atch the full interview with Igor Jablokov.

Pryon CEO Igor Jablokov’s Vision for Democratized AI

Join Machine Meets World, Infinia ML’s ongoing conversation about AI

James Kotecki
Jul 13 · 24 min read

Episode Highlights

This week’s guest is Pryon CEO Igor Jablokov.

AI for Everybody

“Everybody’s going to have access to an AI that can help them do their jobs better and we hope that that’s going to reduce the delta between the haves and the have nots.”

Unimaginable AI

“…there’s a fixation for, ‘Hey, we need an AI to be explainable and so we have to map the types of activities that it’s doing that are going to be displacing certain costs or alleviating shortfalls that we have in terms of human production.’ But in reality, they’re going to be doing new things. We just don’t have a great grasp of what those new things are going to be or how to model them, or how to even describe them.”

Creating the World We Want to Live In

“…people draw inspiration from art. That’s why both my parents were artists and not technologists, but it’s two sides of the same coin. Innovation is a highly creative process. That’s why we’re really passionate about it because we get to essentially create the world that we want to live in, which is mind-blowing to me and how fantastically fun that could be for all of us.”

Watch the show above. You can also hear Machine Meets World as a podcast, join the email list, and contact the show.

Audio + Transcript

James Kotecki:
From Infinia ML, I am James Kotecki, and this is Machine Meets World. We’re talking artificial intelligence today with my guest, the CEO of Pryon, Ivor Jablokov, welcome.

Ivor Jablokov:
Thanks for having me.

James Kotecki:
Igor, you and I have chatted before, but for folks that are just meeting you in this interview for the first time, can you just kind of describe your background? And I think people would be especially interested in your background contributing to Amazon’s Alexa, and then how that connects to the work that you’re doing today.

Ivor Jablokov:
Sure. So I started my career in IBM Microelectronics, and about halfway through that, I ended up joining the IBM Pervasive Computing Division. At the time, it was the island of misfit toys and that’s where a lot of things that people take for granted nowadays, like artificial intelligence and internet of things were being just stated in the company. And so we had some crazy ideas. We said, “Hey, we’re doing these joint projects with Sony, Toshiba on something called the cell architecture for the PlayStation. Why don’t we put a microphone on that?” And everybody started laughing, saying, “Hey, nobody’s going to put a microphone in their house.” And so the following year, we said, “Oh my gosh, let’s not put embedded speech in that device, let’s make cloud-based speech recognition and that way we can free up the local resources for the graphics processing.” And everybody was laughing again, saying, “Hey, no enterprise worth their salt is ever going to allow their customer data to leave their data centers.”

Ivor Jablokov:
And then in the third year, we said, “Oh my gosh, not only can we transcribe people’s speech, we can also answer any questions any humans will ever have.” And by then, people were losing themselves that it was an impossible task. And so we ended up departing and we founded a startup called Yap way back when. Started very small of course. Got seed funding, got venture capital funding. A year after its foundation, we’re joined with folks that used to work on the iPod. So there was an iPod R&D team that we crossed paths with, that ended up leaving Nvidia. And we had a go of it. We were blended into a prototype iPhone that never saw the light of day. And by the end of our experience there, we had dozens of enterprise and carrier customers. We were being used for call mining, for voicemail attacks, messaging, searches. And that’s when Amazon came calling on us and did the acquisition. Google tried to get us as well, but our research team wanted to go somewhere where it was going to be a blank slate opportunity.

James Kotecki:
And so your technology, how much of that was put into Alexa? What’s the right way to frame that?

Ivor Jablokov:
Well, let’s put it this way, what I hear through the grapevine is they still use the word Pryon as the wake word for prototype Alexas and there’s still something called [Yap Stats 00:03:08] where a lot of the transactions now flow through as well, for them to inspect. Look, like anything, right, we’re a company of several dozen people. Amazon has added thousands of people to this issue. So after a while, sure, you can do the DNA test and your 23andMe still has your startup in there, but the amount of sustained investment that an Amazon, a Google, an Apple can make is just immense. Given the amount of time and material they can throw against it. So we’re just blessed to be part of the early stages of it.

