1 00:00:04,400 --> 00:00:07,800 Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio. 2 00:00:11,840 --> 00:00:14,480 Speaker 1: This season of Smart Talks with IBM is all about 3 00:00:14,600 --> 00:00:18,480 Speaker 1: new creators, the developers, data scientists, c t o s 4 00:00:18,560 --> 00:00:23,280 Speaker 1: and other visionaries creatively applying technology in business to drive change. 5 00:00:23,800 --> 00:00:26,680 Speaker 1: They use their knowledge and creativity to develop better ways 6 00:00:26,680 --> 00:00:30,280 Speaker 1: of working, no matter the industry. Join hosts from your 7 00:00:30,320 --> 00:00:33,880 Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to 8 00:00:33,960 --> 00:00:37,480 Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide 9 00:00:37,520 --> 00:00:39,839 Speaker 1: you through the season as your host and provide his 10 00:00:39,920 --> 00:00:43,000 Speaker 1: thoughts and analysis along the way. Look out for new 11 00:00:43,040 --> 00:00:45,480 Speaker 1: episodes of Smart Talks with IBM on the I Heart 12 00:00:45,560 --> 00:00:49,159 Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts, 13 00:00:49,360 --> 00:00:56,040 Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello, 14 00:00:56,200 --> 00:00:59,560 Speaker 1: Welcome to Smart Talks with IBM, a podcast from Pushkin 15 00:00:59,600 --> 00:01:03,600 Speaker 1: Industry is I Heart Radio and IBM. I'm Malcolm Blobwell. 16 00:01:04,319 --> 00:01:09,000 Speaker 1: This season we're talking to new creators, the developers, data scientists, 17 00:01:09,319 --> 00:01:12,479 Speaker 1: c t o s and other visionaries who are creatively 18 00:01:12,520 --> 00:01:16,759 Speaker 1: applying technology and business to drive change. Channeling their knowledge 19 00:01:16,760 --> 00:01:21,280 Speaker 1: and expertise, they're developing more creative and effective solutions no 20 00:01:21,319 --> 00:01:24,920 Speaker 1: matter the industry. Our guest today are Brett Fanoff and 21 00:01:25,040 --> 00:01:29,240 Speaker 1: Don Scott. Brett and Dawn are responsible for creating the 22 00:01:29,280 --> 00:01:34,120 Speaker 1: world's first unmanned, fully autonomous ship to cross the Atlantic Ocean, 23 00:01:34,560 --> 00:01:39,160 Speaker 1: a research vessel they've dubbed the Mayflower four hundred. Brett 24 00:01:39,280 --> 00:01:42,680 Speaker 1: is a director of the Mayflower Autonomous Ship Project and 25 00:01:42,760 --> 00:01:49,440 Speaker 1: Dawn is the CTO of Marine AI. On June, the 26 00:01:49,480 --> 00:01:54,640 Speaker 1: Mayflower four hundred successfully completed its voyage from Plymouth, UK 27 00:01:55,120 --> 00:01:59,920 Speaker 1: to Plymouth, Massachusetts. It's both an homage to the original Mayflower, 28 00:02:00,280 --> 00:02:03,400 Speaker 1: which crossed the Atlantic forundered years earlier, and a bell 29 00:02:03,480 --> 00:02:07,560 Speaker 1: weather for the ways autonomous technology will push the boundaries 30 00:02:07,560 --> 00:02:12,839 Speaker 1: of maritime exploration in the next four years. On today's show, 31 00:02:12,880 --> 00:02:17,560 Speaker 1: the Unlikely Origins of a self directed Ship, some motion misadventures, 32 00:02:17,600 --> 00:02:20,640 Speaker 1: and what AI and machine learning will mean for the 33 00:02:20,680 --> 00:02:25,240 Speaker 1: future of seafaring and beyond, Brett and Dawn spoke with 34 00:02:25,360 --> 00:02:29,799 Speaker 1: Lauren Ober, host of the forthcoming Pushkin podcast The Loudest 35 00:02:29,880 --> 00:02:32,839 Speaker 1: Girl in the World. Lauren is a longtime radio host 36 00:02:32,880 --> 00:02:36,520 Speaker 1: and reporter, helming shows like NPRS, The Big Listen, and 37 00:02:36,680 --> 00:02:42,760 Speaker 1: Spectacular Failures from American public Media. Okay, now, let's get 38 00:02:42,800 --> 00:02:50,280 Speaker 1: to the interview with Brett Fanoff and Don Scott. Don 39 00:02:50,400 --> 00:02:52,960 Speaker 1: and Brett, it's really great to be talking with you 40 00:02:53,000 --> 00:02:56,040 Speaker 1: guys today. I was wondering for each of you, what 41 00:02:56,320 --> 00:02:58,880 Speaker 1: is the draw of the sea? I mean, it's like 42 00:02:58,960 --> 00:03:02,560 Speaker 1: this explain It's a place. It feels so unknown in 43 00:03:02,639 --> 00:03:06,160 Speaker 1: so many ways. Um, but I'm curious, like, what is 44 00:03:06,200 --> 00:03:08,600 Speaker 1: the allure there for me? It's I wanted to be 45 00:03:08,680 --> 00:03:10,800 Speaker 1: I wanted to do aerospace. So I always feel like 46 00:03:10,840 --> 00:03:13,960 Speaker 1: I'm like like the poor cousin of aerospace, but it isn't. 47 00:03:14,040 --> 00:03:17,840 Speaker 1: It's actually it's harder to to do the underwater stuff. 48 00:03:17,919 --> 00:03:20,639 Speaker 1: It's closer. It's just harder than being in space. It's 49 00:03:21,240 --> 00:03:24,840 Speaker 1: it's incredibly hostile and wildly unexplored. And why what I 50 00:03:24,880 --> 00:03:27,200 Speaker 1: like about it is that you know, you can take 51 00:03:27,200 --> 00:03:28,919 Speaker 1: a bucket and go down to the beach, get a 52 00:03:28,919 --> 00:03:31,560 Speaker 1: bucket of water, analyze the bucket of water for the 53 00:03:31,600 --> 00:03:34,639 Speaker 1: next twenty years, and you know, chances are pretty high 54 00:03:34,639 --> 00:03:36,160 Speaker 1: you're gonna have a couple of things in there that 55 00:03:36,200 --> 00:03:39,560 Speaker 1: nobody's ever seen before. And that's every bucket of water 56 00:03:39,720 --> 00:03:42,600 Speaker 1: everywhere in the world, right, So I like the idea 57 00:03:42,680 --> 00:03:44,600 Speaker 1: that you get to discover something new all the time, 58 00:03:44,640 --> 00:03:47,839 Speaker 1: and it's also hard. It's a difficult place to work, 59 00:03:47,880 --> 00:03:50,200 Speaker 1: so it challenges you to come up with new ideas 60 00:03:50,240 --> 00:03:52,480 Speaker 1: and new ways to do things and new materials. And 61 00:03:53,000 --> 00:03:54,640 Speaker 1: that's what I like about it. I don't know, don 62 00:03:54,680 --> 00:03:57,880 Speaker 1: what about you. Yeah, I mean, there's obviously an allure 63 00:03:57,880 --> 00:04:00,880 Speaker 1: and draw there's some great descriptions about why people are 64 00:04:00,960 --> 00:04:03,720 Speaker 1: drawn to the ocean. Talk to the authors and the poets, 65 00:04:03,800 --> 00:04:06,240 Speaker 1: you know, it's it's definitely a real sort of visceral 66 00:04:06,280 --> 00:04:08,280 Speaker 1: feeling that people get. I think you find that the 67 00:04:08,320 --> 00:04:12,160 Speaker 1: people that are involved in ocean engineering and or marine 68 00:04:12,200 --> 00:04:15,080 Speaker 1: sides like that you'll just sort of fall into this 69 00:04:15,200 --> 00:04:19,200 Speaker 1: career by accident. You make proactive decisions to get involved 70 00:04:19,480 --> 00:04:22,960 Speaker 1: in that environment. So you have a bunch of people 71 00:04:23,040 --> 00:04:25,760 Speaker 1: working there that that want to be there and sort 72 00:04:25,760 --> 00:04:30,440 Speaker 1: of have this uh understanding of that this is the 73 00:04:30,440 --> 00:04:32,160 Speaker 1: place they want to be and this is where they 74 00:04:32,160 --> 00:04:35,680 Speaker 1: want to work. So that becomes a very very positive 75 00:04:35,800 --> 00:04:39,320 Speaker 1: work environment workspace because everyone's they want to be there. 