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）的需求限制了人工智能专家对其的使用，并阻碍了更广泛地开发其真正潜力，例如在农业、人文科学等领域。我们的目标是通过新颖的人工智能方法来取代这个繁琐的过程，这些方法能够生成比人工设计的深度神经网络性能更好、计算成本更低的深度神经网络。我们进一步扩展这一想法，以解决复杂的现实世界问题，包括在能源和气候变化等各种应用中发现的标记和未标记数据。预期的结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。

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.