James Kotecki:
And then catch us up to the present day. Because you said, in Amazon, they still use the code word or the wake word Pryon for kind of ton of tests. And Pryon was word that you were using when you were at Yap, I gather, and then you took that word Pryon and made it the name of your new company, which does what?

Ivor Jablokov:
Yeah. It’s a augmented intelligence for the enterprise. It’s basically, how do we bring that experience that people have at home into the workplace? Now, the problem at work is it’s not a homogeneous environment like we have at home. At home, we buy into Apple’s HomeKit or Google Assistant, or the Samsung ecosystem or things of that sort. But when we bring it to work, most of these large scale enterprises grew up through M&A transactions, and so they have a hodgepodge of systems, applications, and data that they’ve gotten collapsed through the decades that they’ve been operating, and so it’s a much tougher problem. And so to think about bringing a natural language interface, on top of all of those things, is a great problem solved.

James Kotecki:
I want to pause for a second and go back to something you said at the very beginning when you were introducing yourself. You’re talking about how people were laughing at you basically, or maybe, at least, certainly poo-pooing the ideas that you had. No one’s ever going to want to put a microphone in, no one’s going to want to do this kind of… Put language in the cloud. As a CEO, as a leader in this space, how do you figure out when it’s worth pushing through an idea that people are laughing at and when to take their skepticism seriously? Because, certainly, there’s a case of kind of selection bias, right? I’m a part of successes, right? And you were certainly a success because you were acquired by Amazon.

James Kotecki:
But there probably were a lot of people who had ideas that other people laughed at, they press forward and guess what? It turns out those ideas were bad, which is why they were getting that kind of feedback. So how do you, especially in the AI space where everything is moving forward so quickly and can be lot of bluster, how do you differentiate between good and bad ideas?

Ivor Jablokov:
Well, I mean, in their defense though, they were also trying to allocate scarce resources, right? And from their standpoint, a product line manager have to show up with an idea that can deliver a hundred million plus in contribution to the company within 12 months of sustained investment. And so they look through these different options that are presented to them and then they figure out which which ones are going to fly, and-

Speaker 3:
I didn’t get that. Could you try again?

Ivor Jablokov:
Which ones are aren’t going to. So from that standpoint, they’re always looking for these sure things.

James Kotecki:
And I think I hear a voice assistant actually talking in the background. Did we accidentally wake one up?

Ivor Jablokov:
Well, we did. We did, unfortunately. There’s-

James Kotecki:
Okay. Well, maybe this voice assistant can contribute to some of these questions I’m asking you. So let’s talk about the advancement of AI as people perceive it. People often perceive it and it’s often framed — and I noticed that on your website, your marketing does some of this as well, which is you frame AI up as an assistant to humans. An assistant, typically, that is kind of doing tedious tasks, offloading things that humans either don’t want to do or it would take them a long time to do, and they’re better served elsewhere anyway. Do you think that’s the right framing going forward? Because it seems to me, eventually, that framing, while useful in the short-term… Because you just think about what people are doing and you offload some of that. It seems like AI is a fundamentally different technology than just adding another person into the mix. And so if you just frame it in human terms like that, do you miss out on opportunities for what it really could be doing?

Ivor Jablokov:
James, I have to tell you that’s one of the most excellent questions I’ve ever gotten and here’s why. From a marketing standpoint, practitioners frame it that way because it’s easy for people to connect the dots on why it’s useful and how it drives a business case. Right? To make people more productive or to reduce costs, right? But Fred Jelinek famously said, “Airplanes don’t flap their wings.” Right? So there’s a fixation for, “Hey, we need an AI to be explainable and so we have to map the types of activities that it’s doing that are going to be displacing certain costs or alleviating shortfalls that we have in terms of human production.” But in reality, they’re going to be doing new things. We just don’t have a great grasp of what those new things are going to be or how to model them, or how to even describe them.

