August 31, 2022 • 29 mins
The world’s first autonomous vessel to cross the Atlantic Ocean is signaling a new era in maritime technology. In this episode of Smart Talks with IBM, Malcolm Gladwell takes on this topic with Lauren Ober, host of The Loudest Girl in the World, and guests Brett Phaneuf and Don Scott, the engineers behind the Mayflower Autonomous Ship project. The two explain how automation and AI allowed them to reimagine the design and use of a ship at sea and their implications beyond maritime navigation.
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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new edition of the Smart Talks podcast series,
which is produced in partnership with IBM. This season of
Smart Talks with IBM is all about new creators, the developers,
data scientists, c t o s, and other visionaries creatively
(00:22):
applying technology and business to drive change. They use their
knowledge and creativity to develop better ways of working, no
matter the industry. Join hosts from your favorite Pushkin Industries
podcast as they use their expertise to deepen these conversations.
Malcolm Gladwell will guide you through this season as your
host to provide his thoughts and analysis along the way.
(00:45):
Look out for new episodes of Smart Talks with IBM
every month on the I Heart Radio app, Apple Podcasts,
or wherever you get your podcasts. And learn more at
IBM dot com slash smart Talks. Hello, Hello, Welcome to
Smart Talks with IBM, a podcast from Pushkin Industries, I
(01:07):
Heart Radio and IBM. I'm Malcolm Blobwell. This season we're
talking to new creators, the developers, data scientists, c t
o s, and other visionaries who are creatively applying technology
and business to drive change. Channeling their knowledge and expertise,
they're developing more creative and effective solutions no matter the industry.
(01:30):
Our guest today are Brett Fanoff and Don Scott. Brett
and Dawn are responsible for creating the world's first unmanned,
fully autonomous ship to cross the Atlantic Ocean, a research
vessel they've dubbed the Mayflower four hundred. Brett is the
director of the Mayflower Autonomous Ship Project and Dawn is
(01:51):
the CTO of Marine AI. On June thirty two, the
Mayflower four hundred successfully completed its voyage from Plymouth, UK
to Plymouth, Massachusetts. It's both an homage to the original Mayflower,
which crossed the Atlantic forundered years earlier, and a bell
weather for the ways autonomous technology will push the boundaries
(02:14):
of maritime exploration in the next four years. On today's show,
the Unlikely Origins of a self directed Ship, some motion misadventures,
and what AI and machine learning will mean for the
future of seafaring and beyond, Brett and Dawn spoke with
Lauren Ober, host of the forthcoming Pushkin podcast The Loudest
(02:37):
Girl in the World. Lauren is a longtime radio host
and reporter, helming shows like NPRS, The Big Listen and
Spectacular failures from American public Media. Okay, now let's get
to the interview with Brett Fanoff and Don Scott. Don
(02:57):
and Brett, it's really great to be talking with you
guys today. I was wondering for each of you, what
is the draw of the sea? I mean, it's like
this expansive place. It feels so unknown in so many ways. Um,
but I'm curious, like, what is the allure there for me?
It's I wanted to be I wanted to do aerospace,
so I always feel like I'm like the poor cousin
(03:19):
of aerospace. But it isn't. It's actually it's harder to
to do the underwater stuff. It's closer. It's just harder
than being in space. It's it's incredibly hostile and wildly unexplored.
And why what I like about it is that you know,
you can take a bucket and go down to the beach,
get a bucket of water, analyze the bucket of water
for the next twenty years, and you know, chances are
(03:41):
pretty high you're gonna have a couple of things in
there that nobody's ever seen before, and that's every bucket
of water everywhere in the world, right, So I like
the idea that you get to discover something new all
the time. And it's also hard. It's a difficult place
to work, so it challenges you to come up with
new ideas and new ways to do things and new materials,
and that's what I like about it. I don't know,
(04:01):
don what about you, Yeah, I mean, um, there's obviously
an allure and draw there's some great descriptions about why
people are drawn to the ocean. Talk to the authors
and the poets, you know, it's it's definitely a real
sort of visceral feeling that people get. I think you're
find that the people that are involved in ocean engineering
and or marine sides like that. You don't just sort
of fall into this career by accident. You make proactive
(04:25):
decisions to get involved in that environment. So you have
a bunch of people working there that that want to
be there and sort of have this uh understanding of
that this is the place they want to be and
this is where they want to work. So that becomes
a very very positive work environment workspace because everyone's they
(04:46):
want to be there, So there's that. Yeah, it's highly collaborative,
isn't it. It's um like anything, there's personalities, but it
tends to be a lot of fun more than anything else.
It's challenging in all the ways that make life interesting.
And then it also tends to be a good time.
And you can't work in the ocean by yourself, like, well,
you can, but it's kind of hard. So, like Brett said,
(05:07):
it's an incredibly collaborative environment. I mean, if you want
to be doing anything of significance, you have to be
working as a group because you need to rely on
each other. It is an incredibly dynamic, hostile environment, very humbling.
So you find you you're going to achieve success as
a collaborative group as opposed to some sort of lone
(05:28):
wolf type out to right. Okay, so we're here to
talk about the Mayflower Autonomous Ship project, which obviously is
very cool. Um, how exactly did you guys decide to
build an autonomous ship and then model it after the Mayflower?
I mean it was just to hold my beer kind
(05:49):
of thing. Um, I'm sure what it really is, it
really was, it really was. Yeah, what it really was
is it was so in meeting with the City of
Plymouth on something else. They were talking about what they
were going to do and maybe build a replica ship,
of which there's already one. And I thought that wasn't
the best idea. And you're talking for anniversary. Yeah, And
(06:12):
so I was a little bit indelicate in my comment
as to how they wanted to proceed with a possible replica.
