1 00:00:09,654 --> 00:00:11,115 The group that I'm particularly 2 00:00:11,814 --> 00:00:13,834 focused on, really, is the 3 00:00:14,214 --> 00:00:15,514 convergence of 4 00:00:16,489 --> 00:00:18,350 response to applications and transformation. 5 00:00:21,529 --> 00:00:24,109 So how do we actually take this technology, 6 00:00:24,835 --> 00:00:26,594 not just at a high level or as 7 00:00:26,594 --> 00:00:28,695 a sort of, you know, an AI whitewashing 8 00:00:28,914 --> 00:00:29,414 type 9 00:00:30,035 --> 00:00:32,515 blur, but how do you actually practically apply 10 00:00:32,515 --> 00:00:33,975 it around some 11 00:00:34,579 --> 00:00:36,200 significant industrial problems? 12 00:00:37,460 --> 00:00:40,359 Earlier this year, we talked to Darren Martin, 13 00:00:40,579 --> 00:00:42,200 CDO of Atkins Realis, 14 00:00:42,564 --> 00:00:45,625 about the relationship between AI and humanity. 15 00:00:46,484 --> 00:00:48,725 We saw how Darren had used big data 16 00:00:48,725 --> 00:00:51,064 in his early career as a clinical psychologist 17 00:00:51,590 --> 00:00:55,210 to identify effective treatments for severe mental illness. 18 00:00:55,989 --> 00:00:58,729 We saw that machine learning and big data 19 00:00:58,789 --> 00:01:00,170 could be powerful tools, 20 00:01:00,734 --> 00:01:02,655 but they were not a fix for every 21 00:01:02,655 --> 00:01:03,155 problem. 22 00:01:04,734 --> 00:01:06,415 But what does it mean to live in 23 00:01:06,415 --> 00:01:09,709 a world where AIs control autonomous devices that 24 00:01:09,709 --> 00:01:10,930 work alongside us? 25 00:01:11,549 --> 00:01:14,930 As machine autonomy replaces or supplements human work, 26 00:01:15,149 --> 00:01:16,930 what does that mean for how we design, 27 00:01:17,149 --> 00:01:18,769 build, and live in cities? 28 00:01:20,734 --> 00:01:22,575 These are some of the questions that Darren 29 00:01:22,575 --> 00:01:25,055 has been examining along with fellow members of 30 00:01:25,055 --> 00:01:26,594 the World Economic Forum's 31 00:01:27,120 --> 00:01:28,500 AI Governance Alliance. 32 00:01:29,439 --> 00:01:31,460 This really boils down to, 33 00:01:31,920 --> 00:01:32,659 for example, 34 00:01:33,040 --> 00:01:35,380 how my robots and people 35 00:01:36,105 --> 00:01:38,025 relate to each other and work with each 36 00:01:38,025 --> 00:01:39,325 other in a safe way 37 00:01:39,704 --> 00:01:41,005 on a mega 38 00:01:41,865 --> 00:01:44,239 project, like an, a project in the Middle 39 00:01:44,239 --> 00:01:45,859 East where we're building huge 40 00:01:47,040 --> 00:01:48,420 cities, and how might we 41 00:01:48,799 --> 00:01:50,739 help achieve that construction 42 00:01:51,520 --> 00:01:52,020 faster 43 00:01:52,635 --> 00:01:53,775 with the AI 44 00:01:54,155 --> 00:01:55,935 and the people working together. 45 00:01:57,115 --> 00:01:59,275 Bringing that together in a safe, practical way 46 00:01:59,275 --> 00:02:00,255 is really important. 47 00:02:00,689 --> 00:02:02,709 But if you then flip out into 48 00:02:03,329 --> 00:02:03,829 different 49 00:02:04,689 --> 00:02:05,750 areas of opportunity 50 00:02:06,770 --> 00:02:08,849 and discuss the pros and cons of this 51 00:02:08,849 --> 00:02:10,935 technology with and the applications 52 00:02:11,875 --> 00:02:14,114 The impact of these technologies will be felt 53 00:02:14,114 --> 00:02:15,254 throughout our society 54 00:02:16,194 --> 00:02:18,775 We will be leaning into conversations about health. 55 00:02:19,500 --> 00:02:21,599 We'll be leaning into conversations about 56 00:02:21,900 --> 00:02:23,439 defense and security. 57 00:02:24,780 --> 00:02:27,099 It has the potential to do great harm, 58 00:02:27,099 --> 00:02:29,004 but also has the potential to 59 00:02:29,465 --> 00:02:30,364 do great good. 60 00:02:45,585 --> 00:02:47,284 Welcome to Engineering Matters. 61 00:02:47,585 --> 00:02:48,724 I'm Johnny Dowling. 62 00:02:49,185 --> 00:02:51,824 And I'm Rian Owen. Earlier this year, we 63 00:02:51,824 --> 00:02:54,620 heard from Darren Martin, chief digital officer of 64 00:02:54,620 --> 00:02:56,240 our partner, Atkins Realis, 65 00:02:56,699 --> 00:02:59,580 about the importance of considering human needs in 66 00:02:59,580 --> 00:03:01,199 our deployment of AI. 67 00:03:01,585 --> 00:03:03,824 In this episode, Darren returns to look at 68 00:03:03,824 --> 00:03:05,745 what it means for humans to live and 69 00:03:05,745 --> 00:03:07,844 work alongside autonomous devices 70 00:03:08,305 --> 00:03:10,544 and what that implies for the relationship between 71 00:03:10,544 --> 00:03:12,009 big tech and architecture, 72 00:03:12,310 --> 00:03:14,169 engineering, and construction professionals. 73 00:03:14,949 --> 00:03:17,669 One day, autonomous devices will be close at 74 00:03:17,669 --> 00:03:20,495 hand wherever we go. But for now, we 75 00:03:20,495 --> 00:03:22,574 are in some corners of the world most 76 00:03:22,574 --> 00:03:24,835 likely to encounter them on the road. 77 00:03:33,189 --> 00:03:35,050 In San Francisco, for example, 78 00:03:35,955 --> 00:03:37,875 one can get in a car without a 79 00:03:37,875 --> 00:03:38,375 driver 80 00:03:39,474 --> 00:03:41,254 and pay a fare and go. 81 00:03:44,900 --> 00:03:47,459 So the Uber concept has evolved to a 82 00:03:47,459 --> 00:03:48,759 a Robot Taxi 83 00:03:49,139 --> 00:03:49,639 concept. 