Ivor Jablokov:
I know when I talked to a fellow who was leading AI for one of the defense services, he said, “We don’t care about explainable AI, we just need it to work.” And so from that standpoint, I think as people get more comfortable with these technologies, they’ll realize new things that these things can do that is beyond our comprehension of what’s actually happening, but they’re still performing units of work that are valuable to organizations. Just right now, it’s easier to say,” Hey, it’s replacing a horse and buggy.” Right? So it will get you to your destination faster than you can conceive of. But nobody was saying, “Hey, this replacement for a horse and buggy can now take a space shuttle from one pad or another.” Or, “It can drive itself on the moon if you have this type of vehicle.” We’re still so early that all people can do is compare it to a one horsepower tool, right?

James Kotecki:
And nobody calls it the horseless carriage anymore, which is of course what they had to call it in the beginning because that was the only way people that way people could conceive of it. I mean, it’s almost like a paradoxical problem, right, because it’s like how do you predict things that, by definition, can’t be predicted by the human brain? But are you able to look around the corner just a little bit more than the average person? I just want to push you on this point just one more time and see if I can get any kind of predictions about the kinds of things that this kind of thing could do, in as much as it can be explained, that just aren’t easily frameable in human replacement terms.

Ivor Jablokov:
Look, the people that can see far ahead of us are what? The folks writing science fiction. Right? That art form is pushing all possible boundaries because it’s looking at what’s happening in life sciences, what’s happening in AI, what’s happening in quantum computing, so on, and what’s happening in physics and trying to extrapolate possible futures for us. Right? They’re literally going in all directions. This is what happens if we’re under the ocean, this is what happens if we’re off-world, this is what happens in between. This is what happens if family units are described in different ways, this is what happens when we’re blended with machines. So they’re the ones really going out and looking at all possible futures for humans, for hybrids, for non-humans. Right? Let’s put them in three easy buckets.

Ivor Jablokov:
People like us, what we have to do, for the work environment, is look ahead five years and work backwards from that. Right? Because that’s the only way that you’re going to be able to attract venture capital, is to say, “Applying capital towards this problem is going to lead to this outcome over the course of the next half decade. And these enterprise customers are buying our product today, and they’re also buying this roadmap because we’re going to converge to where they want to be in five years time, as they go ahead and compete with one another, and try to all become trillion dollar plus market cap companies by reaching that level of productivity.” So for us, we’re half-futurists in some ways because we can see 5 years ahead, instead of 10, 15, 25, where things get a little bit divergent. For us, it can’t be divergent. It has to converge at that five-year mark, and then work backwards.

James Kotecki:
What sci-fi inspires you? I heard that the Y in Pryon, and we can look at the… I don’t think this logo that we see above your head, but I think a previous version of the logo had the Y as kind of the flux capacitor from Back to the Future. So I know that you’re thinking about sci-fi and what other kind of works of fiction inspire you in that realm?

Ivor Jablokov:
Yeah, well, even this logo, here, has a hidden puzzles in it as well. But the flux capacitor, look, Back to the Future, right? You’re working backwards from something that you know and then trying to get to that outcome. It was also the three dots for the first locations where we started the company and also the Y in Yap, in terms of the origins of some of our work as well. The color blue, right, which is a little bit of IBM, a little bit of the Alexa color, as well, that we used in there. So we tend to hide lots of little puzzles in our work. I think half my tweets have puzzles hidden in them as well. I think from my standpoint, science fiction, look, a foundation. I’ve said this before, the fact that-

James Kotecki:
Facts gets off.

Ivor Jablokov:
Yeah, the fact that you can absorb that… And I remember reading it as a kid, for one of the summers our parents sent us to a monastery to learn how to paint and work on a farm, and what have you, and I remember reading it by candlelight and just my mind being blown that, “Oh my gosh, one day there’ll be math that can predict these outcomes.” And if you know that, then you can put steps in place in order to either achieve those outcomes or deflect some of the negative circumstances that are happening. And now, with, certainly, the rise of big data, machine learning, and everything else, he predicted it pretty nicely. Right? It’s almost like Leonardo da Vinci predicting helicopters way back when.

