1 00:00:03,040 --> 00:00:07,039 Speaker 1: Hey, everyone, Welcome to the Restless Ones. I'm Jonathan Strickland. 2 00:00:07,320 --> 00:00:10,719 Speaker 1: As always, my focus is on exploring the intersection of 3 00:00:10,720 --> 00:00:14,440 Speaker 1: technology and business by having conversations with the most forward 4 00:00:14,480 --> 00:00:19,200 Speaker 1: thinking leaders. Throughout my career, I've covered everything from massive 5 00:00:19,239 --> 00:00:24,319 Speaker 1: parallel processing to advanced robotics, but what truly inspires me 6 00:00:24,680 --> 00:00:31,000 Speaker 1: are the stories of innovation and transformation. Today, you're going 7 00:00:31,040 --> 00:00:34,000 Speaker 1: to hear a great conversation I had with Sonia Kassner, 8 00:00:34,120 --> 00:00:38,880 Speaker 1: founder and CEO of Panoai. Her company brings together technologies 9 00:00:38,880 --> 00:00:44,199 Speaker 1: such as imaging, cloud computing, AI, and yes connectivity to 10 00:00:44,400 --> 00:00:49,080 Speaker 1: provide a very important service early detection of forest fires. 11 00:00:49,800 --> 00:00:53,440 Speaker 1: As you'll hear, Panoai partners with customers to deliver data 12 00:00:53,600 --> 00:00:58,600 Speaker 1: needed to identify and respond to forest fires. But before 13 00:00:58,720 --> 00:01:02,720 Speaker 1: I could geek out about Panoai's approach and technology, I 14 00:01:02,760 --> 00:01:10,400 Speaker 1: wanted to learn more about Sonia's journey. Sonia, it is 15 00:01:10,560 --> 00:01:13,600 Speaker 1: a huge pleasure to have you on the show. Welcome 16 00:01:13,640 --> 00:01:14,800 Speaker 1: to the Restless Ones. 17 00:01:15,120 --> 00:01:16,280 Speaker 2: Thank you so much for having me. 18 00:01:16,760 --> 00:01:19,319 Speaker 1: I'm really excited to have a conversation with you, So 19 00:01:19,440 --> 00:01:22,240 Speaker 1: can you kind of give me sort of a story 20 00:01:22,400 --> 00:01:26,399 Speaker 1: of your journey up to now really hitting the highlights. 21 00:01:26,840 --> 00:01:31,560 Speaker 3: Absolutely, I've always worked at the intersection of business, technology, 22 00:01:31,560 --> 00:01:34,520 Speaker 3: and government. Early in my career I was a young 23 00:01:34,600 --> 00:01:38,080 Speaker 3: activist working on political campaigns. I also worked in the 24 00:01:38,280 --> 00:01:41,319 Speaker 3: New York City government under Michael Bloomberg. And I love 25 00:01:41,400 --> 00:01:43,800 Speaker 3: the impact that you can have in the public sector, 26 00:01:44,120 --> 00:01:47,920 Speaker 3: but I also love the innovation and speed and nimbleness 27 00:01:47,920 --> 00:01:50,120 Speaker 3: you can have in the private sector. And in two 28 00:01:50,160 --> 00:01:52,840 Speaker 3: thousand and seven, I decided to move out to California 29 00:01:52,880 --> 00:01:56,840 Speaker 3: and get involved in the green energy industry, which was 30 00:01:57,040 --> 00:02:00,320 Speaker 3: a booming sector in venture capital back then. I went 31 00:02:00,360 --> 00:02:02,400 Speaker 3: to business school at Stanford, I joined one of the 32 00:02:02,480 --> 00:02:06,520 Speaker 3: many venture backed solar startups at the time, and I 33 00:02:06,560 --> 00:02:09,680 Speaker 3: really hope to spend my entire career working on technologies 34 00:02:09,720 --> 00:02:14,079 Speaker 3: that would prevent climate change. And sadly, fast forward fifteen years, 35 00:02:14,400 --> 00:02:16,760 Speaker 3: I think everyone would agree we have not prevented climate change. 36 00:02:16,919 --> 00:02:19,840 Speaker 3: Climate change is now very much clearly. 37 00:02:19,520 --> 00:02:20,160 Speaker 2: All around us. 38 00:02:20,160 --> 00:02:22,680 Speaker 3: We're living it every day, and natural disasters are one 39 00:02:22,720 --> 00:02:25,640 Speaker 3: of the ways we're feeling the effects of climate change first. 40 00:02:26,120 --> 00:02:29,200 Speaker 3: And these natural disasters people are starting to rever to 41 00:02:29,240 --> 00:02:35,560 Speaker 3: them as climate disasters. Floods, hurricanes, wildfires, mud slides, extreme heat, 42 00:02:35,720 --> 00:02:39,160 Speaker 3: extreme cold, tornadoes. They're really serving as a wake up 43 00:02:39,160 --> 00:02:42,359 Speaker 3: call to humanity that climate change is here. Since then, 44 00:02:42,520 --> 00:02:45,919 Speaker 3: I've been working in supply chain manufacturing at companies like Nest, 45 00:02:46,080 --> 00:02:48,560 Speaker 3: where I actually worked on the Nestcam in twenty fourteen, 46 00:02:48,600 --> 00:02:52,800 Speaker 3: one of the first AI cameras. In twenty eighteen twenty nineteen, 47 00:02:52,840 --> 00:02:56,160 Speaker 3: I was thinking about ways that I could apply the 48 00:02:56,400 --> 00:02:59,000 Speaker 3: technology that I was familiar with, coming from Silicon Valley, 49 00:02:59,040 --> 00:03:03,880 Speaker 3: coming from Internet things, to help with tackling natural disasters 50 00:03:03,919 --> 00:03:06,480 Speaker 3: and see if we could make these natural disasters less harmful. 51 00:03:07,040 --> 00:03:09,560 Speaker 3: And as I started to do research, the good news 52 00:03:09,560 --> 00:03:11,120 Speaker 3: I found was actually there was a lot of low 53 00:03:11,160 --> 00:03:14,560 Speaker 3: hanging fruit. The natural disaster industry had not seen a 54 00:03:14,600 --> 00:03:18,960 Speaker 3: lot of innovation for decades, and there's tremendous hunger from 55 00:03:19,240 --> 00:03:23,880 Speaker 3: emergency managers such as fire agencies, such as power utility 56 00:03:23,880 --> 00:03:27,919 Speaker 3: emergency managers. They were asking send us cameras, send us drone, 57 00:03:28,000 --> 00:03:32,760 Speaker 3: send US satellite technology, artificial intelligence, send us better software tools, 58 00:03:32,960 --> 00:03:36,640 Speaker 3: help us use mobile phones better, because we need technology 59 00:03:36,680 --> 00:03:40,080 Speaker 3: as a force multiplier to help us tackle this growing threat. 60 00:03:40,400 --> 00:03:43,080 Speaker 3: And we looked around, there were very few companies that 61 00:03:43,240 --> 00:03:45,960 Speaker 3: have been founded out of Silicon Valley, building tools for 62 00:03:46,000 --> 00:03:48,160 Speaker 3: this industry, and we decided to answer the call, and 63 00:03:48,160 --> 00:03:49,720 Speaker 3: that was the story behind pano. 64 00:03:49,960 --> 00:03:52,320 Speaker 1: Wow and speaking of it another things. Since I have 65 00:03:52,400 --> 00:03:55,600 Speaker 1: you on here, I am somewhat curious as to what 66 00:03:55,640 --> 00:03:59,480 Speaker 1: your first exposure to that concept was, because it's rare 67 00:03:59,480 --> 00:04:02,560 Speaker 1: that I talk someone who is so deeply involved in 68 00:04:02,600 --> 00:04:03,600 Speaker 1: the Internet of Things. 