Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio.
Speaker 1: This season of Smart Talks with IBM is all about
Speaker 1: new creators, the developers, data scientists, c t o s
Speaker 1: and other visionaries creatively applying technology in business to drive change.
Speaker 1: They use their knowledge and creativity to develop better ways
Speaker 1: of working, no matter the industry. Join hosts from your
Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to
Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide
Speaker 1: you through the season as your host and provide his
Speaker 1: thoughts and analysis along the way. Look out for new
Speaker 1: episodes of Smart Talks with IBM on the I Heart
Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello,
Speaker 1: Welcome to Smart Talks with IBM, a podcast from Pushkin
Speaker 1: Industry is I Heart Radio and IBM. I'm Malcolm Blobwell.
Speaker 1: This season we're talking to new creators, the developers, data scientists,
Speaker 1: c t o s and other visionaries who are creatively
Speaker 1: applying technology and business to drive change. Channeling their knowledge
Speaker 1: and expertise, they're developing more creative and effective solutions no
Speaker 1: matter the industry. Our guest today are Brett Fanoff and
Speaker 1: Don Scott. Brett and Dawn are responsible for creating the
Speaker 1: world's first unmanned, fully autonomous ship to cross the Atlantic Ocean,
Speaker 1: a research vessel they've dubbed the Mayflower four hundred. Brett
Speaker 1: is a director of the Mayflower Autonomous Ship Project and
Speaker 1: Dawn is the CTO of Marine AI. On June, the
Speaker 1: Mayflower four hundred successfully completed its voyage from Plymouth, UK
Speaker 1: to Plymouth, Massachusetts. It's both an homage to the original Mayflower,
Speaker 1: which crossed the Atlantic forundered years earlier, and a bell
Speaker 1: weather for the ways autonomous technology will push the boundaries
Speaker 1: of maritime exploration in the next four years. On today's show,
Speaker 1: the Unlikely Origins of a self directed Ship, some motion misadventures,
Speaker 1: and what AI and machine learning will mean for the
Speaker 1: future of seafaring and beyond, Brett and Dawn spoke with
Speaker 1: Lauren Ober, host of the forthcoming Pushkin podcast The Loudest
Speaker 1: Girl in the World. Lauren is a longtime radio host
Speaker 1: and reporter, helming shows like NPRS, The Big Listen, and
Speaker 1: Spectacular Failures from American public Media. Okay, now, let's get
Speaker 1: to the interview with Brett Fanoff and Don Scott. Don
Speaker 1: and Brett, it's really great to be talking with you
Speaker 1: guys today. I was wondering for each of you, what
Speaker 1: is the draw of the sea? I mean, it's like
Speaker 1: this explain It's a place. It feels so unknown in
Speaker 1: so many ways. Um, but I'm curious, like, what is
Speaker 1: the allure there for me? It's I wanted to be
Speaker 1: I wanted to do aerospace. So I always feel like
Speaker 1: I'm like like the poor cousin of aerospace, but it isn't.
Speaker 1: It's actually it's harder to to do the underwater stuff.
Speaker 1: It's closer. It's just harder than being in space. It's
Speaker 1: it's incredibly hostile and wildly unexplored. And why what I
Speaker 1: like about it is that you know, you can take
Speaker 1: a bucket and go down to the beach, get a
Speaker 1: bucket of water, analyze the bucket of water for the
Speaker 1: next twenty years, and you know, chances are pretty high
Speaker 1: you're gonna have a couple of things in there that
Speaker 1: nobody's ever seen before. And that's every bucket of water
Speaker 1: everywhere in the world, right, So I like the idea
Speaker 1: that you get to discover something new all the time,
Speaker 1: and it's also hard. It's a difficult place to work,
Speaker 1: so it challenges you to come up with new ideas
Speaker 1: and new ways to do things and new materials. And
Speaker 1: that's what I like about it. I don't know, don
Speaker 1: what about you. Yeah, I mean, there's obviously an allure
Speaker 1: and draw there's some great descriptions about why people are
Speaker 1: drawn to the ocean. Talk to the authors and the poets,
Speaker 1: you know, it's it's definitely a real sort of visceral
Speaker 1: feeling that people get. I think you find that the
Speaker 1: people that are involved in ocean engineering and or marine
Speaker 1: sides like that you'll just sort of fall into this
Speaker 1: career by accident. You make proactive decisions to get involved
Speaker 1: in that environment. So you have a bunch of people
Speaker 1: working there that that want to be there and sort
Speaker 1: of have this uh understanding of that this is the
Speaker 1: place they want to be and this is where they
Speaker 1: want to work. So that becomes a very very positive
Speaker 1: work environment workspace because everyone's they want to be there.
