Reinventing Our Cities with Physical AI | Jonathan Reichental | FortyGuard Hackathon’26

Well, that was a beautiful introduction. It's great to be here. Thank you so much for that. I am coming to you from beautiful San Francisco here in Northern California. It's quite early in the morning. It's just, just 7 AM, just come up to 7 AM. And I'm delighted to be with all of you wherever you're coming from too. So the first thing I'm going to do is get my presentation up on the screen and make sure that everyone can see it. And if you could just confirm that you can see it there, Snehal. Professor, I can see it. Wonderful, wonderful. Good, good. Well, very happy to share some insights with you today, and I think there's a tremendous intersection with what you're doing in this incredible hackathon and also just overall with the work that FortiGuard is doing to help us build better cities, more habitable cities, and a better future for everyone. I just had an opportunity to bring this kind of presentation, this topic across Australia. I've just come back from a few weeks on a speaking tour where it's been received very, very well, and I will take this actually to Saudi Arabia next week. And then back to New Zealand and Australia again, and continuing to share this important topic. But I wanted to start with a story, actually my own personal story. I live here in a managed apartment, a sort of corporate housing, you could almost say, in just outside of San Francisco. And it's great because if there's ever anything wrong with my apartment, if I need a light bulb changed or the Dishwasher doesn't work, the company comes in and takes care of it. I don't have to worry about any of that. And yeah, it's a real luxury for sure. And I like it. And so about 2 years ago, my clothes washer and dryer, just, they weren't, they neither of them were performing particularly well. They were old. And so I reported it. I created a ticket for the organization and I said, could you take a look? You know, my clothes are Not, not cleaning well and not drying well. Could you, like, take a look at the washer and dryer? So when I came home that day, later that day from wherever I was, and I walked into my apartment and I opened up the, the doors where the washer and dryer were, they had replaced them, which was really cool. They were completely new, a state-of-the-art washer and dryer. I was very impressed, like high-end. I was like, great. So I guess they They didn't, they didn't fix them. They just thought it'd be better to replace them. And one of the things I noticed on it was some Wi-Fi symbols on both devices. And I thought to myself, oh, that's interesting. Being a computer guy myself, I thought that means I can connect these to the internet. I wasn't entirely sure why I would do that, but I thought, well, if I can, I'm going to. So I went ahead and connected my washer and dryer to my Wi-Fi. And then of course there was an app to download and it made sense. You know, I could check the status of my washer and dryer. I could turn it on, turn it off. I could, you know, increase the heat. You know, I could do all sorts of things from the app. So that made sense to me. So anyway, I put my first washing into the washing machine that evening and closed the door and I went to the sofa to Netflix and chill, to sit back and watch a movie. So I don't know, about 45 minutes in, I get a text message on my phone. I pick up my smartphone and lo and behold, there's a text message from my washing machine. And the washing machine is like, your clothes are ready. Your clothes are ready to put into the dryer. And I thought, oh, that's cool. The machine actually texted me. And now I know that it's ready and I can put it into the dryer. Well, I wasn't ready. And I thought, you know, as in I wanted to continue to watch the movie. And so I ignored the message. Well, about 15 minutes later, I got another text message and the washing machine said, hey, you haven't put the washing, the clothes into the dryer yet. Just thought you'd want to know and perhaps you want to do that. And I thought to myself, okay, you know, that's good. That's a reminder. Well, I ignored it again to watch the movie. And then of course I got another text message and another text message. And eventually I jumped up and I went to the washing machine. Took the clothes out and I put it in the dryer. I thought, oh my goodness, that's annoying. I better just do it so the texting stops. And I did. And I put it into the dryer. And of course, the texting stopped. I thought, you know, that's an interesting use of technology. But actually, I had a much bigger epiphany. I sat back on the sofa and I thought to myself, you know what? We're going to be working for the robots in the future. It won't be robots working for us. We're going to be working for them. That was the conclusion I made from that little experience. Now, of course, I had to get up and, and do the work, but that might not be what happens in the near to medium future. Here you see in this image the emergence of a humanoid who, instead of me getting up and putting the washing into the dryer, may be able to do it for me. So that sort of today's limitation might be overcome in the not too distant future as we begin to incorporate a physical AI future into our future, putting intelligence into devices and having those devices independently participate in the world, supporting all sorts of human activities and even supporting themselves as they go about their activities. So we can see the potential for a completely new world. And that's the story I want to tell today in the short time we have, is what happens when we start to build intelligence into the built world, when we start to put intelligence into physical devices. How does that change our world? And particularly, how does that change our cities? And in my assessment, having done this work now for over 30 years and been a leader, particularly in the smart cities movement, I think the change will be profound. And the good news for you watching, many of you, if not all of you, are entrepreneurs or want to be entrepreneurs. This will present significant opportunities. But I want to step back for a moment and just paint the picture of the world in which we're in today and where we're headed. I think this will be useful to you as you think about your hackathon too, and the projects you're working on, to think about the place that cities occupy on our planet and what and where they will occupy in the months and years ahead. I'll just give you some basic facts. I love this picture, by the way. It shows the urban world, and it's— I always look at this and I'm intrigued by the stories it tells, the stories of history, the stories of war, the stories of demographics and geography and political systems and the environment and so much more. It's