Martin, PhD.
Martin talks about how to go about starting an AI strategy, what to avoid, and the challenges and struggles of applying AI at existing businesses. Also, Martin discusses what business leaders should ignore and what business leaders should tune into and prioritize for an effective AI strategy that will propel them toward success in the coming years.
AI Business Strategy Basics - Critical Insights on AI AdoptionJun 21, 2019 36:36
One of the best conversations I ever had on the topic of AI business strategy on the podcast was with the guest I've brought back this week: Madhu Shekar, Head of Digital Innovation for Amazon Internet Services in Bangalore.
I wanted to do a deeper session with Madhu, who has seen a lot of companies go from no AI to beginning with AI, about where to start with AI adoption. How do companies build the expertise and experience with AI that lets them scale it to their organization? He also talks about how to prepare realistically for AI, including data requirements, integration times, and more.
Five AI and Data Science Terms People Get WrongJun 14, 2019 42:31
As it turns out, often times terms like predictive analytics and data science are used incorrectly. By the end of this podcast, you'll have greater clarity on five potentially vague AI and data science terms that are sometimes overused in conversations about AI in the enterprise. This week, I introduce you to German Sanches, who focused his PhD on NLP and has done a lot of AI work in business. He also helps us with our research projects. This episode is all about addressing use-cases in reference to five terms that a lot of folks get wrong.
AI Enterprise Adoption Lessons From Building a National AI StrategyJun 6, 2019 31:32
This week, we interview Arnab Kumar, Founding Manager, Frontier Technologies for the NITI Aayog, the wing of the Indian government focused on rolling out AI into areas like healthcare and agriculture.
In this episode, we talk about critical factors for applying AI at the national level, such as where to begin applying AI and what the low-hanging fruit is for gaining traction, leverage, and data assets that are going to transfer elsewhere.
We also talk about how governments, much like enterprises, need a future vision for critical capabilities they're going to enable with AI.
Finally, Kumar discusses what he thinks are the most transferable lessons for the enterprise from his experience building out a national AI strategy.
Adopting AI at the Department of Homeland SecurityMay 31, 2019 18:47
Erin Knealy is the portfolio manager of the cybersecurity division of the Us Department of Homeland Security. She is the interface between the US government and the startup and tech ecosystems. We speak with her about transferable lessons from the AI use-cases in the public sector into the private sector. How does an existing organization pick the right first AI project? How should look through a lens of opportunity when it comes to AI? In this episode, we discuss how these lessons learned in the public sector can apply to the private sector.
How to get an ROI from AI Internet of Things SolutionsMay 24, 2019 27:38
It's curious to see how much more there is of sensor tech and internet of things than there was 18 months ago. This week, we speak with Cormac Driver, PhD and Head of Product Engineering at Temboo, an IoT vendor.
We talk about how to spot AI and IoT opportunity where sensors and equipment in the physical world can actually deliver ROI and drive value for an enterprise. In addition, Cormac discusses how to get the most out of an IoT project and what's involved in terms of data and infrastructure. Finally, I ask Cormac in what sector IoT will become ubiquitous first.
AI Search Applications for Compliance, Contracts, and Human ResourcesMay 17, 2019 36:17
There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world.
That is the topic of this week's episode of AI in Industry. Our guest is Anke Conzelmann, Director of Product Management at Iron Mountain. Iron Mountain is a four-billion-dollar physical and digital storage company based in the Boston area. They handle the records of some of the largest financial, health care, and retail brands around the world. IConzelmann speaks with us about the future potential of artificial intelligence for search within an enterprise, not just of digital files, but across formats.
What It Looks Like to Adopt AI for Competitive AdvantageMay 10, 2019 24:11
The AI in Industry podcast is all about transferrable lessons. Today we speak with Andrew Byrnes, an investment director at Comet Labs in San Francisco about the competitive edge with AI. What does it look like when companies adopt AI in a way that gives them a competitive advantage? Byrnes breaks down the idea into two categories: automation and augmentation.
AI Integration Challenges in ManufacturingMay 7, 2019 28:40
We did a lot of focus on healthcare for the World Bank, and we presented a lot of that research in South Africa. When I was there, I interviewed DataProft cofounder Frans Cronje about the intersection of AI and manufacturing.
We talk about what's possible with AI in manufacturing today and just how instrumented and challenging it is to add a layer of AI insight into a manufacturing environment. This is much harder than a lot of other domains where data is maybe more accessible, and in some cases it's also higher risk.
Adopting AI into Healthcare WorkflowsApr 26, 2019 21:20
This week we speak with founder and CEO of Aidoc, Elad Walach, about the challenges of adopting AI to become part of a workflow in healthcare. We speak to him about what it is that makes it so challenging to get these tools to become part of the process of treating patients.
How Machines and Robots Learn - the Progression of AIApr 17, 2019 26:01
This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces.
We talk to him about the future of manufacturing and more broadly, how machines and robots learn. Schmidhuber uses the analogy of a baby learning about the world around it. He has a lot of interesting perspectives on how the general progression of making machines more intelligent will affect other industries outside of where AI is arguably best known today: consumer tech and advertising.
If you're in the manufacturing space, this will be an interesting interview to tune into. If you're just interested in what the next phase in AI might be like, I think Schmidhuber actually frames it pretty succinctly.
Ensuring a Positive Posthuman Transition - Perspectives from Jaan TallinApr 11, 2019 23:39
The AI In Industry podcast is often conducted over Skype, and this week's guest happens to be one of its early developers. Jaan Tallinn is recognized as sort of one of the technical leads behind Skype as a platform. I met Jaan while we were both doing round table sessions at the World Government Summit, and in this episode, I talk to Tallinn about a topic that we often don't get to cover on the podcast: the consequences of artificial general intelligence. Where's this going to take humanity in the next hundred years?
How Business Leaders Should Think About AI HardwareApr 4, 2019 22:31
In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions:How will business models fundamentally change with respect to new AI hardware capabilities? How can business leaders think about their AI hardware needs?
SambaNova is one of many firms that's going to be advertising at the Kisaco Research AI Hardware Summit in Beijing June 4th and 5th.
Training Self-Driving Cars in Simulations – The Future of AutomotiveMar 30, 2019 25:26
Danny Lange heads up the AI efforts at Unity, one of the better-known firms in terms of simulations and computer graphics. They work in several different industries, but this week we speak mostly about automotive.
This is a man that has been in the AI game since before it was cool, and now he is working on some cutting-edge projects with Unity. In this interview, we speak with Danny about where simulated environments are becoming valuable.
We hear about simulations mostly in the context of video games, and of course, Unity does apply their technology in that domain, but what about a space like automotive, where navigating within an environment is important?
Certainly we need to have physical cars on the road to drink in data from physical roads and physical environments, but is it possible to splinter some digital cars into digital environments that model the physics, that model the roads, that model the same number of pedestrian risks, and see how well they succeed in all these different environments with no real physical risk of damaging an actual vehicle or an actual person on the road?
As it turns out, there's value there.
Speech Recognition and Transcription in Law and LegalMar 28, 2019 22:18
Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livine, co-founder and CEO of Verbit.ai.
Verbit is a company that focuses on AI for transcription. They use a combination of machine learning and human experts to transcribe audio in different accents, in different noise environments, with different diction, to give people more accurate results and hopefully help the process scale.