Think you said it was a stupid idea, I said,
I said it was stupid. And uh, and there was
more I couldn't resist and and and I said, there
already is one, you know, And it's it's just I
grew up near there. And and so they said, all right,
smart guy, what are you gonna do. I was like, oh,
(06:33):
we should build one that challenges us technologically and from
an engineering perspective and sort of invokes the spirit of
the original risk taking and do something that informs the
next four years. And everybody was like, yeah, you should
do that, and I was like, you know what, I
will hold my beer. And so so I called Don
after the meeting and I was like, oh, Don, we
(06:55):
we have to build an AI. I need Captain Watson
because we're going to build an A ton of a
ship across the Atlantic, and he was like great and
so yeah, and it was just that literally, that glib,
but it also I mean, he and I have been
working on unmanned systems and autonomous systems for a long
time together, twenty plus years, and so I wanted to
see where we could get to, like, how hard could
(07:16):
this be? Right? I mean? And AI, sure, let's do it. Then,
So we built a ship. You mentioned capturing the spirit
of the original Mayflower Journey, and I wonder what exactly
where you're trying to capture. Was it the spirit of
taking risks or was it doing something that hadn't been
done before? What we were trying to do. We knew
(07:37):
it was really hard, right like, and it was a
huge amount of risk to undertake it. Press the real
risk taker. He's the one with the big ideas and
wants to take the risk. I'm I'm a little more
cautious and sort of pragmatic in the sense of, Okay,
what's going to take to do that? We we actually
didn't think we were going to make it, or I
fully expected at some point the ocean we get annoyed
(07:59):
and mighte us, you know, pilgrims like that to me
is what's interesting. The pilgrims took a risk, right, So
every one of them fully expected that they would die
if not on the voyage within like the first year. Right,
That's how it was, and it was worth it to
them to take that risk. So our risk is infinitesimal
(08:20):
by comparison, Right, it's tiny. What was our risk, really,
We'd lose a ship we spent some money on. So
what the knowledge about how to approach these problems is,
and the and the experience that you get to give
people to take risk at that level from an engineering
perspective is really important. Right, somebody had to do the
first open heart surgery and took a risk. Now we're
(08:43):
not doing open heart surgery, right, No one's going to die.
So what's appealing about the risk thing is it has
a technical risk and environmental risk, and then there's a
legislative and regulatory risk. Because we had to have our
fights with various agencies about the fact that they didn't
have a law that said we couldn't so they didn't
get to say no just because they didn't want us to.
And at the same time trying to create a reliable
(09:06):
machine and then some sort of an AI machine learning
based system that would be safe whatever that is in
the middle of the ocean. It's really interesting and gives
people a lot to a lot of purchase for different
people with different skill sets to collaborate. Brett and John
started developing the Mayflower Autono, a ship in It took
them six years to figure out both the software and
(09:29):
the body of the boat itself. In that time, over
seventy people contributed to the project. Lauren asked Don and
Brett what it really took to go from hold my
beer to an actual ship? You know, it is mind
boggling when you think of how many people are involved,
how many people are touching this project, how many interesting
(09:51):
minds doing interesting things, but you have to funnel it
all into this one project. Well that I don't know
if it's that way. I mean, I guess you could
say there was one project, but there were lots of projects,
and so, you know, there was sort of the hardcore
group of people that are trying to build the actual
software that works, and then there's the guys trying to
build the hardware and they have an interface, but they're
(10:13):
parallel pursuits that don't have direct overlap. And then we
said yes a lot to anybody who wanted to help,
because we learned from experience that most people don't last
in terms of the ability to stick out four or
five years focus on the projects very hard. And so
the people that I wanted to stick it out and
bring it to fruition ended up, you know, sticking it
(10:33):
out and that was great, you know. And then there
are all sorts of different things. There was a group
making a web interface so that they could show the
world what we were doing, and you know, then there
was a PR group that was marketing things and sort
of talking about how we tell the world about it,
and we would support them. But it's hard to describe
it as one project. I guess would be my position.
It's lots of interlinked programs, right, Sure, I get that,
(10:56):
I get that. Can you tell me more about how
automate is built into the ship and how it works. Well,
there's tons of automation and Mayflow, I mean Mayflower is
like most robotics systems, right, So you peel it open
and you find you know, programmable logic controllers and motor
drives and also its of other things sensors and industrial
(11:18):
automation that you'd see, you know, in an elevator or
an escalator or industrial machinery for manufacture. And that's one
sort of layer of it. Right, So you've got the
basic analog control, then you've got sort of a veneer
of automation, and then what I would call sophisticated automation,
which don and I have worked on for decades in
the marine space. So all that's in there. And you know,
(11:40):
Donn and I talked really early on if I just
wanted to get across the Atlantic, we could have bought
an old fishing boat, filled up the fisholds with diesel fuel,
and put a cheap autopilot on it and sent it.
It probably would have got across. But so what it's
not reducing risk, and it's not unburdening a person, and
it's not doing anything really clever or sophisticated. And so
(12:03):
what we were more interested in was getting to a
point where instead of having to tell it to do everything,
saying go do this task right, a goal like go
to Plymouth right, and then while you're doing that, oh,
by the way, while you're doing that, collect all this
science data and if you see anything unusual, tell us
and and while you're looking for all these unusual things
and trying to achieve your goal don't hit anything. So
(12:26):
then what role did IBM s technology play in all
of this? Yeah, I mean their their technology is all
over the ship. Probably the main contribution it was the
decision making process or it's it's an automation TOOLM operational
decision manager. It's actually a financial services tool. It's for
your making decisions about the viability of a transaction, whether
(12:50):
it's fraud or order or alone or let's say, And
we were being presented this by one of the ODIUM engineers,
and I remember sitting in the room with Brett thing,
what what in the world does uh financial services product
have to do with marine navigation? And they sort of
were brought to realize by the IBM engineer how this is.
(13:11):
This isn't really so much about financial services as it
is about making making really difficult decisions in a really
complex environment, which is what they do in financial services.
But it's also exactly what we needed to do in
re navigation. And when it's when the system was actually undering,
it would create a log essentially of why that decision
(13:32):
was made, so they can validate that decision and verify
and validate that that that was in fact the right decision.