84 00:03:50,180 --> 00:03:52,979 Now taking the learnings from San Francisco, which 85 00:03:52,979 --> 00:03:55,455 is is very healing, but it's a grid 86 00:03:55,455 --> 00:03:57,534 city. So it's, you know, it's a traditional 87 00:03:57,534 --> 00:04:00,574 American city with the grids, and taking that 88 00:04:00,574 --> 00:04:02,094 to where I live, and I latest it 89 00:04:02,094 --> 00:04:04,689 with our single lane country lanes and, you 90 00:04:04,689 --> 00:04:06,789 know, the all of the complexity that comes 91 00:04:07,330 --> 00:04:08,689 with having a deer in the middle of 92 00:04:08,689 --> 00:04:10,610 the road, and and how does the robot 93 00:04:10,610 --> 00:04:11,405 taxi deal with 94 00:04:13,164 --> 00:04:15,724 that? The ability of AIs to operate potentially 95 00:04:15,724 --> 00:04:17,904 dangerous equipment in public spaces 96 00:04:18,444 --> 00:04:20,605 is being proven in the neat grids of 97 00:04:20,605 --> 00:04:21,105 American 98 00:04:22,209 --> 00:04:24,370 cities. Development now is focused on improving the 99 00:04:24,370 --> 00:04:27,669 safety of these systems and verifying that safety 100 00:04:27,810 --> 00:04:29,110 so that they can be deployed 101 00:04:29,435 --> 00:04:30,254 around the world. 102 00:04:31,035 --> 00:04:33,514 It's bringing these two things together, like like, 103 00:04:33,514 --> 00:04:36,074 what does the consumer need? How might that 104 00:04:36,074 --> 00:04:37,294 be enabled? 105 00:04:38,660 --> 00:04:39,319 And also, 106 00:04:39,620 --> 00:04:41,639 what are the benefits for doing that? 107 00:04:42,259 --> 00:04:44,420 If these autonomous devices can be shown to 108 00:04:44,420 --> 00:04:45,960 be safe enough for our roads, 109 00:04:46,305 --> 00:04:48,225 then they'll be well on their way to 110 00:04:48,225 --> 00:04:50,324 working alongside us everywhere. 111 00:04:54,464 --> 00:04:55,990 We can already see autonomy 112 00:04:59,029 --> 00:04:59,233 autonomy in the workplace at ports around the 113 00:04:59,233 --> 00:04:59,769 world. Here, autonomous rubber tire gantry 114 00:05:00,229 --> 00:05:02,089 cranes move containers around ports 115 00:05:03,555 --> 00:05:04,774 without human intervention. 116 00:05:05,314 --> 00:05:08,514 In applications like this, the autonomous equipment is 117 00:05:08,514 --> 00:05:10,910 separated from human staff by a fence. 118 00:05:11,610 --> 00:05:14,189 Part of the terminal will be exclusively autonomous. 119 00:05:14,490 --> 00:05:16,330 And part of it will be operated by 120 00:05:16,330 --> 00:05:16,830 people. 121 00:05:19,544 --> 00:05:20,524 There are many 122 00:05:21,144 --> 00:05:23,644 quite advanced examples now with 123 00:05:24,104 --> 00:05:26,044 the safety case has been lowered 124 00:05:27,229 --> 00:05:29,470 for the technology, because people are not allowed 125 00:05:29,470 --> 00:05:31,069 in there. And so if the technology goes 126 00:05:31,069 --> 00:05:31,569 wrong, 127 00:05:32,029 --> 00:05:35,285 it's embarrassing, it's expensive, but no one was 128 00:05:35,285 --> 00:05:37,444 hurt. So the profile of it is much 129 00:05:37,444 --> 00:05:37,944 lower. 130 00:05:39,925 --> 00:05:42,725 But to extract the real efficiency benefits of 131 00:05:42,725 --> 00:05:43,810 AI autonomy, 132 00:05:44,350 --> 00:05:45,629 we need to be able to work with 133 00:05:45,629 --> 00:05:47,569 these devices in the same space. 134 00:05:47,949 --> 00:05:49,009 So the question is, 135 00:05:49,550 --> 00:05:51,870 under what conditions can one move away from 136 00:05:51,870 --> 00:05:53,685 a a closed loop scenario? 137 00:05:55,024 --> 00:05:55,524 So 138 00:05:56,384 --> 00:05:58,084 it's about the interoperability 139 00:05:58,625 --> 00:05:59,764 of of humans 140 00:06:00,970 --> 00:06:02,350 and the robot systems. 141 00:06:03,610 --> 00:06:06,009 That could then allow a new generation of 142 00:06:06,009 --> 00:06:08,824 robot workers to work in environments far more 143 00:06:08,824 --> 00:06:11,245 complex and unpredictable than a modern port. 144 00:06:12,904 --> 00:06:14,845 If we then translate that back into 145 00:06:15,420 --> 00:06:16,720 how do we operate 146 00:06:17,259 --> 00:06:18,639 a really complex, 147 00:06:19,259 --> 00:06:22,319 dangerous, multi level, multi layered, multi machine 148 00:06:22,895 --> 00:06:23,395 construction 149 00:06:25,455 --> 00:06:27,775 site. The advantages of using a robot to 150 00:06:27,775 --> 00:06:28,835 do something that 151 00:06:29,455 --> 00:06:31,990 is dangerous, like working at height, working with 152 00:06:31,990 --> 00:06:32,970 heavy loads, 153 00:06:33,750 --> 00:06:35,370 working with dangerous substances, 154 00:06:35,830 --> 00:06:36,650 for example, 155 00:06:37,670 --> 00:06:38,810 is really appealing. 156 00:06:39,745 --> 00:06:42,225 But some workplaces are even more risk laden 157 00:06:42,225 --> 00:06:44,785 than a construction site, like a nuclear power 158 00:06:44,785 --> 00:06:45,285 plant. 159 00:06:49,870 --> 00:06:50,770 We're currently, 160 00:06:51,230 --> 00:06:53,330 talking to a number of research institutes 161 00:06:54,430 --> 00:06:56,670 to really explore how that is gonna work 162 00:06:56,670 --> 00:06:58,644 in the future and start to trial it 163 00:06:58,944 --> 00:07:00,704 in our ways of working. And and as 164 00:07:00,704 --> 00:07:02,165 an organization where 165 00:07:02,704 --> 00:07:04,004 we know we own, 166 00:07:04,944 --> 00:07:06,245 one of the 6 167 00:07:06,610 --> 00:07:07,670 nuclear technologies, 168 00:07:08,930 --> 00:07:11,330 that are licensed on this planet and that 169 00:07:11,330 --> 00:07:12,470 there's a significant 170 00:07:12,770 --> 00:07:13,589 need for 171 00:07:14,074 --> 00:07:14,735 more nuclear 172 00:07:15,115 --> 00:07:16,735 facilities to be built. 