AI for Aviation

AI in aviation has the potential to revolutionize the way we travel, making air transportation more efficient, reliable and safe. Use AI technology allows to optimize routes, reduce fuel consumption, automate flight plans and enable predictive maintenance. The ultimate goal is to reduce the carbon footprint of airlines across the industry.

The ACO recently announced it was partnering with Airbus and Leonardo to develop FlyBiz, an innovative mobile app that will revolutionize the passenger experience on commercial flights by enabling passengers to book seats in advance and purchase onboard service on their smartphones. This AI can also help planes travel shorter distances, improve safety by reducing human error, improve passenger comfort, and streamline airport operations. Furthermore, AI in aviation has the potential to increase urban air mobility and decarbonize air transportation. The potential for AI in aviation is vast. With its ability to process vast amounts of data quickly and accurately, AI is set to transform the industry in a way that will benefit all stakeholders involved.

And if you think this is a far and distant future, think again. Airbus just completed its Autonomous Taxi, Take-Off, and Landing (ATTOL) project that saw an A350-1000XWB perform normally pilot-flown maneuvers entirely on its own in early 2020, while just recently Cathay Pacific announced a partnership targeting 2025 for single-pilot cruise flight operations. For more info on the benefits of single-pilot flight operations, please watch this video.

Replacing pilots, as you might imagine, requires new technology powered by artificial intelligence (AI). Thanks to the increasing computing power available today, machine learning (ML) promises unprecedented efficiency gains and autonomous decision-making. We’ve already seen some pilots sidelined by ML, such as air traffic controllers. Continuous improvement of ML has enabled the AI-driven automation of routine tasks that used to require human interaction. With this shift, ML-driven automation will gradually replace pilots in more and more instances across industries.

In the quest for autonomy, one company is applying sophisticated machine learning techniques to on-the-job training processes to improve productivity and retention rates by ensuring every employee has a clear understanding of their role and how it fits into the larger organization's strategy. For example, one application at Siemens trains its engineers to recognize when a machine is stuck in an incorrect state and to diagnose when a breakdown has occurred. The company's "machine learning specialist" makes sure that the training data set is accurate by automatically detecting duplicates, comparing patterns, and determining if the learned behavior is actually being followed by employees.

Most of these systems are based on traditional technology, where linear computer algorithms react to sensor information based on predefined rules. In contrast, a swarm of tiny flying robots can be programmed to perform the same tasks without human involvement. For example, a swarm could be programmed so that if it detects an obstacle in its path, it makes use of its individual intelligence and chooses the best paths around that obstacle using natural swarming behaviors. And even though we have used this approach for decades, the increasing complexity in today’s airplanes sometimes results in unexpected system behavior, such as misbehaving flight-control systems or flawed MCAS. To address this issue, we studied a number of control-system design principles that are not well known. The first principle is a way to make the system easier to understand and predict by making it more declarative instead of procedural. The second principle is an adaptation of the Bellman equation, which can be used to automate some important decisions in the design process. The third principle is a new way for turning on and off concepts in the design process. The fourth principle uses continuous analysis for both modeling and optimization, where trajectories are automatically generated from a model via simulation or optimization methods such as gradient descent, particle swarm optimization, or genetic algorithms), vision (image processing, computer vision), problem solving (alpha-beta pruning methods) etc..