1 00:00:00,040 --> 00:00:05,000 Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got 2 00:00:05,040 --> 00:00:06,960 Speaker 1: something a little bit different to share with you. It 3 00:00:07,080 --> 00:00:10,680 Speaker 1: is a new edition of the Smart Talks podcast series, 4 00:00:10,720 --> 00:00:14,319 Speaker 1: which is produced in partnership with IBM. This season of 5 00:00:14,360 --> 00:00:18,640 Speaker 1: Smart Talks with IBM is all about new creators, the developers, 6 00:00:19,040 --> 00:00:22,600 Speaker 1: data scientists, c t o s, and other visionaries creatively 7 00:00:22,640 --> 00:00:27,120 Speaker 1: applying technology and business to drive change. They use their 8 00:00:27,160 --> 00:00:30,640 Speaker 1: knowledge and creativity to develop better ways of working, no 9 00:00:30,720 --> 00:00:34,840 Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries 10 00:00:34,920 --> 00:00:38,560 Speaker 1: podcast as they use their expertise to deepen these conversations. 11 00:00:39,040 --> 00:00:41,800 Speaker 1: Malcolm Gladwell will guide you through this season as your 12 00:00:41,840 --> 00:00:45,000 Speaker 1: host to provide his thoughts and analysis along the way. 13 00:00:45,320 --> 00:00:48,600 Speaker 1: Look out for new episodes of Smart Talks with IBM 14 00:00:48,680 --> 00:00:52,040 Speaker 1: every month on the I Heart Radio app, Apple Podcasts, 15 00:00:52,159 --> 00:00:55,480 Speaker 1: or wherever you get your podcasts. And learn more at 16 00:00:55,520 --> 00:01:04,000 Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to 17 00:01:04,080 --> 00:01:07,720 Speaker 1: Smart Talks with IBM, a podcast from Pushkin Industries, I 18 00:01:07,880 --> 00:01:12,560 Speaker 1: Heart Radio and IBM. I'm Malcolm Blobwell. This season we're 19 00:01:12,560 --> 00:01:16,880 Speaker 1: talking to new creators, the developers, data scientists, c t 20 00:01:17,040 --> 00:01:21,000 Speaker 1: o s, and other visionaries who are creatively applying technology 21 00:01:21,000 --> 00:01:24,720 Speaker 1: and business to drive change. Channeling their knowledge and expertise, 22 00:01:24,880 --> 00:01:29,600 Speaker 1: they're developing more creative and effective solutions no matter the industry. 23 00:01:30,200 --> 00:01:34,160 Speaker 1: Our guest today are Brett Fanoff and Don Scott. Brett 24 00:01:34,160 --> 00:01:37,960 Speaker 1: and Dawn are responsible for creating the world's first unmanned, 25 00:01:38,240 --> 00:01:42,480 Speaker 1: fully autonomous ship to cross the Atlantic Ocean, a research 26 00:01:42,560 --> 00:01:46,720 Speaker 1: vessel they've dubbed the Mayflower four hundred. Brett is the 27 00:01:46,840 --> 00:01:51,040 Speaker 1: director of the Mayflower Autonomous Ship Project and Dawn is 28 00:01:51,040 --> 00:01:56,720 Speaker 1: the CTO of Marine AI. On June thirty two, the 29 00:01:56,800 --> 00:02:01,960 Speaker 1: Mayflower four hundred successfully completed its voyage from Plymouth, UK 30 00:02:02,440 --> 00:02:07,280 Speaker 1: to Plymouth, Massachusetts. It's both an homage to the original Mayflower, 31 00:02:07,600 --> 00:02:10,720 Speaker 1: which crossed the Atlantic forundered years earlier, and a bell 32 00:02:10,800 --> 00:02:14,880 Speaker 1: weather for the ways autonomous technology will push the boundaries 33 00:02:14,880 --> 00:02:20,160 Speaker 1: of maritime exploration in the next four years. On today's show, 34 00:02:20,200 --> 00:02:24,880 Speaker 1: the Unlikely Origins of a self directed Ship, some motion misadventures, 35 00:02:24,919 --> 00:02:27,960 Speaker 1: and what AI and machine learning will mean for the 36 00:02:28,000 --> 00:02:32,560 Speaker 1: future of seafaring and beyond, Brett and Dawn spoke with 37 00:02:32,680 --> 00:02:37,119 Speaker 1: Lauren Ober, host of the forthcoming Pushkin podcast The Loudest 38 00:02:37,200 --> 00:02:40,160 Speaker 1: Girl in the World. Lauren is a longtime radio host 39 00:02:40,200 --> 00:02:43,840 Speaker 1: and reporter, helming shows like NPRS, The Big Listen and 40 00:02:44,000 --> 00:02:50,079 Speaker 1: Spectacular failures from American public Media. Okay, now let's get 41 00:02:50,120 --> 00:02:57,600 Speaker 1: to the interview with Brett Fanoff and Don Scott. Don 42 00:02:57,720 --> 00:03:00,280 Speaker 1: and Brett, it's really great to be talking with you 43 00:03:00,320 --> 00:03:03,360 Speaker 1: guys today. I was wondering for each of you, what 44 00:03:03,639 --> 00:03:06,240 Speaker 1: is the draw of the sea? I mean, it's like 45 00:03:06,280 --> 00:03:11,840 Speaker 1: this expansive place. It feels so unknown in so many ways. Um, 46 00:03:11,880 --> 00:03:15,000 Speaker 1: but I'm curious, like, what is the allure there for me? 47 00:03:15,080 --> 00:03:17,239 Speaker 1: It's I wanted to be I wanted to do aerospace, 48 00:03:17,240 --> 00:03:19,480 Speaker 1: so I always feel like I'm like the poor cousin 49 00:03:19,960 --> 00:03:23,519 Speaker 1: of aerospace. But it isn't. It's actually it's harder to 50 00:03:23,520 --> 00:03:26,639 Speaker 1: to do the underwater stuff. It's closer. It's just harder 51 00:03:26,680 --> 00:03:31,200 Speaker 1: than being in space. It's it's incredibly hostile and wildly unexplored. 52 00:03:31,240 --> 00:03:34,000 Speaker 1: And why what I like about it is that you know, 53 00:03:34,160 --> 00:03:35,960 Speaker 1: you can take a bucket and go down to the beach, 54 00:03:36,040 --> 00:03:38,480 Speaker 1: get a bucket of water, analyze the bucket of water 55 00:03:38,680 --> 00:03:41,520 Speaker 1: for the next twenty years, and you know, chances are 56 00:03:41,520 --> 00:03:43,200 Speaker 1: pretty high you're gonna have a couple of things in 57 00:03:43,200 --> 00:03:46,600 Speaker 1: there that nobody's ever seen before, and that's every bucket 58 00:03:46,600 --> 00:03:49,440 Speaker 1: of water everywhere in the world, right, So I like 59 00:03:49,520 --> 00:03:51,560 Speaker 1: the idea that you get to discover something new all 60 00:03:51,600 --> 00:03:54,800 Speaker 1: the time. And it's also hard. It's a difficult place 61 00:03:54,840 --> 00:03:56,840 Speaker 1: to work, so it challenges you to come up with 62 00:03:56,880 --> 00:03:59,640 Speaker 1: new ideas and new ways to do things and new materials, 63 00:03:59,680 --> 00:04:01,640 Speaker 1: and that's what I like about it. I don't know, 64 00:04:01,760 --> 00:04:04,640 Speaker 1: don what about you, Yeah, I mean, um, there's obviously 65 00:04:04,640 --> 00:04:07,760 Speaker 1: an allure and draw there's some great descriptions about why 66 00:04:07,800 --> 00:04:10,480 Speaker 1: people are drawn to the ocean. Talk to the authors 67 00:04:10,480 --> 00:04:12,400 Speaker 1: and the poets, you know, it's it's definitely a real 68 00:04:12,720 --> 00:04:15,160 Speaker 1: sort of visceral feeling that people get. I think you're 69 00:04:15,160 --> 00:04:18,600 Speaker 1: find that the people that are involved in ocean engineering 70 00:04:18,640 --> 00:04:21,719 Speaker 1: and or marine sides like that. You don't just sort 71 00:04:21,720 --> 00:04:25,080 Speaker 1: of fall into this career by accident. You make proactive 72 00:04:25,120 --> 00:04:28,720 Speaker 1: decisions to get involved in that environment. So you have 73 00:04:28,800 --> 00:04:31,919 Speaker 1: a bunch of people working there that that want to 74 00:04:31,920 --> 00:04:35,320 Speaker 1: be there and sort of have this uh understanding of 75 00:04:36,880 --> 00:04:38,919 Speaker 1: that this is the place they want to be and 76 00:04:38,960 --> 00:04:41,520 Speaker 1: this is where they want to work. So that becomes 77 00:04:41,560 --> 00:04:45,960 Speaker 1: a very very positive work environment workspace because everyone's they 78 00:04:46,000 --> 00:04:50,040 Speaker 1: want to be there, So there's that. Yeah, it's highly collaborative, 79 00:04:50,120 --> 00:04:53,760 Speaker 1: isn't it. It's um like anything, there's personalities, but it 80 00:04:54,000 --> 00:04:55,960 Speaker 1: tends to be a lot of fun more than anything else. 81 00:04:56,000 --> 00:04:58,320 Speaker 1: It's challenging in all the ways that make life interesting. 82 00:04:58,360 --> 00:04:59,960 Speaker 1: And then it also tends to be a good time. 83 00:05:00,279 --> 00:05:03,920 Speaker 1: And you can't work in the ocean by yourself, like, well, 84 00:05:03,960 --> 00:05:07,800 Speaker 1: you can, but it's kind of hard. So, like Brett said, 85 00:05:07,800 --> 00:05:11,240 Speaker 1: it's an incredibly collaborative environment. I mean, if you want 86 00:05:11,279 --> 00:05:13,559 Speaker 1: to be doing anything of significance, you have to be 87 00:05:13,760 --> 00:05:16,240 Speaker 1: working as a group because you need to rely on 88 00:05:16,279 --> 00:05:20,680 Speaker 1: each other. It is an incredibly dynamic, hostile environment, very humbling. 89 00:05:21,200 --> 00:05:25,840 Speaker 1: So you find you you're going to achieve success as 90 00:05:25,880 --> 00:05:28,719 Speaker 1: a collaborative group as opposed to some sort of lone 91 00:05:28,760 --> 00:05:32,120 Speaker 1: wolf type out to right. Okay, so we're here to 92 00:05:32,160 --> 00:05:36,760 Speaker 1: talk about the Mayflower Autonomous Ship project, which obviously is 93 00:05:36,880 --> 00:05:41,320 Speaker 1: very cool. Um, how exactly did you guys decide to 94 00:05:41,440 --> 00:05:47,039 Speaker 1: build an autonomous ship and then model it after the Mayflower? 95 00:05:47,600 --> 00:05:49,440 Speaker 1: I mean it was just to hold my beer kind 96 00:05:49,440 --> 00:05:53,240 Speaker 1: of thing. Um, I'm sure what it really is, it 97 00:05:53,279 --> 00:05:56,320 Speaker 1: really was, it really was. Yeah, what it really was 98 00:05:56,400 --> 00:06:00,560 Speaker 1: is it was so in meeting with the City of 99 00:06:00,560 --> 00:06:03,360 Speaker 1: Plymouth on something else. They were talking about what they 100 00:06:03,400 --> 00:06:05,360 Speaker 1: were going to do and maybe build a replica ship, 101 00:06:05,400 --> 00:06:08,440 Speaker 1: of which there's already one. And I thought that wasn't 102 00:06:08,480 --> 00:06:12,520 Speaker 1: the best idea. And you're talking for anniversary. Yeah, And 103 00:06:12,560 --> 00:06:16,560 Speaker 1: so I was a little bit indelicate in my comment 104 00:06:16,640 --> 00:06:19,760 Speaker 1: as to how they wanted to proceed with a possible replica. 105 00:06:19,800 --> 00:06:22,280 Speaker 1: Think you said it was a stupid idea, I said, 106 00:06:22,279 --> 00:06:25,000 Speaker 1: I said it was stupid. And uh, and there was 107 00:06:25,080 --> 00:06:27,280 Speaker 1: more I couldn't resist and and and I said, there 108 00:06:27,279 --> 00:06:29,080 Speaker 1: already is one, you know, And it's it's just I 109 00:06:29,120 --> 00:06:32,160 Speaker 1: grew up near there. And and so they said, all right, 110 00:06:32,240 --> 00:06:33,640 Speaker 1: smart guy, what are you gonna do. I was like, oh, 111 00:06:33,640 --> 00:06:37,360 Speaker 1: we should build one that challenges us technologically and from 112 00:06:37,360 --> 00:06:40,159 Speaker 1: an engineering perspective and sort of invokes the spirit of 113 00:06:40,200 --> 00:06:44,039 Speaker 1: the original risk taking and do something that informs the 114 00:06:44,040 --> 00:06:46,440 Speaker 1: next four years. And everybody was like, yeah, you should 115 00:06:46,480 --> 00:06:48,520 Speaker 1: do that, and I was like, you know what, I 116 00:06:48,560 --> 00:06:52,560 Speaker 1: will hold my beer. And so so I called Don 117 00:06:52,600 --> 00:06:55,359 Speaker 1: after the meeting and I was like, oh, Don, we 118 00:06:55,360 --> 00:06:57,760 Speaker 1: we have to build an AI. I need Captain Watson 119 00:06:57,920 --> 00:06:59,240 Speaker 1: because we're going to build an A ton of a 120 00:06:59,320 --> 00:07:03,400 Speaker 1: ship across the Atlantic, and he was like great and 121 00:07:03,480 --> 00:07:06,719 Speaker 1: so yeah, and it was just that literally, that glib, 122 00:07:06,920 --> 00:07:08,400 Speaker 1: but it also I mean, he and I have been 123 00:07:08,440 --> 00:07:10,960 Speaker 1: working on unmanned systems and autonomous systems for a long 124 00:07:11,000 --> 00:07:14,280 Speaker 1: time together, twenty plus years, and so I wanted to 125 00:07:14,280 --> 00:07:16,040 Speaker 1: see where we could get to, like, how hard could 126 00:07:16,040 --> 00:07:18,600 Speaker 1: this be? Right? I mean? And AI, sure, let's do it. Then, 127 00:07:19,360 --> 00:07:22,960 Speaker 1: So we built a ship. You mentioned capturing the spirit 128 00:07:22,960 --> 00:07:27,640 Speaker 1: of the original Mayflower Journey, and I wonder what exactly 129 00:07:27,680 --> 00:07:30,880 Speaker 1: where you're trying to capture. Was it the spirit of 130 00:07:30,920 --> 00:07:34,280 Speaker 1: taking risks or was it doing something that hadn't been 131 00:07:34,320 --> 00:07:37,960 Speaker 1: done before? What we were trying to do. We knew 132 00:07:37,960 --> 00:07:40,600 Speaker 1: it was really hard, right like, and it was a 133 00:07:40,680 --> 00:07:43,760 Speaker 1: huge amount of risk to undertake it. Press