1 00:00:05,360 --> 00:00:09,560 Speaker 1: No matter the location, from the global South to northern Europe, 2 00:00:09,800 --> 00:00:13,400 Speaker 1: from the far East to the American West Coast, life 3 00:00:13,440 --> 00:00:17,239 Speaker 1: in the modern day city is both exhilarating and exhausting. 4 00:00:18,960 --> 00:00:21,680 Speaker 1: Consider all the different cities you've visited throughout the world, 5 00:00:22,400 --> 00:00:25,119 Speaker 1: then try and take a mental picture of what you've seen. 6 00:00:25,920 --> 00:00:28,720 Speaker 1: Of course, you could focus on the luster of city life, 7 00:00:28,880 --> 00:00:32,840 Speaker 1: but don't forget about the traffic, the commotion, and the confusion. 8 00:00:34,040 --> 00:00:36,680 Speaker 1: I recently traveled to Italy with my family, and I 9 00:00:36,800 --> 00:00:39,400 Speaker 1: was crazy enough to drive through the narrow streets of Rome, 10 00:00:39,880 --> 00:00:45,440 Speaker 1: where local drivers take stop signs as suggestions. But in 11 00:00:45,479 --> 00:00:49,520 Speaker 1: the future, the chaos of busy city streets might become 12 00:00:49,560 --> 00:00:53,440 Speaker 1: distant memories of what city life used to be. The 13 00:00:53,520 --> 00:00:57,560 Speaker 1: rise of AI technology is making cities smarter. Urban planners 14 00:00:57,600 --> 00:00:59,720 Speaker 1: and city engineers are teaming up with some of the 15 00:01:00,000 --> 00:01:04,080 Speaker 1: eating minds in AI. Together they are streamlining how cities 16 00:01:04,120 --> 00:01:08,759 Speaker 1: can be more energy efficient, safer, and enjoyable than ever before. 17 00:01:09,600 --> 00:01:12,600 Speaker 1: Join us as we reimagine urban life and how we 18 00:01:12,720 --> 00:01:18,440 Speaker 1: interact with our surroundings in the smartest cities yet. Welcome 19 00:01:18,600 --> 00:01:23,080 Speaker 1: to Technically Speaking, an Intel podcast produced by iHeartMedia's Ruby 20 00:01:23,080 --> 00:01:27,720 Speaker 1: Studio in partnership with Intel. In every episode, we explore 21 00:01:27,760 --> 00:01:31,880 Speaker 1: how AI innovations are changing the world and revolutionizing the 22 00:01:31,920 --> 00:01:36,240 Speaker 1: way we live. Hey, then I'm grand class. Today we 23 00:01:36,280 --> 00:01:39,440 Speaker 1: take our episode to the city streets, where AI technology 24 00:01:39,560 --> 00:01:43,360 Speaker 1: is already making a tremendous impact across the globe. In 25 00:01:43,360 --> 00:01:47,039 Speaker 1: this episode, we will focus on how Intel's AI technology 26 00:01:47,520 --> 00:01:52,520 Speaker 1: is impacting local infrastructures and changing the urban landscape. Plus 27 00:01:53,040 --> 00:01:56,600 Speaker 1: we'll learn about real world examples where AI has changed 28 00:01:56,600 --> 00:02:01,120 Speaker 1: the way residents of one bustling city live, work, and 29 00:02:01,200 --> 00:02:05,040 Speaker 1: commute every day. But before we go any further, let's 30 00:02:05,040 --> 00:02:10,959 Speaker 1: welcome our guests. Joining us today is Ashshiyadov, the head 31 00:02:10,960 --> 00:02:15,040 Speaker 1: of Strategic Partnership, Alliances and Technical Product Marketing at cap Gemini, 32 00:02:15,240 --> 00:02:18,560 Speaker 1: a global leader in partnering with companies to transform and 33 00:02:18,680 --> 00:02:22,280 Speaker 1: manage their business by harnessing the power of technology. Cap 34 00:02:22,320 --> 00:02:25,360 Speaker 1: Gemini has partnered with Intel to develop the five G 35 00:02:25,680 --> 00:02:29,440 Speaker 1: Roadside Unit, which will improve five G communications between traffic 36 00:02:29,480 --> 00:02:34,280 Speaker 1: monitoring cameras, sensors, vehicles, and pedestrians, making cities run smoother 37 00:02:34,600 --> 00:02:38,120 Speaker 1: and safer for everyone. Welcome to the show, Ashiesh. 38 00:02:38,160 --> 00:02:40,480 Speaker 2: Thank you Graham, It's so nice to be here. This morning. 39 00:02:41,080 --> 00:02:44,680 Speaker 1: We're also joined by a JVS. Ramakrishna, a technology expert 40 00:02:44,800 --> 00:02:48,280 Speaker 1: who lives in Hyderabad, India, where local authorities have leaned 41 00:02:48,320 --> 00:02:51,160 Speaker 1: on Intel zeon servers as part of the solution to 42 00:02:51,200 --> 00:02:55,320 Speaker 1: address challenges around effective traffic management and ensure public safety 43 00:02:55,560 --> 00:02:59,000 Speaker 1: as the city has expanded beyond its limits. The strategy 44 00:02:59,000 --> 00:03:03,200 Speaker 1: has improved traffic flow significantly by identifying congestion hotspots and 45 00:03:03,280 --> 00:03:08,720 Speaker 1: dynamically adjusting signal timings, helping optimize travel times citywide. Today. 46 00:03:09,000 --> 00:03:12,239 Speaker 1: JVS is also the global business unit head for Sustainable 47 00:03:12,320 --> 00:03:15,639 Speaker 1: Smart World at L and T Technology Services, also known 48 00:03:15,680 --> 00:03:18,760 Speaker 1: as LTTS. Welcome to the show, JVS. 49 00:03:19,080 --> 00:03:21,079 Speaker 3: Thank your Graham, pleasure to be here in the show. 50 00:03:26,240 --> 00:03:29,760 Speaker 1: I think before we start discussing solutions on making our 51 00:03:29,800 --> 00:03:32,520 Speaker 1: cities smarter, I'd like to get both of your thoughts 52 00:03:32,560 --> 00:03:36,000 Speaker 1: about some of the common challenges cities face. When some 53 00:03:36,080 --> 00:03:39,120 Speaker 1: people think of busy city life, they think of congestion, 54 00:03:39,920 --> 00:03:42,720 Speaker 1: think of traffic, they think of safety concerns. I'm sure 55 00:03:42,720 --> 00:03:44,880 Speaker 1: some of our audience right now is listening to us 56 00:03:44,880 --> 00:03:48,320 Speaker 1: while in a traffic jam. When it comes to infrastructure, 57 00:03:48,640 --> 00:03:51,600 Speaker 1: how can we plan our cities? What sticks out is 58 00:03:51,640 --> 00:03:55,920 Speaker 1: the common problem most cities face. I'll start with you, Iseshesh. 