69 00:04:04,320 --> 00:04:06,960 Speaker 3: Absolutely so I got involved in the Internet of Things 70 00:04:07,000 --> 00:04:10,520 Speaker 3: industry early on. I had been working in clean tech, 71 00:04:10,920 --> 00:04:15,480 Speaker 3: which went through a boombust cycle, and found myself needing 72 00:04:15,480 --> 00:04:19,000 Speaker 3: to find another industry to work in, and I started 73 00:04:19,160 --> 00:04:23,280 Speaker 3: hearing about Internet of Things, how Internet connectivity like Bluetooth 74 00:04:23,320 --> 00:04:26,880 Speaker 3: and Wi Fi and cellular technologies were going to move 75 00:04:26,920 --> 00:04:30,080 Speaker 3: from just being in your phone and computer into other 76 00:04:30,120 --> 00:04:33,480 Speaker 3: types of devices and really IoT. The theme of IoT 77 00:04:34,120 --> 00:04:37,520 Speaker 3: has always been gathering data that could be used for AI. 78 00:04:38,040 --> 00:04:41,200 Speaker 3: IoT and AI have actually always been coupled. My first 79 00:04:41,240 --> 00:04:44,520 Speaker 3: IoT job was at a company called Whistle, where we 80 00:04:44,520 --> 00:04:47,560 Speaker 3: were making a fitbit for dogs, and we thought it 81 00:04:47,600 --> 00:04:49,520 Speaker 3: was going to be an easy product to make because 82 00:04:49,520 --> 00:04:51,920 Speaker 3: there was already a fitbit for humans. But it turns 83 00:04:51,920 --> 00:04:54,960 Speaker 3: out a fitbit for dogs came with its own new challenges. 84 00:04:55,520 --> 00:04:59,520 Speaker 3: For example, dogs don't carry phones, so Fitbit had Bluetooth 85 00:04:59,600 --> 00:05:01,320 Speaker 3: to just the data back to your phone, but your 86 00:05:01,360 --> 00:05:04,560 Speaker 3: dog was home without a phone, so we actually had 87 00:05:04,600 --> 00:05:08,200 Speaker 3: to put Wi Fi into this dog collar device, and 88 00:05:08,320 --> 00:05:10,159 Speaker 3: in hindsight, it turned out to be one of the 89 00:05:10,200 --> 00:05:14,080 Speaker 3: first IoT devices that had miniaturized Wi Fi. I think 90 00:05:14,120 --> 00:05:16,279 Speaker 3: we were one of the first users of the Athos 91 00:05:16,360 --> 00:05:20,680 Speaker 3: Qualcom chip and it was hard, very very hard. But 92 00:05:20,760 --> 00:05:23,359 Speaker 3: we shipped it in fifteen months and it was a 93 00:05:23,360 --> 00:05:25,640 Speaker 3: great introduction to the world of IoT and that was 94 00:05:25,680 --> 00:05:27,720 Speaker 3: where I spent my career for the next ten years. 95 00:05:28,080 --> 00:05:30,640 Speaker 1: Excellent. I love that story. So you know, I'm curious 96 00:05:30,680 --> 00:05:34,800 Speaker 1: because before you founded Pano, you were an angel investor 97 00:05:35,279 --> 00:05:38,920 Speaker 1: and you were specifically focusing on startups that were centered 98 00:05:38,960 --> 00:05:42,640 Speaker 1: around hardware, which to me is mind blowing because I 99 00:05:42,640 --> 00:05:46,320 Speaker 1: think anyone who's ever been in tech knows hardware is hard. 100 00:05:46,640 --> 00:05:48,920 Speaker 1: It is hard to get that right, it's a long 101 00:05:48,960 --> 00:05:52,159 Speaker 1: development cycle. What kind of drove you in that direction 102 00:05:52,360 --> 00:05:55,840 Speaker 1: to look for companies that you could help establish and 103 00:05:55,920 --> 00:05:57,800 Speaker 1: support that were in the hardware space. 104 00:05:58,680 --> 00:06:01,200 Speaker 3: I love working in hard and that's what we do 105 00:06:01,240 --> 00:06:03,640 Speaker 3: here at Pano as well. There are certain things that 106 00:06:03,720 --> 00:06:06,560 Speaker 3: make hardware harder, but if you know how to do them. 107 00:06:06,720 --> 00:06:09,359 Speaker 3: Then it becomes a great moat, and I think every 108 00:06:09,400 --> 00:06:12,159 Speaker 3: startup needs to find a moat. I think there's a 109 00:06:12,200 --> 00:06:14,679 Speaker 3: great article from one of the y common Air founders 110 00:06:14,680 --> 00:06:18,320 Speaker 3: that every startup needs its schlep right, and if there's 111 00:06:18,320 --> 00:06:20,280 Speaker 3: no schlep, then there's gonna be too much competition, it's 112 00:06:20,279 --> 00:06:21,719 Speaker 3: not going to be a good business. You just have 113 00:06:21,760 --> 00:06:23,680 Speaker 3: to pick what your schlep is going to be. So 114 00:06:24,080 --> 00:06:27,080 Speaker 3: the schlep of hardware is a couple of things. First 115 00:06:27,080 --> 00:06:29,960 Speaker 3: of all, in a hardware company, much more of the 116 00:06:30,000 --> 00:06:33,160 Speaker 3: work you need to do is done with third parties 117 00:06:33,520 --> 00:06:36,640 Speaker 3: for other companies, not within the own walls of your company, 118 00:06:36,880 --> 00:06:40,840 Speaker 3: not by employees. So your engineers are mainly going out 119 00:06:40,880 --> 00:06:44,880 Speaker 3: and selecting technology from third party companies, and they have 120 00:06:44,960 --> 00:06:47,480 Speaker 3: to then work with those vendors, they have to work 121 00:06:47,520 --> 00:06:50,880 Speaker 3: with contract manufacturers. So it's a team sport outside of 122 00:06:50,920 --> 00:06:52,840 Speaker 3: your company. It's also much more of a team sport 123 00:06:52,880 --> 00:06:56,520 Speaker 3: within the company. There's much more interdependencies between mechanical engineers 124 00:06:56,520 --> 00:07:01,839 Speaker 3: and electrical engineers then necessarily between discrete software engineers, so 125 00:07:01,920 --> 00:07:05,480 Speaker 3: that makes it challenging. It's much harder to change scope 126 00:07:05,640 --> 00:07:08,120 Speaker 3: of a hardware product once you've started, so you have 127 00:07:08,200 --> 00:07:11,080 Speaker 3: lex fusibility there, and you also have more specialists within 128 00:07:11,120 --> 00:07:13,400 Speaker 3: your team, so the people on your team are not fungible, 129 00:07:13,760 --> 00:07:17,160 Speaker 3: so you've fixed your scope. You often have a hard deadline, 130 00:07:17,160 --> 00:07:19,080 Speaker 3: like you're shipping for Christmas. In our case of pano 131 00:07:19,120 --> 00:07:21,560 Speaker 3: we're shipping for fire season, and things go wrong a 132 00:07:21,600 --> 00:07:25,080 Speaker 3: lot in hardware and you have to sprint and recover. 133 00:07:25,520 --> 00:07:27,160 Speaker 3: I think it tends to be a good field for 134 00:07:27,200 --> 00:07:30,440 Speaker 3: people who are a bit of adrenaline junkies, where you know, 135 00:07:30,480 --> 00:07:33,480 Speaker 3: you make really really careful plans and then everything still 136 00:07:33,520 --> 00:07:35,880 Speaker 3: goes wrong and you have to figure out how to react. 