really a story of civilization, our place in the world and where we've come from and where we might be going. Despite the fact that we think the whole world is urbanized, it turns out only about 3% of the land has cities on it, only 3%, but that will double over the next few decades to about 6%. And at that point, we're probably going to max out. And in fact, what we find at the end of this century is that humanity begins to decline in numbers, and that has an impact, a profound impact on our cities. But that's a ways away. I don't worry too much about that. You know, it's only Barely 20— it's going to be 2030 here in a few years, so we have about 70 years or so before the tide changes, but we should have it certainly in the back of our minds as we think about the future. About 60% of all humans live in cities, and that number will rise to about 70% by 2050 and higher as we get towards the end of the century. One of the things you can notice here is that a lot of humans live on coastlines. In the region of about 1.5 billion humans are right on the coast. So sea level rise and climate crisis is real for them. For better or for worse, we built many, many homes and cities and urbanization beside rising seas. So this is the world that we find ourselves in today, and we have some clues about where we're headed. Knowing that the current and the future is an urban world, we are an urban species, you know, recognizing that as a fact, the question then becomes, as innovators and entrepreneurs and as city leaders and even just as citizens of our cities around the world, what kind of communities do we want? Isn't that the most important question? What kind of cities do we want? And I always ask The people I work with, my students, when I'm working with cities and even entrepreneurs, I often say, let's reflect on this question for a moment. Let's think about this question. What kind of cities do we want? Remember, we're still in charge here. You know, humans, despite my little story earlier, we get to decide. Our choices dictate our future. And the technologies that we roll out, the capabilities that we have, the artificial intelligence, and that, that is, that is upon us and growing in power. All of that is, is going to shape the tools that we have to create the kinds of cities we want. What we do know is our cities have great needs today. You know, they've been incredible. Let me just tell you this. Cities are the most complicated and successful machines we've ever built, right? Maybe you don't think about it like that, but think about the city in which you come from today. If you're watching live, or perhaps you're watching this on playback, wherever you are in the world, you know, what happened today to make your city function in terms of power, you know, electricity, in terms of water, in terms of transport systems, waste management, economics, moving people around, you know, these complicated urban areas that we've created. Our education system spun up, our businesses spun up. It all sort of had to work. We may have a lot of complaints, but I also think we need to be kind of— we need to recognize how much goes right, you know, for our cities to work. But if we look at a a picture like this, I've deliberately taken a more, you know, a picture of something that you can readily tell that there's needs. Transportation clearly in this picture is not fabulous. You can see there's quite a lot of urban decay, and there's, you know, there's just work that needs to be done. There's risks, you know, you can see people spilling onto the street here. A lot of infrastructure needs, and likely there's some climate-related activities. This is, in the summer at least, extremely hot and difficult for people to be outside, and it's putting enormous pressure on the electrical grid to provide air conditioning and all the power required for fans and cooling. So again, trying to bring this back to the topic you're interested in, we have significant needs to innovate around climate and heat and temperature as we look at this. Now, the good news is I've been in the space for, again, about— I mean, in technology for up to 30 years, but I've been in the urban movement about half of that, maybe a little over half of that. And I've seen the emergence of the smart city, basically using technology and data and sensors to make our cities work better. And I've seen a lot of progress and a lot of positives as, again, technology supporting more efficiency and more insight about how our cities function and what our cities need. But here we are in 2026, you know, the 3rd decade of the 21st century, and we see a new kind of city is emerging. And it's the consequence not only of changing demographics, a more developed world, but also the enormous impact that new technologies are having and the extent to which we can use those to do things we couldn't do before. And so to sort of visualize this, we've gone through a series of revolutions in humanity over the last few hundred years. And to summarize, this is quite a summary page, what has built the world that we have today has been 4 industrial revolutions beginning in the 1700s, beginning out of England back in the 1750s. And no one disputes the enormous impact that automation has had and the transition from an agricultural world to an industrial world to mass production and then moving to electricity. And then the world that we know, the electronics age and information technology heading into the digital world, that's the world we find ourselves in these last few years. And I'm here to make the argument that we are now moving to yet another revolution, A cognitive industrial revolution. This cognitive industrial revolution is not about automating the physical world, or just about automating the physical world, which the industrial revolutions have been about, but this is about automating the mind. For the first time, we are actually codifying and automating thinking. We're building thinking machines. Today, they do things that we tell them to do, but increasingly they are working in an autonomous fashion, making decisions themselves. So I want to come back to that in just a moment, but I see this transition from a world, an urban world of smart cities, where again technology of all types, data, things like sensors, that integration, that That intersection of technologies and new innovation has allowed us to be smarter about delivering city services. And that is the foundation in which we can now build another layer, an intelligence layer that we call— and I think I'm proposing that we call cognitive cities, thinking cities. That seems quite abstract. So let's Go a little deeper into that. What does that mean? And by the way, how does that differ from this idea of a smart city? Well, this is a city that has technologies and processes that allow sort of the