In this episode, Levine explains five different factors that go into getting transcription right and getting AI to be able to aid in the process. In addition, Tom talks about some of the critical factors for where transcription will come into play in terms of bringing value into business.
Speech Recognition and Transcription in Law and LegalMar 28, 2019
Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livine, co-founder and CEO of Verbit.ai.
Verbit is a company that focuses on AI for transcription. They use a combination of machine learning and human experts to transcribe audio in different accents, in different noise environments, with different diction, to give people more accurate results and hopefully help the process scale.
In this episode, Levine explains five different factors that go into getting transcription right and getting AI to be able to aid in the process. In addition, Tom talks about some of the critical factors for where transcription will come into play in terms of bringing value into business.
Why Executives Should Keep Up with AI Trends in BusinessMar 22, 2019 23:12
I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.
This episode, we interview Brooke Wenig, the machine learning practice lead at Databricks. Databricks was founded by the folks who created Apache Spark. Those of you who are technically savvy with AI will be familiar with Apache Spark as an open source language for artificial intelligence and distributed computing.
Wenig works with a lot of companies with Databricks. Databricks is now close to 700 folks and helps implement AI applications into, oftentimes, large enterprise environments. Wenig speaks with us this week about what to look for in an actual data scientist and how to find data science folks with the right skills to be able to communicate to business people, not just to work with models. What should people be capable of; how should they be capable of thinking? Hopefully, some of you will have better interview questions by the end of this podcast.
In addition, we ask Brooke about what the value of covering the cutting edge applications of AI is, looking at what's working in industry. How does that help us in our own business make better decisions?
Read the full article on Emerj.com
The Strengths of the AI Ecosystem in China - Perspectives from a UN LeaderMar 21, 2019 20:30
If you want to understand the international competitive dynamics of artificial intelligence, particularly the US and China, starting with the United Nations is probably not a bad move. This week, I spoke with Irakli Beridze, the head of the Center for Artificial Intelligence and Robotics at the UN, particularly under the wing called UNICRI, the organization's crime and justice division.
Irakli was kind enough to invite me to speak at a recent event in Shanghai held by the UN and by the Shanghai Institutes for International Studies on national security, and when we were there, we talked a good deal about China's unique AI-related strengths.
I spoke with Irakli about the strengths of the ecosystem in China for artificial intelligence and how that stacks up against the US.
In addition, I asked Irakli about what it's going to look like to encourage more and more multilateral action. In other words, how do we get countries to be on the same page so AI doesn't become an arms race?
AutoML and How AI Could Become More Accessible to BusinessesMar 14, 2019 23:43
Discover how so-called autoML, or automated machine learning, could bring AI to more businesses by allowing users to build AI models faster and cheaper.
Read the full article, where we go into further detail, at Emerj.com. Search for "AutoML and How AI Could Become More Accessible to Businesses"
AI for Enterprise Legal Departments - Contract Analysis and MoreMar 7, 2019 23:26
AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms.
He provides some insight into how a company has to make its way into the legal space and the challenges of training an NLP system and collecting data for it.
Read more about AI in legal at Emerj.com
Data Challenges in the Healthcare IndustryFeb 28, 2019 30:19
There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising.
Late last year, we spoke for The World Bank about our proprietary AI in healthcare research, and speaking with governments, it's clear that there are hurdles that healthcare companies have to overcome to access data for training AI systems.
Broadly, most of the folks that we speak with who are innovating in AI and healthcare are frustrated with how hard it is to streamline the data to make use of it for applications such as diagnosing illnesses.
But why is that? That's a question that we asked our guest this week.
Our guest this week is Zhigang Chen, and he speaks about why this problem exists and how it can be overcome. In addition, Chen talks about the AI ecosystem in China and how it differs from Silicon Valley.
Success Factors for AI Business Models - A Venture Capitalist's PerspectiveFeb 21, 2019 21:39
Saying that your company does artificial intelligence might still have a slightly cool ring to it if you're talking to one of your peers at a conference, but it doesn't mean very much to venture capitalists today, who've been battered with machine learning and artificial intelligence in every pitch deck they've seen for the last three or four years.
I wondered, from a venture capitalist perspective, what makes an AI company's value proposition actually strong? What is it that makes an AI startup actually seem like a company that maybe could use AI to really win in the market? Not just to be another company that says they're going to do it or says they are doing it, but where can it actually provide enough of that competitive edge to make a VC want to pull the trigger?
Getting a grasp of the answer to that question seems pretty critical.
This week, we speak with Tim Chang, partner at Mayfield Fund in Menlo Park, California. Chang and I both spoke at the Trans Tech Conference, held every year in Silicon Valley, focused on wellness and health-related technologies.
Chang talks about what it is about an AI company's pitch, product, and market that actually makes AI an enhancement to the business in a way that's compelling to someone who wants to invest potentially millions and millions of dollars.
What Makes a Successful AI Company? - A Venture Capitalist's PerspectiveFeb 14, 2019 24:57
If one wants to start a general search engine, they're going to have to compete with Google. If one wants to start a general eCommerce platform, they'll have to compete with Amazon. But the same dynamics play out on a smaller scale. There are going to be some established players, some big tech giant, be it IBM or someone else, who already has a product.
When it comes to getting a new AI product out to market, how does one compete with the big guys?
This week's guest is Mike Edelhart, who runs Social Starts and Joyance Partners, seed stage investment firms out in the Bay Area. Edelhart has invested in a number of companies, and in this episode, we get his perspective on not only the patterns among successful AI startups and where AI plays a role in their competitive strategy, but what a "land and expand" strategy looks like for a new product that already has larger and more established competitors.
Why It's Exceedingly Difficult to Build and Adopt AI in BusinessFeb 7, 2019 36:08
A lot of AI in the press is CMOs or marketing people talking about what a company can do in a way that really is aspirational. They're speaking about what they can do, but in reality, the things that they're talking about, the capabilities won't be unlocked for maybe a year or more. These are just things on the technology road map, but people speak about them like they exist now.
This week, we speak with Abinash Tripathy, founder and Chief Strategy Officer at Help Shift. They've raised upwards of $40,000,000 in the last six years to apply artificial intelligence to the future of customer service, and we speak about the hard challenges of chatbots and conversational interfaces, as well as how long it's going to be until those are actually robust. This in opposition to how people at large companies might put out a press release touting their own chatbots that simply aren't capable of doing what they say they can to any meaningful degree.
We also talk about where AI can augment and make a difference in existing customer service workflows. Even if we can't have all-capable chatbots to handle banking or insurance or eCommerce questions from people, where can AI easily slide it's way in and actually make a difference today? In this episode, we draw a firm line on where the technology currently stands.
Overall, though, this episode is about the challenges of actually innovating in AI. We talk about why it really is the big companies that do a lot of the actual cutting edge breakthroughs of AI and why others are going to have to license those their technologies from large firms like Google and Amazon.
We also discuss why companies maybe need to have a realistic expectation about where they can apply AI, as well as why actually innovating and coming up with new AI capabilities on their own might just be wholly unreasonable given their data, their company culture, and their density of AI talent.
Read the full interview article on emerj.com
How to Build Data Science Teams for AI ProjectsJan 31, 2019 25:03
This week we interview a leader at Facebook. Jason Sundram is the lead of World.ai at Facebook, which is one of their efforts to work with public data around roads and population and other projects of that kind. But Sundram is also highly involved in the Boston office here, where Facebook will soon have around 650 employees. Many of them focus on data science and artificial intelligence.
