Aerospace & AI - Airbus’ responsible and human-centric approach to AI | #aidatasummit24
Summary
TLDRDer Vortrag diskutiert die Verwendung von KI bei Airbus, einem führenden Hersteller von Flugzeugen und Verteidigungssystemen. Es wird auf die Anwendung von KI in täglichem Geschäftsbetrieb, operativer Unterstützung und game changing Anwendungen eingegangen, wie z.B. Flugzeugdesign. Es wird betont, dass KI nicht nur Compliance, sondern auch Verantwortungsbewusstsein und Werte der Firma fördert. Die Herausforderungen der KI-Regulierung und der Ansatz zur Umsetzung von KI im Einklang mit den Firmenwerten werden ebenso thematisiert wie die Bedeutung des menschzentrierten Ansatzes und der ethischen Gestaltung von KI-Systemen.
Takeaways
- 🚀 Airbus ist bekannt für die Herstellung von Flugzeugen, Verteidigungssystemen, Satelliten und ist der größte Hubschraubenerzeuger der Welt.
- 🤖 Sie setzen künstliche Intelligenz (KI) in verschiedenen Bereichen wie täglichem Geschäftsbetrieb, operativer Unterstützung und gamechanging KI-Anwendungen ein.
- 🔍 Generative KI wird besonders in der Informationssuche und -verarbeitung in technischen und dokumentationsintensiven Prozessen genutzt.
- 🛠 KI wird zur Verbesserung der Kernoperationen eingesetzt, um Effizienz zu steigern, Planungen zu verbessern und Muster zu verstehen.
- 🛩️ KI hat auch das Potenzial, den Flugzeugentwurf und die Produktion von Flugzeugen grundlegend zu verändern, indem sie digitale Zwillinge erstellt und Prozesse automatisiert.
- 🌐 KI wird auch in der Kundeneffizienz unterstützt, beispielsweise durch die Vorhersage von Wartungsbedarf und die Verbesserung des Kundenerlebnisses.
- 🤝 KI-Anwendungen in verschiedenen Abteilungen von Airbus umfassen Satellitenbildanalyse, automatisierte Landungen und Taxifahrten sowie KI-gestützte Testhubschrauber.
- 🏭 Auf der Produktionsebene wird KI bereits genutzt, um Arbeit zu priorisieren, Qualitätsmerkmale zu analysieren und die Produktion effizienter zu gestalten.
- 📚 KI wird auch in der Ingenieurarbeit genutzt, um den Entwurf von Flugzeugen zu verbessern und digitale Zwillinge zu erstellen.
- 📖 Die KI-Akt-Richtlinie von Airbus betont die Verantwortung für KI-Systeme, Transparenz, Datenschutz, Fairness und menschliche Kontrolle und Überwachung.
- 🌟 Eine humanzentrierte Herangehensweise und die Einhaltung von Regelungen und gesellschaftlichen Werten sind zentrale Aspekte der KI-Strategie von Airbus.
Q & A
Wie viele Airbus-Flugzeuge sind täglich im Einsatz?
-Rund 14.000 Airbus-Flugzeuge sind täglich im Einsatz.
In welchen Bereichen ist Airbus tätig?
-Airbus ist im Bereich der Luftfahrt, Verteidigung, Raumfahrt und ist auch der größte Hubschrauberhersteller der Welt tätig.
Was versteht Airbus unter verantwortungsvoller Nutzung von KI?
-Airbus versteht unter verantwortungsvollem Umgang mit KI nicht nur die Einhaltung von Mindestanforderungen, sondern auch die Integration von KI in den Unternehmenswert und eine verantwortungsvolle, ganzheitliche Herangehensweise.
Inwiefern wird KI bei Airbus zur Verbesserung des täglichen Geschäfts eingesetzt?
-KI wird bei Airbus eingesetzt, um den täglichen Geschäftsablauf zu verbessern, z. B. durch die Nutzung generativer KI-Funktionen, um Informationen schneller zu finden und Prozesse effizienter zu gestalten.
Wie wird KI bei Airbus in der Produktion und im Betrieb unterstützt?
-KI unterstützt bei Airbus die Kernoperationen durch maschinelles Lernen, Computervision und Optimierung, um Prozesse effizienter zu gestalten und Planungs- und Vorhersageprozesse zu verbessern.
