On Nov. 13, Facebook announced with great fanfare that it was taking down substantially more posts containing hate speech from its platform than ever before. Facebook removed more than seven million instances of hate speech in the third quarter of 2019, the company claimed, an increase of 59% against the previous quarter. More and more of that hate speech (80%) is now being detected not by humans, they added, but automatically, by artificial intelligence. The new statistics, however, conceal a structural problem Facebook is yet to overcome: not all hate speech is treated equally. The algorithms Facebook currently uses to remove hate speech only work in certain languages. That means it has become easier for Facebook to contain the spread of racial or religious hatred online in the primarily developed countries and communities where global languages like English, Spanish and Mandarin dominate.

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model- based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.

We present, FT-SWRL, a fuzzy temporal extension to the Semantic Web Rule Language (SWRL), which combines fuzzy theories based on the valid-time temporal model to provide a standard approach for modeling imprecise temporal domain knowledge in OWL ontologies. The proposal introduces a fuzzy temporal model for the semantic web, which is syntactically defined as a fuzzy temporal SWRL ontology (SWRL-FTO) with a new set of fuzzy temporal SWRL built-ins for defining their semantics. The SWRL-FTO hierarchically defines the necessary linguistic terminologies and variables for the fuzzy temporal model. An example model demonstrating the usefulness of the fuzzy temporal SWRL built-ins to model imprecise temporal information is also represented. Fuzzification process of interval-based temporal logic is further discussed as a reasoning paradigm for our FT-SWRL rules, with the aim of achieving a complete OWL-based fuzzy temporal reasoning. Literature review on fuzzy temporal representation approaches, both within and without the use of ontologies, led to the conclusion that the FT-SWRL model can authoritatively serve as a formal specification for handling imprecise temporal expressions on the semantic web.

Local ancestry inference (LAI) allows identification of the ancestry of all chromosomal segments in admixed individuals, and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and precision medicine to genome-wide association studies. In recent years, many LAI techniques have been developed in both industry and academic research. However, these methods require large training data sets of human genomic sequences from the ancestries of interest. Such reference data sets are usually limited, proprietary, protected by privacy restrictions, or otherwise not accessible to the public. Techniques to generate training samples that resemble real haploid sequences from ancestries of interest can be useful tools in such scenarios, since a generalized model can often be shared, but the unique human sample sequences cannot. In this work we present a class-conditional VAE-GAN to generate new human genomic sequences that can be used to train local ancestry inference (LAI) algorithms. We evaluate the quality of our generated data by comparing the performance of a state-of-the-art LAI method when trained with generated versus real data.

Machine Learning algorithms based on Brain-inspired Hyperdimensional (HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy-efficiency in different machine learning tasks, such as classification, semi-supervised learning and clustering. A weakness of existing HD computing-based ML algorithms is the fact that they have to be binarized for achieving very high energy-efficiency. At the same time, binarized models reach lower classification accuracies. To solve the problem of the trade-off between energy-efficiency and classification accuracy, we propose the QubitHD algorithm. It stochastically binarizes HD-based algorithms, while maintaining comparable classification accuracies to their non-binarized counterparts. The FPGA implementation of QubitHD provides a 65% improvement in terms of energy-efficiency, and a 95% improvement in terms of the training time, as compared to state-of-the-art HD-based ML algorithms. It also outperforms state-of-the-art low-cost classifiers (like Binarized Neural Networks) in terms of speed and energy-efficiency by an order of magnitude during training and inference.

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.

I find these questions very interesting. During some recent study, I wrote a response to a different question'Will having robots around make people more or less lonely? I postulated that we will be lonelier when Robots offer an alternative to Human friendships. Suitable human companions will not be available, to those who seek them, because those potential companions all have chosen robotic friends. We have to factor in our human nature into all AI related questions.

This post is co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services. In an age where low-latency and data security can be the lifeblood of an organization, containers make it possible for enterprises to meet these needs when harnessing artificial intelligence (AI). Since introducing Azure Cognitive Services in containers this time last year, businesses across industries have unlocked new productivity gains and insights. The combination of both the most comprehensive set of domain-specific AI services in the market and containers enables enterprises to apply AI to more scenarios with Azure than with any other major cloud provider. Organizations ranging from healthcare to financial services have transformed their processes and customer experiences as a result.

Human beings take an average of 27 years to mature, leave home, start a separate household, and start having children. In the US, once a household is formed, the average American has 1.9 children. At 1.9 children per household, the US is below the basic sustainable population level of 2.1 children per woman. Despite widespread concern about population growth, we actually have halved our fertility rate (TFR) from 4.5 to 2.4 globally, pushing us under the basic replenishment rate. As humans, we have a long history of continually renegotiating an equilibrium with our environment.