Last time we talked about personalization in AI with Hussein Mehanna, who was Director of Engineering at Facebook at the time. This time, we'll talk about two topics that all established sectors need to be focusing on:How does one build ML and data science teams? How does one pick an AI project?
For business leaders who are considering hiring data science talent or thinking about how to start with AI in terms of making a difference in their bottom line, this should be a useful episode.
How AI and Data Science Could Better Inform Public Policy DecisionsJan 24, 2019 26:35
One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy.
This week we interview Professor Joan Peckham at the University of Rhode Island. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI. The University of Rhode Island is home to DataSpark, an organization that helps policymakers inform the decisions that they're going to make about the economy, the environment, the opioid crisis, a variety of social issues, based on deeper assessments of the data.
The ability to find objective insights might help policymakers make better decisions about where they allocate budget and what decisions are made. Right now, policymakers are beginning to tune into artificial intelligence as a source of informing their decisions. The same dynamic will likely play out in the C-suite, particularly when the data is actually there.
For more on AI in government, visit Emerj.com
The State of Natural Language Processing in the Sales ProcessJan 17, 2019 25:56
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:How can managers better train salespeople? How can salespeople better find the patterns that lead to closing a deal? The next appointment? A bigger contract?
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
AI for Contract Analysis in the EnterpriseJan 10, 2019 24:10
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:What's possible with AI when it comes to analyzing contracts, and, most importantly Where is the business upside for AI as it relates to contract analysis. How can we analyze contracts not just in a way that saves money, but that allows us to optimize our deals for revenue, for the likelihood that they'll go through? What's that farther vision?
Computer Vision for Medical Diagnostics in the Chest AreaJan 2, 2019 22:38
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.
How AI Will Become More Accessible to RetailersDec 27, 2018 25:38
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.
We talk with Pedro about the future of retail, the future of understanding customers with artificial intelligence. Essentially asking under what circumstances would a retailer need to go beyond intuition in order to inform their understanding and their ability to influence the actions of their customers or their users. In addition to that, Alves talks with us about what has to happen to AI as a technology to become more accessible and within reach of existing enterprises. Knowing now all the points of friction for bringing AI into an existing business, he talks about the transition points that he thinks are going to have to happen over the course of the years ahead in order to make these technologies more accessible to companies.
Machine Learning for Decision Support in Tax and AccountingDec 20, 2018 24:43
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.
An Overview of AI for Wealth Management - What's Possible Today?Dec 18, 2018 30:12
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:Personalization: Helping financial advisors identify the investment preferences of a client and provide personalized advice to a degree that was not possible before. This might involve taking into account factors such as declared, observed, and inferred information around client goals or attitude towards risk. Engagement: Helping wealth advisors communicate the most relevant insights for a client at a preferred time and channel.
Data Challenges in the Defense SectorDec 16, 2018 24:36
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
What It Looks Like to Be Ready for AI Adoption in the EnterpriseDec 7, 2018 23:36
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
How AI Can Help Retailers With Inventory OptimizationDec 2, 2018 20:55
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
When to Upgrade Your Hardware for Artificial IntelligenceNov 25, 2018 19:26
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.
Setting Up Retail Stores for Machine Learning - Cameras, Microphones, and MoreNov 18, 2018 25:37
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.
How to Use AI to Hire and Recruit TalentNov 11, 2018 19:38
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
How to Get a Chatbot to do What One Wants in BusinessNov 4, 2018 28:14
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/
Balancing Machines and Human Employees When Adopting AI in the EnterpriseOct 26, 2018 20:04
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?
How IT Services Firms Can Adapt to Artifical IntelligenceOct 21, 2018 24:59
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.
Predicting Sales Propensity with Artificial Intelligence - Opportunities and ChallengesOct 14, 2018 22:45
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.
Bridging the Data Science Gap - Why Subject-Matter Experts MatterOct 12, 2018 23:11
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.
How Machine Learning Could Help CPG Companies Beat Out Their CompetitorsOct 12, 2018 22:36
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.
AI for Enterprise Search - Challenges and OpportunitiesOct 7, 2018 20:56
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.
How to Determine the Data Needs of an AI Project or InitiativeSep 30, 2018 22:47
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.
Data Collection and Enhancement Strategies for AI Initiatives in BusinessSep 27, 2018 30:48
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.
The State of AI for Sales Enablement, and the Evolution of the CRMSep 23, 2018 23:46
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.
AI for Retail and eCommerce in India - Challenges and OpportunitiesSep 14, 2018 23:14
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
The Future of Drug Discovery and AI - The Role of Man and MachineSep 9, 2018 25:58
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
AI for Government and NGO Social Good Initiatives - an Interview with the Wadhwani InstituteSep 2, 2018 21:02
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
Machine Learning for Video Search and Video Education - How it WorksAug 26, 2018 30:29
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/
AI in Industry: How AI Ethics Impacts the Bottom Line - An Overview of Practical ConcernsAug 20, 2018 26:26
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
How Recommendation Engines Actually Work - Strategies and PrinciplesAug 19, 2018 20:03
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
What Executives Should be Asking about AI Use-Cases in BusinessAug 15, 2018 23:11
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
NLP for Text Summarization and Team CommunicationAug 12, 2018 17:51
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
How to Determine the Best Artificial Intelligence Application Areas in Your BusinessAug 3, 2018 22:09
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
The Financial ROI of AI Hardware - Top-Line and Bottom-Line ImpactJul 30, 2018 24:37
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:
The Future of Advertising and Machine Learning - Audience Targeting, Reach, and MoreJul 29, 2018 19:00
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:
How Existing Businesses Should Organize Their Data Assets for AIJul 23, 2018 30:28
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:
White Collar Automation in Healthcare - What's Possible Today?Jul 15, 2018 21:38
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:
Using NLP for Customer Feedback in Automotive, Banking, and MoreJul 9, 2018 21:07
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:
Can Businesses Use "Emotional" Artificial Intelligence?Jul 1, 2018 25:32
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
Improving Customer Experience with AI, Gaining Quantifiable Insight at ScaleJun 28, 2018 39:39
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:
Better Than Elasticsearch? How Machine Learning is Improving Online SearchJun 24, 2018 26:56
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:
AI Use-Cases for the Future of Real EstateJun 15, 2018 27:26
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:
High Performance Computing in Artificial Intelligence Applications with Paul Martino from Bullpen CapitalJun 11, 2018 19:53
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
Machine Learning for Credit Risk - What's Changing, and What Does it Mean?Jun 3, 2018 27:10
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/
Applications of Machine Vision in Heavy IndustryMay 18, 2018 20:28
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/
Artificial Intelligence for Personalization in Marketing - Current and Future PossibilitiesMay 13, 2018 22:05
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:
Will Artificial Intelligence Become Easier to Use?May 6, 2018 21:44
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/
How to Apply AI to an Existing Business with Larry LaffertyApr 29, 2018 31:19
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/
Will McGinnis (Predikto) - Predictive Maintenance for Trains and Mobile Heavy IndustryApr 21, 2018 25:08
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
Improving Robot Safety and Capability with Artificial Intelligence - with Rodney BrooksApr 18, 2018 25:47
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/
What's the Value of AI Events and Consulting?Apr 15, 2018 23:14
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/
Spoken Voice AI Applications in the Smart Home - with Peter Cahill from VoysisApr 9, 2018 29:11
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/
What Industries Will Adopt Voice-Related AI Applications First?Apr 1, 2018 31:00
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:
Reducing the Friction of AI Adoption in the Enterprise - with Rudina SeseriMar 26, 2018 27:36
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:
NLP for eCommerce Search - Current Challenges and Future PotentialMar 18, 2018 26:21
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:
Robbie Allen from Automated Insights - The Use-Cases of Natural Language GenerationMar 11, 2018 31:03
Episode Summary: Machine learning (ML) can be used to identify objects and pictures or help steer vehicles, but is not best suited for text-based AI applications says Robbie Allen, founder of Automated Insights.