Speaker 1: So there's that. Yeah, it's highly collaborative, isn't it. It's
Speaker 1: um like anything, there's personalities, but it tends to be
Speaker 1: a lot of fun more than anything else. It's challenging
Speaker 1: in all the ways that make life interesting, and then
Speaker 1: it also tends to be a good time. And you
Speaker 1: can't work in the ocean by yourself, like, well, you can,
Speaker 1: but it's kind of hard, so press out. It's an
Speaker 1: incredibly collaborative environment. I mean, if you want to be
Speaker 1: doing anything of significance, you have to be working as
Speaker 1: a group because you need to rely on each other.
Speaker 1: It is an incredibly dynamic, hostile environment, very humbling. So
Speaker 1: you find you you're going to achieve success as a
Speaker 1: collaborative group as opposed to some sort of lone wolf
Speaker 1: type out to right. Okay, so we're here to talk
Speaker 1: about the Mayflower Autonomous Ship project, which obviously is very cool. Um,
Speaker 1: how exactly did you guys decide to build an autonomous
Speaker 1: ship and then model it after the Mayflower? I mean
Speaker 1: it was just to hold my beer kind of thing. Um,
Speaker 1: I'm sure what it really is, it really was, it
Speaker 1: really was. Yeah, what it really was is it was
Speaker 1: so in meeting with the City of Plymouth on something else,
Speaker 1: they were talking about what they were going to do
Speaker 1: and maybe built a replica ship, of which there's already one,
Speaker 1: And I thought that wasn't the best idea, and you're
Speaker 1: talking for anniversary. Yeah, and so I was a little
Speaker 1: bit indelicate in my comment as to how they wanted
Speaker 1: to proceed with a possible replica. You said it was
Speaker 1: a stupid idea, I said, I said it was stupid
Speaker 1: and uh and there was more I couldn't resist and
Speaker 1: and and I said, there already is one, you know,
Speaker 1: and it's it's just I grew up near there. And
Speaker 1: and so they said, all right, smart guy, what are
Speaker 1: you gonna do? I was like, oh, we should build
Speaker 1: one that challenges us technologically and from an engineering perspective
Speaker 1: and sort of invokes the spirit of the original risk
Speaker 1: taking and do something that informs the next four years.
Speaker 1: And everybody was like, yeah, you should do that. And
Speaker 1: I was like, you know what, I will hold my
Speaker 1: beer and uh so, so I called Don after the
Speaker 1: meeting and I was like, oh, Don, we we have
Speaker 1: to build an AI. I need Captain Watson because we're
Speaker 1: going to build an autn of a ship across the Atlantic.
Speaker 1: And he was like great, and so yeah, and it
Speaker 1: was just that literally that glib, but it also I
Speaker 1: mean he and I have been working on unmanned systems
Speaker 1: and autonomous systems for a long time together, twenty plus years,
Speaker 1: and so I wanted to see where we could get to,
Speaker 1: Like how hard could this be? Right I mean? And
Speaker 1: I sure, let's do it. Then, So we built a ship.
Speaker 1: You mentioned capturing the spirit of the original Mayflower journey,
Speaker 1: and I wonder what exactly where you're trying to capture.
