WEBVTT - How AI Is Making Our Cities Smarter and Safer 0:00:05.360 --> 0:00:09.560 No matter the location, from the global South to northern Europe, 0:00:09.800 --> 0:00:13.400 from the far East to the American West Coast, life 0:00:13.440 --> 0:00:17.239 in the modern day city is both exhilarating and exhausting. 0:00:18.960 --> 0:00:21.680 Consider all the different cities you've visited throughout the world, 0:00:22.400 --> 0:00:25.119 then try and take a mental picture of what you've seen. 0:00:25.920 --> 0:00:28.720 Of course, you could focus on the luster of city life, 0:00:28.880 --> 0:00:32.840 but don't forget about the traffic, the commotion, and the confusion. 0:00:34.040 --> 0:00:36.680 I recently traveled to Italy with my family, and I 0:00:36.800 --> 0:00:39.400 was crazy enough to drive through the narrow streets of Rome, 0:00:39.880 --> 0:00:45.440 where local drivers take stop signs as suggestions. But in 0:00:45.479 --> 0:00:49.520 the future, the chaos of busy city streets might become 0:00:49.560 --> 0:00:53.440 distant memories of what city life used to be. The 0:00:53.520 --> 0:00:57.560 rise of AI technology is making cities smarter. Urban planners 0:00:57.600 --> 0:00:59.720 and city engineers are teaming up with some of the 0:01:00.000 --> 0:01:04.080 eating minds in AI. Together they are streamlining how cities 0:01:04.120 --> 0:01:08.759 can be more energy efficient, safer, and enjoyable than ever before. 0:01:09.600 --> 0:01:12.600 Join us as we reimagine urban life and how we 0:01:12.720 --> 0:01:18.440 interact with our surroundings in the smartest cities yet. Welcome 0:01:18.600 --> 0:01:23.080 to Technically Speaking, an Intel podcast produced by iHeartMedia's Ruby 0:01:23.080 --> 0:01:27.720 Studio in partnership with Intel. In every episode, we explore 0:01:27.760 --> 0:01:31.880 how AI innovations are changing the world and revolutionizing the 0:01:31.920 --> 0:01:36.240 way we live. Hey, then I'm grand class. Today we 0:01:36.280 --> 0:01:39.440 take our episode to the city streets, where AI technology 0:01:39.560 --> 0:01:43.360 is already making a tremendous impact across the globe. In 0:01:43.360 --> 0:01:47.039 this episode, we will focus on how Intel's AI technology 0:01:47.520 --> 0:01:52.520 is impacting local infrastructures and changing the urban landscape. Plus 0:01:53.040 --> 0:01:56.600 we'll learn about real world examples where AI has changed 0:01:56.600 --> 0:02:01.120 the way residents of one bustling city live, work, and 0:02:01.200 --> 0:02:05.040 commute every day. But before we go any further, let's 0:02:05.040 --> 0:02:10.959 welcome our guests. Joining us today is Ashshiyadov, the head 0:02:10.960 --> 0:02:15.040 of Strategic Partnership, Alliances and Technical Product Marketing at cap Gemini, 0:02:15.240 --> 0:02:18.560 a global leader in partnering with companies to transform and 0:02:18.680 --> 0:02:22.280 manage their business by harnessing the power of technology. Cap 0:02:22.320 --> 0:02:25.360 Gemini has partnered with Intel to develop the five G 0:02:25.680 --> 0:02:29.440 Roadside Unit, which will improve five G communications between traffic 0:02:29.480 --> 0:02:34.280 monitoring cameras, sensors, vehicles, and pedestrians, making cities run smoother 0:02:34.600 --> 0:02:38.120 and safer for everyone. Welcome to the show, Ashiesh. 0:02:38.160 --> 0:02:40.480 Thank you Graham, It's so nice to be here. This morning. 0:02:41.080 --> 0:02:44.680 We're also joined by a JVS. Ramakrishna, a technology expert 0:02:44.800 --> 0:02:48.280 who lives in Hyderabad, India, where local authorities have leaned 0:02:48.320 --> 0:02:51.160 on Intel zeon servers as part of the solution to 0:02:51.200 --> 0:02:55.320 address challenges around effective traffic management and ensure public safety 0:02:55.560 --> 0:02:59.000 as the city has expanded beyond its limits. The strategy 0:02:59.000 --> 0:03:03.200 has improved traffic flow significantly by identifying congestion hotspots and 0:03:03.280 --> 0:03:08.720 dynamically adjusting signal timings, helping optimize travel times citywide. Today. 