Welche potenziellen Veränderungen durch KI bei der Flugzeugentwicklung werden genannt?
-Generierte KI-Modelle wie Surrogate Modeling haben ein großes Potenzial in der Flugzeugentwicklung, insbesondere bei der Erstellung von digitalen Zwillingen und der Gestaltung von Flugzeugplattformen.
Wie plant Airbus die Nutzung von KI für die Kundenzufriedenheit?
-Airbus plant die Nutzung von KI für die Kundenzufriedenheit durch Predictive Maintenance und die Bereitstellung von maßgeschneiderten Dienstleistungen, um die Kundenerfahrung zu verbessern.
Welche KI-Anwendungen werden bei Airbus in der Produktion und im Engineering genutzt?
-Bei Airbus werden KI-Anwendungen wie Computervision für automatisierte Landungen und Taxifahrten sowie KI zur Unterstützung von Piloten in stressigen Situationen in der Produktion und im Engineering genutzt.
Wie schätzt Airbus die Herausforderungen der KI-Regulierung?
-Airbus sieht KI-Regulierung als Herausforderung, die man sorgfältig und in enger Zusammenarbeit mit Regulierungsbehörden wie der EASA anzugehen hat, um sicherzustellen, dass AI-Anwendungen sicher und verantwortungsvoll eingesetzt werden.
Wie plant Airbus die Einhaltung der KI-Akt-Richtlinien?
-Airbus plant, die KI-Akt-Richtlinien einzuhalten, indem sie eine zentrale AI-Expertise hat, die Projekte leitet, aber auch die Organisation dezentralisiert und Experimente in einem einwandfreien Weg ermöglicht.
Was ist die Rolle der zentralen AI-Gruppe bei Airbus?
-Die zentrale AI-Gruppe bei Airbus ist für den Zugang zu cloud-bereiten Plattformen verantwortlich, die für die Produktion vorgesehen sind, aber auch für die Schaffung von sicheren Sandbox-Umgebungen, in denen Experimente stattfinden können.
Outlines
🚀 Einleitung in die AI-Anwendungen bei Airbus
Der Sprecher stellt sich und Airbus vor, wobei Airbus bekannt ist für die Herstellung von Flugzeugen, Verteidigungssystemen und Satelliten. Es wird erwähnt, dass Airbus auch der größte Hubschraubenerzeuger der Welt ist. Der Schwerpunkt liegt auf der Verantwortungsbewussten Nutzung von KI, die sowohl den gesetzlichen Rahmenbedingungen als auch den Unternehmenswerten entspricht. Es wird auf die verschiedenen Anwendungsbereiche von KI eingegangen, wie der täglichen Geschäftsprozesse, der operativen Unterstützung und der generativen KI für die Informationssuche. Außerdem werden potenzielle gamechanging KI-Anwendungen in der工程设计 und im Bereich der Kundeneffizienz diskutiert.
🤖 KI in der Flugzeugproduktion und -operation
Der zweite Absatz konzentriert sich auf die Anwendung von KI in der Flugzeugproduktion und -operation. Es wird über die Verwendung von KI in der automatisierten Landung und Steuerung von Flugzeugen und Hubschraubern gesprochen, die die Sicherheit erhöhen sollen. Ebenso wird die KI im Bereich der Produktionsplanung und -überwachung thematisiert, wobei die KI dazu beiträgt, Prozesse effizienter zu gestalten und Muster zu verstehen. Die Herausforderungen im Bereich der KI-Regulierung werden angesprochen, insbesondere im Hinblick auf die verschiedenen Sensibilitätsbereiche innerhalb des Unternehmens.
🌐 KI-Regulierung und menschzentrierte Ansätze
In diesem Absatz wird die Rolle der KI-Regulierung und der menschzentrierten Ansätze bei der Entwicklung und dem Einsatz von KI in Airbus besprochen. Es wird erwähnt, dass die KI-Aktivitäten im Unternehmen registriert, eine Risikoklassifizierung vorgenommen und die Governance im Laufe der Zeit gestärkt wird. Es wird auf die Notwendigkeit hingewiesen, frühzeitig Exploration zu ermöglichen und gleichzeitig sicherzustellen, dass dies im Einklang mit den gesetzlichen Rahmenbedingungen und den Unternehmenswerten geschieht. Die KI-Aktivitäten sollen demnach in einem verantwortungsvollen und koordinierten Umfeld stattfinden.