And um, so that's a that's one of the key
IVM tools that are on board. Well, one of the
things you might want to consider about that is the fundamentals, Right,
the theoretical independence of all the AI that we're deploying
now have been sort of understood for decades, right, and
(13:54):
so now we just happened to live in a world
where the microprocesses are up to snuff that they can
deplace some of these very sophisticated theoretical and reality and
all of which IBM has been involved with from inception,
based on its pedigree is in the national business machines.
There isn't an IBM product that I can think of
that we haven't tried to utilize the deploying so it's
(14:15):
it's it's everywhere in the ship. Yeah, I don't think
a lot of people think of technology as as as
a creative pursuit, but I imagine building an autonomous ship
from scratch takes a lot of creativity. And I'm wondering,
do you guys think of your work as creative? Yeah,
engineering is essentially designing technological innovation sort of do you
(14:39):
think of it as a very logical process, and there
is that, for sure, but there's an incredible amount of
innovation involved too, Like there's no template for what we're doing.
And you know, we call it white paper design, where
you're basically given a blank piece of paper and a goal,
which is, okay, ship that's going to cross the Atlantic, Um, okay,
(14:59):
come up with some ideas, right, So I mean it
requires major conceptual leaps and then the technical skill to
realize those those leaps. You're not going to make any
advances just doing things the way you've always done them. Right.
You need to stretch right, and the only way it
stretches what implementing new ideas, like you can spend a decade.
(15:22):
We call it power point engineering right where you do
nothing but think of things. We don't actually do anything,
as opposed to what we call full contact engineering, where
you actually built the boat, right, the software to go
on the boat and send it out on the water.
Get your kick like, get sea sick, you know, all
that sort of fun stuff that happens when you're dont
(15:43):
see trials um. And because that's where you that's where
the actual learning is happening, that's where the actual development
is happening is being out on the ocean. Crossing the
Atlantic is no small voyage for any vessel, but the
Mayflower Autonomous Ship Project is more than just about sailing
from point A to point B. Automation and AI have
(16:05):
game changing implications for the way we design the next
generation of vessels and the way these vessels will behave
and interact at sea. Ships will be able to gather
data from the ocean by themselves, providing humans with critical
information we need to address problems like global warming, ocean pollution,
(16:26):
and our impact on marine life. For instance, the Mayfire
four hundred can sample ocean water for microplastics and record
audio of whale vocalizations. Taking the human factor out of
a ship allows us to explore new designs and functions
that haven't been imagined before. Lauren asked bread and Down
(16:48):
more about this. What are some of the benefits of
having an unmanned vessel, like, how does automation push the
boundaries of what we can do out in the ocean. Well,
the few major apples right or through facets to that
one is you can do some risky things when you
don't have the people there right because no one's going
to be lost at sea. And then the other thing
(17:09):
is you can drive cost down, and I mean cost
financially but also environmental cost, right, because you can use
a far less energy to accomplish a similar goal. And
then what that allows you to do is have more Right.
So instead of say having one fifty million dollar hundred
million dollar research ship, which is the kind of numbers
you're talking about to take scientists to see, you can
(17:31):
have twenty or thirty or forty million dollars or two
million dollar ships that go out and work collaboratively with
space based assets and with one another and collect vast
amounts of data from disparate parts of the ocean. And
then you use that data to create information that informs
where you send the man vessel, right, so that they
get the most out of their time at sea. So
(17:54):
it's about enabling the people. It's about leaving the humans
to do the uniquely human part, which is have the insight,
the intuition and and the creativity. And so you know,
that's why it's important, and we're going to see an
increasing amount of this, and I think it's also important
for people to get comfortable with the idea that these
things will be roaming around and that it's okay. Yeah,
(18:15):
And and on an interim basis, I mean, we're also
talking about this same technology that allows a ship to
sail autonomously also can be used to assist a human
crew now, you know, basically be another set of eyes
and years be a watchkeeper for a manned vessel. Right,
(18:36):
I want to know more about the AI captain. How
did you build it so that it would be comparable
to the way a human captain might direct a ship.
What we're trying to do is augment the person, Right,
We're trying to let them be more of a person
than sort of. They don't have to watch the radar,
they don't have to watch the cameras. Right. The machine
(18:58):
can do all that, and then if it can't do
something safely, if it can't come to a solution, it
can ask a person send a little texic, I don't
know what to do, and then a person can, in
a very calm way, with no stress, tell it what
to do. But in the in the interim, they're doing
something more important, like looking at all the information that's
being produced by the instruments and having insight. You know,
(19:21):
ever since we started sailing, there's been expectation of how
ships interact with each other. Let's see, you know, they've
been codified by the the I M O. Right, they're
called like the regulations to prevent collisions at sea. We
just called them coal ricks. But they're quite nuanced. Like
it's not like they're called rules of the road, you know,
after like the idea of like cars, but they're they're
(19:42):
much more nuanced than like rules for cars. How you
act depends on the type of vessels that are interacting,
like if it's a sail boat or a fishing boat,
or a container ship or a pleasure craft. Like imagine
if you're driving your car down the road and you're
at a stop sign, and then depending whether you could
go or not depended on whether the other car about
(20:03):
the stop sign was a bus or you know, or
something else, Like the rules change anyway. So that's where
humans are are really really good at. Is this nuanced
understanding of these these rules, um squshy squishy rules. Yeah. So,
(20:24):
and that's where we've done. You know, a lot of
our lot of our work on is in that area.
And that's the hardest part of this whole puzzle. I
wonder if the ship ever got into any sticky situations
that the AI captain was able to get it out of.
One time, we had a sailboat come at us in
(20:46):
the night head on reciprocal course, no lights on, no
radar reflector. Everybody was probably asleep and they just had
the autopilot on and um, we easily could have speared them,
or they would have actually hit us because they were
in violation of regulations. But but that's common, right, And see,
when you're crossing, it's so unlikely, it's so fast that
(21:08):
you're going to run into somebody, but it happens. So
we you know, the ship took appropriate action and moved
so that that wouldn't happen. But it's not like it
seems very dramatic at the moment. But you know, you
see these things coming miles away and it unfolds it
like five miles an hour or something, right, So it's yeah,
so it seems more nervous than it is. And I
(21:29):
mean weather was challenging, and we had some failures technical
and mechanical failures in the ship that were very very challenging.
