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Are you interested in artificial intelligence advancing cities?

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Our summary today works with the article titled Intelligent urbanism with artificial intelligence in shaping tomorrow’s smart cities: current developments,, trends, and future directions from 2023, by Zhenjun Yan, Ling Jiang, Xiaoli Huang, Lifang Zhang, and Xinxin Zhou, published in the Journal of Cloud Computing.

This is a great preparation to our next interview with Glenn Drew in episode 320 talking about artificial intelligence as a tool.

Since we are investigating the future of cities, I thought it would be interesting to see how to integrate AI into the urban fabric. This article highlights AI’s potential to improve urban resilience, sustainability and overall quality of life, not without challenges.

[intro music]

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Welcome to today’s What is The Future For Cities podcast and its Research episode; my name is Fanni, and today we will introduce a research by summarising it. The episode really is just a short summary of the original investigation, and, in case it is interesting enough, I would encourage everyone to check out the whole documentation. This conversation was produced and generated with Notebook LM as two hosts dissecting the whole research.

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Speaker 1: Today we’re diving headfirst into a topic that’s increasingly impacting our lives. The rise of AI in shaping the cities of the future.

Speaker 2: Yeah, it’s really fascinating to see how this tech is going from something out of sci fi movies to actually shaping the real world around us.

Speaker 1: Absolutely, and to guide us on this deep dive, we’re turning to some cutting edge research from the Journal of Cloud Computing. The paper is titled Intelligent Urbanism with Artificial Intelligence in Shaping Tomorrow’s Smart Cities. And right off the bat, it hits us with a pretty mind blowing stat.

Speaker 2: Oh, yeah.

Speaker 1: They’re predicting that by 2050, get this, 68 percent of the world’s population will be living in urban areas.

Speaker 2: Wow, that is a huge shift. Think about the implications.

Speaker 1: Exactly. It’s a massive change in how we live as a species, and it’s happening fast.

Speaker 2: And that level of concentrated growth. It’s bound to create some major challenges. For

Speaker 1: sure.

Speaker 2: We’re already seeing it, right? Housing shortages, traffic nightmares, just the strain on resources in general.

Speaker 1: Okay, so more people equals more problems.

Speaker 2: Yeah, pretty much.

Speaker 1: I think anyone who’s ever sat in rush hour traffic can relate to that. But this paper suggests that smart cities that leverage tech like AI could be the key to not just managing this growth, but actually improving urban life.

Speaker 2: The idea is that AI can make cities more efficient, more sustainable. More liveable for everyone.

Speaker 1: Can you give an example?

Speaker 2: Sure. Imagine a system that could adjust traffic lights in real time based on traffic flow, or manage energy consumption across the entire city to reduce waste.

Speaker 1: So it’s like taking something we use every day, like Google Maps.

Speaker 2: Exactly.

Speaker 1: And scaling that kind of problem solving to a city wide level.

Speaker 2: It’s a really exciting concept.

Speaker 1: But before we go too deep, I think it’s worth taking a step back for say, for any listeners who might not be total tech whizzes.

Speaker 2: Sure.

Speaker 1: How would you explain AI and machine learning in simple terms? Like, what are we actually talking about here?

Speaker 2: Okay, at its core, AI is really about machines trying to mimic human intelligence. Okay. And machine learning is a type of AI where computers actually learn from data without, like, step by step programming.

Speaker 1: So instead of a programmer telling the computer exactly what to do, it figures things out on its own based on the data it’s given.

Speaker 2: Exactly. It’s like how those streaming services figure out what movies you like based on your viewing history.

Speaker 1: Ah, so it’s basically pattern recognized on a massive scale.

Speaker 2: Pretty much. Algorithms picking up on those patterns and then Using them to predict what you might want next, what might be useful.

Speaker 1: That makes sense. Okay, so now that we’ve got the basics down, let’s jump into some real world examples of AI in action in smart cities, because this paper is full of them.

Speaker 2: Oh yeah, for sure.

Speaker 1: It even mentions something called gate recognition. Have you heard of that?

Speaker 2: Yeah, it’s definitely an area that’s getting a lot of attention these days. Basically, it turns out that the way a person walks their gait is actually pretty unique.

Speaker 1: Really?

Speaker 2: Yeah, there are 24 different components to human gait, which makes it almost as unique as a fingerprint.