the real 134 00:07:43,840 --> 00:07:46,080 Speaker 1: risk taker. He's the one with the big ideas and 135 00:07:46,320 --> 00:07:48,480 Speaker 1: wants to take the risk. I'm I'm a little more 136 00:07:48,920 --> 00:07:52,720 Speaker 1: cautious and sort of pragmatic in the sense of, Okay, 137 00:07:52,760 --> 00:07:55,720 Speaker 1: what's going to take to do that? We we actually 138 00:07:55,760 --> 00:07:57,360 Speaker 1: didn't think we were going to make it, or I 139 00:07:57,480 --> 00:07:59,760 Speaker 1: fully expected at some point the ocean we get annoyed 140 00:07:59,760 --> 00:08:02,640 Speaker 1: and mighte us, you know, pilgrims like that to me 141 00:08:02,720 --> 00:08:04,640 Speaker 1: is what's interesting. The pilgrims took a risk, right, So 142 00:08:04,720 --> 00:08:08,560 Speaker 1: every one of them fully expected that they would die 143 00:08:09,200 --> 00:08:12,040 Speaker 1: if not on the voyage within like the first year. Right, 144 00:08:12,480 --> 00:08:15,960 Speaker 1: That's how it was, and it was worth it to 145 00:08:16,000 --> 00:08:20,280 Speaker 1: them to take that risk. So our risk is infinitesimal 146 00:08:20,320 --> 00:08:23,480 Speaker 1: by comparison, Right, it's tiny. What was our risk, really, 147 00:08:23,680 --> 00:08:27,440 Speaker 1: We'd lose a ship we spent some money on. So 148 00:08:27,480 --> 00:08:31,680 Speaker 1: what the knowledge about how to approach these problems is, 149 00:08:31,840 --> 00:08:33,720 Speaker 1: and the and the experience that you get to give 150 00:08:33,800 --> 00:08:37,200 Speaker 1: people to take risk at that level from an engineering 151 00:08:37,200 --> 00:08:39,800 Speaker 1: perspective is really important. Right, somebody had to do the 152 00:08:39,840 --> 00:08:43,840 Speaker 1: first open heart surgery and took a risk. Now we're 153 00:08:43,840 --> 00:08:46,000 Speaker 1: not doing open heart surgery, right, No one's going to die. 154 00:08:46,400 --> 00:08:49,040 Speaker 1: So what's appealing about the risk thing is it has 155 00:08:49,040 --> 00:08:52,720 Speaker 1: a technical risk and environmental risk, and then there's a 156 00:08:52,800 --> 00:08:56,160 Speaker 1: legislative and regulatory risk. Because we had to have our 157 00:08:56,240 --> 00:08:58,840 Speaker 1: fights with various agencies about the fact that they didn't 158 00:08:58,840 --> 00:09:00,600 Speaker 1: have a law that said we couldn't so they didn't 159 00:09:00,600 --> 00:09:02,760 Speaker 1: get to say no just because they didn't want us to. 160 00:09:03,520 --> 00:09:06,560 Speaker 1: And at the same time trying to create a reliable 161 00:09:06,600 --> 00:09:09,720 Speaker 1: machine and then some sort of an AI machine learning 162 00:09:09,760 --> 00:09:13,960 Speaker 1: based system that would be safe whatever that is in 163 00:09:14,000 --> 00:09:16,840 Speaker 1: the middle of the ocean. It's really interesting and gives 164 00:09:16,880 --> 00:09:18,880 Speaker 1: people a lot to a lot of purchase for different 165 00:09:18,880 --> 00:09:22,199 Speaker 1: people with different skill sets to collaborate. Brett and John 166 00:09:22,240 --> 00:09:26,520 Speaker 1: started developing the Mayflower Autono, a ship in It took 167 00:09:26,559 --> 00:09:29,480 Speaker 1: them six years to figure out both the software and 168 00:09:29,520 --> 00:09:32,760 Speaker 1: the body of the boat itself. In that time, over 169 00:09:32,840 --> 00:09:37,000 Speaker 1: seventy people contributed to the project. Lauren asked Don and 170 00:09:37,080 --> 00:09:40,040 Speaker 1: Brett what it really took to go from hold my 171 00:09:40,160 --> 00:09:43,520 Speaker 1: beer to an actual ship? You know, it is mind 172 00:09:43,520 --> 00:09:47,359 Speaker 1: boggling when you think of how many people are involved, 173 00:09:47,360 --> 00:09:51,520 Speaker 1: how many people are touching this project, how many interesting 174 00:09:51,559 --> 00:09:54,600 Speaker 1: minds doing interesting things, but you have to funnel it 175 00:09:54,640 --> 00:09:59,000 Speaker 1: all into this one project. Well that I don't know 176 00:09:59,040 --> 00:10:00,560 Speaker 1: if it's that way. I mean, I guess you could 177 00:10:00,559 --> 00:10:03,040 Speaker 1: say there was one project, but there were lots of projects, 178 00:10:03,120 --> 00:10:05,600 Speaker 1: and so, you know, there was sort of the hardcore 179 00:10:05,640 --> 00:10:07,480 Speaker 1: group of people that are trying to build the actual 180 00:10:07,520 --> 00:10:09,839 Speaker 1: software that works, and then there's the guys trying to 181 00:10:09,880 --> 00:10:13,160 Speaker 1: build the hardware and they have an interface, but they're 182 00:10:13,200 --> 00:10:16,840 Speaker 1: parallel pursuits that don't have direct overlap. And then we 183 00:10:16,880 --> 00:10:19,200 Speaker 1: said yes a lot to anybody who wanted to help, 184 00:10:19,840 --> 00:10:22,880 Speaker 1: because we learned from experience that most people don't last 185 00:10:22,960 --> 00:10:25,760 Speaker 1: in terms of the ability to stick out four or 186 00:10:25,800 --> 00:10:28,040 Speaker 1: five years focus on the projects very hard. And so 187 00:10:28,120 --> 00:10:30,480 Speaker 1: the people that I wanted to stick it out and 188 00:10:30,480 --> 00:10:33,760 Speaker 1: bring it to fruition ended up, you know, sticking it 189 00:10:33,800 --> 00:10:35,880 Speaker 1: out and that was great, you know. And then there 190 00:10:35,920 --> 00:10:37,480 Speaker 1: are all sorts of different things. There was a group 191 00:10:37,720 --> 00:10:39,679 Speaker 1: making a web interface so that they could show the 192 00:10:39,720 --> 00:10:41,920 Speaker 1: world what we were doing, and you know, then there 193 00:10:41,960 --> 00:10:44,720 Speaker 1: was a PR group that was marketing things and sort 194 00:10:44,720 --> 00:10:46,920 Speaker 1: of talking about how we tell the world about it, 195 00:10:46,960 --> 00:10:49,960 Speaker 1: and we would support them. But it's hard to describe 196 00:10:49,960 --> 00:10:51,880 Speaker 1: it as one project. I guess would be my position. 