But from the AI captain perspective, the only time that
we got annoyed was. There was a research ship that
shall remain nameless from a university that was coming along
and was going to cross in front of us by
ten twelve miles, which is fine, and they were going along,
(21:50):
but they clearly saw us on there, neither their radar
or their automated identification system which we broadcast, and they
just at some point turned and came directly at us
at a angle that it's the it's the I'm messing
with you angle, Yeah, the angle that allows them to
maintain right of way but makes it very, very difficult
to understand their intent and take action. So the ship
(22:12):
was kind of like, if they had persisted, it would
have ended up kind of going around in circles trying
to avoid them. But but fortunately we had a support
boat that was coming out of Halifax to meet it,
and it physically got in between the Mayflower and this
research boat and so what are you doing? Oh, we
were just going to take a look, and but we
weren't going to get any closer than two miles and
(22:33):
it's like, well, what are you going to see from
two miles away? They absolutely are going to come over
and take a much closer look because they didn't understand
that the vessel was trying to avoid them. You know,
when they see these unmanned systems at sea, they're just
dumb robots, right, They just float around with winder wave power.
They are a bunch of sideists coming back from like
(22:54):
a six week cruise, and there was like, oh, that
looks interesting, let's go take a look. So yeah, and
so that was the only thing that was annoying. Other
than that, it was getting into and out of port.
Getting out of Plymouth was a little challenging. Once we
get outside twelve miles, we had a lot of fishing
boats to dodge, but that was fine. And then out
(23:14):
in the deep sea, it's just it's mostly the sea
that you're concerned with, and it's the fishing grounds are
always the trickiest place because, yeah, because fishing boats do
whatever they want. Yeah, and they're like container ships. They're
not going to change course unless they have to, so
you can pretty much understand what they're what they're doing.
Fishing boats could be going along a nice straight line
(23:36):
and then all of a sudden do a money or worse,
a ninety degree turn, and they don't care about you,
and they just expect you to avoid them, and they
literally there's no one in the wheelhouse. Probably they're all
on the backs of the rules too, we're supposed to
avoid them. And so, but what Brett caught it earlier,
it was things evolved very slowly. Like things don't happen
quickly at sea. It's sort of like, Okay, there's ship,
(23:58):
it's you know, it's it's twenty miles away. I've got
a little bit of time to figure out what I'm
gonna do. You don't ever try to put yourself into
a situation where there's a risk of collision, so you
make decisions that so you don't put yourself at that risk. Right, So,
like I'm not going to cross the street at the
busiest place. I'm gonna cross it dada, you know somewhere say,
(24:22):
fishing boats, container ships, scientists on a cruise. The vast
majority of vessels at sea are still of the not
autonomous variety. To wrap up their conversation, Lauren asked Brett
and Don where the technology they've developed is headed, what
it means for the humans who work at SEE, and
what's next for the two of them. What do you
(24:43):
guys think this type of automation means for the future
of the maritime industry and people who work in at
first of all, like we mentioned, Brett and I have
both worked in the ocean community for decades our entire careers,
Like we haven't a lot of respect for the people
that work in this area. And this isn't about a
(25:06):
replacement technology. It's an augmented augment what's what's the right
how do you say augmented intelligence? I mean, look, ships
have always been the leading edge of technology and almost
every society up until the twentieth century where we started
into flight, and now they're kind of resurging into really
(25:27):
new technological areas. But the point of trying to make
is there was a time when there were no propellers.
There's a time when there are no rudders, right, it
was just sales and steering oars. And then so it's
been this evolution in technology um and ships have always
been right at the absolute forefront of it from design
and engineering and material science. And you know, we've seen
this sort of long evolution of technology and this is
(25:49):
just another thing. So I think you're going to see
lots of areas where really smart port of machine learning
models helped like to improve efficiencies, and so we're at
the advent of of a new way of thinking about
design and implementation of very sophisticated solutions that are based
in vast amounts of data analytics that are hitherto impossible
(26:12):
to address. What is next for the Mayflower Autonomous Ship.
We may do a few things with the Coast Guard,
and there's a few other folks that want us to
do some work on national marine sanctuaries looking at cetacean populations,
and so we'll do that kind of thing with with it,
and more and more people will get involved in its
(26:33):
day to day operation and we'll have less sort of
day to day input, which is fine. And then the
AI Captain is going into a whole bunch of other
projects and programs, and we're just starting off on a
new design for a much larger ship for vast oceanic voyages,
um maybe even a circumnavigation. That's that's quite an effort. Yeah,
(26:57):
And then we're going to connect with with NASA with
the there you know, with the International Space Station and
satellite networks and sort of have them work collaboratively so
the space assets see things and they know there's another
ship asset. So it's almost like a satellite in revers
It's like the inverse satellite at sea. So it sees
something from space and it's as a ship such and
such as over there, ask it to go and look
(27:19):
at that and tell us if what we're seeing is right,
or collect a sample right, and those things will work
collaboratively without people. You kind of opened up Pandora's box here.
So we did this, and now there's all these other
things that we can do. So yeah, and we just
have to pick one that we can do within the
remainder of our lifetime. There you go. Well, I I
(27:40):
hope you. I hope you both get to do all
the new things that you want and have capacity to do.
Thank you both so much for your time and good
luck with future journeys and projects. Thank you, hi everybody.
In the centuries long evolution of maritime technology, the Mayflower
automous ship represents an inflection point. The ship's success indicates
(28:05):
that artificial intelligence and automation are tools ready to be
normalized within the nautical industry, and that the advantages they
provide will change the way we conceive of ship building.
But the technology aboard the Mayflower four hundred has implications
beyond just application at see. Brett and Don's project has
(28:27):
shown that the potential reward for innovative risk taking is
to achieve something unprecedented, and that's true for any industry.
