Artificial intelligence (AI) is becoming an increasingly important part of modern aviation. From predicting traffic congestion and optimizing flight operations to evaluating regulatory compliance and supporting complex airspace decisions, AI offers opportunities to improve safety, efficiency, and operational performance. However, as aviation organizations adopt increasingly sophisticated AI tools, an important Read more
Modernizing the National Airspace System is often discussed in terms of new technologies: cloud computing, artificial intelligence, machine learning, automation, digital twins, and increasingly sophisticated decision-support tools. However, beneath nearly every one of those capabilities is a less visible requirement: high-quality data. Modern air traffic management depends on information arriving Read more
Artificial intelligence in aviation tends to attract two competing narratives. In one, AI solves congestion, delays, staffing challenges, and nearly every other operational problem. In the other, it is an unproven technology that threatens to remove experienced professionals from safety-critical decisions. Neither version accurately describes where AI is headed today in air traffic control.
Airport operators and aviation stakeholders already have access to more operational data than ever before. The challenge is turning that data into a shared, accurate, and forward-looking understanding of airport operations. That is where digital twins are becoming increasingly valuable.
Mosaic ATM’s RTS Agent (Real Time Simulation Agent) combines
machine learning, probabilistic modeling, and knowledge graph analytics to extract predictive insight from streaming track data.
Web-based weather maps make the collection and storage of weather data more efficient & effective. Our blog goes into detail.
Mosaic lays out an approach to developing a flight data analysis suite that can be used to solve common airspace system problems.
We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.
Mosaic is developing a machine learning based tool that assists corporate travel manages and business travelers in making the safest travel decisions possible.
Working in conjunction with subject matter experts, data scientists can swiftly apply statistical tools and uncover emerging trends. This is extremely valuable for companies trying to operate in a disruption. Not only will executives have an accurate representation of their present situation, but new products & services can be devised from these insights.