infrastructure and the leaders of the city working with intelligent systems to perceive what is happening, to reason around what is happening, let's say in a traffic system or an electrical system or a waste management system to make decisions on our behalf or help us as humans make decisions, for our environment to learn every single day and by learning, getting better. So, you know, one of the things about that strikes me about the intelligence age that we find ourselves in Is, you know, once technology learns something, it doesn't unlearn it. Tomorrow it will be as smart as it was today and smarter in what it learns tomorrow, and that will just be cumulative. It's very different to humans. We forget things, we age, you know, humans have some weaknesses in this area where digital systems have none of those weaknesses, right? And they learn and that learning compounds over time. And then, of course, in a cognitive city where you are perceiving, reasoning, and learning, you're then able to act. The systems are able to act, to do things again, either autonomously— there'll be more of that— or with humans in the loop. Now, a lot of— some of this sounds clearly academic, and so I want to spend a lot of time providing a lot of examples so you can get your head wrapped around what I'm proposing today as the future. You know, hopefully this video plays nicely. We hadn't tested it, but it's playing nicely on my side. But at least you can see the image. You know, this is not uncommon where systems are already able to perceive the world and make sense of what's happening. And in this short video, you can see that there's some hardware on a bridge perhaps that has a sensor, camera in this case, and it's able to identify vehicles, what direction they're going, what speed they're going, what type of vehicle, if it's a motorcycle, if it's a car, a van, truck, it can count. And so we've already been instrumentizing the world to allow us to make better decisions. This is very valuable data alone and perception technology for decision makers to be able to do things like maybe design streets better, to see where there are more incidences of accidents perhaps, and then be able to redesign, but also know things like about how much throughput there is such that you can adjust the traffic signals if they're not dynamic. And, you know, we have intelligent trash cans today that optimize for collection. You know, think about this for a moment, that this is what we mean by a smarter city. Why would we collect trash from a receptacle if it was empty? You know, why would we send a vehicle with a person in it, at least today, all the way to a receptacle just to discover that it's empty? It's better to know that it's full and then pick it up, so when it's empty, you don't actually pick it That's optimizing our infrastructure. And by the way, I was in Australia, as I said earlier, and I've used— I'm reusing some of the slides here, and I wanted to give a local example. This is actually Brisbane Airport. And many times airports belong to the city. They're part of the city organization. Here you see lawnmowers that are autonomous. So rather than having to send people out with lawnmowers to cut the grass around the airport, These experimental lawnmowers run 24 hours a day when they're not being charged and keep the grass at the right length. So it's very practical, this stuff. This is not sort of very niche, hard-to-understand applications. And what's great about the continued use of digitalization and now intelligence is the ability to provide better services, to provide more immediate, more accurate services through chatbots, and we see a lot of activity in this area. If you asked me, if one of the questions was, where are cities today using it the most? One of the areas that's happening at real speed is the implementation of chatbots on city websites and all sorts of apps and services that cities provide. You know, the interesting thing, as I think about myself as a citizen, you know, chatbots have existed for a while, but they haven't been very good. Okay, you could go to a city website and type in a question and just get frustrated because you just couldn't get the answer. Today, we've turned a corner. We're getting more accurate, correct answers the first time, and so we don't have to talk to people. What we do need, though, is We do need to have people because today there's still a need to provide human services. Well, I want to sort of begin to focus on one area of this, of cognitive cities, and what it means when cities begin to integrate greater amounts of intelligence with the physical world. And to keep it simple, because after all, we only have a short amount of time together here, You can think of physical AI, the term we use for the integration of AI with the physical world, really having 4 parts. So you've got the generative AI piece, that's a given, and that's the big movement, of course, right now. But it has been since about 2022 largely software-driven, at least for the majority of people and the majority of experiences. that people have with AI on their phones, on their laptops, etc. But now integrating with the physical world, you have the use of sensors, automation, and robotics in general, and greater utilization of data, which feeds all of this. Of course, we know already that data feeds AI, but it also will feed or be created by the sensors and robotics as an input, as input devices. So you've got to have these 4 pieces. And again, as I think about what you're doing in the hackathon, you really are— all of these matter, but particularly, of course, the data that the 4D Guard solutions provide, the AI that's baked into it, and how you'll use that, and whether or not you want to collect more from sensors or build out more sensor networks. There's all sorts of ways to think about the implications of this in the work you're doing. So we come back to the, you know, the basic questions again. Okay, this is interesting, this is interesting technology, but again, let's go back to that first question: what kind of cities do we want? And in answering that question, what problems are we trying to solve, right? As entrepreneurs, as innovators, this is the most important question. If you want to build a successful business, you have to solve a problem. And in fact, not only do you have to solve a problem, but you have to solve a problem that people are prepared to pay money for, right? Sure, you can do volunteer work and you can be altruistic, but to build a successful business, You've got to be able to solve this. You've got to be able to answer this question really well. Is there a problem that you've observed that needs to be solved because it impacts a decent amount of people? And is there a way to create revenue from that? Will people pay you for that solution? So let's again