Speaker 1: That’s wild. So in a smart city context, are we talking about identifying criminals on security cameras, even if their faces are hidden?

Speaker 2: That’s one possibility. Yeah,

Speaker 1: or maybe helping doctors diagnose medical conditions where someone’s walking pattern is affected.

Speaker 2: Exactly. It’s still early days for this tech, but the potential is there.

Speaker 1: Okay, that’s super interesting.

Speaker 2: Lots of possibilities

Speaker 1: now for something I think a lot of people are more familiar with facial recognition. We all use it to unlock our phones, but this paper talks about how it’s being used in smart cities for way more than that.

Speaker 2: Oh, absolutely. In a smart city, facial recognition could be used to boost security in public spaces, streamline payments, etc.

Speaker 1: Even personalized services, so like walking into a store and having your shopping preferences automatically pulled up

Speaker 2: exactly or maybe even getting personalized recommendations as you walk through the city.

Speaker 1: That’s either incredibly cool or creepy depending on how you look at it.

Speaker 2: It’s definitely a powerful technology that raises some questions about privacy and control

Speaker 1: for sure. And speaking of facial recognition, this paper gets into the weeds a bit with the three main modes. 1. 1. 1N and MN. Right. I’ll admit those sound a bit like alphabet soup to me. Can you break those down for us? Sure.

Speaker 2: 1. 1 is the most basic. It’s comparing, like, a live image to a single stored image, like when you unlock your phone. Okay. 1n is when you’re searching for a match in a database. So think like a security system checking your face against a list of employees. Got

Speaker 1: it.

Speaker 2: And then mn is the most complex. It’s comparing multiple faces to a database of multiple faces.

Speaker 1: Wow, so that’s like trying to pick out all your friends in a massive crowd, but for computers.

Speaker 2: Yeah, it takes some serious computational power to pull that off.

Speaker 1: It’s amazing how far AIs come. Okay, let’s shift gears a bit and talk about health care. The paper highlights how AI is changing the game in smart cities when it comes to medical treatment.

Speaker 2: Yeah, for sure.

Speaker 1: What kind of possibilities are we looking at here?

Speaker 2: Imagine AI systems that can help doctors with diagnoses even remotely.

Speaker 1: So faster, more accurate diagnoses, even for people who live far away from specialists.

Speaker 2: Exactly. It could be a game changer for people in rural areas or places with limited access to health care.

Speaker 1: That’s a really powerful application of this technology.

Speaker 2: It has the potential to really improve people’s lives.

Speaker 1: Now, for a topic I’m sure will resonate with everyone listening, transportation.

Speaker 2: Oh yeah.

Speaker 1: I think we can all agree that a smoother commute is a universal desire, and this paper talks about how AI is tackling the age old problem of traffic jams.

Speaker 2: It’s funny you should mention that, because have you noticed how traffic apps are getting so much better at predicting travel times and suggesting alternate routes?

Speaker 1: I have, actually. I just thought it was better data collection, but you’re saying it’s AI.

Speaker 2: It is. AI algorithms are analysing real time data from sensors and GPS to help optimize traffic flow, make things a little less painful.

Speaker 1: Wow, so it’s not just about me getting where I need to go faster, it’s actually helping everyone on the road.

Speaker 2: Exactly. It’s about optimizing the whole system.

Speaker 1: That’s really impressive. Okay, now get ready for this next one because it’s pretty mind blowing. This paper talks about something called digital twin cities. It sounds like something straight out of science fiction.

Speaker 2: It doesn’t it?

Speaker 1: Can you explain what that is?

Speaker 2: So picture this, a virtual 3D replica of a real city, constantly being updated with real time data from sensors, cameras, you name it.

Speaker 1: So like a giant SimCity, but for real?

Speaker 2: Exactly, it’s like a giant urban simulator. That city planners can use to model different scenarios, test ideas, and even predict the future of the city.

Speaker 1: Hold on, so you’re telling me we could have a virtual copy of our city where we can see how traffic flows, where pollution is worse, even what would happen in a natural disaster.

Speaker 2: It’s a powerful tool with a ton of potential.

Speaker 1: That’s incredible. I can see how that would revolutionize urban planning.

Speaker 2: It has the potential to make our cities safer, more efficient, and more resilient.

Speaker 1: So we’re not just talking about designing better buildings, we’re talking about designing better cities as a whole.

