Custom Writing Service http://StudyHub.vip/Artificial-Intelligent-In-Aviation-AI-I

31. Aviation Business Productivity
© General Electric Company, 2014. All Rights Reserved.

32. Why does aviation need AI?

33. As technologies continue to advance, Artificial Intelligence (AI) will play
bigger roles in our futures. This might be concerning to some, but it is a part
of our lives. AI has already been incorporated in many industries; the
aviation industry has already successfully implemented the early stage of
automation in daily flight operations with flight management systems (FMS)
or autopilot.
With the limited capacity of airspace and aerodromes, the aviation industry is
faced with the challenge of accommodating surging air traffic demands. New
kinds of flight operations, such as drones, commercial space travel and the
personal air vehicles of the future, are making the problem even more
difficult. The modern aviation system dynamically adapts and evolves on a
regular basis to meet demands with real-time information sharing. But as the
system rapidly increases its complexity, we are reaching our limit. Traditional
automation cannot handle such ‘information explosions’. Human cognition
and performance may need to be further augmented with the support of
artificial intelligence.

34. How can AI contribute to aviation?

35. Safety is one of the key features in this industry. Aviation is the safest mode of
transportation, given that the industry demands the highest level of safety standards and
risk management. The validation process for machine intelligence had to be rigorous, so
not all early attempts were successful. It had to be proven that AI operates safely; is
interoperable with the current system; supports the human-centric system; and is
applied through smooth and stable transition with globally harmonized manner. The key
to achieving well-guided AI is to establish a human-machine coexisting environment,
where the machine becomes a ‘sidekick’ that supports humans, instead of being a
prospective ‘rival’.
While early automation was providing support with simple and repetitive tasks, today AI
is expected to deliver further capabilities by learning and mimicking human behaviors.
AI is carrying out human tasks and in certain cases, even out-performing them. This can
be achieved from the data, which fuels AI. Whether it be for individual flights or systems,
the aviation industry has gathered standardized data that creates and develops
centralized, streamlined and globally accessible information exchange systems. The
industry has already worked to build the cornerstone for AI, and with open data sharing,
AI could change the direction of how we make decisions.

36. Where are we and where are we
heading?

37. we illustrated an optimistic view of AI implementation in aviation. But
we have many challenges to overcome. We do not fully understand what
AI is capable of, and how AI will impact the industry. Concern is growing
that AI would bring new opportunities as well as new problems, just as
historically, other inventions have. Moreover, in considering the huge
impact will have on our society, we need to work together to achieve
common goals, and not represent the interests of individuals or
corporations. The ‘positive use of AI for all’ is an emerging topic, with AI
United
helping people lead enhanced lives and helping to accelerate the
.
Nations Sustainable Development Goals (SDGs)
Aviation plays an important role in connecting the world, we need to
remember those pioneers and adventurers who proactively explored the
new horizon with their innovative thoughts and actions.

38. Artificial Intelligence in Aviation/Travel
The revenue of commercial airlines worldwide is predicted to reach
$824 billion in 2018. In this highly competitive industry, the corporations
with highest technological advancement will prevail.
Revenue of airlines predicted to reach $824 billion in 2018
AI in the aviation industry is disrupting the way companies approach their
data, operations, and revenue stream.
The world’s leading airlines are already using artificial intelligence to
improve operational efficiency, avoid costly mistakes, and increase customer
satisfaction.
There are many different areas where machine learning can empower the
aviation business. They can mainly be broken down into three categories:
•Fleet & operations management
•Customer service and retention
•Autonomous machines and processes
•How to bring AI to your business?
At MindTitan, we believe that airline companies can benefit from applying AI
to all three areas.

39. Fleet & operations management
Airlines and flight operators can significantly reduce their operational costs
and overhead by optimizing their fleets and operations with AI-powered
systems.
Potential areas for applying AI in the travel industry include:
Dynamic pricing – to maximize revenue, airlines are optimizing their base
published fare that has already been calculated based on journey
characteristics and broad segmentation, and further adjusting the fare after
evaluating details about the travellers and current market conditions. Airline
companies are using many different variables to determine the flight ticket
prices: indicator whether the travel is during the holidays, the number of free
seats in the plane, etc.
Pricing optimization – similarly to dynamic pricing, these algorithms are
looking for ways to optimize the sales revenue in the longer term to ensure all
flights are optimally booked.
Flight delay prediction – as flight delays are dependent on a huge number of
factors, including weather conditions and what’s happening in other airports,
an intelligent system can be applied to analyze huge data sets in real time to
predict delays and re-book customers’ flights in time.