look at some implementations of physical AI today and how it's changing the game and creating more Cognitive cities. Here's a solution for better traffic management. There's a couple of vendors, actually, there's a growing number of vendors in this space. So in this instance, there's sensors that are on city vehicles, city fleets, and they are— because cities have lots of, you know, a big city has a lot of vehicles, they travel around the city every day. going about their business, doing deliveries, bringing workers to construction sites. They're riding on public safety vehicles like fire trucks and police cars. And these relatively small, unintrusive sensors look to see, are vehicles parked where they shouldn't be? So in this image, you see a car parked in a bus lane. And so it identifies the car, it identifies the the bus lane, and on the back end, it can then issue a citation. It can actually mail it out or send an email. It can collect the revenue then. So it's a complete ecosystem for better management. Of course, it can raise revenue, but it's also supposed to be a deterrent. Of course, we don't want cars to be in the bus lane. Now I want to take you to an airport. I really like airports a lot. I actually consult with some airports, and airports are often an extension of a city, but mostly you can think of an airport as kind of a mini city. Everything that you'd find in a city, you pretty much find in an airport. Everything from public safety to waste management to power to plumbing to water systems, heat management, or temperature management. It's all there in an airport. And so here's the airport of Vegas. Some of you might know this image if you've visited or gambled or went to a show in Las Vegas. A fun place to go. It's more family-oriented today. You don't need to gamble. You can, you can simply go and see a great magic show or Cirque du Soleil. I like to bring friends and family here from time to time. And one of the things airports like to know is the journey for passengers from what's called the curb to gate. Right? When a person is dropped off at the curb, like all of us have, what do we do from the curb? And what happens between that moment and when we step on the plane at the gate? It's a journey, right? There's— you've got to maybe check in. You might have to put your baggage, you know, check in your baggage. Then you go through security, you go to stores, you go to the bathroom, perhaps you drink some coffee. Yeah, you walk around. I mean, there's so many things that people— and so airports need want to know that. Why? Because they want to create more efficiencies, they want to create a better environment, they want to make improvements. They want to know, for example, when a security line is getting really long, should they open up another one? What can they learn? And so intelligence about the airport environment is very, very important. And, and so Vegas, the international airport, they wanted to increase their ability to get intelligence and do something about it. And one thing I think intuitively, you know, is that airports have a lot of cameras, right? Every airport has a lot of cameras. Sometimes you can see them, sometimes you can't, but they have a lot of cameras. So what, rather than putting in a completely new system, what McCarran did was they worked with an emerging AI company to embed intelligence into their existing camera system. Essentially intercepting the image as it's captured by the camera, processing it, you know, sending it to the cloud to be processed, and then presenting decision-based information to airport leaders. And to give you an idea of that, here's just a rendering so you can see what I'm talking about. Here's a smartphone with some template information here. City leaders can— sorry, airport leaders can see real-time information You know, you can see about how much time the average time is at the checkpoint, the wait time, how many passengers are in the airport, or in this case, in a particular terminal. It could say— you could have a lot more here. You could have things like, where's the food cart for flight, you know, 58 to Dubai? Or, you know, where is the airplane? You know, there's lots of things. Of course, some of you might be thinking about privacy implications. Yeah, that's very important, and there's opportunity there to manage privacy and to do it in a way that it's a win-win for everybody. But we see physical AI in our cities in different ways. We're starting to see more use of robotics. I think that's one of the huge growth areas. Some of you might have seen in your cities or might be anticipating the arrival of delivery robots. I see these. I get to travel to a lot of places, And it's always fun to see what type of delivery, autonomous delivery robots are being used, if at all, and how they are navigating around our urban environments. Here's a popular one called Otto. And, you know, robots, the integration of AI with physical hardware, you know, robotics is an obvious emergence here. They take— they will take many forms and they will have many different functions. Here's a good one, you know, relevant to your interests in the temperature hackathon. These robots are building and maintaining solar panels. And now think about solar panels. First of all, it's hard work, right? That this is often in high-temperature areas and hostile environments. It could be out of this, you know, renewable energy can be, you know, out in the ocean, it could be in the desert. And in this case, the solar panels can be in places that are hard to get to and hard to work in. So robots seem to be a good solution to that. They work 24 hours a day, they don't complain about heat, and they have strength too. So they're dealing with, you know, intensive physical environments. This is a vendor called RoboForce, and they rent out fleets of robots for different tasks. One of the things I like about, you know, thinking about this is, let's say you are a buyer or you have a need for 80 robots and to do like to work in a factory or to work on a construction site. You only have to train one, right? Train one robot and that robot then, that information then is disseminated to all the other robots. And if a robot breaks and has to be replaced, it can learn everything all the other robots know pretty much immediately. Our cities, you know, are getting more intelligent such that we can start to deploy all sorts of autonomous transport systems and in this case drones that have many, many activities already, as you know, from public safety to delivery and building inspections and much more. In fact, here's an example of food delivery. This is DoorDash. In fact, this is some DoorDash being used in a community to deliver food. The other day I saw a video on Instagram of a Somebody actually in a park getting food delivered to them from