AI Cameras Revolutionizing Transportation and Beyond: A Conversation with Chris Piche, Founder and CEO of Smarter AI

AI Cameras Revolutionizing Transportation and Beyond: A Conversation with Chris Piche, Founder and CEO of Smarter AI

Hello, welcome to Secure Talk, your trusted source of information on the latest threat trends, tools, and technology related to cyber-security and compliance. In this episode, we have a fascinating conversation lined up for you with an industry visionary who is at the forefront of innovation in the world of AI-powered cameras and unmanned systems. Our guest today is Chris Piche, the Founder and CEO of Smarter AI, a pioneering software platform for AI cameras. With a background in transformative technologies and years of experience in the industry, Chris will shed light on how AI cameras are revolutionizing transportation, security, and beyond.

Join us as we delve into the cutting-edge advancements in AI cameras and the crucial role they play in autonomous driving technology. Chris will share his insights into the challenges and opportunities of securing these cameras against cyber threats, ensuring that the data they capture remains safe and confidential. From detecting complacent or distracted driving to transforming the way we perceive and address road safety, AI cameras are poised to make a profound impact on our daily lives.

Let’s jump into this enlightening discussion with Chris Piche, where we explore the future of AI cameras and the exciting possibilities they hold in reshaping the landscape of transportation and beyond.

Mark Shriner: Welcome to Secure Talk. My name is Mark Shriner, and I’ll be your host for this episode of Secure Talk. Today we’ll be talking with Chris Piche, who is the Founder and CEO of Smarter AI. And we’ll be talking to Chris about something called AI-powered cameras, AI infrastructure, maybe fully autonomous vehicles, and the future of AI cameras and unmanned systems. But before we do that, I want to say hi to Chris. Chris, how are you today?

Chris Piche: I‘m great. Thanks for having me, Mark.

Mark Shriner: It’s my pleasure. And whereabouts you located?

Chris Piche: I’m in the greatest city in the world, Las Vegas, Nevada.

Mark Shriner: Really? It’s interesting because I looked at your LinkedIn profile. And I saw a couple of other cities. So I was like, you did live in Singapore for a while?

Chris Piche: I lived in Singapore from 2015 until 2020.

Mark Shriner: I was there from 2008 to 2012 and have been going there off and on for many years for business. But what’s the biggest contrast for you Singapore and the greatest city in the world, Las Vegas?

Chris Piche: I’ll go with breakfast.

Mark Shriner: You get those free local breakfasts. The locals get a lot of good deals.

Chris Piche: I don’t know about free, but you can get something good for $1 or $2.

Mark Shriner: That’s pretty much free these days. I looked at your LinkedIn profile, and you’ve worked for a variety of different ventures and different companies, several of them in the AI space. Why don’t you just give me a little bit of background about how you got into this space? And exactly what is Smarter AI?

Chris Piche: About ten years ago, I was running a small company in Canada where I’m from. It was called Eyeball Networks. Eyeball Networks was the world leader in something called NAT Traversal technology. So in other words, we were the world leaders and in something most people have never heard of. But fortunately for us, it turned out that NAT Traversal was a foundational technology for smartphones. And our NAT Traversal technology was acquired by another great Canadian company called BlackBerry. And we became a small part of the BlackBerry smartphones.

Mark Shriner: Awesome. Can you just back up and explain what that technology that NAT traversal is?

Chris Piche: Sure. In most voice-over IP or video calls, like the call you and I are making now, the call has to traverse a couple of NATs. NAT stands for(Network Address Translation). NAT is a security feature of most routers and firewalls. NATs were designed for a couple of reasons, including the modern shortage of IP addresses, which the founding fathers of the Internet did not anticipate.

Mark Shriner: And is never going to catch on a few of these addresses.

Chris Piche: They anticipated such widespread use of the Internet. So IPv4 doesn’t have enough capacity for all of the Internet-connected computers and mobile devices today. It was one of the original motivations for Network Address Translation to multiplex many connected devices onto a single public IP address. Of course, we’re here to talk about security today. And an added benefit of Network Address Translation is that it hides the private IP address of a computer or mobile device from the public Internet. So for those two reasons- scalability and security – Network Address Translation is built into most Internet routers and firewalls, including mobile data networks which connect mobile devices. Then BlackBerry developed a smartphone to compete with the iPhone and FaceTime. BlackBerry wanted to make its ubiquitous BlackBerry Messenger service the killer feature of the BlackBerry smartphone with video calling. But for BBM video calls to work, it needed NAT Traversal technology to deliver video packets across these NATs in 3G and other mobile data networks. Eyeball Networks, being the world leader in NAT Traversal, was incorporated into BlackBerry OS and communications infrastructure.

