Generating Knowledge from High-dimensional and Incrementally Acquired Data

来自紧急情况的复杂数据，例如从正在进行的病毒暴发或活跃的丛林火灾中获得的数据，通常是逐步收到的。这种情况的分析不能等到紧急情况结束时才有完整的数据集。该项目的目的是通过开发创新的基于神经网络的方法来克服当前人工智能工具的这一严重缺陷，这些方法可以从连续的数据流中学习，并从语义或数学上提取和解释隐藏的知识。该项目的预期成果包括开发新方法、训练有素的人工智能研究人员以及一些关键的真实应用程序，这些应用程序将为澳大利亚和世界带来重大利益。

Complex data from emergencies, e.g., data acquired from an ongoing viral outbreak or actively moving bush fire are often received progressively. The analysis of such situations cannot wait until the complete data set is available at the end of the emergency. The aim of this project is to overcome this serious deficiency of current AI tools by developing innovative Neural Network based methods that can learn from continuous data streams and extract and interpret the hidden knowledge either semantically or mathematically. The expected outcomes of this project include the development of novel methods, highly trained AI researchers and a number of critical real applications that will bring significant benefits to Australia and the world.