59 00:03:56,680 --> 00:04:00,440 Speaker 4: So traffic is definitely one of the most challenging part 60 00:04:00,520 --> 00:04:04,520 Speaker 4: of city life and there are many ways to solve it. 61 00:04:04,720 --> 00:04:06,720 Speaker 4: But for me also personally, I have a lot of 62 00:04:06,800 --> 00:04:09,400 Speaker 4: thoughts around how we can do it. Let's continue to 63 00:04:09,440 --> 00:04:11,240 Speaker 4: talk and we can tell more into it. 64 00:04:12,000 --> 00:04:13,720 Speaker 1: Absolutely jvs. 65 00:04:14,280 --> 00:04:17,960 Speaker 3: While technology plays a very important role, you know, I 66 00:04:18,000 --> 00:04:21,120 Speaker 3: come from part in India where traffic is like all pervesu. 67 00:04:21,240 --> 00:04:24,480 Speaker 3: I mean it's like everywhere and you would see millions 68 00:04:24,520 --> 00:04:27,000 Speaker 3: of people on the roads. But I think it started 69 00:04:27,040 --> 00:04:30,560 Speaker 3: with master about planning. Urban planning is the key in 70 00:04:30,640 --> 00:04:33,680 Speaker 3: terms of the city growth and how do the road 71 00:04:33,680 --> 00:04:37,400 Speaker 3: infrastructure is planned. Then follows the technology infrastructure. 72 00:04:37,880 --> 00:04:40,840 Speaker 4: The cities are already planned, you cannot erase the lines 73 00:04:40,880 --> 00:04:44,800 Speaker 4: and create new roads. But I still feel technology can 74 00:04:44,800 --> 00:04:47,719 Speaker 4: do a lot of good even with all those I 75 00:04:47,720 --> 00:04:53,080 Speaker 4: would say misplanned roads, right, That's where the power of 76 00:04:53,160 --> 00:04:54,280 Speaker 4: technology comes in. 77 00:04:54,760 --> 00:04:58,400 Speaker 1: Yeah. And it's interesting beside that because generally cities have 78 00:04:58,440 --> 00:05:01,719 Speaker 1: been planned from a historical perspective. If for example, in Perth, 79 00:05:01,760 --> 00:05:04,520 Speaker 1: it was an agricultural type city long time ago, so 80 00:05:04,560 --> 00:05:07,880 Speaker 1: the roads are designed for getting access to farmlands, and 81 00:05:07,920 --> 00:05:11,080 Speaker 1: now it's you know, obviously the metropolitan area has grown, 82 00:05:11,600 --> 00:05:14,640 Speaker 1: and perhaps that leads us into what is a smart city? 83 00:05:15,279 --> 00:05:17,680 Speaker 1: Maybe we could start a shish with what's your definition 84 00:05:17,760 --> 00:05:20,360 Speaker 1: of what a quite smart city is? 85 00:05:21,240 --> 00:05:25,040 Speaker 4: So I would say a smart city's city that is 86 00:05:25,480 --> 00:05:29,920 Speaker 4: keeping the efficiency and quality of life of its citizens 87 00:05:30,080 --> 00:05:33,599 Speaker 4: at the foremost. Where I would say you are using 88 00:05:33,600 --> 00:05:39,119 Speaker 4: and integrating technology and communication efficiently so that everyday life 89 00:05:39,160 --> 00:05:44,880 Speaker 4: becomes easier and the challenges become less and less as 90 00:05:45,120 --> 00:05:48,400 Speaker 4: the city becomes smarter and smarters. 91 00:05:48,880 --> 00:05:51,000 Speaker 3: I've been trying to figure out this definition for last 92 00:05:51,040 --> 00:05:54,359 Speaker 3: more eight nine years now, just to set the context. 93 00:05:55,000 --> 00:05:57,800 Speaker 3: In cities like San Francisco, Birth and all, they have 94 00:05:57,839 --> 00:06:01,920 Speaker 3: been planned historically. But most of the developing world where 95 00:06:01,960 --> 00:06:04,560 Speaker 3: there as a lot of infrastructure activity is happening, I think 96 00:06:04,560 --> 00:06:07,479 Speaker 3: that's where they have an opportunity to replan. But the 97 00:06:07,520 --> 00:06:11,440 Speaker 3: problem statements are different to different cities. What is a 98 00:06:11,480 --> 00:06:15,320 Speaker 3: smart city? In one small town of India where they 99 00:06:15,360 --> 00:06:19,400 Speaker 3: had a problem of unpredictable water supply, So people who 100 00:06:19,400 --> 00:06:21,840 Speaker 3: are like probably queuing up late night, you know, waking 101 00:06:22,000 --> 00:06:24,600 Speaker 3: for a water supply. If they get an SMS, they 102 00:06:24,640 --> 00:06:26,400 Speaker 3: know exactly when it's going to come. The water is 103 00:06:26,440 --> 00:06:29,000 Speaker 3: going to come right, so that's smart for them, Whereas 104 00:06:29,000 --> 00:06:32,680 Speaker 3: in big cities like Mumbai and Aderabat, it's all about traffic, safety, 105 00:06:32,960 --> 00:06:35,720 Speaker 3: utilities and a waste measurement so on. So I think 106 00:06:35,720 --> 00:06:38,320 Speaker 3: the definition varies, but ultimate it's all about production of 107 00:06:38,360 --> 00:06:40,440 Speaker 3: the wastage efficiencies, quality of life. 108 00:06:41,000 --> 00:06:44,360 Speaker 4: I come from a small village in India and near 109 00:06:44,400 --> 00:06:47,760 Speaker 4: our village there is hardly any water, and today that 110 00:06:47,880 --> 00:06:50,800 Speaker 4: area is thriving. We may talk about a first world 111 00:06:50,800 --> 00:06:53,960 Speaker 4: country where smartness is more to do with sensing so 112 00:06:54,000 --> 00:06:55,600 Speaker 4: that I don't have to wait even one minute on 113 00:06:55,680 --> 00:06:59,240 Speaker 4: a traffic light, to somebody in a third world country 114 00:06:59,360 --> 00:07:03,560 Speaker 4: or a West country where even getting that water is smart. 