Thiel's criticism appears to refer to Google's 2018 decision not to renew its contract with the Department of Defense, which allowed the agency to review drone footage with the company's artificial intelligence tools. The same year, Google faced backlash for working on "Dragonfly," a project to create a censored search engine for China. However, in December, CEO Sundar Pichai announced there were no plans to launch it.

In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the majority of existing systems have exploited either only flat side information or only hierarchical side information due to the challenges brought by the heterogeneity. In this paper, we investigate the problem of exploiting heterogeneous side information for recommendations. Specifically, we propose a novel framework jointly captures flat and hierarchical side information with mathematical coherence. We demonstrate the effectiveness of the proposed framework via extensive experiments on various real-world datasets. Empirical results show that our approach is able to lead a significant performance gain over the state-of-the-art methods.

The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets, termed Pascal-C, Coco-C and Cityscapes-C, contain a large variety of image corruptions. We show that a range of standard object detection models suffer a severe performance loss on corrupted images (down to 30-60% of the original performance). However, a simple data augmentation trick - stylizing the training images - leads to a substantial increase in robustness across corruption type, severity and dataset. We envision our comprehensive benchmark to track future progress towards building robust object detection models. Benchmark, code and data are available at: http://github.com/bethgelab/robust-detection-benchmark

We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to be obscure, long-tail artists and generally have little or no available user preference data associated with them. This creates a cold-start problem for collaborative filtering-based recommendation algorithms that depend on large amounts of such information to make accurate recommendations. In this paper, we compare the performance of three standard recommender system algorithms (Item-Item Neighborhood (IIN), Alternating Least Squares for Implicit Feedback (ALS), and Bayesian Personalized Ranking (BPR)) on the task of local music recommendation using the Million Playlist Dataset. To do this, we modify the standard evaluation procedure such that the algorithms only rank tracks by local artists for each of the eight different cities. Despite the fact that techniques based on matrix factorization (ALS, BPR) typically perform best on large recommendation tasks, we find that the neighborhood-based approach (IIN) performs best for long-tail local music recommendation.

Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learning methods are beginning to get applied to recommender systems. Current survey papers on deep learning in recommender systems provide a historical overview and taxonomy of recommender systems based on type. Our paper addresses the gaps of providing a taxonomy of deep learning approaches to address recommender systems problems in the areas of cold start and candidate generation in recommender systems. We outline different challenges in recommender systems into those related to the recommendations themselves (include relevance, speed, accuracy and scalability), those related to the nature of the data (cold start problem, imbalance and sparsity) and candidate generation. We then provide a taxonomy of deep learning techniques to address these challenges. Deep learning techniques are mapped to the different challenges in recommender systems providing an overview of how deep learning techniques can be used to address them. We contribute a taxonomy of deep learning techniques to address the cold start and candidate generation problems in recommender systems. Cold Start is addressed through additional features (for audio, images, text) and by learning hidden user and item representations. Candidate generation has been addressed by separate networks, RNNs, autoencoders and hybrid methods. We also summarize the advantages and limitations of these techniques while outlining areas for future research.

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"While my company and the museum have distinct missions, both are important contributors to our society," said Whitney Museum of American Art vice chairman Warren B. Kanders. This statement, salvaged from a letter leaked by ARTnews in December, sets the tone as the opening visual for Forensic Architecture's installation at the Whitney Biennial--a 15-minute video delivering the collective's most recent foray into artificial intelligence, titled Triple Chaser. The London-based architecture and science research group chose to respond to the Kanders tear gas and munitions scandal not with a withdrawal from the biennial, but with the creation of a work of art-as-social justice tool, a submission that infiltrates the subject of derision's own institution. Their video, created in collaboration with director Laura Poitras and Praxis Films, is narrated by David Byrne cooly explaining how FA approached the training of a computer program to track and recognize images of "Triple Chaser" tear gas canisters and subsequently reduce the amount of human labor needed to do so. The program is trained to recognize the canisters, so named for the way they break into three distinct pieces after being fired, and not become used to identifying just the degraded landscapes they usually occur in.

David Harbour, best known for playing supernaturally beleaguered small-town cop Jim Hopper, is now on to even stranger things. In the mockumentary Frankenstein's Monster's Monster, Frankenstein, he plays a puffed-up version of himself, investigating the life of his father, David Harbour Junior, after he unearths footage of his dad's televised stage play while killing rats in his mother's attic. What Harbour discovers--the bizarre artifact that is the play Frankenstein's Monster's Monster, Frankenstein and lots more scandal besides--challenges everything he thinks he knows about his father in just about 30 minutes. Stranger Things' Noah Schnapp Has a Crush on Zendaya What Stranger Things' High Viewership Numbers Actually Mean What he finds is also bizarrely funny. In real life, Harbour's dad's name is Ken and he's in real estate.

Abstract: In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline, such as disk I/O and data preprocessing, do not run on accelerators. As accelerators continue to improve, these earlier stages will increasingly become the bottleneck. In this paper, we introduce "data echoing," which reduces the total computation used by earlier pipeline stages and speeds up training whenever computation upstream from accelerators dominates the training time. Data echoing reuses (or "echoes") intermediate outputs from earlier pipeline stages in order to reclaim idle capacity.