AUTOMAKERS, while focused on building self-driving cars, are also keen on using technology to improve existing processes and functions. Porsche is one such company. Although a global brand, the company employs just 30,000 employees -- a fraction of the workforce in comparison to many of its competitors. However, its size is also an advantage. It allows the company to innovate more intimately.

Back in the day, the idea of interacting with robots and computers seemed out of an episode of The Jetsons. But fast-forward to today, and some of the most far-fetched ideas back then have become our reality, and you know what? In Back to The Future II, they used tablets to get Marty to sign up to save the Clock Tower, and then this massive shark pops out of a sign, which freaked 1984 Marty out. The only thing is – do you remember how pixelated the shark was? Magic Leap is augmenting reality to look like a whale can literally crash into a gymnasium, without so much as a drop of water.

Facial recognition technology has spread prodigiously. Google, Microsoft, Apple and others have built it into apps to compile albums of people who hang out together. It verifies who you are at airports and is the latest biometric to unlock your mobile phone where facial recognition apps abound. Need to confirm your identity for a £1,000 bank transfer? Just look into the camera.

A trio of teens took the top awards at the Fortnite World Cup this weekend in New York. At the biggest "Fortnite" tournament to date, with $30 million in prizes awarded, Kyle "Bugha" Giersdorf, 16, won $3 million Sunday by beating 99 others for the solo title. Those 100 made the cut from the 40 million players globally who initially entered the competition. The previous day, Emil "Nyhrox" Bergquist Pedersen, 16, of Norway and Thomas "Aqua" Arnould, 17, of Austria won $1.5 million each when they were crowned the top duo. Need more evidence that esports is big business?

We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment. We performed a user-study with nine participants across different expertise levels to examine their perception of our workflow and to collect suggestions to fill the gap between our system and framework. The evaluation confirms that our tightly integrated system leads to an informed machine learning process while disclosing opportunities for further extensions.

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing.

M. E. J. Newman, 1, 2 George T. Cantwell, 1 and Jean Gabriel Young 2 1 Department of Physics, University of Michigan, Ann Arbor, Michigan, USA 2 Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of objects into groups. In the context of classification algorithms, for instance, it is often used to compare discovered classes to known ground truth and hence to quantify algorithm performance. Here we argue that the standard mutual information, as commonly defined, omits a crucial term which can become large under real-world conditions, producing results that can be substantially in error. We demonstrate how to correct this error and define a mutual information that works in all cases. We discuss practical implementation of the new measure and give some example applications. I. INTRODUCTION Mutual information is widely used in physics, statistics, and machine learning as a tool for comparing different labelings of a set of objects [1]. For instance, within physics it is used in statistical mechanics for comparing states of spin models [2] and particularly in network science for comparing partitions of networks into communities, mutual information being perhaps the standard measure for quantifying the performance of community detection algorithms [3, 4]: it tells us the extent to which the set of communities found by an algorithm agree with a given set of ground-truth communities. In machine learning and statistics, mutual information is similarly used in classification problems to quantify the similarity of different labelings of sets of objects [5]. For instance, we might attempt to deduce characteristics of a set of users of an online service, such as their age group or gender, and then calibrate our algorithm by using mutual information to compare our results against known characteristics of a test set of users. Imagine then that we have some set of individuals or objects, such as people, documents, email messages, or aerial photographs, among many other possibilities. Each object can be classified or labeled as belonging to one of several types, groups, or communities. People could be labeled by sex, race, or blood type for instance; documents by topic; aerial photographs by type of terrain, and so forth. Now imagine we have two different sets of labels for our objects, one inferred by some algorithm and the other assigned for instance by human experts. The mutual information of the two labelings represents the amount of information that the first labeling gives us about the second--in effect, how good the algorithm is at mimicking the human experts.

With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.

Logistic regression (LR) is widely used in clinical prediction because it is simple to deploy and easy to interpret. Nevertheless, being a linear model, LR has limited expressive capability and often has unsatisfactory performance. Generalized additive models (GAMs) extend the linear model with transformations of input features, though feature interaction is not allowed for all GAM variants. In this paper, we propose a factored generalized additive model (F-GAM) to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual. We evaluate F-GAM on prediction of two targets, postoperative acute kidney injury and acute respiratory failure, from a single-center database. We find superior model performance of F-GAM in terms of AUPRC and AUROC compared to several other GAM implementations, random forests, support vector machine, and a deep neural network. We find that the model interpretability is good with results with high face validity.