115 00:07:03,680 --> 00:07:05,280 Speaker 2: I agree with you, and. 116 00:07:05,200 --> 00:07:09,400 Speaker 1: That actually leads us to thinking more about these sorts 117 00:07:09,400 --> 00:07:12,880 Speaker 1: of smart solutions. As she she've worked with five G 118 00:07:13,240 --> 00:07:16,960 Speaker 1: commercializations for a number of years. Now. Before we get 119 00:07:16,960 --> 00:07:20,160 Speaker 1: into the role five G plays in smart cities, we're 120 00:07:20,160 --> 00:07:23,200 Speaker 1: going to be hearing in this podcast episode the term 121 00:07:23,360 --> 00:07:27,000 Speaker 1: RSU or roadside unit a lot. Can you just help 122 00:07:27,080 --> 00:07:30,400 Speaker 1: us dissect what this actually means? What does five G 123 00:07:30,960 --> 00:07:32,080 Speaker 1: RSU mean? 124 00:07:33,040 --> 00:07:37,240 Speaker 4: So five G is not just a fast data on 125 00:07:37,280 --> 00:07:40,200 Speaker 4: your phone or that five giken on your right top 126 00:07:40,240 --> 00:07:44,440 Speaker 4: corner of your phone. It enables much more because of 127 00:07:44,480 --> 00:07:47,680 Speaker 4: the low latency and high speed of the data. For example, 128 00:07:47,760 --> 00:07:52,560 Speaker 4: these roadside units could be just single server, harmless, small 129 00:07:52,600 --> 00:07:57,040 Speaker 4: service sitting somewhere and that could monitor and sense if 130 00:07:57,080 --> 00:07:59,680 Speaker 4: there is traffic. They can even sense if there is 131 00:07:59,720 --> 00:08:02,760 Speaker 4: a distin on the way the automous vehicle can take 132 00:08:02,800 --> 00:08:06,120 Speaker 4: a break, so they could enable that kind of interaction 133 00:08:06,440 --> 00:08:11,320 Speaker 4: between the pedestrian and the car. They could ensure that 134 00:08:11,480 --> 00:08:14,720 Speaker 4: the lights the signals are to the point where there 135 00:08:14,760 --> 00:08:18,160 Speaker 4: is less and less congestion, So it could be making 136 00:08:18,240 --> 00:08:22,640 Speaker 4: your traffic smarter. Because of the low latency, you could 137 00:08:22,640 --> 00:08:26,440 Speaker 4: have all those signals going out to the vehicles, to 138 00:08:26,600 --> 00:08:31,119 Speaker 4: the phones, if not the smart cars, and then making 139 00:08:31,200 --> 00:08:35,280 Speaker 4: it consistent also the signals on the lights based on 140 00:08:35,320 --> 00:08:38,679 Speaker 4: the sensors, on where the pedestrians are, where you could 141 00:08:39,160 --> 00:08:43,120 Speaker 4: prioritize the pedestrians, you could prioritize the bikers, you could 142 00:08:43,120 --> 00:08:46,640 Speaker 4: prioritize the congestion. All those things can be done with 143 00:08:46,960 --> 00:08:47,800 Speaker 4: the RSUs. 144 00:08:48,520 --> 00:08:52,000 Speaker 1: I'm just thinking of another scenario, particularly around school zones 145 00:08:52,080 --> 00:08:54,520 Speaker 1: where I live. It would be nice to be able 146 00:08:54,520 --> 00:08:58,560 Speaker 1: to prioritize school students crossing the road or notifying cars 147 00:08:58,559 --> 00:09:01,360 Speaker 1: to slide down and watch out during those pick up 148 00:09:01,400 --> 00:09:03,000 Speaker 1: and drop off times at school. 149 00:09:03,440 --> 00:09:07,280 Speaker 4: So, Graham, the thing is, once you have connectivity, once 150 00:09:07,320 --> 00:09:11,480 Speaker 4: you have cameras, the use cases expand. They can tailor 151 00:09:11,520 --> 00:09:15,120 Speaker 4: to your needs. You could even sense based on the height, 152 00:09:15,320 --> 00:09:19,920 Speaker 4: based on the uniforms, based on timing. You could do 153 00:09:19,960 --> 00:09:22,760 Speaker 4: all those wonderful things. It's just a matter of your 154 00:09:22,760 --> 00:09:24,839 Speaker 4: imagination and how far you can take it. 155 00:09:25,720 --> 00:09:29,760 Speaker 1: And you've talked a little bit about the technological benefit 156 00:09:29,840 --> 00:09:33,400 Speaker 1: of the five G protocol. Why is five G a 157 00:09:33,480 --> 00:09:34,160 Speaker 1: game changer? 158 00:09:34,760 --> 00:09:35,640 Speaker 2: All right, thank you. 159 00:09:35,760 --> 00:09:39,000 Speaker 4: This is my favorite topic because I really believe five 160 00:09:39,080 --> 00:09:42,000 Speaker 4: G is going to make the change that's needed in 161 00:09:42,080 --> 00:09:46,120 Speaker 4: the society. When LT came, you could see a lot 162 00:09:46,120 --> 00:09:50,080 Speaker 4: of applications, You could see a lot of enablement of 163 00:09:50,120 --> 00:09:53,920 Speaker 4: the app stores, but still latency was a big concern. 164 00:09:54,800 --> 00:09:58,560 Speaker 4: With five G, the latency reduced, the throughput increased, so 165 00:09:58,640 --> 00:10:02,880 Speaker 4: now you can be in more reliable situations. LT was 166 00:10:02,920 --> 00:10:06,080 Speaker 4: a good step because it moved from just the voice 167 00:10:06,120 --> 00:10:10,320 Speaker 4: calls to more an application world. But with five G 168 00:10:11,080 --> 00:10:13,520 Speaker 4: you can take it to a real life level. You're 169 00:10:13,559 --> 00:10:17,360 Speaker 4: just not playing games. You're moving beyond. You're impacting and 170 00:10:17,400 --> 00:10:22,240 Speaker 4: touching real lives now, on traffic signals, on waste management systems, 171 00:10:22,600 --> 00:10:25,960 Speaker 4: on power grades, and these are all real examples that 172 00:10:25,960 --> 00:10:28,640 Speaker 4: are happening in cities. You see it in Singapore, you 173 00:10:28,679 --> 00:10:32,160 Speaker 4: see it in Barcelona, you see it in Dubai. So 174 00:10:32,160 --> 00:10:35,800 Speaker 4: so many of these countries are already deploying these use cases. 175 00:10:37,640 --> 00:10:40,840 Speaker 1: Let's pause here for a second to analyze what Asheesh 176 00:10:41,040 --> 00:10:44,560 Speaker 1: just said. You heard her mention both LT and five 177 00:10:44,600 --> 00:10:48,400 Speaker 1: G and the differences between the two. LT stands for 178 00:10:48,480 --> 00:10:51,880 Speaker 1: long term evolution. And when it was introduced, it served 179 00:10:51,920 --> 00:10:55,680 Speaker 1: as a significant upgrade from existing three G technology, but 180 00:10:55,720 --> 00:10:58,400 Speaker 1: it only offers a speed of one hundred megabits per second. 