76 00:04:39,640 --> 00:04:43,360 Speaker 1: So there's that. Yeah, it's highly collaborative, isn't it. It's 77 00:04:43,480 --> 00:04:47,080 Speaker 1: um like anything, there's personalities, but it tends to be 78 00:04:47,120 --> 00:04:49,400 Speaker 1: a lot of fun more than anything else. It's challenging 79 00:04:49,400 --> 00:04:51,240 Speaker 1: in all the ways that make life interesting, and then 80 00:04:51,279 --> 00:04:53,400 Speaker 1: it also tends to be a good time. And you 81 00:04:53,440 --> 00:04:56,920 Speaker 1: can't work in the ocean by yourself, like, well, you can, 82 00:04:57,000 --> 00:05:00,839 Speaker 1: but it's kind of hard, so press out. It's an 83 00:05:00,880 --> 00:05:04,160 Speaker 1: incredibly collaborative environment. I mean, if you want to be 84 00:05:04,240 --> 00:05:07,200 Speaker 1: doing anything of significance, you have to be working as 85 00:05:07,200 --> 00:05:09,360 Speaker 1: a group because you need to rely on each other. 86 00:05:09,640 --> 00:05:14,159 Speaker 1: It is an incredibly dynamic, hostile environment, very humbling. So 87 00:05:15,160 --> 00:05:18,640 Speaker 1: you find you you're going to achieve success as a 88 00:05:18,680 --> 00:05:21,640 Speaker 1: collaborative group as opposed to some sort of lone wolf 89 00:05:21,720 --> 00:05:25,000 Speaker 1: type out to right. Okay, so we're here to talk 90 00:05:25,040 --> 00:05:31,200 Speaker 1: about the Mayflower Autonomous Ship project, which obviously is very cool. Um, 91 00:05:31,240 --> 00:05:36,040 Speaker 1: how exactly did you guys decide to build an autonomous 92 00:05:36,040 --> 00:05:40,520 Speaker 1: ship and then model it after the Mayflower? I mean 93 00:05:40,560 --> 00:05:43,520 Speaker 1: it was just to hold my beer kind of thing. Um, 94 00:05:43,560 --> 00:05:46,600 Speaker 1: I'm sure what it really is, it really was, it 95 00:05:46,640 --> 00:05:49,599 Speaker 1: really was. Yeah, what it really was is it was 96 00:05:51,560 --> 00:05:54,400 Speaker 1: so in meeting with the City of Plymouth on something else, 97 00:05:54,480 --> 00:05:56,560 Speaker 1: they were talking about what they were going to do 98 00:05:56,600 --> 00:05:59,240 Speaker 1: and maybe built a replica ship, of which there's already one, 99 00:05:59,360 --> 00:06:02,280 Speaker 1: And I thought that wasn't the best idea, and you're 100 00:06:02,320 --> 00:06:06,040 Speaker 1: talking for anniversary. Yeah, and so I was a little 101 00:06:06,080 --> 00:06:10,279 Speaker 1: bit indelicate in my comment as to how they wanted 102 00:06:10,320 --> 00:06:13,160 Speaker 1: to proceed with a possible replica. You said it was 103 00:06:13,200 --> 00:06:16,040 Speaker 1: a stupid idea, I said, I said it was stupid 104 00:06:16,440 --> 00:06:19,120 Speaker 1: and uh and there was more I couldn't resist and 105 00:06:19,120 --> 00:06:20,760 Speaker 1: and and I said, there already is one, you know, 106 00:06:20,800 --> 00:06:23,800 Speaker 1: and it's it's just I grew up near there. And 107 00:06:23,800 --> 00:06:25,599 Speaker 1: and so they said, all right, smart guy, what are 108 00:06:25,640 --> 00:06:26,839 Speaker 1: you gonna do? I was like, oh, we should build 109 00:06:26,880 --> 00:06:31,200 Speaker 1: one that challenges us technologically and from an engineering perspective 110 00:06:31,240 --> 00:06:34,080 Speaker 1: and sort of invokes the spirit of the original risk 111 00:06:34,200 --> 00:06:37,760 Speaker 1: taking and do something that informs the next four years. 112 00:06:37,760 --> 00:06:40,240 Speaker 1: And everybody was like, yeah, you should do that. And 113 00:06:40,320 --> 00:06:41,760 Speaker 1: I was like, you know what, I will hold my 114 00:06:41,839 --> 00:06:45,599 Speaker 1: beer and uh so, so I called Don after the 115 00:06:45,640 --> 00:06:48,240 Speaker 1: meeting and I was like, oh, Don, we we have 116 00:06:48,279 --> 00:06:51,000 Speaker 1: to build an AI. I need Captain Watson because we're 117 00:06:51,000 --> 00:06:53,120 Speaker 1: going to build an autn of a ship across the Atlantic. 118 00:06:53,240 --> 00:06:57,600 Speaker 1: And he was like great, and so yeah, and it 119 00:06:57,680 --> 00:07:00,400 Speaker 1: was just that literally that glib, but it also I 120 00:07:00,440 --> 00:07:02,280 Speaker 1: mean he and I have been working on unmanned systems 121 00:07:02,279 --> 00:07:05,440 Speaker 1: and autonomous systems for a long time together, twenty plus years, 122 00:07:05,440 --> 00:07:07,880 Speaker 1: and so I wanted to see where we could get to, 123 00:07:08,000 --> 00:07:09,640 Speaker 1: Like how hard could this be? Right I mean? And 124 00:07:10,000 --> 00:07:12,920 Speaker 1: I sure, let's do it. Then, So we built a ship. 125 00:07:13,400 --> 00:07:17,760 Speaker 1: You mentioned capturing the spirit of the original Mayflower journey, 126 00:07:17,760 --> 00:07:21,400 Speaker 1: and I wonder what exactly where you're trying to capture. 