🛠️ KI-Prinzipien und Anpassungsfähige Governance
Der vierte Absatz beschäftigt sich mit den KI-Prinzipien von Airbus und der Anpassungsfähigen Governance. Es wird betont, dass die KI-Prinzipien auf europäischen Werten und den Unternehmenswerten beruhen und wie diese in den企业文化 und die KI-Implementierung einfließen. Es wird auf die Bedeutung von Sicherheit, Nachhaltigkeit, Verantwortlichkeit, Transparenz, Datenschutz, Fairness und menschlicher Agency in den KI-Systemen hingewiesen. Schließlich wird die Bedeutung der fruchtbaren Zusammenarbeit und des Austauschs mit anderen Unternehmen und Behörden im Hinblick auf die zukünftige Entwicklung von KI und deren Regulierung hervorgehoben.
Mindmap
Keywords
💡AI
💡generative AI
💡Regulierung
💡Digitale Zwillinge
💡Prädiktive Wartung
💡Ethik durch Entwurf
💡KI-Prinzipien
💡Regulierungssandkasten
💡Human Centric
💡Autonomie
Highlights
Abus is known for manufacturing aircraft, with 14,000 operational every day.
Abus has divisions in defense, space, and is the largest helicopter manufacturer in the world.
Abus is focused on a responsible approach to AI, aligned with company values.
AI is applied to improve daily business, operations support, and game-changing applications.
Generative AI is used to find information efficiently within technical documentation.
AI is utilized in machine learning, computer vision, and optimization for core operations.
Surrogate modeling in engineering design has significant potential.
AI is used for customer efficiency, predictive maintenance, and improving the customer experience.
AI is being researched for autonomy in flight, single pilot operations, and safety improvements.
Abus operates its own satellite fleets for imagery analysis and various services.
AI features are tested on aircraft for automated landing and taxiing.
The company uses AI for complex operations on the shop floor, prioritizing work, and quality analysis.
AI is combined with robotics in aviation for human-machine augmentation.
AI is used in the engineering process to create digital twins for aircraft design.
Abus has different areas of sensitivity due to regulation in aircraft quality inspection.
The use of generative AI is seen across the company for chatbots and information retrieval.
Abus encourages a culture of open-source within the organization for AI innovation.
AI Act encompasses AI use cases and sectorial regulations, important for aircraft regulation.
A human-centric approach to AI is valued, with different levels of augmentation outlined.
Abus aligns with the human-centric approach and values human expertise.
The company follows a human-centric approach to developing new aircraft programs.
Abus focuses on responsible AI aspects and regulation, considering what is technically possible, sustainable, and preferable.
Ethics by Design is an approach that considers stakeholders and ethical values from the early stages of a project.
Abus has developed AI principles that align with European and company values.
Adaptive governance and AI principles are key for building safe and reliable AI systems.
Abus is looking forward to the interplay between AI regulation and innovation.
Transcripts
for having me um and thanks for joining
the session and the talk um so yes J
Schon back thanks for the introduction
um just a word about Abus so you
probably know us from from the aircraft
you you fly and that we manufacture
there's about I think 14,000 abis
aircraft operational every day flying
taking off every two seconds um so
that's what we're most known for we also
uh have larger divisions in defense and
space for example the EUR fighter you
may know um as well as large satellite
programs we are also the largest
helicopter manufacturer in the world uh
so that's you know broad divisions uh we
try to be quite coordinated on the AI
front um and yes something that's really
keen and sort of important to us is not
just sort of the compliance of the
minimal approach to AI but really how do
we U merge a take on AI um with our
company values and a responsible
approach uh companywide so happy share
with you about that um starting um with
an overall picture of where AI is
relevant where do we employ it um some
examples of use cases maybe focus on gen
um I'll show you a bit on uh the
regulation challenges we have in
different areas of the company and how
that leads us to really think very
closely around regulation but also sort
of what is our way how do we you know
responsibly handle the
technology uh oh sorry can I go back
that's that one right yeah okay so first
off um the different application Fields
the value streams where do we apply AI
um on the one side it's improving sort
of the Daily Business this can be you
know as part of your your workspace
tooling that that you know is quite
important just to have like available to
you um in terms of you know uh
generative AI features it will also come
through you know features coming through
applications we all use um but something
we see quite a bit is is especially
around generative AI just the sort of
daily tasks of finding information so
you know we're we have very technical
language we have tons of documentation
processes references so a lot of jobs in
abis and Engineering or customer support
that really rely on finding information
at the right point in time that's
relevant and take that taking a lot of
time out of their job so here things
like gen are really really interesting
um in terms of operation support so also
when I look at sort of traditional AI
maybe machine learning computer vision
uh optimization there's a lot of fields