137 00:07:36,240 --> 00:07:39,520 Speaker 3: My thinking is that actually is pretty similar to disaster management, 138 00:07:39,560 --> 00:07:41,520 Speaker 3: and that's actually part of also what drew me into 139 00:07:41,520 --> 00:07:44,080 Speaker 3: getting into the disaster management industry as well. 140 00:07:44,600 --> 00:07:48,880 Speaker 1: Wow, great insight, And we've already alluded to the fact 141 00:07:48,880 --> 00:07:51,880 Speaker 1: that pano is in hardware space. That panel relies a 142 00:07:51,880 --> 00:07:54,120 Speaker 1: lot on hardware. Can you talk a bit about the 143 00:07:54,200 --> 00:07:58,760 Speaker 1: types of hardware PANOAI is using in order to detect 144 00:07:59,280 --> 00:08:02,520 Speaker 1: forest fires. What actually is the technology that's being used 145 00:08:02,520 --> 00:08:03,840 Speaker 1: out in the field. 146 00:08:04,080 --> 00:08:06,120 Speaker 3: Yeah, I can describe the hardware and maybe how the 147 00:08:06,160 --> 00:08:10,400 Speaker 3: whole system works as well. So Pano's solution is a 148 00:08:10,440 --> 00:08:15,520 Speaker 3: solution for detecting and alerting on new fire ignitions within 149 00:08:15,640 --> 00:08:19,800 Speaker 3: minutes and pushing out alerts that come with time lapse 150 00:08:19,920 --> 00:08:23,800 Speaker 3: video of the fire growing and location information GPS coordinates 151 00:08:24,120 --> 00:08:27,720 Speaker 3: so that first responders can see the fire, make an 152 00:08:27,720 --> 00:08:30,720 Speaker 3: assessment about what kind of response is necessary, share the 153 00:08:30,720 --> 00:08:33,600 Speaker 3: fire information with each other over text and email, and 154 00:08:33,920 --> 00:08:38,200 Speaker 3: then create a coordinated aggressive response to send helicopters, planes, 155 00:08:38,240 --> 00:08:40,800 Speaker 3: bulldozers go nip that fire in the butt is actually 156 00:08:40,880 --> 00:08:44,040 Speaker 3: modeled after an scam or a ring doorbell, very very 157 00:08:44,080 --> 00:08:47,760 Speaker 3: similar idea. From a user's perspective, it's almost the same 158 00:08:47,840 --> 00:08:50,199 Speaker 3: as a nestcam or ring doorbell that says, somebody's stealing 159 00:08:50,200 --> 00:08:50,600 Speaker 3: your package. 160 00:08:50,640 --> 00:08:51,240 Speaker 2: Here's a video. 161 00:08:51,480 --> 00:08:52,800 Speaker 3: And actually, I think it's a big part of why 162 00:08:52,880 --> 00:08:55,360 Speaker 3: the product's been so popular and being widely adopted because 163 00:08:55,360 --> 00:08:58,800 Speaker 3: the customers actually are used to this concept. To the 164 00:08:58,840 --> 00:09:02,280 Speaker 3: customer seems really simple. Behind the scenes, the whole system 165 00:09:02,360 --> 00:09:06,480 Speaker 3: is incredibly complicated. So it starts with a piece of 166 00:09:06,520 --> 00:09:09,400 Speaker 3: equipment that we design a manufacturer called a Pane station. 167 00:09:10,040 --> 00:09:15,520 Speaker 3: This includes about forty components, including two high definition security cameras. 168 00:09:15,679 --> 00:09:17,960 Speaker 3: These are off the shelf top of the line six 169 00:09:18,040 --> 00:09:22,959 Speaker 3: megapixel security cameras designed for ruggedized environments, and about forty 170 00:09:22,960 --> 00:09:26,079 Speaker 3: other components. We assemble them in our factory in San Francisco. 171 00:09:26,679 --> 00:09:30,520 Speaker 3: The systems also include an edge computer which has logic 172 00:09:30,559 --> 00:09:33,599 Speaker 3: on how we control the cameras. We also include networking 173 00:09:33,760 --> 00:09:35,640 Speaker 3: equipment so we can send the data up to the 174 00:09:35,679 --> 00:09:40,800 Speaker 3: cloud over cellular broadband connectivity. We have power management. Sometimes 175 00:09:40,800 --> 00:09:43,079 Speaker 3: we need to include a backup battery. Sometimes we need 176 00:09:43,080 --> 00:09:46,080 Speaker 3: to include solar panels. Every site is a little bit different, 177 00:09:46,280 --> 00:09:49,360 Speaker 3: so it's a configured to order supply chain, which is 178 00:09:49,400 --> 00:09:50,280 Speaker 3: not for the faint of heart. 179 00:09:50,760 --> 00:09:52,160 Speaker 2: And we mount. 180 00:09:51,880 --> 00:09:56,800 Speaker 3: These systems typically on existing structures like cell towers, water tanks, 181 00:09:57,040 --> 00:10:01,520 Speaker 3: government communications towers, sometimes even private home homes or chairlifts 182 00:10:01,520 --> 00:10:03,760 Speaker 3: at a ski resort. We get really creative on where 183 00:10:03,800 --> 00:10:07,120 Speaker 3: to put these and we've actually found five G to 184 00:10:07,160 --> 00:10:09,319 Speaker 3: be a really great technology for us. One of our 185 00:10:09,320 --> 00:10:12,040 Speaker 3: investors is t Mobile, and they've been a phenomenal partner 186 00:10:12,320 --> 00:10:14,480 Speaker 3: in terms of allowing us to migrate from four G 187 00:10:14,559 --> 00:10:17,280 Speaker 3: to five G, and we're one of the first industrial 188 00:10:17,400 --> 00:10:20,960 Speaker 3: users of five G technology, which has further range into 189 00:10:21,000 --> 00:10:24,520 Speaker 3: the forest and higher performance than four G. So Once 190 00:10:24,520 --> 00:10:28,000 Speaker 3: we mount these systems, we rotate the cameras three hundred 191 00:10:28,040 --> 00:10:31,560 Speaker 3: and sixty degrees every minute, and we take ten frames 192 00:10:31,559 --> 00:10:34,840 Speaker 3: with each rotation, and we send this up to the cloud. 193 00:10:35,000 --> 00:10:38,480 Speaker 3: We put these images through a computer vision deep learning 194 00:10:38,520 --> 00:10:42,800 Speaker 3: algorithm and it draws bounding boxes on the images where 195 00:10:42,800 --> 00:10:46,080 Speaker 3: it thinks there's smoke. Each one of these bounding boxes 196 00:10:46,120 --> 00:10:49,120 Speaker 3: is reviewed by a human analyst in our Paneo Intelligence 197 00:10:49,120 --> 00:10:52,520 Speaker 3: center to make sure that it's really truly smoke. This 198 00:10:52,559 --> 00:10:55,320 Speaker 3: is called human in the loop AI. It's actually a 199 00:10:55,400 --> 00:10:58,760 Speaker 3: very common approach with computer vision AI because it's so 200 00:10:58,840 --> 00:11:01,560 Speaker 3: easy for a human to use damage as ground truth 201 00:11:01,640 --> 00:11:02,520 Speaker 3: to be sure. 202 00:11:02,920 --> 00:11:03,760 Speaker 2: In that way, when. 203 00:11:03,600 --> 00:11:06,480 Speaker 3: We trigger an alert out to our customer, we have 204 00:11:06,559 --> 00:11:09,720 Speaker 3: almost perfect signal to noise ratio, so the customers know 205 00:11:09,760 --> 00:11:11,440 Speaker 3: when they get an alert from PANO, this is going 206 00:11:11,480 --> 00:11:13,320 Speaker 3: to be smoke, and I don't have to worry about 207 00:11:13,320 --> 00:11:16,160 Speaker 3: being spammed. Then the customers get all the other rich 208 00:11:16,200 --> 00:11:17,920 Speaker 3: information they need to go control the fire. 209 00:11:18,440 --> 00:11:21,400 Speaker 1: I'm glad you brought up the human loop element because 210 00:11:21,840 --> 00:11:25,720 Speaker 1: anyone who's followed