0:03:09.000 --> 0:03:12.239 JVS is also the global business unit head for Sustainable 0:03:12.320 --> 0:03:15.639 Smart World at L and T Technology Services, also known 0:03:15.680 --> 0:03:18.760 as LTTS. Welcome to the show, JVS. 0:03:19.080 --> 0:03:21.079 Thank your Graham, pleasure to be here in the show. 0:03:26.240 --> 0:03:29.760 I think before we start discussing solutions on making our 0:03:29.800 --> 0:03:32.520 cities smarter, I'd like to get both of your thoughts 0:03:32.560 --> 0:03:36.000 about some of the common challenges cities face. When some 0:03:36.080 --> 0:03:39.120 people think of busy city life, they think of congestion, 0:03:39.920 --> 0:03:42.720 think of traffic, they think of safety concerns. I'm sure 0:03:42.720 --> 0:03:44.880 some of our audience right now is listening to us 0:03:44.880 --> 0:03:48.320 while in a traffic jam. When it comes to infrastructure, 0:03:48.640 --> 0:03:51.600 how can we plan our cities? What sticks out is 0:03:51.640 --> 0:03:55.920 the common problem most cities face. I'll start with you, Iseshesh. 0:03:56.680 --> 0:04:00.440 So traffic is definitely one of the most challenging part 0:04:00.520 --> 0:04:04.520 of city life and there are many ways to solve it. 0:04:04.720 --> 0:04:06.720 But for me also personally, I have a lot of 0:04:06.800 --> 0:04:09.400 thoughts around how we can do it. Let's continue to 0:04:09.440 --> 0:04:11.240 talk and we can tell more into it. 0:04:12.000 --> 0:04:13.720 Absolutely jvs. 0:04:14.280 --> 0:04:17.960 While technology plays a very important role, you know, I 0:04:18.000 --> 0:04:21.120 come from part in India where traffic is like all pervesu. 0:04:21.240 --> 0:04:24.480 I mean it's like everywhere and you would see millions 0:04:24.520 --> 0:04:27.000 of people on the roads. But I think it started 0:04:27.040 --> 0:04:30.560 with master about planning. Urban planning is the key in 0:04:30.640 --> 0:04:33.680 terms of the city growth and how do the road 0:04:33.680 --> 0:04:37.400 infrastructure is planned. Then follows the technology infrastructure. 0:04:37.880 --> 0:04:40.840 The cities are already planned, you cannot erase the lines 0:04:40.880 --> 0:04:44.800 and create new roads. But I still feel technology can 0:04:44.800 --> 0:04:47.719 do a lot of good even with all those I 0:04:47.720 --> 0:04:53.080 would say misplanned roads, right, That's where the power of 0:04:53.160 --> 0:04:54.280 technology comes in. 0:04:54.760 --> 0:04:58.400 Yeah. And it's interesting beside that because generally cities have 0:04:58.440 --> 0:05:01.719 been planned from a historical perspective. If for example, in Perth, 0:05:01.760 --> 0:05:04.520 it was an agricultural type city long time ago, so 0:05:04.560 --> 0:05:07.880 the roads are designed for getting access to farmlands, and 0:05:07.920 --> 0:05:11.080 now it's you know, obviously the metropolitan area has grown, 0:05:11.600 --> 0:05:14.640 and perhaps that leads us into what is a smart city? 0:05:15.279 --> 0:05:17.680 Maybe we could start a shish with what's your definition 0:05:17.760 --> 0:05:20.360 of what a quite smart city is? 0:05:21.240 --> 0:05:25.040 So I would say a smart city's city that is 0:05:25.480 --> 0:05:29.920 keeping the efficiency and quality of life of its citizens 0:05:30.080 --> 0:05:33.599 at the foremost. Where I would say you are using 0:05:33.600 --> 0:05:39.119 and integrating technology and communication efficiently so that everyday life 0:05:39.160 --> 0:05:44.880 becomes easier and the challenges become less and less as 0:05:45.120 --> 0:05:48.400 the city becomes smarter and smarters. 0:05:48.880 --> 0:05:51.000 I've been trying to figure out this definition for last 0:05:51.040 --> 0:05:54.359 more eight nine years now, just to set the context. 