1 00:00:02,040 --> 00:00:05,120 Speaker 1: On last week's episode, we discussed new opportunities in the 2 00:00:05,160 --> 00:00:09,119 Speaker 1: future of manufacturing in a more connected future. We heard 3 00:00:09,160 --> 00:00:13,040 Speaker 1: from Irene Patrick, Senior director of Industrial Innovation in the 4 00:00:13,080 --> 00:00:17,160 Speaker 1: Internet of Things Group at Intel, Pat McCusker, CEO of 5 00:00:17,239 --> 00:00:20,639 Speaker 1: fast Radius, a company which is using three D printing 6 00:00:20,800 --> 00:00:24,920 Speaker 1: on an industrial scale to reimagine the supply chain, and 7 00:00:25,079 --> 00:00:28,920 Speaker 1: Sean Peterson, founder of strong Arm Technologies, a company using 8 00:00:29,000 --> 00:00:39,159 Speaker 1: data to help improve workers safety. One of the world's 9 00:00:39,159 --> 00:00:43,000 Speaker 1: oldest and most established industries is still changing and innovating 10 00:00:43,200 --> 00:00:46,640 Speaker 1: day after day. You know ever more connective future. The 11 00:00:46,640 --> 00:00:50,320 Speaker 1: way we cultivate our crops and livestock is modernizing and 12 00:00:50,440 --> 00:00:53,960 Speaker 1: becoming more and more precise. The next generation of wireless 13 00:00:53,960 --> 00:00:57,800 Speaker 1: innovation with future five G networks will create new opportunities 14 00:00:57,840 --> 00:01:00,920 Speaker 1: for more efficient farming and irrigation techniques with new ways 15 00:01:00,960 --> 00:01:04,160 Speaker 1: to gather and process data straight from the field. Thanks 16 00:01:04,200 --> 00:01:07,800 Speaker 1: to support from Teamobile, for Business Today will explore how 17 00:01:07,840 --> 00:01:11,360 Speaker 1: advancements in five G connectivity will enable innovations in the 18 00:01:11,440 --> 00:01:25,560 Speaker 1: agriculture industry that could shape the future of farming. Unlocking 19 00:01:26,240 --> 00:01:30,200 Speaker 1: the ability of farmers to make real time decisions is 20 00:01:30,560 --> 00:01:33,760 Speaker 1: really what makes the difference it's great that you could 21 00:01:33,760 --> 00:01:35,959 Speaker 1: see what you should have done a week later, but 22 00:01:36,160 --> 00:01:39,080 Speaker 1: it's really truly magical when you can make that decision 23 00:01:39,440 --> 00:01:42,039 Speaker 1: while there's an opportunity to still change whatever you need 24 00:01:42,080 --> 00:01:47,440 Speaker 1: to change to improve your practices or your decisions. That's 25 00:01:47,520 --> 00:01:52,120 Speaker 1: Julian Sanchez. He's Director of Precision Agriculture for the Intelligent 26 00:01:52,240 --> 00:01:54,720 Speaker 1: Solutions Group at John Deer, which is one of the 27 00:01:54,800 --> 00:01:59,919 Speaker 1: largest manufacturers of agricultural equipment in the world. Essentially, julian 28 00:02:00,080 --> 00:02:03,040 Speaker 1: job is to leverage new technology, including five G and 29 00:02:03,080 --> 00:02:07,320 Speaker 1: AI to make farming faster and more efficient. But what 30 00:02:07,440 --> 00:02:11,560 Speaker 1: exactly is precision agriculture. If you had a farmer that 31 00:02:11,720 --> 00:02:16,000 Speaker 1: decided I'm going to just grow one plant, and I'm 32 00:02:16,040 --> 00:02:20,520 Speaker 1: going to dedicate all of my resources at maximizing the 33 00:02:20,680 --> 00:02:24,520 Speaker 1: yield the output of that one plant, and guarantee you 34 00:02:24,600 --> 00:02:27,160 Speaker 1: that plant would do quite well. So just imagine this 35 00:02:27,200 --> 00:02:30,959 Speaker 1: farmer putting that plant in the best possible conditions, giving 36 00:02:31,000 --> 00:02:34,480 Speaker 1: it just the right nutrients and the right fertilizers, and 37 00:02:34,520 --> 00:02:36,720 Speaker 1: putting the seed in the ground exactly on the day 38 00:02:36,760 --> 00:02:39,280 Speaker 1: when it would maximize the amount of sun that plant 39 00:02:39,280 --> 00:02:43,360 Speaker 1: would get throughout the growing season. The objective of precision 40 00:02:43,360 --> 00:02:48,640 Speaker 1: agriculture is to try to enable farmers to farm hundreds 41 00:02:48,760 --> 00:02:51,840 Speaker 1: or thousands or tens of thousands of acres with that 42 00:02:51,880 --> 00:02:57,360 Speaker 1: same mentality. Precision agriculture is providing farmers with the technology 43 00:02:57,520 --> 00:03:00,160 Speaker 1: and the tools to allow them to do microman men 44 00:03:00,200 --> 00:03:03,320 Speaker 1: at scale where they're able to make the best possible 45 00:03:03,360 --> 00:03:07,520 Speaker 1: decisions that maximize the output of each individual plant. In 46 00:03:07,520 --> 00:03:10,000 Speaker 1: other words, the goal of precision of farming is to 47 00:03:10,040 --> 00:03:14,560 Speaker 1: allow farmers to give crops individual attention at scale, and 48 00:03:14,600 --> 00:03:17,880 Speaker 1: this can help better manage resources like water and minimize 49 00:03:17,919 --> 00:03:21,680 Speaker 1: the use of pesticides and fertilizers, and of course create 50 00:03:21,720 --> 00:03:26,040 Speaker 1: more value. You have a business that sometimes has pressures 51 00:03:26,080 --> 00:03:30,320 Speaker 1: of external pressures, whether it be whether whether it be 52 00:03:30,360 --> 00:03:33,760 Speaker 1: availability of labor, whether it be trade, and so a 53 00:03:33,760 --> 00:03:37,760 Speaker 1: farmer is always looking to make improvements to maximize their 54 00:03:37,760 --> 00:03:42,000 Speaker 1: margins and improve their productivity. When we look at technologies 55 00:03:42,080 --> 00:03:46,160 Speaker 1: like five G coming down the pipeline, the increased bandwidth 56 00:03:46,200 --> 00:03:50,280 Speaker 1: and the lower latencies have tremendous opportunities to unlock value 57 00:03:50,280 --> 00:03:53,040 Speaker 1: and the types of solutions that could be then rolled 58 00:03:53,080 --> 00:03:55,080 Speaker 1: out into market and put in the hands of farmers. 59 00:03:56,960 --> 00:04:00,320 Speaker 1: The value that Julian mentions is all about better understanding 60 00:04:00,400 --> 00:04:03,600 Speaker 1: how to optimize the food we grow. Modern day farming 61 00:04:03,680 --> 00:04:07,720 Speaker 1: already involves the use of computers, algorithms, and technology just 62 00:04:07,760 --> 00:04:10,600 Speaker 1: as much as it does tractors and soil. But as 63 00:04:10,640 --> 00:04:14,680 Speaker 1: wireless networks become faster and the infrastructure reaches further as 64 00:04:14,680 --> 00:04:17,200 Speaker 1: with the promise of future five gen networks, we can 65 00:04:17,240 --> 00:04:21,200 Speaker 1: expect the agricultural industry to become even more precise and efficient. 