Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little bit different to share with you. It
Speaker 1: is a new edition of the Smart Talks podcast series,
Speaker 1: which is produced in partnership with IBM. This season of
Speaker 1: Smart Talks with IBM is all about new creators, the developers,
Speaker 1: data scientists, c t o s, and other visionaries creatively
Speaker 1: applying technology and business to drive change. They use their
Speaker 1: knowledge and creativity to develop better ways of working, no
Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries
Speaker 1: podcast as they use their expertise to deepen these conversations.
Speaker 1: Malcolm Gladwell will guide you through this season as your
Speaker 1: host to provide his thoughts and analysis along the way.
Speaker 1: Look out for new episodes of Smart Talks with IBM
Speaker 1: every month on the I Heart Radio app, Apple Podcasts,
Speaker 1: or wherever you get your podcasts. And learn more at
Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to
Speaker 1: Smart Talks with IBM, a podcast from Pushkin Industries, I
Speaker 1: Heart Radio and IBM. I'm Malcolm Blobwell. This season we're
Speaker 1: talking to new creators, the developers, data scientists, c t
Speaker 1: o s, and other visionaries who are creatively applying technology
Speaker 1: and business to drive change. Channeling their knowledge and expertise,
Speaker 1: they're developing more creative and effective solutions no matter the industry.
Speaker 1: Our guest today are Brett Fanoff and Don Scott. Brett
Speaker 1: and Dawn are responsible for creating the world's first unmanned,
Speaker 1: fully autonomous ship to cross the Atlantic Ocean, a research
Speaker 1: vessel they've dubbed the Mayflower four hundred. Brett is the
Speaker 1: director of the Mayflower Autonomous Ship Project and Dawn is
Speaker 1: the CTO of Marine AI. On June thirty two, 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 expansive place. It feels so unknown in so many ways. Um,
Speaker 1: but I'm curious, like, what is the allure there for me?
Speaker 1: It's I wanted to be I wanted to do aerospace,
Speaker 1: so I always feel like I'm like the poor cousin
Speaker 1: of aerospace. But it isn't. It's actually it's harder to
Speaker 1: to do the underwater stuff. It's closer. It's just harder
Speaker 1: than being in space. It's it's incredibly hostile and wildly unexplored.
Speaker 1: And why what I like about it is that you know,
Speaker 1: you can take a bucket and go down to the beach,
Speaker 1: get a bucket of water, analyze the bucket of water
Speaker 1: for the next twenty years, and you know, chances are
Speaker 1: pretty high you're gonna have a couple of things in
Speaker 1: there that nobody's ever seen before, and that's every bucket
Speaker 1: of water everywhere in the world, right, So I like
Speaker 1: the idea that you get to discover something new all
Speaker 1: the time. And it's also hard. It's a difficult place
Speaker 1: to work, so it challenges you to come up with
Speaker 1: new ideas and new ways to do things and new materials,
Speaker 1: and that's what I like about it. I don't know,
Speaker 1: don what about you, Yeah, I mean, um, there's obviously
Speaker 1: an allure and draw there's some great descriptions about why
Speaker 1: people are drawn to the ocean. Talk to the authors
Speaker 1: and the poets, you know, it's it's definitely a real
Speaker 1: sort of visceral feeling that people get. I think you're
Speaker 1: find that the people that are involved in ocean engineering
Speaker 1: and or marine sides like that. You don't just sort
Speaker 1: of fall into this career by accident. You make proactive
Speaker 1: decisions to get involved in that environment. So you have
Speaker 1: a bunch of people working there that that want to
Speaker 1: be there and sort of have this uh understanding of
Speaker 1: that this is the place they want to be and
Speaker 1: this is where they want to work. So that becomes
Speaker 1: a very very positive work environment workspace because everyone's they
Speaker 1: want to be there, So there's that. Yeah, it's highly collaborative,
Speaker 1: isn't it. It's um like anything, there's personalities, but it
Speaker 1: tends to be a lot of fun more than anything else.
Speaker 1: It's challenging in all the ways that make life interesting.
Speaker 1: And then it also tends to be a good time.