0:05:55.000 --> 0:05:57.800 In cities like San Francisco, Birth and all, they have 0:05:57.839 --> 0:06:01.920 been planned historically. But most of the developing world where 0:06:01.960 --> 0:06:04.560 there as a lot of infrastructure activity is happening, I think 0:06:04.560 --> 0:06:07.479 that's where they have an opportunity to replan. But the 0:06:07.520 --> 0:06:11.440 problem statements are different to different cities. What is a 0:06:11.480 --> 0:06:15.320 smart city? In one small town of India where they 0:06:15.360 --> 0:06:19.400 had a problem of unpredictable water supply, So people who 0:06:19.400 --> 0:06:21.840 are like probably queuing up late night, you know, waking 0:06:22.000 --> 0:06:24.600 for a water supply. If they get an SMS, they 0:06:24.640 --> 0:06:26.400 know exactly when it's going to come. The water is 0:06:26.440 --> 0:06:29.000 going to come right, so that's smart for them, Whereas 0:06:29.000 --> 0:06:32.680 in big cities like Mumbai and Aderabat, it's all about traffic, safety, 0:06:32.960 --> 0:06:35.720 utilities and a waste measurement so on. So I think 0:06:35.720 --> 0:06:38.320 the definition varies, but ultimate it's all about production of 0:06:38.360 --> 0:06:40.440 the wastage efficiencies, quality of life. 0:06:41.000 --> 0:06:44.360 I come from a small village in India and near 0:06:44.400 --> 0:06:47.760 our village there is hardly any water, and today that 0:06:47.880 --> 0:06:50.800 area is thriving. We may talk about a first world 0:06:50.800 --> 0:06:53.960 country where smartness is more to do with sensing so 0:06:54.000 --> 0:06:55.600 that I don't have to wait even one minute on 0:06:55.680 --> 0:06:59.240 a traffic light, to somebody in a third world country 0:06:59.360 --> 0:07:03.560 or a West country where even getting that water is smart. 0:07:03.680 --> 0:07:05.280 I agree with you, and. 0:07:05.200 --> 0:07:09.400 That actually leads us to thinking more about these sorts 0:07:09.400 --> 0:07:12.880 of smart solutions. As she she've worked with five G 0:07:13.240 --> 0:07:16.960 commercializations for a number of years. Now. Before we get 0:07:16.960 --> 0:07:20.160 into the role five G plays in smart cities, we're 0:07:20.160 --> 0:07:23.200 going to be hearing in this podcast episode the term 0:07:23.360 --> 0:07:27.000 RSU or roadside unit a lot. Can you just help 0:07:27.080 --> 0:07:30.400 us dissect what this actually means? What does five G 0:07:30.960 --> 0:07:32.080 RSU mean? 0:07:33.040 --> 0:07:37.240 So five G is not just a fast data on 0:07:37.280 --> 0:07:40.200 your phone or that five giken on your right top 0:07:40.240 --> 0:07:44.440 corner of your phone. It enables much more because of 0:07:44.480 --> 0:07:47.680 the low latency and high speed of the data. For example, 0:07:47.760 --> 0:07:52.560 these roadside units could be just single server, harmless, small 0:07:52.600 --> 0:07:57.040 service sitting somewhere and that could monitor and sense if 0:07:57.080 --> 0:07:59.680 there is traffic. They can even sense if there is 0:07:59.720 --> 0:08:02.760 a distin on the way the automous vehicle can take 0:08:02.800 --> 0:08:06.120 a break, so they could enable that kind of interaction 0:08:06.440 --> 0:08:11.320 between the pedestrian and the car. They could ensure that 0:08:11.480 --> 0:08:14.720 the lights the signals are to the point where there 0:08:14.760 --> 0:08:18.160 is less and less congestion, So it could be making 0:08:18.240 --> 0:08:22.640 your traffic smarter. Because of the low latency, you could 0:08:22.640 --> 0:08:26.440 have all those signals going out to the vehicles, to 0:08:26.600 --> 0:08:31.119 the phones, if not the smart cars, and then making 0:08:31.200 --> 0:08:35.280 it consistent also the signals on the lights based on 0:08:35.320 --> 0:08:38.679 the sensors, on where the pedestrians are, where you could 0:08:39.160 --> 0:08:43.120 prioritize the pedestrians, you could prioritize the bikers, you could 0:08:43.120 --> 0:08:46.640 prioritize the congestion. All those things can be done with 0:08:46.960 --> 0:08:47.800 the RSUs. 0:08:48.520 --> 0:08:52.000 I'm just thinking of another scenario, particularly around school zones 0:08:52.080 --> 0:08:54.520 where I live. It would be nice to be able 0:08:54.520 --> 0:08:58.560 to prioritize school students crossing the road or notifying cars 0:08:58.559 --> 0:09:01.360 to slide down and watch out during those pick up 0:09:01.400 --> 0:09:03.000 and drop off times at school. 