173 00:07:17,754 --> 00:07:18,334 The interoperability 174 00:07:18,714 --> 00:07:20,014 of of humans 175 00:07:20,714 --> 00:07:23,115 and the use cases of robots to keep 176 00:07:23,115 --> 00:07:25,379 people safe and to to, 177 00:07:26,000 --> 00:07:28,660 make sure we can design, run, and maintain 178 00:07:28,879 --> 00:07:31,439 those facilities in the safest way is really 179 00:07:31,439 --> 00:07:31,939 attractive. 180 00:07:33,785 --> 00:07:36,125 There's a whole community of people that have 181 00:07:36,665 --> 00:07:39,245 spent their whole lives working in those environments 182 00:07:39,720 --> 00:07:40,699 and they're extremely 183 00:07:41,240 --> 00:07:42,699 proud of what they've done 184 00:07:43,319 --> 00:07:44,620 and long may that continue. 185 00:07:45,240 --> 00:07:48,014 Their ability to get suited up and and 186 00:07:48,014 --> 00:07:49,154 operate in that environment 187 00:07:49,615 --> 00:07:52,014 and then move away from that environment, it 188 00:07:52,014 --> 00:07:53,954 might take a whole day to spend 189 00:07:54,740 --> 00:07:56,360 around 40 minutes or 190 00:07:56,899 --> 00:07:58,740 maybe an hour in that environment, but it 191 00:07:58,740 --> 00:08:00,259 would take a whole day to kinda go 192 00:08:00,259 --> 00:08:01,160 through that process. 193 00:08:01,540 --> 00:08:03,779 So your time on tools and your ability 194 00:08:03,779 --> 00:08:04,279 to 195 00:08:04,854 --> 00:08:07,354 interact and perform accurate maintenance 196 00:08:07,814 --> 00:08:10,774 could be significantly improved if the human safety 197 00:08:10,774 --> 00:08:11,274 factor 198 00:08:11,654 --> 00:08:12,474 could be lowered. 199 00:08:17,759 --> 00:08:20,800 An engineering business like Atkins Realis is well 200 00:08:20,800 --> 00:08:22,895 placed to develop systems that can work in 201 00:08:22,895 --> 00:08:24,274 these hazardous environments. 202 00:08:24,975 --> 00:08:27,214 On one level, engineering is all about the 203 00:08:27,214 --> 00:08:28,995 assessment and control of risk. 204 00:08:29,569 --> 00:08:30,389 But as engineers, 205 00:08:30,769 --> 00:08:33,509 architects, and construction managers build new infrastructure, 206 00:08:34,049 --> 00:08:36,450 they must also think about creating an environment 207 00:08:36,450 --> 00:08:38,450 that is suitable for the use of autonomous 208 00:08:38,450 --> 00:08:38,950 machines. 209 00:08:40,014 --> 00:08:40,754 But the 210 00:08:41,534 --> 00:08:42,034 disadvantages 211 00:08:42,975 --> 00:08:45,134 that occur when you have these closed loop 212 00:08:45,134 --> 00:08:46,815 systems, you and you don't have people and 213 00:08:46,815 --> 00:08:49,300 machines together, There's still many things the machines 214 00:08:49,300 --> 00:08:50,759 can't do very well, 215 00:08:51,300 --> 00:08:53,620 not nearly as well as humans. So the 216 00:08:53,620 --> 00:08:55,160 advantage of that interoperability 217 00:08:56,285 --> 00:08:59,085 of these two entities is is appealing. It's 218 00:08:59,165 --> 00:09:01,565 and for me, personally, it is really appealing. 219 00:09:01,565 --> 00:09:02,305 Like like, 220 00:09:03,090 --> 00:09:05,649 from an academic perspective it's appealing, but also 221 00:09:05,649 --> 00:09:06,950 from a real value 222 00:09:07,330 --> 00:09:08,230 in terms of 223 00:09:08,690 --> 00:09:10,070 doing things faster 224 00:09:10,529 --> 00:09:11,429 and safer, 225 00:09:12,595 --> 00:09:14,774 which is right at the ethos of 226 00:09:15,394 --> 00:09:16,694 the construction industry. 227 00:09:18,115 --> 00:09:19,334 From a design perspective, 228 00:09:19,670 --> 00:09:22,070 how do you how does one design a 229 00:09:22,070 --> 00:09:24,149 construction site? How do you even design a 230 00:09:24,149 --> 00:09:24,649 city? 231 00:09:25,190 --> 00:09:26,870 Where, as I mentioned before, you know, the 232 00:09:26,870 --> 00:09:29,450 the robot taxis can operate in? But also, 233 00:09:29,575 --> 00:09:30,475 how do we actually design 234 00:09:30,855 --> 00:09:32,554 buildings so that we can 235 00:09:33,894 --> 00:09:35,035 design for manufacturing 236 00:09:36,134 --> 00:09:37,035 with people, 237 00:09:37,710 --> 00:09:38,850 with 3 d printing, 238 00:09:39,389 --> 00:09:42,610 and with robotic devices that have high mobility 239 00:09:42,830 --> 00:09:44,450 and interoperability with people. 240 00:09:45,714 --> 00:09:47,574 And I think I think that whole piece 241 00:09:47,875 --> 00:09:48,375 is 242 00:09:49,394 --> 00:09:51,334 we're scratching the surface of that. 243 00:10:01,945 --> 00:10:04,904 Construction sites and the building materials supply chains 244 00:10:04,904 --> 00:10:07,384 that support them can be designed to support 245 00:10:07,384 --> 00:10:09,164 the use of autonomous devices. 246 00:10:09,950 --> 00:10:12,830 We can imagine robots able to manipulate and 247 00:10:12,830 --> 00:10:14,529 install large modular components 248 00:10:14,910 --> 00:10:16,450 alongside human workers, 249 00:10:16,884 --> 00:10:18,904 And this may give us the pace needed 250 00:10:18,965 --> 00:10:21,445 to build new cities to house a rapidly 251 00:10:21,445 --> 00:10:22,745 growing, urbanizing 252 00:10:23,125 --> 00:10:24,424 and aging population. 