66 00:04:21,839 --> 00:04:25,039 Speaker 1: In this episode, we'll dive into the world of precision farming, 67 00:04:25,560 --> 00:04:27,599 Speaker 1: take a look at how censors and robotics can be 68 00:04:27,680 --> 00:04:31,320 Speaker 1: used in the field, and discussed how greater connectivity could 69 00:04:31,320 --> 00:04:35,960 Speaker 1: transform one of the world's oldest industries. I'm as Lachen 70 00:04:36,680 --> 00:04:42,679 Speaker 1: this is This Time Tomorrow. So cart before this series, 71 00:04:42,720 --> 00:04:45,679 Speaker 1: when I thought about farming, technology wasn't the first place 72 00:04:45,720 --> 00:04:48,279 Speaker 1: my mind would go, Oh really, because I think tractors 73 00:04:48,320 --> 00:04:52,599 Speaker 1: are the original Tesla's actually all right, And in the 74 00:04:52,680 --> 00:04:55,240 Speaker 1: very first episode of This Time Tomorrow, we spoke to 75 00:04:55,320 --> 00:04:58,320 Speaker 1: Durga Malady of Qualcom and he said that agriculture is 76 00:04:58,320 --> 00:05:01,839 Speaker 1: an area that he's most excited out five G transforming. Yeah, 77 00:05:01,839 --> 00:05:03,600 Speaker 1: I mean, if you think about it, there's just so 78 00:05:03,680 --> 00:05:06,839 Speaker 1: much data to collect from farms. You know, first of all, 79 00:05:07,120 --> 00:05:11,080 Speaker 1: they often span huge geographical areas, and there are all 80 00:05:11,200 --> 00:05:14,440 Speaker 1: kinds of equipment needed to tend to crops. You tractors, 81 00:05:14,480 --> 00:05:17,599 Speaker 1: combine harvesters, irrigation systems. I know a lot about this, 82 00:05:19,080 --> 00:05:21,880 Speaker 1: and the promise of future five G networks is all 83 00:05:21,880 --> 00:05:25,440 Speaker 1: about gathering data from more sources, combining it, analyzing it, 84 00:05:25,640 --> 00:05:28,760 Speaker 1: and making recommendations in real time. We've looked at everything 85 00:05:28,800 --> 00:05:33,159 Speaker 1: from fashion to auto to manufacturing, and there's absolutely no 86 00:05:33,200 --> 00:05:36,159 Speaker 1: reason that agriculture should be any different. Right. We've covered 87 00:05:36,160 --> 00:05:38,240 Speaker 1: all those topics on this time tomorrow and this is 88 00:05:38,680 --> 00:05:41,800 Speaker 1: sadly our last episode of this season. One of the 89 00:05:41,800 --> 00:05:45,640 Speaker 1: big challenges in five G, though, is access. Farmlands are 90 00:05:45,640 --> 00:05:48,560 Speaker 1: often very large and sparsely populated, which has made it 91 00:05:48,680 --> 00:05:51,680 Speaker 1: challenging to network them. Some people believe five SHE could 92 00:05:51,680 --> 00:05:54,800 Speaker 1: actually play a role though, in solving that. Late last year, 93 00:05:54,839 --> 00:05:57,720 Speaker 1: the SEC announced a nine billion dollar fund for five 94 00:05:57,800 --> 00:06:01,279 Speaker 1: D development in rural areas that Ammon said, we must 95 00:06:01,360 --> 00:06:05,200 Speaker 1: ensure that FIG narrows rather than widens, the digital divide, 96 00:06:05,560 --> 00:06:08,480 Speaker 1: and that rural Americans received the benefits that come from 97 00:06:08,480 --> 00:06:11,480 Speaker 1: what is innovation, and actually billion dollars of that fund 98 00:06:11,800 --> 00:06:15,400 Speaker 1: has been a marks specifically for precision agriculture. I'm a 99 00:06:15,400 --> 00:06:18,000 Speaker 1: New Yorker, so I've never actually seen a real farm before, 100 00:06:19,560 --> 00:06:22,400 Speaker 1: but no, in all seriousness, precision farming was a new 101 00:06:22,440 --> 00:06:25,239 Speaker 1: concept for me, but it actually reminds me of many 102 00:06:25,400 --> 00:06:29,040 Speaker 1: other topics we've discussed on this show. Data and higher 103 00:06:29,080 --> 00:06:34,680 Speaker 1: bandwidth could allow for unprecedented specificity in manufacturing in the 104 00:06:34,680 --> 00:06:37,880 Speaker 1: automobile industry, and in this episode, I spoke with a 105 00:06:37,920 --> 00:06:40,640 Speaker 1: guy named Kirk Stevie who is a farmer in western 106 00:06:40,720 --> 00:06:44,760 Speaker 1: Minnesota and also an engineer by he is and Kirk 107 00:06:44,839 --> 00:06:48,159 Speaker 1: is working with Series Imaging, a company that uses aerial 108 00:06:48,200 --> 00:06:51,640 Speaker 1: photos and videos to provide farmers with information like whether 109 00:06:51,760 --> 00:06:53,840 Speaker 1: or not a plant has been watered enough and if 110 00:06:53,839 --> 00:06:57,000 Speaker 1: it's at risk for developing a disease. You mentioned tractors 111 00:06:57,000 --> 00:07:00,960 Speaker 1: and Tesla's since tractors, the advancements and every cultural machinery 112 00:07:01,040 --> 00:07:03,719 Speaker 1: are pretty amazing. And for this episode, I spoke with 113 00:07:03,720 --> 00:07:07,080 Speaker 1: George Cantor, who is a robotics professor at Connigie Mellon 114 00:07:07,480 --> 00:07:09,720 Speaker 1: and he and his team are building robots that work 115 00:07:09,760 --> 00:07:14,080 Speaker 1: autonomously or semi autonomously on farms. Before we get to George, though, 116 00:07:14,280 --> 00:07:16,680 Speaker 1: I want to go back to Julian Sanchez of John Deer. 117 00:07:19,480 --> 00:07:23,880 Speaker 1: There's something really really exciting about agriculture. You know, ultimately 118 00:07:24,280 --> 00:07:26,400 Speaker 1: it's a space that at a at a macro level, 119 00:07:26,600 --> 00:07:30,320 Speaker 1: it matters, right, So all the crabs being grown out 120 00:07:30,360 --> 00:07:33,280 Speaker 1: there ultimately resolved in the food we eat. In some 121 00:07:33,360 --> 00:07:35,880 Speaker 1: cases they result in many of the products that are 122 00:07:35,960 --> 00:07:39,000 Speaker 1: key to our society. Ensuring that we have a productive 123 00:07:39,040 --> 00:07:42,080 Speaker 1: and healthy food supply is essential, but so is protecting 124 00:07:42,080 --> 00:07:46,160 Speaker 1: our environment. Many believe that incorporating technology can help the 125 00:07:46,200 --> 00:07:50,560 Speaker 1: agricultural industry reduce its environmental footprint by being more efficient, 126 00:07:51,080 --> 00:07:54,520 Speaker 1: and that's where collecting and analyzing data can play such 127 00:07:54,560 --> 00:07:57,800 Speaker 1: an important role. One of the things that John Deere 128 