Artificial Intelligence in Industry with Daniel Faggella (Artificial Intelligence)

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.

Direct download: Muril_Serrurier_October_2019_AI_in_Industry.mp3
Category:Artificial Intelligence -- posted at: 5:07pm PDT

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.

Direct download: AI_in_Industry-Wijay-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:50pm PDT

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. 

Direct download: AI_in_Industry-AI_ROI_Analyst-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:57am PDT

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?

Direct download: AI_in_Industry-David_Carmona_ROI-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 2:42pm PDT

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.

Direct download: AI_in_Industry-_Robert_Gendron_-_Vicor_-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 9:32am PDT

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.

Direct download: AI_in_Industry-Jonathan_Ross-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 1:37pm PDT

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.

Direct download: AI_in_Industry-Charles_Martin_ROI-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:37pm PDT

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.

Direct download: AI_in_Industry-Moe_Tanabian-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 3:20pm PDT

This month, we focus on the ROI of AI, and our guest this week is Sankar Narayanan, Chief Practice Officer at Fractal Analytics, a global AI & analytics firm headquartered in New York City.

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.

Direct download: AI_in_Industry-Sankar_Naranyan-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:13am PDT

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

Direct download: AI_in_Industry-Analyst-Getting-Started-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:20pm PDT

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.

Direct download: AI_in_Industry-Jan_Kautz-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:56am PDT

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.

Direct download: AI_in_Industry-Jan_Neumann-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:00pm PDT

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.

Direct download: AI_in_Industry-David_Carmona_Adoption-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 7:00am PDT

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.

Direct download: Vlad_Sejnoha-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 11:33am PDT

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.

 

Direct download: AI_in_Industry-Jerry_Overton-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:59am PDT

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

Direct download: AI_in_Industry-Elif_Tutuk-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:45pm PDT

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.

Direct download: AI_in_Industry-Jay_Budzik-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 11:10am PDT

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.

Direct download: AI_in_Industry-Dave_Touretzky-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 7:00am PDT

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.

Direct download: AI_in_Industry-Spark_Congition-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 4:19pm PDT

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.

Direct download: AI_in_Industry-Charles_Martin-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:27pm PDT

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. 

Direct download: AI_in_Industry-Madhu_Shekar-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:10pm PDT

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.

Direct download: AI_in_Industry-German_Sanchis-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:53pm PDT

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.

Direct download: AI_in_Industry-Arnab_Kumar-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 3:47pm PDT

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.

Direct download: AI_in_Industry-Erin_Keneally-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:05am PDT

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.

Direct download: AI_in_Industry-Cormac_Driver-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:47pm PDT

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.

Direct download: AI_in_Industry-Anke_Conzelmann-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 3:09pm PDT

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.

Direct download: AI_in_Industry-Andrew_Byrnes-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:15pm PDT

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.

Direct download: AI_in_Industry-Frans_Cronje-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 11:23am PDT

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.

Direct download: AI_in_Industry-Elad_Walach-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:08pm PDT

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.

Direct download: AI_in_Industry-Jurgen_Schmidhuber-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 11:12am PDT

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?

Direct download: AI_in_Industry-Jaan_Tallinn-Mixdown_1_1.mp3
Category:Artificial Intelligence -- posted at: 3:22pm PDT

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.

Direct download: AI_in_Industry-Marshall_Choy-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 8:50am PDT

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.

Direct download: AI_in_Industry-Danny_Lange-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 9:01am PDT

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.

Direct download: AI_in_Industry-Tom_Livne-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 11:13am PDT

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.

Direct download: AI_in_Industry-Tom_Livne-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 11:13am PDT

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

Direct download: AI_in_Industry-Brooke_Wenig-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 2:02pm PDT

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?

Direct download: AI_in_Industry-Irakli_Beridze-Mixdown_2.mp3
Category:Artificial Intelligence -- posted at: 11:02am PDT

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

Direct download: AI_in_Industry-Lars_Mahler-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 2:33pm PDT

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.

Direct download: AI_in_Industry-Zhigang_Chen-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 11:53am PDT

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.

Direct download: AI_in_Industry-Tim_Chang_-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 1:33pm PDT

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.

Direct download: AI_in_Industry-Mike_Edelhart-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 8:41am PDT

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

Direct download: AI_in_Industry-Abinash_Tripathy_-Mixdown_1.mp3
Category:Artificial Intelligence -- posted at: 11:07am PDT

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.

Direct download: AI_in_Industry-Jason_Sundram-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 9:53am PDT

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

Direct download: AI_in_Industry-Joan_Peckham-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:01pm PDT

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.

Direct download: AI_in_Industry-Yufeng_Deng-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 1:54pm PDT

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.

Direct download: AI_in_Industry-Pedro_Alves-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:26pm PDT

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.