Ivor Jablokov:
So, look, whether it’s Bezos talking about the Star Trek inspired him for the Amazon Echo or others, I mean, people draw inspiration from art. That’s why both my parents were artists and not technologists, but it’s two sides of the same coin, right? Innovation is a highly creative process. That’s why we’re really passionate about it because we get to essentially create the world that we want to live in, which is mind-blowing to me and how fantastically fun that could be for all of us.

James Kotecki:
Let’s swing all the way back from the far flung future and snap right back to the very immediate presence. I talked to you four months ago, just as the kind of national lockdowns we’re starting, especially where we live here in North Carolina. Now it’s been four months since then, we’ve had four months of kind of a pandemic lockdown living and working from home, and all kinds of things. What has the pandemic taught you about the way that people work with and adapt to artificial intelligence?

Ivor Jablokov:
Okay. So let me go more general than that. So I think the tech industry fetishized all these technologies that we’re on, right? Look at these Zooms, Slacks, things like that. Look, everybody can work from home, we can be disconnected, we can live in a virtual reality world, right, where we never have to go into a workplace. And after most of us have experienced it over the course of the last quarter or so, we found how much of that was bunk. Right? That we’re missing the human connection of experiencing these worlds for ourselves, interacting with people, interacting with museums, interacting with restaurants, interacting with different cultures, going to different places, and what that does for our cognitive ability and brain elasticity. And so a half of what we’ve been thinking would be new and novel, and interesting and drive more efficiencies, we saw how much was removed from our experiences.

Ivor Jablokov:
So that’s one lesson, I think, all of us took from all this. That, “Hey, you know what? We’re actually still human. We’re humans that use technology, but we’re still human too.” Right? And we’ve been human far longer than we’ve been humans with technology. And then the second thing is, “All right, how do we reorder the things that we’re building, in order to benefit people that are trying to remediate the pandemic?” In our case, it was working with companies like K4Connect, which is backed by Intel Capital. Scott’s team is great, they’re trying to build technical solutions in senior living communities and we felt like, “Hey, that’s going to be an area of technology that nobody else is going to touch because they don’t understand it and here’s a great partner that’s trying to envision how virus-related intelligence goes to their caregivers in order to help that population, since they’re the most impacted by this issue.” And then, certainly, other places like hospital systems and what have you, that we’ve been working behind the scenes.

Ivor Jablokov:
The rest, I think, when we were pitching things like knowledge assistance to these Fortune 500 companies, it was a nice to have. Because they’re like, “Hey, whatever. We can always tap on the shoulders of our people and get them information that they need, and there’s all these other ways to get them information.” Now, they’re all beating a door down. They’re saying that whole, what are called a process for us, having staff meetings where we can brief people on post has all broken down and we need a better way of delivering knowledge to them.

James Kotecki:
You’re talking about the way to use AI to, effectively, search within your company’s corpus of knowledge, documents, emails, messages, whatever, and extract insights from that, especially in a world where you don’t work in the same building. Is that what you’re talking about?

Ivor Jablokov:
Yeah. Yeah. Or just staying in touch. All of these things that we said, eventually, there would be AI in the center of all of these companies, where I know people have been using these cliches saying, “That’s years ahead.” Well, years have transformed into months, and months have been transformed into weeks, weeks have transformed into days.

James Kotecki:
And tell me a little bit more about what’s going on with K4Connect. So I am an elderly person and I’m in a nursing home, and the pandemic is raging, what, especially for people who don’t know what K4Connect does, I know a little bit about them, but what is my experience now, thanks to this partnership that you have?” Or maybe the experience is on the caregiver side. What are they able to do now, thanks to what you’re bringing to the table?