Speaker 2: That’s the big idea.

Speaker 1: It sounds like AI is really becoming an integral part of how we design, build, and experience our cities in the future.

Speaker 2: It is, and the possibilities are really just starting to open up.

Speaker 1: Pretty wild concept.

Speaker 2: It is, yeah.

Speaker 1: I can see how having a virtual city to play around with would be super helpful for planning and testing, but like, how detailed and accurate can these digital twins actually be?

Speaker 2: It’s not just some static 3D model. Okay, so

Speaker 1: more than just a pretty picture.

Speaker 2: Yeah, it’s a dynamic representation of the city, constantly being updated with real time data from all sorts of sensors and IoT devices.

Speaker 1: So we’re talking about data on traffic, energy use, even things like air quality, all flowing into this digital twin in real time.

Speaker 2: Exactly. It’s like having a, I don’t know, a digital pulse on the city. You can actually see how it’s breathing, how it’s reacting.

Speaker 1: So it’s not just about seeing the city, it’s about understanding how it actually works, right?

Speaker 2: And as the technology gets better, these digital twins are becoming more and more sophisticated.

Speaker 1: But how do we actually use this information? Is it just for urban planners or can everyday people benefit from it?

Speaker 2: The benefits can extend to everyone. Imagine a city app that uses the digital twin to give you personalized info and recommendations.

Speaker 1: Oh, I like where this is going.

Speaker 2: So you could see the best route to work or find the nearest port with, I don’t know, picnic tables available. Maybe even get alerts about potential flooding in your area.

Speaker 1: So it’s like having a virtual assistant for city living.

Speaker 2: Exactly. Making our lives easier and smoother.

Speaker 1: Speaking of real world examples, are there any cities that are already doing this well, using AI to improve their infrastructure and services?

Speaker 2: Oh, absolutely. Singapore, for example. Oh, yeah. They’re using AI to do all sorts of things, optimize traffic flow, reduce energy consumption, improve public safety. Wow. They even have an AI powered chatbot.

Speaker 1: Really?

Speaker 2: Yeah. Citizens can use it to report issues or access government services.

Speaker 1: So they’re not just making the city smarter, they’re making the government smarter, too.

Speaker 2: Yeah. More responsive to people’s needs.

Speaker 1: Any other examples come to mind?

Speaker 2: Barcelona is another one. They’re using AI for things like managing parking, making trash collection more efficient, even giving personalized recommendations to tourists.

Speaker 1: It’s fascinating how different cities are applying AI to solve their own unique problems.

Speaker 2: Yeah, every city has its own priorities, its own goals, and AI is flexible enough to be adapted to different situations.

Speaker 1: Okay, so we’ve talked about the what of AI in smart cities, but what about the why? Why should we care about all of this? What’s the bigger picture?

Speaker 2: That’s a great question. Ultimately, the whole point of smart cities is to create urban spaces that are good for everyone, not just technologically advanced.

Speaker 1: So it’s about more than just cool gadgets and futuristic tech.

Speaker 2: It is. It’s about creating a better future, a future that’s more equitable, more sustainable, more liveable for everyone.

Speaker 1: With everything going on in the world, climate change, inequality, you name it.

Speaker 2: Exactly.

Speaker 1: It feels like the stakes are pretty high.

Speaker 2: But these smart cities, they give us tools to address these challenges.

Speaker 1: It’s a pretty inspiring vision. Honestly,

Speaker 2: it is. It requires a lot of collaboration, a lot of innovation, a willingness to embrace new ideas.

Speaker 1: Speaking of new ideas, what are some of the key trends and developments that we should be watching for in the future of AI and smart cities? What’s coming next?

Speaker 2: One trend is the rise of edge computing. Basically, it’s about processing data closer to the source. Instead of sending it all to a central cloud server.

Speaker 1: So instead of everything going to a giant data centre, we’re bringing the computing power to the devices themselves.

Speaker 2: Exactly. It can make things faster, more efficient, more secure, too.

Speaker 1: And that’s really important for things like self driving cars, right?

Speaker 2: Anything that needs real time decision making.

Speaker 1: So we’re not just collecting data faster, we’re acting on it faster, too. Makes sense.

Speaker 2: Another trend is the growing importance of what’s called explainable AI, or xAI.

