In this era of fast-paced technological advancements, the aviation industry has been at the forefront of adopting modern technologies that are changing the way airlines, airports, and MROs operate. From advancing safety measures to optimizing operations, the involvement of artificial intelligence (AI) and Machine Learning (ML) is signifying a shift towards smarter, and more adaptive systems. The added ability to analyze large amounts of data in real time and the ability to make informed decisions based on the data collected has made AI and ML indispensable tools. However, the aviation industry is still barely at the inception of what AI and ML are truly capable of providing them.

What is Artificial Intelligence?

In simple words, artificial intelligence refers to the ability to mimic human intelligence in machines that are designed to perform tasks that usually require human intelligence. AI as a concept has existed since the 1950s but the work to develop this technology has massively increased over the last 10 years mainly due to these three factors.

• The advanced ability to collect and store large amounts of data.
• The large increase in computing power.
• The development of powerful algorithms.

We have already seen the integration of AI in other day-to-day sectors like Telecommunication, Research, Health Care, etc. We are now seeing an ongoing trend in the aviation industry to integrate AI into aviation software to improve the safety, efficiency, and decision-making process in different sections of the industry. However, the development and integration of AI in the aviation industry still has a long way to go.

What is Machine Learning?

Machine Learning is a field of Artificial intelligence that concentrates on the development of statistical models and algorithms that provide platforms with the capability of performing work without any defined instruction but are rather trained through large amounts of data to understand patterns and make decisions or predictions based on the data.

ML is an important aspect of artificial intelligence that is playing a key role in the advancement of technology in the aviation industry. ML is going to be critical when it comes to detecting potential issues early and preventing failures or analyzing historical data to identify patterns and factors to provide information that will allow aviation personnel to make informed decisions. Machine Learning has the potential to advance various sections of the aviation industry as the development of machine learning continues.

Ways in which AI and ML can change the aviation industry

Listed below are some of the aspects of the aviation industry that can be advanced through AI and ML.

• Maintenance: AI and ML can be utilized to further advance aircraft and airport maintenance so that personnel can detect issues early and prevent failures.
• Safety: These technologies will advance safety intelligence by highlighting vulnerabilities and by predicting failures. They will be a major factor when it comes to analyzing emerging risks, classifying risks based on occurrences, and creating a Safety Risk portfolio to allow for the prioritization of safety issues.
• Flight Operations: AI and ML will enable users to optimize flight routes, scheduling, and fuel consumption. AI and ML will work toward identifying the most efficient flight paths, ways to reduce fuel costs, and minimizing flight delays.
• Aviation personnel training: AI and ML-based tools will be invaluable when it comes to optimizing and training airport and aircraft personnel. These technologies will directly improve the skills and decision-making abilities of these personnel.

These are just some of the aspects that will advance with the continuous improvement and integration of AI and ML in aviation software but there is so much more that can be achieved.

Hurdles along the way

The integration of AI and ML is invaluable to the aviation industry, but many challenges need to be overcome before we find the perfect application of these technologies. Some of the hurdles are listed below:

• Overcoming model bias: AI and ML-based solutions can be subject to bias and it’s very important to overcome this bias to maintain the integrity of the solution.
• Ethical conundrum: The usage of AI raises many ethical questions. There is a need for ethical guidelines in AI development so that the solution can be considered trustworthy.
• Stability and Robustness of AI application: These technologies are statistics-driven and it’s critical to understand that the outcome can be highly varied based on small variations in the input. There is a need to create new methods to verify the stability and robustness of these applications.
• Scaling solutions: Even with modern technology it’s very difficult to train and deploy models as it requires significant resources. This makes it difficult to scale the solutions.

Conclusions

The integration of AI and ML will lead to smarter, more efficient, and safer systems. These technologies will change the game as they keep on developing, be it maintenance, safety, flight operations, or personnel training, all these sections will advance in ways that have never been seen before. However, there are challenges that need to be overcome along the way and it is a must to address these challenges and overcome them.