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AI is Reshaping the Aviation Industry

Aviation Business
Following up on our blog post on the top 5 technologies that will reshape aviation, let's look at probably the most important and game changing technology. Today we're talking about Artificial Intelligence (AI). There are few technologies that have such a profound impact on our lives as AI systems do already. The potential is great, but there is also a risk of negative impacts and progress is moving at breakneck pace. In today's blog post, we will give a brief overview about the state of AI, explore the role of AI in aviation discussing risks and challenges, and provide appropriate application examples from aviation.

What's behind AI?

Artificial intelligence is defined as intelligence demonstrated by machines rather than natural intelligence expressed by people or animals. It is the study of rational or intelligent agents, which are systems that can understand their environment, make decisions, and perform actions that maximize their chances of achieving specific goals. The following five domains of AI are particularly important in terms of their ability to influence the future of digital products and services:
Machine Learning
Machine Learning (ML) allows machines and computer systems to process, analyze, and interpret data in order to solve real-life problems. ML systems can learn and take actions on their own through processing big amounts of data.
Neural Network
This AI branch is known as "deep learning," as artificial brain neurons are used to solve complex issues. The Neural Network helps machines approach the functioning of the human brain. Risk analysis, market research, forecasting, stock exchange prediction or facial recognition are popular fields where the technology is being used.
Natural Language Processing
Natural Language Processing (NLP) gives a computer program the ability to understand spoken or written human language. This allows us to communicate with machines in a natural way, and they will understand our human language.
Expert Systems
Expert Systems are AI-based systems that learn and simulate human decision-making abilities. To solve complex subjects, such systems do not use conventional programming but logical notations to accomplish that goal.
This branch focuses on the design and development of robots by incorporating both science and engineering techniques. The goal of robot deployment is to assist people with time-consuming and heavy jobs.
In many areas AI has already become a key driver of today's change. With the advancement of AI, experts even speak of a new dimension in the digital revolution. But, despite the fact that the theory has been around for decades, why are deep learning and AI just now becoming increasingly relevant? The critical factor for neural networks to operate effectively is the availability of data. We've seen some significant technological advancements in the last few years. Memory has become much cheaper, data storage has become much easier, computer processing speeds have continued to exceed past marks and also significant algorithmic modifications have been developed. The internet connected world enables a fast data exchange and has caused the available data volumes to explode. Supercomputing is being used in an increasing number of fields and is expected to significantly boost AI development. At this point in time, when technology has finally advanced to the point where critical data sets are available in large amounts, AI is experiencing a Renaissance.

What are recent AI developments and trends?

There is so much that artificial intelligence is being utilized for, and so much more potential, that it is difficult to narrow down the most recent advancements to a few application areas. Nevertheless, we have selected few examples to showcase some exciting AI developments and trends in recent years before we dive deep into the aviation industry.
The use of AI is going to make healthcare more accurate and less costly. AI-powered radiology assistants, programs to diagnose potentially fatal blood diseases at an early stage, and software used in cancer screenings, diagnostic tests, and blood work are just a few examples of AI applications in healthcare. During the last year and a half, the pandemic has had a considerable impact on our lives. The world has hoped that a vaccine would be developed quickly. But a new vaccine usually takes years, if not decades, to develop. However, barely three months after the first reported cases, vaccine candidates for covid-19 were already being tested on humans. The record-breaking pace of vaccine development was made possible in part by AI models that helped researchers in analyzing massive amounts of data about corona viruses.
The field of autonomous driving is a well-known AI application and the technology improved considerably in recent years. In 2020 robotaxi services have been introduced to the public and tested in several Chinese cities. Robotaxis are driverless cars or shuttle busses that you can order to your location when you need them like traditional taxis. This gets us one step closer to commercialization of fully automated driving. But until then, there are still some challenges to be solved, such as government regulations and accurate map coverage.
The technique by which AI "learns" is usually through the training by humans or machine learning, where the bot learns through data processing. But from the latest developments AI robots gain the ability to learn through observation. Nvidia introduced a robot capable of observing and learning human activities. This development is more hands-off method from the usual training of robots and will definitely be a game changer in robotics.
Facial-recognition systems are trained to create 'faceprints' of people by mapping the geometry of certain facial features using a large number of images and classify a face into categories such as gender, age, and race, as well as to compare it to other faceprints in databases. For the past few years, facial recognition has seemed to be in trend all over. It was invading many areas of life, and both private and public organizations started using it for a variety of purposes, including surveillance in airports. But in 2020, IBM, Amazon, and Microsoft declared that they would stop developing facial recognition software due to worries that it may reinforce racial and gender discrimination. IBM also called for a review of the technology and how it is applied all across the industry. Despite the concerns, facial recognition software is being widely used in numerous countries, e.g., China and India.
AI significantly improves communication between machines and humans. Natural language processing (NLP) is the branch of AI that enables computers to understand text and spoken words in the same way that humans can. Previously, computers relied on text-based and graphical user interfaces, but NLP introduced powerful conversational user interfaces that we are all familiar with from our everyday lives. Google Assistant, Amazon's Alexa, and Apple's Siri have changed the way people interact with their devices, and they are still advancing. Chatbots have become commonplace and are being used in a variety of industries. In 2018 Google introduced their AI-powered "Smart Compose" feature for Gmail that is able to complete sentences quite accurately before the author does. NLP is undeniably a game-changing technology that is used in more and more areas. We look forward to seeing how far it will progress in the next years.
The financial sector is a broad field of application for AI. Nowadays AI technology is already being used to help manage equity funds. Intelligent systems are able to consider a large number of variables, so they can outperform a human management. The technology will become a game changer in finance disrupting traditional trading and investing practices.