Mark Shriner: Let me just stop you there. Because I think everything’s all connected here. Because you’re in Singapore. I was in Singapore. I actually had a Blackberry phone. I was working for a Swiss company. We had a lot of customers in the banking sector. So big requirement for security. So they’re like, “No, no, no, you can’t have your iPhone. You’ve got to use a BlackBerry device.” And I remember, I’m still like, my self-esteem is still not fully recovered. But I was hanging out with a bunch of friends down at Clark Quay having beers, and everybody had their super slick iPhones, and I brought out this big silver Blackberry, and people were like, what is that? Is that a Soviet device or something?

Chris Piche: Did it have the keyboard?

Mark Shriner: It did. Anyway, please continue. So that led into.

Chris Piche: That led to me spending a year helping to integrate our NAT Traversal technology into the BlackBerry operating system and BlackBerry Messenger and launching the first BlackBerry smartphones. I had a front-row seat to how quickly a market could be disrupted by a technology transformation. At the beginning of my year with Blackberry, their stock price was more than $100, and their market cap was $20 or $30 billion, which at the time made BlackBerry by far the biggest company in Canada and one of the biggest in the world. And by the end of my year with Blackberry, its stock price was down to about $6 billion.

Mark Shriner: Oh, my God, you remember that year.

Chris Piche: Everybody watching and listening can do the math. It was about a 95% loss in 1 year. All because of a key BlackBerry metric calleddaily  activations.” Let me explain daily activations. At that time, BlackBerry had 50 or 60 million active subscribers. A typical mobile phone has a life of 2 to 3 years. I’ll use round numbers to simplify the math. If BlackBerry will lose 100,000 phone subscriptions on any given day because my phone is too old, my friends are making fun of me, so I need to upgrade. And if we’re losing 100,000 phones, we have to activate 100,000 new phones per day just to maintain our customer base, and we have to activate more than 100,000 new phones to grow. And during my one year at Blackberry, these daily activations fell off a cliff. Because, even though you were loyal to Blackberry, your friends were replacing them with iPhones.

Mark Shriner: And another interesting thing, though, I remember when all this happened, but one of the markets that were stubbornly bullish on BlackBerry was Indonesia. And I can’t remember the reason why, but the Indonesian market held out for several years.

Chris Piche: Apple was lauded for its iPhone product innovation, but something that Apple did equally well, if not even better than product innovation, was its business model innovation. You may remember that at the time that the iPhone was introduced, it was laughed at by executives from Microsoft, Nokia, and BlackBerry. They said, this is a beautiful phone, but Number 1, it’s going to cripple the mobile networks. It pulls too much data, and the networks won’t be able to support it. And number 2, who is going to spend $800 or $900 for a phone?  Nobody will spend $800 or $900 on a phone. Apple’s business innovation was to approach every number 2 and number 3 mobile operator in every country around the world and tell them, “Look, we have an opportunity for you to leapfrog your competition and become number 1.” All you have to do is give Apple your marketing budget for the next three years, and we’ll put it behind our iPhone advertising campaign. In exchange, we’ll give you the exclusive right to sell iPhones in your country for three years. And because most people replace their phones in less than three years, and we’ll create so much demand for these iPhones that people won’t care whether you’re AT&T, Verizon or T-Mobile, they’re going to buy an iPhone from whoever is selling it. And after three years, you’ll be number 1 in your country. Your observation about BlackBerry in Indonesia was correct. BlackBerry was able to do this by building similar business relationships with Indonesian carriers.

Mark Shriner: I remember something along the lines of just the coverage that the devices had in Indonesia was superior to what the iPhones could get at the time. And maybe it was because they had a relationship with the predominant carrier, I don’t recall, but it was very interesting. So when I went to Indonesia, nobody laughed at me.

