Since the 1950s, artificial intelligence (AI) has been a recurring topic in research. However, this field has only recently gained significant momentum because of the advances in technology and algorithms, along with new AI techniques such as machine learning methods for structured data, modern deep learning, and natural language processing for unstructured data. Although companies are eager to join the fray of this new AI trend and take advantage of its potential benefits, it is unclear what implications AI will have on society now and in the long term. Using the five dimensions of sustainability to structure the analysis, we explore the impacts of AI on several domains. We find that there is a significant impact on all five dimensions, with positive and negative impacts, and that value, collaboration, sharing responsibilities; ethics will play a vital role in any future sustainable development of AI in society. Our exploration provides a foundation for in-depth discussions and future research collaborations.

Automatic segmentation of retinal blood vessels from fundus images is of great importance in assessing the condition of vascular network in human eyes. The task is primary challenging due to the low contrast of images, the variety of vessels and potential pathology. Previous studies have proposed shallow machine learning based methods to tackle the problem. However, these methods require specific domain knowledge, and the efficiency and robustness of these methods are not satisfactory for medical diagnosis. In recent years, deep learning models have made great progress in various segmentation tasks. In particular, Fully Convolutional Network and U-net have achieved promising results in end-to-end dense prediction tasks. In this study, we propose a novel encoder-decoder architecture based on the vanilla U-net architecture for retinal blood vessels segmentation. The proposed deep learning architecture integrates hybrid dilation convolutions and pixel transposed convolutions in the encoder-decoder model. Such design enables global dense feature extraction and resolves the common “gridding” and “checkerboard” issues in the regular U-net. Furthermore, the proposed network can be efficiently and directly implemented for any semantic segmentation applications. We evaluate the proposed network on two retinal blood vessels data sets. The experimental results show that our proposed model outperforms the baseline U-net model.