40. Airline companies are using many different variables to determine
the flight ticket prices.
Flight route optimization – think machine learning-enabled
systems that can find optimal flight routes, leading to optimally
timed and booked flights, lower operational costs and higher
customer retention. For this use case, various route characteristics,
such as flight efficiency, air navigation charges and expected level of
congestion can be analyzed.
, one of the leading global
Amadeus
–
Avoiding travel disruption
distribution systems (GDS), has introduced Schedule Recovery
system, aiming to help airlines mitigate the risks of travel disruption.
Crew scheduling – flying personnel of major U.S. carriers have
grown and now often exceed $1.3 billion a year and are the second
largest item (next to fuel cost) of the total operating cost of major U.S.
carriers. what’s the optimal way to schedule an airline’s crew to
maximize their time and increase employee retention?

41. Fraud detection – by analyzing specific customers’ flight and
purchase patterns and coupling it with historic data, algorithms are
able to point out suspicious credit card transaction and eliminate
fraudulent cases, saving airline and travel companies millions of
, director of product
John McBride
to
. According
dollars every year
management for PROS, a software provider that works with airlines
including Lufthansa, Emirates and Southwest, a number of operators
have already introduced dynamic pricing on some ticket searches.
Machine learning can also benefit the air freight
industry. For example, predictive models help to forecast whether a
product will be shipped on time, and find the most optimal shipping
routes. In addition, intelligent systems can help identify problematic
incidents and solve them in time.

42. Customer service and retention
Enhanced customer experience is an area where both the aviation
and travel companies can strongly benefit.
Artificial intelligence can be applied to optimize pricing strategies,
increase customer engagement, and improve the overall flight
experience.
Here’s a list of potential AI use cases for the travel
industry:
- Recommendation engines for tailored offers.
- Sentiment analysis on social media .
.
- Chatbots and customer service automation

43. Recommendation engines for tailored offers – behavior-tracking
techniques, metadata, and purchase history allow making highly
personalized offers to
customers, increasing retention and a customer’s lifetime value.
Sentiment analysis on social media – when paired with intelligent
algorithms, social media feedback can be used to evaluate customer
reactions close to real-time, giving valuable insight for improving customer
experience.
Chatbots and customer service automation – Kayak, a popular travel
directly from your
plan your next trip
booking service, allows you to
Facebook Messenger app. Their chatbot is human-like, understands simple
questions and responds in a casual, conversational style.
% of customer service and support
25
that as much as
Gartner predicts
operations will rely on the virtual assistant technology by 2018.
Facial recognition and biometrics pave way to seamless airport security
processes. A similar approach could be applied to track how people move
across in the airport, getting a better sense of the flow of travellers.

44. Autonomous machines and processes
loading, fueling, cleaning, and aircraft safety checks.
using AI
Airbus, one of the leading aerospace companies, is currently
to analyses data coming from various factories, predicting when
variations in the manufacturing processes occur. This allows them to
tackle the problems earlier, when it’s easier and less costly, or even
prevent them completely.

45. How to bring AI to your business?
When working with companies in the aviation and travel business, we
usually see a lot of low-hanging fruits for personalizing customer
service and optimizing operations.
Before you take the first step to bring artificial
intelligence into your company, we recommend that
you consider the following questions:
What are the key areas where you’d like to see improvement?
Is it in flight optimization, customer service or some other
department?
Are you sure that AI is the optimal solution to these problems?
Do you have the required data for the algorithms to learn from or do
you need to first set up a data infrastructure

46. How the 4 Largest Airlines Use Artificial Intelligence
The U.S. commercial airline system is an economic engine which
.
2016
in
billion in operating revenue
168.2
$
generated an estimated
125.2
percent of operating revenue or $
74.5
Ticket fares represented
, the overall category of transportation represented
2016
. In
billion
of the national GDP.
percent
2.7
approximately
double over the next two
Airline passenger traffic is projected to
. Today, leading airlines are exploring how AI can help them
decades
keep pace with customer demand and improve operational efficacy,
speed and customer satisfaction.