127 00:07:21,560 --> 00:07:25,720 Speaker 1: Was it the spirit of taking risks or was it 128 00:07:25,760 --> 00:07:29,880 Speaker 1: doing something that hadn't been done before. What we were 129 00:07:29,880 --> 00:07:32,240 Speaker 1: trying to do we knew was really hard, right, like, 130 00:07:32,400 --> 00:07:35,480 Speaker 1: and it was a huge amount of risk to undertake it. 131 00:07:35,840 --> 00:07:38,080 Speaker 1: Press the real risk taker. He's the one with the 132 00:07:38,080 --> 00:07:40,600 Speaker 1: big ideas and wants to take the risk. I'm I'm 133 00:07:40,600 --> 00:07:44,600 Speaker 1: a little more cautious and sort of pragmatic in the 134 00:07:44,640 --> 00:07:46,680 Speaker 1: sense of, Okay, what's going to take to do that? 135 00:07:47,440 --> 00:07:49,560 Speaker 1: We we actually didn't think we were going to make it, 136 00:07:49,680 --> 00:07:51,840 Speaker 1: or I fully expected at some point the ocean we 137 00:07:51,920 --> 00:07:55,080 Speaker 1: get annoyed and smite us, you know. Pilgrims like that 138 00:07:55,120 --> 00:07:57,200 Speaker 1: to me is what's interesting. Pilgrims took a risk, right, 139 00:07:57,240 --> 00:08:01,240 Speaker 1: so every one of them fully expected that they would die, 140 00:08:01,880 --> 00:08:04,720 Speaker 1: if not on the voyage within like the first year, right, 141 00:08:05,160 --> 00:08:08,640 Speaker 1: That's how it was, and it was worth it to 142 00:08:08,680 --> 00:08:12,960 Speaker 1: them to take that risk. So our risk is infinitesimal 143 00:08:13,000 --> 00:08:16,160 Speaker 1: by comparison, Right, it's tiny. What was our risk really, 144 00:08:16,360 --> 00:08:20,120 Speaker 1: We'd lose a ship we spent some money on. So 145 00:08:20,160 --> 00:08:24,360 Speaker 1: what the knowledge about how to approach these problems is, 146 00:08:24,520 --> 00:08:26,400 Speaker 1: and the and the experience that you get to give 147 00:08:26,480 --> 00:08:29,880 Speaker 1: people to take risk at that level from an engineering 148 00:08:29,880 --> 00:08:32,480 Speaker 1: perspective is really important. Right, somebody had to do the 149 00:08:32,520 --> 00:08:36,520 Speaker 1: first open heart surgery and took a risk. Now we're 150 00:08:36,520 --> 00:08:38,640 Speaker 1: not doing open heart surgery. Right, no one's going to die. 151 00:08:39,080 --> 00:08:41,719 Speaker 1: So what's appealing about the risk thing is it has 152 00:08:41,720 --> 00:08:45,400 Speaker 1: a technical risk and environmental risk, and then there's a 153 00:08:45,480 --> 00:08:48,840 Speaker 1: legislative and regulatory risk because we had to have our 154 00:08:48,920 --> 00:08:51,520 Speaker 1: fights with various agencies about the fact that they didn't 155 00:08:51,520 --> 00:08:53,280 Speaker 1: have a law that said we couldn't so they didn't 156 00:08:53,280 --> 00:08:55,440 Speaker 1: get to say no just because they didn't want us to. 157 00:08:56,200 --> 00:08:59,240 Speaker 1: And at the same time, trying to create a reliable 158 00:08:59,280 --> 00:09:02,400 Speaker 1: machine and then some sort of an AI machine learning 159 00:09:02,440 --> 00:09:06,640 Speaker 1: based system that would be safe whatever that is in 160 00:09:06,640 --> 00:09:09,520 Speaker 1: the middle of the ocean. It's really interesting and gives 161 00:09:09,559 --> 00:09:11,559 Speaker 1: people a lot to a lot of purchase for different 162 00:09:11,559 --> 00:09:14,880 Speaker 1: people with different skill sets to collaborate. Brett and John 163 00:09:14,920 --> 00:09:18,920 Speaker 1: started developing the Mayflower auton of a ship in It 164 00:09:18,960 --> 00:09:21,800 Speaker 1: took them six years to figure out both the software 165 00:09:22,040 --> 00:09:24,920 Speaker 1: and the body of the boat itself. In that time, 166 00:09:25,240 --> 00:09:29,640 Speaker 1: over seventy people contributed to the project. Lauren asked Don 167 00:09:29,640 --> 00:09:32,640 Speaker 1: and Brett what it really took to go from hold 168 00:09:32,679 --> 00:09:35,880 Speaker 1: my beer to an actual ship? You know, it is 169 00:09:35,960 --> 00:09:40,040 Speaker 1: mind boggling when you think of how many people are involved, 170 00:09:40,040 --> 00:09:44,199 Speaker 1: how many people are touching this project, how many interesting 171 00:09:44,240 --> 00:09:47,280 Speaker 1: minds doing interesting things, but you have to funnel it 172 00:09:47,320 --> 00:09:51,680 Speaker 1: all into this one project. Well that I don't know 173 00:09:51,720 --> 00:09:53,200 Speaker 1: if it's that way. I mean, I guess you could 174 00:09:53,200 --> 00:09:55,719 Speaker 1: say there was one project, but there were lots of projects, 175 00:09:55,800 --> 00:09:58,280 Speaker 1: and so, you know, there was sort of the hardcore 176 00:09:58,320 --> 00:10:00,200 Speaker 1: group of people that are trying to build the full 177 00:10:00,240 --> 00:10:02,520 Speaker 1: software that works, and then there's the guys trying to 178 00:10:02,559 --> 00:10:05,840 Speaker 1: build the hardware and they have an interface, but they're 179 00:10:05,880 --> 00:10:09,520 Speaker 1: parallel pursuits that don't have direct overlap. And then we 180 00:10:09,559 --> 00:10:11,880 Speaker 1: said yes a lot to anybody who wanted to help, 181 00:10:12,520 --> 00:10:15,559 Speaker 1: because we learned from experience that most people don't last 182 00:10:15,640 --> 00:10:18,439 Speaker 1: in terms of the ability to stick out four or 183 00:10:18,480 --> 00:10:20,720 Speaker 1: five years focus on the projects very hard, and so 184 00:10:20,800 --> 00:10:23,160 Speaker 1: the people that I wanted to stick it out and 185 00:10:23,160 --> 00:10:26,440 Speaker 1: bring it to fruition ended up you know, sticking it 186 00:10:26,440 --> 00:10:28,560 Speaker 1: out and that was great, you know. And then there 187 00:10:28,600 --> 00:10:30,160 Speaker 1: are all sorts of different things. There was a group 188 00:10:30,400 --> 00:10:32,360 Speaker 1: making a web interface so that they could show the 189 00:10:32,400 --> 00:10:34,600 Speaker 1: world what we were doing, and you know, then there 190 00:10:34,640 --> 00:10:37,360 Speaker 1: was a PR group that was marketing things and sort 191 00:10:37,400 --> 00:10:39,600 Speaker 1: of talking about how we tell the world about it, 192 00:10:39,600 --> 00:10:42,640 Speaker 1: and we would support them. But it's hard to describe 193 00:10:42,640 --> 00:10:44,560 Speaker 1: it as one project. I guess would be my position. 