Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up "What if?" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats...

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.

Conversation s contain a wide spectrum of multimodal information that gives us hints about the emotions and moods of the speaker. In this paper, we developed a system that supports humans to analyze conversations. O ur main contribution is the identification of appropriat e multimodal features and the integration of such features into verbatim conversation transcripts . We demonstrate the ability of our system to take in a wide range of multimodal information and automatically generated a prediction score for the depression state of the individual. Our experiments showed that this approach yielded better performance than the baseline model . Furthermore, the multimodal narrative approach makes it easy to integrate learnings from other disciplines, such as conversational analys is and psychology. Lastly, this interdisciplinary and automated approach is a step towards emulating how practitioners record the course of treatment as well as emulating how conversational analysts have been analyzing conversations by hand.

The ingenious use of quantum effects has led to a significant number of quantum machine learning algorithms that offer computational speedups [1, 2]. While awaiting the demonstration of these quantum algorithms on full-fledge quantum computers equipped with quantum error correction, quantum computing has transitioned from theoretical ideas to the noisy intermediate-scale quantum (NISQ) technology era [3]. Hybrid quantum-classical algorithms using short-depth circuits are particularly suitable for implementation on NISQ devices. Many notable experimental demonstrations of NISQ devices employ hybrid algorithms for data classification [4] and quantum chemistry [5]. An ongoing quest is to find interesting applications on quantum computers with increasingly lower noise profile but not reaching a low enough threshold to enable continuous quantum error correction. Here we propose a hybrid quantum-classical algorithm that utilizes dissipative quantum dynamics for temporal information processing on gate-model NISQ quantum processors. Our approach exploits dissipative quantum systems as universal approximators for nonlinear maps with short-term or fading memory, important in a broad class of real-world problems including spoken digit recognition [6], neural modeling [7] and machine learning tasks (e.g., speech processing and natural language processing) [8, 9]. This is a quantum analogue of the universal function approximation property neural networks enjoy [10], but for nonlinear mappings from sequential input to sequential output data [11-13].

After a school shooting in Parkland, Florida left 17 people dead, RealNetworks decided to make its facial recognition technology available for free to schools across the US and Canada. If school officials could detect strangers on their campuses, they might be able to stop shooters before they got to a classroom. Anxious to keep children safe from gun violence, thousands of schools reached out with interest in the technology. Dozens started using SAFR, RealNetworks' facial recognition technology. From working with schools, RealNetworks, the streaming media company, says it's learned an important lesson: Facial recognition isn't likely an effective tool for preventing shootings.

I grew up during the 80s and 90s, the kind of kid who built his own computers late into the night and heard the dial-up modem's tones as my personal anthem. During this time of great promise in mankind's technological potential I remember watching early documentaries on artificial intelligence (AI). My experiences as a software programmer, patent-holding inventor and successful entrepreneur during the past three decades were fueled by this wide-eyed optimism of my youth. I still draw upon that feeling daily, but more than ever we need to see past it if we are to create the best possible future for AI, a technology I believe that will transform the world as we know it. As more people are connected online, it is more obvious than ever that such blind optimism is as anachronistic as a five and a quarter inch floppy.

January 25, 2020 - The AI revolution is upon us. How much is Croatia lagging behind, and are we going to do something about it? But even if we start those processes, where would we be in comparison to the rest of the world? What are other countries already doing and what should we be aware of? Fortunately, a fear of missing out is spreading around the globe or at least among some countries.

Massive bushfires have destroyed millions of acres in Australia over the past few months. RAND's Melissa Finucane, a community resilience expert who grew up in a remote region of New South Wales, has watched in anguish. Experiences from previous disasters have highlighted concrete steps that can help communities start to recover right away, she says. She also notes that rural Australians have "a special kind of resilience," with perspectives and wisdom from years of hard experience. But still, measuring the effects that fires and other disasters have on people's mental health, social, and economic needs remains a unique challenge.

The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson 1,, Robert Salomone 2,, and Fred Roosta 3,, † 1 Department of Statistics, UC Berkeley, Berkeley, CA, 94720, USA. Abstract: Stein importance sampling [42] is a widely applicable technique based on kernelized Stein discrepancy [43], which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples are obtained via the simulation of a Markov chain, and applies to an arbitrary underlying Polish space. We prove that Stein importance sampling yields consistent estimators for quantities related to a target distribution of interest by using samples obtained from a geometrically ergodic Markov chain with a possibly unknown invariant measure that differs from the desired target. The approach is shown to be valid under conditions that are satisfied for a large number of unadjusted samplers, and is capable of retaining consistency when data subsampling is used. Along the way, a universal theory of reproducing Stein kernels is established, which enables the construction of kernelized Stein discrepancy on general Polish spaces, and provides sufficient conditions for kernels to be convergence-determining on such spaces. These results are of independent interest for the development of future methodology based on kernelized Stein discrepancies. 1. Introduction Our problem of interest is the efficient computation of integrals with respect to some target probability measure π . Adopting the Monte Carlo approach, π is approximated by an empirical distribution formed from samples drawn according to π . However, in many problems of interest, it is not possible to simulate according to π exactly, and so further approximate methods must be used. Arguably the most widely employed and general approach is Markov Chain Monte Carlo (MCMC); successively drawing samples as a realization of a Markov chain. The dominant approach to MCMC involves the simulation of a process that is π -ergodic, often constructed by the Metropolis-Hastings algorithm from an underlying irreducible and aperiodic Markov chain [58]. However, there has been significant recent interest in so-called unadjusted MCMC approaches [14, 19, 29, 45]. A common strategy with these methods is the approximate numer-All authors are supported in part by the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), under Australian Research Council grant CE140100049. For the same computational effort, one can achieve substantially lower variance of estimates at the cost of incurring additional (asymptotic) bias. Despite poorer asymptotic guarantees [21], the ensuing Markov chains are often rapidly mixing, and perform particularly well in high dimensional settings [20].