any sort of AI, even things like 211 00:11:25,760 --> 00:11:30,720 Speaker 1: image recognition, you realize that there are outlier cases where 212 00:11:31,080 --> 00:11:33,000 Speaker 1: I think of it as like Paradelia where you're looking 213 00:11:33,080 --> 00:11:34,720 Speaker 1: up at the sky and you see a cloud and 214 00:11:34,760 --> 00:11:37,480 Speaker 1: it is in the shape of something, and as a person, 215 00:11:37,520 --> 00:11:40,320 Speaker 1: you know that's not actually the thing. It's not really 216 00:11:40,360 --> 00:11:42,440 Speaker 1: a cat up in the sky. It just looks like one, 217 00:11:42,640 --> 00:11:45,840 Speaker 1: but to an image recognition algorithm, it might fool it 218 00:11:45,880 --> 00:11:49,080 Speaker 1: into thinking it's a cat. And so having that human 219 00:11:49,480 --> 00:11:53,720 Speaker 1: element to be that step before sending messages out, before 220 00:11:53,840 --> 00:11:56,800 Speaker 1: you start to see agencies commit resources toward going to 221 00:11:56,840 --> 00:12:00,840 Speaker 1: a potentially remote location that is not easy to get to, 222 00:12:00,880 --> 00:12:03,920 Speaker 1: which could be taking their attention away from other very real, 223 00:12:04,240 --> 00:12:05,680 Speaker 1: very present emergencies. 224 00:12:06,200 --> 00:12:06,400 Speaker 2: Yeah. 225 00:12:06,440 --> 00:12:09,720 Speaker 3: Absolutely, And the good news is cameras as an AI 226 00:12:09,880 --> 00:12:13,320 Speaker 3: sensor are really phenomenal. And that's actually a core part 227 00:12:13,360 --> 00:12:17,160 Speaker 3: of Pano's long term vision and strategy is we've decided 228 00:12:17,200 --> 00:12:21,640 Speaker 3: to specialize in computer vision AI. We just hired an 229 00:12:21,679 --> 00:12:25,240 Speaker 3: amazing VP of engineering. He has a PhD in computer vision, 230 00:12:25,760 --> 00:12:28,839 Speaker 3: and we brought on a leader with computer vision and 231 00:12:28,920 --> 00:12:32,880 Speaker 3: expertise to lead our engineering effort because we think that 232 00:12:33,400 --> 00:12:36,360 Speaker 3: camera sensors are going to be the most important for 233 00:12:36,480 --> 00:12:41,079 Speaker 3: emergency management, whether those cameras are stationary like our first product, 234 00:12:41,280 --> 00:12:44,199 Speaker 3: or whether those cameras are on a drone or satellites. 235 00:12:44,360 --> 00:12:46,640 Speaker 3: With camera data, you can run your algorithm and you 236 00:12:46,679 --> 00:12:50,079 Speaker 3: do object detection to detect the signal you're trying to detect, 237 00:12:50,240 --> 00:12:52,720 Speaker 3: but you can also have a human verify in real time. 238 00:12:52,800 --> 00:12:55,680 Speaker 3: Because the camera data is ground truth, you can combine 239 00:12:55,840 --> 00:12:59,360 Speaker 3: artificial intelligence and human intelligence together in real time with 240 00:12:59,400 --> 00:13:02,120 Speaker 3: camera data beautifully, and so that makes it, I think, 241 00:13:02,200 --> 00:13:04,280 Speaker 3: much more useful and much more powerful. 242 00:13:12,679 --> 00:13:15,680 Speaker 1: You also mentioned five G connectivity, which is obviously something 243 00:13:15,720 --> 00:13:18,280 Speaker 1: we really like to talk about on this show, and 244 00:13:18,440 --> 00:13:21,160 Speaker 1: it's interesting because you were bringing up a different sort 245 00:13:21,160 --> 00:13:24,160 Speaker 1: of flavor of five G than what I often talk about. 246 00:13:24,240 --> 00:13:26,800 Speaker 1: I'm often talking about the ultra high frequency five G, 247 00:13:26,920 --> 00:13:31,960 Speaker 1: where you have very high throughput, very low latency. But 248 00:13:32,280 --> 00:13:34,520 Speaker 1: for those who do not know, there are other bands 249 00:13:34,520 --> 00:13:37,400 Speaker 1: of five G, and as you were eluding, one of 250 00:13:37,440 --> 00:13:40,160 Speaker 1: those bands is one where you are able to get 251 00:13:40,360 --> 00:13:45,160 Speaker 1: much further broadcast range than you would with other cellular technologies. 252 00:13:45,200 --> 00:13:48,000 Speaker 1: So you mentioned about the partnership with T Mobile. Can 253 00:13:48,000 --> 00:13:50,160 Speaker 1: you talk a little bit more about that and how 254 00:13:50,320 --> 00:13:53,720 Speaker 1: five G has played a part in pano AI's operation. 255 00:13:54,520 --> 00:13:58,480 Speaker 3: Yes, absolutely, so, we've been partnering with T Mobile for 256 00:13:58,559 --> 00:14:00,200 Speaker 3: over a year now. I think we were in the 257 00:14:00,320 --> 00:14:03,400 Speaker 3: twenty twenty two class of the five G Open Innovation 258 00:14:03,520 --> 00:14:07,360 Speaker 3: Lab and they help facilitate a partnership with T Mobile 259 00:14:07,800 --> 00:14:09,800 Speaker 3: over a year ago, and that has been a really 260 00:14:09,840 --> 00:14:14,280 Speaker 3: successful partnership. T Mobile helped us with technical support that 261 00:14:14,360 --> 00:14:16,640 Speaker 3: allowed us to migrate from four G to five G 262 00:14:17,280 --> 00:14:21,440 Speaker 3: sooner than almost any other industrial application, and this has 263 00:14:21,480 --> 00:14:23,800 Speaker 3: really been terrific for us, and I think it's just 264 00:14:23,840 --> 00:14:26,360 Speaker 3: going to continue to be more powerful over time as 265 00:14:26,400 --> 00:14:29,120 Speaker 3: we unlock more and more features that take advantage of 266 00:14:29,120 --> 00:14:31,960 Speaker 3: the performance of five G. So the benefits for our 267 00:14:32,480 --> 00:14:35,680 Speaker 3: system of five G are, first of all, the range 268 00:14:35,800 --> 00:14:40,240 Speaker 3: as you referred to. We're installing our PANA stations on 269 00:14:40,400 --> 00:14:45,560 Speaker 3: mountaintop locations, typically at the wildland urban interface or deep 270 00:14:45,640 --> 00:14:48,320 Speaker 3: in the forest. That's where you need to have wildfire cameras. 271 00:14:48,320 --> 00:14:50,320 Speaker 3: You don't need to have wildfare cameras right and downtown. 272 00:14:50,960 --> 00:14:53,520 Speaker 3: And sometimes our locations are not very close to a 273 00:14:53,520 --> 00:14:55,360 Speaker 3: cell tower. They can be far from a cell tower. 274 00:14:55,360 --> 00:14:58,280 Speaker 3: We could be mounting on an abandoned four service lookout 275 00:14:58,320 --> 00:15:00,840 Speaker 3: tower that's miles into the forest. So the fact that 276 00:15:00,880 --> 00:15:04,320 Speaker 3: five G provides for further range from the cell tower 277 00:15:04,720 --> 00:15:06,520 Speaker 3: means that we might be able to use cellular in 278 00:15:06,560 --> 00:15:08,760 Speaker 3: a location where otherwise cellular would not be an option. 