00:07:58,040 --> 00:08:01,400 Speaker 1: has done in terms of rolling out tools and technologies 129 00:08:01,480 --> 00:08:05,760 Speaker 1: that help farmers adopt and embrace this idea of precision 130 00:08:05,800 --> 00:08:11,040 Speaker 1: agriculture is everything from putting the right sensors in the 131 00:08:11,120 --> 00:08:14,800 Speaker 1: machines so that the machines can sense where each seat 132 00:08:14,880 --> 00:08:18,160 Speaker 1: is being placed, how much fertilizer, how many nutrients are 133 00:08:18,200 --> 00:08:21,320 Speaker 1: being applied to each plant, and then at the end 134 00:08:21,320 --> 00:08:25,280 Speaker 1: of the season also sends what the yield of your 135 00:08:25,320 --> 00:08:28,040 Speaker 1: crops were, and so putting all of those sensors in 136 00:08:28,080 --> 00:08:30,560 Speaker 1: the machines, but then giving the farmers the ability to 137 00:08:31,000 --> 00:08:34,120 Speaker 1: transmit all of that data to a platform where they 138 00:08:34,120 --> 00:08:37,080 Speaker 1: can easily view it, they can easily analyze it, they 139 00:08:37,080 --> 00:08:40,319 Speaker 1: can share that data with their trusted advisors, and then 140 00:08:40,440 --> 00:08:44,199 Speaker 1: make better decisions for the next growing season. We are 141 00:08:44,200 --> 00:08:47,840 Speaker 1: in the age of big data, and agriculture is no exception. 142 00:08:48,360 --> 00:08:52,240 Speaker 1: But in order to transmit that data, farmers need connectivity, 143 00:08:52,760 --> 00:08:56,480 Speaker 1: which in rural areas is often lacking or incomplete. So 144 00:08:56,640 --> 00:09:00,720 Speaker 1: today farmers will in some cases be on the phone 145 00:09:00,880 --> 00:09:03,719 Speaker 1: with operators that are in the vehicles and saying, hey, 146 00:09:03,760 --> 00:09:06,320 Speaker 1: what are you seeing right now? Is the vehicle performing 147 00:09:06,360 --> 00:09:08,200 Speaker 1: the way it's supposed to? Are we seeing what we 148 00:09:08,280 --> 00:09:11,360 Speaker 1: expected out of the crops? And essentially in real time 149 00:09:11,440 --> 00:09:15,640 Speaker 1: sending instructions to operators about what to do. Today, farmers 150 00:09:15,679 --> 00:09:19,240 Speaker 1: share important data on the phone. Tomorrow, the promise of 151 00:09:19,280 --> 00:09:22,080 Speaker 1: future five gene networks is to collect and share that 152 00:09:22,200 --> 00:09:26,360 Speaker 1: data in real time, automatically and even directly to machines. 153 00:09:28,480 --> 00:09:30,920 Speaker 1: If we thought about a world in which you had 154 00:09:31,320 --> 00:09:35,080 Speaker 1: in rural America full coverage with no death spots that unlocked, 155 00:09:35,200 --> 00:09:39,320 Speaker 1: you know less than one second real time connectivity that 156 00:09:39,440 --> 00:09:43,560 Speaker 1: would have a tremendous impact on farmers. Farming is a 157 00:09:43,600 --> 00:09:48,120 Speaker 1: real time business where having the most recent data and 158 00:09:48,160 --> 00:09:50,920 Speaker 1: when I say recent, yes within the last five seconds 159 00:09:50,960 --> 00:09:53,800 Speaker 1: of what is happening in a vehicle and what kind 160 00:09:53,840 --> 00:09:56,640 Speaker 1: of performance you're getting out of the vehicle and doing 161 00:09:56,679 --> 00:10:00,559 Speaker 1: the job. Having that information at your fingertips in real 162 00:10:00,640 --> 00:10:04,440 Speaker 1: time allows you to make the necessary adjustments that really 163 00:10:04,520 --> 00:10:08,480 Speaker 1: ultimately impact your bottom line. And with AI, some of 164 00:10:08,520 --> 00:10:11,079 Speaker 1: these key decisions that need to take place in real 165 00:10:11,120 --> 00:10:15,239 Speaker 1: time can be made without human input. One of my 166 00:10:15,240 --> 00:10:20,040 Speaker 1: my favorite examples is a harvesting combine. Is the vehicle 167 00:10:20,120 --> 00:10:22,200 Speaker 1: you used to go through the field at the end 168 00:10:22,200 --> 00:10:25,320 Speaker 1: of the season and harvest the crops. This is essentially 169 00:10:25,320 --> 00:10:29,520 Speaker 1: a factory on wheels where conditions are changing literally by 170 00:10:29,520 --> 00:10:34,679 Speaker 1: the second. It's a tremendously difficult job. And our harvesting 171 00:10:34,720 --> 00:10:40,280 Speaker 1: combines are equipped with an artificial intelligence system that continuously 172 00:10:40,400 --> 00:10:44,200 Speaker 1: takes images of the crop that is going through the 173 00:10:44,200 --> 00:10:47,280 Speaker 1: machine as the machine tries to separate essentially the grain 174 00:10:47,440 --> 00:10:49,120 Speaker 1: from the rest of the plant and you know, just 175 00:10:49,160 --> 00:10:52,800 Speaker 1: hold onto the grain. It's using a convolutional neural network 176 00:10:53,000 --> 00:10:56,800 Speaker 1: to make decisions as to whether the grain samples are 177 00:10:56,840 --> 00:11:00,319 Speaker 1: the right quality, and it's automatically making a just ments 178 00:11:00,360 --> 00:11:03,440 Speaker 1: to this factory on wheels so that it can maintain 179 00:11:03,480 --> 00:11:08,560 Speaker 1: its optimal performance. We've seen already on this time tomorrow 180 00:11:08,600 --> 00:11:11,720 Speaker 1: how AI and five G can come together to deliver 181 00:11:11,800 --> 00:11:14,720 Speaker 1: on the promise of the Internet of things, connected devices 182 00:11:14,760 --> 00:11:17,240 Speaker 1: talking to one another as they monitor their environment to 183 00:11:17,280 --> 00:11:22,120 Speaker 1: optimize decisions. This could be particularly useful for farmers because 184 00:11:22,200 --> 00:11:27,240 Speaker 1: they're constantly dealing with highly variable processes and environments. Farming 185 00:11:27,440 --> 00:11:33,360 Speaker 1: is a domain where every inch of every field across 186 00:11:33,440 --> 00:11:37,600 Speaker 1: the world is different from the inch of field that's 187 00:11:37,720 --> 00:11:41,079 Speaker 1: right next to it. It might be the soil that's different, 188 00:11:41,400 --> 00:11:44,840 Speaker 1: it might be the topography, the micro topography of that field, 189 00:11:44,880 --> 00:11:47,480 Speaker 1: where the inch next to the other one gets a 190 00:11:47,480 --> 00:11:50,160 Speaker 1: little bit less water, and so all of a sudden, 191 00:11:50,160 --> 00:11:53,080 Speaker 1: when you start looking at the opportunity and agriculture to 192 00:11:53,160 --> 00:11:56,040 Speaker 1: try to get your arms around all