their favorite restaurant, like the drone coming to where they are no matter where they are, which is not how I thought about it. I thought the drone would come to your home, but you could be anywhere and have the drone come to you and drop off your favorite sandwich or milkshake or boba drink, whatever it is that you like, right? Maybe the most obvious near-term example of physical AI is the emergence of autonomous vehicles. And here in San Francisco, we're embracing these, and in fact all over Silicon Valley where I am, this is becoming more and more common. Not everywhere yet. For example, I was in Australia and we don't see them yet, but it's only a matter of time that we'll see autonomous vehicles. But the story about autonomous vehicles is a good example of us thinking much bigger about what it means when you start to deploy physical AI. Let me explain that to you. You could say, wow, it's amazing that cars drive themselves or will be driving themselves everywhere. Well, because, well, frankly, a lot of people don't like to drive, and isn't it nicer to be able to have a snooze in a car? Or, you know, look at your phone, maybe you flick through TikTok or whatever it is that you do, or just even work, right, and have someone else drive. And that's great, and it's also safer. Autonomous vehicles are safer today. Humans are not good at driving, unfortunately. We kill each other at a rate of about 1.5 million car deaths per year. That's a horrific number, and autonomous vehicles will reduce that. But that's not really the That's not just the story. That's a big story in and of itself. The bigger story is that we can redesign our cities. We can build a different type of city when cars drive themselves. Think for a moment about the city that you live in. How much of the city has been designed to accommodate cars? Or specifically, how much of the city that you live in has been built to accommodate humans driving cars? For example, traffic signals. Here in the US, we have the grid system, parking lots, multi-story parking lots, signs, even lanes. All of that becomes optional and in fact goes away over time. Autonomous vehicles don't need the same physical clues that humans do, and so we can start to think about our cities completely different. Do we need traffic signals? No. Do we need lanes? No. Do we need the grid system? No. Do we need parking spaces? No. You know, how might you think about that when cars drive themselves? And so I pose this as sort of one example of, as we move into the era of cognitive cities, it's not just about doing what we do today better. It's not just about having more intelligence. It is the enablement it gives us to redesign and rethink our cities almost from scratch. And that's what entrepreneurship and innovation to me is all about, is rethinking the future, is rethinking what's possible. You know, does every department in a city become a robotics department? It's a little thought there. And ultimately, what are the implications? of physical AI? That's the question that city leaders and entrepreneurs and all sorts of people, all sorts of stakeholders need to think about. What are the implications of the cognitive city with physical AI? And maybe to bring it back on target, on topic, is what does the intelligence layer about temperature help us do? How does that get integrated? It's a very, very important, essential dataset because you can't separate, you know, temperature from our cities. They, they are coupled together, right? And you can't separate it from physical AI. Temperature plays an important part here. So some of the implications are related to our workforce. What talent do we need? What talent do we not need? There's lots of questions about the future of work. What's the role of humans? You know, we've more ethical questions than ever before. This is a topic that's consuming an awful lot of my time in a positive way, is thinking about the ethical implications of the cognitive industrial revolution. You know, what does it mean to privacy? What does it mean to humans in the loop? What does it mean to our future? How should we manage data, and what data do we need? The role of data is now really top of mind. Is, is, I, I would argue, apart from humans, outside of the human asset, is data is the most important asset in every organization. And then how do we manage all of this? What's the governance for ensuring safety? security processes, that things do run well, that there is recourse? How do we have oversight? What is the AI governance and city governance required for a world of physical AI? By the way, just one way to look at these 4, just 4 areas of many, is every one of these presents opportunities for innovation, right? I'm not negative on these. I'm actually, I look at a challenge as a something to be solved, and that what gets solved is an opportunity. Okay, so what's the timeline look like here as I begin to wrap up my short talk with you today? Right now, we're seeing a lot of progress, we're seeing a lot of good data, of course, we're seeing chatbots be integrated around cities, we're seeing back-office automation in our communities, we're seeing a lot of activity happening rapidly, and including many of the examples. Within 3 years, and by the way, these are, you know, smart guesses, I could be maybe not optimistic enough, or perhaps I'm being too optimistic, right? And so within 3 years, we're seeing inspections regularly, deliveries, you know, a lot of security activities with physical AI. And then as we kind of go out more to the 5 to 10 year, that's where things really begin to change. So we're at the beginning, you know, don't feel like you're late to the game. You're exactly right. But as we get into the early and mid-2030s, we're gonna see extensive robotic integration in our world, extensive humanoid coworkers. Everything that can be autonomous will be autonomous, and there'll be an awful lot of agentic AI and full automation as a response. So as I come to the end here, hopefully I've said something that was interesting to you and something that maybe inspired you or helped you think. What happens when our cities begin to think and act? What happens when that happens? That's another question for you to ponder. What happens when our cities begin to think and act? What does that help us do? What does that enable? What challenges does that present. So with that, I'll conclude. Here's a barcode for you, or QR code, if you want to learn more about my, my work. And there's a lot of free stuff, so, you know, you can, you can read a lot of my articles and see a lot of my videos, a ton of free things that complement what we've done today. So I hope you enjoyed this and it was valuable to you. It was a real pleasure. I'm going to stop sharing now and hand it back to Sunil, so he will Maybe have some questions for us.