194 00:10:44,559 --> 00:10:48,720 Speaker 1: It's lots of interlinked programs, right, Sure, I get that, 195 00:10:48,840 --> 00:10:52,120 Speaker 1: I get that. Can you tell me more about how 196 00:10:52,200 --> 00:10:56,320 Speaker 1: automation is built into the ship and how it works. Well, 197 00:10:56,480 --> 00:11:00,000 Speaker 1: there's tons of automation and may Far, I mean Mayflower 198 00:11:00,200 --> 00:11:04,560 Speaker 1: is like most robotics systems, right. So you peel it 199 00:11:04,600 --> 00:11:07,560 Speaker 1: open and you find you know, programmable logic controllers and 200 00:11:07,600 --> 00:11:10,160 Speaker 1: motor drives and also its of other things sensors and 201 00:11:10,880 --> 00:11:13,800 Speaker 1: industrial automation that you'd see, you know in an elevator 202 00:11:13,880 --> 00:11:17,800 Speaker 1: or an escalator or industrial machinery for manufacture. And that's 203 00:11:17,840 --> 00:11:20,200 Speaker 1: one sort of layer of it. Right. So you've got 204 00:11:20,240 --> 00:11:22,679 Speaker 1: the basic analog control. Then you've got sort of a 205 00:11:22,760 --> 00:11:26,520 Speaker 1: veneer of automation, and then what I would call sophisticated automation, 206 00:11:26,559 --> 00:11:29,560 Speaker 1: which Don and I have worked on for decades in 207 00:11:29,600 --> 00:11:32,480 Speaker 1: the marine space. So all that's in there. And you know, 208 00:11:32,679 --> 00:11:35,480 Speaker 1: Don and I talked really early on if I just 209 00:11:35,520 --> 00:11:37,320 Speaker 1: wanted to get across the Atlantic, we could have bought 210 00:11:37,320 --> 00:11:40,200 Speaker 1: an old fishing boat, filled up the fish hoolds with 211 00:11:40,280 --> 00:11:42,800 Speaker 1: diesel fuel, and put a cheap autopilot on it and 212 00:11:42,840 --> 00:11:45,520 Speaker 1: sent it. It probably would have got across. But so 213 00:11:45,679 --> 00:11:50,720 Speaker 1: what it's not reducing risk, and it's not unburdening a person, 214 00:11:51,559 --> 00:11:55,800 Speaker 1: and it's not doing anything really clever or sophisticated. And 215 00:11:55,880 --> 00:11:58,160 Speaker 1: so what we were more interested in was getting to 216 00:11:58,200 --> 00:12:00,040 Speaker 1: a point where instead of having to tell it to 217 00:12:00,040 --> 00:12:03,920 Speaker 1: do everything, saying go do this task right, a goal 218 00:12:04,160 --> 00:12:06,640 Speaker 1: like go to Plymouth right, and then while you're doing that, ah, 219 00:12:06,679 --> 00:12:08,920 Speaker 1: by the way, while you're doing that, collect all this 220 00:12:09,000 --> 00:12:11,600 Speaker 1: science data and if you see anything unusual, tell us 221 00:12:11,720 --> 00:12:14,559 Speaker 1: and and while you're looking for all these unusual things 222 00:12:14,559 --> 00:12:18,920 Speaker 1: and trying to achieve your goal, don't hit anything. So 223 00:12:18,960 --> 00:12:22,199 Speaker 1: then what role did IBM S technology play in all 224 00:12:22,240 --> 00:12:26,240 Speaker 1: of this? Yeah? I mean their their technology is all 225 00:12:26,280 --> 00:12:29,640 Speaker 1: over the ship. Probably the main contribution it was the 226 00:12:29,679 --> 00:12:34,320 Speaker 1: decision making process or it's it's an automation TOOLM operational 227 00:12:34,360 --> 00:12:38,200 Speaker 1: decision manager. It's actually a financial services tool. It's for 228 00:12:38,840 --> 00:12:43,400 Speaker 1: your making decisions about the viability of a transaction, whether 229 00:12:43,440 --> 00:12:46,680 Speaker 1: it's fraud or order or alone or let's say, And 230 00:12:47,200 --> 00:12:51,439 Speaker 1: we were being presented this by one of the ODIUM engineers, 231 00:12:51,520 --> 00:12:53,040 Speaker 1: and I remember sitting in the room with Brett thing, 232 00:12:53,200 --> 00:12:56,679 Speaker 1: what what in the world does financial services product have 233 00:12:56,800 --> 00:12:59,760 Speaker 1: to do with marine navigation And they sort of were 234 00:12:59,760 --> 00:13:03,679 Speaker 1: brought to realize by the IBM engineer how this is. 235 00:13:04,120 --> 00:13:06,600 Speaker 1: This isn't really so much about financial services as it 236 00:13:06,679 --> 00:13:10,400 Speaker 1: is about making making really difficult decisions in a really 237 00:13:10,440 --> 00:13:14,040 Speaker 1: complex environment, which is what they do in financial services. 238 00:13:14,080 --> 00:13:16,400 Speaker 1: But it's also exactly what we needed to do in 239 00:13:17,080 --> 00:13:20,640 Speaker 1: re navigation. And when the when the system was actually rounding, 240 00:13:20,960 --> 00:13:25,480 Speaker 1: it would create a log essentially of why that decision 241 00:13:25,559 --> 00:13:29,080 Speaker 1: was made, so they can validate that decision and verify 242 00:13:29,160 --> 00:13:31,520 Speaker 1: and validate that that that was in fact the right decision. 243 00:13:32,040 --> 00:13:34,319 Speaker 1: And um, so that's a that's one of the key 244 00:13:34,640 --> 00:13:37,000 Speaker 1: IVM tools that are on board. Well, one of the 245 00:13:37,040 --> 00:13:40,320 Speaker 1: things you might want to consider about that is the fundamentals, right, 246 00:13:40,400 --> 00:13:43,120 Speaker 1: the theoretical independence of all the AI that we're deploying 247 00:13:43,160 --> 00:13:46,920 Speaker 1: now have been sort of understood for decades, right, and 248 00:13:46,960 --> 00:13:48,960 Speaker 1: so now we just happened to live in a world 249 00:13:49,520 --> 00:13:52,200 Speaker 1: where the microprocesses are up to snuff that they can 250 00:13:52,240 --> 00:13:55,280 Speaker 1: deploy some of these very sophisticated theoretical and reality and 251 00:13:55,320 --> 00:13:57,600 Speaker 1: all of which IBM has been involved with from inception 252 00:13:58,280 --> 00:14:00,880 Speaker 1: based on its pedigree is in a national business machines. 253 00:14:01,280 --> 00:14:04,480 Speaker 1: There isn't an IBM product that I can think of 254 00:14:04,800 --> 00:14:08,520 Speaker 1: that we haven't tried to utilize the deploying so it's 255 00:14:08,600 --> 00:14:11,400 Speaker 1: it's it's everywhere in the ship. Yeah. I don't think 256 00:14:11,400 --> 00:14:15,240 Speaker 1: a lot of people think of technology as as as 257 00:14:15,240 --> 00:14:19,840 Speaker 1: a creative pursuit, but I imagine building an autonomous ship 258 00:14:19,880 --> 00:14:23,160 Speaker 1: from scratch takes a lot of creativity. And I'm wondering, 259 00:14:23,280 --> 00:14:27,280 Speaker 1: do you guys think of your work as creative? Yeah, 260 00:14:27,480 --> 00:14:32,040 Speaker 1: engineering is essentially designing technological innovation sort of. You think 261 00:14:32,080 --> 00:14:35,240 Speaker 1: of it as a very logical process, and there is that, 262 00:14:35,400 --> 00:14:39,160 Speaker 1: for sure, but there's an incredible amount of innovation involved too, 263 00:14:39,440 --> 00:14:42,200 Speaker 1: Like there's no template for what we're doing, and you know, 264 00:14:42,240 --> 00:14:45,400 Speaker 1: we call it white paper design where you're basically given 265 00:14:45,440 --> 00:14:49,080 Speaker 1: a blank piece of paper and a goal, which is, okay, 266 00:14:49,200 --> 00:14:52,600 Speaker 1: ship that's going to cross the Atlantic, Um, okay, come 267 00:14:52,680 --> 00:14:55,680 Speaker 1: up with some ideas, right, So I mean it requires 268 00:14:56,480 --> 00:14:59,920 Speaker 1: major conceptual leaps and then the technical skill to realize 269 00:15:00,280 --> 00:15:03,440 Speaker 1: those those sleeps. You're not going to make any advances 270 00:15:03,520 --> 00:15:06,120 Speaker 1: just doing things the way you've always done them. Right. 