Chris Piche: The origin of Smarter AI began a few years after Apple transformed and disrupted the phone market. I was living in Singapore, helping a friend of mine who had founded a fintech company, and searching for my next venture. Eventually, I foresaw that the same transformation/disruption that had happened now ten years ago in the phone market was going to happen in the camera market. You were sitting in Clark Quay with your friends and your Blackberry, and maybe your friends had other feature phones like  Nokia, Ericsson, and Motorola flip phones.  Your feature phones could do two things: make a call and send a text message. That’s all. And I don’t know about you, but I used my Blackberry or Nokia phone for about 5 minutes per day for a couple of text messages and a couple of phone calls, and that was all. Today, instead of 5 minutes, I’ve got five smartphones! And instead of 5 minutes, I use them closer to 5 hours per day. And that’s because smartphones are so much more useful. Instead of doing two things, they can do 200 or 2,000 things. I saw that the same thing would happen to cameras. Legacy cameras, like feature phones, do two things: they either display a video on a screen in real-time or store video on a disk. And even though the video contains lots of valuable data, most of it is untapped because nobody ever watches the video. And I thought that thanks to a couple of enabling technologies, namely neural networks and neural network accelerators, it would soon be feasible to make AI cameras with enough computational capacity to “watch” this video for us. And in the same way that smartphones transformed and then disrupted the phone market, I saw that AI would do the same for cameras. So, instead of displaying video on a screen or storing it on a disk without ever watching it, AI cameras would see, listen and understand what’s happening around them and notify us when something important or interesting happens. And I saw that this would transform and disrupt cameras, just as we witnessed in the phone market.

Mark Shriner: Can you maybe walk me through the use case? I’m assuming that you would train the AI camera, for example, if you’re driving a car and something runs in front of the car, it does a signal or something like and this is probably a basic thing, or if it’s, I don’t know, walk me through a use case of an AI camera?

Chris Piche: Well, there are many use cases. Some of the common use cases that most people will be familiar with are counting and recognizing people, counting and recognizing cars, and understanding roads and traffic infrastructure, like stop signs and road lanes. And these are the building blocks for one of the topics that we’ll get into shortly, autonomous driving. The concept of autonomous driving is based on the premise that the car sees and understands its surroundings and recognizes the objects around it objects, which can then be used to navigate the car.

Mark Shriner: How far are we out from fully autonomous vehicles? You can say, given context, because I understand that they’re more effective in certain scenarios. And they are and others the truck example is, they’re pretty good on the open highway, but you need somebody for that last mile to drive in heavy traffic. But in terms of just like normal passenger cars, how far out are we?

Chris Piche: That’s a  very interesting question. In terms of technology, we’re already either there or we’re very close to being there. The next question is, how quickly will we deploy autonomous driving technology? And this is actually a societal question. Tesla made a very interesting comment about this, I think it was last week. When asked the same question – how quickly will we deploy this technology –  the preface to Tesla’s answer was that its Full Self Driving technology is already ten times safer than a human driver. And that’s very interesting because Elon Musk has very famously declared, every year for at least the last 6 or 7 years, that this would be the year that Tesla would deploy Full Self Driving to its entire fleet, and I think it was 3 or 4 years ago that he declared that Full Self Driving would drive from Los Angeles to New York and back again. As we all know, this hasn’t happened yet. What this shows is that, while Elon obviously understood autonomous driving technology, he didn’t fully understand wthe double standard that we have for accidents caused by human drivers versus autonomous drivers. e Even here in the greatest city in the world,  Las Vegas, car accidents happen every hour of every day,  which is not newsworthy; but if 1 Tesla is involved in an accident, it becomes front page news, with an implicit or explicit presumption that the accident was caused by Tesla Full Self Driving.

Mark Shriner: Or the batteries blew up.

Chris Piche: Yes, or the battery blew up. What Elon didn’t understand was the higher safety standard that needs to be achieved for autonomous driving to be widely accepted.

Mark Shriner: I like the 10 to 1 rule there. But I don’t even think that’s going to be serious enough because you get into these ethical situations where if I’m driving down the road, and a lady pushing her baby buggy jumps in front of me, and I have a choice to swerve around her and hit a school bus. And how does the machine process that? And what decision does it make, and whatever decision is made, it makes, it’s going to be wrong. And it’s not going to be the driver may be somewhat liable. But ultimately, the money is going to it’s going to come back upstream to the software or the camera or the car company. And I’m just wondering, are they willing to take that liability on? So the 10 to 1 thing, I would say that the profitability of these the sales of these autonomous vehicles would have to be significantly higher than the potential downside of these ethical dilemmas in addition to just the regular normal crashes.

