Aerospace sectors needs implementation of highly efficient artificial intelligence applications that can automate aerospace workflows, enable us to control the soft landing on space craft mission. There are lots of potential risks that need to be analyzed, and mitigated with the help of artificial intelligence. Before utilizing artificial intelligence in aerospace and defense sectors we need to analyze the risks properly. In this article we will explore several challenges of ai in aerospace sectors.

Technology limitation due to inefficiency in Autonomous Navigation & Decision-Making

Autonomous unmanned aerial vehicle face major challenge to capture real time field data, understand the geographical conditions, predict weather forecast, analyze potential risk before making any accident. Our technology keeps on evolving with time but it has not revolutionized to the extent that it can accurately predict the potential or vulnerable threat to aerospace vehicle and make informed decision for safeguarding the damage or risk of life of passengers inside. AI model in aerospace sectors are trained on training data which is limited. To ensure that autonomous navigation is fully controlled by artificial intelligence we would have to make use of big data analytics to collect enormous flight simulation data and perform required flight activities to avoid flight accidents.

Ethical issues with the privacy concerns

Training data of aerospace accidents are prepared with different case scenarios. These data are analyzed properly by artificial intelligence and make the machine learning algorithm more sophisticated, robust and secure. Countries might have strict data protection compliance regulations and hence it becomes a challenging task for ai aerospace solutions to prepare the training data across the globe.

Legal issues with the artificial intelligence in aerospace

When research and development in aerospace is done with the help of artificial intelligence then it might become legal issues as the same type of ideas might be generated and innovative ideas might not be unique which causes copyright or patent issue.

Human ai interaction and training

Perhaps the most difficult task to train the aerospace vehicle with human voice. AI system need to capture the facial expression, find the sentinel analysis to determine the emotions of the passengers whether they afraid, in fear or terror due to inside circumstances. With current technology this response is too late which delays the life saving activities to safeguard the passengers from life threatening situations.

Complexity in flight autonomous systems

Dynamics of autonomous unmanned vehicle are analyzed by the customized ai enabled cockpits. They create a virtual space and make all possible events that can cause damage to vehicle or risk to human life, use predictive ai technology for predicting accurate weather conditions. However these data is not fully reliable. They might show results that are completely different from the reality. In the event when the ai system is not trained they do not take wise decision.

Conclusion

With effective implementation of ai in aerospace we can automate the aerospace data workflows, integrate predictive data analytics tools, enabling real time data analysis, train ai model with big data to make it more sophisticated and robust. By analyzing the risks properly we can mitigate the potential vulnerability issues of aerospace vehicles and eliminate inefficiency, and improve accuracy.