253 00:10:28,870 --> 00:10:31,509 As we think about how much infrastructure's gotta 254 00:10:31,509 --> 00:10:34,205 be built to replace the aging infrastructure we've 255 00:10:34,205 --> 00:10:34,705 got, 256 00:10:35,245 --> 00:10:37,404 how the population keeps on growing, and there 257 00:10:37,404 --> 00:10:38,705 are many more sustainable 258 00:10:39,519 --> 00:10:40,420 energy transition 259 00:10:40,960 --> 00:10:43,060 initiatives that have to be built 260 00:10:43,519 --> 00:10:44,019 to 261 00:10:45,360 --> 00:10:46,660 make sure we 262 00:10:47,040 --> 00:10:49,139 are able to all survive on this planet. 263 00:10:49,475 --> 00:10:52,115 I think that the ability to think about 264 00:10:52,115 --> 00:10:52,934 how would 265 00:10:53,475 --> 00:10:55,075 how do we design it in a way 266 00:10:55,075 --> 00:10:57,975 that allow for modular construction, design for manufacturing, 267 00:10:58,034 --> 00:10:59,095 and how would we 268 00:10:59,590 --> 00:11:00,889 design it so that 269 00:11:01,350 --> 00:11:03,210 there were some closed loop 270 00:11:03,830 --> 00:11:05,669 environments where it's really not safe for a 271 00:11:05,669 --> 00:11:08,065 human being to be in, but we designed 272 00:11:08,125 --> 00:11:09,644 also for the for those two things to 273 00:11:09,644 --> 00:11:10,304 come together. 274 00:11:11,164 --> 00:11:13,264 The urban planners of the near future 275 00:11:13,725 --> 00:11:16,649 shouldn't just be considering how construction sites can 276 00:11:16,649 --> 00:11:19,370 safely allow for humans and machines to work 277 00:11:19,370 --> 00:11:19,870 together. 278 00:11:20,570 --> 00:11:22,330 They need to think about how to make 279 00:11:22,330 --> 00:11:23,629 the best use of space 280 00:11:24,254 --> 00:11:24,995 within cities 281 00:11:25,375 --> 00:11:27,075 where new forms of transportation 282 00:11:27,455 --> 00:11:28,115 are possible. 283 00:11:29,134 --> 00:11:30,835 When I bring this back to an AEC, 284 00:11:30,975 --> 00:11:32,754 an architecture engineering construction 285 00:11:33,215 --> 00:11:33,715 situation, 286 00:11:34,929 --> 00:11:38,049 the master planners, the architects of the new 287 00:11:38,049 --> 00:11:38,549 cities, 288 00:11:39,649 --> 00:11:42,149 the retrofitters of our existing cities, 289 00:11:42,544 --> 00:11:44,164 how are we thinking about 290 00:11:44,544 --> 00:11:45,044 transportation, 291 00:11:45,825 --> 00:11:48,065 how are we thinking about social value, so 292 00:11:48,065 --> 00:11:49,284 how are we thinking about 293 00:11:49,799 --> 00:11:52,919 walkways that allow people to walk and cycle 294 00:11:52,919 --> 00:11:55,660 and scoot for example and be more active 295 00:11:56,200 --> 00:11:57,100 and be safer 296 00:11:57,504 --> 00:12:00,465 but also how might we be thinking about 297 00:12:00,465 --> 00:12:02,725 the utilization of car parking space? 298 00:12:03,264 --> 00:12:05,285 Is that now social housing opportunities? 299 00:12:06,429 --> 00:12:09,409 We've seen older people returning back to cities. 300 00:12:10,029 --> 00:12:13,809 Is that community space for communities to 301 00:12:14,589 --> 00:12:15,089 experience 302 00:12:15,654 --> 00:12:18,315 different social interactions than our cities have typically 303 00:12:19,014 --> 00:12:20,714 seen and how might you 304 00:12:21,575 --> 00:12:22,075 design 305 00:12:23,180 --> 00:12:25,360 a roadway system for robot 306 00:12:25,980 --> 00:12:26,480 cars 307 00:12:27,180 --> 00:12:29,500 preferably robot taxis because if we all have 308 00:12:29,500 --> 00:12:32,274 robot cars parked outside wherever we choose to 309 00:12:32,274 --> 00:12:35,394 live and work, we're taking up unnecessary real 310 00:12:35,394 --> 00:12:36,535 estate space that 311 00:12:37,235 --> 00:12:39,519 just doesn't really add a lot of value 312 00:12:39,519 --> 00:12:41,460 other than the prestige of owning the car. 313 00:12:42,720 --> 00:12:45,039 We live today in cities that have for 314 00:12:45,039 --> 00:12:45,539 decades 315 00:12:45,924 --> 00:12:47,384 been closed loop systems, 316 00:12:48,164 --> 00:12:50,725 designed to allow for the widespread use of 317 00:12:50,725 --> 00:12:51,225 unsafe, 318 00:12:51,764 --> 00:12:53,544 unreliably controlled machines. 319 00:12:55,129 --> 00:12:57,529 We mark off huge swathes of our cities 320 00:12:57,529 --> 00:13:01,230 for cars, segregating roads from pavements and dedicating 321 00:13:01,290 --> 00:13:03,470 space for them to sit unused. 322 00:13:04,654 --> 00:13:07,134 Now urban planners should be thinking beyond these 323 00:13:07,134 --> 00:13:07,955 risk controls. 324 00:13:08,335 --> 00:13:10,434 If truly safe autonomous vehicles 325 00:13:10,735 --> 00:13:13,455 designed to be shared and used constantly were 326 00:13:13,455 --> 00:13:13,955 available, 327 00:13:14,480 --> 00:13:16,720 what other uses could we find for those 328 00:13:16,720 --> 00:13:17,220 spaces? 329 00:13:18,559 --> 00:13:20,179 If you are gonna be in a car, 330 00:13:20,399 --> 00:13:22,179 let's make sure that car is a robot 331 00:13:22,445 --> 00:13:24,524 vehicle that isn't just going to park and 332 00:13:24,524 --> 00:13:26,705 take up precious real estate. 333 00:13:27,085 --> 00:13:28,524 I can trust you in a study at 334 00:13:28,524 --> 00:13:29,504 the moment between 335 00:13:30,559 --> 00:13:32,660 Cranfield University, Open University 336 00:13:33,040 --> 00:13:34,820 and Oxford and Cambridge 337 00:13:35,360 --> 00:13:37,620 looking at the some of the connections between 338 00:13:37,679 --> 00:13:38,179 those 339 00:13:38,815 --> 00:13:41,774 cities, those educational institutes and how do we 340 00:13:41,774 --> 00:13:42,754 create thriving 341 00:13:43,375 --> 00:13:43,875 natural 342 00:13:44,415 --> 00:13:45,875 corridors between waterways, 343 00:13:46,759 --> 00:13:49,480 cycle path routes, and so on to make 344 00:13:49,480 --> 00:13:50,620 the the connectivity 345 00:13:50,920 --> 00:13:53,100 of those parts of the world better. 