181 00:10:58,880 --> 00:11:01,400 Speaker 1: Five G, on the other hand, delivers up to twenty 182 00:11:01,480 --> 00:11:05,120 Speaker 1: gigabits per second. That's the speed improvement of two hundred 183 00:11:05,160 --> 00:11:09,079 Speaker 1: times now. Five GEN. Near edge is the intersection of 184 00:11:09,120 --> 00:11:13,800 Speaker 1: two technologies, five G network technology and edge computing. It 185 00:11:13,840 --> 00:11:16,800 Speaker 1: brings the power of local computing in your home, in 186 00:11:16,840 --> 00:11:20,200 Speaker 1: your car, or at the traffic lights, together with high 187 00:11:20,280 --> 00:11:23,640 Speaker 1: speed mobile networks. All this means is that these edge 188 00:11:23,640 --> 00:11:27,440 Speaker 1: computers can make super fast decisions at the locations where 189 00:11:27,440 --> 00:11:31,040 Speaker 1: it's critically needed. With that in mind, I asked Ashish 190 00:11:31,160 --> 00:11:35,000 Speaker 1: how cap Geminis roadside units work and how five G 191 00:11:35,160 --> 00:11:39,040 Speaker 1: technology can help vehicles communicate with one another to ensure 192 00:11:39,240 --> 00:11:40,600 Speaker 1: safety on the road. 193 00:11:43,040 --> 00:11:47,160 Speaker 4: So roadside units and autonomous A vehicles will go hand 194 00:11:47,160 --> 00:11:51,680 Speaker 4: in hand. This will become more relevant than the autonomous 195 00:11:51,760 --> 00:11:57,440 Speaker 4: vehicles come into play. So imagine a traffic signal not needed. 196 00:11:58,280 --> 00:12:00,880 Speaker 4: That could be the future. Why is it needed today, 197 00:12:01,160 --> 00:12:05,440 Speaker 4: Because when somebody takes a break, other person needs to 198 00:12:05,480 --> 00:12:09,040 Speaker 4: come in. What if you can coordinate the two vehicles. 199 00:12:09,400 --> 00:12:12,200 Speaker 4: The edge can tell the vehicle at this moment, this 200 00:12:12,320 --> 00:12:15,080 Speaker 4: vehicle is going to intersect, so you don't need to stop. 201 00:12:15,120 --> 00:12:18,840 Speaker 4: You can slow down, and then the same signal from 202 00:12:18,960 --> 00:12:21,560 Speaker 4: vehicle to vehicle can go to other vehicles which can 203 00:12:21,720 --> 00:12:25,000 Speaker 4: tell them to slow down. So the traffic might slow 204 00:12:25,080 --> 00:12:26,959 Speaker 4: down a bit, but you don't need to stop in 205 00:12:27,000 --> 00:12:29,320 Speaker 4: a traffic signal. That's the extent that it can go 206 00:12:29,360 --> 00:12:32,520 Speaker 4: to because there's a communication between vehicle to vehicle. There's 207 00:12:32,559 --> 00:12:36,320 Speaker 4: a communication between signal to vehicle. So for now it 208 00:12:36,360 --> 00:12:41,559 Speaker 4: can be more efficiency, but going forward it can be autonomous. 209 00:12:41,640 --> 00:12:44,480 Speaker 3: That's the future of it and JVS. 210 00:12:44,480 --> 00:12:47,840 Speaker 1: Where have you seen other areas or other cities that 211 00:12:47,920 --> 00:12:51,160 Speaker 1: I've used this sort of roadside technology And it's a 212 00:12:51,200 --> 00:12:54,679 Speaker 1: two part kind of question. Is that plus using some 213 00:12:54,720 --> 00:12:58,760 Speaker 1: of the new kind of AI type technology that's coming 214 00:12:58,800 --> 00:13:02,680 Speaker 1: about and being able to improve the overall infrastructure of 215 00:13:02,720 --> 00:13:03,320 Speaker 1: these cities. 216 00:13:04,040 --> 00:13:07,520 Speaker 3: Yeah, So this technology has been extensively adopted in India. 217 00:13:07,840 --> 00:13:10,880 Speaker 3: More than hundred cities have implemented po or less similar 218 00:13:10,920 --> 00:13:14,800 Speaker 3: technologies where roads that units play significant route. The biggest 219 00:13:14,880 --> 00:13:17,560 Speaker 3: challenge is traffic enforcement in this part of the world, 220 00:13:18,000 --> 00:13:21,479 Speaker 3: and to do the traffic enforcement, we need to consistently 221 00:13:21,760 --> 00:13:24,960 Speaker 3: read the license plate numbers in terms of the violations, 222 00:13:24,960 --> 00:13:28,000 Speaker 3: whether they're crossing the red light, or somebody's coming in 223 00:13:28,040 --> 00:13:30,080 Speaker 3: the opposite direction and so on and so forth. It's 224 00:13:30,120 --> 00:13:34,160 Speaker 3: extremely important from latency point of view because the MOMENTI process, 225 00:13:34,760 --> 00:13:37,240 Speaker 3: it has to coordinate with the red light and decide 226 00:13:37,240 --> 00:13:39,000 Speaker 3: whether it's a violation or not, and then it has 227 00:13:39,040 --> 00:13:43,559 Speaker 3: to immediately notify the violation along with the evidences. 228 00:13:43,880 --> 00:13:47,280 Speaker 4: I can really see this RSU getting more useful for 229 00:13:47,440 --> 00:13:51,400 Speaker 4: violations because in India it's a very common practice for 230 00:13:51,480 --> 00:13:54,920 Speaker 4: people to not honor the traffic light. So one of 231 00:13:54,920 --> 00:13:58,600 Speaker 4: the things that's happening these days which is leading to 232 00:13:58,760 --> 00:14:02,920 Speaker 4: very good traffic management India is those sensors on the 233 00:14:02,960 --> 00:14:06,360 Speaker 4: traffic lights and with the IRISU coind off the situation, 234 00:14:06,400 --> 00:14:08,600 Speaker 4: you can read the number plate and you can get 235 00:14:08,640 --> 00:14:13,079 Speaker 4: to the violators automatically. And I hear that these days 236 00:14:13,400 --> 00:14:15,880 Speaker 4: a lot of tickets that are coming to homes with that. 237 00:14:16,640 --> 00:14:20,080 Speaker 3: At this moment, we run twenty five smart City Command centers. 238 00:14:20,440 --> 00:14:23,440 Speaker 3: Almost every city has got this feature. And trust me, 239 00:14:23,720 --> 00:14:27,720 Speaker 3: in some intersections the moment the tickets come next the morning, 240 00:14:27,720 --> 00:14:30,400 Speaker 3: you would find everybody behind the line. So it's quite 241 00:14:30,400 --> 00:14:31,560 Speaker 3: effective in this part of the world. 