Democratisation of Deep Learning: Neural Architecture Search at Low Cost

人工设计基于深度学习的神经网络（DNN）的需求限制了它们对AI专家的使用，并阻碍了更广泛地开发它们的真正潜力，例如，农业和人文学科我们的目标是通过能够生成DNN的新AI方法来取代这个繁琐的过程，这些方法可以比手动设计的DNN执行得更好，计算成本更低。我们进一步扩展了这一想法，以解决复杂的现实问题，包括能源和气候变化等各种应用中的标记和未标记数据。预期成果包括新的人工智能方法，训练有素的人工智能研究人员和一些将为澳大利亚和世界带来重大利益的关键应用程序。

The need to manually design Deep Learning-based Neural Networks (DNNs) limits their usage to AI experts and hinders the exploitation of their true potential more broadly, e.g., in farming, humanities. We aim to replace this tedious process through novel AI methods capable of generating DNNs that can perform significantly better and at a lower computational cost than manually designed DNNs. We further expand this idea to solve complex real-world problems with both labelled and unlabelled data found in various applications including energy and climate change. The expected outcomes include the novel AI methods, highly trained AI researchers and a number of critical applications that will bring significant benefits to Australia and the world.