Ivor Jablokov:
Well, think of this way, right? So the caregivers want to maximize their time working with the residents, right? And one of the things that you can’t do is go into Google every time you have a question of, “Okay, how do I deal with an Alzheimer’s patient, to try to install a mask on them? What do we do with these new activities that they’re used to staying in connection with each other, but now I can’t do that? So how do I replace that activity?” And think about how ludicrous it is whenever we have different medical tests that we undertake. Right? And then you read something in your blood, “Oh, your potassium level is low,” and blah, blah, blah, blah, blah. And then we’re not sure what that means, and we go ahead and copy and paste that into Google. Right? In order to find that out.

Ivor Jablokov:
Well, that should be a private search, right? Anything related to ourselves, medical issues, and things of that sort shouldn’t actually be going to open-source consumer sources and stuff like that. They derive ads from that, they end up learning something about us that we probably don’t want them to know, and so I think a lot more of that… Think of even a big enterprise contemplating M&A, right? So they’re trying to figure things out about these companies and yet they’re revealing their targets by having their employees typing in the company names that they’re interested in there. And that gives, what, an unfair advantage to the larger tech companies that are hoovering all of this information to understand what the industry’s buying patterns are likely to be over the course of the next half decade.

James Kotecki:
So it’s about making AI more private, more customized to the needs of patients in caregiving setting.

Ivor Jablokov:
Right. Make it more private and make it faster so that people can get curated knowledge that’s specific to the things that they do, so they don’t have to wade through the open-source morass.

James Kotecki:
And I think, you and I have discussed this before, but given your background, it’s important to clarify I think, this is not necessarily a matter of people doing voice searches, right? For you, it’s more about the AI on the backend of, you can get questions in there from typing it or asking it, or maybe eventually thinking it and it can pick up your brainwaves. But for you, it’s really about the AI in the backend. It’s answering those question however they come in.

Ivor Jablokov:
Yeah, we built an insight engine. Whether you want to type into it, in natural language, whether you want to talk to it or eventually provide other forms of input. Whether it’s a human providing input or maybe you have an automated process that’s asking for the query is just available to you.

James Kotecki:
I want to talk to you about an article that you wrote in January, which is six months ago, it seems like six years ago, in Forbes, and the title, I’m just going to read it till I get it right, was, “What Politicians Don’t Understand About the AI Debate.” And of course, there’s been a lot of discussion about what the government is doing right and wrong in the middle of the pandemic, but of course AI is still progressing. So what did you say in that article about what politicians don’t get about AI? And then, now flashing forward six months, what would you say if you wrote that article today?

Ivor Jablokov:
Yeah, so I feel it’s election-era fear-mongering in order to get people to press one button or another, about that particular issue. The point is, humans have adapted to new forms of technology in the past, and yet the challenges that we encounter, we always assume arrogantly that these are the biggest challenges that humanity has ever faced. Nobody’s ever faced a threat like AI in the past. And guess what? People said about industrialization, people said this about the Information Age, so on and so forth. And somehow, because we work on relatively geologic time, we can deal with it. We can absorb, people can retrain, people can adapt, people can say, “Hey, I’m not going to get this college diploma, I’m going to get that one because I’m going to predict that it’s going to be more relevant to where the market’s going to be 15 to 30 years down the road.” And guess what? When I even look at my diploma, relative to what I do now, I already see a drifting and hyperspecialize, right? Just from getting a generic computer engineering degree.

Ivor Jablokov:
So from that standpoint, that already happens within our lifetimes. Nobody should be entering higher ed assuming that, 30 years in the future, you’re going to be doing that same thing. You’re going to be an eternal student. It’s a double-edged sword too, right? The negative side is you’re going to be impacted by AI, but the positive side is you’re going to be impacted by AI. Right? It’s going to be helping you adapt and learn at a brisker pace than even I’ve been able to do. And the ones that know how to leverage that are going to have an advantage, an economic advantage.

