The decentralisation movement hit countless industries hard and fast with promises to revolutionise the way stakeholders interact. For the most part, this is very true. One such example is DeFi -- we have seen financial primitives (such as lending and borrowing) be reborn using the innovations of programmable blockchains. While these technologies are cutting edge and push the boundaries of what we thought was possible, it is time for them to take their next evolutionary step; with artificial intelligence and deep learning. This is the cutting edge of the cutting edge.

Although practically attractive with high prediction and classification power, complicated learning methods often lack interpretability and reproducibility, limiting their scientific usage. A useful remedy is to select truly important variables contributing to the response of interest. We develop a method for deep learning inference using knockoffs, DeepLINK, to achieve the goal of variable selection with controlled error rate in deep learning models. We show that DeepLINK can also have high power in variable selection with a broad class of model designs. We then apply DeepLINK to three real datasets and produce statistical inference results with both reproducibility and biological meanings, demonstrating its promising usage to a broad range of scientific applications. Software data have been deposited in GitHub (). Preprocessed data matrices for the four publicly available data sets can be downloaded with the corresponding link: Zeller microbiome ([67][1]), Yu microbiome ([68][2]), murine scRNA-seq ([69][3]), and human scRNA-seq ([70][4]). [1]: #ref-67 [2]: #ref-68 [3]: #ref-69 [4]: #ref-70

Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.

At Snips, we have spent the past year building technologies for assistants, from analyzing your location patterns to extracting relevant stuff from your email and chat messages. But we don't want to be just a technology company. We want to build products on top of our technology, simplifying the way we interact with our devices on a daily basis. Compared to product categories like calendars or photo-sharing apps, assistants are relatively new. As such, designers around the world have been experimenting with various ideas, from bots to voice assistants, proactive suggestions or physical devices.