242 00:14:31,880 --> 00:14:37,000 Speaker 1: Yeah, that might also lead me to I guess another challenge. Particularly, 243 00:14:37,480 --> 00:14:40,600 Speaker 1: I'd like to get your thoughts about India's overall approach 244 00:14:40,680 --> 00:14:45,160 Speaker 1: to this, because in Europe and American and in some 245 00:14:45,240 --> 00:14:49,840 Speaker 1: regards Canada and Australia, we are very much concerned about 246 00:14:49,920 --> 00:14:53,560 Speaker 1: some of the privacy aspects of this, of the sensors 247 00:14:53,560 --> 00:14:57,720 Speaker 1: and cameras and being able to automatically detect violations and 248 00:14:57,760 --> 00:15:02,320 Speaker 1: things like that. How some of the safeguards being put 249 00:15:02,360 --> 00:15:05,960 Speaker 1: in place so that it can protect people's informations about 250 00:15:05,960 --> 00:15:07,479 Speaker 1: themselves and their movements. 251 00:15:07,960 --> 00:15:11,040 Speaker 3: First of all, all this data that is being captured 252 00:15:11,120 --> 00:15:15,080 Speaker 3: at the near edge is anonymous meus, you have license plates, 253 00:15:15,120 --> 00:15:18,720 Speaker 3: you wondn't have any information about that person because there 254 00:15:18,800 --> 00:15:22,680 Speaker 3: is nothing called people databas here. Number two most important 255 00:15:22,680 --> 00:15:25,320 Speaker 3: thing is that once you get the data, once the 256 00:15:25,320 --> 00:15:29,160 Speaker 3: tickets are generated, it goes into the data centers where 257 00:15:29,920 --> 00:15:34,280 Speaker 3: the data privacy is given the most highest priority in 258 00:15:34,360 --> 00:15:36,400 Speaker 3: terms of data availability point of view. 259 00:15:37,600 --> 00:15:40,080 Speaker 4: Let me chime in and the technology part that takes 260 00:15:40,080 --> 00:15:44,320 Speaker 4: care of this. The moment edge comes into play, security 261 00:15:44,400 --> 00:15:46,680 Speaker 4: does become a concern because you are out of the 262 00:15:46,840 --> 00:15:50,680 Speaker 4: data center hole security now, especially when you are on 263 00:15:50,720 --> 00:15:54,560 Speaker 4: a standalone edge. But the best part about edge is 264 00:15:54,600 --> 00:15:59,080 Speaker 4: also that the data is with the enterprise. If it's 265 00:15:59,160 --> 00:16:02,640 Speaker 4: the police to department or the traffic management department that's 266 00:16:02,680 --> 00:16:07,920 Speaker 4: handling the edge, then the data is only with that department. 267 00:16:08,640 --> 00:16:12,560 Speaker 1: Okay, great, And we talked a lot about the edge 268 00:16:12,640 --> 00:16:16,480 Speaker 1: and near edge and kind of related to the privacy issue. 269 00:16:16,920 --> 00:16:21,360 Speaker 1: Are you working with that data in terms of generating 270 00:16:21,400 --> 00:16:24,760 Speaker 1: new AI models or some sort of machine learning side 271 00:16:24,760 --> 00:16:28,400 Speaker 1: of things to continually improve the system as a whole. 272 00:16:29,120 --> 00:16:33,840 Speaker 4: That is correct, that data, even when it's anonymous, can 273 00:16:33,880 --> 00:16:38,200 Speaker 4: be used for analytics and can be fed into the 274 00:16:38,240 --> 00:16:42,320 Speaker 4: machine learning engine so that it can create more insights 275 00:16:42,520 --> 00:16:45,680 Speaker 4: as well as more behavior modification for the use cases 276 00:16:45,720 --> 00:16:48,720 Speaker 4: going forward, enabling more efficiency for the users. 277 00:16:48,840 --> 00:16:53,760 Speaker 3: Absolutely, yes, Okay, we do very extensively the data from 278 00:16:53,760 --> 00:16:56,200 Speaker 3: an anonymous point of view, but we are also very 279 00:16:56,200 --> 00:16:59,120 Speaker 3: of the fact that depending on where we put these 280 00:16:59,160 --> 00:17:01,680 Speaker 3: censors we can collect the data. There are a lot 281 00:17:01,680 --> 00:17:05,080 Speaker 3: of biases also getting introduced into the system, so there 282 00:17:05,119 --> 00:17:08,760 Speaker 3: is always some sort of judgment that comes from the 283 00:17:08,800 --> 00:17:13,600 Speaker 3: officers there. Whether the model is really reasonably unbiassed is 284 00:17:13,640 --> 00:17:17,840 Speaker 3: always a challenge which we've been tackling on a regular basis. 285 00:17:17,960 --> 00:17:19,960 Speaker 3: For example, we know how to product the traffic. We 286 00:17:20,040 --> 00:17:23,080 Speaker 3: even know where most of the vehicles are stolen and 287 00:17:23,400 --> 00:17:26,680 Speaker 3: where to get tratory them and and what atom and everything. 288 00:17:26,400 --> 00:17:32,080 Speaker 1: Is one coming up next on Technically Speaking and Intel Podcast. 289 00:17:33,480 --> 00:17:37,000 Speaker 3: There's a huge appetite in public infrastructure to adopt technology. 290 00:17:37,240 --> 00:17:39,320 Speaker 3: I think we need to focus more and more on 291 00:17:39,560 --> 00:17:40,720 Speaker 3: affordable use cases. 292 00:17:41,119 --> 00:17:43,199 Speaker 1: We'll be right back after a brief message from our 293 00:17:43,240 --> 00:17:54,359 Speaker 1: partners at Intel, Welcome back to Technically Speaking, an Intel Podcast. 294 00:17:54,640 --> 00:18:01,360 Speaker 1: I'm here now with Ashishiadav and Javis Rama Krishna. I'll 295 00:18:01,400 --> 00:18:04,440 Speaker 1: switch now a little bit to perhaps a real world 296 00:18:04,480 --> 00:18:09,120 Speaker 1: case study. JVS. You live in Hyderabad, incredibly busy city 297 00:18:09,119 --> 00:18:11,919 Speaker 1: in India. I'd like you to introduce the city to 298 00:18:11,960 --> 00:18:14,240 Speaker 1: the audience and how many people live there, How do 299 00:18:14,280 --> 00:18:17,080 Speaker 1: you describe the city, and maybe a bit of a 300 00:18:17,160 --> 00:18:19,280 Speaker 1: day in a life of a typical resident and some 301 00:18:19,359 --> 00:18:20,720 Speaker 1: of the challenges that they face. 302 00:18:21,560 --> 00:18:24,560 Speaker 3: Absolutely, absolutely, that's been my favorite subject for some time. 303 00:18:25,040 --> 00:18:28,959 Speaker 3: Hyderabad is one of the upcoming, fastest growing city in 304 00:18:29,000 --> 00:18:31,359 Speaker 3: Asia and it is in the southern part of the 305 00:18:31,359 --> 00:18:34,520 Speaker 3: India give and take maybe more than a ten million population. 