47. To learn how the top four U.S. airlines are using AI, we researched this sector
in depth to help answer questions business leaders are asking today,
including:
How are industry leaders like American Airlines and Delta Airlines using AI
today?
What have been the tangible results of these airline AI applications?
What are the trends across airline AI applications – and how will they impact
the industry’s future?
This article aims to present a comprehensive look at how the four leading
commercial passenger airlines are using AI. Companies were ranked based
sourced from company financial reports.
operation revenue
2016
on
Before we begin exploring each company, we’ll present the common patterns
that emerged throughout our research in this sector.

48. Artificial Intelligence in the Airline Sector – Insights Up
Front
The most popular AI applications from the top four industry leaders
currently using AI appear to be:
AI Assistants: Responding to customer inquiries and responding to voice
commands for domestic airline flight info and ticket availability through
interactions using natural language (see American Airlines and United
below)
Smart Logistics: Machine learning algorithms are being applied to data
to help automate airline operations. (see Southwest below).
Facial Recognition: Facial recognition technology is being used to
perform customer identity verification and to match passengers to their
luggage through kiosks (see Delta below, and you may want to read our full
)
cases” article here
-
“facial recognition use
In the full article below, we’ll explore the AI applications of each airline
individually. We will begin with American Airlines, the #1 ranking U.S.
commercial airline based on 2016 revenue figures.

49. American Airlines
In 2017, the current leading airline focused its annual app development
, on “artificial intelligence, drones and augmented and
HackWars
competition,
virtual reality” technologies. HackWars IV, was a 24-hour hack-a-thon that
reportedly brought out over 700 “designers, developers and IT” professionals.
Participants worked in teams aiming to come up with an idea for an innovative
app that would be beneficial for both “customers and employees.”
The 1st place team, “Team Avatar,” reportedly designed an app that would allow
users to determine the size of their luggage in advance of arriving to airport or at
a kiosk before proceeding to the gate. The winning team also claimed that their
app would allow users to “prepay for any potential expenses” associated with
their luggage.
”

50. Most likely in an effort to protect the idea, American Airlines did not
show a demonstration of how “Team Avatar’s” application would
function in the official video. Based on the three categories of interest
in the competition, it is possible that the winning app was developed
Dan
Founder
TechEmergence
using AI but this is not confirmed.
states:
Faggella
“If anything, ‘HackWars’ is a demonstration of AA’s eagerness to
innovate (and to let the press know about it), but it’s symbolic of the
current nascent stage of AI: Businesses all know they should be applying
AI, but are having a hard time finding where and how. If nothing else,
AA seems to at least be making the effort, and we hope to see more
traction with the firm over the years ahead.

51. Delta Airlines
investment in four
000
,
600
$
, Delta announced a reported
2017
In May
, including one that will
service bag checking kiosks
-
automated self
. The airline selected
facial recognition technology
incorporate
Minneapolis-St. Paul International Airport to debut the four self-service
kiosks, and claims that facial recognition technology will be used to
verify customer identity by matching customer faces to passport photos.
While Delta Airlines doesn’t seem to have their own YouTube video of the
new self-service bag check kiosk, WCCO – CBS Minnesota explained the
technology well in a video from earlier this year:
Evidence of the airlines interest in integrating more self-service and
automation into its operations is evident in its previous initiatives such
.
in via the Fly Delta Mobile app”
-
“ticketing kiosks and check
as

52. “We are dependent on technology initiatives to provide customer service
and operational effectiveness in order to compete in the current business
environment. For example, we have made and continue to make significant
investments in delta.com, mobile device applications, check-in kiosks,
customer service applications, airport information displays and
related initiatives, including security for these initiatives. The
performance, reliability and security of the technology are critical to our
Annual Report
2017
–
ability to serve customers.”
Delta claims that previous innovations mentioned above have helped to
drastically improved
streamline customer traffic in airports and have also “
.” However, the airline does not specifically
customer satisfaction scores
.
press release
provide any data pertaining to customer feedback in the
(Readers interested in customer service AI applications may want to read
fast food AI uses
about the innovative AI kiosk ideas that we covered in our
.)
cases article

53. Southwest
The airline shows limited evidence of AI implementation, but there is some
evidence of Southwest using machine learning to improve operations. Jeff
Hamlet, former Director of Air Operations Assurance at Southwest Airlines
that he and his team used machine learning techniques such as
stated
has
time series analysis and pattern recognition to enhance their data mining
capabilities.
refers to a method for evaluating a series of data points
Time series analysis
that are ordered according to time. This type of analysis is often used to
identify trends or patterns.
Hamlet claims that these approaches enabled his team to identify potential
flight glitches found in pilots’ data reports. These findings were then
relayed to air traffic control at the site of arrival. Hamlet concludes that in
this reported instance, contributed