271 00:15:06,200 --> 00:15:09,840 Speaker 1: You need to stretch, right, and the only way it 272 00:15:09,880 --> 00:15:13,680 Speaker 1: stretch is what implementing new ideas like you can spend 273 00:15:13,920 --> 00:15:16,960 Speaker 1: a decade. We call it power point engineering right where 274 00:15:16,960 --> 00:15:18,840 Speaker 1: you do nothing but think of things. We don't actually 275 00:15:18,920 --> 00:15:23,560 Speaker 1: do anything, as opposed to what we call full contact engineering, 276 00:15:23,560 --> 00:15:28,040 Speaker 1: where you actually built the boat right the software to 277 00:15:28,080 --> 00:15:30,240 Speaker 1: go on the boat and send it out on the water, 278 00:15:31,440 --> 00:15:34,080 Speaker 1: get your kicked, like, get sea sick, you know, all 279 00:15:34,080 --> 00:15:35,960 Speaker 1: that sort of fun stuff that happens when you don't 280 00:15:36,000 --> 00:15:40,480 Speaker 1: see trials. Um. And because that's where you that's where 281 00:15:40,480 --> 00:15:43,160 Speaker 1: the actual learning is happening, that's where the actual development 282 00:15:43,240 --> 00:15:46,560 Speaker 1: is happening, is being out on the ocean. Crossing the 283 00:15:46,560 --> 00:15:49,920 Speaker 1: Atlantic is no small voyage for any vessel, but the 284 00:15:50,000 --> 00:15:53,760 Speaker 1: Mayflower Autonomous Ship Project is more than just about sailing 285 00:15:53,840 --> 00:15:58,120 Speaker 1: from point A to point B. Automation and AI have 286 00:15:58,320 --> 00:16:01,560 Speaker 1: game changing implications for the way we design the next 287 00:16:01,640 --> 00:16:05,000 Speaker 1: generation of vessels and the way these vessels will behave 288 00:16:05,360 --> 00:16:09,080 Speaker 1: and interact at sea. Ships will be able to gather 289 00:16:09,200 --> 00:16:13,400 Speaker 1: data from the ocean by themselves, providing humans with critical 290 00:16:13,440 --> 00:16:18,720 Speaker 1: information we need to address problems like global warming, ocean pollution, 291 00:16:19,160 --> 00:16:22,720 Speaker 1: and our impact on marine life. For instance, the Mayfire 292 00:16:22,800 --> 00:16:27,560 Speaker 1: four hundred can sample ocean water from microplastics and record 293 00:16:27,720 --> 00:16:32,760 Speaker 1: audio of whale vocalizations. Taking the human factor out of 294 00:16:32,760 --> 00:16:36,600 Speaker 1: a ship allows us to explore new designs and functions 295 00:16:36,640 --> 00:16:40,600 Speaker 1: that haven't been imagined before. Lauren asked bread and Down 296 00:16:41,000 --> 00:16:43,720 Speaker 1: more about this. What are some of the benefits of 297 00:16:43,760 --> 00:16:48,280 Speaker 1: having an unmanned vessel, like, how does automation push the 298 00:16:48,320 --> 00:16:51,720 Speaker 1: boundaries of what we can do out in the ocean? Well, 299 00:16:51,720 --> 00:16:54,320 Speaker 1: the few major variables right or true facets to that. 300 00:16:54,400 --> 00:16:57,520 Speaker 1: One is you can do some risky things when you 301 00:16:57,520 --> 00:16:59,520 Speaker 1: don't have the people there, right, because no one's going 302 00:16:59,560 --> 00:17:02,280 Speaker 1: to be law cost at sea. And then the other 303 00:17:02,320 --> 00:17:04,320 Speaker 1: thing is you can drive cost down, and I mean 304 00:17:04,359 --> 00:17:07,600 Speaker 1: cost financially but also environmental cost RCT because you can 305 00:17:08,000 --> 00:17:10,920 Speaker 1: use a far less energy to accomplish a similar goal. 306 00:17:11,600 --> 00:17:14,360 Speaker 1: And then what that allows you to do is have more. Right, 307 00:17:14,400 --> 00:17:18,919 Speaker 1: So instead of say having one fifty million dollar hundred 308 00:17:18,920 --> 00:17:21,399 Speaker 1: million dollar research ship, which is the kind of numbers 309 00:17:21,400 --> 00:17:24,360 Speaker 1: you're talking about to take scientists to see, you can 310 00:17:24,440 --> 00:17:28,359 Speaker 1: have twenty or thirty or forty million dollars or two 311 00:17:28,440 --> 00:17:31,679 Speaker 1: million dollar ships that go out and work collaboratively with 312 00:17:31,720 --> 00:17:34,720 Speaker 1: space based assets and with one another and collect vast 313 00:17:34,720 --> 00:17:37,479 Speaker 1: amounts of data from disparate parts of the ocean. And 314 00:17:37,520 --> 00:17:41,240 Speaker 1: then you use that data to create information that informs 315 00:17:41,240 --> 00:17:44,120 Speaker 1: where you send the man vessel, right, so that they 316 00:17:44,160 --> 00:17:46,919 Speaker 1: get the most out of their time at sea. So 317 00:17:46,920 --> 00:17:49,919 Speaker 1: it's about enabling the people. It's about leaving the humans 318 00:17:49,960 --> 00:17:52,080 Speaker 1: to do the uniquely human part, which is have the 319 00:17:52,160 --> 00:17:56,080 Speaker 1: insight and the intuition and and the creativity. And so 320 00:17:56,520 --> 00:17:59,520 Speaker 1: you know, that's why it's important. And we're going to 321 00:17:59,560 --> 00:18:01,360 Speaker 1: see an freezing amount of this. And I think it's 322 00:18:01,359 --> 00:18:03,800 Speaker 1: also important for people to get comfortable with the idea 323 00:18:03,840 --> 00:18:07,600 Speaker 1: that these things will be roaming around and that it's okay. Yeah, 324 00:18:07,800 --> 00:18:10,400 Speaker 1: And and on an insim basis, I mean, we're also 325 00:18:10,520 --> 00:18:13,840 Speaker 1: talking about this same technology that allows a ship to 326 00:18:14,040 --> 00:18:18,399 Speaker 1: sail autonomously also can be used to assist a human 327 00:18:18,440 --> 00:18:24,000 Speaker 1: crew now, you know, basically be another set of eyes 328 00:18:24,200 --> 00:18:29,160 Speaker 1: and years be a watchkeeper for a manned vessel. Right, 329 00:18:29,520 --> 00:18:33,480 Speaker 1: I want to know more about the AI captain. How 330 