The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark. It is under consideration for acceptance in TPLP.

The developer of a popular app which transforms users' faces to predict how they will look as older people has insisted they are not accessing users' photographs without permission. FaceApp, which was launched by a Russian developer in 2017, uses artificial intelligence allowing people to see how they would look with different hair colour, eye colour or as a different gender. The app has topped download charts again this week, after users homed in on its ageing filter, which has since been used by dozens of celebrities and prominent figures to picture how they will supposedly look in several decades' time. This surge of interest has in turn created concerns that FaceApp is systematically harvesting users' images. People who upload their image to the app transfer the picture to a server controlled by the developer, with the photograph processing done remotely, rather than on their phone. These concerns have been heightened by growing awareness of online privacy issues in recent years and the fact that the developer is based in Russia, where many high-profile online misinformation campaigns have been based, in addition to a loosely-phrased privacy policy.

TTEC Holdings, Inc. (NASDAQ: TTEC), a leading digital global customer experience (CX) technology and services company focused on the design, implementation and delivery of transformative customer experience, engagement and growth solutions, has recently been recognized by Chief Learning Officer magazine as a 2019 LearningElite Silver Award winner. This robust, peer-reviewed ranking and benchmarking program recognizes those organizations that employ exemplary workforce development strategies that deliver significant business results. Special emphasis was placed this year on how these learning teams are helping their organizations adapt to and prepare for change. Winners were recently announced during the ninth annual LearningElite Awards program at the CLO Symposium conference. "TTEC is honored to be recognized as an elite learning organization and appreciates this award from Chief Learning Officer," said Steve Pollema, Executive Vice President, TTEC Digital.

When Mark Zuckerberg told Congress Facebook would use artificial intelligence to detect fake news posted on the social media site, he wasn't particularly specific about what that meant. Given my own work using image and video analytics, I suggest the company should be careful. Despite some basic potential flaws, AI can be a useful tool for spotting online propaganda – but it can also be startlingly good at creating misleading material. Researchers already know that online fake news spreads much more quickly and more widely than real news. My research has similarly found that online posts with fake medical information get more views, comments and likes than those with accurate medical content.

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.