0:09:03.440 --> 0:09:07.280 So, Graham, the thing is, once you have connectivity, once 0:09:07.320 --> 0:09:11.480 you have cameras, the use cases expand. They can tailor 0:09:11.520 --> 0:09:15.120 to your needs. You could even sense based on the height, 0:09:15.320 --> 0:09:19.920 based on the uniforms, based on timing. You could do 0:09:19.960 --> 0:09:22.760 all those wonderful things. It's just a matter of your 0:09:22.760 --> 0:09:24.839 imagination and how far you can take it. 0:09:25.720 --> 0:09:29.760 And you've talked a little bit about the technological benefit 0:09:29.840 --> 0:09:33.400 of the five G protocol. Why is five G a 0:09:33.480 --> 0:09:34.160 game changer? 0:09:34.760 --> 0:09:35.640 All right, thank you. 0:09:35.760 --> 0:09:39.000 This is my favorite topic because I really believe five 0:09:39.080 --> 0:09:42.000 G is going to make the change that's needed in 0:09:42.080 --> 0:09:46.120 the society. When LT came, you could see a lot 0:09:46.120 --> 0:09:50.080 of applications, You could see a lot of enablement of 0:09:50.120 --> 0:09:53.920 the app stores, but still latency was a big concern. 0:09:54.800 --> 0:09:58.560 With five G, the latency reduced, the throughput increased, so 0:09:58.640 --> 0:10:02.880 now you can be in more reliable situations. LT was 0:10:02.920 --> 0:10:06.080 a good step because it moved from just the voice 0:10:06.120 --> 0:10:10.320 calls to more an application world. But with five G 0:10:11.080 --> 0:10:13.520 you can take it to a real life level. You're 0:10:13.559 --> 0:10:17.360 just not playing games. You're moving beyond. You're impacting and 0:10:17.400 --> 0:10:22.240 touching real lives now, on traffic signals, on waste management systems, 0:10:22.600 --> 0:10:25.960 on power grades, and these are all real examples that 0:10:25.960 --> 0:10:28.640 are happening in cities. You see it in Singapore, you 0:10:28.679 --> 0:10:32.160 see it in Barcelona, you see it in Dubai. So 0:10:32.160 --> 0:10:35.800 so many of these countries are already deploying these use cases. 0:10:37.640 --> 0:10:40.840 Let's pause here for a second to analyze what Asheesh 0:10:41.040 --> 0:10:44.560 just said. You heard her mention both LT and five 0:10:44.600 --> 0:10:48.400 G and the differences between the two. LT stands for 0:10:48.480 --> 0:10:51.880 long term evolution. And when it was introduced, it served 0:10:51.920 --> 0:10:55.680 as a significant upgrade from existing three G technology, but 0:10:55.720 --> 0:10:58.400 it only offers a speed of one hundred megabits per second. 0:10:58.880 --> 0:11:01.400 Five G, on the other hand, delivers up to twenty 0:11:01.480 --> 0:11:05.120 gigabits per second. That's the speed improvement of two hundred 0:11:05.160 --> 0:11:09.079 times now. Five GEN. Near edge is the intersection of 0:11:09.120 --> 0:11:13.800 two technologies, five G network technology and edge computing. It 0:11:13.840 --> 0:11:16.800 brings the power of local computing in your home, in 0:11:16.840 --> 0:11:20.200 your car, or at the traffic lights, together with high 0:11:20.280 --> 0:11:23.640 speed mobile networks. All this means is that these edge 0:11:23.640 --> 0:11:27.440 computers can make super fast decisions at the locations where 0:11:27.440 --> 0:11:31.040 it's critically needed. With that in mind, I asked Ashish 0:11:31.160 --> 0:11:35.000 how cap Geminis roadside units work and how five G 0:11:35.160 --> 0:11:39.040 technology can help vehicles communicate with one another to ensure 0:11:39.240 --> 0:11:40.600 safety on the road. 0:11:43.040 --> 0:11:47.160 So roadside units and autonomous A vehicles will go hand 0:11:47.160 --> 0:11:51.680 in hand. This will become more relevant than the autonomous 0:11:51.760 --> 0:11:57.440 vehicles come into play. So imagine a traffic signal not needed. 0:11:58.280 --> 0:12:00.880 That could be the future. Why is it needed today, 0:12:01.160 --> 0:12:05.440 Because when somebody takes a break, other person needs to 0:12:05.480 --> 0:12:09.040 come in. What if you can coordinate the two vehicles. 