306 00:18:35,119 --> 00:18:38,639 Speaker 3: But it has got two distinct qualities. One part of 307 00:18:38,680 --> 00:18:42,120 Speaker 3: the city is foreign years old legacy city. The other 308 00:18:42,200 --> 00:18:44,600 Speaker 3: part of the city is akin to probably a San 309 00:18:44,640 --> 00:18:47,639 Speaker 3: Francisco or Perth. So what we're able to do is 310 00:18:47,680 --> 00:18:51,320 Speaker 3: we planned almost I think ten thousand cameras and one 311 00:18:51,400 --> 00:18:54,680 Speaker 3: hundred thousand community cameras to be brought into one network. 312 00:18:54,960 --> 00:18:57,680 Speaker 3: There's a huge, massive command center that didn't put in 313 00:18:58,000 --> 00:18:59,840 Speaker 3: from the security point of which is very normal in 314 00:18:59,840 --> 00:19:03,280 Speaker 3: any city. But what is more important areas the city 315 00:19:03,320 --> 00:19:06,000 Speaker 3: has taken a view of to do more and more 316 00:19:06,119 --> 00:19:09,360 Speaker 3: use AA from the safety point of view, to identify 317 00:19:09,400 --> 00:19:12,960 Speaker 3: the hotspots and coordinate with the first responders at patrolling 318 00:19:13,080 --> 00:19:15,600 Speaker 3: vehicles to go on time and all. So what I 319 00:19:15,600 --> 00:19:17,960 Speaker 3: should tell you is that in the whole process using 320 00:19:18,000 --> 00:19:21,440 Speaker 3: AA and the core technology, they were able to improve 321 00:19:22,040 --> 00:19:26,000 Speaker 3: on first responders time to average to around eight minutes 322 00:19:26,040 --> 00:19:29,879 Speaker 3: from us by fifteen to twenty minutes. Very important. The 323 00:19:29,920 --> 00:19:33,920 Speaker 3: safety parameters so are so well monitored by the government. 324 00:19:34,480 --> 00:19:36,760 Speaker 3: I think that's a plus point for the city. The 325 00:19:36,800 --> 00:19:39,119 Speaker 3: crime rates have come down. Second part is on the 326 00:19:39,119 --> 00:19:42,199 Speaker 3: traffic side. A lot of work has gone into master planning, 327 00:19:42,640 --> 00:19:45,600 Speaker 3: especially in the new part of the city and almost 328 00:19:45,760 --> 00:19:50,199 Speaker 3: around two hundred and twenty five intersections we're having traffic 329 00:19:50,280 --> 00:19:54,679 Speaker 3: enforcement technologies could be a red light violation, speed and 330 00:19:54,760 --> 00:19:59,359 Speaker 3: all camera based, vision based analytics and also to really 331 00:19:59,400 --> 00:20:02,000 Speaker 3: identify the the patterns of their traffic and help their 332 00:20:02,000 --> 00:20:06,080 Speaker 3: transportation planning. We are even putting close to one hundred 333 00:20:06,080 --> 00:20:10,159 Speaker 3: and seventifare intersections the ATYCC cameras which do the traffic 334 00:20:10,200 --> 00:20:13,199 Speaker 3: classification and content. This actually helped us to give a 335 00:20:13,280 --> 00:20:17,639 Speaker 3: real time congestion index in every arm of the intersection. Okay, 336 00:20:17,800 --> 00:20:21,119 Speaker 3: so the number of possibilities are very using technologists. But 337 00:20:21,160 --> 00:20:24,080 Speaker 3: all of these things use vision, they use edge compute, 338 00:20:24,160 --> 00:20:24,720 Speaker 3: they use air. 339 00:20:26,760 --> 00:20:30,760 Speaker 1: AI technology didn't just help city officials in Hyderabad deal 340 00:20:30,880 --> 00:20:34,720 Speaker 1: with issues of traffic and congestion. It also allowed the 341 00:20:34,760 --> 00:20:39,119 Speaker 1: city to share infrastructure information across various agencies so that 342 00:20:39,160 --> 00:20:42,919 Speaker 1: they could improve the safety, security, and quality of the 343 00:20:43,040 --> 00:20:47,160 Speaker 1: utility companies. City officials can also plan better for big 344 00:20:47,200 --> 00:20:50,920 Speaker 1: events like how many cars and how many people will 345 00:20:50,960 --> 00:20:54,040 Speaker 1: stream into their city on any given day, and they 346 00:20:54,080 --> 00:20:58,360 Speaker 1: can prepare accordingly to ensure greater public safety for everyone involved. 347 00:20:58,920 --> 00:21:01,840 Speaker 1: And all of those insights, it's a dependent on AI. 348 00:21:07,359 --> 00:21:10,920 Speaker 1: Going back to the technology in the solution, JVS, you're 349 00:21:11,040 --> 00:21:13,960 Speaker 1: part of the L and T Technology Services which actually 350 00:21:14,000 --> 00:21:17,560 Speaker 1: created the LTTS Fusion platform. Can you tell us a 351 00:21:17,600 --> 00:21:20,600 Speaker 1: little bit about that platform and what role Intel played 352 00:21:20,640 --> 00:21:22,200 Speaker 1: in its AI functionality. 353 00:21:22,920 --> 00:21:25,960 Speaker 3: Sure. What we've found is that there are many applications 354 00:21:26,000 --> 00:21:29,040 Speaker 3: IT applications which are coming in trying to solve a 355 00:21:29,080 --> 00:21:31,639 Speaker 3: traffic problem or trying to solve this safety problem and 356 00:21:31,680 --> 00:21:34,320 Speaker 3: so on. But at a city level, when we started 357 00:21:34,320 --> 00:21:37,399 Speaker 3: analyzing so many cities, we found that you need to 358 00:21:37,440 --> 00:21:41,320 Speaker 3: have a platform which can actually try to get the 359 00:21:41,359 --> 00:21:46,640 Speaker 3: insights out of geospecial data, video data, structured data all 360 00:21:46,680 --> 00:21:51,680 Speaker 3: together and generate some insights or recommendations to the operator there. 361 00:21:52,240 --> 00:21:55,720 Speaker 3: So we have got into this act created what we 362 00:21:55,800 --> 00:21:59,520 Speaker 3: call Fusion its LTTUS platform. So this platform has got 363 00:21:59,600 --> 00:22:02,880 Speaker 3: feature of developing applications on the top of it, could 364 00:22:02,880 --> 00:22:06,160 Speaker 3: be traffic, to be safety or could be multi agency operations. 