279 00:15:09,360 --> 00:15:12,120 Speaker 3: Second of all, the latency and the broadcast and the 280 00:15:12,200 --> 00:15:15,560 Speaker 3: upload speeds and the bandwidth are actually very beneficial for 281 00:15:15,640 --> 00:15:17,840 Speaker 3: us as well. I mean, remember we're an upload application, 282 00:15:17,920 --> 00:15:21,520 Speaker 3: not a download application in terms of latency. This really 283 00:15:21,520 --> 00:15:24,920 Speaker 3: comes into play when we're using the cameras in optical 284 00:15:25,000 --> 00:15:28,560 Speaker 3: zoom mode. So our standard operating mode is patrol mode. 285 00:15:28,600 --> 00:15:32,200 Speaker 3: We're rotating the cameras one rotation per minute, scanning the 286 00:15:32,280 --> 00:15:36,480 Speaker 3: landscape looking for smoke. But once we find smoke, then 287 00:15:36,800 --> 00:15:40,240 Speaker 3: either Pano or our customers will take one of those 288 00:15:40,280 --> 00:15:43,920 Speaker 3: two cameras out of rotation and zoom in on the 289 00:15:44,000 --> 00:15:47,160 Speaker 3: smoke using the very powerful optical zoom capabilities of the camera. 290 00:15:47,560 --> 00:15:51,720 Speaker 3: And this actually provides much richer visual information of what's 291 00:15:51,760 --> 00:15:54,640 Speaker 3: going on with the fire. It allows the fire agencies 292 00:15:54,720 --> 00:15:57,880 Speaker 3: to much more clearly see road access, to see which 293 00:15:57,920 --> 00:16:01,360 Speaker 3: way the wind is blowing, to see strokes nearby. It 294 00:16:01,360 --> 00:16:04,560 Speaker 3: can really have a huge impact on firefighter safety. And 295 00:16:05,040 --> 00:16:07,320 Speaker 3: the challenge is as you're going to navigate the camera, 296 00:16:07,400 --> 00:16:09,160 Speaker 3: if you're on a four G connection, you have to 297 00:16:09,200 --> 00:16:12,680 Speaker 3: wait to send the instructions out to the camera, and 298 00:16:12,800 --> 00:16:15,360 Speaker 3: it can be a nuisance and slow down the navigation 299 00:16:15,440 --> 00:16:18,080 Speaker 3: and take you longer to find the fire and zoom 300 00:16:18,120 --> 00:16:19,840 Speaker 3: in to where you want to get to. It's not 301 00:16:19,880 --> 00:16:22,280 Speaker 3: the same as performing open heart surgery, but the latency 302 00:16:22,320 --> 00:16:24,880 Speaker 3: really will be of great advantage. And then finally, in 303 00:16:25,000 --> 00:16:28,240 Speaker 3: terms of the upload bandwidth, these cameras are capable of 304 00:16:28,280 --> 00:16:31,320 Speaker 3: thirty frame per second video, but on four G we're 305 00:16:31,320 --> 00:16:35,200 Speaker 3: typically uploading about ten frames a minute. Depending on the 306 00:16:35,200 --> 00:16:37,280 Speaker 3: four G connection we can go a little bit above that, 307 00:16:37,360 --> 00:16:39,120 Speaker 3: but with five G we can go to the full 308 00:16:39,480 --> 00:16:43,040 Speaker 3: thirty frame per second video, which, again, when you're staring 309 00:16:43,080 --> 00:16:45,040 Speaker 3: at a fire, you want all the visual information you 310 00:16:45,080 --> 00:16:45,480 Speaker 3: can get. 311 00:16:45,960 --> 00:16:48,680 Speaker 1: Yeah, that's incredible. Well, can you tell me a little 312 00:16:48,680 --> 00:16:50,800 Speaker 1: bit more about the technologies that are on the back end. 313 00:16:50,800 --> 00:16:53,960 Speaker 1: I'm assuming that you have a mixture of cloud computing 314 00:16:53,960 --> 00:16:57,160 Speaker 1: and on premises compute that are processing all this images. 315 00:16:57,160 --> 00:16:58,720 Speaker 1: Can you kind of give us a big picture of 316 00:16:58,760 --> 00:16:59,520 Speaker 1: how that works. 317 00:17:00,160 --> 00:17:00,440 Speaker 2: Sure. 318 00:17:00,680 --> 00:17:03,360 Speaker 3: I love the theme that a technology is developed by 319 00:17:03,360 --> 00:17:07,360 Speaker 3: one industry and then other sectors find ways to harvest 320 00:17:07,359 --> 00:17:10,719 Speaker 3: that technology and use it in new ways. So at 321 00:17:10,800 --> 00:17:14,680 Speaker 3: PANO we're harnessing many technologies that were developed for other industries. 322 00:17:14,960 --> 00:17:18,199 Speaker 3: Five G is a perfect example, was developed primarily for 323 00:17:18,400 --> 00:17:22,080 Speaker 3: cell phone users, but we're using it for wildfire detection. 324 00:17:22,600 --> 00:17:25,399 Speaker 3: The AI that we use was really primarily developed for 325 00:17:25,400 --> 00:17:28,840 Speaker 3: the self driving car industry, and we're harnessing that for 326 00:17:28,920 --> 00:17:33,040 Speaker 3: fire detection. Satellite technology is really critical for us. We're 327 00:17:33,119 --> 00:17:37,359 Speaker 3: using geostationary satellite data that was developed by Noah, the 328 00:17:37,600 --> 00:17:41,040 Speaker 3: National Weather Service. Those have been very useful as a 329 00:17:41,080 --> 00:17:46,640 Speaker 3: supplemental detection method using thermal sensors. We're harnessing cloud tools. 330 00:17:46,680 --> 00:17:49,760 Speaker 3: I mean we ingest massive amounts of data. If we 331 00:17:49,760 --> 00:17:53,960 Speaker 3: were not able to harness scalable cloud technologies and Kubernetes, 332 00:17:54,240 --> 00:17:56,600 Speaker 3: there's no way we could do what we're doing today. 333 00:17:56,880 --> 00:17:59,399 Speaker 3: We use SaaS tools like Twilio to push out our 334 00:17:59,400 --> 00:18:04,200 Speaker 3: notification and scalable fashion. So there's dozens of modern day 335 00:18:04,400 --> 00:18:07,080 Speaker 3: technologies that were developed over the past ten years that 336 00:18:07,200 --> 00:18:10,159 Speaker 3: are included in our solution that our customers are totally 337 00:18:10,200 --> 00:18:12,480 Speaker 3: unaware of don't have to think about as Our job 338 00:18:12,600 --> 00:18:15,120 Speaker 3: is to go shop for the best technologies like five 339 00:18:15,160 --> 00:18:17,800 Speaker 3: G and bring them to our customers as as soon 340 00:18:17,800 --> 00:18:19,200 Speaker 3: as they become readily available. 341 00:18:19,760 --> 00:18:23,760 Speaker 1: I love that explanation as well. It is another inspiring 342 00:18:23,800 --> 00:18:27,120 Speaker 1: approach to this and the idea of using that specifically 343 00:18:27,200 --> 00:18:31,520 Speaker 1: for a climate oriented task here where we know for 344 00:18:31,560 --> 00:18:34,040 Speaker 1: a fact that we're going to have more of these 345 00:18:34,040 --> 00:18:38,119 Speaker 1: climate events. It's interesting to me about your business because 346 00:18:38,160 --> 00:18:41,320 Speaker 1: when I first heard about what PANO does, to me, 347 00:18:41,440 --> 00:18:43,400 Speaker 1: it was immediately one of those things where I thought, Oh, 348 00:18:43,440 --> 00:18:45,720 Speaker 1: that sounds like something that you would think of as 349 00:18:45,720 --> 00:18:49,560 Speaker 1: an adjunct to a government agency for example. Can you 350 00:18:49,600 --> 00:18:52,959 Speaker 1: talk a bit about what thinking went into making it 351 00:18:53,080 --> 00:18:56,960 Speaker 1: a business, how did that sort of blossom? 