0:12:09.400 --> 0:12:12.200 The edge can tell the vehicle at this moment, this 0:12:12.320 --> 0:12:15.080 vehicle is going to intersect, so you don't need to stop. 0:12:15.120 --> 0:12:18.840 You can slow down, and then the same signal from 0:12:18.960 --> 0:12:21.560 vehicle to vehicle can go to other vehicles which can 0:12:21.720 --> 0:12:25.000 tell them to slow down. So the traffic might slow 0:12:25.080 --> 0:12:26.959 down a bit, but you don't need to stop in 0:12:27.000 --> 0:12:29.320 a traffic signal. That's the extent that it can go 0:12:29.360 --> 0:12:32.520 to because there's a communication between vehicle to vehicle. There's 0:12:32.559 --> 0:12:36.320 a communication between signal to vehicle. So for now it 0:12:36.360 --> 0:12:41.559 can be more efficiency, but going forward it can be autonomous. 0:12:41.640 --> 0:12:44.480 That's the future of it and JVS. 0:12:44.480 --> 0:12:47.840 Where have you seen other areas or other cities that 0:12:47.920 --> 0:12:51.160 I've used this sort of roadside technology And it's a 0:12:51.200 --> 0:12:54.679 two part kind of question. Is that plus using some 0:12:54.720 --> 0:12:58.760 of the new kind of AI type technology that's coming 0:12:58.800 --> 0:13:02.680 about and being able to improve the overall infrastructure of 0:13:02.720 --> 0:13:03.320 these cities. 0:13:04.040 --> 0:13:07.520 Yeah, So this technology has been extensively adopted in India. 0:13:07.840 --> 0:13:10.880 More than hundred cities have implemented po or less similar 0:13:10.920 --> 0:13:14.800 technologies where roads that units play significant route. The biggest 0:13:14.880 --> 0:13:17.560 challenge is traffic enforcement in this part of the world, 0:13:18.000 --> 0:13:21.479 and to do the traffic enforcement, we need to consistently 0:13:21.760 --> 0:13:24.960 read the license plate numbers in terms of the violations, 0:13:24.960 --> 0:13:28.000 whether they're crossing the red light, or somebody's coming in 0:13:28.040 --> 0:13:30.080 the opposite direction and so on and so forth. It's 0:13:30.120 --> 0:13:34.160 extremely important from latency point of view because the MOMENTI process, 0:13:34.760 --> 0:13:37.240 it has to coordinate with the red light and decide 0:13:37.240 --> 0:13:39.000 whether it's a violation or not, and then it has 0:13:39.040 --> 0:13:43.559 to immediately notify the violation along with the evidences. 0:13:43.880 --> 0:13:47.280 I can really see this RSU getting more useful for 0:13:47.440 --> 0:13:51.400 violations because in India it's a very common practice for 0:13:51.480 --> 0:13:54.920 people to not honor the traffic light. So one of 0:13:54.920 --> 0:13:58.600 the things that's happening these days which is leading to 0:13:58.760 --> 0:14:02.920 very good traffic management India is those sensors on the 0:14:02.960 --> 0:14:06.360 traffic lights and with the IRISU coind off the situation, 0:14:06.400 --> 0:14:08.600 you can read the number plate and you can get 0:14:08.640 --> 0:14:13.079 to the violators automatically. And I hear that these days 0:14:13.400 --> 0:14:15.880 a lot of tickets that are coming to homes with that. 0:14:16.640 --> 0:14:20.080 At this moment, we run twenty five smart City Command centers. 0:14:20.440 --> 0:14:23.440 Almost every city has got this feature. And trust me, 0:14:23.720 --> 0:14:27.720 in some intersections the moment the tickets come next the morning, 0:14:27.720 --> 0:14:30.400 you would find everybody behind the line. So it's quite 0:14:30.400 --> 0:14:31.560 effective in this part of the world. 0:14:31.880 --> 0:14:37.000 Yeah, that might also lead me to I guess another challenge. Particularly, 0:14:37.480 --> 0:14:40.600 I'd like to get your thoughts about India's overall approach 0:14:40.680 --> 0:14:45.160 to this, because in Europe and American and in some 0:14:45.240 --> 0:14:49.840 regards Canada and Australia, we are very much concerned about 0:14:49.920 --> 0:14:53.560 some of the privacy aspects of this, of the sensors 0:14:53.560 --> 0:14:57.720 and cameras and being able to automatically detect violations and 0:14:57.760 --> 0:15:02.320 things like that. How some of the safeguards being put 0:15:02.360 --> 0:15:05.960 in place so that it can protect people's informations about 0:15:05.960 --> 0:15:07.479 themselves and their movements. 