54. United Airlines
a collaboration with
announced
, United Airlines
2017
In September
Amazon Alexa called “United skill.” The app reportedly allows Alexa
users to find answers to the most common questions about United
flights by communicating through natural language.
skill
Alexa
announcing their
blog post
A screen shot from United’s
Once users add “United skill” to their existing Alexa app, they are able
to ask Alexa common questions about flight statuses, flight times and
amenities. Though United skill, examples of commands that Alexa can
process include:
“Alexa, ask United: what is the status of flight 959?”
“Alexa, ask United: does flight 869 have Wi-Fi?”
“Alexa, ask United to check me in.”

55. However, the app has some limitations. For example, commands must be
phrased in a very specific way and information on certain features such as
airline check-in are restricted to domestic flights.
on Amazon’s website, so far, United skill has
reviews published
Based on
had a mixed reception. Some complaints include incorrect flight times and
routine misunderstanding of crucial elements of vocal commands such as
“flight number.”
As the pioneering airline to integrate Alexa functionality, it is expected that
there will be a learning curve. It will be interesting to see what
improvements Amazon will make over time and if the collaboration will
ultimately prove mutually beneficial for both companies.
(Readers with a strong interest in Amazon’s conversational interface
–
Comparison
Chatbot
technology may want to read our full article titled: “
“.)
Facebook, Microsoft, Amazon, and Google

56. Concluding Thoughts on AI in Commercial Airline Sector
AI is being explored in the commercial airline segment of the aviation
industry and is being integrated across multiple areas including customer
service, airport and flight operations. Airport development will be a particular
published by the
annual report
area of importance according to an
International Air Transport Association.
The association anticipates that the cost of airport development, specifically
improving and modernizing existing infrastructure and operations, will
exceed $1 trillion over the next fifteen years. Therefore, innovation will be a
critical building block of these efforts. Specifically, AI and self-service airport
kiosks and apps should mesh well into this industry outlook.
In aviation, the transmission and translation of data is fundamental to market
competition and safe flying. Jeff Hamlet former Director of Air Operations
Assurance at Southwest Airlines and Ashok N. Srivastava, the project manager
that efficient data management
posit
for the Aviation Safety Program at NASA
is achieved through the continued creation of new algorithms.
These algorithms or apps would be tailored to the new problems that are
being reported by pilots, the FAA, and others involved in aeronautics space.
Policies and procedures that affect the transmission of data are of
fundamental importance to the future of machine learning in aeronautics.

57. Thus, we can anticipate that machine learning algorithms will continue to play
an important role in how leading airlines translate their data interpretation into
valuable outcomes for their companies.
It is also important to consider the economic impact as it pertains to job growth.
As a bustling multi-billion dollar industry, it is anticipated that over the next 20
years we will see widespread and lasting growth in the commercial aviation job
“expanding
market. Global economic expansion has contributed to airlines
,
2016
to satisfy growing consumer demand. In
their fleets and flight schedules”
and
million supply chain jobs
67.7
the aviation industry sustained an estimated
.
added output
-
trillion in global value
3.0
produced $
While the leading commercial passenger airlines are relatively early-adopters of
AI, industry projections depict a business environment primed for innovation
and automation. We will continue to monitor how AI emerges throughout the
industry as we anticipate wider implementation in the coming years.

58. Aviation is starting to adopt AI
in many ways in order to
streamline business and
improve customer experience

59. The aviation industry
Especially the commercial aviation sector, is constantly striving to improve
both the way it works and its customer satisfaction.
. Though AI in the aviation
artificial intelligence
that end, it has begun using
To
industry is still in the nascent stage, some progress has been made already as
certain leading carriers invest in AI.
facial
start with, certain use uses are being implemented such as
To
in, customer queries and answers, aircraft fuel
-
, baggage check
recognition
optimization and factory operations optimization. But AI can potentially go far
beyond the current use cases.
To make a long story short, AI can redefine how the aviation industry goes
Ways
5
about its work. (To learn more about AI in business, check out
.)
Companies May Want to Consider Using AI

60. The Context
The global aviation industry has been growing exponentially.

the U.S. commercial aviation industry: In the next two
of
example
the
Take
decades, passenger count is expected to double.
In 2016, the U.S. commercial aviation industry generated an operating revenue
of $168.2 billion. This is an opportunity for exponential growth which needs to
be handled well.
The aviation industry needs to move beyond its present ways of working and
find better ways to optimize resources, improve customer satisfaction and
safety records, control costs and be more responsible environmentally.
Data is key to unlocking the potential, and the aviation industry must leverage
AI. So, while both the business case and context of AI in the aviation industry
is set, we need to discuss the use cases being implemented currently.