Direct download: AI_in_Industry-Sharda_Cherwoo-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 12:44pm PDT

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

 

Read the full interview article on Emerj.com

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

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

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

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

Read our full interview article on Sunday at Emerj.com

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

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

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

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

Read the full interview article on emerj.com

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AI has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. Searching for content within videos is more challenging because video is not just voice and sound, it is also a collection of moving and still images on screen. How could AI work to overcome that challenge?

In this episode of the AI in Industry podcast, we interview Manish Gupta, CEO and co-founder of VideoKen, about the future of video search as machine learning is increasingly integrated into the process. Dr. Gupta talks about how video is becoming more searchable and discusses his own forecasts about what that will look like in the future. He also predicts what machine learning will allow Youtube to do as people continue to search for more specific video content.

Our Content Lead, Raghav Bharadwaj, joins us for this interview.

See the full video article here: www.techemergence.com/machine-learning-video-search-video-education-how-it-works/

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Companies with wells of data at their disposal may find themselves asking how they can use them in meaningful ways. Generally speaking, a clean set of data is the foundation for AI applications, but business owners may not know how exactly to organize their data in a way that allows them to best leverage AI. How exactly does a business transition from having data with the potential for usefulness to having data that’s going to allow for an accurate, helpful machine learning tool—one that can actually help solve business problems?

In this episode of the podcast, we speak with Bryon Jacob, Co-founder and Chief Technology Officer at data.world, a company that offers products and services that help enterprises manage their data. In our conversation, Bryon walks us through the common errors companies make when creating and organizing data sets, and how these companies can transition to a more organized and meaningful data management system.

The details in this interview should provide business leaders with a better understanding of some of the processes involved in getting started with AI initiatives, and how to hire data science-related roles into a company.

See the full interview article with Bryon Jacob live at: 

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

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

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

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

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

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

 

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

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

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

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

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

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

 

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

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

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

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

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

 

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

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

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

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

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

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

Read the full article:

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

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

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

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

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

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

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

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

Episode summary: In this episode of AI in Industry, we speak with Andy Terrel, the Chief Data Scientist at REX - Real Estate Exchange Inc., about how AI is being used in the real estate sector today.

Looking ahead ten years into the future, Andy paints a picture of the areas where he believes AI will change the real estate business. Andy explores how marketing in real estate might change in the future with chatbots and conversational interfaces in real estate which are high value per ticket interactions - a process that will likely vary greatly from the chatbot applications we see for smaller B2C purchases (in the fashion sector, eCommerce, etc).

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

https://www.techemergence.com/ai-use-cases-future-real-estate/

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

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

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

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

 

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Episode summary: In this week’s episode of AI in Industry, we speak with Larry Lafferty, the President and CEO of Veloxiti. Larry has been building large AI projects for DARPA and other large private companies for the last 30 years.

In this interview, Larry explains three critical factors to applying artificial intelligence in the enterprise (with insights especially relevant for companies who aren’t very familiar with AI and data science).

AI vendors and business leaders should find the “how to” insights in this interview useful – particularly Larry’s details on organizing data and defining an AI-applicable business problem.

Interested readers can listen to the full interview with Larry here: https://www.techemergence.com/how-to-apply-ai-…h-larry-lafferty/

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

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

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

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

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

 

 

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

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

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

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

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

Episode summary: One of the key challenges that enterprises face in adopting artificial intelligence is finding skilled data science talent; ). Business leaders want to know when it's best to hire AI talent, to "upskill" existing workers, or simply to bring in AI consultants - and the answers aren't always obvious.

In this episode of AI in Industry we speak with Nikolaos Vasiloglou from MLTrain about how AI consulting and AI training events can be used to upgrade an existing team’s skills. Nikolaos also distinguishes the right and wrong circumstances to bring on AI consultants, and shares his tips on how training, upskilling, and consulting can level up an existing company’s AI capabilities.

Listeners can find out  how to set realistic goals for re-training existing teams for new AI skill sets. Lastly, we also explore how AI consultants can support developer and engineering teams to produce fruitful real-world AI applications (without developing unhealthy reliance on outside experts).

Interested readers can also listen to our previous episode of AI in Industry (here) where we look at overcoming the data and talent challenges of AI in life sciences

Interested readers can listen to the full interview with Nikolaos here:https://www.techemergence.com/whats-the-value-of-ai-events-and-consulting/

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

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

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

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

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

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

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

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

Interested readers can see the full interview with Ail here:

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

 

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

Episode summary: There are many challenges to bringing AI into an enterprise for example the lack of skilled AI talent, or issues around data organization. In this week's episode, we focus on AI adoption in the enterprise from an investor’s perspective.

We expect that founders looking to sell B2B enterprise AI-products and people in enterprises who are looking for the right qualities in an AI firm which would ease integration, would find this episode relatable. We speak with Rudina Seseri from Glasswing Ventures about what are the pain points for AI integration in the enterprise and at the other end of the spectrum, some factors that are aiding AI adoption.