346 00:13:59,274 --> 00:14:01,915 Autonomous devices will change the way that we 347 00:14:01,915 --> 00:14:03,455 build and design cities. 348 00:14:03,850 --> 00:14:05,690 They will increase the pace at which we 349 00:14:05,690 --> 00:14:07,769 build and create new space for us to 350 00:14:07,769 --> 00:14:08,509 build in. 351 00:14:08,970 --> 00:14:10,990 AIs are pattern matching machines. 352 00:14:11,664 --> 00:14:13,284 They can mimic human communication, 353 00:14:13,904 --> 00:14:15,845 allowing for their control by anyone, 354 00:14:16,384 --> 00:14:18,384 and they can select designs that are safe 355 00:14:18,384 --> 00:14:19,125 and buildable. 356 00:14:19,940 --> 00:14:22,259 Taken together, this will allow for a tailored 357 00:14:22,259 --> 00:14:23,160 design process 358 00:14:23,620 --> 00:14:26,039 previously only available to the very rich. 359 00:14:26,580 --> 00:14:29,585 And so when I think about high end 360 00:14:29,585 --> 00:14:31,845 luxury goods, there have been some organizations 361 00:14:32,865 --> 00:14:35,205 that have really brought in 362 00:14:35,585 --> 00:14:37,524 the consumer into the personalization 363 00:14:38,450 --> 00:14:38,950 process. 364 00:14:39,730 --> 00:14:41,110 So hyper personalization. 365 00:14:43,250 --> 00:14:46,529 Consumers of trainers and sunglasses can adapt designs 366 00:14:46,529 --> 00:14:49,125 to their own taste, but within the options 367 00:14:49,125 --> 00:14:50,664 offered by a drop down menu. 368 00:14:53,445 --> 00:14:55,465 What would it mean if this personalization 369 00:14:56,279 --> 00:14:58,200 could be taken beyond a set of fixed 370 00:14:58,200 --> 00:14:58,700 choices 371 00:14:59,080 --> 00:15:01,639 and intersections of the economy more vital to 372 00:15:01,639 --> 00:15:03,500 life than luxury consumer goods? 373 00:15:03,894 --> 00:15:06,774 But all of these are within parameters, so 374 00:15:06,774 --> 00:15:07,514 the manufacturers 375 00:15:08,535 --> 00:15:10,475 are able to offer a premium 376 00:15:11,699 --> 00:15:12,199 differentiated 377 00:15:12,659 --> 00:15:13,159 product, 378 00:15:13,620 --> 00:15:15,799 but something that they can still affordably 379 00:15:16,259 --> 00:15:19,559 manufacture and make profit from in a sustainable 380 00:15:20,179 --> 00:15:21,205 and safe way. 381 00:15:22,325 --> 00:15:24,965 Around the world, cities are now being planned 382 00:15:24,965 --> 00:15:25,785 from scratch. 383 00:15:26,325 --> 00:15:28,884 Robot workers will deliver the pace needed to 384 00:15:28,884 --> 00:15:29,625 house 1,000,000, 385 00:15:30,289 --> 00:15:32,769 and AI systems will allow residents to have 386 00:15:32,769 --> 00:15:34,690 a real say in what their new homes 387 00:15:34,690 --> 00:15:35,589 will look like. 388 00:15:35,970 --> 00:15:37,065 Where we need to go 389 00:15:37,865 --> 00:15:39,404 is the ability to 390 00:15:40,105 --> 00:15:40,924 draw in 391 00:15:41,945 --> 00:15:43,004 enough parameters 392 00:15:43,705 --> 00:15:46,125 so the consumer can have ownership 393 00:15:46,919 --> 00:15:49,339 and identify with that modular experience, 394 00:15:49,799 --> 00:15:51,659 but at the same time, the benefits 395 00:15:52,600 --> 00:15:53,100 of, 396 00:15:53,720 --> 00:15:56,495 the construction process, the efficiencies, the sustainability 397 00:15:56,795 --> 00:15:58,014 of it, the ongoing 398 00:15:58,875 --> 00:16:01,115 operational maintenance of it are much better than 399 00:16:01,115 --> 00:16:02,415 we've achieved in the past. 400 00:16:09,059 --> 00:16:10,039 AI and autonomy 401 00:16:10,340 --> 00:16:12,179 will allow us to build cities in a 402 00:16:12,179 --> 00:16:12,840 new way. 403 00:16:13,254 --> 00:16:15,115 We'll be able to rapidly construct 404 00:16:15,495 --> 00:16:15,995 personalized 405 00:16:16,295 --> 00:16:16,795 homes 406 00:16:17,174 --> 00:16:19,014 and to interact and travel in a more 407 00:16:19,014 --> 00:16:21,894 human way, free from the risk and waste 408 00:16:21,894 --> 00:16:22,580 of cars. 409 00:16:23,940 --> 00:16:26,740 But the climate crisis makes clear that even 410 00:16:26,740 --> 00:16:29,720 designing cities from scratch is not enough. 411 00:16:30,259 --> 00:16:32,200 We must leverage these new technologies 412 00:16:32,615 --> 00:16:35,595 to consider and analyze our whole planet. 413 00:16:37,174 --> 00:16:39,014 Part of my job and other people's at 414 00:16:39,014 --> 00:16:41,399 Atkins Real Estate's job is to constantly 415 00:16:42,740 --> 00:16:44,040 look at 416 00:16:44,740 --> 00:16:47,639 what do certain technology partners bring 417 00:16:48,180 --> 00:16:49,240 to our customers, 418 00:16:49,834 --> 00:16:52,014 and what value together do we bring 419 00:16:52,475 --> 00:16:55,274 to drive a better societal outcome for our 420 00:16:55,274 --> 00:16:55,774 customers. 421 00:16:56,314 --> 00:16:58,314 And one of the examples that, you know, 422 00:16:58,314 --> 00:16:59,580 we're really interested in 423 00:17:00,379 --> 00:17:02,399 is the impact of climate change 424 00:17:02,940 --> 00:17:04,880 and the impact it has on society 425 00:17:05,420 --> 00:17:06,799 and thinking about resiliency. 