of that variability 193 00:11:56,400 --> 00:12:00,480 Speaker 1: and help farmers maximize how they do their job. What's 194 00:12:00,520 --> 00:12:03,199 Speaker 1: exciting and the reason I wake up every morning is 195 00:12:03,280 --> 00:12:07,520 Speaker 1: that I feel like, despite the tremendous progress that has 196 00:12:07,559 --> 00:12:11,000 Speaker 1: been made in the industry, we are really really just 197 00:12:11,160 --> 00:12:13,920 Speaker 1: at the beginning of the journey and unlocking all of 198 00:12:13,960 --> 00:12:17,319 Speaker 1: that value. And I was curious about how Julian is 199 00:12:17,320 --> 00:12:20,520 Speaker 1: planning to leverage five G to help execute his vision 200 00:12:20,640 --> 00:12:24,200 Speaker 1: of the future of filming. Five G is an infrastructure 201 00:12:24,559 --> 00:12:28,800 Speaker 1: if it can help reduce or hopefully eliminate some of 202 00:12:28,840 --> 00:12:32,840 Speaker 1: the prevalent dark spots and connectivity. All of a sudden, 203 00:12:32,880 --> 00:12:37,000 Speaker 1: you can really imagine having solutions that are truly trying 204 00:12:37,040 --> 00:12:43,359 Speaker 1: to execute sort of coordinated logistics activities, coordinated semi autonomous 205 00:12:43,400 --> 00:12:48,559 Speaker 1: and fully autonomous operations in the fields by leveraging low latency, 206 00:12:48,679 --> 00:12:52,760 Speaker 1: high bandwidth five G solutions. While that's possible right now, 207 00:12:52,960 --> 00:12:56,240 Speaker 1: they exist in a in a very localized manner because 208 00:12:56,320 --> 00:13:00,200 Speaker 1: you cannot be relying on full coverage and connectivity to 209 00:13:00,240 --> 00:13:06,360 Speaker 1: be able to transmit data through a cloud. Garrot does 210 00:13:06,400 --> 00:13:09,400 Speaker 1: a factory on wheels that can readjust itself based on 211 00:13:09,440 --> 00:13:13,720 Speaker 1: images that it's taking in real time. Impress you. Steven 212 00:13:13,720 --> 00:13:16,480 Speaker 1: Spielberg made a movie, one of his first movies, I think, 213 00:13:17,120 --> 00:13:20,560 Speaker 1: was about a truck that went wrong, A bad a 214 00:13:20,600 --> 00:13:24,000 Speaker 1: bad truck. Hopefully the trucks will go right exactly. I 215 00:13:24,000 --> 00:13:25,920 Speaker 1: mean that I hope it's not that situation, but that's 216 00:13:25,920 --> 00:13:28,240 Speaker 1: sort of what this reminds me of. But I also know, 217 00:13:28,880 --> 00:13:31,480 Speaker 1: you know, much like having a warehouse in the cloud, 218 00:13:32,160 --> 00:13:35,360 Speaker 1: that these sort of things are the future in their 219 00:13:35,520 --> 00:13:39,800 Speaker 1: given industries. Absolutely, and the future is closer than we think. 220 00:13:39,920 --> 00:13:42,000 Speaker 1: I mean, people often ask how close are we to 221 00:13:42,040 --> 00:13:46,280 Speaker 1: the robotics revolution. Many ways, it's already arrived. Robots are 222 00:13:46,320 --> 00:13:49,079 Speaker 1: machines that make decisions for themselves, and that's exactly what 223 00:13:49,200 --> 00:13:52,280 Speaker 1: Julian was describing with that combine harvest. Yeah, and we 224 00:13:52,320 --> 00:13:54,679 Speaker 1: know from some of our previous episodes that future five 225 00:13:54,720 --> 00:13:58,200 Speaker 1: G networks could really support the expansion of robotics because 226 00:13:58,200 --> 00:14:00,200 Speaker 1: these moving robots will be able to connect to a 227 00:14:00,240 --> 00:14:03,880 Speaker 1: network that allows for really fast processing. Right. And despite 228 00:14:03,880 --> 00:14:07,040 Speaker 1: all the machinery built by John Deer and others, farming 229 00:14:07,120 --> 00:14:10,040 Speaker 1: remains one of the most labor intensive industries in the world. 230 00:14:10,320 --> 00:14:13,240 Speaker 1: There's so many tasks like picking fruit, to name just one, 231 00:14:13,640 --> 00:14:17,160 Speaker 1: that rely on people. And according to a California Farm 232 00:14:17,200 --> 00:14:21,640 Speaker 1: Bureau survey, I've also done my homework more than farmers 233 00:14:21,720 --> 00:14:24,760 Speaker 1: in the past five years have been unable to obtain 234 00:14:25,000 --> 00:14:27,160 Speaker 1: all the workers they need for the production of their 235 00:14:27,160 --> 00:14:30,720 Speaker 1: main crop. At the same time, growing populations and changing 236 00:14:30,720 --> 00:14:33,760 Speaker 1: climate is putting new pressure on food production, and these 237 00:14:33,760 --> 00:14:36,200 Speaker 1: are issues that George Cantor spends a lot of time 238 00:14:36,320 --> 00:14:43,480 Speaker 1: thinking about. First of all, everybody eats, so this touches 239 00:14:43,560 --> 00:14:45,920 Speaker 1: everyone on the planet. When you look at the way 240 00:14:45,960 --> 00:14:49,320 Speaker 1: food is produced now, we have several very serious challenges. 241 00:14:49,640 --> 00:14:53,000 Speaker 1: The rate at which we are increasing yields is not 242 00:14:53,360 --> 00:14:55,400 Speaker 1: high enough to meet the rate at which we are 243 00:14:55,480 --> 00:15:00,160 Speaker 1: increasing people. Climate change is changing the way agriculture real 244 00:15:00,160 --> 00:15:02,960 Speaker 1: production is done. It's making some regions more productive, it's 245 00:15:03,000 --> 00:15:06,080 Speaker 1: making other regions less productive. We're starting to grasp the 246 00:15:06,120 --> 00:15:11,240 Speaker 1: implications of our sort of highly chemical driven agricultural system. 247 00:15:11,240 --> 00:15:14,520 Speaker 1: It's a very big, complicated problem, and I think technology 248 00:15:14,560 --> 00:15:16,760 Speaker 1: can be part of the solution of that problem. According 249 00:15:16,800 --> 00:15:19,800 Speaker 1: to George, much of the solution relies on giving farmers 250 00:15:19,920 --> 00:15:24,920 Speaker 1: better information that people who grow plants for a living, 251 00:15:25,400 --> 00:15:28,920 Speaker 1: the more they know about the status of those plants, 252 00:15:28,960 --> 00:15:31,400 Speaker 1: the better they can do at making decisions that help 253 00:15:31,440 --> 00:15:35,120 Speaker 1: grow the plants. So, for example, when do apply fertilizer, 254 00:15:35,160 --> 00:15:39,120 Speaker 1: when to apply pesticide in a way that maximizes your 255 00:15:39,120 --> 00:15:42,040 Speaker 1: crop growth but at the same time minimizes the environment 256 00:15:42,240 --> 00:15:45,480 Speaker 1: impact that that decision might have. George is a