197 00:10:51,880 --> 00:10:56,040 Speaker 1: It's lots of interlinked programs, right, Sure, I get that, 198 00:10:56,160 --> 00:10:59,440 Speaker 1: I get that. Can you tell me more about how 199 00:10:59,520 --> 00:11:03,640 Speaker 1: automate is built into the ship and how it works. Well, 200 00:11:03,760 --> 00:11:07,559 Speaker 1: there's tons of automation and Mayflow, I mean Mayflower is 201 00:11:08,080 --> 00:11:12,199 Speaker 1: like most robotics systems, right, So you peel it open 202 00:11:12,200 --> 00:11:15,199 Speaker 1: and you find you know, programmable logic controllers and motor 203 00:11:15,280 --> 00:11:18,760 Speaker 1: drives and also its of other things sensors and industrial 204 00:11:18,800 --> 00:11:21,240 Speaker 1: automation that you'd see, you know, in an elevator or 205 00:11:21,240 --> 00:11:25,360 Speaker 1: an escalator or industrial machinery for manufacture. And that's one 206 00:11:25,640 --> 00:11:27,640 Speaker 1: sort of layer of it. Right, So you've got the 207 00:11:27,640 --> 00:11:30,400 Speaker 1: basic analog control, then you've got sort of a veneer 208 00:11:30,520 --> 00:11:33,839 Speaker 1: of automation, and then what I would call sophisticated automation, 209 00:11:33,880 --> 00:11:36,880 Speaker 1: which don and I have worked on for decades in 210 00:11:36,920 --> 00:11:39,800 Speaker 1: the marine space. So all that's in there. And you know, 211 00:11:40,000 --> 00:11:42,800 Speaker 1: Donn and I talked really early on if I just 212 00:11:42,840 --> 00:11:44,640 Speaker 1: wanted to get across the Atlantic, we could have bought 213 00:11:44,640 --> 00:11:48,280 Speaker 1: an old fishing boat, filled up the fisholds with diesel fuel, 214 00:11:48,320 --> 00:11:50,480 Speaker 1: and put a cheap autopilot on it and sent it. 215 00:11:50,559 --> 00:11:54,480 Speaker 1: It probably would have got across. But so what it's 216 00:11:54,520 --> 00:11:58,960 Speaker 1: not reducing risk, and it's not unburdening a person, and 217 00:11:59,000 --> 00:12:03,280 Speaker 1: it's not doing anything really clever or sophisticated. And so 218 00:12:03,360 --> 00:12:05,600 Speaker 1: what we were more interested in was getting to a 219 00:12:05,600 --> 00:12:08,000 Speaker 1: point where instead of having to tell it to do everything, 220 00:12:08,640 --> 00:12:11,720 Speaker 1: saying go do this task right, a goal like go 221 00:12:11,840 --> 00:12:13,960 Speaker 1: to Plymouth right, and then while you're doing that, oh, 222 00:12:14,000 --> 00:12:16,240 Speaker 1: by the way, while you're doing that, collect all this 223 00:12:16,320 --> 00:12:18,920 Speaker 1: science data and if you see anything unusual, tell us 224 00:12:19,040 --> 00:12:21,880 Speaker 1: and and while you're looking for all these unusual things 225 00:12:21,880 --> 00:12:26,240 Speaker 1: and trying to achieve your goal don't hit anything. So 226 00:12:26,280 --> 00:12:29,520 Speaker 1: then what role did IBM s technology play in all 227 00:12:29,559 --> 00:12:33,559 Speaker 1: of this? Yeah, I mean their their technology is all 228 00:12:33,600 --> 00:12:36,960 Speaker 1: over the ship. Probably the main contribution it was the 229 00:12:37,000 --> 00:12:41,640 Speaker 1: decision making process or it's it's an automation TOOLM operational 230 00:12:41,679 --> 00:12:45,640 Speaker 1: decision manager. It's actually a financial services tool. It's for 231 00:12:46,160 --> 00:12:50,720 Speaker 1: your making decisions about the viability of a transaction, whether 232 00:12:50,760 --> 00:12:54,040 Speaker 1: it's fraud or order or alone or let's say, And 233 00:12:54,480 --> 00:12:58,760 Speaker 1: we were being presented this by one of the ODIUM engineers, 234 00:12:58,840 --> 00:13:00,360 Speaker 1: and I remember sitting in the room with Brett thing, 235 00:13:00,520 --> 00:13:03,880 Speaker 1: what what in the world does uh financial services product 236 00:13:03,960 --> 00:13:06,920 Speaker 1: have to do with marine navigation? And they sort of 237 00:13:06,920 --> 00:13:11,000 Speaker 1: were brought to realize by the IBM engineer how this is. 238 00:13:11,440 --> 00:13:13,920 Speaker 1: This isn't really so much about financial services as it 239 00:13:14,000 --> 00:13:17,720 Speaker 1: is about making making really difficult decisions in a really 240 00:13:17,760 --> 00:13:21,360 Speaker 1: complex environment, which is what they do in financial services. 241 00:13:21,400 --> 00:13:23,760 Speaker 1: But it's also exactly what we needed to do in 242 00:13:24,400 --> 00:13:27,960 Speaker 1: re navigation. And when it's when the system was actually undering, 243 00:13:28,280 --> 00:13:32,800 Speaker 1: it would create a log essentially of why that decision 244 00:13:32,880 --> 00:13:36,400 Speaker 1: was made, so they can validate that decision and verify 245 00:13:36,480 --> 00:13:38,840 Speaker 1: and validate that that that was in fact the right decision. 246 00:13:39,360 --> 00:13:41,640 Speaker 1: And um, so that's a that's one of the key 247 00:13:41,920 --> 00:13:44,320 Speaker 1: IVM tools that are on board. Well, one of the 248 00:13:44,360 --> 00:13:47,640 Speaker 1: things you might want to consider about that is the fundamentals, Right, 249 00:13:47,720 --> 00:13:50,440 Speaker 1: the theoretical independence of all the AI that we're deploying 250 00:13:50,480 --> 00:13:54,240 Speaker 1: now have been sort of understood for decades, right, and 251 00:13:54,280 --> 00:13:56,280 Speaker 1: so now we just happened to live in a world 252 00:13:56,840 --> 00:13:59,520 Speaker 1: where the microprocesses are up to snuff that they can 253 00:13:59,559 --> 00:14:02,600 Speaker 1: deplace some of these very sophisticated theoretical and reality and 254 00:14:02,640 --> 00:14:04,920 Speaker 1: all of which IBM has been involved with from inception, 255 00:14:05,600 --> 00:14:08,160 Speaker 1: based on its pedigree is in the national business machines. 256 00:14:08,600 --> 00:14:11,800 Speaker 1: There isn't an IBM product that I can think of 257 00:14:12,120 --> 00:14:15,839 Speaker 1: that we haven't tried to utilize the deploying so it's 258 00:14:15,920 --> 00:14:18,720 Speaker 1: it's it's everywhere in the ship. Yeah, I don't think 259 00:14:18,720 --> 00:14:22,560 Speaker 1: a lot of people think of technology as as as 260 00:14:22,600 --> 00:14:27,160 Speaker 1: a creative pursuit, but I imagine building an autonomous ship 261 00:14:27,200 --> 00:14:30,480 Speaker 1: from scratch takes a lot of creativity. And I'm wondering, 262 00:14:30,600 --> 00:14:34,520 Speaker 1: do you guys think of your work as creative? Yeah, 263 00:14:34,800 --> 00:14:39,200 Speaker 1: engineering is essentially designing technological innovation sort of do you 264 00:14:39,200 --> 00:14:42,280 Speaker 1: think of it as a very logical process, and there 265 00:14:42,360 --> 00:14:45,080 Speaker 1: is that, for sure, but there's an incredible amount of 266 00:14:45,200 --> 00:14:48,760 Speaker 1: innovation involved too, Like there's no template for what we're doing. 