426 00:17:08,035 --> 00:17:10,755 The risks and uncertainties of climate change have 427 00:17:10,755 --> 00:17:13,255 undermined the functioning of insurance markets. 428 00:17:13,715 --> 00:17:16,549 Without understanding the impact of climate change and 429 00:17:16,549 --> 00:17:18,809 the resilience of our buildings and infrastructure, 430 00:17:19,670 --> 00:17:21,850 many regions are becoming increasingly 431 00:17:22,470 --> 00:17:22,970 uninsurable. 432 00:17:23,835 --> 00:17:25,755 And just, you know, at the moment there 433 00:17:25,755 --> 00:17:28,394 are some questions being asked around the bond 434 00:17:28,394 --> 00:17:28,894 market, 435 00:17:29,275 --> 00:17:31,214 the insurance bond market and 436 00:17:32,319 --> 00:17:33,299 with the 437 00:17:33,679 --> 00:17:36,179 frequency and the the impact of 438 00:17:36,720 --> 00:17:38,099 climate change and weather, 439 00:17:38,799 --> 00:17:42,095 impact on on critical infrastructure across the globe, 440 00:17:42,554 --> 00:17:44,815 how do we keep on paying for it, 441 00:17:44,875 --> 00:17:47,855 who underwrites it and it's a bet that 442 00:17:47,914 --> 00:17:49,769 in the past used to come off quite 443 00:17:49,769 --> 00:17:51,549 nicely. It's quite a profitable industry, 444 00:17:51,930 --> 00:17:53,529 but actually a lot of these bets are 445 00:17:53,529 --> 00:17:54,910 starting to become 446 00:17:55,289 --> 00:17:55,950 loss making. 447 00:17:56,490 --> 00:17:58,830 And in some places now, 448 00:17:59,275 --> 00:18:00,335 all over the world. 449 00:18:00,795 --> 00:18:03,994 Our current tools don't allow for reliable assessment 450 00:18:03,994 --> 00:18:05,375 of risk and resilience. 451 00:18:06,369 --> 00:18:08,869 Getting more precision from AI 452 00:18:09,570 --> 00:18:11,509 about the exact nature 453 00:18:12,450 --> 00:18:13,430 or and likelihood 454 00:18:13,809 --> 00:18:14,789 of resiliency 455 00:18:16,095 --> 00:18:19,535 impacts and also putting in remedial action to 456 00:18:19,535 --> 00:18:21,394 mitigate against them where possible 457 00:18:22,095 --> 00:18:22,595 is 458 00:18:23,470 --> 00:18:25,809 really important because if you own real estate 459 00:18:25,950 --> 00:18:27,549 or you own a business or you run 460 00:18:27,549 --> 00:18:29,789 a school, you wanna make sure that it 461 00:18:29,789 --> 00:18:32,210 can be insured and protected. 462 00:18:34,095 --> 00:18:37,535 Atkins Realis is already using AI systems to 463 00:18:37,535 --> 00:18:40,734 analyze the impact of climate change across tens 464 00:18:40,734 --> 00:18:41,954 of thousands of properties 465 00:18:42,340 --> 00:18:43,160 at a time. 466 00:18:43,940 --> 00:18:46,279 We've done work with the federal emergency 467 00:18:46,580 --> 00:18:48,759 management agency, FEMA, in the US, 468 00:18:49,299 --> 00:18:50,200 where we've 469 00:18:50,835 --> 00:18:53,154 been able to do surveys on properties that 470 00:18:53,154 --> 00:18:55,255 have been impacted by, severe 471 00:18:55,555 --> 00:18:56,934 climate change and prioritise 472 00:18:57,394 --> 00:18:59,015 the surveying of those properties 473 00:19:00,049 --> 00:19:01,350 to measure the impact 474 00:19:02,370 --> 00:19:03,490 and to help, 475 00:19:03,890 --> 00:19:05,190 free up funds 476 00:19:05,490 --> 00:19:06,710 to get people back 477 00:19:07,424 --> 00:19:09,684 in those homes or in new homes. 478 00:19:11,265 --> 00:19:12,325 That's a 150,000 479 00:19:13,985 --> 00:19:16,164 potential surveys that needed to be done. 480 00:19:17,539 --> 00:19:19,000 Relying on human assessment 481 00:19:19,299 --> 00:19:21,940 would leave those impacted by climate change in 482 00:19:21,940 --> 00:19:24,840 vulnerable situations for weeks or months. 483 00:19:25,705 --> 00:19:27,785 AI can allow support to be delivered where 484 00:19:27,785 --> 00:19:30,365 it's most needed, when it's most needed. 485 00:19:31,305 --> 00:19:34,345 With, being able to use AI to 0 486 00:19:34,345 --> 00:19:34,859 in on 487 00:19:35,819 --> 00:19:36,299 around, 488 00:19:36,619 --> 00:19:38,460 1 5th of them that really needed to 489 00:19:38,460 --> 00:19:40,240 be done as a huge priority 490 00:19:40,940 --> 00:19:41,440 to, 491 00:19:41,980 --> 00:19:43,200 make sure people were 492 00:19:43,500 --> 00:19:45,544 helped and looked after as a result of 493 00:19:45,544 --> 00:19:46,044 that, 494 00:19:47,144 --> 00:19:47,644 climate 495 00:19:48,024 --> 00:19:50,524 impact on their lives. And so 496 00:19:50,984 --> 00:19:53,869 zeroing in and removing waste and prioritizing and 497 00:19:54,029 --> 00:19:56,430 getting help and resources to people is something 498 00:19:56,430 --> 00:19:57,490 that that I think 499 00:19:57,950 --> 00:19:59,349 AI is a really good example of where 500 00:19:59,349 --> 00:20:00,430 it can do it, and I think we 501 00:20:00,430 --> 00:20:01,809 need to get better at that. 502 00:20:03,674 --> 00:20:06,394 AI based analysis can identify the impact of 503 00:20:06,394 --> 00:20:08,815 climate events across cities and regions. 504 00:20:09,880 --> 00:20:12,519 One vision of the interaction of machine learning 505 00:20:12,519 --> 00:20:15,000 and city planners has already been tried in 506 00:20:15,000 --> 00:20:15,500 Canada. 507 00:20:15,960 --> 00:20:18,519 In this vision, tech led the way and 508 00:20:18,519 --> 00:20:19,625 engineering followed. 