Chris Piche: You’re absolutely right. In the United States, drunk driving kills about 10,000 people every year, and complacent and distracted driving kills another 10,000. This is mostly people texting and driving. And you’re absolutely right. We take these statistics for granted. But if autonomous driving causes even one death, we as a society consider this less acceptable than the 10,000 deaths caused by complacent and distracted driving.

Mark Shriner: And both of those are terrible numbers and terrible behaviors. I’m a cyclist, mostly mountain bike, but I do get on the road. And I can’t believe how many cars either go by me or I go by them and people just staring at their phones and it scares me. It’s like, “Wow.”

Chris Piche: I don’t know which part of the country you live in, but wherever you live, you’re very brave to ride a bicycle on the roads.

Mark Shriner: Maybe you can say stupid.

Chris Piche: In Las Vegas, once or twice every year, cyclists are killed by cars or trucks. There was one accident last year where a truck collided with an entire platoon of cyclists, an entire cycling team.

Mark Shriner: That was in Phoenix.

Chris Piche: I think it was on the highway from Las Vegas to Phoenix.

Mark Shriner: It’s pretty scary. So the other issue that I think may be an issue, or may not be an issue, but it’s perceived as an issue. And if it’s perceived societally as an issue, then it’s an issue, and that would be the security of these cameras. So let’s just talk about the cameras, whether it’s cameras that Smarter AI is involved with or just out in the market. And these cameras are also integrated with other components of autonomous vehicles. The security of these devices is because if they can be hacked, the vehicle can be guided certain direction, and it can malfunction. What steps are being taken to ensure the security of these cameras and other devices?

Chris Piche:  Many steps are being taken. There are general regulations regarding the collection, processing, and storage of personal data. And there are regulations specific to govern vehicle cameras to ensure the cyber security of vehicle cameras and data, both in transit and at rest. And another aspect that we have to consider, Mark, is that autonomous transportation systems rely on neural network models, commonly known as AI models. These AI models perform only as well as the quality of their training data. So not only do we have to secure cameras and data from hackers, but we also have to consider the security of the underlying training data.  A few months agoTesla reported a breach, where some employees within the company had made unauthorized use of videos recorded from customer vehicle cameras. The videos were intended for use as training data, but some employees had reportedly used the videos for other unauthorized purposes. We must secure camera data not only from hackers outside of our companies but also need security tools and processes inside our companies.

Mark Shriner: Yep, it makes a lot of sense. And so I’m what I’m hearing you say that it’s a priority. There are some standards or regulations that govern the deployment of these devices. When you talk about the training, do you create your own training data for these cameras, or do your customers say, “Hey, we want you to use this training data? How does that work?”

Chris Piche: Smarter AI is a software platform for AI cameras. It’s the same for cameras as Android or iOS are for phones. The idea for Smarter AI goes back to my time developing the BlackBerry smartphone. All smartphones, from Apple, Blackberry, Nokia, or any other failed smartphone company, are all based on the same chips and manufactured in the same factories. So what’s the difference between these smartphones? The only difference is the software platform. It was iOS that enabled Apple and Android eventually enabled Google to achieve product market fit.  In other words, to create a compelling user experience that would generate consumer demand. So when I founded Smarter AI, I knew that the key to product market fit would lie in the software platform. I founded Smarter AI  to develop such a software platform for AI cameras and to collaborate with third-party chip makers and camera manufacturers.  And just like your iPhone or Android device enables each customer to use different apps, Smarter AI enables each customer to use different AI models to suit their needs. Let me give you a  simple example of 2 different customers using the same cameras based on the Smarter AI platform. The first customer can use an AI model for counting people,  while the second customer with the same camera can use an AI model for counting cars. Here is another example from recent history. 3 years ago, the manager of a bank with Smarter AI cameras would use an AI model to find people wearing masks and then notify security guards and the police of a bank robbery.  But two years ago, the same bank manager would use an AI model to find people not wearing masks and then notify public health authorities.  That’s the power of Smarter AI cameras. In the same way that each end Android or iPhone customer can choose the apps that they need or want, each Smarter AI customer can choose  AI models to meet their needs.