In this episode of AI in Industry, we speak with Robbie about what is possible in generating text with AI and why rules based processes are a big part of natural language generation (NLG). We also explore which industries are likely to adopt such NLG techniques and in what ways can NLG help in business intelligence applications in the near future.
You can listen to the full interview with Robbie here:https://www.techemergence.com/robbie-allen-from-automated-insights-the-use-cases-of-natural-language-generation
Applying AI to Legal Contracts - What's Possible NowMar 3, 2018 25:35
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:https://www.techemergence.com/applying-ai-legal-contracts-whats-possible-now/
Artificial Intelligence for Team CommunicationFeb 25, 2018 24:12
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.
Artificial Intelligence for Content Marketing and Content CreationFeb 17, 2018 27:18
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:
Overcoming Challenges in Spoken Voice based Natural Language Processing (NLP) for business useFeb 11, 2018 22:05
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:
Natural Language Processing - Current Applications and Future PossibilitiesFeb 4, 2018 47:39
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:
How Microtasking Helps Optimize AI-Based Search - in Media, eCommerce and MoreFeb 1, 2018 25:31
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:
AI for Sales Forecasting - How it Works and Where it MattersJan 29, 2018 25:55
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:
Overcoming the Data and Talent Challenges of AI in Life SciencesJan 24, 2018 26:24
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:
Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AIJan 21, 2018 23:55
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
AI Recommendation Engines for Big Purchases - Will You Buy Your Home or Car Using AI?Jan 14, 2018 22:02
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
The Future of Medical Machine Vision - Possibilities for Diagnostics and MoreJan 7, 2018 27:30
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:
Building and Retaining a Data Science TeamDec 31, 2017 28:50
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:
AI for IoT Security - with Dr. Bob Baxley of BastilleDec 24, 2017 28:47
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:
AI for Social Influence and Behavior Manipulation with Dr. Charles IsbellDec 17, 2017 24:35
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:
Ben Goertzel on How Blockchain Might Make AI More AccessibleDec 10, 2017 30:54
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:
Machine Learning with Less Training Data - Approaches and TrendsDec 4, 2017 27:30
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:
How Chatbots Work, and How They EvolveNov 27, 2017 26:34
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:
Machine Vision for Advertising - Possibilities in Social and Online MediaNov 19, 2017 30:43
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:
Modeling Biology with Machine Learning - with Turbine.ai's CEO Kristóf Zsolt SzalayNov 12, 2017 23:24
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:
What Chatbots Can Do, and Cannot DoNov 6, 2017 22:18
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:
How Can Businesses Get NLP to Work?Oct 30, 2017 23:34
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:
AI for Theft Prevention and Process Adherence - with Alan O'Herlihy from EverseenOct 22, 2017 20:50
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:
Qrativ's Murali Aravamudan on "What's Possible" for AI in Drug DiscoveryOct 14, 2017 26:36
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:
AI in Healthcare IT Security - Why Hospitals are TargetsOct 8, 2017 21:38
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:
NLP for Customer Service - How Does it Work?Oct 2, 2017 34:29
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:
Computer Vision for Body Language - How it Works and How it Could be UsedSep 24, 2017 26:00
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:
AI for Cameras and Computer Vision - with Algolux's Allan BenchetritSep 17, 2017 29:00
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:
Tamr's Eliot Knudsen on the Automation of ProcurementSep 10, 2017 24:45
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:
AI Use-Cases in the CRM - with Bastiaan Janmaat of DataFoxSep 3, 2017 26:56
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:
Surviving the Machine Age - Technological Job Loss with Kevin LaGrandeurAug 26, 2017 25:14
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:What skills are least "automate-able" in the next decade? What middle class professions have the greatest risk of automation, and what should those professionals be doing now to hedge against job loss? What should business leaders be doing now to prepare for "phasing out" work while still taking care of their employees?
For more interviews with AI executives and researchers (and more insight on applying AI in your organization) - visit us online at:
Might AI Need Standards to Scale? - with Konstantinos Karachalios of the IEEEAug 20, 2017 28:44
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.
Predictive Maintenance for Equipment and Machinery - with Predii's Tilak KasturiAug 13, 2017 27:40
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:
Artificial Intelligence and the Future of Programmatic AdvertisingAug 6, 2017 37:21
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:
AI for Real-Time Personalization - with LiftIgniter's Adam SpectorJul 29, 2017 29:08
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.
See more interviews with AI industry innovators at:
Bringing AI into an Old, Large, Existing Business - with Muriel Serrurier Schepper of RabobankJul 23, 2017 28:13
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:
Where is AI Making it's Way into Hospitals? - with Sangeeta Chakraborty of AYASDIJul 16, 2017 21:11
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,
Marshall Brain on Technological Unemployment and the Role of Man and MachineJul 8, 2017 26:02
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:
Obstacles to Progress in Machine Learning - for NLP, Autonomous Vehicles, and MoreJul 3, 2017 21:47
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).
See more episodes online at:
Machine Learning for Fraud Detection - Modern Applications and RisksJun 25, 2017 27:44
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.
See more episodes at:
The Future of AI in Heavy IndustryJun 18, 2017 33:39
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.
Rebellion Research's Alexander Fleiss - How AI is Eating FinanceJun 12, 2017 27:11
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.
The Challenges and Opportunities of Healthcare Data - with Remedy HealthJun 5, 2017 20:37
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.
How Innovative Healthcare Companies Use AI to Put Patients FirstMay 28, 2017 22:32
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.
Prescriptive Analytics Driving the Smart Enterprise with Ann Miura-KoMay 21, 2017 21:00
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.
Gary Swart on Defensibility and Scale for AI CompaniesMay 14, 2017 24:32
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.
Deep Learning on Front Line Against New Malware AttacksMay 7, 2017 23:02
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.
Scopely and the Uses of AI and Analytics in GamingApr 30, 2017 25:43
One of the most clear insights from our recent consensus in marketing and advertising was that companies who have more digital touch points along the path to conversion—and more conversion in general—have an advantage when applying AI and ML technologies. In this week's episode, Scopely Co-Founder Ankur Bulsara shines a light on this dynamic and describes how gaming companies are taking advantage of digital trails and applying machine learning technologies. We don't cover much gaming on the TechEmergence podcast, so this interview is a bit off the beaten path. Bulsara speaks about how dialed-in and instrumented the mobile gaming environment is and how data is used to leverage higher conversions over time, as well as how Scopely's systems are set in place to ensure success of their business model. We think his insights on how gaming companies leverage higher conversions with (and without) machine learning can serve as an analogy for companies in other industries that are considering how to set in place similar, optimal digital processes over time.