00:18:33,520 --> 00:18:37,040 Speaker 1: did you build it so that it would be comparable 331 00:18:37,200 --> 00:18:39,840 Speaker 1: to the way a human captain might direct a ship. 332 00:18:40,240 --> 00:18:42,560 Speaker 1: What we're trying to do is augment the person, right, 333 00:18:42,560 --> 00:18:46,119 Speaker 1: We're trying to let them be more of a person 334 00:18:46,200 --> 00:18:48,440 Speaker 1: than sort of. They don't have to watch the radar, 335 00:18:48,480 --> 00:18:51,080 Speaker 1: they don't have to watch the cameras. Right. The machine 336 00:18:51,119 --> 00:18:52,959 Speaker 1: can do all that, and then if you can't do 337 00:18:53,040 --> 00:18:55,560 Speaker 1: something safely, if it can't come to a solution, it 338 00:18:55,840 --> 00:18:58,800 Speaker 1: can ask a person send a little texic, I don't 339 00:18:58,800 --> 00:19:00,879 Speaker 1: know what to do, and a person can, in a 340 00:19:01,000 --> 00:19:04,840 Speaker 1: very calm way, with no stress, tell it what to do. 341 00:19:05,080 --> 00:19:08,679 Speaker 1: But in the in the interim, they're doing something more important, 342 00:19:08,760 --> 00:19:11,679 Speaker 1: like looking at all the information that's being produced by 343 00:19:11,680 --> 00:19:14,600 Speaker 1: the instruments and having insight. You know, Ever since we 344 00:19:14,680 --> 00:19:17,480 Speaker 1: started sailing, there's been expectation of how ships interact with 345 00:19:17,520 --> 00:19:21,080 Speaker 1: each other. Let's see, you know, they've been codified by 346 00:19:21,200 --> 00:19:24,040 Speaker 1: the what the I M O. Right, they're called like 347 00:19:24,119 --> 00:19:27,119 Speaker 1: the regulations to prevent collisions at sea. We just call 348 00:19:27,200 --> 00:19:30,560 Speaker 1: them coal ricks. But they're quite nuanced. Like it's not 349 00:19:30,640 --> 00:19:33,000 Speaker 1: like they're called rules of the road, you know, after 350 00:19:33,119 --> 00:19:35,600 Speaker 1: like the idea of like cars, but they're they're much 351 00:19:35,640 --> 00:19:40,080 Speaker 1: more nuanced than like rules for cars. How you act 352 00:19:40,160 --> 00:19:43,000 Speaker 1: depends on the type of vessels that are interacting, like 353 00:19:43,040 --> 00:19:45,159 Speaker 1: if it's a sail boat or a fishing boat, or 354 00:19:45,200 --> 00:19:49,520 Speaker 1: a a container ship or a pleasure craft. Like imagine 355 00:19:49,520 --> 00:19:51,560 Speaker 1: if you're driving your car down the road and you're 356 00:19:51,560 --> 00:19:54,040 Speaker 1: at a stop sign, and then depending whether you could 357 00:19:54,040 --> 00:19:56,399 Speaker 1: go or not, depending on whether the other car about 358 00:19:56,440 --> 00:20:00,320 Speaker 1: the stop sign was a thrust, a bus or you know, 359 00:20:01,119 --> 00:20:05,240 Speaker 1: or something else, like, the rules change anyway. So that's 360 00:20:05,240 --> 00:20:08,520 Speaker 1: where humans are are really really good at. Is this 361 00:20:08,720 --> 00:20:17,399 Speaker 1: nuanced understanding of these these rules, um squashy rules. Yeah. So, 362 00:20:17,520 --> 00:20:19,320 Speaker 1: and that's where we've done. You know a lot of 363 00:20:19,320 --> 00:20:22,600 Speaker 1: our lot of our work on is in that area. 364 00:20:22,960 --> 00:20:27,800 Speaker 1: And that's the hardest part of this whole possit r. 365 00:20:28,800 --> 00:20:31,160 Speaker 1: I wonder if the ship ever got into any sticky 366 00:20:31,240 --> 00:20:34,119 Speaker 1: situations that the AI captain was able to get it 367 00:20:34,160 --> 00:20:38,560 Speaker 1: out of. One time we had a sailboat come at 368 00:20:38,640 --> 00:20:42,920 Speaker 1: us in the night head on reciprocal course, no lights on, 369 00:20:43,200 --> 00:20:46,600 Speaker 1: no radar reflector. Everybody was probably asleep and they just 370 00:20:46,640 --> 00:20:50,280 Speaker 1: had the autopilot on and um, we easily could have 371 00:20:50,320 --> 00:20:52,639 Speaker 1: speared them, or they would have actually hit us because 372 00:20:52,640 --> 00:20:58,359 Speaker 1: they were in violation of race regulations. But but that's common, right. 373 00:20:58,400 --> 00:21:00,720 Speaker 1: Let's see, when you're crossing, it's so likely, it's so 374 00:21:00,760 --> 00:21:03,800 Speaker 1: fast that you're going to run into somebody, but it happens, 375 00:21:03,800 --> 00:21:06,959 Speaker 1: so we you know, the ship took appropriate action and 376 00:21:07,000 --> 00:21:09,480 Speaker 1: moved so that that wouldn't happen. But it's not like 377 00:21:09,640 --> 00:21:12,560 Speaker 1: it seems very dramatic at the moment. But you know, 378 00:21:12,640 --> 00:21:15,240 Speaker 1: you see these things coming miles away and it unfolds 379 00:21:15,240 --> 00:21:18,000 Speaker 1: it like five miles an hour or something, right, So 380 00:21:18,040 --> 00:21:21,240 Speaker 1: it's yeah, so it seems more nervous than it is. 381 00:21:21,320 --> 00:21:23,959 Speaker 1: And I mean, weather was challenging, and we had some 382 00:21:24,080 --> 00:21:26,600 Speaker 1: failures technical and mechanical failures in the ship that were 383 00:21:26,720 --> 00:21:30,159 Speaker 1: very very challenging. But from the AI captain perspective, the 384 00:21:30,200 --> 00:21:33,280 Speaker 1: only time that we got annoyed was there was a 385 00:21:33,359 --> 00:21:36,680 Speaker 1: research ship that shall remain nameless from a university that 386 00:21:36,760 --> 00:21:39,359 Speaker 1: was coming along and was going to cross in front 387 00:21:39,359 --> 00:21:41,800 Speaker 1: of us by ten twelve miles, which is fine, and 388 00:21:41,960 --> 00:21:44,520 Speaker 1: they were going along, but they clearly saw us on there, 389 00:21:45,320 --> 00:21:49,000 Speaker 1: neither their radar or their automated identification system which we broadcast, 390 00:21:49,080 --> 00:21:51,760 Speaker 1: and they just at some point turned and came directly 391 00:21:51,800 --> 00:21:54,960 Speaker 1: at us at at an angle that it's the it's 392 00:21:55,000 --> 00:21:57,760 Speaker 1: the I'm messing with you angle, Yeah, the angle that 393 00:21:57,800 --> 00:22:01,080 Speaker 1: allows them to maintain right of way but makes it 394 00:22:01,359 --> 00:22:04,479 Speaker 1: very very difficult to understand their intent and take action. 