Mark Shriner: How many companies are in your space?

Chris Piche: In AI cameras?

Mark Shriner: Well, no, in the specific space that Smarter AI is in?

Chris Piche: Not many companies are focusing on the software platform. There are some competitive products at Microsoft, Azure, and AWS, but those products are slightly different. Those products are tightly coupled to the cloud infrastructure that those companies provide. In other words, if you want to make AI cameras that can only work with Microsoft Azure or cameras that can only work with FES, those two companies have software platforms that will allow you to do that. I  believe we are the only company offering a cloud-agnostic platform for AI cameras.

Mark Shriner: I can see the value in that. I’m just curious, doesn’t it matter what tech space you’re in? Well, up until recently, I should say one of the biggest challenges is finding qualified developers anywhere in the world. It but you’re in a relatively new space. How did you deal with that challenge?

Chris Piche: You’re absolutely right. That remains one of our primary challenges. I’m happy to report that it’s still as challenging as it’s ever been. If we’re talking about highly skilled computer scientists, system software developers and machine learning experts, regardless of the state of the general economy or market, the demand for people with those skills is it’s continuously rising,   so that’s absolutely a  significant challenge for us.

Mark Shriner: Let me ask you in terms of your business model. Do you sell your platform directly to the camera manufacturers you partner with them, or let’s say you go to the military and say,“We could design these cameras that could be battlefield ready to identify adverse events and make a recommendation or at least report them so they’re aware of it?” Because if you’re out there, you’re busy, the camera spots something that you should need to be alert for. Do you deal with the end customer or are you talking with the manufacturers?

Chris Piche: The answer is both.  Smarter AI works with several manufacturers to ensure the availability of cameras with popular form factors and specifications, in the same way that Apple and Google ensure the availability of Android and iOS with popular phone and tablet form factors and specifications. And Smarter AI also works with channel partners and customers to understand their requirements and, in cases where off-the-shelf form factors and specifications aren’t enough, to provide bespoke camera hardware.

Mark Shriner: Excellent. Well, let me ask you this, what has been the coolest application for an AI camera that you’ve seen to date, and then make a prediction of what we’re going to see or be able to do, say, five years from now?

Chris Piche: That’s easy. The coolest AI cameras are vehicle cameras that detect complacent or distracted driving, especially texting and driving. This technology is already being mandated for vehicles in America, Europe, China, Australia, and more. Looking ahead to 5 years, we’ll see a huge reduction in texting and driving and other forms of complacent and distracted driving. And we’ll also see a change y in how we perceive texting and driving. Today most of us agree that impaired driving is selfish and dangerous. Most of us would never drink and drive. And yet, the same people will drive through a school zone with their faces buried in an email, text message or Twitter post. That will be a remarkable change. In 5 years, we’ll think about texting and driving in the same way that we think about drunk driving today.

Mark Shriner: Well, I certainly hope so. We’ve made these big changes before. Drinking and driving is a great example of something that just is no longer acceptable at all. Smoking in certain situations is no longer acceptable. I mean, people used to smoke on airplanes. It’s kind of crazy. You want to sit in the smoking section. I’m like on an airplane. What can go wrong?

Chris Piche: Or smoking in offices. You just want to go to work, not to subject yourself to secondhand smoke, right?

Mark Shriner: I certainly hope your predictions are true. I was kind of hoping there’ll be because you’re in Vegas to get these super cool glasses. And they would tell me when I should hold or hit, you know. I’m sure that’s the common man. I’m sure it’s covered the way we already are out there.

Chris Piche: Sure. There are some sharp people here in Vegas working on that. But our casinos also have cyber security people working on countermeasures.

Mark Shriner: I’m sure, Chris. If our listeners or viewers want to get more information about Smarter AI or connect with you, what’s the best way for them to do that?

Chris Piche: Smarter AI’s website is And you can find me at,, and

Mark Shriner: Yeah. And I can put a link to your website in the show notes as well. Enjoyed this conversation and we’d like to wish you a great summer.

Chris Piche: Thank you for having me, Mark.

Mark Shriner: Cheers, man.