352 00:18:57,320 --> 00:19:00,880 Speaker 3: So it absolutely is a service that should be provided 353 00:19:00,880 --> 00:19:04,119 Speaker 3: by government agencies, so you're absolutely right there. And then 354 00:19:04,200 --> 00:19:08,479 Speaker 3: government agencies typically select vendors by running an RFP, and 355 00:19:08,560 --> 00:19:11,480 Speaker 3: that's how they select the vendors that then deliver the 356 00:19:11,520 --> 00:19:14,800 Speaker 3: government services that you're used to. And actually we've been 357 00:19:14,880 --> 00:19:17,920 Speaker 3: very impressed with the rigor that government agencies have used 358 00:19:17,960 --> 00:19:19,200 Speaker 3: when selecting vendors. 359 00:19:20,040 --> 00:19:22,959 Speaker 1: Excellent. Can you talk a bit about sort of the 360 00:19:23,000 --> 00:19:26,440 Speaker 1: skills that are needed for a business leader who wants 361 00:19:26,480 --> 00:19:30,000 Speaker 1: to work closely with governments? What sort of skill set 362 00:19:30,040 --> 00:19:32,560 Speaker 1: does that person need and what sort of expectations should 363 00:19:32,560 --> 00:19:32,879 Speaker 1: they have? 364 00:19:33,600 --> 00:19:36,360 Speaker 3: Yeah, so I had worked in government, but selling into 365 00:19:36,359 --> 00:19:39,280 Speaker 3: government was not my skill set. The first person who 366 00:19:39,359 --> 00:19:42,760 Speaker 3: joined our team, I'm very fortunate, is our chief commercial officer, 367 00:19:42,880 --> 00:19:47,160 Speaker 3: Arvinsattiam and he did have ten years of experience selling 368 00:19:47,200 --> 00:19:50,439 Speaker 3: into government. He had been working at Cisco for many years, 369 00:19:50,520 --> 00:19:53,240 Speaker 3: and for the past ten of those years he was 370 00:19:53,280 --> 00:19:56,760 Speaker 3: one of the leaders of their government IoT business, where 371 00:19:56,920 --> 00:20:00,960 Speaker 3: he sold million dollar deals into sy like the city 372 00:20:01,000 --> 00:20:04,119 Speaker 3: of Barcelona that led to wiring up the entire city 373 00:20:04,119 --> 00:20:06,159 Speaker 3: to be a smart city with smart street lamps and 374 00:20:06,200 --> 00:20:09,600 Speaker 3: smart parking meter, smart trash cans, and so he is 375 00:20:09,880 --> 00:20:13,399 Speaker 3: exceptional at learning how to navigate the challenges of selling 376 00:20:13,400 --> 00:20:17,240 Speaker 3: into government buyers. So I would say that selling into 377 00:20:17,240 --> 00:20:19,960 Speaker 3: government we have found to be more difficult than selling 378 00:20:19,960 --> 00:20:24,040 Speaker 3: into any other segment. So you need both sellers and 379 00:20:24,119 --> 00:20:27,160 Speaker 3: you also need regulatory We have regulatory advisors as well. 380 00:20:27,440 --> 00:20:29,960 Speaker 3: But I will also say a really important part of 381 00:20:29,960 --> 00:20:33,680 Speaker 3: Pano's business success has been that we don't only sell 382 00:20:33,720 --> 00:20:37,560 Speaker 3: to government. So our business model is that we deploy 383 00:20:37,600 --> 00:20:40,920 Speaker 3: in a region and we sell subscriptions to multiple types 384 00:20:40,960 --> 00:20:46,280 Speaker 3: of entities. So we sell to government agencies city, county, state, federal, 385 00:20:46,640 --> 00:20:49,520 Speaker 3: but we also sell to power utilities. They're one of 386 00:20:49,560 --> 00:20:54,160 Speaker 3: our largest customer segments. They have a lot of wildfire risk. 387 00:20:54,320 --> 00:20:57,520 Speaker 3: Their power lines run through high fire risk areas and 388 00:20:57,560 --> 00:21:00,879 Speaker 3: so they're really focused on protecting their ass that's we 389 00:21:00,920 --> 00:21:04,639 Speaker 3: also sell to other property owners like ski resorts, the 390 00:21:04,680 --> 00:21:10,240 Speaker 3: forestry industry, other private landowners, real estate developers. So it's 391 00:21:10,280 --> 00:21:12,879 Speaker 3: been really important as a venture backed company that we 392 00:21:13,480 --> 00:21:17,119 Speaker 3: have a mixture of public sector and private sector customers 393 00:21:17,320 --> 00:21:21,280 Speaker 3: in order to meet the business objectives. And also actually 394 00:21:21,359 --> 00:21:24,040 Speaker 3: we found these two types of buyers to be complementary. 395 00:21:24,119 --> 00:21:28,600 Speaker 3: We found that our private sector customers appreciate the fact 396 00:21:28,640 --> 00:21:30,720 Speaker 3: that we also have a public sector business. In fact, 397 00:21:30,880 --> 00:21:34,560 Speaker 3: we've done multiple deals where we've actually signed up both 398 00:21:34,560 --> 00:21:36,679 Speaker 3: a public sector and a private sector customer at the 399 00:21:36,680 --> 00:21:40,800 Speaker 3: same time. For example, in the Telluride area, we signed 400 00:21:40,840 --> 00:21:44,560 Speaker 3: up the Telleride Fire Department and SA Miguel Power Company 401 00:21:44,600 --> 00:21:47,360 Speaker 3: as well at the same time for the pilot. This 402 00:21:47,400 --> 00:21:51,560 Speaker 3: is called a public private partnership and both parties feel 403 00:21:51,600 --> 00:21:56,320 Speaker 3: great about this collaboration, and it's because the fire agency 404 00:21:56,400 --> 00:22:00,159 Speaker 3: feels great that the utility is participating and contributing to 405 00:22:00,200 --> 00:22:04,199 Speaker 3: support protecting their assets, and the power utilities know that 406 00:22:04,320 --> 00:22:07,240 Speaker 3: if the fire agencies are participating as customers that they're 407 00:22:07,280 --> 00:22:10,240 Speaker 3: going to use this product because the enterprise customers can't 408 00:22:10,240 --> 00:22:12,639 Speaker 3: suppress the fire on their own. The end users of 409 00:22:12,640 --> 00:22:14,119 Speaker 3: the product are the fire agencies. 410 00:22:14,800 --> 00:22:17,880 Speaker 1: It's really interesting. It also makes me think of cases 411 00:22:17,960 --> 00:22:22,520 Speaker 1: where there are issues with power utilities and their close 412 00:22:22,560 --> 00:22:26,680 Speaker 1: association with fires. I'm reminded of rolling brownouts that were 413 00:22:26,720 --> 00:22:30,200 Speaker 1: needed in times of high winds, for example, where there 414 00:22:30,240 --> 00:22:33,360 Speaker 1: was a concern that utility lines could potentially cause fires, 415 00:22:33,680 --> 00:22:37,119 Speaker 1: and potentially down the line, a company like PANO could 416 00:22:37,240 --> 00:22:40,760 Speaker 1: end up delivering lots of information that could lead to 417 00:22:40,840 --> 00:22:44,600 Speaker 1: things like infrastructure overhaul. Like it's interesting to me how 418 00:22:44,720 --> 00:22:49,639 Speaker 1: your role could expand beyond the immediate detection of fires 419 00:22:49,640 --> 00:22:53,879 Speaker 1: but also ultimately contribute to improving infrastructure in places that 420 00:22:53,920 --> 00:22:56,959 Speaker 1: need it. And it's because you're able to gather the 421 00:22:57,000 --> 00:23:00,600 Speaker 1: information that's necessary before anyone can make those kinds of decisions. 