robotics 257 00:15:45,520 --> 00:15:50,160 Speaker 1: researcher at Carnegie Mellon. Alongside better information, he's interested in 258 00:15:50,160 --> 00:15:53,240 Speaker 1: giving farmers better tools to leverage it. He and his 259 00:15:53,280 --> 00:15:56,640 Speaker 1: team are building robots designed to do jobs traditionally handled 260 00:15:56,640 --> 00:16:00,720 Speaker 1: by people, and despite concerns about displacing a labor, George 261 00:16:00,760 --> 00:16:04,920 Speaker 1: believes this automation is necessary. Some of the industries United States, 262 00:16:04,960 --> 00:16:10,080 Speaker 1: like apples and grapes, rely heavily on manual labor. It's 263 00:16:10,080 --> 00:16:14,520 Speaker 1: a terrible job, it's tedious, the environmental conditions are really rough, 264 00:16:14,680 --> 00:16:18,160 Speaker 1: and they're just having a very difficult time finding people 265 00:16:18,200 --> 00:16:21,360 Speaker 1: to do this work. And so they're very interested in 266 00:16:21,480 --> 00:16:25,360 Speaker 1: automating some of the things that people do currently. They 267 00:16:25,400 --> 00:16:29,640 Speaker 1: are difficult to automate because they require really intricate manipulation 268 00:16:29,680 --> 00:16:31,440 Speaker 1: with the environment. You know, if you if you pick 269 00:16:31,480 --> 00:16:33,280 Speaker 1: an apple off a tree, you have to grab it 270 00:16:33,320 --> 00:16:35,240 Speaker 1: just so, and you have to pull it off just so. 271 00:16:35,800 --> 00:16:38,040 Speaker 1: Sometimes you have to reach around branches and push things 272 00:16:38,040 --> 00:16:41,120 Speaker 1: out of the way, and robotics isn'to that state yet, 273 00:16:41,160 --> 00:16:45,000 Speaker 1: but that's that's where we're going. We're moving into manipulation 274 00:16:45,040 --> 00:16:49,360 Speaker 1: and touching plants, tackling problems like pruning and harvesting and 275 00:16:49,400 --> 00:16:51,920 Speaker 1: weeding and things like that. That's been our holy grail 276 00:16:52,000 --> 00:16:54,360 Speaker 1: for a while. In order for robots to be able 277 00:16:54,400 --> 00:16:57,960 Speaker 1: to accurately and effectively manipulate their environment, they need to 278 00:16:58,000 --> 00:17:00,560 Speaker 1: be able to sense it, which is something George has 279 00:17:00,560 --> 00:17:03,400 Speaker 1: been working on for more than a decade. I'm interested 280 00:17:03,440 --> 00:17:08,000 Speaker 1: in sensing the plants and inferring what their health is 281 00:17:08,080 --> 00:17:10,760 Speaker 1: from whatever it is we can sense. We can never 282 00:17:10,880 --> 00:17:13,360 Speaker 1: sense directly the things we want to sense, the things 283 00:17:13,440 --> 00:17:16,000 Speaker 1: we really want to sense, or things like what's going 284 00:17:16,040 --> 00:17:18,680 Speaker 1: on inside the leave for how is the water status 285 00:17:18,680 --> 00:17:21,120 Speaker 1: in the stem? We can't sense those things directly. This 286 00:17:21,200 --> 00:17:25,600 Speaker 1: process of taking things like camera images or local environmental 287 00:17:25,640 --> 00:17:28,040 Speaker 1: readings or whatever it is we can measure and using 288 00:17:28,080 --> 00:17:30,320 Speaker 1: that to infer what's going on inside the plant is 289 00:17:30,400 --> 00:17:33,080 Speaker 1: I think really interesting. And we've been spending the last 290 00:17:33,080 --> 00:17:36,600 Speaker 1: fifteen years on the sensing side of things. Sensing and modeling. 291 00:17:36,640 --> 00:17:38,840 Speaker 1: You take measurements and then you build up a model 292 00:17:38,880 --> 00:17:41,280 Speaker 1: of what you're looking at and you reason about what's there. 293 00:17:41,760 --> 00:17:44,200 Speaker 1: We've gotten really good at it, and so now it's 294 00:17:44,240 --> 00:17:47,400 Speaker 1: time to build upon that and move into the actually 295 00:17:47,440 --> 00:17:49,679 Speaker 1: reaching out and making the cut side of things. And 296 00:17:49,720 --> 00:17:52,720 Speaker 1: that's why you know, we're moving into the manipulation in 297 00:17:52,720 --> 00:17:56,920 Speaker 1: the agriculture space for sure. In George's vision, sensing and 298 00:17:57,000 --> 00:18:01,199 Speaker 1: manipulation powered by data connectivity and pros saying come together 299 00:18:01,320 --> 00:18:04,360 Speaker 1: to enable the vision for precision farming at scale and 300 00:18:04,520 --> 00:18:10,200 Speaker 1: improve outcomes for farmers. Some people consider precision farming as 301 00:18:10,280 --> 00:18:14,320 Speaker 1: making decisions at the scale of blocks that are tens 302 00:18:14,320 --> 00:18:17,680 Speaker 1: of acres. Some people consider precision farming to be making 303 00:18:17,680 --> 00:18:21,679 Speaker 1: decisions at the scale of individual plants. But basically it 304 00:18:21,720 --> 00:18:27,639 Speaker 1: means tailoring your treatment to the specific environment and plant 305 00:18:27,720 --> 00:18:31,320 Speaker 1: status at wherever it is you're applying that treatment. The 306 00:18:31,320 --> 00:18:33,679 Speaker 1: old way, the non precision way to farm, is just 307 00:18:33,720 --> 00:18:36,440 Speaker 1: to plant the same thing everywhere in the field and 308 00:18:36,840 --> 00:18:38,639 Speaker 1: treat it the same way throughout the course of the 309 00:18:38,680 --> 00:18:41,720 Speaker 1: growing season. But if you were to instead, you know, 310 00:18:41,800 --> 00:18:43,960 Speaker 1: take what you know about that field and break it 311 00:18:44,040 --> 00:18:47,720 Speaker 1: up into smaller areas where you have different microclimates and 312 00:18:47,760 --> 00:18:50,480 Speaker 1: maybe there's a different slope or the the sun is 313 00:18:50,640 --> 00:18:54,359 Speaker 1: coming in from a different direction, and then adapt what 314 00:18:54,600 --> 00:18:58,040 Speaker 1: you plant and how you treat it after you plant 315 00:18:58,080 --> 00:19:01,359 Speaker 1: it based on those factors that that becomes precision farming. 316 00:19:01,840 --> 00:19:05,480 Speaker 1: Five G capabilities are still improving, but the abasity to 317 00:19:05,480 --> 00:19:10,400 Speaker 1: connect to low latency, high bandwidth mobile internet could transform agriculture. 