This paper also summarizes the main strategies our players have developed in our This paper presents iNNK, a multiplayer drawing game where human playtesting. Certainly, a lot more effort is needed to empower citizens players team up against an NN. The players need to successfully to be more familiar with AI and to engage the technology communicate a secret code word to each other through drawings, critically. Through our game, we have seen evidence that playful without being deciphered by the NN. With this game, we aim experience can turn people from passive users into creative and reflective to foster a playful environment where players can, in a small way, thinkers, a crucial step towards a more mature relationship go from passive consumers of NN applications to creative thinkers with AI. and critical challengers.

The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information. We apply a reinforcement learning framework which emulates this format by providing explanations based on the explainee's current mental model. We conduct novel online human experiments where explanations generated by various explanation methods are selected and presented to participants, using policies which observe participants' mental models, in order to optimize an interpretability proxy. Our results suggest that mental model-based policies (anchored in our proposed state representation) may increase interpretability over multiple sequential explanations, when compared to a random selection baseline. This work provides insight into how to select explanations which increase relevant information for users, and into conducting human-grounded experimentation to understand interpretability.

Many social services including online dating, social media, recruitment and online learning, largely rely on \matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

Through superposition, a quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning is a subfield of quantum computing that explores the potential of quantum computing to enhance machine learning algorithms. An approach of quantum machine learning named quantum ensembles of quantum classifiers consists of using superposition to build an exponentially large ensemble of classifiers to be trained with an optimization-free learning algorithm. In this work, we investigate how the quantum ensemble works with the addition of an optimization method. Experiments using benchmark datasets show the improvements obtained with the addition of the optimization step.

Fast Fourier transform, Wavelets, and other well-known transforms in signal processing have a structured representation as a product of sparse matrices which are referred to as butterfly structures. Research in the recent past have used such structured linear networks along with randomness as pre-conditioners to improve the computational performance of large scale linear algebraic operations. With the advent of deep learning and AI and the computational efficiency of such structured matrices, it is natural to study sparse linear deep networks in which the location of the non-zero weights are predetermined by the butterfly structure. This work studies, both theoretically and empirically, the feasibility of training such networks in different scenarios. Unlike convolutional neural networks, which are structured sparse networks designed to recognize local patterns in lattices representing a spatial or a temporal structure, the butterfly architecture used in this work can replace any dense linear operator with a gadget consisting of a sequence of logarithmically (in the network width) many sparse layers, containing a total of near linear number of weights. This improves on the quadratic number of weights required in a standard dense layer, with little compromise in expressibility of the resulting operator. We show in a collection of empirical experiments that our proposed architecture not only produces results that match and often outperform existing known architectures, but it also offers faster training and prediction in deployment. This empirical phenomenon is observed in a wide variety of experiments that we report, including both supervised prediction on NLP and vision data, as well as in unsupervised representation learning using autoencoders. Preliminary theoretical results presented in the paper explain why training speed and outcome are not compromised by our proposed approach.

Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient indexing and search. It has recently been shown that variational autoencoders, with Bernoulli latent representations parametrized by neural nets, can be successfully trained to learn such codes in supervised and unsupervised scenarios, improving on more traditional methods thanks to their ability to handle the binary constraints architecturally. However, the scenario where labels are scarce has not been studied yet. This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. The first augments the variational autoencoder's training objective to jointly model the distribution over the data and the class labels. The second approach exploits the annotations to define an additional pairwise loss that enforces consistency between the similarity in the code (Hamming) space and the similarity in the label space. Our experiments show that both methods can significantly increase the hash codes' quality. The pairwise approach can exhibit an advantage when the number of labelled points is large. However, we found that this method degrades quickly and loses its advantage when labelled samples decrease. To circumvent this problem, we propose a novel supervision method in which the model uses its label distribution predictions to implement the pairwise objective. Compared to the best baseline, this procedure yields similar performance in fully supervised settings but improves the results significantly when labelled data is scarce. Our code is made publicly available at https://github.com/amacaluso/SSB-VAE.

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

In this paper, we propose a novel loss function for training Generative Adversarial Networks (GANs) aiming towards deeper theoretical understanding as well as improved performance for the underlying optimization problem. The new loss function is based on cumulant generating functions giving rise to \emph{Cumulant GAN}. Relying on a recently-derived variational formula, we show that the corresponding optimization problem is equivalent to R{\'e}nyi divergence minimization, thus offering a (partially) unified perspective of GAN losses: the R{\'e}nyi family encompasses Kullback-Leibler divergence (KLD), reverse KLD, Hellinger distance and $\chi^2$-divergence. Wasserstein GAN is also a member of the proposed cumulant GAN. In terms of stability, we rigorously prove the exponential convergence of cumulant GAN to the Nash equilibrium for a linear discriminator, Gaussian distributions and the standard gradient descent algorithm. Finally, we experimentally demonstrate that image generation is generally more robust relative to Wasserstein GAN and it is substantially improved in terms of inception score when weaker discriminators are considered.

Guavus, a pioneer in AI-based analytics for communications service providers (CSPs), announced the launch of Guavus-IQ -- a comprehensive product portfolio that provides a unique multi-perspective analytics experience for CSPs. Guavus-IQ delivers highly instrumented analytics insights to CSPs on how each subscriber is experiencing their network and services (bringing the outside perspective in) and how their network is impacting their subscribers (understanding how their internal operations are impacting their customers). This single, real-time "outside-in/inside-out" perspective helps operators identify subscriber behavioral patterns and better understand their operational environments. This enables them to increase revenue opportunities through data monetization and improved customer experience (CX), as well as reduce costs through automated, closed-loop actions. In addition, Guavus-IQ has been designed to be'operator-friendly' for CSPs -- it doesn't require the operator to be a data science specialist or expert.

Facial recognition technology is employed for various purposes, whether for biometric identification on the airports, or on the public CCTV cameras. Many smartphones now have this technology for unlocking their system. On states level, many countries use facial recognition technology for mass surveillance also. However, despite it being used in 98 countries of the world, there still are some that do not approve of it, and some countries have even banned it. In the US, 59% of the citizens believe that facial recognition technology should be implemented, especially for law enforcement.