Speaker 1: Was it the spirit of taking risks or was it
Speaker 1: doing something that hadn't been done before. What we were
Speaker 1: trying to do we knew was really hard, right, like,
Speaker 1: and it was a huge amount of risk to undertake it.
Speaker 1: Press the real risk taker. He's the one with the
Speaker 1: big ideas and wants to take the risk. I'm I'm
Speaker 1: a little more cautious and sort of pragmatic in the
Speaker 1: sense of, Okay, what's going to take to do that?
Speaker 1: We we actually didn't think we were going to make it,
Speaker 1: or I fully expected at some point the ocean we
Speaker 1: get annoyed and smite us, you know. Pilgrims like that
Speaker 1: to me is what's interesting. Pilgrims took a risk, right,
Speaker 1: so every one of them fully expected that they would die,
Speaker 1: if not on the voyage within like the first year, right,
Speaker 1: That's how it was, and it was worth it to
Speaker 1: them to take that risk. So our risk is infinitesimal
Speaker 1: by comparison, Right, it's tiny. What was our risk really,
Speaker 1: We'd lose a ship we spent some money on. So
Speaker 1: what the knowledge about how to approach these problems is,
Speaker 1: and the and the experience that you get to give
Speaker 1: people to take risk at that level from an engineering
Speaker 1: perspective is really important. Right, somebody had to do the
Speaker 1: first open heart surgery and took a risk. Now we're
Speaker 1: not doing open heart surgery. Right, no one's going to die.
Speaker 1: So what's appealing about the risk thing is it has
Speaker 1: a technical risk and environmental risk, and then there's a
Speaker 1: legislative and regulatory risk because we had to have our
Speaker 1: fights with various agencies about the fact that they didn't
Speaker 1: have a law that said we couldn't so they didn't
Speaker 1: get to say no just because they didn't want us to.
Speaker 1: And at the same time, trying to create a reliable
Speaker 1: machine and then some sort of an AI machine learning
Speaker 1: based system that would be safe whatever that is in
Speaker 1: the middle of the ocean. It's really interesting and gives
Speaker 1: people a lot to a lot of purchase for different
Speaker 1: people with different skill sets to collaborate. Brett and John
Speaker 1: started developing the Mayflower auton of a ship in It
Speaker 1: took them six years to figure out both the software
Speaker 1: and the body of the boat itself. In that time,
Speaker 1: over seventy people contributed to the project. Lauren asked Don
Speaker 1: and Brett what it really took to go from hold
Speaker 1: my beer to an actual ship? You know, it is
Speaker 1: mind boggling when you think of how many people are involved,
Speaker 1: how many people are touching this project, how many interesting
Speaker 1: minds doing interesting things, but you have to funnel it
Speaker 1: all into this one project. Well that I don't know
Speaker 1: if it's that way. I mean, I guess you could
Speaker 1: say there was one project, but there were lots of projects,
Speaker 1: and so, you know, there was sort of the hardcore
Speaker 1: group of people that are trying to build the full
Speaker 1: software that works, and then there's the guys trying to
Speaker 1: build the hardware and they have an interface, but they're
Speaker 1: parallel pursuits that don't have direct overlap. And then we
Speaker 1: said yes a lot to anybody who wanted to help,
Speaker 1: because we learned from experience that most people don't last
Speaker 1: in terms of the ability to stick out four or
Speaker 1: five years focus on the projects very hard, and so
Speaker 1: the people that I wanted to stick it out and
Speaker 1: bring it to fruition ended up you know, sticking it
Speaker 1: out and that was great, you know. And then there
Speaker 1: are all sorts of different things. There was a group
Speaker 1: making a web interface so that they could show the
Speaker 1: world what we were doing, and you know, then there
Speaker 1: was a PR group that was marketing things and sort
Speaker 1: of talking about how we tell the world about it,
Speaker 1: and we would support them. But it's hard to describe
Speaker 1: it as one project. I guess would be my position.