What Does it Take to Improve Marketing Results with AI?Apr 28, 2017 25:46
In this episode, we speak with Co-founder and CEO Alex Holub of Vidora, about how AI can be put to work to improve marketing results. Holub touches on the resources needed—time, money, in-house or outside expertise, calibration, and data— in order to leverage AI in a realistic way. It's safe to say that today, some businesses are not yet set up to be leveraging AI, while others should be seriously considering taking the leap to using machine learning. Holub draws some firm lines as to what kinds of businesses are primed to take advantage of AI, and what it takes to flip the switch and make AI a useful and inspired revenue driver in the marketing domain.
AI Healthcare Applications – and Why Doctors Don't Want to Be ReplacedApr 23, 2017 24:04
I'm always a little shocked when I see how much venture investing goes into the healthcare space, which brings me to the subject of this week's episode: just how the healthcare industry is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management talks about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches on which innovations have a better chance of getting blocked and redirected by parties of interest and those that have more promise in being accepted and rolled out sooner. By the end of this episode, listeners will have a more clear picture of practical considerations in healthcare technology adoption, reasons that are often less about quality or potential of the technology and more about clarity on ROI for investors.
Data-Driven Software and the Future of Enterprise TechApr 16, 2017 22:33
At TechEmergence, we like to look around the corner at where AI is impacting industries and how people can make better business decisions based on that information. AI and software is an emerging topic of interest to many companies, and in this episode we get a venture capitalist's perspective on where AI will play a vital and necessary role with real results in software and industry.
Jake Flomenberg, a partner with venture capital firm Accel in Palo Alto, shared his insights on how software can integrate AI in intuitive and valuable ways for users. He cites some of the companies that Accel has invested in to illustrate some of the potential software features that may be introduced to the enterprise in the next five years or so. Flomenberg's insights may be useful for anyone building a business or planning to buy a product or service from a software vendor in the near future. If you're interested in getting other founders' perspectives on the feedback and interest shown by investors in their startups, our AI startup consensus on investor sentiment is a good place to start.
A VC's Take On Business Process AutomationsApr 9, 2017 29:01
In some ways, investors in AI have to do a lot of what we do at TechEmergence, which is sort through marketing fluff and determine what's actually working and what's more of a pipe dream, as well as what's coming up in the next five years that seems inevitable and what's more likely to flop. In this episode we're joined by Li Jiang, a venture capitalist with GSV Capital whom I was connected with through Bootstrap Labs as a pre-event interview — we'll both be at Bootstrap Labs' Applied AI event in San Francisco on May 11. This week, Jiang speaks about the current areas of AI applications that he sees driving value in business, as well as what technologies he believes will make a long-term impact in terms of automation. His insights on where AI automations are generating cost savings and increased efficiency, as well as what roles might be completely replaced or significantly augmented by AI, are useful nuggets for companies who are thinking through some of their own business processes and are eager to identify low-hanging fruit.
Genetic Algorithms Evolve Simple Solutions Across IndustriesApr 2, 2017 24:28
As it turns out, survival of the fittest applies as much to algorithms as it does to amoebas, at least when we're talking about genetic algorithms. We recently interviewed Dr. Jay Perrret, CTO of Aria Networks, a company that uses genetic algorithm-based technology for solving some of industry's toughest problems, from optimization of business networks to pinpointing genetic patterns correlated with specific diseases. Dr. Perrett has been working for years in this domain, testing algorithms that use variations of parameters in order to gradually arrive at a best result, when there's no simple way to program a solution. In this episode, Dr. Perrett discusses how genetic algorithms (GA) work and ways that they can be tested and applied in a business context. He provides two very useful case studies, including a recent example with Facebook that involved planning out an optimal (and massive) data network.
Art of Artificial Intelligence in Marketing OptimizationMar 26, 2017 25:03
Getting beyond the marketing and jargon on the homepage of AI companies and figuring out what's actually happening, what results are being driven in business, is part of our job at TechEmergence. Shaking those answers out of founders is not always easy, but we didn't have to do much shaking with Yohai Sabag, chief data scientist for Optimove, a marketing AI and automation company in Israel. In this episode, he speaks about what humans are needed for in the optimization process, and what facets can be automated or distributed to a machine. Sabag gives an excellent walk-through of how marketers can use the "human-machine feedback loop" to optimize individual campaigns at scale.
Fundamentals of Natural Language Generation in Business IntelligenceMar 19, 2017 33:50
You might be aware that some of the articles online about sports or financial performance of companies are article written by machines; this machine learning-based technology is the burgeoning field of natural language generation (NLG), which aims to create written content as humans would—in context— but at greater speed and scale. Yseop is one such enterprise software company, whose product suite turns data into written insight, explanations, and narrative. In this episode we interview Yseop's Vice President Matthieu Rauscher, who talks about the fundamentals of natural language generation in business, and what conditions need to be in place in order to drive key objectives. Rauscher also addresses the difference between discover-oriented machine learning (ML) and production-level ML, and why different industries might be drawn to one over the other.
DarkTrace's Justin Fier - Malicious AI and the Dark Side of Data SecurityMar 12, 2017 29:35
There is in fact a dark side to AI, although we’re certainly not at the point where we need to fear terminators, but it’s certainly been leveraged toward malicious aims in a business context. In data security, tremendous venture dollars are going into preventing fraud and theft, but this same brand of technology is also being use by the “bad guys” to try and steal that information and break into those systems. In this episode, I speak with Justin Fier, director of cyber intelligence at Dark Trace, who speaks about the malicious uses of AI and how companies like Dark Trace have been forced to fight these “AI assailants”.
Startup Artificial Intelligence Companies in ChinaMar 5, 2017 23:13
Most of our recent investor interviews have been Bay area investors, like Accenture and Canvas, and we don't usually get to speak with investors overseas, particularly in Asia. This week, however, we interviewed Tak Lo, a partner with Zeroth.ai, an accelerator program and cohort investing firm based in Hong Kong and focused on startup artificial intelligence (AI) and machine learning (ML) companies. Lo speaks about when he saw AI take off in China and the differences in that rise compared to the U.S. He also gives valuable insight on consumer differences in how the two populations interact with technology, and how these differences in the Asian market drive different business opportunities in China than in the U.S.
How Data Lakes Support ML in Industry - with Cloudera's Amr AwadallahFeb 27, 2017 29:25
If you're going to apply machine learning (ML) in a business context, you need a lot of data, and algorithms across the board perform better with more recent, rich, and relevant data. Today, there are companies whose entire business models are predicated on helping others make sense of and use of this type of information. In this episode, we speak with the CTO and Co-Founder of one such company—Palo Alto-based Cloudera. CTO Amr Awadallah, PhD, speaks with us this week about where he sees "data lakes" (or "data hubs", Cloudera's preferred term) and warehouses play an important role in ML applications in business. Based on his experiences helping a variety of companies in many countries set up data lakes, Amwadallah is able to distill and communicate these uses in three broad categories that apply across industries as companies look to solve tougher problems and ask more complex questions using unstructured data.