But like the original Mayflower Voyage four n years ago,
it may require a leap of faith. On the next
episode of Smart Talks with IBM, what does it take
to create a sustainability focused global supply chain innovative and
(28:52):
equitable enough to connect our modern world? We talk with
Sherry Highness, IBM's global sustainability services leader and offering leader
for a sustainable supply chain. Smart Talks with IBM is
produced by Molly Sosha, David jaw, Royston Reserve, Matt Romano,
and Edith Russelo with Jacob Goldstein. Our engineers are Jason Gambrel,
(29:17):
Sarah Bruger and Ben Tolliday. Theme song by Gramascope. Special
thanks to Colly mcglory, Andy Kelly, Kathy Callaghan and the
eight Bar and IBM teams, as well as the Pushkin
marketing team. Smart Talks with IBM is a production of
Pushkin Industries and I Heart Media. To find more Pushkin podcasts,
(29:41):
listen to the i Heart Radio app, Apple Podcasts, or
wherever you listen to podcasts. I'm Malcolm Glacko. This is
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they're developing more creative and effective solutions no matter the industry.
(01:30):
Our guest today are Brett Fanoff and Don Scott. Brett
and Dawn are responsible for creating the world's first unmanned,
fully autonomous ship to cross the Atlantic Ocean, a research
vessel they've dubbed the Mayflower four hundred. Brett is the
director of the Mayflower Autonomous Ship Project and Dawn is
(01:51):
the CTO of Marine AI. On June thirty two, the
Mayflower four hundred successfully completed its voyage from Plymouth, UK
to Plymouth, Massachusetts. It's both an homage to the original Mayflower,
which crossed the Atlantic forundered years earlier, and a bell
weather for the ways autonomous technology will push the boundaries
(02:14):
of maritime exploration in the next four years. On today's show,
the Unlikely Origins of a self directed Ship, some motion misadventures,
and what AI and machine learning will mean for the
future of seafaring and beyond, Brett and Dawn spoke with
Lauren Ober, host of the forthcoming Pushkin podcast The Loudest
(02:37):
Girl in the World. Lauren is a longtime radio host
and reporter, helming shows like NPRS, The Big Listen and
Spectacular failures from American public Media. Okay, now let's get
to the interview with Brett Fanoff and Don Scott. Don
(02:57):
and Brett, it's really great to be talking with you
guys today. I was wondering for each of you, what
is the draw of the sea? I mean, it's like
this expansive place. It feels so unknown in so many ways. Um,
but I'm curious, like, what is the allure there for me?
It's I wanted to be I wanted to do aerospace,
so I always feel like I'm like the poor cousin
(03:19):
of aerospace. But it isn't. It's actually it's harder to
to do the underwater stuff. It's closer. It's just harder
than being in space. It's it's incredibly hostile and wildly unexplored.
And why what I like about it is that you know,
you can take a bucket and go down to the beach,
get a bucket of water, analyze the bucket of water
for the next twenty years, and you know, chances are
(03:41):
pretty high you're gonna have a couple of things in
there that nobody's ever seen before, and that's every bucket
of water everywhere in the world, right, So I like
the idea that you get to discover something new all
the time. And it's also hard. It's a difficult place
to work, so it challenges you to come up with
new ideas and new ways to do things and new materials,
and that's what I like about it. I don't know,
(04:01):
don what about you, Yeah, I mean, um, there's obviously
an allure and draw there's some great descriptions about why
people are drawn to the ocean. Talk to the authors
and the poets, you know, it's it's definitely a real
sort of visceral feeling that people get. I think you're
find that the people that are involved in ocean engineering
and or marine sides like that. You don't just sort
of fall into this career by accident. You make proactive
(04:25):
decisions to get involved in that environment. So you have
a bunch of people working there that that want to
be there and sort of have this uh understanding of
that this is the place they want to be and
this is where they want to work. So that becomes
a very very positive work environment workspace because everyone's they
(04:46):
want to be there, So there's that. Yeah, it's highly collaborative,
isn't it. It's um like anything, there's personalities, but it
tends to be a lot of fun more than anything else.
It's challenging in all the ways that make life interesting.
And then it also tends to be a good time.
And you can't work in the ocean by yourself, like, well,
you can, but it's kind of hard. So, like Brett said,
(05:07):
it's an incredibly collaborative environment. I mean, if you want
to be doing anything of significance, you have to be
working as a group because you need to rely on
each other. It is an incredibly dynamic, hostile environment, very humbling.
So you find you you're going to achieve success as
a collaborative group as opposed to some sort of lone
(05:28):
wolf type out to right. Okay, so we're here to
talk about the Mayflower Autonomous Ship project, which obviously is
very cool. Um, how exactly did you guys decide to
build an autonomous ship and then model it after the Mayflower?
I mean it was just to hold my beer kind
(05:49):
of thing. Um, I'm sure what it really is, it
really was, it really was. Yeah, what it really was
is it was so in meeting with the City of
Plymouth on something else. They were talking about what they
were going to do and maybe build a replica ship,
of which there's already one. And I thought that wasn't
the best idea. And you're talking for anniversary. Yeah, And
(06:12):
so I was a little bit indelicate in my comment
as to how they wanted to proceed with a possible replica.