Speaker 1: It's lots of interlinked programs, right, Sure, I get that,
Speaker 1: I get that. Can you tell me more about how
Speaker 1: automation is built into the ship and how it works. Well,
Speaker 1: there's tons of automation and may Far, I mean Mayflower
Speaker 1: is like most robotics systems, right. So you peel it
Speaker 1: open and you find you know, programmable logic controllers and
Speaker 1: motor drives and also its of other things sensors and
Speaker 1: industrial automation that you'd see, you know in an elevator
Speaker 1: or an escalator or industrial machinery for manufacture. And that's
Speaker 1: one sort of layer of it. Right. So you've got
Speaker 1: the basic analog control. Then you've got sort of a
Speaker 1: veneer of automation, and then what I would call sophisticated automation,
Speaker 1: which Don and I have worked on for decades in
Speaker 1: the marine space. So all that's in there. And you know,
Speaker 1: Don and I talked really early on if I just
Speaker 1: wanted to get across the Atlantic, we could have bought
Speaker 1: an old fishing boat, filled up the fish hoolds with
Speaker 1: diesel fuel, and put a cheap autopilot on it and
Speaker 1: sent it. It probably would have got across. But so
Speaker 1: what it's not reducing risk, and it's not unburdening a person,
Speaker 1: and it's not doing anything really clever or sophisticated. And
Speaker 1: so what we were more interested in was getting to
Speaker 1: a point where instead of having to tell it to
Speaker 1: do everything, saying go do this task right, a goal
Speaker 1: like go to Plymouth right, and then while you're doing that, ah,
Speaker 1: by the way, while you're doing that, collect all this
Speaker 1: science data and if you see anything unusual, tell us
Speaker 1: and and while you're looking for all these unusual things
Speaker 1: and trying to achieve your goal, don't hit anything. So
Speaker 1: then what role did IBM S technology play in all
Speaker 1: of this? Yeah? I mean their their technology is all
Speaker 1: over the ship. Probably the main contribution it was the
Speaker 1: decision making process or it's it's an automation TOOLM operational
Speaker 1: decision manager. It's actually a financial services tool. It's for
Speaker 1: your making decisions about the viability of a transaction, whether
Speaker 1: it's fraud or order or alone or let's say, And
Speaker 1: we were being presented this by one of the ODIUM engineers,
Speaker 1: and I remember sitting in the room with Brett thing,
Speaker 1: what what in the world does financial services product have
Speaker 1: to do with marine navigation And they sort of were
Speaker 1: brought to realize by the IBM engineer how this is.
Speaker 1: This isn't really so much about financial services as it
Speaker 1: is about making making really difficult decisions in a really
Speaker 1: complex environment, which is what they do in financial services.
Speaker 1: But it's also exactly what we needed to do in
Speaker 1: re navigation. And when the when the system was actually rounding,
Speaker 1: it would create a log essentially of why that decision
Speaker 1: was made, so they can validate that decision and verify
Speaker 1: and validate that that that was in fact the right decision.
Speaker 1: And um, so that's a that's one of the key
Speaker 1: IVM tools that are on board. Well, one of the
Speaker 1: things you might want to consider about that is the fundamentals, right,
Speaker 1: the theoretical independence of all the AI that we're deploying
Speaker 1: now have been sort of understood for decades, right, and
Speaker 1: so now we just happened to live in a world
Speaker 1: where the microprocesses are up to snuff that they can
Speaker 1: deploy some of these very sophisticated theoretical and reality and
Speaker 1: all of which IBM has been involved with from inception
Speaker 1: based on its pedigree is in a national business machines.