Hello, Professor. It was a great session. I'll quickly paste the link to your website in the chat so that people go, because like I know there are a lot of case studies and relevant books and every other resources that they can access from directly from there. So it's a good resource, guys. I have personally. learned a lot from Professor Jonathan. Uh, as we already told you guys, he is an advisor to FortiGuard. We used to meet him every now and then, and we still do, and we have a deep connection with him, and we still learn a lot from him. So there's a lot to unpack here. Professor, people are asking, where can I join your classes? Where are your classes available?

Uh, thank you. I appreciate that they're asking that question. I, I have a lot of Courses online, a lot of videos on YouTube, a lot of videos on LinkedIn Learning and Coursera. What I would recommend is, uh, Ella's put the link to my website in the chat there. If you go to our Q&A and go to— well, it's very easy, it's reichental.com, just my last name, reichental.com, and that will give you links to pretty much everything. It'll give you access to everything. Yes, yes, indeed. I see that. So I would encourage people to, to go there as a starting point and reach out to me. I'm on LinkedIn, happy to answer your questions, happy to tell you more. So I'm available for you.

I think they're just writing up all the questions as well. Majorly, people watch the recordings back, and there are a few people who come up looking up to the sessions to just join in live as well. I'll just paste the link for Your LinkedIn as well in the chat for everyone so that they can check it out. There are people who want to share their 2 AI projects to get views on that. How can they share it with you? Can they approach you on LinkedIn?