318 00:19:11,000 --> 00:19:14,720 Speaker 1: The flip side is that right now, a lot of 319 00:19:14,720 --> 00:19:17,840 Speaker 1: the environments we work in they don't even have any 320 00:19:17,920 --> 00:19:21,440 Speaker 1: cellular at all. Forget about four G or even three 321 00:19:21,440 --> 00:19:24,199 Speaker 1: G or even one X like they have nothing, and 322 00:19:24,240 --> 00:19:26,800 Speaker 1: so if they're not going to cover those areas, then 323 00:19:26,840 --> 00:19:28,600 Speaker 1: it's not going to have the impact that it could 324 00:19:28,640 --> 00:19:32,600 Speaker 1: have Without the connectivity. The things you can do, how 325 00:19:32,640 --> 00:19:35,320 Speaker 1: far you can go with precision agriculture is limited, and 326 00:19:35,359 --> 00:19:37,320 Speaker 1: the more you know about what's going on in the plants, 327 00:19:37,320 --> 00:19:40,560 Speaker 1: the better you can grow stuff. The biggest barrier right 328 00:19:40,600 --> 00:19:44,360 Speaker 1: now to getting that information is not sensing it. We've 329 00:19:44,359 --> 00:19:47,560 Speaker 1: got great sensor technologies, it's transmitting it to wherever it 330 00:19:47,600 --> 00:19:50,000 Speaker 1: needs to be. And so that's one thing that I 331 00:19:50,000 --> 00:19:52,360 Speaker 1: think five G has the potential to have a big 332 00:19:52,400 --> 00:20:01,080 Speaker 1: impact on the future. With five G is coming today, 333 00:20:01,400 --> 00:20:04,359 Speaker 1: T Mobile is leading the five G charge with thirty 334 00:20:04,400 --> 00:20:08,359 Speaker 1: billion dollars invested in their network to deliver new capabilities. 335 00:20:08,880 --> 00:20:12,919 Speaker 1: Improved connectivity, and true mobility provided by an advanced network 336 00:20:12,960 --> 00:20:15,520 Speaker 1: from T Mobile for business could change the way we 337 00:20:15,600 --> 00:20:18,720 Speaker 1: all live and work. The five G era will take 338 00:20:18,760 --> 00:20:22,080 Speaker 1: the best technologies available today in the wireless space so 339 00:20:22,119 --> 00:20:25,560 Speaker 1: that you can offer new capabilities to your business customers. 340 00:20:26,080 --> 00:20:28,640 Speaker 1: T Mobile for Business knows that the future of business 341 00:20:28,640 --> 00:20:32,240 Speaker 1: will be powered by advancements in wireless networks. With these 342 00:20:32,280 --> 00:20:35,120 Speaker 1: new technologies opening the doors for better ways to get 343 00:20:35,160 --> 00:20:38,879 Speaker 1: the job done. Business is changing. Learn more at t 344 00:20:39,040 --> 00:20:46,000 Speaker 1: Mobile for Business dot com. To Garrett, George underlined the 345 00:20:46,040 --> 00:20:49,479 Speaker 1: importance of delivering five G to rural areas, which in 346 00:20:49,520 --> 00:20:53,600 Speaker 1: some cases don't even have three G today. Policymakers and 347 00:20:53,680 --> 00:20:56,320 Speaker 1: telcos are aware of that challenge and taking steps to 348 00:20:56,359 --> 00:20:59,560 Speaker 1: address it. The other Teain George mentioned that policymakers are 349 00:20:59,600 --> 00:21:02,399 Speaker 1: thinking of about what should be thinking about is how 350 00:21:02,440 --> 00:21:05,719 Speaker 1: new technologies can change the labor market, in some cases 351 00:21:06,080 --> 00:21:09,840 Speaker 1: making sudden kinds of jobs obsolete, although the George points 352 00:21:09,840 --> 00:21:14,080 Speaker 1: out many agricultural jobs are desirable and difficult to fill. Yeah, 353 00:21:14,119 --> 00:21:16,480 Speaker 1: we can't think of technology and a vacuum, though, we 354 00:21:16,520 --> 00:21:20,040 Speaker 1: need to think about the political and social consequences early 355 00:21:20,080 --> 00:21:23,840 Speaker 1: on in the process of building this technology out. But 356 00:21:23,880 --> 00:21:25,800 Speaker 1: on the other side of that, I think precision farming 357 00:21:26,119 --> 00:21:29,760 Speaker 1: could change the lifestyle for farmers for the better by 358 00:21:29,800 --> 00:21:32,119 Speaker 1: giving them the ability to keep eyes on their farm 359 00:21:32,440 --> 00:21:35,159 Speaker 1: without having to literally walk in the field, you know. 360 00:21:35,200 --> 00:21:37,280 Speaker 1: I spoke about this with Kirk Stevie, who is a 361 00:21:37,280 --> 00:21:47,000 Speaker 1: farmer and researcher who recently took over his family farm. 362 00:21:47,080 --> 00:21:50,160 Speaker 1: I like to say, I'm I'm part farmer and I'm 363 00:21:50,240 --> 00:21:53,760 Speaker 1: part geek. My farm with my family in western Minnesota, 364 00:21:53,840 --> 00:21:57,159 Speaker 1: where you run corn and soybeans and um. Also I 365 00:21:57,200 --> 00:22:01,399 Speaker 1: work as a research scientist and precision anymous the geek 366 00:22:01,480 --> 00:22:03,919 Speaker 1: part of Kirk works with the company based out of 367 00:22:03,920 --> 00:22:08,080 Speaker 1: California called Series Imaging that provides tools for precision farming, 368 00:22:08,560 --> 00:22:10,320 Speaker 1: but he also spends a lot of time on his 369 00:22:10,359 --> 00:22:13,840 Speaker 1: family farm. I live four hours from the farm, and 370 00:22:13,880 --> 00:22:16,480 Speaker 1: so I usually go stay out there with my uncle 371 00:22:16,600 --> 00:22:19,720 Speaker 1: Mark and aunt Becky that I farm with, and stay 372 00:22:19,760 --> 00:22:22,320 Speaker 1: for two three weeks. And so when I'm out there, 373 00:22:22,359 --> 00:22:26,399 Speaker 1: we're going hard. We're waking up before dawn and grabbing 374 00:22:26,440 --> 00:22:28,720 Speaker 1: a bite to eat, and then we're going and jumping 375 00:22:28,720 --> 00:22:32,480 Speaker 1: in the tractors to to plant or to harvest and 376 00:22:32,640 --> 00:22:36,600 Speaker 1: putting in sixteen seventeen eighteen hour days. We're planting beans, 377 00:22:36,640 --> 00:22:40,119 Speaker 1: were planting corn, dealing with fertilizer and machinery and so forth. 378 00:22:40,400 --> 00:22:43,480 Speaker 1: I was curious what has changed most since Kirk's grandfather 379 00:22:43,560 --> 00:22:47,680 Speaker 1: ran the family farm. What's really changed is every piece 380 00:22:47,680 --> 00:22:50,679 Speaker 1: of equipment it has a computer in it. And so 381 00:22:50,760 --> 00:22:54,960 Speaker 1: for most modern farms, they have a computer that says 382 00:22:55,000 --> 00:22:58,200 Speaker 1: powerful or more powerful than you know your regular desktop 383 00:22:58,320 --> 00:23:01,000 Speaker 1: or laptop computer sitting right in the h and so 384 00:23:01,000 --> 00:23:03,720 Speaker 1: you've got to know a lot about computers, GPS, and 385 00:23:03,800 --> 00:23:06,960 Speaker 1: you know, tying some of that technology to your traditional 386 00:23:07,000 --> 00:23:10,680 Speaker 1: machinery and what your classical farmers know and do well 387 00:23:10,960 --> 00:23:14,199 Speaker 1: to succeed in agriculture. The traditional skills of farming that 388 00:23:14,240 --> 00:23:17,520 Speaker 1: have been honed over thousands of years are not going anywhere. 