Speaker 1: And you can't work in the ocean by yourself, like, well,
Speaker 1: you can, but it's kind of hard. So, like Brett said,
Speaker 1: it's an incredibly collaborative environment. I mean, if you want
Speaker 1: to be doing anything of significance, you have to be
Speaker 1: working as a group because you need to rely on
Speaker 1: each other. It is an incredibly dynamic, hostile environment, very humbling.
Speaker 1: So you find you you're going to achieve success as
Speaker 1: a collaborative group as opposed to some sort of lone
Speaker 1: wolf type out to right. Okay, so we're here to
Speaker 1: talk about the Mayflower Autonomous Ship project, which obviously is
Speaker 1: very cool. Um, how exactly did you guys decide to
Speaker 1: build an autonomous ship and then model it after the Mayflower?
Speaker 1: I mean it was just to hold my beer kind
Speaker 1: of thing. Um, I'm sure what it really is, it
Speaker 1: really was, it really was. Yeah, what it really was
Speaker 1: is it was so in meeting with the City of
Speaker 1: Plymouth on something else. They were talking about what they
Speaker 1: were going to do and maybe build a replica ship,
Speaker 1: of which there's already one. And I thought that wasn't
Speaker 1: the best idea. And you're talking for anniversary. Yeah, And
Speaker 1: so I was a little bit indelicate in my comment
Speaker 1: as to how they wanted to proceed with a possible replica.
Speaker 1: Think you said it was a stupid idea, I said,
Speaker 1: I said it was stupid. And uh, and there was
Speaker 1: more I couldn't resist and and and I said, there
Speaker 1: already is one, you know, And it's it's just I
Speaker 1: grew up near there. And and so they said, all right,
Speaker 1: smart guy, what are you gonna do. I was like, oh,
Speaker 1: we should build one that challenges us technologically and from
Speaker 1: an engineering perspective and sort of invokes the spirit of
Speaker 1: the original risk taking and do something that informs the
Speaker 1: next four years. And everybody was like, yeah, you should
Speaker 1: do that, and I was like, you know what, I
Speaker 1: will hold my beer. And so so I called Don
Speaker 1: after the meeting and I was like, oh, Don, we
Speaker 1: we have to build an AI. I need Captain Watson
Speaker 1: because we're going to build an A ton of a
Speaker 1: ship across the Atlantic, and he was like great and
Speaker 1: so yeah, and it was just that literally, that glib,
Speaker 1: but it also I mean, he and I have been
Speaker 1: working on unmanned systems and autonomous systems for a long
Speaker 1: time together, twenty plus years, and so I wanted to
Speaker 1: see where we could get to, like, how hard could
Speaker 1: this be? Right? I mean? And AI, sure, let's do it. Then,
Speaker 1: So we built a ship. You mentioned capturing the spirit
Speaker 1: of the original Mayflower Journey, and I wonder what exactly
Speaker 1: where you're trying to capture. Was it the spirit of
Speaker 1: taking risks or was it doing something that hadn't been
Speaker 1: done before? What we were trying to do. We knew
Speaker 1: it was really hard, right like, and it was a
Speaker 1: huge amount of risk to undertake it. Press the real
Speaker 1: risk taker. He's the one with the big ideas and
Speaker 1: wants to take the risk. I'm I'm a little more
Speaker 1: cautious and sort of pragmatic in the sense of, Okay,
Speaker 1: what's going to take to do that? We we actually
Speaker 1: didn't think we were going to make it, or I
Speaker 1: fully expected at some point the ocean we get annoyed
Speaker 1: and mighte us, you know, pilgrims like that to me
Speaker 1: is what's interesting. The pilgrims took a risk, right, So
Speaker 1: 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 Autono, a ship in It took
Speaker 1: them six years to figure out both the software and
Speaker 1: the body of the boat itself. In that time, over
Speaker 1: seventy people contributed to the project. Lauren asked Don and
Speaker 1: Brett what it really took to go from hold my
Speaker 1: beer to an actual ship? You know, it is mind
Speaker 1: 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 actual
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: automate is built into the ship and how it works. Well,
Speaker 1: there's tons of automation and Mayflow, I mean Mayflower is
Speaker 1: like most robotics systems, right, So you peel it open
Speaker 1: and you find you know, programmable logic controllers and motor
Speaker 1: drives and also its of other things sensors and industrial
Speaker 1: automation that you'd see, you know, in an elevator or
Speaker 1: an escalator or industrial machinery for manufacture. And that's one
Speaker 1: sort of layer of it. Right, So you've got the
Speaker 1: basic analog control, then you've got sort of a veneer
Speaker 1: 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: Donn 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 fisholds with diesel fuel,
Speaker 1: and put a cheap autopilot on it and sent it.