Based on these few examples, it is clear that AI already has a significant impact on many aspects of our lives, and we can only speculate on how far AI will progress in the coming years. The mere fact that we discuss the machine potential ability to develop own consciousness at some point in time indicates how far-reaching the implications can be. It reminds us of our responsibility to keep control of a tool rather than becoming an object of the tool.

What are recent AI developments and trends?

Artificial intelligence is already being applied across several areas in aviation. It contributes to the optimization of flight operations, the disruption of enterprise data processing, the improvement of customer experience, and the maximization of revenue. Predictive maintenance, pattern recognition, auto-scheduling, targeted advertising, and customer feedback analysis are some of the disciplines powered by AI, which significantly improve the overall performance within aviation. Following are a few examples of application areas where AI is effectively being used by airlines.
A special price just for you
The concept of personalized dynamic pricing goes beyond the commonly used dynamic pricing that is currently employed by the majority of airline operators. The concept of airlines adjusting prices solely based on supply and demand is becoming outdated. Using big data analytics and AI powered systems, airline e-commerce can gain access to many more essential customer variables and create a more profitable pricing in real-time. A typical example is that Mac users are charged more than Windows users. Many e-commerce platforms in a variety of industries are already using this technology. However, despite its profitability, this pricing generates strong negative perceptions of fairness, so companies are sometimes reluctant to implement it.
(A)I can predict your flight delay
Airlines, airports, and passengers all suffer from flight delays. Therefore, an accurate estimation of flight delays is crucial for airlines since the results can be used to increase customer satisfaction and airlines profitability. But, because there are a variety of causes for delays, such as weather-related air traffic congestion, technical issues, boarding difficulties, or the airline's incapacity, conventional prediction models are insufficient. Deep learning models, on the other hand, are able to analyze huge amounts of historical data and learn from the complex interdependencies between the different variables. Thus, deep learning technology enable delays to be predicted before they appear on the departure board with much higher accuracy.
Always choose the best route
Nowadays dispatchers are responsible for safely planning the most effective route for each aircraft. They collaborate with pilots to guarantee a flight's safe routing and operation. Therefore, present and expected weather, recorded air turbulence, aircraft performance, safety regulations, air traffic-control compliance, and traffic volume must all be considered, making the planning process a very complicated workflow. Artificial intelligence and machine learning can be used to enhance efficiency and sustainability of flight operations by optimizing routes and enhancing airline traffic prediction and flow. Alaska Airlines implemented an AI flight monitoring and routing platform to enable the airline and its employees to plan the most efficient routes by providing dispatchers with additional tools to quickly make informed decisions. The software anticipates future circumstances and manages exceptions through rapid and even better accuracy processing of large data inputs. As a result, the Airline could save 480,000 gallons of fuel and avoid 4,600 tons of carbon emissions in just six months.
Crew planning is an AI problem
The airline's flying personnel are a substantial cost element. Therefore, it is imperative for the airlines to optimize their crew scheduling process. However, it is a complex task where a lot of data and aspects must be considered. The planning process can be very time consuming, and the decisions made may not be the most resource efficient. This is where artificial intelligence comes into play, so different crew scheduling systems and algorithms have been developed. Simply explained possible functionality, these systems are capable of solving an optimization problem. As soon as the problem is solved, a viable schedule is proposed to an intelligent agent, which might accept or reject the plan but also rate it based on pre-defined criteria. Each time a schedule is accepted the system recognizes successful patterns and improves its decisions over time. Trained with a lot of data, such a system can improve crew scheduling significantly.
OEMs develops a wide range of solutions that are supported by AI. Airbus, for example, claims to focus on six major AI-related fields: knowledge extraction, computer vision, anomaly detection, conversational assistance, decision-making and autonomous flight. Airbus is using AI to analyze data from various factories and predict when variations in their manufacturing processes may occur. This enables them to address problems earlier, when it is easier and less expensive. The company also launched numerous other projects with topics ranging from urban air mobility to earth observation.

In manufacturing, there is a predetermined production plan, but MRO is a much more difficult problem than typical manufacturing, which is why MROs can significantly benefit from AI-powered systems. Predictive maintenance is a popular application field. AI technology can help identify any issues proactively. Following identification, the AI will be capable of creating and issuing a work order. This will help to expedite the inspection and repair processes to reduce maintenance turnaround times. The critical factor in this case is data collection.

Major challenge before the next step

The advancement of artificial intelligence make it feel like everything is possible. But in aviation, AI is restricted to non-critical ground activities, because aviation-specific challenges in terms of reliability, cyber security and real-time factor still needs to be resolved for more complex tasks, such as air traffic control or aircraft operations.

The required certification of such technology is a major challenge to the application of artificial intelligence. Aircraft components and software are safety-critical. Therefore, they must meet strict quality requirements., and complex AI systems has so far been unable to be deployed due to a lack of corresponding certification processes. Therefore, it is essential to develop standards for technologies that aren't rigorously designed but adapt flexibly in real time. That is why fully self-flying aircraft await somewhere in a distant future.

To the horizon and beyond.

The aviation business is being reshaped by artificial intelligence as well as other emerging technologies such as robotics, the Internet of Things, unmanned aircraft systems and hybrid and electric airplanes. Investing in Artificial Intelligence and Big Data can be considered as a potential way of increasing safety, efficiency, and sustainability. In this way aviation infrastructure and airspace utilization can be improved. We at Aeroji keep up with the most recent technological advancements and continue to pursue our vision of a world in which the aviation industry meets at eye-level and does business as easy as booking a flight.

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