61. AI Use Cases in Aviation
Passenger Identification

The idea is to have machines perform end-to-end passenger identification and check-in
at the airport. Delta Airlines has been testing this process. Delta has been keen on using
in
-
and check
kiosks
AI for some time, as is evident in its initiatives such as ticketing
, Delta announced it was going to invest
2017
. In May
mobile app
via the Fly Delta
$600,000 in four automated self-service bag checking kiosks, including one that will
also have facial recognition technology. The experiment is being carried out at
Minneapolis-St. Paul International Airport. According to Delta, previous experiments
have helped streamline customer flow at the airport and improve customer satisfaction
scores.

According to the Delta annual report: We are dependent on technology initiatives to
provide customer service and operational effectiveness in order to compete in the
current business environment. For example, we have made and continue to make
in kiosks,
-
applications, check
mobile device
significant investments in delta.com,
customer service applications, airport information displays and related initiatives,
including security for these initiatives.
 As already stated, AI in aviation is in the nascent stage, but some use cases are already
being implemented by some major U.S. carriers. These use cases are described below.

62. AI Use Cases in Aviation
Baggage Screening

In 2017,American Airlines conducted an app development
competition with the goal of having an app developed for
making baggage screening easier for passengers.

The competition, named HackWars, was themed upon
virtual
and
augmented
and
drones
artificial intelligence,
. The winner, known as “Team Avatar,” developed
reality
an app that would not only allow passengers determine
their baggage size before arriving at the airport, but also
prepay any potential baggage-related expenses.

63. AI Use Cases in Aviation
Customer Assistance

United Airlines is using Amazon’s Alexa to have certain common
customer queries answered. In September 2017, United announced a
collaboration with Alexa. The feature is known as the United skill.

To get started, all passengers need to do is to add the United skill to
their Alexa app and then start asking questions. Alexa answers
common queries correctly, such as the status of a flight by number,
on a flight.
WiFi
in requests and availability of
-
check

The reviews so far have been mixed, which points to the fact that
there is a learning curve, and it is still a long way to go before AI can
fully handle customer assistance.

64. Challenges and Tasks
Data Confidentiality Management

Humongous volumes of data will be in use as the aviation industry embraces AI, and that will
give rise to data confidentiality risks.

However, the need to properly manage data isn't exactly a new challenge for airlines. One
incident has already come to light, when it was revealed that Emirates, a leading airline,
leaked customer data to third parties without authorization. It was found that customer details
such as name, email, itinerary, phone number and even passport number were shared with
third-party service providers such as Boxever, Coremetrics, Crazy Egg, Facebook and
Google.

Though Emirates policy states that there will be some data sharing, the policy is pretty
ambiguous.
Since the aviation industry has only recently embarked on the AI journey, fully embracing AI is
going to be a challenging task. The following challenges come to mind. (For more on current AI
uses, see What AI Can Do for the Enterprise.)

65. Challenges and Tasks
Tracking Progress

Tracking progress is an enormous challenge that airlines
will face. The first thing they need to do is to develop
that will help them develop and process accurate
analytics
data. However, that in itself is a challenge. What kind of
analytics will help?
For example, customer satisfaction is going to be one of the
most important factors in success. What kind of analytics
will determine that airlines have been improving on
customer satisfaction parameters?

66. Challenges and Tasks
Managing Investments

AI needs huge investments, and probably the biggest risk in
this is that smaller, especially budget airlines are going to miss
out on reaping the benefits of AI fully. Does that mean that the
performance of the smaller carriers will be impacted? That
might not be the case, because we might be moving toward
more acquisitions and mergers.

Bigger airlines will have a massive appetite for acquiring
smaller airlines with an eye on the market. It is not all gloom
and doom though, because smaller airlines like Southwest have
already shown some initiatives toward embracing AI.

207. ‫غ‬

289. Conclusion
It is surprising that a sector as important as aviation has woken up to AI so late. As AI in
aviation picks up its pace, there could probably be a few mergers, acquisitions or even
closure of small airlines which will not be able to afford the investments.
Now, AI seems the best option to take aviation to the next level.

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291. Thank You