422 00:23:01,320 --> 00:23:04,000 Speaker 3: Yes, you're spot on. I mean, once again, I think 423 00:23:04,040 --> 00:23:08,480 Speaker 3: you know our long term vision better than we do exactly. 424 00:23:08,640 --> 00:23:11,320 Speaker 3: You know, our first solution at PANO is focused on 425 00:23:11,960 --> 00:23:15,720 Speaker 3: delivering real time, actionable intelligence during the response phase of 426 00:23:15,760 --> 00:23:18,080 Speaker 3: an incident. And by the way, I should also mention 427 00:23:18,200 --> 00:23:22,760 Speaker 3: our customers use our solution during fire season for enabling 428 00:23:22,920 --> 00:23:26,000 Speaker 3: rapid suppression of fires, but they also use our solution 429 00:23:26,480 --> 00:23:30,359 Speaker 3: in the rainy season for enabling safer and more manageable 430 00:23:30,400 --> 00:23:34,000 Speaker 3: controlled burning. In fire season, our solution is used for 431 00:23:34,200 --> 00:23:37,359 Speaker 3: pushing out real time actionable information to help with suppression, 432 00:23:37,720 --> 00:23:40,480 Speaker 3: and that's called the response phase of a natural disaster. 433 00:23:40,800 --> 00:23:44,280 Speaker 3: But there's three other phases of natural disaster management. There's 434 00:23:44,320 --> 00:23:49,000 Speaker 3: the recovery phase. There's the mitigation phase, which is hardening 435 00:23:49,240 --> 00:23:52,200 Speaker 3: your system, which would be for example, decisions around bearing 436 00:23:52,240 --> 00:23:56,679 Speaker 3: power lines or relocating assets, for example, making a decision 437 00:23:56,680 --> 00:23:58,800 Speaker 3: that you're going to change your deck material to make 438 00:23:58,800 --> 00:23:59,600 Speaker 3: it non flammable. 439 00:23:59,600 --> 00:24:00,920 Speaker 2: That would all be part of mitigation. 440 00:24:01,480 --> 00:24:04,280 Speaker 3: And then there's preparedness, which is okay, making sure you 441 00:24:04,280 --> 00:24:06,919 Speaker 3: have your evacuation routes planned out, making sure you have 442 00:24:06,920 --> 00:24:09,600 Speaker 3: a way to contact people, making sure you have shelters 443 00:24:09,640 --> 00:24:12,359 Speaker 3: with enough supplies. That's all preparedness, and then all of 444 00:24:12,359 --> 00:24:15,600 Speaker 3: that feeds back into the response and the cycle continues. 445 00:24:16,000 --> 00:24:19,160 Speaker 3: To your point, the data that we are creating we 446 00:24:19,240 --> 00:24:22,640 Speaker 3: create in our tool. After we push out an incident, 447 00:24:22,800 --> 00:24:25,480 Speaker 3: that data doesn't just go away. We've created an archive 448 00:24:25,960 --> 00:24:29,000 Speaker 3: of all of these fire incidents that we've alerted on, 449 00:24:29,200 --> 00:24:32,120 Speaker 3: so we have a very accurate record of exactly where 450 00:24:32,160 --> 00:24:35,359 Speaker 3: and when the fire started, and we ingest third party 451 00:24:35,440 --> 00:24:38,639 Speaker 3: data as well that allows us to have data on 452 00:24:38,720 --> 00:24:41,919 Speaker 3: how that fire was responded to. We ingest all of 453 00:24:41,960 --> 00:24:44,959 Speaker 3: that into our database, and we now have a treasure 454 00:24:45,000 --> 00:24:49,480 Speaker 3: trove of fire activity fire response all over the country. 455 00:24:49,760 --> 00:24:52,399 Speaker 3: And as our deployments increase, that data is just growing 456 00:24:52,440 --> 00:24:54,920 Speaker 3: and growing. And when I think about the mission of 457 00:24:55,000 --> 00:24:57,400 Speaker 3: PANO and the impact that we can have, I see 458 00:24:57,520 --> 00:25:00,440 Speaker 3: as twofold. One impact we can have is the impact 459 00:25:00,440 --> 00:25:02,919 Speaker 3: of improving real time response and lessening the harm of 460 00:25:02,960 --> 00:25:06,040 Speaker 3: wildfires in the immediate term, but I believe that we 461 00:25:06,080 --> 00:25:11,120 Speaker 3: can have additional impact by improving hardening decisions. There's going 462 00:25:11,160 --> 00:25:14,719 Speaker 3: to be trillions of dollars of investment made to harden 463 00:25:14,800 --> 00:25:18,480 Speaker 3: our infrastructure and rebuild our cities, rebuild our transportation in 464 00:25:18,480 --> 00:25:22,160 Speaker 3: the face of climate change. It's going to cost billions 465 00:25:22,200 --> 00:25:27,080 Speaker 3: of dollars to bury power lines to create microgrids. That's 466 00:25:27,119 --> 00:25:30,200 Speaker 3: going to take decades, and we're going to need to 467 00:25:30,200 --> 00:25:33,240 Speaker 3: spend billions for new helicopters and new planes. How are 468 00:25:33,280 --> 00:25:36,840 Speaker 3: we going to decide how to prioritize those investments, and 469 00:25:36,880 --> 00:25:39,359 Speaker 3: which investments are needed and which ones are not needed. 470 00:25:39,720 --> 00:25:42,720 Speaker 3: All of that needs data. There's no data today. There's 471 00:25:42,720 --> 00:25:46,520 Speaker 3: a federal agency that created a national Fire Incident Database, 472 00:25:46,560 --> 00:25:49,639 Speaker 3: and they created a database with ninety six fields that 473 00:25:49,680 --> 00:25:52,480 Speaker 3: should be populated for every fire incident. And when you 474 00:25:52,520 --> 00:25:54,919 Speaker 3: look at the database, only two of the fields are populated. 475 00:25:55,400 --> 00:25:58,320 Speaker 3: One field is when was the fire called in and 476 00:25:58,359 --> 00:26:01,480 Speaker 3: how many acres was it when it was And even 477 00:26:01,480 --> 00:26:04,080 Speaker 3: those two fields are suspect, right, how do we know 478 00:26:04,240 --> 00:26:08,119 Speaker 3: if that's really accurate information? But with Panos technology, you 479 00:26:08,160 --> 00:26:11,360 Speaker 3: can absolutely get that information accurately and many, many more 480 00:26:11,400 --> 00:26:13,520 Speaker 3: of those ninety six fields can be populated as well, 481 00:26:13,760 --> 00:26:16,840 Speaker 3: and data can really be an enabler for us to 482 00:26:16,920 --> 00:26:18,160 Speaker 3: adapt to climate change. 