0:15:07.960 --> 0:15:11.040 First of all, all this data that is being captured 0:15:11.120 --> 0:15:15.080 at the near edge is anonymous meus, you have license plates, 0:15:15.120 --> 0:15:18.720 you wondn't have any information about that person because there 0:15:18.800 --> 0:15:22.680 is nothing called people databas here. Number two most important 0:15:22.680 --> 0:15:25.320 thing is that once you get the data, once the 0:15:25.320 --> 0:15:29.160 tickets are generated, it goes into the data centers where 0:15:29.920 --> 0:15:34.280 the data privacy is given the most highest priority in 0:15:34.360 --> 0:15:36.400 terms of data availability point of view. 0:15:37.600 --> 0:15:40.080 Let me chime in and the technology part that takes 0:15:40.080 --> 0:15:44.320 care of this. The moment edge comes into play, security 0:15:44.400 --> 0:15:46.680 does become a concern because you are out of the 0:15:46.840 --> 0:15:50.680 data center hole security now, especially when you are on 0:15:50.720 --> 0:15:54.560 a standalone edge. But the best part about edge is 0:15:54.600 --> 0:15:59.080 also that the data is with the enterprise. If it's 0:15:59.160 --> 0:16:02.640 the police to department or the traffic management department that's 0:16:02.680 --> 0:16:07.920 handling the edge, then the data is only with that department. 0:16:08.640 --> 0:16:12.560 Okay, great, And we talked a lot about the edge 0:16:12.640 --> 0:16:16.480 and near edge and kind of related to the privacy issue. 0:16:16.920 --> 0:16:21.360 Are you working with that data in terms of generating 0:16:21.400 --> 0:16:24.760 new AI models or some sort of machine learning side 0:16:24.760 --> 0:16:28.400 of things to continually improve the system as a whole. 0:16:29.120 --> 0:16:33.840 That is correct, that data, even when it's anonymous, can 0:16:33.880 --> 0:16:38.200 be used for analytics and can be fed into the 0:16:38.240 --> 0:16:42.320 machine learning engine so that it can create more insights 0:16:42.520 --> 0:16:45.680 as well as more behavior modification for the use cases 0:16:45.720 --> 0:16:48.720 going forward, enabling more efficiency for the users. 0:16:48.840 --> 0:16:53.760 Absolutely, yes, Okay, we do very extensively the data from 0:16:53.760 --> 0:16:56.200 an anonymous point of view, but we are also very 0:16:56.200 --> 0:16:59.120 of the fact that depending on where we put these 0:16:59.160 --> 0:17:01.680 censors we can collect the data. There are a lot 0:17:01.680 --> 0:17:05.080 of biases also getting introduced into the system, so there 0:17:05.119 --> 0:17:08.760 is always some sort of judgment that comes from the 0:17:08.800 --> 0:17:13.600 officers there. Whether the model is really reasonably unbiassed is 0:17:13.640 --> 0:17:17.840 always a challenge which we've been tackling on a regular basis. 0:17:17.960 --> 0:17:19.960 For example, we know how to product the traffic. We 0:17:20.040 --> 0:17:23.080 even know where most of the vehicles are stolen and 0:17:23.400 --> 0:17:26.680 where to get tratory them and and what atom and everything. 0:17:26.400 --> 0:17:32.080 Is one coming up next on Technically Speaking and Intel Podcast. 0:17:33.480 --> 0:17:37.000 There's a huge appetite in public infrastructure to adopt technology. 0:17:37.240 --> 0:17:39.320 I think we need to focus more and more on 0:17:39.560 --> 0:17:40.720 affordable use cases. 0:17:41.119 --> 0:17:43.199 We'll be right back after a brief message from our 0:17:43.240 --> 0:17:54.359 partners at Intel, Welcome back to Technically Speaking, an Intel Podcast. 0:17:54.640 --> 0:18:01.360 I'm here now with Ashishiadav and Javis Rama Krishna. I'll 0:18:01.400 --> 0:18:04.440 switch now a little bit to perhaps a real world 0:18:04.480 --> 0:18:09.120 case study. JVS. You live in Hyderabad, incredibly busy city 0:18:09.119 --> 0:18:11.919 in India. I'd like you to introduce the city to 0:18:11.960 --> 0:18:14.240 the audience and how many people live there, How do 0:18:14.280 --> 0:18:17.080 you describe the city, and maybe a bit of a 0:18:17.160 --> 0:18:19.280 day in a life of a typical resident and some 0:18:19.359 --> 0:18:20.720 of the challenges that they face. 0:18:21.560 --> 0:18:24.560 Absolutely, absolutely, that's been my favorite subject for some time. 0:18:25.040 --> 0:18:28.959 Hyderabad is one of the upcoming, fastest growing city in 0:18:29.000 --> 0:18:31.359 Asia and it is in the southern part of the 0:18:31.359 --> 0:18:34.520 India give and take maybe more than a ten million population. 