here where um you know they support our
core operations help us bring more
efficiency to processes be better at
planning at uh uh forecasting
understanding patterns and so on so um
this is really important also when you
think further on sort of there's the
operational improvements but there's
also sort of gamechanging AI
applications when I look at how we how
engineering design is done so things
like surrogate modeling have really big
potential already U utilized in the
design processes um if you look at for
example you know how we design an
aircraft you know that's a very very
heavy process and we're sort of just
preparing to launch a new aircraft
program meaning we'll design the whole
aircraft platform again from scratch so
having endtoend
um AI type you know capabilities data
continuity and means to create true
digital twins is going to be a true
GameChanger in for example the whole
design of an aircraft we have customer
efficiency so this is supporting our
customers uh with a flying product so
things like you know predictive
maintenance really having that
understanding and of the aircraft's
operation um in the fleet you know you
can imagine it's quite different if
you're uh operating an aircraft in the
desert versus you know in in northern
Alaska so um I can help really give
customized services and really improve
the customer experience but it can also
be implemented in the products s so the
aircraft the helicopters and so on um
where you know that's uh when you look
towards autonomy of flight single pilot
operations augmentation of pilots and
improving safety of the aircraft
themselves so that's a longer sort of
research but is very actively looked at
I can tell you as
well and some examples from the
different divisions here uh so on the
left side you see uh satellite imagery
analytics services from defense and
space so we operate our own satellite
fleets and have satellite uh imagery
analysis widely available since quite a
long time this can be for example object
detection of assets uh for intelligence
purposes but also for sort of you know
climate monitoring uh Climate Services
analysis of n natural catastrophes or
Insurance Services even um on the
products themselves for the aircraft in
the middle you see um essentially one of
a350
of our own test aircraft that utilizes
AI features with the computer vision for
example to help with automated Landing
automated taxiing um basically
augmenting as well the pilot we're
testing single pilot operations so to
increase the safety of aircraft that the
pilot is incapacitated can the aircraft
still land can AI support a pilot in
very stressful situations so these are
things that are being researched that
will take a longer time and similar on
on the helicopter we have our test Fleet
helicopters equipped here as well we
call this the pyer lab where um it also
supports I believe a 360 sort of view
with object detection and sort of seen
understanding yeah and of course in the
process in the company itself so um many
areas that are like really interesting
is on the shop floor itself uh very
complex operations that require a lot of
information you have to be very careful
to do the right steps at the right time
a lot of validation there but here as
well I mean workers already utilize AI
capability is a lot when it comes to
sort of prioritizing the right you know
outstanding work um to make sure you
know you don't disturb the production to
uh analyze quality occurrences across
the you know 10 different final assembly
lines we have for aircraft so that's
already working quite a bit um AI for
example in combination with
robotics Aviation is still a very manual
labor in most of the cases um when
robots come into play it's usually not
sort of you know a single production
with you know thousands of of aircraft a
day is more sort of human machine
augmentation where the robot needs to
understand where it is really needs to
understand its context and on the right
side is the engineering what I mentioned
the sort of you know the digital twin
the whole design process um that's a
really large field as
well um and a bit of a focus on some of
the challenges that lead to where I'm
going to end um if you look at you know
those proses I showed before you can
imagine there's quite different areas of
sensitivity some of the processes we
have are under regulation so of course
the Quality Inspection of an aircraft is
very related to safety and air
worthiness so not just do we have ai act
to care about what we also have the
asasa which is the European Aviation
sort of safety administration that
regulates the process that we have so
they also have a say and if we basically
augment our quality process with AI they
also so are part of the regulation and
sort of validating um how we change the
process and use the processes so that
can be quite different than when you're
in like a support function um and just
supporting an administrative task one
area we see what I mentioned in the
beginning is with Gen um that's really
interesting where we see a lot of value
in the short term all across the company
is utilizing gen for sort of types of
chat Bots that understand your data
scope your reference documentation quite
well and can just help you and find
really information really well um we do
a lot of prototyping across the company
in many different fields and processes
and find that that is something that you
know brings a lot of value across all of
the functions just to name a few
examples that can in include for example
for procurement or legal analyzing uh