267 00:14:49,000 --> 00:14:51,720 Speaker 1: And you know, we call it white paper design, where 268 00:14:51,720 --> 00:14:54,960 Speaker 1: you're basically given a blank piece of paper and a goal, 269 00:14:55,440 --> 00:14:59,760 Speaker 1: which is, okay, ship that's going to cross the Atlantic, Um, okay, 270 00:14:59,760 --> 00:15:02,280 Speaker 1: come up with some ideas, right, So I mean it 271 00:15:02,400 --> 00:15:06,720 Speaker 1: requires major conceptual leaps and then the technical skill to 272 00:15:06,760 --> 00:15:10,200 Speaker 1: realize those those leaps. You're not going to make any 273 00:15:10,240 --> 00:15:13,440 Speaker 1: advances just doing things the way you've always done them. Right. 274 00:15:13,520 --> 00:15:17,160 Speaker 1: You need to stretch right, and the only way it 275 00:15:17,240 --> 00:15:21,920 Speaker 1: stretches what implementing new ideas, like you can spend a decade. 276 00:15:22,200 --> 00:15:24,440 Speaker 1: We call it power point engineering right where you do 277 00:15:24,520 --> 00:15:26,800 Speaker 1: nothing but think of things. We don't actually do anything, 278 00:15:27,360 --> 00:15:31,080 Speaker 1: as opposed to what we call full contact engineering, where 279 00:15:31,120 --> 00:15:35,480 Speaker 1: you actually built the boat, right, the software to go 280 00:15:35,520 --> 00:15:37,560 Speaker 1: on the boat and send it out on the water. 281 00:15:38,760 --> 00:15:41,400 Speaker 1: Get your kick like, get sea sick, you know, all 282 00:15:41,400 --> 00:15:43,280 Speaker 1: that sort of fun stuff that happens when you're dont 283 00:15:43,320 --> 00:15:47,800 Speaker 1: see trials um. And because that's where you that's where 284 00:15:47,840 --> 00:15:50,480 Speaker 1: the actual learning is happening, that's where the actual development 285 00:15:50,560 --> 00:15:53,880 Speaker 1: is happening is being out on the ocean. Crossing the 286 00:15:53,880 --> 00:15:57,240 Speaker 1: Atlantic is no small voyage for any vessel, but the 287 00:15:57,320 --> 00:16:01,120 Speaker 1: Mayflower Autonomous Ship Project is more than just about sailing 288 00:16:01,160 --> 00:16:05,440 Speaker 1: from point A to point B. Automation and AI have 289 00:16:05,640 --> 00:16:08,880 Speaker 1: game changing implications for the way we design the next 290 00:16:08,960 --> 00:16:12,320 Speaker 1: generation of vessels and the way these vessels will behave 291 00:16:12,680 --> 00:16:16,400 Speaker 1: and interact at sea. Ships will be able to gather 292 00:16:16,520 --> 00:16:20,720 Speaker 1: data from the ocean by themselves, providing humans with critical 293 00:16:20,760 --> 00:16:26,040 Speaker 1: information we need to address problems like global warming, ocean pollution, 294 00:16:26,480 --> 00:16:30,080 Speaker 1: and our impact on marine life. For instance, the Mayfire 295 00:16:30,120 --> 00:16:34,880 Speaker 1: four hundred can sample ocean water for microplastics and record 296 00:16:35,040 --> 00:16:40,080 Speaker 1: audio of whale vocalizations. Taking the human factor out of 297 00:16:40,080 --> 00:16:43,920 Speaker 1: a ship allows us to explore new designs and functions 298 00:16:43,960 --> 00:16:47,920 Speaker 1: that haven't been imagined before. Lauren asked bread and Down 299 00:16:48,320 --> 00:16:51,040 Speaker 1: more about this. What are some of the benefits of 300 00:16:51,080 --> 00:16:55,640 Speaker 1: having an unmanned vessel, like, how does automation push the 301 00:16:55,640 --> 00:16:59,040 Speaker 1: boundaries of what we can do out in the ocean. Well, 302 00:16:59,040 --> 00:17:01,640 Speaker 1: the few major apples right or through facets to that 303 00:17:01,720 --> 00:17:04,840 Speaker 1: one is you can do some risky things when you 304 00:17:04,840 --> 00:17:06,879 Speaker 1: don't have the people there right because no one's going 305 00:17:06,920 --> 00:17:09,760 Speaker 1: to be lost at sea. And then the other thing 306 00:17:09,840 --> 00:17:11,960 Speaker 1: is you can drive cost down, and I mean cost 307 00:17:12,040 --> 00:17:15,480 Speaker 1: financially but also environmental cost, right, because you can use 308 00:17:15,520 --> 00:17:19,000 Speaker 1: a far less energy to accomplish a similar goal. And 309 00:17:19,040 --> 00:17:21,680 Speaker 1: then what that allows you to do is have more Right. 310 00:17:21,720 --> 00:17:26,240 Speaker 1: So instead of say having one fifty million dollar hundred 311 00:17:26,240 --> 00:17:28,680 Speaker 1: million dollar research ship, which is the kind of numbers 312 00:17:28,720 --> 00:17:31,720 Speaker 1: you're talking about to take scientists to see, you can 313 00:17:31,760 --> 00:17:35,679 Speaker 1: have twenty or thirty or forty million dollars or two 314 00:17:35,760 --> 00:17:39,000 Speaker 1: million dollar ships that go out and work collaboratively with 315 00:17:39,040 --> 00:17:42,040 Speaker 1: space based assets and with one another and collect vast 316 00:17:42,040 --> 00:17:44,800 Speaker 1: amounts of data from disparate parts of the ocean. And 317 00:17:44,840 --> 00:17:48,560 Speaker 1: then you use that data to create information that informs 318 00:17:48,560 --> 00:17:51,440 Speaker 1: where you send the man vessel, right, so that they 319 00:17:51,480 --> 00:17:54,239 Speaker 1: get the most out of their time at sea. So 320 00:17:54,240 --> 00:17:57,240 Speaker 1: it's about enabling the people. It's about leaving the humans 321 00:17:57,280 --> 00:17:59,840 Speaker 1: to do the uniquely human part, which is have the insight, 322 00:18:00,080 --> 00:18:04,040 Speaker 1: the intuition and and the creativity. And so you know, 323 00:18:04,160 --> 00:18:07,199 Speaker 1: that's why it's important, and we're going to see an 324 00:18:07,240 --> 00:18:09,440 Speaker 1: increasing amount of this, and I think it's also important 325 00:18:09,440 --> 00:18:11,440 Speaker 1: for people to get comfortable with the idea that these 326 00:18:11,440 --> 00:18:14,920 Speaker 1: things will be roaming around and that it's okay. Yeah, 327 00:18:15,119 --> 00:18:17,720 Speaker 1: And and on an interim basis, I mean, we're also 328 00:18:17,840 --> 00:18:21,159 Speaker 1: talking about this same technology that allows a ship to 329 00:18:21,359 --> 00:18:25,720 Speaker 1: sail autonomously also can be used to assist a human 330 00:18:25,760 --> 00:18:31,320 Speaker 1: crew now, you know, basically be another set of eyes 331 00:18:31,520 --> 00:18:36,480 Speaker 1: and years be a watchkeeper for a manned vessel. Right, 