Speaker 1: It probably would have got across. But so what it's
Speaker 1: not reducing risk, and it's not unburdening a person, and
Speaker 1: it's not doing anything really clever or sophisticated. And so
Speaker 1: what we were more interested in was getting to a
Speaker 1: point where instead of having to tell it to do everything,
Speaker 1: saying go do this task right, a goal like go
Speaker 1: to Plymouth right, and then while you're doing that, oh,
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 uh financial services product
Speaker 1: have to do with marine navigation? And they sort of
Speaker 1: were 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 it's when the system was actually undering,
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: deplace 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 the 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 do you
Speaker 1: think of it as a very logical process, and there
Speaker 1: is that, for sure, but there's an incredible amount of
Speaker 1: innovation involved too, Like there's no template for what we're doing.
Speaker 1: And you know, we call it white paper design, where
Speaker 1: you're basically given a blank piece of paper and a goal,
Speaker 1: which is, okay, ship that's going to cross the Atlantic, Um, okay,
Speaker 1: come up with some ideas, right, So I mean it
Speaker 1: requires major conceptual leaps and then the technical skill to
Speaker 1: realize those those leaps. You're not going to make any
Speaker 1: advances 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: stretches what implementing new ideas, like you can spend a decade.
Speaker 1: We call it power point engineering right where you do
Speaker 1: nothing but think of things. We don't actually do anything,
Speaker 1: as opposed to what we call full contact engineering, where
Speaker 1: you actually built the boat, right, the software to go
Speaker 1: on the boat and send it out on the water.
Speaker 1: Get your kick like, get sea sick, you know, all
Speaker 1: that sort of fun stuff that happens when you're dont
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 for 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 apples right or through 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 lost at sea. And then the other thing
Speaker 1: is you can drive cost down, and I mean cost
Speaker 1: financially but also environmental cost, right, because you can use
Speaker 1: a far less energy to accomplish a similar goal. And
Speaker 1: 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 insight,
Speaker 1: the intuition and and the creativity. And so you know,
Speaker 1: that's why it's important, and we're going to see an
Speaker 1: increasing amount of this, and I think it's also important
Speaker 1: for people to get comfortable with the idea that these
Speaker 1: things will be roaming around and that it's okay. Yeah,
Speaker 1: And and on an interim 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 it 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 then a person can, in
Speaker 1: a very calm way, with no stress, tell it what
Speaker 1: to do. But in the in the interim, they're doing
Speaker 1: something more important, like looking at all the information that's
Speaker 1: being produced by the instruments and having insight. You know,
Speaker 1: ever since we started sailing, there's been expectation of how
Speaker 1: ships interact with each other. Let's see, you know, they've
Speaker 1: been codified by the the I M O. Right, they're
Speaker 1: called like the regulations to prevent collisions at sea. We
Speaker 1: just called them coal ricks. But they're quite nuanced. Like
Speaker 1: it's not like they're called rules of the road, you know,
Speaker 1: after like the idea of like cars, but they're they're
Speaker 1: much more nuanced than like rules for cars. How you
Speaker 1: act depends on the type of vessels that are interacting,
Speaker 1: like if it's a sail boat or a fishing boat,
Speaker 1: or 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 depended on whether the other car about
Speaker 1: the stop sign was a bus or you know, or
Speaker 1: something else, Like the rules change anyway. So that's where
Speaker 1: humans are are really really good at. Is this nuanced
Speaker 1: understanding of these these rules, um squshy squishy 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 puzzle. I
Speaker 1: wonder if the ship ever got into any sticky situations
Speaker 1: that the AI captain was able to get it out of.