483 00:26:18,800 --> 00:26:22,040 Speaker 1: Sonya, You've talked about so much here with the Internet 484 00:26:22,040 --> 00:26:24,760 Speaker 1: of things, artificial intelligence, the era of big data, the 485 00:26:24,840 --> 00:26:27,720 Speaker 1: ability for us to be able to do something meaningful 486 00:26:27,800 --> 00:26:29,600 Speaker 1: with that data. I think a lot of these things 487 00:26:29,600 --> 00:26:32,760 Speaker 1: all started to converge in a really exciting way around 488 00:26:32,840 --> 00:26:35,520 Speaker 1: the same time. The future you're describing as one that 489 00:26:35,560 --> 00:26:38,639 Speaker 1: I expect we're going to see lots of other businesses 490 00:26:38,960 --> 00:26:42,840 Speaker 1: focus on. I hope to see more organizations take that 491 00:26:42,920 --> 00:26:47,320 Speaker 1: approach of how can we leverage the massive amounts of 492 00:26:47,359 --> 00:26:51,719 Speaker 1: information we collect in ways that have a real positive 493 00:26:52,080 --> 00:26:53,560 Speaker 1: outcome in the real world. 494 00:26:54,320 --> 00:26:55,520 Speaker 2: Absolutely, you're spot on. 495 00:26:55,720 --> 00:26:58,280 Speaker 3: I think there's a hopeful message here, which is that 496 00:26:58,720 --> 00:27:01,159 Speaker 3: we as humanity have been work working on trying to 497 00:27:01,200 --> 00:27:04,960 Speaker 3: find solutions to slow down or reverse climate change for decades, 498 00:27:05,000 --> 00:27:07,560 Speaker 3: and I think many of those I'm optimistic about, and 499 00:27:07,600 --> 00:27:11,320 Speaker 3: I think human innovation will find a way to reverse 500 00:27:11,320 --> 00:27:13,600 Speaker 3: climate change, but it's going to take decades. There has 501 00:27:13,680 --> 00:27:17,280 Speaker 3: been almost no innovation that has gone into adaptation to 502 00:27:17,280 --> 00:27:19,960 Speaker 3: climate change, and as a result, what that means is 503 00:27:19,960 --> 00:27:22,560 Speaker 3: that there's so much low hanging fruit. We have so 504 00:27:22,640 --> 00:27:28,440 Speaker 3: many technology tools that are perfectly suited to help cope 505 00:27:28,440 --> 00:27:32,760 Speaker 3: with the effects of climate change, sensors, drones, satellites, five G, 506 00:27:33,320 --> 00:27:38,159 Speaker 3: big data software tools, and the entrepreneurship wave of the 507 00:27:38,200 --> 00:27:40,960 Speaker 3: climate adaptation field is just beginning. Panel is one of 508 00:27:41,000 --> 00:27:43,480 Speaker 3: the first companies in this field, and we're hoping to 509 00:27:43,520 --> 00:27:46,720 Speaker 3: blaze a trail here and we're hoping other entrepreneurs will 510 00:27:46,760 --> 00:27:50,200 Speaker 3: see that technology really can make climate change less harmful 511 00:27:50,359 --> 00:27:52,879 Speaker 3: and that we can save lives we can save homes, 512 00:27:52,920 --> 00:27:56,440 Speaker 3: we can save trees, we can save animal life. Let's 513 00:27:56,520 --> 00:27:59,080 Speaker 3: put our heads together and think about ways that we 514 00:27:59,119 --> 00:28:02,160 Speaker 3: can innovate to lessen the harms of a climate change. 515 00:28:02,160 --> 00:28:04,600 Speaker 2: It's already here today. 516 00:28:05,840 --> 00:28:08,080 Speaker 1: I had a couple more questions to ask Sonia before 517 00:28:08,080 --> 00:28:16,159 Speaker 1: I could let her go. Well, next up, what is 518 00:28:16,240 --> 00:28:19,400 Speaker 1: the best piece of advice you have ever received? 519 00:28:19,720 --> 00:28:20,960 Speaker 2: A professor of mine? 520 00:28:20,960 --> 00:28:25,400 Speaker 3: In college senior year, I was taking a behavioral economics class, 521 00:28:26,000 --> 00:28:28,439 Speaker 3: and he was explaining that people do not do a 522 00:28:28,440 --> 00:28:33,320 Speaker 3: good job of intuitively understanding probabilities. So people often are 523 00:28:33,359 --> 00:28:36,800 Speaker 3: afraid of losing something, even if it's a small amount 524 00:28:36,880 --> 00:28:38,800 Speaker 3: or a low risk of losing. People are grasped what 525 00:28:38,840 --> 00:28:41,760 Speaker 3: they have. They don't want to lose something. They buy 526 00:28:41,800 --> 00:28:43,480 Speaker 3: lottery tickets because they think they have a chance of 527 00:28:43,520 --> 00:28:47,959 Speaker 3: winning something big. So what he encouraged us is take small, 528 00:28:48,040 --> 00:28:51,959 Speaker 3: positive expected value risks in life. And I think that 529 00:28:52,040 --> 00:28:52,800 Speaker 3: was really good advice. 530 00:28:53,200 --> 00:28:55,200 Speaker 1: I think so too. Yeah, I like that a lot. 531 00:28:55,320 --> 00:28:58,680 Speaker 1: Generally speaking, humans are really bad at assessing risk unless 532 00:28:58,680 --> 00:29:02,080 Speaker 1: we put a lot of effort toward it, I find. Yeah, 533 00:29:02,120 --> 00:29:07,600 Speaker 1: what does the term restless ones mean to you? 534 00:29:07,600 --> 00:29:09,400 Speaker 3: You know? I love the name of this podcast, The 535 00:29:09,440 --> 00:29:14,200 Speaker 3: Restless Ones, and it made me think of not being 536 00:29:14,400 --> 00:29:17,760 Speaker 3: satisfied with accepting the world for how it is, but 537 00:29:17,920 --> 00:29:20,320 Speaker 3: saying I don't like how the world is today. I 538 00:29:20,320 --> 00:29:22,080 Speaker 3: think the world should be different, and I'm going to 539 00:29:22,080 --> 00:29:22,920 Speaker 3: go do something about it. 540 00:29:23,360 --> 00:29:32,200 Speaker 1: Excellent. I couldn't agree more. Thanks again to Sonia Castner 541 00:29:32,280 --> 00:29:35,560 Speaker 1: of pano Ai for talking with us on The Restless Ones. 542 00:29:35,960 --> 00:29:40,520 Speaker 1: Sonia's story reminded me that innovation isn't restricted to inventing 543 00:29:40,560 --> 00:29:43,920 Speaker 1: the next light bulb. Innovation can involve finding a new 544 00:29:43,960 --> 00:29:46,640 Speaker 1: way to use that light bulb to tackle tough problems. 545 00:29:47,040 --> 00:29:51,200 Speaker 1: Taking advantage of advancements in technologies ranging from IoT deployments, 546 00:29:51,520 --> 00:29:55,800 Speaker 1: image capture and artificial intelligence, big data analysis, and yes, 547 00:29:56,000 --> 00:30:00,880 Speaker 1: advanced wireless technologies makes pano Ai a true pioneer. Moreover, 548 00:30:01,200 --> 00:30:03,680 Speaker 1: I think Sonya's story reminds us that in the face 549 00:30:03,760 --> 00:30:07,760 Speaker 1: of huge challenges, there is opportunity, and if we can 550 00:30:07,800 --> 00:30:11,640 Speaker 1: look at climate change as an opportunity to dedicate engineering 551 00:30:11,720 --> 00:30:15,280 Speaker 1: and innovation to protecting ourselves and future generations and with 552 00:30:15,360 --> 00:30:26,040 Speaker 1: any luck, eventually reverse some of those changes, that's truly inspiring. Again, 553 00:30:26,160 --> 00:30:29,360 Speaker 1: thank you Sonia Kastner, and thank you listeners for checking 554 00:30:29,360 --> 00:30:31,800 Speaker 1: out the Restless Ones, be sure to look through our 555 00:30:31,840 --> 00:30:34,800 Speaker 1: back catalog of conversations with leaders at the intersection of 556 00:30:34,840 --> 00:30:37,840 Speaker 1: business and tech, and come back for more great discussions 557 00:30:38,040 --> 00:30:41,480 Speaker 1: with the future Restless Ones. I'm Jonathan Strickland.