0:18:35.119 --> 0:18:38.639 But it has got two distinct qualities. One part of 0:18:38.680 --> 0:18:42.120 the city is foreign years old legacy city. The other 0:18:42.200 --> 0:18:44.600 part of the city is akin to probably a San 0:18:44.640 --> 0:18:47.639 Francisco or Perth. So what we're able to do is 0:18:47.680 --> 0:18:51.320 we planned almost I think ten thousand cameras and one 0:18:51.400 --> 0:18:54.680 hundred thousand community cameras to be brought into one network. 0:18:54.960 --> 0:18:57.680 There's a huge, massive command center that didn't put in 0:18:58.000 --> 0:18:59.840 from the security point of which is very normal in 0:18:59.840 --> 0:19:03.280 any city. But what is more important areas the city 0:19:03.320 --> 0:19:06.000 has taken a view of to do more and more 0:19:06.119 --> 0:19:09.360 use AA from the safety point of view, to identify 0:19:09.400 --> 0:19:12.960 the hotspots and coordinate with the first responders at patrolling 0:19:13.080 --> 0:19:15.600 vehicles to go on time and all. So what I 0:19:15.600 --> 0:19:17.960 should tell you is that in the whole process using 0:19:18.000 --> 0:19:21.440 AA and the core technology, they were able to improve 0:19:22.040 --> 0:19:26.000 on first responders time to average to around eight minutes 0:19:26.040 --> 0:19:29.879 from us by fifteen to twenty minutes. Very important. The 0:19:29.920 --> 0:19:33.920 safety parameters so are so well monitored by the government. 0:19:34.480 --> 0:19:36.760 I think that's a plus point for the city. The 0:19:36.800 --> 0:19:39.119 crime rates have come down. Second part is on the 0:19:39.119 --> 0:19:42.199 traffic side. A lot of work has gone into master planning, 0:19:42.640 --> 0:19:45.600 especially in the new part of the city and almost 0:19:45.760 --> 0:19:50.199 around two hundred and twenty five intersections we're having traffic 0:19:50.280 --> 0:19:54.679 enforcement technologies could be a red light violation, speed and 0:19:54.760 --> 0:19:59.359 all camera based, vision based analytics and also to really 0:19:59.400 --> 0:20:02.000 identify the the patterns of their traffic and help their 0:20:02.000 --> 0:20:06.080 transportation planning. We are even putting close to one hundred 0:20:06.080 --> 0:20:10.159 and seventifare intersections the ATYCC cameras which do the traffic 0:20:10.200 --> 0:20:13.199 classification and content. This actually helped us to give a 0:20:13.280 --> 0:20:17.639 real time congestion index in every arm of the intersection. Okay, 0:20:17.800 --> 0:20:21.119 so the number of possibilities are very using technologists. But 0:20:21.160 --> 0:20:24.080 all of these things use vision, they use edge compute, 0:20:24.160 --> 0:20:24.720 they use air. 0:20:26.760 --> 0:20:30.760 AI technology didn't just help city officials in Hyderabad deal 0:20:30.880 --> 0:20:34.720 with issues of traffic and congestion. It also allowed the 0:20:34.760 --> 0:20:39.119 city to share infrastructure information across various agencies so that 0:20:39.160 --> 0:20:42.919 they could improve the safety, security, and quality of the 0:20:43.040 --> 0:20:47.160 utility companies. City officials can also plan better for big 0:20:47.200 --> 0:20:50.920 events like how many cars and how many people will 0:20:50.960 --> 0:20:54.040 stream into their city on any given day, and they 0:20:54.080 --> 0:20:58.360 can prepare accordingly to ensure greater public safety for everyone involved. 0:20:58.920 --> 0:21:01.840 And all of those insights, it's a dependent on AI. 0:21:07.359 --> 0:21:10.920 Going back to the technology in the solution, JVS, you're 0:21:11.040 --> 0:21:13.960 part of the L and T Technology Services which actually 0:21:14.000 --> 0:21:17.560 created the LTTS Fusion platform. Can you tell us a 0:21:17.600 --> 0:21:20.600 little bit about that platform and what role Intel played 0:21:20.640 --> 0:21:22.200 in its AI functionality. 