509 00:20:20,265 --> 00:20:21,884 Google Sidewalk Labs, 510 00:20:22,424 --> 00:20:24,265 it's, it's this company as part of the 511 00:20:24,265 --> 00:20:26,204 Alphabet group that was set up, 512 00:20:26,664 --> 00:20:27,164 and 513 00:20:27,929 --> 00:20:30,169 its first project was to look at the 514 00:20:30,169 --> 00:20:31,149 Quayside project, 515 00:20:31,769 --> 00:20:34,349 on the South Bank of Lake Ontario, 516 00:20:34,730 --> 00:20:36,349 which was a port area 517 00:20:37,404 --> 00:20:38,705 back in Toronto, 518 00:20:39,245 --> 00:20:41,745 and then the idea there was that 519 00:20:42,125 --> 00:20:45,265 big government had aligned with big tech, Google, 520 00:20:45,485 --> 00:20:46,640 and that this 521 00:20:47,839 --> 00:20:48,339 large, 522 00:20:49,200 --> 00:20:51,200 real estate project was gonna be put put 523 00:20:51,200 --> 00:20:53,839 in and that it would it had some 524 00:20:53,839 --> 00:20:55,599 fantastic benefits. I was looking at some of 525 00:20:55,599 --> 00:20:57,154 the stats and I'm not an expert on 526 00:20:57,154 --> 00:20:58,694 on this area, but just looking 527 00:20:59,075 --> 00:20:59,894 at the stats, 528 00:21:00,275 --> 00:21:03,369 it said by 2040, it would generate around 529 00:21:03,369 --> 00:21:03,869 44,000 530 00:21:04,650 --> 00:21:07,130 jobs. So it's an industrial pool, and it 531 00:21:07,130 --> 00:21:08,910 was being turned into a, 532 00:21:10,144 --> 00:21:12,144 you know, as we have in the UK, 533 00:21:12,144 --> 00:21:13,525 you know, some really good 534 00:21:13,984 --> 00:21:14,484 waterside 535 00:21:15,505 --> 00:21:16,005 retail 536 00:21:16,384 --> 00:21:16,884 business 537 00:21:17,839 --> 00:21:18,339 and 538 00:21:18,720 --> 00:21:19,220 residential, 539 00:21:20,240 --> 00:21:22,180 quadrants and and and locations. 540 00:21:22,559 --> 00:21:23,059 And 541 00:21:23,519 --> 00:21:26,454 it was reducing emissions of that industrial site 542 00:21:26,454 --> 00:21:27,355 by 89%, 543 00:21:28,134 --> 00:21:30,234 and it was reducing the cost for 544 00:21:30,535 --> 00:21:31,434 social housing, 545 00:21:31,894 --> 00:21:32,394 assets 546 00:21:32,855 --> 00:21:35,539 that were being put into that alongside some 547 00:21:35,539 --> 00:21:37,079 higher end housing assets. 548 00:21:37,380 --> 00:21:39,779 But the typical cost for a social housing 549 00:21:39,779 --> 00:21:40,599 asset was 550 00:21:41,460 --> 00:21:41,960 40% 551 00:21:42,420 --> 00:21:43,845 below the market rate. 552 00:21:46,164 --> 00:21:48,964 Sidewalks Labs' plan had been to offer more 553 00:21:48,964 --> 00:21:51,944 jobs and cheaper housing in an attractive environment, 554 00:21:52,329 --> 00:21:54,429 managed and maintained using sophisticated 555 00:21:54,730 --> 00:21:55,230 monitoring. 556 00:21:55,929 --> 00:21:56,909 But by 2020, 557 00:21:57,210 --> 00:21:58,750 the project had fallen apart. 558 00:21:59,450 --> 00:22:01,865 Stymied by the impacts of COVID and by 559 00:22:01,865 --> 00:22:04,045 a lack of trust in the monitoring systems 560 00:22:04,105 --> 00:22:05,484 used to manage the district. 561 00:22:06,424 --> 00:22:08,664 Darren believes that projects like this need to 562 00:22:08,664 --> 00:22:10,445 draw on the established expertise 563 00:22:10,869 --> 00:22:11,690 of architecture, 564 00:22:11,990 --> 00:22:14,009 engineering, and construction businesses. 565 00:22:17,190 --> 00:22:18,325 Big Tech came in 566 00:22:18,804 --> 00:22:20,984 and did a deal with big government, 567 00:22:22,244 --> 00:22:25,384 and local government, and local sentiment, and human 568 00:22:26,005 --> 00:22:26,505 permitting 569 00:22:27,130 --> 00:22:27,630 considerations, 570 00:22:28,650 --> 00:22:29,150 objections, 571 00:22:30,250 --> 00:22:31,309 community resistance 572 00:22:32,570 --> 00:22:34,910 wasn't dealt with in the same way 573 00:22:35,455 --> 00:22:37,634 that perhaps Atkins Realis would have 574 00:22:38,255 --> 00:22:38,755 understood 575 00:22:39,295 --> 00:22:40,115 the nuances 576 00:22:41,134 --> 00:22:44,275 of engaging with local government, understanding the planning 577 00:22:44,850 --> 00:22:45,350 rules, 578 00:22:45,730 --> 00:22:47,830 understanding how to engage with communities 579 00:22:48,369 --> 00:22:50,769 to help them understand what the changes and 580 00:22:50,769 --> 00:22:53,015 what the benefits are for them, but also 581 00:22:53,015 --> 00:22:55,275 to adapt the master planning concept 582 00:22:55,815 --> 00:22:57,914 based on consultation with those communities. 583 00:22:59,015 --> 00:23:01,255 But just as Alphabet is moving out of 584 00:23:01,255 --> 00:23:01,994 city building, 585 00:23:02,470 --> 00:23:05,210 other big tech players are moving into engineering. 586 00:23:06,150 --> 00:23:06,650 Cognizant 587 00:23:08,309 --> 00:23:09,450 have put forward 588 00:23:10,164 --> 00:23:10,904 and acquired 589 00:23:11,205 --> 00:23:12,184 7 companies 590 00:23:12,565 --> 00:23:15,065 in the with engineering focus since 2021. 591 00:23:15,765 --> 00:23:18,744 So Cognizant typically would have been, you know, 592 00:23:19,160 --> 00:23:20,380 Indian HQed 593 00:23:20,920 --> 00:23:23,100 IT and Business Process Outsourcing Organization 594 00:23:24,119 --> 00:23:26,619 competing with the likes of, you know, IBM, 595 00:23:27,000 --> 00:23:29,744 Japan's Fujitsu, and so on. Probably doing so 596 00:23:29,744 --> 00:23:31,045 on a lower price point. 597 00:23:32,224 --> 00:23:33,365 It made a 1,300,000,000 598 00:23:34,144 --> 00:23:34,644 acquisition 599 00:23:35,025 --> 00:23:36,884 of an engineering company 600 00:23:37,359 --> 00:23:38,420 in June 24. 601 00:23:39,680 --> 00:23:40,180 Tata, 602 00:23:40,799 --> 00:23:43,059 for example, bought an automotive 603 00:23:43,440 --> 00:23:44,819 engineering company recently. 