395 00:22:05,000 --> 00:22:07,880 Speaker 1: So the ship was kind of like, if they had persisted, 396 00:22:07,920 --> 00:22:09,600 Speaker 1: it would have ended up kind of going around in 397 00:22:09,640 --> 00:22:13,520 Speaker 1: circles trying to avoid them. But but fortunately we had 398 00:22:13,520 --> 00:22:15,399 Speaker 1: a support boat that was coming out of Halifax to 399 00:22:15,480 --> 00:22:18,879 Speaker 1: meet it, and it physically got in between the Mayflower 400 00:22:18,920 --> 00:22:21,560 Speaker 1: and this research boat and so what are you doing? Oh, 401 00:22:21,800 --> 00:22:23,719 Speaker 1: we were just going to take a look, and but 402 00:22:23,760 --> 00:22:25,840 Speaker 1: we weren't going to get any closer than two miles 403 00:22:25,840 --> 00:22:26,920 Speaker 1: and it's like, well, what are you going to see 404 00:22:26,920 --> 00:22:29,479 Speaker 1: from two miles away? They absolutely are going to come 405 00:22:29,480 --> 00:22:32,560 Speaker 1: over and take a much closer look because they didn't 406 00:22:33,600 --> 00:22:37,479 Speaker 1: understand that the vessel was trying to avoid them. You know, 407 00:22:37,600 --> 00:22:39,800 Speaker 1: when they see these unmanned systems at sea, they're just 408 00:22:39,960 --> 00:22:44,280 Speaker 1: dumb robots, right, they just float around with winder wave power. 409 00:22:45,200 --> 00:22:46,880 Speaker 1: There are a bunch of side just coming back from 410 00:22:46,880 --> 00:22:49,560 Speaker 1: like a six week cruise, and I was like, oh, 411 00:22:49,640 --> 00:22:53,359 Speaker 1: that looks interesting, let's go take a look. Yeah, And 412 00:22:53,480 --> 00:22:56,200 Speaker 1: so that was the only thing that was annoying. Other 413 00:22:56,280 --> 00:22:58,800 Speaker 1: than that, it was getting into and out of port. 414 00:22:59,400 --> 00:23:01,920 Speaker 1: Getting out of Limouth was a little challenging. Once we 415 00:23:01,960 --> 00:23:03,720 Speaker 1: get outside twelve miles, we had a lot of fishing 416 00:23:03,720 --> 00:23:07,320 Speaker 1: boats to dodge, but that was fine. And then out 417 00:23:07,320 --> 00:23:09,920 Speaker 1: in the deep sea, it's just it's mostly the sea 418 00:23:09,960 --> 00:23:13,040 Speaker 1: that you're concerned with, and it's the fishing grounds are 419 00:23:13,080 --> 00:23:17,560 Speaker 1: always the trickiest place because yeah, because fishing boats do 420 00:23:17,560 --> 00:23:20,480 Speaker 1: whatever they want. Yeah, and they're like container ships. They're 421 00:23:20,480 --> 00:23:22,719 Speaker 1: not going to change course unless they have to, so 422 00:23:22,760 --> 00:23:25,360 Speaker 1: you can pretty much understand what they're what they're doing. 423 00:23:25,400 --> 00:23:29,080 Speaker 1: Fishing boats could be going along a nice straight line 424 00:23:29,200 --> 00:23:32,240 Speaker 1: and then all of a sudden do a money or 425 00:23:32,320 --> 00:23:35,359 Speaker 1: worse than iity degreeturn and they don't care about you, 426 00:23:35,400 --> 00:23:37,199 Speaker 1: and they just expect you to avoid them. And they 427 00:23:37,320 --> 00:23:39,520 Speaker 1: literally there's no one in the wheelhouse. Probably they're all 428 00:23:39,560 --> 00:23:41,399 Speaker 1: on the bad Those of the rules too, we're supposed 429 00:23:41,400 --> 00:23:44,960 Speaker 1: to avoid them and so, but what Bratt got it earlier? 430 00:23:44,960 --> 00:23:48,679 Speaker 1: It was things evolved very slowly, Like things don't happen 431 00:23:48,800 --> 00:23:50,879 Speaker 1: quickly at sea. It's sort of like, Okay, there's ship, 432 00:23:50,920 --> 00:23:55,159 Speaker 1: it's you know, it's it's twenty miles away. I've got 433 00:23:55,200 --> 00:23:56,640 Speaker 1: a little bit of time to figure out what I'm 434 00:23:56,640 --> 00:23:59,679 Speaker 1: gonna do. You don't ever try to put yourself into 435 00:24:00,080 --> 00:24:03,440 Speaker 1: situation where there's a risk of collision, so you make 436 00:24:03,480 --> 00:24:08,280 Speaker 1: decisions that so you don't put yourself at that risk. Right, So, 437 00:24:08,440 --> 00:24:10,399 Speaker 1: like I'm not going to cross the street at the 438 00:24:10,440 --> 00:24:16,119 Speaker 1: busiest place, I'm gonna cross it ata somewhere saying fishing boats, 439 00:24:16,200 --> 00:24:20,000 Speaker 1: container ships, scientists on a cruise. The vast majority of 440 00:24:20,040 --> 00:24:23,840 Speaker 1: vessels at sea are still of the not autonomous variety. 441 00:24:23,880 --> 00:24:26,840 Speaker 1: To wrap up their conversation, Lauren asked Brett and Don 442 00:24:27,080 --> 00:24:30,800 Speaker 1: where the technology they've developed is headed, what it means 443 00:24:30,840 --> 00:24:34,040 Speaker 1: for the humans who work at SEE, and what's next 444 00:24:34,080 --> 00:24:36,800 Speaker 1: for the two of them. What do you guys think 445 00:24:37,160 --> 00:24:40,240 Speaker 1: this type of automation means for the future of the 446 00:24:40,280 --> 00:24:44,680 Speaker 1: maritime industry and people who work in at First of all, 447 00:24:45,560 --> 00:24:47,720 Speaker 1: like we mentioned, Brett and I have both worked in 448 00:24:48,040 --> 00:24:51,960 Speaker 1: the ocean community for decades our entire careers, Like we 449 00:24:52,000 --> 00:24:53,960 Speaker 1: have enough in quite a lot of respect for the 450 00:24:54,000 --> 00:24:58,320 Speaker 1: people that work in this area. And this isn't about 451 00:24:58,359 --> 00:25:04,600 Speaker 1: a replacement technology an augmented augment What's what's the right 452 00:25:04,800 --> 00:25:10,199 Speaker 1: augmented intelligence? I mean, look, ships have always been the 453 00:25:10,280 --> 00:25:13,200 Speaker 1: leading edge of technology and almost every society up until 454 00:25:13,240 --> 00:25:17,400 Speaker 1: the twentieth century where we started into flight, and now 455 00:25:17,480 --> 00:25:22,480 Speaker 1: they're kind of resurging into really new technological areas. But 456 00:25:22,600 --> 00:25:24,000 Speaker 1: the point I'm trying to make is there was a 457 00:25:24,000 --> 00:25:25,960 Speaker 1: time when there were no propellers. There's a time when 458 00:25:25,960 --> 00:25:28,600 Speaker 1: there are no rudders, right, it was just sales and 459 00:25:28,640 --> 00:25:31,080 Speaker 1: steering oars. And then so it's been this evolution in 460 00:25:31,119 --> 00:25:33,760 Speaker 1: technology um and ships have always been right at the 461 00:25:33,800 --> 00:25:37,440 Speaker 1: absolute forefront of it from design and engineering and material science, 462 00:25:37,480 --> 00:25:40,840 Speaker 1: and you know, we've seen this sort of long evolution 463 00:25:40,840 --> 00:25:42,840 Speaker 1: of technology, and this is just another thing. So I 464 00:25:42,840 --> 00:25:45,639 Speaker 1: think you're going to see lots of areas where really 465 00:25:45,680 --> 00:25:50,520 Speaker 1: smart port of machine learning models helped, like to improve efficiencies. 