Ivor Jablokov:
There is a big vision for this company and here’s what it is. When we talk about how AI is going to disrupt things. The thing that triggered the Information Age was that only-large scale companies could afford computing, right? These were the big mainframes, right, that IBM and others were driving into these enterprises. And it took visionaries like Steve Jobs and Bill Gates in order to democratize access to that, and drop a PC or Mac on everybody’s desktop. And so the same thing’s going to… And that allowed the Information Age to be democratized for the rest of us and to onboard ourselves to the information superhighway.

Ivor Jablokov:
That same thing’s coming for AI. Right now, this is the playground for a relative few data scientists, for big companies that have the hardware and software to compete. Sooner or later, that will get democratized, it will be dropped on everybody’s desktop. It doesn’t matter if you’re a doc worker, if you’re a nurse, if you’re a cafeteria worker, if you’re a lineman, everybody’s going to have access to an AI that can help them do their jobs better and we hope that that’s going to reduce the delta between the haves and the have nots.

James Kotecki:
I totally get where you’re going with that, but there is one difference between the revolution with Bill Gates and the revolution that you’re talking about now, which was that when the information revolution hit my desk at home, I literally got a computer, a new computer, a piece of furniture on my desktop. Or maybe I got a new graphical interface that I didn’t have access to before. I can drag and drop, I can teach my parents how to drag and drop and put things in a graphical trashcan, and it all kind of makes sense.

James Kotecki:
With AI, a lot of the applications are… A lot of what AI can do is just do the things that you’re already doing, but make them work better behind the scenes. So my question to you, not disputing at all what you’re saying, but will it feel as revolutionary to the individuals who are being impacted or will it just feel more like a gradual sense that everything is getting better? Or at least there’s fewer failures when I do a search or when some kind of filter is in place, or all the things that would use AI already to do.

Ivor Jablokov:
Yeah, so look, I think you’re right. There’s an evolutionary component and there’s a revolutionary component, and the revolutionary stuff doesn’t happen on a schedule, right? So we can predict, “Hey, the new Apple devices coming out later this year are going to most likely have better cameras and faster speed, and this and this. And there’s probably going to be one revolutionary thing that’s going to be a surprise, that gets thrown in there.” But you don’t get more than one surprise, right? And then they may wait on some surprises because something else needs to happen.

Ivor Jablokov:
So it’s not just the software, it’s also the hardware. Right? Edge-based processing that’s disconnected to any sort of cloud service, that’s going to allow to do certain private things completely disconnected from the network. That’s coming, right? And to have software that can better take advantage of that, so that you can use it in more robust environments and disconnected environments, and secure environments. That’s part of that drive and fascination. Think about this way. It was a relative few folks that knew how to solder these boards together, right in the early days of the personal computer, but then somebody figured out the packaging of it. Here’s a screen, here’s a keyboard, the motherboard is in there, just plug it into a power and you’re ready to go.

Ivor Jablokov:
The same thing will have to happen with AI, but on the software front, I deeply believe in natural language, right? Because if you look backwards in time, relative few of us became computer scientists, mathematicians, engineers, and scientists, and what have you, that learned the language of computers, right? The ones and zeros, assembly code, all of the different programming languages you can think of. That was open to a smaller population. With natural language now, with semantic interaction, you can just say things, and stuff happens behind the scenes.

Ivor Jablokov:
Now the machine, the relationship between humans and the machine actually reversed itself, where the machines have enough processing power and enough senses now where they’ll be able to see gestures, right? Whether we’re smiling or not, facial reco, and things like that. Once they fuse all of these senses together, you can just start talking through, “Hey, I want to write a computer program. I want it to do this, I want to do that, I want it to get that information there, I want it to fuse it with this. When you get to this part of the process, I want you to go ask Bob a question about what we should do,” blah, blah, blah. All this stuff gets put together and now you have augmented intelligence, and you didn’t have to have a PhD in data science in order to do that. That’s the breakthroughs that I’m looking for, that are going to, I think, democratize access to it.