365 00:22:06,680 --> 00:22:09,960 Speaker 3: Video ingestion has been the core technology for US, which 366 00:22:10,040 --> 00:22:12,280 Speaker 3: is where we have been working with Intel. Most of 367 00:22:12,320 --> 00:22:16,120 Speaker 3: our infrastructure runs on Intel architecture here and it's quite 368 00:22:16,200 --> 00:22:18,920 Speaker 3: useful from that perspective. But what we're also trying to 369 00:22:18,960 --> 00:22:22,040 Speaker 3: do now in North America is we started working with 370 00:22:22,119 --> 00:22:25,560 Speaker 3: Intel primarily to use some of the confondents like Getty 371 00:22:25,560 --> 00:22:30,040 Speaker 3: and Sceinscape to actually do a lot of edge side analytics, 372 00:22:30,280 --> 00:22:33,800 Speaker 3: especially from the highwast point of view, and we get 373 00:22:33,800 --> 00:22:36,160 Speaker 3: the insights and on the top of it, we are 374 00:22:36,160 --> 00:22:38,920 Speaker 3: now able to put in a Fusion platform from the 375 00:22:38,920 --> 00:22:41,520 Speaker 3: command center point of view, which will through analytics, which 376 00:22:41,520 --> 00:22:45,200 Speaker 3: will do sort of business rules or can throw recommendations 377 00:22:45,200 --> 00:22:45,920 Speaker 3: on the top of it. 378 00:22:46,640 --> 00:22:51,440 Speaker 1: And when you're implementing it, what's the top challenge you've faced? 379 00:22:51,760 --> 00:22:53,679 Speaker 3: There is a big need in this part of the 380 00:22:53,680 --> 00:22:56,520 Speaker 3: world in this type of traffic to have a green 381 00:22:56,680 --> 00:23:00,199 Speaker 3: channel for any ambulance going in. We have tried a 382 00:23:00,200 --> 00:23:03,760 Speaker 3: lot of technologies to really communicate using radios wherein the 383 00:23:03,800 --> 00:23:06,720 Speaker 3: ambulance goes and then the signal turns green, and so 384 00:23:06,720 --> 00:23:09,840 Speaker 3: on and so forth. But the challenge is considering the latencies, 385 00:23:10,359 --> 00:23:13,120 Speaker 3: considering the infrastructure of word we are. It didn't really 386 00:23:13,119 --> 00:23:15,960 Speaker 3: scale the way we want. Probably technologies like Fiji will 387 00:23:16,240 --> 00:23:18,560 Speaker 3: ease out to some extent, but there is still the 388 00:23:18,720 --> 00:23:22,320 Speaker 3: challenge in terms of clearing the people from the intersection 389 00:23:22,640 --> 00:23:24,440 Speaker 3: in this part of the world, after it turns green, 390 00:23:24,840 --> 00:23:26,560 Speaker 3: is still going to be on the grown. There's a 391 00:23:26,640 --> 00:23:30,320 Speaker 3: huge appetite in public infrastructure to adopt technology. I think 392 00:23:30,560 --> 00:23:33,640 Speaker 3: we need to focus more and more on affordable use cases. 393 00:23:34,400 --> 00:23:37,040 Speaker 1: And this is actually now question for both of you, 394 00:23:37,840 --> 00:23:40,800 Speaker 1: how do you see ten years in the future given 395 00:23:41,000 --> 00:23:45,560 Speaker 1: the growth and speed of technology development as well as 396 00:23:45,880 --> 00:23:50,440 Speaker 1: societal changes and being more adoptive of these sorts of technologies. 397 00:23:51,040 --> 00:23:53,199 Speaker 1: It's just where do you think we're going to be 398 00:23:53,240 --> 00:23:54,120 Speaker 1: in ten years time? 399 00:23:55,080 --> 00:23:58,840 Speaker 4: So cram in ten years not just technology challenges. We're 400 00:23:58,840 --> 00:24:01,719 Speaker 4: going to have these natural resource challenges that are going 401 00:24:01,760 --> 00:24:05,560 Speaker 4: to intensify, so using them efficiently is going to be 402 00:24:05,600 --> 00:24:09,119 Speaker 4: one of the key challenges. Let's talk with very basic 403 00:24:09,160 --> 00:24:13,920 Speaker 4: things water energy. The usage is something that you see 404 00:24:13,960 --> 00:24:18,440 Speaker 4: cities saying if you water your gardens, you will be penalized. 405 00:24:18,840 --> 00:24:19,920 Speaker 2: Still, you go for a. 406 00:24:19,880 --> 00:24:23,399 Speaker 4: Walk on the sidewalk, you see overflowing gardens with water 407 00:24:23,520 --> 00:24:29,080 Speaker 4: and lush green grass. So you can definitely have observation 408 00:24:29,240 --> 00:24:33,119 Speaker 4: units that can send data to cities. Especially in a 409 00:24:33,160 --> 00:24:37,359 Speaker 4: country like United States, you cannot have people resources patrolling 410 00:24:37,800 --> 00:24:41,919 Speaker 4: from the city, so you certainly can have sensors that 411 00:24:42,000 --> 00:24:44,840 Speaker 4: can send the data to city. You can certainly have 412 00:24:45,400 --> 00:24:49,960 Speaker 4: energy efficiently being used in terms of renewable energy. If 413 00:24:50,000 --> 00:24:53,280 Speaker 4: some areas are producing excess energy, it can be diverted 414 00:24:53,320 --> 00:24:56,880 Speaker 4: to other areas which need more, and you can have 415 00:24:57,000 --> 00:25:00,600 Speaker 4: tabs on which areas use and why, and you can 416 00:25:00,680 --> 00:25:04,800 Speaker 4: even go to the extent of making those appliances more 417 00:25:05,040 --> 00:25:08,600 Speaker 4: energy efficient based on the analytics. So, in my opinion, 418 00:25:09,320 --> 00:25:12,800 Speaker 4: energy is going to be a key factor in terms 419 00:25:12,800 --> 00:25:16,360 Speaker 4: of how we will see the world change in next 420 00:25:16,440 --> 00:25:19,919 Speaker 4: ten years. Technology will continue to grow, we have to 421 00:25:20,119 --> 00:25:23,000 Speaker 4: channelize it towards how do we make it more efficient 422 00:25:23,400 --> 00:25:27,800 Speaker 4: so that we use minimal without impacting the quality of life. 423 00:25:27,560 --> 00:25:31,280 Speaker 1: For people JIVS. Whe do you see ourselves in ten years. 