Speaker 1: Explainable AI. That sounds intriguing. What’s that all about?

Speaker 2: Traditional AI algorithms, they can be like black boxes. Okay. We know what goes in, we know what comes out, but we don’t really understand how the decision was made.

Speaker 1: Yeah, that can be a little unnerving, especially when we’re talking about important

Speaker 2: stuff. Exactly. So XAI aims to make the decision making process more transparent, so we can actually understand how the AI is arriving at its conclusions.

Speaker 1: So we can trust the AI more, basically.

Speaker 2: And that’s crucial for things like healthcare or finance or criminal justice. Where the stakes are really high.

Speaker 1: If we’re going to let AI make decisions that impact people’s lives, we need to know how those decisions are being made.

Speaker 2: Absolutely. Transparency and accountability are essential.

Speaker 1: Any other trends on your radar?

Speaker 2: We’re also seeing some amazing advancements in generative AI, which is basically using AI to create new content, like text, images, even music.

Speaker 1: Oh yeah, those AI generated images are everywhere these days. Some of them are pretty wild.

Speaker 2: They are, yeah. And the potential applications are huge. In a smart city context, we could use it to design buildings, create personalized learning experiences, even generate realistic simulations for urban planning.

Speaker 1: So it’s not just about AI analyzing data, it’s about AI creating things,

Speaker 2: too. Exactly. It’s about augmenting our own creativity, helping us solve problems in new ways.

Speaker 1: It feels like we’re just at the tip of the iceberg with all of this.

Speaker 2: We are, yeah. And that’s what makes it so exciting.

Speaker 1: There’s so much potential, so much still to explore.

Speaker 2: I can’t wait to see what the future holds.

Speaker 1: It’s mind blowing to think about how much AI is already changing our cities.

Speaker 2: Yeah, it really is.

Speaker 1: And it sounds like we’re just at the beginning of this whole technological revolution, right?

Speaker 2: I think so, yeah. AI has the potential to completely reshape how we live in cities in the years to come.

Speaker 1: But with any powerful tech, there are Always concerns, potential downsides, things we need to be careful about.

Speaker 2: Oh, for sure. And it’s important to talk about those, address them head on.

Speaker 1: Absolutely. So let’s get into some of those potential downsides of AI and smart cities. What are some of the things we need to be wary of?

Speaker 2: One of the biggest concerns you hear is about jobs.

Speaker 1: Makes

Speaker 2: sense. As AI gets more and more sophisticated, it can start to automate tasks that humans used to do.

Speaker 1: Yeah, it’s natural to worry about AI taking over jobs.

Speaker 2: It is, and it’s something we need to be really thoughtful about. As some jobs become automated, we need to focus on creating new opportunities, training people for the jobs of the future.

Speaker 1: So it’s not just about stopping AI, it’s about adapting to it.

Speaker 2: Exactly, and another big concern is data privacy. Smart cities rely on tons of data about their residents.

Speaker 1: And with all that data being collected, it’s a bit unnerving to think about what could happen if it falls into the wrong hands.

Speaker 2: Absolutely. We need to have really strong protections in place to make sure this data is handled responsibly, that people’s privacy is respected.

Speaker 1: So it’s a balancing act, right? We want to use data to make cities better, but we also need to protect individual rights.

Speaker 2: It is, for sure. We need to be very careful about how this data is collected, how it’s used, how it’s stored.

Speaker 1: Okay, so jobs, privacy, what else should we be thinking about?

Speaker 2: Another big one is bias. AI algorithms, they’re trained on data. And if that data reflects existing biases in society, then those biases can get baked into the AI’s decisions.

Speaker 1: So basically, if we feed the AI bad data, it’s going to make bad decisions.

Speaker 2: Yeah, it’s like the old saying, garbage in, garbage out.

Speaker 1: This has been an incredible deep dive into the world of AI and smart cities. I think what’s clear is that this technology has amazing potential. But it’s also clear that we need to move forward thoughtfully and carefully.

Speaker 2: The future of our cities is ultimately in our hands, and it’s up to all of us to make sure we’re shaping it in a way that benefits everyone.

Speaker 1: I love that. It’s a good reminder that the future isn’t just something that happens to us. It’s something we create. I like that. And by talking about these issues, by engaging in these conversations about AI and its impact, I think we can really help steer things in the right direction.

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What is the future for cities podcast?

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