Speaker 1: One time, we had a sailboat come at us in
Speaker 1: the night head on reciprocal course, no lights on, no
Speaker 1: radar reflector. Everybody was probably asleep and they just had
Speaker 1: the autopilot on and um, we easily could have speared them,
Speaker 1: or they would have actually hit us because they were
Speaker 1: in violation of regulations. But but that's common, right, And see,
Speaker 1: when you're crossing, it's so unlikely, it's so fast that
Speaker 1: you're going to run into somebody, but it happens. So
Speaker 1: we you know, the ship took appropriate action and moved
Speaker 1: so that that wouldn't happen. But it's not like it
Speaker 1: seems very dramatic at the moment. But you know, you
Speaker 1: see these things coming miles away and it unfolds it
Speaker 1: like five miles an hour or something, right, So it's yeah,
Speaker 1: so it seems more nervous than it is. And I
Speaker 1: mean weather was challenging, and we had some failures technical
Speaker 1: and mechanical failures in the ship that were very very challenging.
Speaker 1: But from the AI captain perspective, the only time that
Speaker 1: we got annoyed was. There was a research ship that
Speaker 1: shall remain nameless from a university that was coming along
Speaker 1: and was going to cross in front of us by
Speaker 1: ten twelve miles, which is fine, and they were going along,
Speaker 1: but they clearly saw us on there, neither their radar
Speaker 1: or their automated identification system which we broadcast, and they
Speaker 1: just at some point turned and came directly at us
Speaker 1: at a angle that it's the it's the I'm messing
Speaker 1: with you angle, Yeah, the angle that allows them to
Speaker 1: maintain right of way but makes it very, very difficult
Speaker 1: to understand their intent and take action. So the ship
Speaker 1: was kind of like, if they had persisted, it would
Speaker 1: have ended up kind of going around in circles trying
Speaker 1: to avoid them. But but fortunately we had a support
Speaker 1: boat that was coming out of Halifax to meet it,
Speaker 1: and it physically got in between the Mayflower and this
Speaker 1: research boat and so what are you doing? Oh, we
Speaker 1: were just going to take a look, and but we
Speaker 1: weren't going to get any closer than two miles and
Speaker 1: it's like, well, what are you going to see from
Speaker 1: two miles away? They absolutely are going to come over
Speaker 1: and take a much closer look because they didn't understand
Speaker 1: 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: They are a bunch of sideists coming back from like
Speaker 1: a six week cruise, and there was like, oh, that
Speaker 1: looks interesting, let's go take a look. So 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 Plymouth 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 worse,
Speaker 1: a ninety degree turn, 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 backs of the rules too, we're supposed to
Speaker 1: avoid them. And so, but what Brett caught 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: a situation where there's a risk of collision, so you
Speaker 1: make 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 dada, you know somewhere say,
Speaker 1: fishing boats, container ships, scientists on a cruise. The vast
Speaker 1: majority of vessels at sea are still of the not
Speaker 1: autonomous variety. To wrap up their conversation, Lauren asked Brett
Speaker 1: and Don where the technology they've developed is headed, what
Speaker 1: it means for the humans who work at SEE, and
Speaker 1: what's next for the two of them. What do you
Speaker 1: guys think this type of automation means for the future
Speaker 1: of the maritime industry and people who work in at
Speaker 1: first of all, like we mentioned, Brett and I have
Speaker 1: both worked in the ocean community for decades our entire careers,
Speaker 1: Like we haven't a lot of respect for the people
Speaker 1: that work in this area. And this isn't about a
Speaker 1: replacement technology. It's an augmented augment what's what's the right
Speaker 1: how do you say augmented intelligence? I mean, look, ships
Speaker 1: have always been the leading edge of technology and almost
Speaker 1: every society up until the twentieth century where we started
Speaker 1: into flight, and now they're kind of resurging into really
Speaker 1: new technological areas. But the point of trying to make
Speaker 1: is there was a time when there were no propellers.