Speaker 1: There isn't an IBM product that I can think of
Speaker 1: that we haven't tried to utilize the deploying so it's
Speaker 1: it's it's everywhere in the ship. Yeah. I don't think
Speaker 1: a lot of people think of technology as as as
Speaker 1: a creative pursuit, but I imagine building an autonomous ship
Speaker 1: from scratch takes a lot of creativity. And I'm wondering,
Speaker 1: do you guys think of your work as creative? Yeah,
Speaker 1: engineering is essentially designing technological innovation sort of. You think
Speaker 1: of it as a very logical process, and there is that,
Speaker 1: for sure, but there's an incredible amount of innovation involved too,
Speaker 1: Like there's no template for what we're doing, and you know,
Speaker 1: we call it white paper design where you're basically given
Speaker 1: a blank piece of paper and a goal, which is, okay,
Speaker 1: ship that's going to cross the Atlantic, Um, okay, come
Speaker 1: up with some ideas, right, So I mean it requires
Speaker 1: major conceptual leaps and then the technical skill to realize
Speaker 1: those those sleeps. You're not going to make any advances
Speaker 1: just doing things the way you've always done them. Right.
Speaker 1: You need to stretch, right, and the only way it
Speaker 1: stretch is what implementing new ideas like you can spend
Speaker 1: a decade. We call it power point engineering right where
Speaker 1: you do nothing but think of things. We don't actually
Speaker 1: do anything, as opposed to what we call full contact engineering,
Speaker 1: where you actually built the boat right the software to
Speaker 1: go on the boat and send it out on the water,
Speaker 1: get your kicked, like, get sea sick, you know, all
Speaker 1: that sort of fun stuff that happens when you don't
Speaker 1: see trials. Um. And because that's where you that's where
Speaker 1: the actual learning is happening, that's where the actual development
Speaker 1: is happening, is being out on the ocean. Crossing the
Speaker 1: Atlantic is no small voyage for any vessel, but the
Speaker 1: Mayflower Autonomous Ship Project is more than just about sailing
Speaker 1: from point A to point B. Automation and AI have
Speaker 1: game changing implications for the way we design the next
Speaker 1: generation of vessels and the way these vessels will behave
Speaker 1: and interact at sea. Ships will be able to gather
Speaker 1: data from the ocean by themselves, providing humans with critical
Speaker 1: information we need to address problems like global warming, ocean pollution,
Speaker 1: and our impact on marine life. For instance, the Mayfire
Speaker 1: four hundred can sample ocean water from microplastics and record
Speaker 1: audio of whale vocalizations. Taking the human factor out of
Speaker 1: a ship allows us to explore new designs and functions
Speaker 1: that haven't been imagined before. Lauren asked bread and Down
Speaker 1: more about this. What are some of the benefits of
Speaker 1: having an unmanned vessel, like, how does automation push the
Speaker 1: boundaries of what we can do out in the ocean? Well,
Speaker 1: the few major variables right or true facets to that.
Speaker 1: One is you can do some risky things when you
Speaker 1: don't have the people there, right, because no one's going
Speaker 1: to be law cost at sea. And then the other
Speaker 1: thing is you can drive cost down, and I mean
Speaker 1: cost financially but also environmental cost RCT because you can
Speaker 1: use a far less energy to accomplish a similar goal.
Speaker 1: And then what that allows you to do is have more. Right,
Speaker 1: So instead of say having one fifty million dollar hundred
Speaker 1: million dollar research ship, which is the kind of numbers
Speaker 1: you're talking about to take scientists to see, you can
Speaker 1: have twenty or thirty or forty million dollars or two
Speaker 1: million dollar ships that go out and work collaboratively with
Speaker 1: space based assets and with one another and collect vast
Speaker 1: amounts of data from disparate parts of the ocean. And
Speaker 1: then you use that data to create information that informs
Speaker 1: where you send the man vessel, right, so that they
Speaker 1: get the most out of their time at sea. So
Speaker 1: it's about enabling the people. It's about leaving the humans
Speaker 1: to do the uniquely human part, which is have the
Speaker 1: insight and the intuition and and the creativity. And so
Speaker 1: you know, that's why it's important. And we're going to
Speaker 1: see an freezing amount of this. And I think it's
Speaker 1: also important for people to get comfortable with the idea
Speaker 1: that these things will be roaming around and that it's okay. Yeah,
Speaker 1: And and on an insim basis, I mean, we're also
Speaker 1: talking about this same technology that allows a ship to
Speaker 1: sail autonomously also can be used to assist a human
Speaker 1: crew now, you know, basically be another set of eyes
Speaker 1: and years be a watchkeeper for a manned vessel. Right,
Speaker 1: I want to know more about the AI captain. How
Speaker 1: did you build it so that it would be comparable
Speaker 1: to the way a human captain might direct a ship.