332 00:18:36,840 --> 00:18:40,800 Speaker 1: I want to know more about the AI captain. How 333 00:18:40,840 --> 00:18:44,400 Speaker 1: did you build it so that it would be comparable 334 00:18:44,560 --> 00:18:47,159 Speaker 1: to the way a human captain might direct a ship. 335 00:18:47,560 --> 00:18:49,880 Speaker 1: What we're trying to do is augment the person, Right, 336 00:18:49,880 --> 00:18:53,439 Speaker 1: We're trying to let them be more of a person 337 00:18:53,520 --> 00:18:55,760 Speaker 1: than sort of. They don't have to watch the radar, 338 00:18:55,840 --> 00:18:58,400 Speaker 1: they don't have to watch the cameras. Right. The machine 339 00:18:58,440 --> 00:19:00,240 Speaker 1: can do all that, and then if it can't do 340 00:19:00,400 --> 00:19:02,879 Speaker 1: something safely, if it can't come to a solution, it 341 00:19:03,160 --> 00:19:06,120 Speaker 1: can ask a person send a little texic, I don't 342 00:19:06,119 --> 00:19:08,080 Speaker 1: know what to do, and then a person can, in 343 00:19:08,119 --> 00:19:11,720 Speaker 1: a very calm way, with no stress, tell it what 344 00:19:11,760 --> 00:19:14,280 Speaker 1: to do. But in the in the interim, they're doing 345 00:19:14,359 --> 00:19:18,280 Speaker 1: something more important, like looking at all the information that's 346 00:19:18,320 --> 00:19:21,320 Speaker 1: being produced by the instruments and having insight. You know, 347 00:19:21,400 --> 00:19:23,919 Speaker 1: ever since we started sailing, there's been expectation of how 348 00:19:23,960 --> 00:19:26,800 Speaker 1: ships interact with each other. Let's see, you know, they've 349 00:19:26,800 --> 00:19:30,920 Speaker 1: been codified by the the I M O. Right, they're 350 00:19:30,920 --> 00:19:34,119 Speaker 1: called like the regulations to prevent collisions at sea. We 351 00:19:34,240 --> 00:19:37,520 Speaker 1: just called them coal ricks. But they're quite nuanced. Like 352 00:19:37,560 --> 00:19:39,959 Speaker 1: it's not like they're called rules of the road, you know, 353 00:19:40,040 --> 00:19:42,680 Speaker 1: after like the idea of like cars, but they're they're 354 00:19:42,760 --> 00:19:47,080 Speaker 1: much more nuanced than like rules for cars. How you 355 00:19:47,160 --> 00:19:50,200 Speaker 1: act depends on the type of vessels that are interacting, 356 00:19:50,240 --> 00:19:52,160 Speaker 1: like if it's a sail boat or a fishing boat, 357 00:19:52,400 --> 00:19:56,840 Speaker 1: or a container ship or a pleasure craft. Like imagine 358 00:19:56,840 --> 00:19:58,880 Speaker 1: if you're driving your car down the road and you're 359 00:19:58,880 --> 00:20:01,360 Speaker 1: at a stop sign, and then depending whether you could 360 00:20:01,400 --> 00:20:03,720 Speaker 1: go or not depended on whether the other car about 361 00:20:03,760 --> 00:20:08,560 Speaker 1: the stop sign was a bus or you know, or 362 00:20:08,600 --> 00:20:12,720 Speaker 1: something else, Like the rules change anyway. So that's where 363 00:20:12,880 --> 00:20:16,600 Speaker 1: humans are are really really good at. Is this nuanced 364 00:20:16,720 --> 00:20:24,720 Speaker 1: understanding of these these rules, um squshy squishy rules. Yeah. So, 365 00:20:24,840 --> 00:20:26,639 Speaker 1: and that's where we've done. You know, a lot of 366 00:20:26,640 --> 00:20:29,919 Speaker 1: our lot of our work on is in that area. 367 00:20:30,280 --> 00:20:36,239 Speaker 1: And that's the hardest part of this whole puzzle. I 368 00:20:36,280 --> 00:20:39,280 Speaker 1: wonder if the ship ever got into any sticky situations 369 00:20:39,320 --> 00:20:41,880 Speaker 1: that the AI captain was able to get it out of. 370 00:20:44,000 --> 00:20:46,359 Speaker 1: One time, we had a sailboat come at us in 371 00:20:46,400 --> 00:20:50,600 Speaker 1: the night head on reciprocal course, no lights on, no 372 00:20:50,760 --> 00:20:54,120 Speaker 1: radar reflector. Everybody was probably asleep and they just had 373 00:20:54,119 --> 00:20:58,160 Speaker 1: the autopilot on and um, we easily could have speared them, 374 00:20:58,359 --> 00:21:00,239 Speaker 1: or they would have actually hit us because they were 375 00:21:00,240 --> 00:21:06,120 Speaker 1: in violation of regulations. But but that's common, right, And see, 376 00:21:06,160 --> 00:21:08,440 Speaker 1: when you're crossing, it's so unlikely, it's so fast that 377 00:21:08,480 --> 00:21:11,280 Speaker 1: you're going to run into somebody, but it happens. So 378 00:21:11,320 --> 00:21:14,679 Speaker 1: we you know, the ship took appropriate action and moved 379 00:21:14,680 --> 00:21:17,040 Speaker 1: so that that wouldn't happen. But it's not like it 380 00:21:17,080 --> 00:21:20,040 Speaker 1: seems very dramatic at the moment. But you know, you 381 00:21:20,080 --> 00:21:22,600 Speaker 1: see these things coming miles away and it unfolds it 382 00:21:22,760 --> 00:21:26,639 Speaker 1: like five miles an hour or something, right, So it's yeah, 383 00:21:26,680 --> 00:21:29,439 Speaker 1: so it seems more nervous than it is. And I 384 00:21:29,440 --> 00:21:32,320 Speaker 1: mean weather was challenging, and we had some failures technical 385 00:21:32,320 --> 00:21:35,119 Speaker 1: and mechanical failures in the ship that were very very challenging. 386 00:21:35,840 --> 00:21:38,000 Speaker 1: But from the AI captain perspective, the only time that 387 00:21:38,000 --> 00:21:41,439 Speaker 1: we got annoyed was. There was a research ship that 388 00:21:41,440 --> 00:21:45,440 Speaker 1: shall remain nameless from a university that was coming along 389 00:21:45,480 --> 00:21:47,200 Speaker 1: and was going to cross in front of us by 390 00:21:47,280 --> 00:21:50,159 Speaker 1: ten twelve miles, which is fine, and they were going along, 391 00:21:50,200 --> 00:21:53,480 Speaker 1: but they clearly saw us on there, neither their radar 392 00:21:53,600 --> 00:21:57,200 Speaker 1: or their automated identification system which we broadcast, and they 393 00:21:57,240 --> 00:21:59,439 Speaker 1: just at some point turned and came directly at us 394 00:21:59,480 --> 00:22:03,080 Speaker 1: at a angle that it's the it's the I'm messing 395 00:22:03,080 --> 00:22:05,919 Speaker 1: with you angle, Yeah, the angle that allows them to 396 00:22:06,040 --> 00:22:09,720 Speaker 1: maintain right of way but makes it very, very difficult 397 00:22:09,760 --> 00:22:12,840 Speaker 1: to understand their intent and take action. So the ship 398 00:22:12,960 --> 00:22:15,520 Speaker 1: was kind of like, if they had persisted, it would 399 