Speaker 1: There's a time when there are no rudders, right, it
Speaker 1: was just sales and steering oars. And then so it's
Speaker 1: been this evolution in technology um and ships have always
Speaker 1: been right at the absolute forefront of it from design
Speaker 1: and engineering and material science. And you know, we've seen
Speaker 1: this sort of long evolution of technology and this is
Speaker 1: just another thing. So I think you're going to see
Speaker 1: lots of areas where really smart port of machine learning
Speaker 1: models helped like to improve efficiencies, and so we're at
Speaker 1: the advent of of a new way of thinking about
Speaker 1: design and implementation of very sophisticated solutions that are based
Speaker 1: in vast amounts of data analytics that are hitherto impossible
Speaker 1: to address. What is next for the Mayflower Autonomous Ship.
Speaker 1: We may do a few things with the Coast Guard,
Speaker 1: and there's a few other folks that want us to
Speaker 1: do some work on national marine sanctuaries looking at cetacean populations,
Speaker 1: and so we'll do that kind of thing with with it,
Speaker 1: and more and more people will get involved in its
Speaker 1: day to day operation and we'll have less sort of
Speaker 1: day to day input, which is fine. And then the
Speaker 1: AI Captain is going into a whole bunch of other
Speaker 1: projects and programs, and we're just starting off on a
Speaker 1: new design for a much larger ship for vast oceanic voyages,
Speaker 1: um maybe even a circumnavigation. That's that's quite an effort. Yeah,
Speaker 1: And then we're going to connect with with NASA with
Speaker 1: the there you know, with the International Space Station and
Speaker 1: satellite networks and sort of have them work collaboratively so
Speaker 1: the space assets see things and they know there's another
Speaker 1: ship asset. So it's almost like a satellite in revers
Speaker 1: It's like the inverse satellite at sea. So it sees
Speaker 1: something from space and it's as a ship such and
Speaker 1: such as over there, ask it to go and look
Speaker 1: at that and tell us if what we're seeing is right,
Speaker 1: or collect a sample right, and those things will work
Speaker 1: collaboratively without people. You kind of opened up Pandora's box here.
Speaker 1: So we did this, and now there's all these other
Speaker 1: things that we can do. So yeah, and we just
Speaker 1: have to pick one that we can do within the
Speaker 1: remainder of our lifetime. There you go. Well, I I
Speaker 1: hope you. I hope you both get to do all
Speaker 1: the new things that you want and have capacity to do.
Speaker 1: Thank you both so much for your time and good
Speaker 1: luck with future journeys and projects. Thank you, hi everybody.
Speaker 1: In the centuries long evolution of maritime technology, the Mayflower
Speaker 1: automous ship represents an inflection point. The ship's success indicates
Speaker 1: that artificial intelligence and automation are tools ready to be
Speaker 1: normalized within the nautical industry, and that the advantages they
Speaker 1: provide will change the way we conceive of ship building.
Speaker 1: But the technology aboard the Mayflower four hundred has implications
Speaker 1: beyond just application at see. Brett and Don's project has
Speaker 1: shown that the potential reward for innovative risk taking is
Speaker 1: to achieve something unprecedented, and that's true for any industry.
Speaker 1: But like the original Mayflower Voyage four n years ago,
Speaker 1: it may require a leap of faith. On the next
Speaker 1: episode of Smart Talks with IBM, what does it take
Speaker 1: to create a sustainability focused global supply chain innovative and
Speaker 1: equitable enough to connect our modern world? We talk with
Speaker 1: Sherry Highness, IBM's global sustainability services leader and offering leader
Speaker 1: for a sustainable supply chain. Smart Talks with IBM is
Speaker 1: produced by Molly Sosha, David jaw, Royston Reserve, Matt Romano,
Speaker 1: and Edith Russelo with Jacob Goldstein. Our engineers are Jason Gambrel,
Speaker 1: Sarah Bruger and Ben Tolliday. Theme song by Gramascope. Special
Speaker 1: thanks to Colly mcglory, Andy Kelly, Kathy Callaghan and the
Speaker 1: eight Bar and IBM teams, as well as the Pushkin
Speaker 1: marketing team. Smart Talks with IBM is a production of
Speaker 1: Pushkin Industries and I Heart Media. To find more Pushkin podcasts,
Speaker 1: listen to the i Heart Radio app, Apple Podcasts, or
Speaker 1: wherever you listen to podcasts. I'm Malcolm Glacko. This is
Speaker 1: a paid advertisement from IBM.