contracts authoring contracts
identifying for example liability and
Legacy contracts uh quite a complex task
for you know a huge Legacy amount of
contracts you have engineering support
answering technical customer inquiries
so this can be from well we have an app
for example where the active Fleet the
airlines themselves raise technical
questions to us and we have hundreds of
Engineers on our side analyzing and
making sure they give the right answers
that can be referencing to manuals that
can be analyzing from previous requests
that this asked before so they take
quite a long time to analyze these
topics we see that with gen they can
very easily find similar questions from
before you know directly find the
reference to the right manual and really
speed up that whole process so it's
quite it's quite promising
overall um and just a word on on our
approach of that is as well because like
I'm from the central team for AI we have
a lot of technical experts we run like
the key projects um but it's such a
large company you have to democratize
and Federate the organization and allow
for this experimentation to happen
across the company in a compliant way um
so we as the center of excellence really
tried to um give access to cloud ready
platforms that are already pretty much
in the architectural environment you
would put things into production but the
really sort of secured sandbo spaces um
that we can open to any business team
that's you know technically well vered
we have a large population of very well
technically vers people who've upskilled
themselves in coding analytics Ai and so
on so that's been a huge Ena but it
raises all these questions so right if I
even increase the exploration all across
the company you want to make sure you do
that in the right way and with AI Act of
course you need to be aware of all
initiatives running have everything
registered do the right risk
classification and so I think it's
really an opportunity to think of how do
you allow early exploration and a
compliant sort of safe way and that over
time the governance that you also impose
um sort of really strengthens so when
you put things into production you do it
very meticulously very well so these are
the questions that really raise and you
I think you as a central team you really
have to find aners to that um one word
is also sort of the whole culture around
that so from uh building a kind of you
know open source culture within the
confines of of an organization is quite
enabling here as
well um but yeah so you you still have
to ensure that basically all these
imposing aspects of you know
regulation um aligning that with your
company values is is all in line and
still happening um and you do it with
the right Spirit of course there's a lot
of fear in all areas of the company um
around what will I take my job away do
we do I trust my employer to handle that
responsibly uh so um there's a lot of
these questions coming in we think it's
quite important to to address us the
right way just to give you an idea um on
how this works with the AI act I
mentioned it a bit before already so AI
act overall encompasses of course you
know AI type use cases and for the
sectorial regulations so anything that
is under regulated processes on the
aircraft itself we'll have the ASA
regulation as well um so quite
interesting to see how this will play
out in terms of like how do we manage um
regulatory sandboxes will this come from
measa or the German governments the
national governments how will that be
supported so something we also
appreciate I think the exchange with
bitcom um and the peers here quite a bit
to see that you know the right approach
the right guidance
um yes and on the Outlook when it comes
to Human Centric approach so as's Vision
as well aligns with that quite nicely
with us so they when they lay out how
this will work um they basically see it
there different levels of augmentation
um with the level one being assistance
to the human so this is basically human
using AI assistance but still in full
control of the workflow level two sort
of human machine pairing where it's like
a coordination and the human can still
uh basically um you know has control
over the decisions and level three is
when you know the systems are already
kind of autonomously start to make
decisions take take actions but but are
still overridden by the human there will
possibly be a level four for like sort
of full autonomy um but as you can see
aasa is taking a very sort of human
Centric approach to this and I feel I
mean we feel like this is well aligned
with our company culture as well we
really value the the human Centric
approach we value the highly the human
expertise in everything we do so um we
align with that quite well to give you
an idea on how this looks like in
aviation thinking about all these
processes on sort of production and on
the aircraft we're looking around it's a
bit hard to see here but uh level one
sort of the first use cases will be
validated by asasa probably next year
the level two guidance uh the guidance
itself um is already available and we
expect first use cases to be deployed 20
2035 and then 2050 plus is like the the
later almost at ionomy so far away for
the aircraft itself but you can see
basically if we think around developing
the new aircraft program this will
really come into question if we will see
this some but even if it's not autonomy
it's really also making the aircraft and
the whole system
safer so when we really think about how
do we how we go about all that
responsible aspects and the the