Interested readers can see the full interview with Rudina here:

https://www.techemergence.com/reducing-friction-ai-adoption-enterprise-rudina-seseri/

 

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

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

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

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

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

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

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

Episode Summary: Machine learning (ML) can be used to identify objects and pictures or help steer vehicles, but is not best suited for text-based AI applications says Robbie Allen, founder of Automated Insights.

In this episode of AI in Industry, we speak with Robbie about what is possible in generating text with AI and why rules based processes are a big part of natural language generation (NLG). We also explore which industries are likely to adopt such NLG techniques and in what ways can NLG help in business intelligence applications in the near future.

You can listen to the full interview with Robbie here:

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In this episode of AI in industry, we speak with Michael Johnson, the director of research and innovation for Interactions llc, in Boston MA. Michael explores the inbound (human to machine) and outbound (machine to human) applications of voice based natural language processing (NLP) and also talks about attaching a timeframe to how soon small and medium enterprises (SMEs) would have access to this technology in a financially sensible manner.

 Although NLP is often associated with chat or text interfaces, voice is important for applications in call centers, mobile phones, smart home devices, and more. In addition, Michael explains that voice involves unique challenges that text does not have to deal with - including background noise and accents, which need to be overcome to deliver a good user experience.

 See the full interview article with Michael Johnston live at:

www.techemergence.com/overcoming-challenges-spoken-voice-based-natural-language-processing-nlp-business-use

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

In order to shed more light on the growing applications of natural language processing, we speak with Vlad Sejnoha (CTO of Nuance Communications) about the current and near-term applications of NLP for voice and text across industries.

In this podcast interview, Vlad breaks down real-world NLP use-cases in industries like banking, healthcare, automotive, and customer service.

For the full article of this episode, visit:

TechEmergence.com/natural-language-processing-current-applications-and-future-possibilities

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

This week on AI in Industry we interview Vito Vishnepolsky of Clickworker. Clickworker is a large microtasking marketplace that crowdsources the search optimization work for many of the world's leading search engines.

So how does crowdsourced human work play a role in making sure eCommerce and media searches give users what they want? That's exactly what we explore this week. Vito’s perspective is valuable because he has a finger on the pulse of crowdsourced demand, handing business development for various crowdsourced AI support services - both for tech giants and startups.

Read the full article online at TechEmergence:

TechEmergence.com/how-microtasking-helps-optimize-ai-based-search

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

Sales forecasting is big business. If you can better predict how much of a certain product or service you will sell in a given day, you can better stock inventory, better staff your facilities, and ultimately keep more margin in your business's accounts.

This week on AI in Industry we interview Dr. John-Paul B Clarke, professor at Georgia Tech and co-founder / Chief Scientist at Pace (previously called "Prix"). Dr. Clarke shares details about how sales predictions are done today, and what AI advancements may allow for in helping businesses sell everything from groceries to hotel rooms.

Read the full interview article online at: 

techemergence.com/ai-sales-forecasting-works-matters

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

In this episode of AI in industry, Innoplexus CEO Gunjan Bhardwaj explores how pharma giants are working to overcome two critical challenges with AI: Data, and talent.

Pharmaceutical data is challenging because the same term (say "EGFR") might be referred to as a "protein", a "biomarker", or a "target". Gunjan explores how this kind of relevance and context for data - and how pharma companies may need to hire the talent issues involved with making life sciences and computer sciences teams work together productively.

See the full interview article online at:

techemergence.com/overcoming-data-talent-challenges-ai-life-sciences

 

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

This week’s episode covers the medical applications of machine vision for the diagnosis and treatment of cancer. Medical science has integrated AI since the late 90s, and it’s been useful in the fight against cancer. This week’s guest is Dr. Alexandre Le Bouthillier, founder of Imagia. Imagia is a medical imaging company which specializes in using AI and machine learning to detect cancer in its early stages so that oncologists can make quicker, more accurate diagnoses for patients.

AI is a useful  tool in the detection of breast cancer, colon cancer, and lung cancer. It can even detect genetic mutations, something humans certainly cannot. Learn just how important AI has been over the last two decades in developing the medical infrastructure necessary for patients to have a chance at surviving and even curing their cancer.

See the full interview article - with images and audio included - on TechEmergence:

TechEmergence.com/the-future-of-medical-machine-vision-possibilities-for-diagnostics-and-more

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

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:

TechEmergence.com

Direct download: AI_in_Industry-Rajkumar_Bondugula-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 2:43pm PDT

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:

TechEmergence.com

Direct download: AI_in_Industry-Bob_Baxley-Mixdown.mp3
Category:Artificial Intelligence -- posted at: 5:06pm PDT