Think you said it was a stupid idea, I said,
I said it was stupid. And uh, and there was
more I couldn't resist and and and I said, there
already is one, you know, And it's it's just I
grew up near there. And and so they said, all right,
smart guy, what are you gonna do. I was like, oh,
(06:33):
we should build one that challenges us technologically and from
an engineering perspective and sort of invokes the spirit of
the original risk taking and do something that informs the
next four years. And everybody was like, yeah, you should
do that, and I was like, you know what, I
will hold my beer. And so so I called Don
after the meeting and I was like, oh, Don, we
(06:55):
we have to build an AI. I need Captain Watson
because we're going to build an A ton of a
ship across the Atlantic, and he was like great and
so yeah, and it was just that literally, that glib,
but it also I mean, he and I have been
working on unmanned systems and autonomous systems for a long
time together, twenty plus years, and so I wanted to
see where we could get to, like, how hard could
(07:16):
this be? Right? I mean? And AI, sure, let's do it. Then,
So we built a ship. You mentioned capturing the spirit
of the original Mayflower Journey, and I wonder what exactly
where you're trying to capture. Was it the spirit of
taking risks or was it doing something that hadn't been
done before? What we were trying to do. We knew
(07:37):
it was really hard, right like, and it was a
huge amount of risk to undertake it. Press the real
risk taker. He's the one with the big ideas and
wants to take the risk. I'm I'm a little more
cautious and sort of pragmatic in the sense of, Okay,
what's going to take to do that? We we actually
didn't think we were going to make it, or I
fully expected at some point the ocean we get annoyed
(07:59):
and mighte us, you know, pilgrims like that to me
is what's interesting. The pilgrims took a risk, right, So
every one of them fully expected that they would die
if not on the voyage within like the first year. Right,
That's how it was, and it was worth it to
them to take that risk. So our risk is infinitesimal
(08:20):
by comparison, Right, it's tiny. What was our risk, really,
We'd lose a ship we spent some money on. So
what the knowledge about how to approach these problems is,
and the and the experience that you get to give
people to take risk at that level from an engineering
perspective is really important. Right, somebody had to do the
first open heart surgery and took a risk. Now we're
(08:43):
not doing open heart surgery, right, No one's going to die.
So what's appealing about the risk thing is it has
a technical risk and environmental risk, and then there's a
legislative and regulatory risk. Because we had to have our
fights with various agencies about the fact that they didn't
have a law that said we couldn't so they didn't
get to say no just because they didn't want us to.
And at the same time trying to create a reliable
(09:06):
machine and then some sort of an AI machine learning
based system that would be safe whatever that is in
the middle of the ocean. It's really interesting and gives
people a lot to a lot of purchase for different
people with different skill sets to collaborate. Brett and John
started developing the Mayflower Autono, a ship in It took
them six years to figure out both the software and
(09:29):
the body of the boat itself. In that time, over
seventy people contributed to the project. Lauren asked Don and
Brett what it really took to go from hold my
beer to an actual ship? You know, it is mind
boggling when you think of how many people are involved,
how many people are touching this project, how many interesting
(09:51):
minds doing interesting things, but you have to funnel it
all into this one project. Well that I don't know
if it's that way. I mean, I guess you could
say there was one project, but there were lots of projects,
and so, you know, there was sort of the hardcore
group of people that are trying to build the actual
software that works, and then there's the guys trying to
build the hardware and they have an interface, but they're
(10:13):
parallel pursuits that don't have direct overlap. And then we
said yes a lot to anybody who wanted to help,
because we learned from experience that most people don't last
in terms of the ability to stick out four or
five years focus on the projects very hard. And so
the people that I wanted to stick it out and
bring it to fruition ended up, you know, sticking it
(10:33):
out and that was great, you know. And then there
are all sorts of different things. There was a group
making a web interface so that they could show the
world what we were doing, and you know, then there
was a PR group that was marketing things and sort
of talking about how we tell the world about it,
and we would support them. But it's hard to describe
it as one project. I guess would be my position.
It's lots of interlinked programs, right, Sure, I get that,
(10:56):
I get that. Can you tell me more about how
automate is built into the ship and how it works. Well,
there's tons of automation and Mayflow, I mean Mayflower is
like most robotics systems, right, So you peel it open
and you find you know, programmable logic controllers and motor
drives and also its of other things sensors and industrial
(11:18):
automation that you'd see, you know, in an elevator or
an escalator or industrial machinery for manufacture. And that's one
sort of layer of it. Right, So you've got the
basic analog control, then you've got sort of a veneer
of automation, and then what I would call sophisticated automation,
which don and I have worked on for decades in
the marine space. So all that's in there. And you know,
(11:40):
Donn and I talked really early on if I just
wanted to get across the Atlantic, we could have bought
an old fishing boat, filled up the fisholds with diesel fuel,
and put a cheap autopilot on it and sent it.
It probably would have got across. But so what it's
not reducing risk, and it's not unburdening a person, and
it's not doing anything really clever or sophisticated. And so
(12:03):
what we were more interested in was getting to a
point where instead of having to tell it to do everything,
saying go do this task right, a goal like go
to Plymouth right, and then while you're doing that, oh,
by the way, while you're doing that, collect all this
science data and if you see anything unusual, tell us
and and while you're looking for all these unusual things
and trying to achieve your goal don't hit anything. So
(12:26):
then what role did IBM s technology play in all
of this? Yeah, I mean their their technology is all
over the ship. Probably the main contribution it was the
decision making process or it's it's an automation TOOLM operational
decision manager. It's actually a financial services tool. It's for
your making decisions about the viability of a transaction, whether
(12:50):
it's fraud or order or alone or let's say, And
we were being presented this by one of the ODIUM engineers,
and I remember sitting in the room with Brett thing,
what what in the world does uh financial services product
have to do with marine navigation? And they sort of
were brought to realize by the IBM engineer how this is.
(13:11):
This isn't really so much about financial services as it
is about making making really difficult decisions in a really
complex environment, which is what they do in financial services.
But it's also exactly what we needed to do in
re navigation. And when it's when the system was actually undering,
it would create a log essentially of why that decision
(13:32):
was made, so they can validate that decision and verify
and validate that that that was in fact the right decision.
And um, so that's a that's one of the key
IVM tools that are on board. Well, one of the
things you might want to consider about that is the fundamentals, Right,
the theoretical independence of all the AI that we're deploying
now have been sort of understood for decades, right, and
(13:54):
so now we just happened to live in a world
where the microprocesses are up to snuff that they can
deplace some of these very sophisticated theoretical and reality and
all of which IBM has been involved with from inception,
based on its pedigree is in the national business machines.