Speaker 1: What we're trying to do is augment the person, right,
Speaker 1: We're trying to let them be more of a person
Speaker 1: than sort of. They don't have to watch the radar,
Speaker 1: they don't have to watch the cameras. Right. The machine
Speaker 1: can do all that, and then if you can't do
Speaker 1: something safely, if it can't come to a solution, it
Speaker 1: can ask a person send a little texic, I don't
Speaker 1: know what to do, and a person can, in a
Speaker 1: very calm way, with no stress, tell it what to do.
Speaker 1: But in the in the interim, they're doing something more important,
Speaker 1: like looking at all the information that's being produced by
Speaker 1: the instruments and having insight. You know, Ever since we
Speaker 1: started sailing, there's been expectation of how ships interact with
Speaker 1: each other. Let's see, you know, they've been codified by
Speaker 1: the what the I M O. Right, they're called like
Speaker 1: the regulations to prevent collisions at sea. We just call
Speaker 1: them coal ricks. But they're quite nuanced. Like it's not
Speaker 1: like they're called rules of the road, you know, after
Speaker 1: like the idea of like cars, but they're they're much
Speaker 1: more nuanced than like rules for cars. How you act
Speaker 1: depends on the type of vessels that are interacting, like
Speaker 1: if it's a sail boat or a fishing boat, or
Speaker 1: a a container ship or a pleasure craft. Like imagine
Speaker 1: if you're driving your car down the road and you're
Speaker 1: at a stop sign, and then depending whether you could
Speaker 1: go or not, depending on whether the other car about
Speaker 1: the stop sign was a thrust, a bus or you know,
Speaker 1: or something else, like, the rules change anyway. So that's
Speaker 1: where humans are are really really good at. Is this
Speaker 1: nuanced understanding of these these rules, um squashy rules. Yeah. So,
Speaker 1: and that's where we've done. You know a lot of
Speaker 1: our lot of our work on is in that area.
Speaker 1: And that's the hardest part of this whole possit r.
Speaker 1: I wonder if the ship ever got into any sticky
Speaker 1: situations that the AI captain was able to get it
Speaker 1: out of. One time we had a sailboat come at
Speaker 1: us in the night head on reciprocal course, no lights on,
Speaker 1: no radar reflector. Everybody was probably asleep and they just
Speaker 1: had the autopilot on and um, we easily could have
Speaker 1: speared them, or they would have actually hit us because
Speaker 1: they were in violation of race regulations. But but that's common, right.
Speaker 1: Let's see, when you're crossing, it's so likely, it's so
Speaker 1: fast that you're going to run into somebody, but it happens,
Speaker 1: so we you know, the ship took appropriate action and
Speaker 1: moved so that that wouldn't happen. But it's not like
Speaker 1: it seems very dramatic at the moment. But you know,
Speaker 1: you see these things coming miles away and it unfolds
Speaker 1: it like five miles an hour or something, right, So
Speaker 1: it's yeah, so it seems more nervous than it is.
Speaker 1: And I mean, weather was challenging, and we had some
Speaker 1: failures technical and mechanical failures in the ship that were
Speaker 1: very very challenging. But from the AI captain perspective, the
Speaker 1: only time that we got annoyed was there was a
Speaker 1: research ship that shall remain nameless from a university that
Speaker 1: was coming along and was going to cross in front
Speaker 1: of us by ten twelve miles, which is fine, and
Speaker 1: they were going along, but they clearly saw us on there,
Speaker 1: neither their radar or their automated identification system which we broadcast,
Speaker 1: and they just at some point turned and came directly
Speaker 1: at us at at an angle that it's the it's
Speaker 1: the I'm messing with you angle, Yeah, the angle that
Speaker 1: allows them to maintain right of way but makes it
Speaker 1: very very difficult to understand their intent and take action.