00:22:15,520 --> 00:22:17,600 Speaker 1: have ended up kind of going around in circles trying 400 00:22:17,600 --> 00:22:21,280 Speaker 1: to avoid them. But but fortunately we had a support 401 00:22:21,280 --> 00:22:23,080 Speaker 1: boat that was coming out of Halifax to meet it, 402 00:22:23,080 --> 00:22:26,480 Speaker 1: and it physically got in between the Mayflower and this 403 00:22:26,600 --> 00:22:29,239 Speaker 1: research boat and so what are you doing? Oh, we 404 00:22:29,240 --> 00:22:31,240 Speaker 1: were just going to take a look, and but we 405 00:22:31,240 --> 00:22:33,240 Speaker 1: weren't going to get any closer than two miles and 406 00:22:33,240 --> 00:22:34,400 Speaker 1: it's like, well, what are you going to see from 407 00:22:34,400 --> 00:22:36,960 Speaker 1: two miles away? They absolutely are going to come over 408 00:22:36,960 --> 00:22:41,480 Speaker 1: and take a much closer look because they didn't understand 409 00:22:41,520 --> 00:22:44,800 Speaker 1: that the vessel was trying to avoid them. You know, 410 00:22:44,920 --> 00:22:47,119 Speaker 1: when they see these unmanned systems at sea, they're just 411 00:22:47,280 --> 00:22:51,600 Speaker 1: dumb robots, right, They just float around with winder wave power. 412 00:22:52,520 --> 00:22:54,280 Speaker 1: They are a bunch of sideists coming back from like 413 00:22:54,320 --> 00:22:57,040 Speaker 1: a six week cruise, and there was like, oh, that 414 00:22:57,080 --> 00:23:00,679 Speaker 1: looks interesting, let's go take a look. So yeah, and 415 00:23:00,800 --> 00:23:03,520 Speaker 1: so that was the only thing that was annoying. Other 416 00:23:03,600 --> 00:23:06,119 Speaker 1: than that, it was getting into and out of port. 417 00:23:06,720 --> 00:23:09,240 Speaker 1: Getting out of Plymouth was a little challenging. Once we 418 00:23:09,280 --> 00:23:11,040 Speaker 1: get outside twelve miles, we had a lot of fishing 419 00:23:11,040 --> 00:23:14,640 Speaker 1: boats to dodge, but that was fine. And then out 420 00:23:14,640 --> 00:23:17,240 Speaker 1: in the deep sea, it's just it's mostly the sea 421 00:23:17,280 --> 00:23:20,400 Speaker 1: that you're concerned with, and it's the fishing grounds are 422 00:23:20,400 --> 00:23:24,879 Speaker 1: always the trickiest place because, yeah, because fishing boats do 423 00:23:24,880 --> 00:23:27,800 Speaker 1: whatever they want. Yeah, and they're like container ships. They're 424 00:23:27,800 --> 00:23:30,040 Speaker 1: not going to change course unless they have to, so 425 00:23:30,080 --> 00:23:32,680 Speaker 1: you can pretty much understand what they're what they're doing. 426 00:23:32,720 --> 00:23:36,399 Speaker 1: Fishing boats could be going along a nice straight line 427 00:23:36,520 --> 00:23:39,880 Speaker 1: and then all of a sudden do a money or worse, 428 00:23:40,000 --> 00:23:42,679 Speaker 1: a ninety degree turn, and they don't care about you, 429 00:23:42,720 --> 00:23:44,520 Speaker 1: and they just expect you to avoid them, and they 430 00:23:44,640 --> 00:23:46,879 Speaker 1: literally there's no one in the wheelhouse. Probably they're all 431 00:23:46,880 --> 00:23:48,760 Speaker 1: on the backs of the rules too, we're supposed to 432 00:23:48,800 --> 00:23:52,280 Speaker 1: avoid them. And so, but what Brett caught it earlier, 433 00:23:52,280 --> 00:23:56,000 Speaker 1: it was things evolved very slowly. Like things don't happen 434 00:23:56,119 --> 00:23:58,200 Speaker 1: quickly at sea. It's sort of like, Okay, there's ship, 435 00:23:58,240 --> 00:24:02,520 Speaker 1: it's you know, it's it's twenty miles away. I've got 436 00:24:02,520 --> 00:24:03,960 Speaker 1: a little bit of time to figure out what I'm 437 00:24:03,960 --> 00:24:07,040 Speaker 1: gonna do. You don't ever try to put yourself into 438 00:24:07,080 --> 00:24:10,439 Speaker 1: a situation where there's a risk of collision, so you 439 00:24:10,560 --> 00:24:15,560 Speaker 1: make decisions that so you don't put yourself at that risk. Right, So, 440 00:24:15,760 --> 00:24:17,720 Speaker 1: like I'm not going to cross the street at the 441 00:24:17,760 --> 00:24:20,960 Speaker 1: busiest place. I'm gonna cross it dada, you know somewhere say, 442 00:24:22,720 --> 00:24:26,760 Speaker 1: fishing boats, container ships, scientists on a cruise. The vast 443 00:24:26,800 --> 00:24:29,240 Speaker 1: majority of vessels at sea are still of the not 444 00:24:29,520 --> 00:24:33,680 Speaker 1: autonomous variety. To wrap up their conversation, Lauren asked Brett 445 00:24:33,760 --> 00:24:37,560 Speaker 1: and Don where the technology they've developed is headed, what 446 00:24:37,640 --> 00:24:40,239 Speaker 1: it means for the humans who work at SEE, and 447 00:24:40,320 --> 00:24:43,320 Speaker 1: what's next for the two of them. What do you 448 00:24:43,359 --> 00:24:46,919 Speaker 1: guys think this type of automation means for the future 449 00:24:47,040 --> 00:24:50,000 Speaker 1: of the maritime industry and people who work in at 450 00:24:51,480 --> 00:24:54,159 Speaker 1: first of all, like we mentioned, Brett and I have 451 00:24:54,320 --> 00:24:58,880 Speaker 1: both worked in the ocean community for decades our entire careers, 452 00:24:59,040 --> 00:25:01,600 Speaker 1: Like we haven't a lot of respect for the people 453 00:25:01,640 --> 00:25:06,080 Speaker 1: that work in this area. And this isn't about a 454 00:25:06,119 --> 00:25:11,159 Speaker 1: replacement technology. It's an augmented augment what's what's the right 455 00:25:11,160 --> 00:25:16,639 Speaker 1: how do you say augmented intelligence? I mean, look, ships 456 00:25:16,680 --> 00:25:19,280 Speaker 1: have always been the leading edge of technology and almost 457 00:25:19,280 --> 00:25:22,919 Speaker 1: every society up until the twentieth century where we started 458 00:25:22,920 --> 00:25:27,399 Speaker 1: into flight, and now they're kind of resurging into really 459 00:25:27,440 --> 00:25:30,760 Speaker 1: new technological areas. But the point of trying to make 460 00:25:30,880 --> 00:25:32,720 Speaker 1: is there was a time when there were no propellers. 