Machine Learning for Media Monitoring - with Signal Chief Data ScientistFeb 19, 2017 31:00
One facet of business that nearly any industry has in common is the need to stay on top of news in their respective market, including competitor strategies or understanding changes in news related to the field. Media monitoring is a domain that machine learning (ML) is well suited for, with it's ability to coax out headlines, contextual information, and financial data from the seemingly endless stream of social, blog, and other information on the web today. Signal is a company that uses ML specifically for these purposes. In this episode, we speak with Signal Media's Chief Data Scientist and Co-founder Dr. Miguel Martinez, who dives into real business use cases illustrating the use of machine learning for media monitoring across industries.
Tuning Machine Learning Algorithms with Scott ClarkFeb 12, 2017 24:51
What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.
How to Raise Money for Your AI Startup – with Ben Narasin of Canvas VenturesFeb 5, 2017 30:44
In this episode, recorded live at Canvas Ventures in Portola Valley, I speak with Ben Narasin, a partner with Canvas and an avid venture investor in AI and ML companies, some of which we've interviewed (Crowdflower and Mulesoft), along with many others that we haven't (like Siri). Ben doesn't look for AI to invest in; instead, he looks for companies to invest in, a subtle but important difference in a business world increasingly caught up in the explosion of AI and ML technologies.
From investments in Nuance to more recent one such as Houzz, Narasin has solid ideas as to what makes an investment interesting when AI is involved, what might actually add value to a model with AI, and what's wholly irrelevant when it comes to overall business model. Besides making important distinctions on where investments can make a return and how to raise money for your AI startup, this interview is also chock full of great analogies (give me golden dragons all day long—anyone?)
How to Learn Machine Learning – an Investor's PerspectiveJan 30, 2017 24:24
There’s been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and grasp and understand leveraging related technologies in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, who I met through an investment banking friend in Boston.
BootstrapLabs invests in Bay area companies, and Levy also travels around the world speaking about investing in AI companies and raising funds for new ventures. In this episode, Levy gives his perspective on what investors and executives get wrong about ML and and AI, and discusses how they can get up to speed on the applications for these technologies and leverage them and related expertise to really make a difference (i.e. increased ROI) in their businesses.
Machine Learning in InfosecurityJan 22, 2017 22:50
Uday Veeramachaneni is taking a new approach to machine learning in infosecurity, AKA infosec. Traditionally, infosec has approached predicting attacks in two ways: through a system of hand-designed rules, and through anomaly detection, a technique that detects statistical outliers in the data. The problem with these approaches, Veermachaneni says, is that the signal-to-noise ratio is too low. In this episode, Veermachaneni discusses how his company, PatternEx, is using machine learning to provide more accurate attack prediction. He also discusses the cooperative role of man and machine in building robust AI applications in data security and walks us through a common security attack scenario.
How to Hire Machine Learning Talent - with HIRED's Parshu KulkarniJan 15, 2017 22:51
When it comes to finding an expert on interviewing and finding machine learning (ML) talent, Parshu Kulkarni may just be the guy to ask. Not only is Kulkarni one of a small subsegment of the global population with an advanced degree in data science who has also been hired to work in tech companies like eBay, but he's been on the unique side hiring of ML and AI talent. Today, Kulkarni works full-time as Head of Data Science at Hired, Inc., a giant platform for hiring top talent in tech and other areas. In this episode, he provide an interesting distinction between what individuals with experience in data science look for in potential hires versus those who do not have the tech background tend to look for, and also dives into the supply-and-demand landscape for data scientists now and in the future—an interesting interview for anyone looking to hire or be hired in the ML and AI space.
How Algorithms Improve Advertising - AI for Marketing OptimizationJan 8, 2017 25:43
In marketing, there are lots of applications in AI and machine learning (ML), from recommendation engines to predictive analytics and beyond. At the company Adgorithms, there are even more ambitious projects underway - like automating the process of marketing altogether by having a machine run and generate ads, or test and spend the marketing budget of a company. Or Shani, CEO of Adgorithms, focuses on the quantitative aspects and optimization of online advertising, using algorithms to improve advertising processes. In this interview, Shani talks about how Adgorithms' smart marketing platform "Albert" meshes with humans’ role in marketing, and also discusses how these roles might change over the next 5 to 10 years as we move towards ever more automated marketing processes.
Automating White Collar Work - Two Examples and a Look ForwardJan 1, 2017 26:17
Not all knowledge work can be crunched by a program, but there are some hard-to-automate business processes that a select few entities are making an attempt to automate now. Boston-based Rage Frameworks, Inc. is one such company, and in this episode we speak with Senior Vice President (SVP) Joy Dasgupta about specific applications of automation technologies applied to white collar environments. Rage Frameworks has developed intelligent machines that have been able to take over process that, prior to the emergence of AI and automation technologies, would have required thousands of people to accomplish. These developments are a microcosm of what is to come, and the process is not without its ethical considerations (as discussed in a previous interview with Yoshua Bengio). But Dasgupta's insights provide a concrete glimpse into how these processes are being automated in the knowledge workplace today and what that might mean or look like decades from now.
When and How Will Autonomous Cars be Mainstream?Dec 25, 2016 29:17
This week we speak with CEO and Founder of Nexar Inc., Eran Shir, whose company has created a dashboard app that allows drivers to mount a smartphone, which then collects visual information and other data, such as speed from your accelerometer, in order to help detect and prevent accidents. The app also serves as a way to reconstruct what happens in a collision - a unique solution in a big and untapped market. In this episode, Shir gives his vision of a world where the roads are filled with cyborgs, rather than autonomous robots, i.e. people augmented with new sensory information that trigger notifications, warnings or prompts for safer driving behavior, amongst a network of cloud-connected cars. He also touches on what the transition might look like in response to the question - when will autonomous cars be mainstream?
How to Leverage Data Assets for Business - with Kenneth CukierDec 22, 2016 26:25
In this episode, we speak with Senior Editor for the Economist in digital and data products and Co-author of "Big Data: A Revolution that Will Transform How We Work, Live and Think", Kenneth Cukier, who speaks on the technologies that underlie big data and make it what it is today. Cukier addresses common misconceptions about machine learning and dives into how companies can catch up with this technology by thinking through, assessing ROI, and making sense of the dynamics of big data. Listen for Cukier's apt analogy in comparing machine learning technology to the dynamics of computing from decades ago.
How Executives Can Learn Machine LearningDec 19, 2016 24:23
What are executives missing the boat on and what do they need to think about when it comes to AI and ML? This week, we speak with John Straw, who has had a number of businesses in the UK and US, currently a senior advisor to McKinsey & Co., and who works with a lot of executive teams in terms of finding new applications for AI and finding ROI for those technologies in industry. We speak this week about how executives can get up to speed, what degree of knowledge and in what way they should learn it so they can find opportunities in their own companies. Straw also touches on what he sees as the biggest areas of oversight, in terms of preventing companies from finding those applications that can keep them up to speed with competitors and the big technology players.
Artificial Intelligence in Stock Trading - Future Trends and ApplicationsDec 15, 2016 24:37
In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. This week, we’re joined by CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services.
Three Scenarios for the Future of Work in an AI EconomyDec 11, 2016 26:06
Market research and trends is important when discussing AI and business, but it's also worthwhile to contemplate the ethical and social implications further down the line. How will countries deal with potential unemployment problems? How might countries collaborate to hedge against the risks that AI poses to the future of work and other economic facets? A relatively small group is helping people do just that i.e. getting organizations and countries to think through how they could hedge against the grander risks inherent in a world powered by AI.