This is for the hackathon itself. Well, I'm a mentor, so you can either reach out directly or go through the hackathon. go through the hackathon link, you know, if you're— whoever your contact is at FortiGuard, and they will coordinate it. But I'm also open if people want to come directly as well. But I want to make sure that FortiGuard has visibility to you and your team and what you're up to. So you choose, but I'm here for you.

There's one question, interesting one. As cities become capable of sensing and responding to their surroundings in real time, How do we decide which decisions should be left to AI and which should always require human judgment?

Yeah, that's a big question. Appreciate you asking it. I'm not sure that we know the answer to that yet. I think we're gonna decide that as a society. What we find historically is that we are— once our confidence is built, we do begin to defer more and more to technology. Many organizations today are deploying agents, and, you know, agentic AI means that not only does it complete a function for us, but it also makes decisions on our behalf. We create guardrails, we sort of say, you know, we define the conditions, then it makes a decision on our behalf. We don't know for sure what those guardrails are ultimately. How much are we prepared to allow AI to be autonomous and make decisions? Right now, there's certainly a strong belief that there should be humans in the loop, that we should have input where it comes to, you know, human safety. That would be a good one, I think, a good criteria. Can humans get hurt? The extent to which there's risk, I think that would be another measure related to that. But we've got to decide. As a society, as humanity, we have to decide when, when we can entirely give up human judgment or, or, or say, no, that's, that's a bridge too far. We humans need to be involved in, in that judgment.