There isn't an IBM product that I can think of
that we haven't tried to utilize the deploying so it's
(14:15):
it's it's everywhere in the ship. Yeah, I don't think
a lot of people think of technology as as as
a creative pursuit, but I imagine building an autonomous ship
from scratch takes a lot of creativity. And I'm wondering,
do you guys think of your work as creative? Yeah,
engineering is essentially designing technological innovation sort of do you
(14:39):
think of it as a very logical process, and there
is that, for sure, but there's an incredible amount of
innovation involved too, Like there's no template for what we're doing.
And you know, we call it white paper design, where
you're basically given a blank piece of paper and a goal,
which is, okay, ship that's going to cross the Atlantic, Um, okay,
(14:59):
come up with some ideas, right, So I mean it
requires major conceptual leaps and then the technical skill to
realize those those leaps. You're not going to make any
advances just doing things the way you've always done them. Right.
You need to stretch right, and the only way it
stretches what implementing new ideas, like you can spend a decade.
(15:22):
We call it power point engineering right where you do
nothing but think of things. We don't actually do anything,
as opposed to what we call full contact engineering, where
you actually built the boat, right, the software to go
on the boat and send it out on the water.
Get your kick like, get sea sick, you know, all
that sort of fun stuff that happens when you're dont
(15:43):
see trials um. And because that's where you that's where
the actual learning is happening, that's where the actual development
is happening is being out on the ocean. Crossing the
Atlantic is no small voyage for any vessel, but the
Mayflower Autonomous Ship Project is more than just about sailing
from point A to point B. Automation and AI have
(16:05):
game changing implications for the way we design the next
generation of vessels and the way these vessels will behave
and interact at sea. Ships will be able to gather
data from the ocean by themselves, providing humans with critical
information we need to address problems like global warming, ocean pollution,
(16:26):
and our impact on marine life. For instance, the Mayfire
four hundred can sample ocean water for microplastics and record
audio of whale vocalizations. Taking the human factor out of
a ship allows us to explore new designs and functions
that haven't been imagined before. Lauren asked bread and Down
(16:48):
more about this. What are some of the benefits of
having an unmanned vessel, like, how does automation push the
boundaries of what we can do out in the ocean. Well,
the few major apples right or through facets to that
one is you can do some risky things when you
don't have the people there right because no one's going
to be lost at sea. And then the other thing
(17:09):
is you can drive cost down, and I mean cost
financially but also environmental cost, right, because you can use
a far less energy to accomplish a similar goal. And
then what that allows you to do is have more Right.
So instead of say having one fifty million dollar hundred
million dollar research ship, which is the kind of numbers
you're talking about to take scientists to see, you can
(17:31):
have twenty or thirty or forty million dollars or two
million dollar ships that go out and work collaboratively with
space based assets and with one another and collect vast
amounts of data from disparate parts of the ocean. And
then you use that data to create information that informs
where you send the man vessel, right, so that they
get the most out of their time at sea. So
(17:54):
it's about enabling the people. It's about leaving the humans
to do the uniquely human part, which is have the insight,
the intuition and and the creativity. And so you know,
that's why it's important, and we're going to see an
increasing amount of this, and I think it's also important
for people to get comfortable with the idea that these
things will be roaming around and that it's okay. Yeah,
(18:15):
And and on an interim basis, I mean, we're also
talking about this same technology that allows a ship to
sail autonomously also can be used to assist a human
crew now, you know, basically be another set of eyes
and years be a watchkeeper for a manned vessel. Right,
(18:36):
I want to know more about the AI captain. How
did you build it so that it would be comparable
to the way a human captain might direct a ship.
What we're trying to do is augment the person, Right,
We're trying to let them be more of a person
than sort of. They don't have to watch the radar,
they don't have to watch the cameras. Right. The machine
(18:58):
can do all that, and then if it can't do
something safely, if it can't come to a solution, it
can ask a person send a little texic, I don't
know what to do, and then a person can, in
a very calm way, with no stress, tell it what
to do. But in the in the interim, they're doing
something more important, like looking at all the information that's
being produced by the instruments and having insight. You know,
(19:21):
ever since we started sailing, there's been expectation of how
ships interact with each other. Let's see, you know, they've
been codified by the the I M O. Right, they're
called like the regulations to prevent collisions at sea. We
just called them coal ricks. But they're quite nuanced. Like
it's not like they're called rules of the road, you know,
after like the idea of like cars, but they're they're
(19:42):
much more nuanced than like rules for cars. How you
act depends on the type of vessels that are interacting,
like if it's a sail boat or a fishing boat,
or a container ship or a pleasure craft. Like imagine
if you're driving your car down the road and you're
at a stop sign, and then depending whether you could
go or not depended on whether the other car about
(20:03):
the stop sign was a bus or you know, or
something else, Like the rules change anyway. So that's where
humans are are really really good at. Is this nuanced
understanding of these these rules, um squshy squishy rules. Yeah. So,
(20:24):
and that's where we've done. You know, a lot of
our lot of our work on is in that area.
And that's the hardest part of this whole puzzle. I
wonder if the ship ever got into any sticky situations
that the AI captain was able to get it out of.
One time, we had a sailboat come at us in
(20:46):
the night head on reciprocal course, no lights on, no
radar reflector. Everybody was probably asleep and they just had
the autopilot on and um, we easily could have speared them,
or they would have actually hit us because they were
in violation of regulations. But but that's common, right, And see,
when you're crossing, it's so unlikely, it's so fast that
(21:08):
you're going to run into somebody, but it happens. So
we you know, the ship took appropriate action and moved
so that that wouldn't happen. But it's not like it
seems very dramatic at the moment. But you know, you
see these things coming miles away and it unfolds it
like five miles an hour or something, right, So it's yeah,
so it seems more nervous than it is. And I
(21:29):
mean weather was challenging, and we had some failures technical
and mechanical failures in the ship that were very very challenging.