389 00:23:18,000 --> 00:23:20,640 Speaker 1: But what is changing is the ability to leverage new 390 00:23:20,680 --> 00:23:24,720 Speaker 1: technologies like AI and five G to augment those skills. 391 00:23:25,320 --> 00:23:29,280 Speaker 1: In that sense, Kirk, the self described part geek, part farmer, 392 00:23:29,680 --> 00:23:32,080 Speaker 1: might be what the future looks like. As well as 393 00:23:32,119 --> 00:23:35,040 Speaker 1: working on his family farm, Kirk is a remote sensing 394 00:23:35,080 --> 00:23:39,320 Speaker 1: scientist with Series Imaging, a company that specializes in providing 395 00:23:39,400 --> 00:23:43,080 Speaker 1: high quality aerial images of farms to inform decision making. 396 00:23:43,400 --> 00:23:46,439 Speaker 1: I was integrating satellite data and imagery into the farming 397 00:23:46,440 --> 00:23:50,840 Speaker 1: operation already, but looking for alternatives, and that led me 398 00:23:50,920 --> 00:23:54,000 Speaker 1: to series where I was finding that they really had 399 00:23:54,040 --> 00:23:57,919 Speaker 1: a science driven product with next gen technology that I 400 00:23:57,960 --> 00:24:01,520 Speaker 1: wanted to integrate on the farm eries. Cameras can see 401 00:24:01,520 --> 00:24:04,560 Speaker 1: that water stress where an irrigation device is not functioning 402 00:24:04,560 --> 00:24:07,280 Speaker 1: properly fourteen days before you can see it with your 403 00:24:07,280 --> 00:24:10,240 Speaker 1: eyes at least, and so that allows the growers a 404 00:24:10,320 --> 00:24:13,040 Speaker 1: chance to correct it and respond. They can get a 405 00:24:13,080 --> 00:24:15,720 Speaker 1: sense that they're putting too much water on, not enough, 406 00:24:16,040 --> 00:24:18,480 Speaker 1: or if there are issues, and so really it ties 407 00:24:18,480 --> 00:24:22,560 Speaker 1: into conserving water, which is obviously outstanding, but obviously it 408 00:24:22,680 --> 00:24:26,960 Speaker 1: saves the grower money as well. Our cameras are custom built, 409 00:24:27,000 --> 00:24:30,240 Speaker 1: so they're sensitive to different parts of the spectrum um 410 00:24:30,440 --> 00:24:34,359 Speaker 1: thermal imaging being one. When plants aren't having enough water 411 00:24:34,600 --> 00:24:37,920 Speaker 1: put on them and they start a defense mechanism where 412 00:24:37,960 --> 00:24:40,640 Speaker 1: there's stamata close up and that heats up the canopy 413 00:24:40,960 --> 00:24:43,280 Speaker 1: and we can pick that up with our thermal cameras. 414 00:24:43,560 --> 00:24:46,840 Speaker 1: Stomata are tiny openings in the plant that allow for 415 00:24:46,880 --> 00:24:49,960 Speaker 1: the intake of carbon dioxide and release of other vapors 416 00:24:50,000 --> 00:24:53,600 Speaker 1: like water and oxygen. The human eye would be unable 417 00:24:53,640 --> 00:24:56,680 Speaker 1: to see if these stomata were open or closed, but 418 00:24:56,800 --> 00:25:00,200 Speaker 1: thermal imaging from above can trace their heat pattern and 419 00:25:00,240 --> 00:25:02,640 Speaker 1: then indicate to a farmer that they need to water 420 00:25:02,680 --> 00:25:05,760 Speaker 1: a specific patch of crops before it's too late. This 421 00:25:05,800 --> 00:25:11,200 Speaker 1: is precision farming in action. Certainly growers, they're very accustomed 422 00:25:11,240 --> 00:25:14,960 Speaker 1: to seeing that variability in the field. The tricky part 423 00:25:15,119 --> 00:25:18,439 Speaker 1: is how do we quantify that very precisely and deliver 424 00:25:18,520 --> 00:25:21,040 Speaker 1: it in a format to attractor so it can act 425 00:25:21,160 --> 00:25:23,840 Speaker 1: and do something with it. Uh that's where we can 426 00:25:23,840 --> 00:25:26,920 Speaker 1: show value. And so we have a network of pilots 427 00:25:26,960 --> 00:25:30,320 Speaker 1: that have apps in the fields built right in, and 428 00:25:30,359 --> 00:25:33,320 Speaker 1: so they'll have a flight plan that we've custom designed, 429 00:25:33,760 --> 00:25:36,760 Speaker 1: and those pilots go up and execute and fly the 430 00:25:36,800 --> 00:25:40,320 Speaker 1: fields based off the flight plan in this app, collect 431 00:25:40,359 --> 00:25:43,920 Speaker 1: all the data and loaded up to the cloud where 432 00:25:43,960 --> 00:25:46,680 Speaker 1: it's kicked to our processing team on the backside out 433 00:25:46,680 --> 00:25:51,160 Speaker 1: in California, and then within twenty four hours, the grower, 434 00:25:51,280 --> 00:25:54,280 Speaker 1: the agronomous what have you. They can view this imagery 435 00:25:54,440 --> 00:25:57,199 Speaker 1: on their smartphones while they're walking the fields, and so 436 00:25:57,240 --> 00:26:00,359 Speaker 1: it's very close to having instant access to a broad 437 00:26:00,400 --> 00:26:03,159 Speaker 1: scale field data. Twenty four to forty eight hours may 438 00:26:03,240 --> 00:26:05,680 Speaker 1: not seem like a long time, but to a crop 439 00:26:05,760 --> 00:26:08,960 Speaker 1: needing water, it can make a huge difference. So one 440 00:26:08,960 --> 00:26:11,080 Speaker 1: of the things that makes Kirk most excited about the 441 00:26:11,119 --> 00:26:14,359 Speaker 1: future is the ability to collect, process, and serve that 442 00:26:14,480 --> 00:26:17,560 Speaker 1: data to farmers in real time. This is something Kirk 443 00:26:17,640 --> 00:26:21,679 Speaker 1: hopes five G will make possible. Right now. You know, 444 00:26:21,760 --> 00:26:25,160 Speaker 1: we deliver our imagery just for even a visual observation 445 00:26:25,240 --> 00:26:28,080 Speaker 1: if a grower wants to look at it right now. 446 00:26:28,160 --> 00:26:31,719 Speaker 1: With the existing network, even at that level, there are obstacles. 447 00:26:31,800 --> 00:26:34,639 Speaker 1: You can't even see your data on a phone on 448 00:26:34,680 --> 00:26:38,760 Speaker 1: many fields, and so if five G was ubiquitous, I 449 00:26:38,760 --> 00:26:41,480 Speaker 1: mean that would mean instant access after the data is 450 00:26:41,520 --> 00:26:44,320 Speaker 1: collected from your field, you know, you wouldn't have any issues. 