Speaker 1: So the ship was kind of like, if they had persisted,
Speaker 1: it would have ended up kind of going around in
Speaker 1: circles trying to avoid them. But but fortunately we had
Speaker 1: a support boat that was coming out of Halifax to
Speaker 1: meet it, and it physically got in between the Mayflower
Speaker 1: and this research boat and so what are you doing? Oh,
Speaker 1: we were just going to take a look, and but
Speaker 1: we weren't going to get any closer than two miles
Speaker 1: and it's like, well, what are you going to see
Speaker 1: from two miles away? They absolutely are going to come
Speaker 1: over and take a much closer look because they didn't
Speaker 1: understand that the vessel was trying to avoid them. You know,
Speaker 1: when they see these unmanned systems at sea, they're just
Speaker 1: dumb robots, right, they just float around with winder wave power.
Speaker 1: There are a bunch of side just coming back from
Speaker 1: like a six week cruise, and I was like, oh,
Speaker 1: that looks interesting, let's go take a look. Yeah, And
Speaker 1: so that was the only thing that was annoying. Other
Speaker 1: than that, it was getting into and out of port.
Speaker 1: Getting out of Limouth was a little challenging. Once we
Speaker 1: get outside twelve miles, we had a lot of fishing
Speaker 1: boats to dodge, but that was fine. And then out
Speaker 1: in the deep sea, it's just it's mostly the sea
Speaker 1: that you're concerned with, and it's the fishing grounds are
Speaker 1: always the trickiest place because yeah, because fishing boats do
Speaker 1: whatever they want. Yeah, and they're like container ships. They're
Speaker 1: not going to change course unless they have to, so
Speaker 1: you can pretty much understand what they're what they're doing.
Speaker 1: Fishing boats could be going along a nice straight line
Speaker 1: and then all of a sudden do a money or
Speaker 1: worse than iity degreeturn and they don't care about you,
Speaker 1: and they just expect you to avoid them. And they
Speaker 1: literally there's no one in the wheelhouse. Probably they're all
Speaker 1: on the bad Those of the rules too, we're supposed
Speaker 1: to avoid them and so, but what Bratt got it earlier?
Speaker 1: It was things evolved very slowly, Like things don't happen
Speaker 1: quickly at sea. It's sort of like, Okay, there's ship,
Speaker 1: it's you know, it's it's twenty miles away. I've got
Speaker 1: a little bit of time to figure out what I'm
Speaker 1: gonna do. You don't ever try to put yourself into
Speaker 1: situation where there's a risk of collision, so you make
Speaker 1: decisions that so you don't put yourself at that risk. Right, So,
Speaker 1: like I'm not going to cross the street at the
Speaker 1: busiest place, I'm gonna cross it ata somewhere saying fishing boats,
Speaker 1: container ships, scientists on a cruise. The vast majority of
Speaker 1: vessels at sea are still of the not autonomous variety.
Speaker 1: To wrap up their conversation, Lauren asked Brett and Don
Speaker 1: where the technology they've developed is headed, what it means
Speaker 1: for the humans who work at SEE, and what's next
Speaker 1: for the two of them. What do you guys think
Speaker 1: this type of automation means for the future of the
Speaker 1: maritime industry and people who work in at First of all,
Speaker 1: like we mentioned, Brett and I have both worked in
Speaker 1: the ocean community for decades our entire careers, Like we
Speaker 1: have enough in quite a lot of respect for the
Speaker 1: people that work in this area. And this isn't about
Speaker 1: a replacement technology an augmented augment What's what's the right
Speaker 1: augmented intelligence? I mean, look, ships have always been the
Speaker 1: leading edge of technology and almost every society up until
Speaker 1: the twentieth century where we started into flight, and now
Speaker 1: they're kind of resurging into really new technological areas. But
Speaker 1: the point I'm trying to make is there was a
Speaker 1: time when there were no propellers. There's a time when
Speaker 1: there are no rudders, right, it was just sales and
Speaker 1: steering oars. And then so it's been this evolution in
Speaker 1: technology um and ships have always been right at the
Speaker 1: absolute forefront of it from design and engineering and material science,
Speaker 1: and you know, we've seen this sort of long evolution
Speaker 1: of technology, and this is just another thing. So I
Speaker 1: think you're going to see lots of areas where really
Speaker 1: smart port of machine learning models helped, like to improve efficiencies.