604 00:23:45,875 --> 00:23:46,375 Accenture 605 00:23:46,994 --> 00:23:47,815 have bought 606 00:23:48,275 --> 00:23:48,775 IoT 607 00:23:49,555 --> 00:23:50,055 companies, 608 00:23:50,835 --> 00:23:53,494 a whole range of industrial and engineering 609 00:23:54,269 --> 00:23:54,769 expertise. 610 00:23:56,109 --> 00:23:58,609 As the space between big tech and engineering 611 00:23:58,670 --> 00:23:59,170 closes, 612 00:23:59,630 --> 00:24:01,890 those leading and investing in these sectors 613 00:24:02,285 --> 00:24:04,924 should really think critically about the skills each 614 00:24:04,924 --> 00:24:05,825 sector brings. 615 00:24:06,204 --> 00:24:07,744 You have to ask the question, 616 00:24:08,125 --> 00:24:08,625 is 617 00:24:09,085 --> 00:24:11,984 a company like Cognizant becoming an engineering company? 618 00:24:18,990 --> 00:24:23,365 Professional services businesses in architecture, engineering, and construction 619 00:24:23,904 --> 00:24:26,625 should not, Darren believes, try to themselves into 620 00:24:26,625 --> 00:24:27,444 tech firms. 621 00:24:28,384 --> 00:24:30,500 Big tech is not accustomed to dealing with 622 00:24:30,500 --> 00:24:32,759 the human complexity of urban planning. 623 00:24:33,460 --> 00:24:35,059 This is not a space in which you 624 00:24:35,059 --> 00:24:37,240 can just move fast and break things. 625 00:24:37,734 --> 00:24:40,615 So how do these 2 sectors work together 626 00:24:40,615 --> 00:24:43,115 in a new age of AI driven autonomy? 627 00:24:43,815 --> 00:24:45,494 What can they learn from a project like 628 00:24:45,494 --> 00:24:46,555 Sidewalk Labs? 629 00:24:47,799 --> 00:24:50,059 Perhaps there wasn't the, 630 00:24:50,599 --> 00:24:51,099 awareness 631 00:24:51,400 --> 00:24:52,059 and the, 632 00:24:53,000 --> 00:24:53,500 sensitivity 633 00:24:54,279 --> 00:24:55,099 and experience 634 00:24:55,404 --> 00:24:57,265 that an architecture engineering and 635 00:24:57,565 --> 00:24:59,105 construction company would apply 636 00:24:59,565 --> 00:25:01,424 in getting that project to work. 637 00:25:01,805 --> 00:25:04,205 You can work out, for example, when the 638 00:25:04,205 --> 00:25:05,345 lifts are being used 639 00:25:05,869 --> 00:25:08,349 and, you know, are people exiting the building, 640 00:25:08,349 --> 00:25:10,029 and how many people are in the in 641 00:25:10,029 --> 00:25:11,390 the lifts, and what's the weight of the 642 00:25:11,390 --> 00:25:11,890 lifts. 643 00:25:12,269 --> 00:25:14,625 There are there are a number of less 644 00:25:14,625 --> 00:25:15,764 intrusive ways 645 00:25:16,224 --> 00:25:17,125 of understanding 646 00:25:17,744 --> 00:25:18,625 how the, 647 00:25:19,024 --> 00:25:20,325 buildings and the 648 00:25:20,625 --> 00:25:22,484 the environment are being used 649 00:25:22,900 --> 00:25:26,180 that don't require, you know, quite personal sense 650 00:25:26,180 --> 00:25:28,340 of information to be shared. So once you're 651 00:25:28,340 --> 00:25:29,160 in a community, 652 00:25:29,460 --> 00:25:31,755 people don't want to feel that they're being 653 00:25:31,755 --> 00:25:33,674 observed. Their movements are you know, who are 654 00:25:33,674 --> 00:25:35,914 they talking to? How long are they spending 655 00:25:35,914 --> 00:25:36,734 in these spaces? 656 00:25:38,230 --> 00:25:41,289 The experienced human planners of an AEC business 657 00:25:41,669 --> 00:25:44,329 can work alongside local governments and citizens 658 00:25:44,710 --> 00:25:46,970 to deliver projects that really consider 659 00:25:47,454 --> 00:25:47,954 human 660 00:25:48,414 --> 00:25:49,954 needs, desires, and privacy. 661 00:25:51,855 --> 00:25:54,894 While emerging technologies will have a vital part 662 00:25:54,894 --> 00:25:57,679 to play, it is this human expertise and 663 00:25:57,679 --> 00:25:58,179 experience 664 00:25:58,480 --> 00:26:00,740 that will be key to meeting human needs. 665 00:26:02,559 --> 00:26:04,980 We form part of a partner ecosystem, 666 00:26:05,519 --> 00:26:08,085 which in some cases might be tech enabled, 667 00:26:08,545 --> 00:26:10,005 in many cases it will. 668 00:26:11,984 --> 00:26:13,204 We're highly differentiated 669 00:26:13,664 --> 00:26:16,279 from Big Tech in our ability 670 00:26:16,660 --> 00:26:17,160 to 671 00:26:18,420 --> 00:26:21,940 create environments that are both progressive from an 672 00:26:21,940 --> 00:26:26,325 economic standpoint but sympathetic to to communities and 673 00:26:26,325 --> 00:26:27,464 enhance the communities. 674 00:26:41,484 --> 00:26:44,144 Engineering Matters is a production of Reebi Media. 675 00:26:44,205 --> 00:26:46,285 This episode was written and produced by Will 676 00:26:46,285 --> 00:26:48,205 North and hosted by me, Rianne Owen, and 677 00:26:48,205 --> 00:26:50,840 by Johnny Dowling, editing and series supervision by 678 00:26:50,840 --> 00:26:53,640 John Young, sound engineering by Ross Macpherson, and 679 00:26:53,640 --> 00:26:55,480 the human intelligence that steers our use of 680 00:26:55,480 --> 00:26:56,505 data is Rory Harris. 681 00:26:57,065 --> 00:26:58,984 Special thanks to our partner for this episode, 682 00:26:58,984 --> 00:27:01,464 Atkins Realis, and thank you for listening. You 683 00:27:01,464 --> 00:27:03,144 can find us on all podcast apps and 684 00:27:03,144 --> 00:27:04,210 on our website engineeringmatters. 685 00:27:04,910 --> 00:27:05,410 Rebi.media 686 00:27:05,789 --> 00:27:06,610 and on LinkedIn.