424 00:25:31,880 --> 00:25:33,960 Speaker 3: The biggest challenge is going to be because of the 425 00:25:34,000 --> 00:25:37,560 Speaker 3: limited resources on the planet. I think sustainability is the 426 00:25:37,600 --> 00:25:39,840 Speaker 3: way I think all of us actually we have to 427 00:25:39,880 --> 00:25:42,000 Speaker 3: spend a lot of time and energy and money in 428 00:25:42,080 --> 00:25:47,119 Speaker 3: the technology supporting sustainability, either in terms of environment, water, power, 429 00:25:47,680 --> 00:25:50,800 Speaker 3: renewable sources, and so on and so forth. While we are 430 00:25:50,840 --> 00:25:53,240 Speaker 3: talking about all these things, I think what also comes 431 00:25:53,280 --> 00:25:56,240 Speaker 3: to my mind is the cybersecurity is going to be 432 00:25:56,240 --> 00:26:00,199 Speaker 3: an extremely important aspect. The more connected we are in 433 00:26:00,800 --> 00:26:04,360 Speaker 3: the more the cybersecurity is going to disup the way 434 00:26:04,359 --> 00:26:07,400 Speaker 3: we do and our habits down the line. I think 435 00:26:07,640 --> 00:26:10,080 Speaker 3: these are the two things which are going to change 436 00:26:10,080 --> 00:26:11,000 Speaker 3: the way we live. 437 00:26:11,480 --> 00:26:13,840 Speaker 4: And living in the United States. In case you are 438 00:26:14,119 --> 00:26:18,200 Speaker 4: hit with some natural disaster. The quick recovery in terms 439 00:26:18,240 --> 00:26:22,280 Speaker 4: of using the technology to ensure that the areas can 440 00:26:22,320 --> 00:26:25,320 Speaker 4: be brought up quickly. That's another thing that technology will 441 00:26:25,320 --> 00:26:26,159 Speaker 4: play a big role in. 442 00:26:26,880 --> 00:26:29,520 Speaker 1: Yeah, and I like the fact that you know, we 443 00:26:29,840 --> 00:26:32,560 Speaker 1: have talked about the big cities, but also talked about 444 00:26:32,600 --> 00:26:35,159 Speaker 1: some of the technology going to the smaller cities. And 445 00:26:35,200 --> 00:26:37,480 Speaker 1: I think that's what it's all about, is the technology 446 00:26:37,480 --> 00:26:41,160 Speaker 1: being able to be diffuse to all corners of the planet. 447 00:26:41,840 --> 00:26:43,840 Speaker 1: And just with that, I think we'll leave it this. 448 00:26:44,040 --> 00:26:45,920 Speaker 1: I thank you so much for your time. 449 00:26:46,640 --> 00:26:48,200 Speaker 2: And JBS listening to you. 450 00:26:48,320 --> 00:26:50,440 Speaker 4: I would like to visit Hyderabayt the next time I'm 451 00:26:50,440 --> 00:26:51,440 Speaker 4: in India. 452 00:26:51,680 --> 00:26:54,439 Speaker 3: You are welcome, Ashish, I think you should visit. The 453 00:26:54,440 --> 00:26:56,880 Speaker 3: way they are doing seeing is believing. 454 00:26:57,119 --> 00:26:58,960 Speaker 1: All right. Thank your Shish, Thank you, JVS. 455 00:26:59,400 --> 00:27:01,399 Speaker 2: Thank you what pleasure talking to both of you. 456 00:27:01,640 --> 00:27:03,600 Speaker 3: Thank you Graham for the time and ashes. Thank you 457 00:27:04,000 --> 00:27:07,080 Speaker 3: pleasure meeting you all. 458 00:27:07,119 --> 00:27:10,040 Speaker 1: Thank you to A SHOESH and JVS for the insights 459 00:27:10,080 --> 00:27:14,680 Speaker 1: to today's episode of Technically Speaking. Many of you listening 460 00:27:14,720 --> 00:27:17,960 Speaker 1: to this podcast will have experienced the frustrations of city 461 00:27:18,000 --> 00:27:23,600 Speaker 1: traffic stuck on freeways, roundabouts, and intersections. My takeaway from 462 00:27:23,640 --> 00:27:26,919 Speaker 1: this discussion is that using the new advances in low cost, 463 00:27:27,119 --> 00:27:31,000 Speaker 1: low power, highly efficient edge devices will reduce the time 464 00:27:31,040 --> 00:27:34,240 Speaker 1: spent commuting in your car. Think about it this way. 465 00:27:34,760 --> 00:27:37,480 Speaker 1: If the adoption of smart city technologies saves you just 466 00:27:37,600 --> 00:27:40,880 Speaker 1: ten minutes a day or five minutes each way in commuting, 467 00:27:41,400 --> 00:27:44,520 Speaker 1: the time saving will compound and mean an extra full 468 00:27:44,560 --> 00:27:47,280 Speaker 1: week to spend with your family and friends per year. 469 00:27:48,040 --> 00:27:51,159 Speaker 1: According to the World Bank, currently fifty six percent of 470 00:27:51,160 --> 00:27:54,159 Speaker 1: the world's population live in cities. This is projected to 471 00:27:54,160 --> 00:27:57,520 Speaker 1: increase to seventy percent by twenty forty five. I believe 472 00:27:57,520 --> 00:28:00,479 Speaker 1: that city is with a high livability score will attract 473 00:28:00,480 --> 00:28:04,199 Speaker 1: the best talent and the best investment if they can 474 00:28:04,359 --> 00:28:07,720 Speaker 1: utilize their new trends in AI, edge devices and computing 475 00:28:07,760 --> 00:28:12,240 Speaker 1: techniques to improve the life of residence. As with all technology, 476 00:28:12,640 --> 00:28:16,119 Speaker 1: privacy protections of the individual must be topmost in mind, 477 00:28:16,680 --> 00:28:19,800 Speaker 1: as we may fall into an unwonted scenario of feeling 478 00:28:19,920 --> 00:28:24,479 Speaker 1: like we're always being watched. However, I am confident that 479 00:28:24,520 --> 00:28:27,879 Speaker 1: if we continue to examine and discuss the potential of 480 00:28:28,000 --> 00:28:32,400 Speaker 1: smart city technology and use AI ethically, we will see 481 00:28:32,440 --> 00:28:36,600 Speaker 1: metropolises from all corners of the globe continue to grow 482 00:28:36,920 --> 00:28:42,680 Speaker 1: and prosper. Join us again on Tuesday, June fourth, we'll 483 00:28:42,680 --> 00:28:50,360 Speaker 1: be exploring the technologies impacting the future of retail. Technically 484 00:28:50,360 --> 00:28:53,880 Speaker 1: Speaking was produced by a Ruby Studio from iHeartRadio in 485 00:28:53,960 --> 00:28:57,720 Speaker 1: partnership with Intel and hosted by me Graham Class. Our 486 00:28:57,720 --> 00:29:01,440 Speaker 1: executive producer is Molly Socio Our EP of post production 487 00:29:01,680 --> 00:29:05,720 Speaker 1: is James Foster, and our supervising producer is Nikir Swinton. 488 00:29:06,720 --> 00:29:10,320 Speaker 1: This episode was edited by Sierra Spreen and written by 489 00:29:10,400 --> 00:29:12,440 Speaker 1: Nick Firshaw.