About the Presentation
Artificial Intelligence (AI) is rapidly transforming our world, and the Defence industry is no exception. As an Australian sovereign industry priority and a priority for Australia's Defence capability, it presents challenges and opportunities for Defence Industry leaders and managers.
In a recent talk at Avalon 2023, I addressed the importance of AI fluency for Defence Industry leaders and managers, not just software engineers. It is crucial for leadership to understand how AI is used in the Defence context, from electronic warfare to object detection, classification and tracking, to name but a few.
Some examples I covered include the following:
Object detection: Machine learning algorithms can automatically detect objects' presence in real-time feeds from sensors such as cameras, radars, and lidars. This capability can help identify potential threats and enable quick response times. For example, one can train a machine-learning model to detect the presence of drones in a specific area by analysing live video feeds.
Object classification: Machine learning algorithms can be used to classify different types of objects based on their features, such as size, shape, and movement patterns. For example, a machine learning model can be trained to classify different aircraft types. This capability can help identify potential threats and enable more targeted response strategies.
Object tracking: Machine learning algorithms can be used to track the movement of objects in real time, even as they change direction or speed. This capability can help predict the trajectory of potential threats and enable more accurate targeting. For example, a machine learning model can be used to track the flight path of a missile or drone and predict its next move by fusing information from multiple different sensors.
I also spoke about predictive maintenance, which involves using data and analytics to predict when maintenance is needed based on the actual condition and performance of the asset. This approach relies on real-time monitoring and analysis of the asset's data, such as vibration, temperature, and pressure sensors, to detect early signs of potential issues. Predictive maintenance can help identify problems before they become serious, allowing maintenance to be scheduled at the optimal time to avoid costly downtime and prevent more extensive damage.
In the context of managing Defence assets such as aircraft, predictive maintenance is becoming increasingly important as the complexity and sophistication of modern aircraft continue to grow. By using data analytics to predict when maintenance is needed, Defence organisations can improve operational readiness, reduce maintenance costs, and increase safety by identifying and addressing issues before they become critical. Overall, while scheduled maintenance is a proven approach that has been used for decades, predictive maintenance offers a more proactive and data-driven alternative that can provide significant benefits for managing Defence assets such as aircraft.
To make informed assessments about AI capabilities intended for Defence acquisition, leaders and managers need to develop fluency in AI-related terminology and concepts. These concepts include machine learning, deep neural networks, and human-machine teaming. AI has a significant disruptive impact on a range of industries worldwide, and the ADF will have unique requirements for any AI capabilities it acquires for use on the battlefield. It is essential to understand these issues to take full advantage of the opportunities and avoid potential pitfalls. One of the key takeaways from my talk was that AI presents a tremendous opportunity for the Defence industry to increase efficiency and effectiveness. However, significant challenges, such as governance, risk and compliance considerations, must be addressed.
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