In this episode, we speak with Jerome Glenn, head of the Millennium Project, an initiative that focuses on research implementing the organizational means, operational priorities, and financing structures necessary to achieve the Millennium Development Goals or (MDGs). Glenn talks about how he gets principalities of the world to bring their big industrial players and the public to talk through possible scenarios that are 30, 40, even 50 years in the future, and about ways we might potentially hedge against risks and make the most of the upsides of AI in a global economy.
The Future of Advertising Attribution with Machine LearningDec 8, 2016 20:13
A medium-size business with a $20m marketing budget can run into issues when aiming to track an attribute, what marketing dollars brought in customers, etc. But when you're managing $90B for customers all over the world and working in every conceivable channel, things get all the more complicated. Josh Sutton, global head of Data and AI at Publicis.Sapient, speaks in this episode about the future of advertising attribution with machine learning. Specifically, Sutton discusses how his team of publicists is working on managing, tracking, and determining cohorts and attribution across more channels and numerous clients, and touches on ways that the company is applying ML to make sense of marketing data and spend marketing dollars more effectively.
Five Year Trends in Medical AI ApplicationsDec 4, 2016 21:27
I remember reading an article in Scientific American years ago about a poster of a person looking in the direction people sitting in a school dining room, and that this poster would make people sitting in the dining room less likely to litter. This seems like an absurd example of holding people accountable for their actions, but as it turns out, there are a lot more serious consequences to ensuring behavior change through observation, and one area where this matters is medicine.
Today, there’s a major issue with people who don't adhere to their medical regimens, only to relapse or experience more serious symptoms later on. This week's guest, Cory Kidd, CEO of Catalia Health and known for his work at MIT on human-robotic interaction, is working to help solve this problem by developing a robot that adds some of that physical presence and accountability. This is likely one of many novel medical AI applications that we're likely to see roll out in healthcare over the next decade.
Cogitai's Mark Ring - Going Beyond Reinforcement LearningDec 1, 2016 20:45
Today's episode is about continual learning, a focus of Cogitai, a company dedicated to building AI's that interact and learn from the real world. Cogitai's Cofound and CEO Mark Ring talks about the differences between supervised and reinforcement, and how Cogitai intends to take reinforcement learning in the direction of continual learning. Ring also touches on where he sees an opportunity for applying continual learning in domains like vehicles, consumer apps, etc., and improving abstract levels of understanding by machines.
Applying Computational Linguistics to Streamline the Legal LandscapeNov 27, 2016 22:47
There’s not that many serial tech entrepreneurs in the legal space, but Gary Sangha is one of them. Sangha is CEO and founder of Lit IQ, which is applying machine learning and computational linguistics to legal documents to help lawyers avoid making drafting mistakes. In this episode, Sangha talks about where this type of software is most useful and legitimate, what the legal landscape in relationship to machine learning may look like in the next few years, and how this technology may apply across industries.
OpenAI's Ilya Sutskever on Preparing for the Future of IntelligenceNov 24, 2016 21:28
Some organizations are leveraging artificial intelligence (AI) to help the world with research, some to help companies with marketing, and some are intent on ensuring that the future of AI doesn’t result in the end of humanity. Theres’a good likelihood that if you're reading this interview, that you're already familiar with OpenAI, an organization with the sole purpose of ensuring that the future of man and machines is a friendly one, and that the concentration of power and intelligence isn’t centralized in a way that would make AI a dangerous tool. In this episode, we speak with Ilya Sutskever, research director for Open AI. This was a fun but frustrating interview; Sutskever held his cards close to his chest, but we gain some perspective on what he considers to be areas of importance regarding the future of AI and considerations for safely furthering advances in the field.
Future Applications of Machine Vision - an Interview with Cortica's CEONov 20, 2016 21:39
Right now, you can take a picture of a flower in your garden and post it on social media to see if anyone knows its proper name. Wouldn’t it be nice, though, if a machine could identify the correct name and species in the picture you just took? Solving this problem in applications of machine vision is something that CEO Igal Raichelgauz and his team are working on at Cortica, a machine learning company that is not focused on deep learning, but is instead taking a more "shallow" approach. In this episode, Raichelgauz articulates Cortica's approach, which is based on neurology and goes against some of the current approaches in getting machines to learn. We discuss some of these primary differences and dive into Cortica's goals for applying machine vision in consumer products.
What is a GPU, and How Are Companies Using Them Now?Nov 17, 2016 15:05
This week’s guest is Kimberly Powell, senior director of business development at NVIDIA. In an interview conducted at the 2016 AI Summit in San Francisco, Powell spoke with TechEmergence about GPUs and the factors that are making them easier to use, how Nvidia and others are working to make this technology more accessible to small businesses and startups, and about some of Nvidia’s and other similar players' innovations in the deep learning field.
Accenture's CTO on: The Economic Impact of Artificial IntelligenceNov 13, 2016 21:01
Accenture is a pretty large company in the tech space, providing services to many of the Fortune 500 and global equivalents. They recently conducted a study of their own, combined with expertise from economists and AI researchers, about the longer-term economic impact of artificial intelligence on economies around the world. In this episode, I speak with Chief Technology Officer Paul Daughtery, who has been with Accenture since 1986, who was joined by Global Technology R&D Lead Marc Carrel-Billiard. We met up at a coffee shop after an AI Summit in San Francisco, and I asked Paul and Marc about what they had learned from this newly-published study and what they consider to be the significant impacts of AI and automation on the future job market.
Crowdsourcing a Machine Learning Hedge FundNov 10, 2016 18:20
Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off is in finance and hedge funds. In this episode, we speak with Richard Craib, founder of Numerai, about the company's model for pooling data science talent, using "anonymous" models to train financial data, and competing against one another, in which winners are rewarded in bitcoin to exchange through virtual markets. Craib speaks about his overarching vision for the company, and also delves into the past, present, and future of AI applications in finance.
When Will Chatbots Reach Human Level Sophistication?Nov 6, 2016 21:43
What does the world look like when we can replicate human expertise in an assistant? Are we close to developing human-level chatbots that we can ask about law or medical conditions? We dive into this topic with Founder and CEO of exClone Dr. Riza Berkan, whose personal assistant and chat-bot company is leveraging day-to-day human conversational templates in machine learning technology in order to better approach the tough task of replicating human expertise through a machine. Berkan talks about the edge layer of his company's “secret sauce”, and touches on the future applications of what might manifest in this field in 5 to 10 years in medical and other consumer applications.
Deep Learning Applications for Enterprise with Skymind’s Chris NicholsonNov 2, 2016 20:50
In one of our most recent consensus, we took a close look at future trends in artificial intelligence consumer applications, but it's also interesting to see what’s happening now in businesses. Chris Nicholson is the CEO of Skymind.io, which offers deep learning applications that integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current trends that he sees across industries and best practices for implementing AI solutions to gain consistent return on investment.
Shopify's Kit - The AI Personal Marketing AssistantOct 30, 2016 23:18
We've interviewed a number of guests on TechEmergence, but very few who have had a serious part of their career in selling automobiles. But Michael Perry did just that for 5 years before founding Kit, his third startup - an AI application that works in marketing for small businesses and was acquired by Shopify in April 2016. In this episode, Perry speaks about how Kit and Shopify leverage AI on a daily basis, and how a “non-tech” person with no formal background in AI or data science can build a team for an AI project.