451 00:26:44,640 --> 00:26:46,280 Speaker 1: Not only could you look at it and make a 452 00:26:46,359 --> 00:26:49,920 Speaker 1: qualitative decision about scouting, you can also obviously sink it 453 00:26:50,000 --> 00:26:53,600 Speaker 1: up instantly with your equipment for action. And so when 454 00:26:53,640 --> 00:26:57,160 Speaker 1: we have that more seamless and efficient loop, that's going 455 00:26:57,240 --> 00:27:06,120 Speaker 1: to make it even better. Available now from my Heart, 456 00:27:06,200 --> 00:27:09,680 Speaker 1: a new series presented by Tembile for business, The Restless 457 00:27:09,720 --> 00:27:13,440 Speaker 1: Ones join host Johnathon Strickland as he explores the upcoming 458 00:27:13,480 --> 00:27:16,400 Speaker 1: five year revolution and the business leaders who stand right 459 00:27:16,480 --> 00:27:19,640 Speaker 1: on the cutting edge. There are certain decision makers who 460 00:27:19,640 --> 00:27:21,960 Speaker 1: are restless they know there is a better way to 461 00:27:22,000 --> 00:27:25,600 Speaker 1: get things done, and they're ready, curious and excited for 462 00:27:25,600 --> 00:27:29,280 Speaker 1: the next technological innovation to unlock their vision of the future. 463 00:27:30,200 --> 00:27:34,640 Speaker 1: These Restless Ones are in pursuit of bigger, better, smarter, stronger. 464 00:27:35,240 --> 00:27:39,679 Speaker 1: They seek new partners, new strategies, new processes. They pursue 465 00:27:39,720 --> 00:27:43,800 Speaker 1: innovative platforms and solutions to propel their teams, businesses, and 466 00:27:43,920 --> 00:27:47,720 Speaker 1: industries forward. In each episode, we'll learn more from the 467 00:27:47,720 --> 00:27:50,760 Speaker 1: Restless Ones themselves and dive deep into how they think 468 00:27:50,760 --> 00:27:54,280 Speaker 1: of five year revolution could propel their business forward. The 469 00:27:54,320 --> 00:27:56,760 Speaker 1: Restless Ones is now available on the I Heart Radio 470 00:27:56,800 --> 00:28:06,840 Speaker 1: app or wherever you listen to podcasts. So, Carol, we 471 00:28:06,880 --> 00:28:09,520 Speaker 1: live in a city, New York City, the Big Apple, 472 00:28:10,320 --> 00:28:12,800 Speaker 1: and unless we're on the subway or in an elevator, 473 00:28:13,359 --> 00:28:16,440 Speaker 1: we usually have some kind of access to a WISE network. 474 00:28:16,920 --> 00:28:19,520 Speaker 1: But for rural America where they grow the Apples, it 475 00:28:19,560 --> 00:28:22,520 Speaker 1: can be quite a different experience. Yeah, there are areas 476 00:28:22,560 --> 00:28:26,000 Speaker 1: that just don't have the infrastructure for mobile broadband, but 477 00:28:26,119 --> 00:28:29,879 Speaker 1: five G has such promising potential for agriculture, so it 478 00:28:29,920 --> 00:28:32,920 Speaker 1: will be crucial to make sure it is widely deployed 479 00:28:33,200 --> 00:28:36,040 Speaker 1: to allow for all the amazing future applications we have 480 00:28:36,040 --> 00:28:39,480 Speaker 1: discussed in this episode. Well, that wraps up this series 481 00:28:39,680 --> 00:28:43,320 Speaker 1: of This Time Tomorrow. It's been fun, it has and 482 00:28:43,360 --> 00:28:45,880 Speaker 1: it's a good way of thinking about the future. It is. 483 00:28:46,040 --> 00:28:48,880 Speaker 1: You brought up this concept of the adjacent possible in 484 00:28:48,920 --> 00:28:51,880 Speaker 1: the first episode, and we spent the last eight episodes 485 00:28:52,160 --> 00:28:54,240 Speaker 1: exploring what that might look like in all kinds of 486 00:28:54,280 --> 00:28:57,760 Speaker 1: different industries. We started off in Silicon Valley with some 487 00:28:57,880 --> 00:29:00,320 Speaker 1: researchers who were key to building the technology G that 488 00:29:00,440 --> 00:29:04,200 Speaker 1: underlies five G. We spoke with a NASA scientist, local 489 00:29:04,240 --> 00:29:08,160 Speaker 1: officials trying to improve their communities, researchers, roboticists, and of 490 00:29:08,200 --> 00:29:11,480 Speaker 1: course business leaders. And it's clear that although we don't 491 00:29:11,520 --> 00:29:14,680 Speaker 1: know exactly when and how five G will transform key industries, 492 00:29:15,040 --> 00:29:17,760 Speaker 1: the next generation of wireless has the potential to be 493 00:29:17,840 --> 00:29:20,920 Speaker 1: even more transformative than the jump from those old Nokia 494 00:29:20,960 --> 00:29:24,560 Speaker 1: phones to our beloved smartphones. Yeah, and as Paul Dillinger, 495 00:29:24,600 --> 00:29:27,280 Speaker 1: who you'll remember is the head of global Product Innovation 496 00:29:27,320 --> 00:29:30,000 Speaker 1: at Levi's, said in our episode on the future of retail, 497 00:29:30,440 --> 00:29:33,160 Speaker 1: we don't know what the future will look like, but 498 00:29:33,280 --> 00:29:35,760 Speaker 1: we know it will be different than today and it 499 00:29:35,760 --> 00:29:38,680 Speaker 1: will be really interesting. To see how wireless networks change 500 00:29:38,720 --> 00:29:42,080 Speaker 1: standards for businesses and consumers in the coming years. As 501 00:29:42,160 --> 00:29:45,480 Speaker 1: five G moves from the building phase to the here 502 00:29:45,520 --> 00:29:50,080 Speaker 1: and now, as this time tomorrow becomes today, We'll see 503 00:29:50,080 --> 00:29:55,680 Speaker 1: you there. This maybe the end of season one, but 504 00:29:55,800 --> 00:29:59,040 Speaker 1: we're not finished yet with this conversation. Stay tuned for 505 00:29:59,120 --> 00:30:01,240 Speaker 1: us to pick up the conver station later this year 506 00:30:01,560 --> 00:30:04,800 Speaker 1: with continued support from our friends at T Mobile for Business. 507 00:30:13,160 --> 00:30:15,920 Speaker 1: No matter what you're after, T Mobile for Business is 508 00:30:16,000 --> 00:30:18,960 Speaker 1: here with a network born mobile and built from the 509 00:30:18,960 --> 00:30:22,280 Speaker 1: ground up for the next wave of innovation for mobile 510 00:30:22,280 --> 00:30:26,320 Speaker 1: broadband to IoT to workforce mobility, and everything in between. 511 00:30:26,680 --> 00:30:29,440 Speaker 1: T Mobile for Business is committed to helping you move 512 00:30:29,520 --> 00:30:32,720 Speaker 1: your business forward with the products and services you need, 513 00:30:33,120 --> 00:30:36,200 Speaker 1: as well as the dedicated, award winning customer service you'd 514 00:30:36,200 --> 00:30:40,360 Speaker 1: expect from America's most loved wireless company. Business is changing. 515 00:30:40,840 --> 00:30:43,560 Speaker 1: Learn more at T Mobile for Business dot com.