461 00:25:32,720 --> 00:25:35,000 Speaker 1: There's a time when there are no rudders, right, it 462 00:25:35,080 --> 00:25:37,440 Speaker 1: was just sales and steering oars. And then so it's 463 00:25:37,440 --> 00:25:40,600 Speaker 1: been this evolution in technology um and ships have always 464 00:25:40,640 --> 00:25:42,919 Speaker 1: been right at the absolute forefront of it from design 465 00:25:42,960 --> 00:25:46,640 Speaker 1: and engineering and material science. And you know, we've seen 466 00:25:46,720 --> 00:25:49,199 Speaker 1: this sort of long evolution of technology and this is 467 00:25:49,240 --> 00:25:50,920 Speaker 1: just another thing. So I think you're going to see 468 00:25:50,920 --> 00:25:54,840 Speaker 1: lots of areas where really smart port of machine learning 469 00:25:54,880 --> 00:25:58,920 Speaker 1: models helped like to improve efficiencies, and so we're at 470 00:25:58,920 --> 00:26:02,200 Speaker 1: the advent of of a new way of thinking about 471 00:26:02,240 --> 00:26:08,040 Speaker 1: design and implementation of very sophisticated solutions that are based 472 00:26:08,080 --> 00:26:12,920 Speaker 1: in vast amounts of data analytics that are hitherto impossible 473 00:26:12,960 --> 00:26:18,000 Speaker 1: to address. What is next for the Mayflower Autonomous Ship. 474 00:26:19,320 --> 00:26:21,520 Speaker 1: We may do a few things with the Coast Guard, 475 00:26:21,800 --> 00:26:23,679 Speaker 1: and there's a few other folks that want us to 476 00:26:23,680 --> 00:26:28,800 Speaker 1: do some work on national marine sanctuaries looking at cetacean populations, 477 00:26:28,920 --> 00:26:31,280 Speaker 1: and so we'll do that kind of thing with with it, 478 00:26:31,359 --> 00:26:33,120 Speaker 1: and more and more people will get involved in its 479 00:26:33,160 --> 00:26:35,760 Speaker 1: day to day operation and we'll have less sort of 480 00:26:35,880 --> 00:26:39,199 Speaker 1: day to day input, which is fine. And then the 481 00:26:39,240 --> 00:26:41,360 Speaker 1: AI Captain is going into a whole bunch of other 482 00:26:41,359 --> 00:26:44,000 Speaker 1: projects and programs, and we're just starting off on a 483 00:26:44,040 --> 00:26:49,040 Speaker 1: new design for a much larger ship for vast oceanic voyages, 484 00:26:49,280 --> 00:26:57,119 Speaker 1: um maybe even a circumnavigation. That's that's quite an effort. Yeah, 485 00:26:57,200 --> 00:26:59,679 Speaker 1: And then we're going to connect with with NASA with 486 00:26:59,800 --> 00:27:02,119 Speaker 1: the there you know, with the International Space Station and 487 00:27:02,200 --> 00:27:06,000 Speaker 1: satellite networks and sort of have them work collaboratively so 488 00:27:06,040 --> 00:27:08,760 Speaker 1: the space assets see things and they know there's another 489 00:27:08,800 --> 00:27:11,639 Speaker 1: ship asset. So it's almost like a satellite in revers 490 00:27:11,840 --> 00:27:14,960 Speaker 1: It's like the inverse satellite at sea. So it sees 491 00:27:15,000 --> 00:27:17,080 Speaker 1: something from space and it's as a ship such and 492 00:27:17,119 --> 00:27:19,800 Speaker 1: such as over there, ask it to go and look 493 00:27:19,800 --> 00:27:21,680 Speaker 1: at that and tell us if what we're seeing is right, 494 00:27:21,760 --> 00:27:24,040 Speaker 1: or collect a sample right, and those things will work 495 00:27:24,400 --> 00:27:28,480 Speaker 1: collaboratively without people. You kind of opened up Pandora's box here. 496 00:27:28,840 --> 00:27:31,920 Speaker 1: So we did this, and now there's all these other 497 00:27:31,960 --> 00:27:34,959 Speaker 1: things that we can do. So yeah, and we just 498 00:27:35,000 --> 00:27:36,679 Speaker 1: have to pick one that we can do within the 499 00:27:36,720 --> 00:27:40,280 Speaker 1: remainder of our lifetime. There you go. Well, I I 500 00:27:40,320 --> 00:27:42,920 Speaker 1: hope you. I hope you both get to do all 501 00:27:42,960 --> 00:27:45,520 Speaker 1: the new things that you want and have capacity to do. 502 00:27:45,960 --> 00:27:48,800 Speaker 1: Thank you both so much for your time and good 503 00:27:48,880 --> 00:27:53,920 Speaker 1: luck with future journeys and projects. Thank you, hi everybody. 504 00:27:55,560 --> 00:27:59,679 Speaker 1: In the centuries long evolution of maritime technology, the Mayflower 505 00:27:59,800 --> 00:28:05,480 Speaker 1: automous ship represents an inflection point. The ship's success indicates 506 00:28:05,480 --> 00:28:09,760 Speaker 1: that artificial intelligence and automation are tools ready to be 507 00:28:09,880 --> 00:28:13,920 Speaker 1: normalized within the nautical industry, and that the advantages they 508 00:28:13,960 --> 00:28:17,480 Speaker 1: provide will change the way we conceive of ship building. 509 00:28:18,760 --> 00:28:23,280 Speaker 1: But the technology aboard the Mayflower four hundred has implications 510 00:28:23,320 --> 00:28:27,600 Speaker 1: beyond just application at see. Brett and Don's project has 511 00:28:27,600 --> 00:28:31,359 Speaker 1: shown that the potential reward for innovative risk taking is 512 00:28:31,400 --> 00:28:36,600 Speaker 1: to achieve something unprecedented, and that's true for any industry. 513 00:28:36,960 --> 00:28:39,640 Speaker 1: But like the original Mayflower Voyage four n years ago, 514 00:28:40,160 --> 00:28:43,760 Speaker 1: it may require a leap of faith. On the next 515 00:28:43,760 --> 00:28:47,160 Speaker 1: episode of Smart Talks with IBM, what does it take 516 00:28:47,200 --> 00:28:52,440 Speaker 1: to create a sustainability focused global supply chain innovative and 517 00:28:52,640 --> 00:28:56,320 Speaker 1: equitable enough to connect our modern world? We talk with 518 00:28:56,480 --> 00:29:01,760 Speaker 1: Sherry Highness, IBM's global sustainability services leader and offering leader 519 00:29:02,000 --> 00:29:06,520 Speaker 1: for a sustainable supply chain. Smart Talks with IBM is 520 00:29:06,520 --> 00:29:11,160 Speaker 1: produced by Molly Sosha, David jaw, Royston Reserve, Matt Romano, 521 00:29:11,560 --> 00:29:16,840 Speaker 1: and Edith Russelo with Jacob Goldstein. Our engineers are Jason Gambrel, 522 00:29:17,200 --> 00:29:23,680 Speaker 1: Sarah Bruger and Ben Tolliday. Theme song by Gramascope. Special 523 00:29:23,680 --> 00:29:27,880 Speaker 1: thanks to Colly mcglory, Andy Kelly, Kathy Callaghan and the 524 00:29:27,960 --> 00:29:31,640 Speaker 1: eight Bar and IBM teams, as well as the Pushkin 525 00:29:32,080 --> 00:29:36,080 Speaker 1: marketing team. Smart Talks with IBM is a production of 526 00:29:36,120 --> 00:29:40,840 Speaker 1: Pushkin Industries and I Heart Media. To find more Pushkin podcasts, 527 00:29:41,080 --> 00:29:44,280 Speaker 1: listen to the i Heart Radio app, Apple Podcasts, or 528 00:29:44,360 --> 00:29:50,160 Speaker 1: wherever you listen to podcasts. I'm Malcolm Glacko. This is 529 00:29:50,160 --> 00:29:52,480 Speaker 1: a paid advertisement from IBM.