Martin Ford on the Rise of Workforce AutomationOct 27, 2016 25:36
Martin Ford started off as a software entrepreneur in Silicon Valley, but became better known for his speaking and writing on robotics' and automation's influence on the job market after writing his best-selling book, Rise of the Robots: Technology and the Threat of a Jobless Future. In this episode, Martin talks about why he believes 'white collar' jobs (as opposed to blue) are at a higher risk for automation, and gives his predictions on how automation and robotics will impact the job market over the next 5 to 10 years.
Scaling Virtual Assistant Services for EnterpriseOct 23, 2016 22:45
As Senior Director and World Wide Head of the Cognitive Innovation Group at Nuance Communications, Mark Hanson works on bringing Nuance lab innovations to business applications, with the guiding goals of improving customer experience and business efficiency. In this episode, Hanson speaks about natural language processing (NLP), where he believes this technology is headed in the future and where it's driving value now, and how companies are applying NLP in Silicon Valley and elsewhere.
Human Resource Management Meets Predictive AnalyticsOct 16, 2016 22:43
How do you know if you’ve made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on best hiring decisions. In this episode, Pasha Roberts, chief scientist at Talent Analytics, tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.
Zillow: Data-Driven Real Estate Appraisals at Your FingertipsOct 13, 2016 20:45
Big data is often a buzz word, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, chief analytics officer and economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.
Network Intrusion Detection Using Machine LearningOct 9, 2016 28:40
When Google’s DeepMind won against one of the best modern Go champions, is used multiple AI approaches and exposed gaps in some individual strategies. This even has shed more light on AI, but also on the utility in combining approaches to AI for individual problems. Data security is one of these problem areas where multiple AI approaches is being used to make our information safer. Dr. Sal Stolfo has been a professor at Columbia in Computer Science since 1972 and is now also the CEO of Allure Security, with a focus on engineering network intrusion detection solutions using AI applications. In this episode, Stolfo talks about the various styles of AI and statical methods that have been and are being used to detect malicious activity, as well as how he believes the future of security is going to have to adapt as increasing amounts of data become available.
MuleSoft's CTO Envisions Connected Machine Learning NetworkOct 6, 2016 00
This episode's guest is Uri Sarid, CTO at Mulesoft. Sarid speaks about where he believes the future of machine learning (ML) applications in industry might go - he thinks applications might stay small and niche-based, and will develop based on how well they each serve their individual purposes. He also speaks on his belief that companies will get used to dealing with disparate ML technologies and that finding ways to connect these technologies will be an important path for future trends in technology development.
Could Swarm Intelligence Be Used to Teach AI?Oct 2, 2016 32:41
It isn’t by chance that birds fly in flocks and fish swim in schools - they’re actually smarter when they act in a group. Could it be possible to extend that collective intelligence to human beings, and even AI? Louis Rosenberg is a PhD from Stanford, previously founder of Immersion and who now runs Unanimous AI, a company focusing on harnessing swarm intelligence with human beings. In this episode, Rosenberg speaks about how this collective-intelligence approach has been applied to human beings in terms of garnering improvements in a range of predictions, and he also touches on what this type of swarm intelligence might mean when we talk about multiple AI’s in the future.
How Companies Can Get Started Using Machine Learning for BusinessSep 29, 2016 21:28
Predictive analytics and machine learning are all the rage in Silicon Valley, but how do companies actually derive value by leveraging these technologies? We asked this question to Dr. Ronen Meiri, CTO and Founder of DMWay, a predictive analytics and machine learning platform company based in Israel. In this episode, Ronen speaks about what his company does and how smart executives are starting to make decisions how to choose and decide on the a smart, user-friendly platform that fits their business' needs.
The Business Value of Unstructured Data - with LoopAI Chief Scientist Patrick EhlenSep 25, 2016 36:14
Our guest in this episode has spent a large part of his life on figuring out how to make machines more intelligent. LoopAI Chief Scientist Patrick Ehlen has worked on a number of important projects, from DARPA projects to big-company AI solutions at places like AT&T. LoopAI works on getting AI to make sense and meaning of unstructured text, and Ehlen talks about the potential business applications for this technology and where it's making way its way into industry. Ehlen also touches on the implications for developers in the nascent AI field - like LoopAI - that are vying to implement its technology as an industry standard, and how such organizations will have to market themselves and deliver services to develop a thriving AI ecosystem.
Pitching Angel Investors on Technologies They Don't UnderstandSep 22, 2016 17:50
This week's guest is Senior Vice President of SPARK, an economic development organization dedicated to getting startups and other early-stage companies off the ground in Ann Arbor. Skip Simms speaks on how to convey complex technologies to investors who don’t necessarily have your technical expertise, and still close the deal and get the investment. Simms talks about companies he’s seen do this well (and not so well), and how aspiring companies can do a better job of convincing investors to get in on new or unfamiliar technologies, something many AI company founders will have to deal with in some shape or form in launching a new entity.
Why Big Data in Business Still Needs Human IntuitionSep 18, 2016 24:26
For some companies, big data remains an abstraction; for others, it's an integral part of the lifeblood of a business. Mat Harris is vice president at Sojern, a travel marketing platform that has leveraged big data to grow $3 billion in bookings and 1/3 of a billion traveler profiles across its platform. In this episode, Harris speaks about how Sojern and other businesses are using a combination of their data and other sources of data (what he calls third and "second" data sources) in order to make informed marketing decisions and better market their services to buyers. Harris sheds light on the direct ROI for big data in different businesses, and it's an interesting episode from the perspective of an executive who is using big data to make decisions on business directions.
Investing in Artificial Intelligence - With Motus Ventures' Robert SeidlSep 15, 2016 27:28
Companies looking to raise money are often asking what investors think of their company, their industry, and how they're making investment decisions in related companies. In this episode, I ask these questions of Robert Seidel, who is managing partner of Motus Ventures, an investment firm focusing on autonomous Vehicles and the IoT. Seidl talks about various data sources and the people and networks from which investors draw information when they don’t have what they need on-hand and need to make important investment decisions. He also shares his perspective on the high-energy and competitive investment world of AI, including his thoughts on the most exciting (and confusing) areas in the industry.
The Future of Chatbots and Personal Assistants at Nuance's AI LabSep 11, 2016 22:52
This week's interview was recorded live at Nuance's Silicon Valley office with guest Charlie Ortiz, director of the AI and Natural Language (NL) Processing Lab for Nuance Communications in Silicon Valley. In this episode, Ortiz speaks about what he sees as the most important developments in natural language processing (NLP) over the last few years, what advancements brought us to where we are today, and where progress might take NLP in the coming years ahead (both at Nuance and beyond).
Comet Labs' Saman Farid - An Investor's Take on the AI LandscapeSep 8, 2016 24:51
Fifteen years ago, investing in AI may have seemed a bit far-fetched, but today it's not at all a rare occurrence; however, it's more rare to find entire firms dedicated to investing entirely in AI. In today's episode, we're joined by Saman Farid, co-founder of Comet Labs, an investment firm focused on investment in AI companies across industries. He speaks about his investment hypothesis in the future of AI, why he’s decided to hone his funds in this domain, and the different domains where he believes AI is ripe to disrupt on a global level in the coming few years.
DeepMind's Nando de Freitas - Why Deep Learning is Like Building with LegosSep 4, 2016 24:13
One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.