regulation um when you see it bring it
sort of out with the you know the
furthest is you're trying to you
whatever is feasible you approach um if
it's technically possible
the next question is is it sustainable
um so um does this make sense on the
long term is it acceptable in the sense
of compliance so do we you know fulfill
all the rules to deploy system but then
we really want to focus on the
preferable so essentially allowing
Innovation making use of innovation but
doing things the right way that we
believe are in line with our values with
you know European core values um with
the company overall we don't think that
this necessarily has to mean
um that you know this slows you down in
terms of innovation but it's really more
a ways of aligning things with your
corporate culture and making sure that
um making sure that you take these
concerns early on and are basically
developing even safer systems because
you anticipate much
better so what we try to approach is
that what we call it towards ethics by
Design so this can be as simple as very
early on in your project basically uh
you know giving room to understand who
are your stakeholders who are your
actual customers and users of the system
and just asking them about their
concerns and basically just making sure
that you have principles you know
ethical values that you adhere to and
you make that you know voicing of
concern part of the conversation and
include that in your risk management we
believe it basically Fosters trust with
the employees with the teams that you uh
try to assist with AI we believe it
creates Innovation and value you you
still focus on valuable use cases um and
it contributes to risk reduction because
you're much more anticipative of um
basically in a structured way being able
to voice concerns structurally as well
as sort of just by giving the
space so what we've done is we've been
aligning what we call these AI
principles it's really you know coming
from the core of European values but
also from our company uh values that
really merge and this is something we've
and lined on as sort of a culture to
implement AI across the whole group it
starts with safety first so that's at
the heart of everything we do we look at
saem systems from the safety perspective
um especially in the sort of production
related areas I've talked about while
being in sustainability we Implement AI
for societal good for the good of our
employees as well in a sustainable
manner we the systems that we build that
we integrate um uh we ensure that we
take accountability for them and we sure
transparency for them uh privacy data
protection of course always an important
Point
fairness I mean monitoring for buys you
know fairness equality is always a core
thing and what I mentioned the human
agency and oversight in any system that
we build we value the the human Centric
expertise and build systems around the
employee and the
human to give an idea what the this
looks like in this Innovation cycle in
this Federated uh network of teams
implementing AI it can be as simple as
you know having like a lab stage an
early stage where you have a sort of
preliminary ethics assessment along with
your qualification of a use case uh this
area where you raise concerns you may
have sort of a checklist uh that you
later on bring in as well just to make
sure that you're you know uh having good
intent with your system that you know
stakeholders and users of a system are
wellard
um but that still is lightweight so
ideally if you have like a lowrisk use
case that you know doesn't e concerns
you can still sort of you know smooth
sale through the first phase and there's
really not much of sort of heavy
governance that You' be approaching um
if of course you're really going in a
system that's sort of in ethical
challenging territory you would have
these kind of questions pop up early on
and even avoid such a use case being
addressed um or be very diligent and go
through like like an expert Board of
really looking in detail um around the
challenges you want to be very much
aware about um and anything that gets
deployed goes through that sort of
meticulous process for like a real
ethical
assessment that sounds very fluff but
how can you actually translate that into
sort of credible sort of technical
aspects and sort of associated
mitigation measures and strategies just
to take you a few examples so what I've
listed on the left side I like our
principles around this and they can lead
to identifying specific Harms that can
lead to basically identifying the
associated security threats and defining
technical mitigation measures so this
can be things like you know implementing
guard rails bias checks and outputs um
safety filters and output filtering um
so we believe that basically by
approaching projects like this in sort
of an
Adaptive um governance is a good way to
build safe and reliable AI
systems so in terms of key takeaways so
we all um very much interested in
looking forward to the interplay and the
regulation coming from the AI office but
also from author um regulation
authorities democratizing an i for us
basically is a scaling and wide
Innovation um but adaptive governance is
really important and AI principles I
would recommend everybody to sort of
identify their corporate culture blend
that with the value is important to you
and make that sort of the mindset in
your company and for us ethics is lot of
steering it doesn't have to be a break
but it's actually a steering wheel in
your AI approaches thank you
[Applause]
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