But from the AI captain perspective, the only time that
we got annoyed was. There was a research ship that
shall remain nameless from a university that was coming along
and was going to cross in front of us by
ten twelve miles, which is fine, and they were going along,
(21:50):
but they clearly saw us on there, neither their radar
or their automated identification system which we broadcast, and they
just at some point turned and came directly at us
at a angle that it's the it's the I'm messing
with you angle, Yeah, the angle that allows them to
maintain right of way but makes it very, very difficult
to understand their intent and take action. So the ship
(22:12):
was kind of like, if they had persisted, it would
have ended up kind of going around in circles trying
to avoid them. But but fortunately we had a support
boat that was coming out of Halifax to meet it,
and it physically got in between the Mayflower and this
research boat and so what are you doing? Oh, we
were just going to take a look, and but we
weren't going to get any closer than two miles and
(22:33):
it's like, well, what are you going to see from
two miles away? They absolutely are going to come over
and take a much closer look because they didn't understand
that the vessel was trying to avoid them. You know,
when they see these unmanned systems at sea, they're just
dumb robots, right, They just float around with winder wave power.
They are a bunch of sideists coming back from like
(22:54):
a six week cruise, and there was like, oh, that
looks interesting, let's go take a look. So yeah, and
so that was the only thing that was annoying. Other
than that, it was getting into and out of port.
Getting out of Plymouth was a little challenging. Once we
get outside twelve miles, we had a lot of fishing
boats to dodge, but that was fine. And then out
(23:14):
in the deep sea, it's just it's mostly the sea
that you're concerned with, and it's the fishing grounds are
always the trickiest place because, yeah, because fishing boats do
whatever they want. Yeah, and they're like container ships. They're
not going to change course unless they have to, so
you can pretty much understand what they're what they're doing.
Fishing boats could be going along a nice straight line
(23:36):
and then all of a sudden do a money or worse,
a ninety degree turn, and they don't care about you,
and they just expect you to avoid them, and they
literally there's no one in the wheelhouse. Probably they're all
on the backs of the rules too, we're supposed to
avoid them. And so, but what Brett caught it earlier,
it was things evolved very slowly. Like things don't happen
quickly at sea. It's sort of like, Okay, there's ship,
(23:58):
it's you know, it's it's twenty miles away. I've got
a little bit of time to figure out what I'm
gonna do. You don't ever try to put yourself into
a situation where there's a risk of collision, so you
make decisions that so you don't put yourself at that risk. Right, So,
like I'm not going to cross the street at the
busiest place. I'm gonna cross it dada, you know somewhere say,
(24:22):
fishing boats, container ships, scientists on a cruise. The vast
majority of vessels at sea are still of the not
autonomous variety. To wrap up their conversation, Lauren asked Brett
and Don where the technology they've developed is headed, what
it means for the humans who work at SEE, and
what's next for the two of them. What do you
(24:43):
guys think this type of automation means for the future
of the maritime industry and people who work in at
first of all, like we mentioned, Brett and I have
both worked in the ocean community for decades our entire careers,
Like we haven't a lot of respect for the people
that work in this area. And this isn't about a
(25:06):
replacement technology. It's an augmented augment what's what's the right
how do you say augmented intelligence? I mean, look, ships
have always been the leading edge of technology and almost
every society up until the twentieth century where we started
into flight, and now they're kind of resurging into really
(25:27):
new technological areas. But the point of trying to make
is there was a time when there were no propellers.
There's a time when there are no rudders, right, it
was just sales and steering oars. And then so it's
been this evolution in technology um and ships have always
been right at the absolute forefront of it from design
and engineering and material science. And you know, we've seen
this sort of long evolution of technology and this is
(25:49):
just another thing. So I think you're going to see
lots of areas where really smart port of machine learning
models helped like to improve efficiencies, and so we're at
the advent of of a new way of thinking about
design and implementation of very sophisticated solutions that are based
in vast amounts of data analytics that are hitherto impossible
(26:12):
to address. What is next for the Mayflower Autonomous Ship.
We may do a few things with the Coast Guard,
and there's a few other folks that want us to
do some work on national marine sanctuaries looking at cetacean populations,
and so we'll do that kind of thing with with it,
and more and more people will get involved in its
(26:33):
day to day operation and we'll have less sort of
day to day input, which is fine. And then the
AI Captain is going into a whole bunch of other
projects and programs, and we're just starting off on a
new design for a much larger ship for vast oceanic voyages,
um maybe even a circumnavigation. That's that's quite an effort. Yeah,
(26:57):
And then we're going to connect with with NASA with
the there you know, with the International Space Station and
satellite networks and sort of have them work collaboratively so
the space assets see things and they know there's another
ship asset. So it's almost like a satellite in revers
It's like the inverse satellite at sea. So it sees
something from space and it's as a ship such and
such as over there, ask it to go and look
(27:19):
at that and tell us if what we're seeing is right,
or collect a sample right, and those things will work
collaboratively without people. You kind of opened up Pandora's box here.
So we did this, and now there's all these other
things that we can do. So yeah, and we just
have to pick one that we can do within the
remainder of our lifetime. There you go. Well, I I
(27:40):
hope you. I hope you both get to do all
the new things that you want and have capacity to do.
Thank you both so much for your time and good
luck with future journeys and projects. Thank you, hi everybody.
In the centuries long evolution of maritime technology, the Mayflower
automous ship represents an inflection point. The ship's success indicates
(28:05):
that artificial intelligence and automation are tools ready to be
normalized within the nautical industry, and that the advantages they
provide will change the way we conceive of ship building.
But the technology aboard the Mayflower four hundred has implications
beyond just application at see. Brett and Don's project has
(28:27):
shown that the potential reward for innovative risk taking is
to achieve something unprecedented, and that's true for any industry.
But like the original Mayflower Voyage four n years ago,
it may require a leap of faith. On the next
episode of Smart Talks with IBM, what does it take
to create a sustainability focused global supply chain innovative and
(28:52):
equitable enough to connect our modern world? We talk with
Sherry Highness, IBM's global sustainability services leader and offering leader
for a sustainable supply chain. Smart Talks with IBM is
produced by Molly Sosha, David jaw, Royston Reserve, Matt Romano,
and Edith Russelo with Jacob Goldstein. Our engineers are Jason Gambrel,
(29:17):
Sarah Bruger and Ben Tolliday. Theme song by Gramascope. Special
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