0:21:22.920 --> 0:21:25.960 Sure. What we've found is that there are many applications 0:21:26.000 --> 0:21:29.040 IT applications which are coming in trying to solve a 0:21:29.080 --> 0:21:31.639 traffic problem or trying to solve this safety problem and 0:21:31.680 --> 0:21:34.320 so on. But at a city level, when we started 0:21:34.320 --> 0:21:37.399 analyzing so many cities, we found that you need to 0:21:37.440 --> 0:21:41.320 have a platform which can actually try to get the 0:21:41.359 --> 0:21:46.640 insights out of geospecial data, video data, structured data all 0:21:46.680 --> 0:21:51.680 together and generate some insights or recommendations to the operator there. 0:21:52.240 --> 0:21:55.720 So we have got into this act created what we 0:21:55.800 --> 0:21:59.520 call Fusion its LTTUS platform. So this platform has got 0:21:59.600 --> 0:22:02.880 feature of developing applications on the top of it, could 0:22:02.880 --> 0:22:06.160 be traffic, to be safety or could be multi agency operations. 0:22:06.680 --> 0:22:09.960 Video ingestion has been the core technology for US, which 0:22:10.040 --> 0:22:12.280 is where we have been working with Intel. Most of 0:22:12.320 --> 0:22:16.120 our infrastructure runs on Intel architecture here and it's quite 0:22:16.200 --> 0:22:18.920 useful from that perspective. But what we're also trying to 0:22:18.960 --> 0:22:22.040 do now in North America is we started working with 0:22:22.119 --> 0:22:25.560 Intel primarily to use some of the confondents like Getty 0:22:25.560 --> 0:22:30.040 and Sceinscape to actually do a lot of edge side analytics, 0:22:30.280 --> 0:22:33.800 especially from the highwast point of view, and we get 0:22:33.800 --> 0:22:36.160 the insights and on the top of it, we are 0:22:36.160 --> 0:22:38.920 now able to put in a Fusion platform from the 0:22:38.920 --> 0:22:41.520 command center point of view, which will through analytics, which 0:22:41.520 --> 0:22:45.200 will do sort of business rules or can throw recommendations 0:22:45.200 --> 0:22:45.920 on the top of it. 0:22:46.640 --> 0:22:51.440 And when you're implementing it, what's the top challenge you've faced? 0:22:51.760 --> 0:22:53.679 There is a big need in this part of the 0:22:53.680 --> 0:22:56.520 world in this type of traffic to have a green 0:22:56.680 --> 0:23:00.199 channel for any ambulance going in. We have tried a 0:23:00.200 --> 0:23:03.760 lot of technologies to really communicate using radios wherein the 0:23:03.800 --> 0:23:06.720 ambulance goes and then the signal turns green, and so 0:23:06.720 --> 0:23:09.840 on and so forth. But the challenge is considering the latencies, 0:23:10.359 --> 0:23:13.120 considering the infrastructure of word we are. It didn't really 0:23:13.119 --> 0:23:15.960 scale the way we want. Probably technologies like Fiji will 0:23:16.240 --> 0:23:18.560 ease out to some extent, but there is still the 0:23:18.720 --> 0:23:22.320 challenge in terms of clearing the people from the intersection 0:23:22.640 --> 0:23:24.440 in this part of the world, after it turns green, 0:23:24.840 --> 0:23:26.560 is still going to be on the grown. There's a 0:23:26.640 --> 0:23:30.320 huge appetite in public infrastructure to adopt technology. I think 0:23:30.560 --> 0:23:33.640 we need to focus more and more on affordable use cases. 0:23:34.400 --> 0:23:37.040 And this is actually now question for both of you, 0:23:37.840 --> 0:23:40.800 how do you see ten years in the future given 0:23:41.000 --> 0:23:45.560 the growth and speed of technology development as well as 0:23:45.880 --> 0:23:50.440 societal changes and being more adoptive of these sorts of technologies. 0:23:51.040 --> 0:23:53.199 It's just where do you think we're going to be 0:23:53.240 --> 0:23:54.120 in ten years time? 0:23:55.080 --> 0:23:58.840 So cram in ten years not just technology challenges. We're 0:23:58.840 --> 0:24:01.719 going to have these natural resource challenges that are going 0:24:01.760 --> 0:24:05.560