Ivor Jablokov:
It’s not just, “Hey, you now have access to this Python library and you have to take 120 hours of MOOCs and God knows what, in order to figure out how it’d work.” No, it’s going to sit there and allow you to be you, and yet take proprietary knowledge you have about these workflows to benefit your own situation. That’s what I’m excited about.

James Kotecki:
I would love to end it there on an optimistic note, but I do have one more question, which is about kind of global power dynamics. Because you talked about election year fear-mongering and that’s of course talking about the US election, but when we look at the relationship between AI and the powers of different nation states, I believe it was Putin who said something along the lines of, “Whoever controls AI, controls the future.” And you can debate how far along Russia is along that path, but there’s probably some grain of truth to that, right? I know you’ve done some national security work, looking at the relationship between entrepreneurship and kind of geopolitical issues, so can you end us on a macro, geopolitical, security note? How do you see AI shaping the balance of power in the coming years?

Ivor Jablokov:
Well, right, I mean, it’s what gets described as hyperwar, right? Is that having AI as part of the intelligence process is helpful. So that the nation states that take advantage of that are going to have greater awareness about the world and how they react with allies and adversaries. And then in a kinetic war or space, it’s also a similar, right? So the entities that are going to be able to react faster, whether it’s drones or… Let’s say there’s a decapitation strike against your chain of command. What happens? Are you able to react and get the remaining forces to counter a strike within a period of time when humans are still going to find it’s a chaotic situation. And so there’s a lot of opportunities for having a proper defensive posture and that doesn’t even include any of the stuff that needs to happen in cyberspace, right? Where all of the attacks, the asymmetric attacks, by folks that maybe can’t go against us pound for pound on the material side, but they can leverage cyber warfare against us in cyberspace. So all of those things are being worked on behind the scenes.

James Kotecki:
And gosh, especially at a time when we were all even more reliant on the internet than ever, to do our jobs, and we’re not physically connected, that becomes even more serious.

Ivor Jablokov:
Yeah. Listen, forget about the internet, right? Crawl down further in Maslow’s hierarchy. Right? Your water supply and your electrical supply, and everything else that you can think of, in your hospitals and everything else, those are all interconnected now and somebody that can figure out… What happens if you open up your tap water and nothing flows out anymore? That’s a problem. Without water, forget it. Then it’s like, “Okay, fine. Now we can disrupt the food supply chains.” Those were some of the most worrisome things in the pandemic, right? Look at the island of Manhattan, right? Where within 48 hours time, if you disrupt supply chain, there’s no food on the island, right? People don’t realize how just in time manage those supply chains are right. So those are the things that those decision makers are worried about.

James Kotecki:
Well, we kind of flipped it because I like to usually end on an optimistic note, which you did in the previous question, and this question was a little bit more chilling, but certainly food for thought. Ivor Jablokov, CEO of Pryon, thank you so much for being a guest today on Machine Meets World.

Ivor Jablokov:
Thanks for having me.

James Kotecki:
And thank you so much for watching. Machine Meets World is a production of Infinia ML. You can sign up for our email list if you go to infiniaml. com, a little pop up will pop up and you can sign up for our email there, you can listen to this as a podcast, you can get it as a video, you can do all kinds of stuff. We really appreciate it. I am James Kotecki and that is what happens when Machine Meets World.

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Originally published at https://infiniaml.com on July 13, 2020.

Machine Meets World from Infinia ML

Weekly Interviews with AI Leaders

James Kotecki

Written by

VP of Marketing & Communications for Infinia ML, a machine learning company. Speaker from North Carolina to South Korea.

Machine Meets World from Infinia ML

Infinia ML’s weekly interview show with leaders in artificial intelligence.

James Kotecki

Written by

VP of Marketing & Communications for Infinia ML, a machine learning company. Speaker from North Carolina to South Korea.

Machine Meets World from Infinia ML

Infinia ML’s weekly interview show with leaders in artificial intelligence.

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