Okay. Are there any certain fields or applications where we should be more cautious about using physical AI? And what factors should we consider to make sure the benefits of an innovation outweighs its costs?

I'd have to think about that, but I'll give you my quick answer because we only have a few moments here. And that is, the answer is you have to have a solution that people want. You have to have a solution that people are comfortable with. You know, the marketplace is actually quite a good guide to what is acceptable. If you build something that is beyond the pale, as we sometimes say, the market will tell you. It won't respond to your product. It'll push back. It will give you guidance on what's possible and what's not. And of course, as an innovator and an entrepreneur, you need to test your ideas. you need to talk to a lot of people, and then you've got to experiment before you pilot and then go to production. The question of, you know, benefits outweighing the cost is very like the prior question. It's very similar in that we have to decide. Nobody really knows today because this is so new and so fresh, like, what should we not do? You know, that really is the question. And we don't know the answer right now as we look at the world of AI. It's really a race. It's who can be the first, who can do the thing better and faster and get it into the market faster than anyone else. Eventually, some order comes to this because the market creates order. And we'll have a better idea of what society finds acceptable and what it doesn't.

So, Professor, I have a question before anyone asks this. Where do you think, like, smart cities right now fail? Like, where are they experiencing the failure most? Is it the technology, the budget, or is it something completely else?

No, it's not one thing. The question of failure, I think, is an interesting one. Cities do a lot of things right, and cities actually succeed at a lot of things. They often don't get credit for it. That's what I say. If a city does 10 things and gets 9 things right, we'll focus on the one thing it failed at, right, or didn't go well, right? So We do need to recognize the great success cities have in their smart city efforts. Of course, budget is always an issue. I think skill is also an issue. You know, a mayor and a city manager might have great ambition. I think that's wonderful and should, but do they have the ability to execute? Do they have the workforce? Do they have the partners to execute? One thing that I've seen in a long career in government is the extent to which cities often underestimate what's required, not only in effort, technology, but also in budget, you know, and talent. You know, so I think it's a cliché where, you know, projects often are over— they go too long. we might have said it'll take a year, but actually takes 2 years. I think estimation skills are very valuable here. Maybe the last point to bring it real home is, is the data ready? Does the city have the data that it needs for the project in a way that is good quality, accessible, current? You know, it's good quality data because a lot of these smart city and increasingly the cognitive city Activities are going to entirely rely on good datasets.

You, you guys know where to contract for good datasets. You are already using one of them for temperature in this very hackathon. So we are one of them as well. Uh, one more question, which is that what do you think is the biggest challenge? I think it's similar to mine in making a city truly intelligent— technology, infrastructure, or the people. It's along the same lines.

I'll answer this and then, in fact, I need to leave myself.

No worries.

I'm sorry, I have to leave for another something at the hour here. I would like to leave on an optimistic high note in this question. You know, things like talent, technology, people, projects, all of that can be dealt with. There's ways to deal with it. What is harder to deal with is the absence of vision. And so I think the biggest challenge today for cities to become intelligent is that, that the city, all its stakeholders have a bold vision for what they can become, that they have a vision that is aligned with their resources and their environment, their context. That is agreed upon and then is executed upon. So having a— having the right vision is extremely important.

All right. So that brings us to the end of a truly thought-provoking session. A huge thank you to Professor Jonathan. There's a lot to sit with here. The shift towards physical AI, the opportunities it's creating in the public sector and every other sector which is there, and a real sense of where cities are actually heading next. If there's one thing to take away for all of us, it's this, that the most impactful projects aren't just the ones that work. They are the ones that anticipate the future and build for it. So carry that mindset into these final days, guys. Uh, think about not just what you can build, but why it matters and who it matters for. So thank you again, Professor. That was just fantastic. And to everyone still building, keep going strong. Uh, every session is recorded and shared afterwards, and the full schedule is there on the hackathon page. See you at the next one. Thank you so much, Professor.

Thank you. My pleasure. Good luck to everybody. It's a wonderful hackathon. Thank you.

Thank you.