Speaker 1: And so we're at the advent of of a new
Speaker 1: way of thinking about design and implementation of very sophisticated
Speaker 1: solutions that are based in vast amounts of data analytics
Speaker 1: that are hitherto impossible to address. What is next for
Speaker 1: the Mayflower Autonomous Ship. We may do a few things
Speaker 1: with the Coast Guard, and there's a few other folks
Speaker 1: that want us to do some work on national marine
Speaker 1: sanctuaries looking at cetacean populations, and so we'll do that
Speaker 1: kind of thing with with it, and more and more
Speaker 1: people will get involved in its day to day operation
Speaker 1: and we'll have less sort of day to day input,
Speaker 1: which is fine. And then the Captain is going into
Speaker 1: a whole bunch of other projects and programs, and we're
Speaker 1: just starting off on a new design for a much
Speaker 1: larger ship for vast oceanic voyages, um maybe even a circumnavigation.
Speaker 1: That's that's quite an effort. Yeah, And then we're going
Speaker 1: to connect with with NASA with there, you know, with
Speaker 1: the International Space Station and satellite networks and sort of
Speaker 1: have them work collaboratively so the space assets see things
Speaker 1: and they know there's another ship asset. So it's almost
Speaker 1: like a satellite in revers it's like the inverse satellite
Speaker 1: at sea, so it sees something from space and it's
Speaker 1: as a ship such and such as over there ask
Speaker 1: it to go and look at that and tell us
Speaker 1: if what we're seeing is right, or collect a sample right,
Speaker 1: and those things will work collaboratively without people. You kind
Speaker 1: of opened up Pandora's box here. So we did this,
Speaker 1: and now there's all these other things that we can do.
Speaker 1: So yeah, and we just have to pick one that
Speaker 1: we can do within the remainder of our lifetime. There
Speaker 1: you go. Well, I I hope you. I hope you
Speaker 1: both get to do all the new things that you
Speaker 1: want and have capacity to do. Thank you both so
Speaker 1: much for your time and good luck with future journeys
Speaker 1: and projects. Thank you, hi everybody. In the centuries long
Speaker 1: evolution of maritime technology, the Mayflower Autonomous Ship represents an
Speaker 1: inflection point. The ship's success indicates that artificial intelligence and
Speaker 1: automation are tools ready to be normalized within the nautical industry,
Speaker 1: and that the advantages they provide will change the way
Speaker 1: we conceive of shipbuilding. But the technology aboard the Mayflower
Speaker 1: four hundred has implications beyond just application at see. Brett
Speaker 1: and Don's project has shown that the potential reward for
Speaker 1: innovative risk taking is to achieve something unprecedented, and that's
Speaker 1: true for any industry, but like the original Mayflower Voyage
Speaker 1: for hun years ago, it may require a leap of faith.
Speaker 1: On the next episode of Smart Talks with IBM, what
Speaker 1: does it take to create a sustainability focused global supply
Speaker 1: chain innovative and equitable enough to connect our modern world?
Speaker 1: We talk with Sherry Highness, IBMS global sustainability services leader
Speaker 1: and offering leader for a sustainable supply chain. Smart Talks
Speaker 1: with IBM is produced by Molly Sosha, David jaw, Royston Reserve,
Speaker 1: Matt Romano, and Edith Russelo with Jacob Goldstein. Our engineers
Speaker 1: are Jason Gambrel, Sarah Bruger and Ben Tolliday. Theme song
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Speaker 1: Callaghan and the eight Bar and IBM teams, as well
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