We observe a rapid increase in machine learning models for learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair prediction outcomes. This is indeed a positive proliferation. All available models however learn latent embeddings, therefore the produced representations do not have the semantic meaning of the input. Our aim here is to learn fair representations that are directly interpretable in the original input domain. We cast this problem as a data-to-data translation; to learn a mapping from data in a source domain to a target domain such that data in the target domain enforces fairness definitions, such as statistical parity or equality of opportunity. Unavailability of fair data in the target domain is the crux of the problem. This paper provides the first approach to learn a highly unconstrained mapping from source to target by maximizing (conditional) dependence of residuals - the difference between data and its translated version - and protected characteristics. The usage of residual statistics ensures that our generated fair data should only be an adjustment of the input data, and this adjustment should reveal the main difference between protected characteristic groups. When applied to CelebA face image dataset with gender as protected characteristic, our model enforces equality of opportunity by adjusting eyes and lips regions. In Adult income dataset, also with gender as protected characteristic, our model achieves equality of opportunity by, among others, obfuscating wife and husband relationship. Visualizing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and the prediction performance.

In classification problems, the goal is to decide the class membership of a set of observations, by using available information on features and class membership of a training data set. Decision trees are one of the most popular models for solving this problem, due to their effectiveness and high interpretability. In this work, we focus on constructing univariate binary decision trees of prespecified depth. In a univariate binary decision tree, each internal node contains a test regarding the value of one single feature of the data set, while the leaves contain the target classes. The problem of constructing (learning) a classification tree (CTCP), is the problem of finding a set of optimal tests (decision checks), such that the assignment of target classes to rows satisfies a certain criteria. A commonly encountered objective is accuracy, measured as the number of correct predictions in a training set. As the problem of learning optimal decision trees is an NPcomplete problem (Hyafil and Rivest 1976), heuristics such as CART (Breiman et al. 1984) and ID3 (Quinlan 1986) are widely used. These greedy algorithms build a tree recursively, starting from a single node.

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.

The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment. However, artificial multisensory perception remains an open issue especially in situations of sensory uncertainty and conflicts. In this work, we extend previous studies on audio-visual (AV) conflict resolution in complex environments. In particular, we focus on quantitatively assessing the contribution of semantic congruency during an AV spatial localization task. In addition to conflicts in the spatial domain (i.e. spatially misaligned stimuli), we consider gender-specific conflicts with male and female avatars. Our results suggest that while semantically related stimuli affect the magnitude of the visual bias (perceptually shifting the location of the sound towards a semantically congruent visual cue), humans still strongly rely on environmental statistics to solve AV conflicts. Together with previously reported results, this work contributes to a better understanding of how multisensory integration and conflict resolution can be modelled in artificial agents and robots operating in real-world environments.

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the legal small and large test dataset.

Future warfare will likely be conducted by armies of robots and humans enhanced by gene editing and drugs, according to a new Ministry of Defence report. As the world becomes more volatile due to increased threats from terrorism and climate change, "new areas of conflict" will also open up, including space and cyberspace, it is thought. In an analysis developed with experts from around the world, the potential challenges facing the UK are laid out. The document, entitled The Future Starts Today, also warns of an increasing risk from nuclear and chemical weapons as technology rapidly advances. 'Killer robots' ban blocked by US and Russia at UN meeting'Killer robots' ban blocked by US and Russia at UN meeting "This report makes clear that we are living in a world that is becoming rapidly more dangerous, with intensifying challenges from state aggressors who flout the rules, terrorists who want to harm our way of life and the technological race with our adversaries," said defence secretary Gavin Williamson.

Work is underway to deploy Corti, an AI system that detects heart attacks during emergency phone calls, and it could be coming to some of the biggest cities in Europe. Following plans announced earlier this year to roll Corti out in more cities, this summer the European Emergency Number Association (EENA), whose members include cities like London, Paris, Milan, and Munich, will deliver AI-powered assistance to emergency 112 operators. In initial trials, this assistance was found to identify cardiac arrest events more quickly than human operators. Emergency call centers from Seattle to Singapore also want to make Corti part of their operations, but there's no global standard for organizations working to save lives. Some are fine with the idea of deploying the AI through the cloud, while others with privacy concerns require the AI system to operate from on-premise servers.

If you're attending the All Things Open conference in Raleigh, NC this year be sure to check out our Lightning Talk series on Tuesday, October 23. This is an amazing line-up of quick talks you won't want to miss. Speakers have five minutes to enlighten the audience about an open source topic they are passionate about. We've got everything from containers to AI and Itseo to Blockchain, Raspberry Pi and more. Grab your lunch, find a seat, warm up your Twitter fingers, and get ready for the fastest hour at All Things Open.

Europe's new privacy rules risk becoming outdated less than five months after being put in place, the chairman of Nokia warned today as he urged policymakers to update legislation as more companies invest in artificial intelligence (AI) technology. Speaking at an AI event in Finland, Risto Siilasmaa warned that the EU's General Data Protection Regulation (GDPR) was "largely designed before anyone in Brussels had heard the term machine learning". "We need to regularly update the rules and make sure they are cutting-edge and respond to these new needs," he said. "I was talking to a large number of commissioners and director generals about machine learning last autumn, and I gave them a lesson in...

The late physicist Stephen Hawking's last writings predict that a breed of superhumans will take over, having used genetic engineering to surpass their fellow beings. In Brief Answers to the Big Questions, to be published on Oct. 16 and excerpted today in the UK's Sunday Times (paywall), Hawking pulls no punches on subjects like machines taking over, the biggest threat to earth, and the possibilities of intelligent life in space. Hawking delivers a grave warning on the importance of regulating AI, noting that "in the future AI could develop a will of its own, a will that is in conflict with ours." A possible arms race over autonomous-weapons should be stopped before it can start, he writes, asking what would happen if a crash similar to the 2010 stock market Flash Crash happened with weapons. In short, the advent of super-intelligent AI would be either the best or the worst thing ever to happen to humanity.