466 00:25:50,560 --> 00:25:53,920 Speaker 1: And so we're at the advent of of a new 467 00:25:53,920 --> 00:25:57,960 Speaker 1: way of thinking about design and implementation of very sophisticated 468 00:25:58,920 --> 00:26:03,320 Speaker 1: solutions that are based in vast amounts of data analytics 469 00:26:03,320 --> 00:26:07,919 Speaker 1: that are hitherto impossible to address. What is next for 470 00:26:08,119 --> 00:26:13,119 Speaker 1: the Mayflower Autonomous Ship. We may do a few things 471 00:26:13,200 --> 00:26:15,760 Speaker 1: with the Coast Guard, and there's a few other folks 472 00:26:15,800 --> 00:26:19,080 Speaker 1: that want us to do some work on national marine 473 00:26:19,080 --> 00:26:22,720 Speaker 1: sanctuaries looking at cetacean populations, and so we'll do that 474 00:26:22,800 --> 00:26:24,640 Speaker 1: kind of thing with with it, and more and more 475 00:26:24,640 --> 00:26:26,680 Speaker 1: people will get involved in its day to day operation 476 00:26:26,720 --> 00:26:29,479 Speaker 1: and we'll have less sort of day to day input, 477 00:26:29,560 --> 00:26:33,280 Speaker 1: which is fine. And then the Captain is going into 478 00:26:33,320 --> 00:26:35,560 Speaker 1: a whole bunch of other projects and programs, and we're 479 00:26:35,760 --> 00:26:37,840 Speaker 1: just starting off on a new design for a much 480 00:26:37,920 --> 00:26:44,640 Speaker 1: larger ship for vast oceanic voyages, um maybe even a circumnavigation. 481 00:26:46,800 --> 00:26:50,320 Speaker 1: That's that's quite an effort. Yeah, And then we're going 482 00:26:50,359 --> 00:26:53,359 Speaker 1: to connect with with NASA with there, you know, with 483 00:26:53,400 --> 00:26:56,240 Speaker 1: the International Space Station and satellite networks and sort of 484 00:26:56,320 --> 00:27:00,560 Speaker 1: have them work collaboratively so the space assets see things 485 00:27:00,600 --> 00:27:03,080 Speaker 1: and they know there's another ship asset. So it's almost 486 00:27:03,160 --> 00:27:05,840 Speaker 1: like a satellite in revers it's like the inverse satellite 487 00:27:05,840 --> 00:27:08,760 Speaker 1: at sea, so it sees something from space and it's 488 00:27:08,760 --> 00:27:11,680 Speaker 1: as a ship such and such as over there ask 489 00:27:11,760 --> 00:27:13,239 Speaker 1: it to go and look at that and tell us 490 00:27:13,240 --> 00:27:15,720 Speaker 1: if what we're seeing is right, or collect a sample right, 491 00:27:15,760 --> 00:27:19,440 Speaker 1: and those things will work collaboratively without people. You kind 492 00:27:19,440 --> 00:27:22,360 Speaker 1: of opened up Pandora's box here. So we did this, 493 00:27:22,920 --> 00:27:25,440 Speaker 1: and now there's all these other things that we can do. 494 00:27:26,000 --> 00:27:28,479 Speaker 1: So yeah, and we just have to pick one that 495 00:27:28,520 --> 00:27:31,200 Speaker 1: we can do within the remainder of our lifetime. There 496 00:27:31,240 --> 00:27:34,199 Speaker 1: you go. Well, I I hope you. I hope you 497 00:27:34,240 --> 00:27:36,560 Speaker 1: both get to do all the new things that you 498 00:27:36,600 --> 00:27:39,640 Speaker 1: want and have capacity to do. Thank you both so 499 00:27:39,720 --> 00:27:43,480 Speaker 1: much for your time and good luck with future journeys 500 00:27:43,480 --> 00:27:49,240 Speaker 1: and projects. Thank you, hi everybody. In the centuries long 501 00:27:49,320 --> 00:27:54,760 Speaker 1: evolution of maritime technology, the Mayflower Autonomous Ship represents an 502 00:27:54,760 --> 00:28:00,119 Speaker 1: inflection point. The ship's success indicates that artificial intelligence and 503 00:28:00,280 --> 00:28:05,200 Speaker 1: automation are tools ready to be normalized within the nautical industry, 504 00:28:05,440 --> 00:28:08,280 Speaker 1: and that the advantages they provide will change the way 505 00:28:08,320 --> 00:28:13,600 Speaker 1: we conceive of shipbuilding. But the technology aboard the Mayflower 506 00:28:13,680 --> 00:28:18,800 Speaker 1: four hundred has implications beyond just application at see. Brett 507 00:28:18,800 --> 00:28:22,280 Speaker 1: and Don's project has shown that the potential reward for 508 00:28:22,359 --> 00:28:27,120 Speaker 1: innovative risk taking is to achieve something unprecedented, and that's 509 00:28:27,160 --> 00:28:31,399 Speaker 1: true for any industry, but like the original Mayflower Voyage 510 00:28:31,400 --> 00:28:34,919 Speaker 1: for hun years ago, it may require a leap of faith. 511 00:28:35,920 --> 00:28:39,200 Speaker 1: On the next episode of Smart Talks with IBM, what 512 00:28:39,320 --> 00:28:43,320 Speaker 1: does it take to create a sustainability focused global supply 513 00:28:43,440 --> 00:28:47,840 Speaker 1: chain innovative and equitable enough to connect our modern world? 514 00:28:48,280 --> 00:28:53,000 Speaker 1: We talk with Sherry Highness, IBMS global sustainability services leader 515 00:28:53,240 --> 00:28:58,600 Speaker 1: and offering leader for a sustainable supply chain. Smart Talks 516 00:28:58,600 --> 00:29:02,640 Speaker 1: with IBM is produced by Molly Sosha, David jaw, Royston Reserve, 517 00:29:03,040 --> 00:29:08,600 Speaker 1: Matt Romano, and Edith Russelo with Jacob Goldstein. Our engineers 518 00:29:08,600 --> 00:29:13,520 Speaker 1: are Jason Gambrel, Sarah Bruger and Ben Tolliday. Theme song 519 00:29:14,080 --> 00:29:19,120 Speaker 1: by Granmascope. Special thanks to Colly mcglory, Andy Kelly, Kathy 520 00:29:19,240 --> 00:29:23,440 Speaker 1: Callaghan and the eight Bar and IBM teams, as well 521 00:29:23,480 --> 00:29:28,160 Speaker 1: as the Pushkin Marketing team. Smart Talks with IBM is 522 00:29:28,160 --> 00:29:31,680 Speaker 1: a production of Pushkin Industries and I Heart Media. To 523 00:29:31,760 --> 00:29:35,240 Speaker 1: find more Pushkin podcasts, listen to the I Heart Radio app, 524 00:29:35,560 --> 00:29:41,400 Speaker 1: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Glacko. 525 00:29:42,360 --> 00:29:45,120 Speaker 1: This is a paid advertisement from IBM.