The ominous spread of coronavirus has bolstered the case for such advances as telemedicine; drones; artificial intelligence/machine learning; Big Data; and more flexible regulation of health care personnel and institutions. During a social conversation via FaceTime, her grandson, a physician, realized Mom was in the early stages of septic shock. A day's delay in treatment might have proven fatal. Similar tales emerge from professional telemedicine doctors. The advantages of telemedicine for, say, a migrant worker family on a remote ranch whose child becomes ill in the wee hours.

We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains. The core of our method is an adversarial autoencoder that can: (1) learn to produce domain-invariant embeddings to reduce the difference between domains; (2) learn the data distribution for each domain and correctly perform data imputation on missing data. For MDL, we use the Maximum Mean Discrepancy (MMD) measure to align the domain distributions. For DI, we use an adversarial approach where a generator fill in information for missing data and a discriminator tries to distinguish between real and imputed values. Finally, using the universal feature representation in the embeddings, we train a classifier using MTL that given input from any domain, can predict labels for all domains. We demonstrate the superior performance of our approach compared to other state-of-art methods in three distinct settings, DG-DI in image recognition with unstructured data, MTL-DI in grade estimation with structured data and MDMTL-DI in a selection process using mixed data.

In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with an MTL component so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using real-world data from different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

Machine-learning (ML) models are increasingly used today in making decisions that affect real people's lives, and, because of that, there is a huge need to ensure that the models and their decisions are interpretable by their human users. Motivated by this need, there has bee a lot of interest recently in the ML community in studying Interpretable models [18]. There is currently no consensus on what interpretability means, and no benchmarks for evaluating interpretability [5, 10]. The only consensus is that simpler models such as linear regression or decision trees are considered more interpretable than complex models like, say, deep neural nets. However, two general principles for approaching interpretability have emerged in the literature that are relevant to our paper.

"The funny thing about life is that it's temporary; that is to say, temporary in the'temporal' sense of the word, meaning that all living things and all that we do are subject to the precepts and effects of time." Many organisms perform best at certain hours of the day. The slug species Arion subfuscus, living in almost total darkness, knowing nothing about the Gregorian calendar, lays its eggs between the last week of August and the first week of September.1 Bees forage for nectar, knowing the best times to visit the best fields and the exact timing of nectar secretions for individual species of flowers. In the mid-20th century, the Austrian Nobel laureate Karl von Frisch provided enormous insights on honeybee communication and foraging time. He discovered that bees have internal clocks that tell them not only where the nectar is to be found but also precisely when that food will be ready. "I know of no other living creature," he wrote in his book on bee language, "that learns so easily as the bee when, according to its'internal clock,' to come to the table."2 Even without a light clue, the plants were able to tell time.

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related.

Machine learning applications in the legal field are numerous and diverse. In order to make contribution to both the machine learning community and the legal community, we have made efforts to create a model compatible with the classification of text sequences, valuing the interpretability of the results. The purpose of this paper is to classify legal proceedings in three possible status classes, which are (i) archived proceedings, (ii) active proceedings and (iii) suspended proceedings. Our approach is composed by natural language processing, supervised and unsupervised deep learning models and performed remarkably well in the classification task. Furthermore we had some insights regarding the patterns learned by the neural network applying tools to make the results more interpretable.

Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' preferences. Personalized RS are successfully applied in platforms such as Netflix, Amazon or YouTube. However, they are missing in gastronomic platforms such as TripAdvisor, where moreover we can find millions of images tagged with users' tastes. This paper explores the potential of using those images as sources of information for modeling users' tastes and proposes an image-based classification system to obtain personalized recommendations, using a convolutional autoencoder as feature extractor. The proposed architecture will be applied to TripAdvisor data, using users' reviews that can be defined as a triad composed by a user, a restaurant, and an image of it taken by the user. Since the dataset is highly unbalanced, the use of data augmentation on the minority class is also considered in the experimentation. Results on data from three cities of different sizes (Santiago de Compostela, Barcelona and New York) demonstrate the effectiveness of using a convolutional autoencoder as feature extractor, instead of the standard deep features computed with convolutional neural networks.

The European Parliament's proposal to create a new legal status for artificial intelligence (AI) and robots brought into focus the idea of electronic legal personhood. This discussion, however, is hugely controversial. While some scholars argue that the proposed status could contribute to the coherence of the legal system, others say that it is neither beneficial nor desirable. Notwithstanding this prospect, we conducted a survey (N=3315) to understand online users' perceptions of the legal personhood of AI and robots. We observed how the participants assigned responsibility, awareness, and punishment to AI, robots, humans, and various entities that could be held liable under existing doctrines. We also asked whether the participants thought that punishing electronic agents fulfills the same legal and social functions as human punishment. The results suggest that even though people do not assign any mental state to electronic agents and are not willing to grant AI and robots physical independence or assets, which are the prerequisites of criminal or civil liability, they do consider them responsible for their actions and worthy of punishment. The participants also did not think that punishment or liability of these entities would achieve the primary functions of punishment, leading to what we define as the punishment gap. Therefore, before we recognize electronic legal personhood, we must first discuss proper methods of satisfying the general population's demand for punishment.

Artificial intelligence (AI) will change the way we learn and work in the near future. Nearly 400 million workers globally will change their occupations in the next 10 years, and business schools are uniquely situated to respond to the shifts coming to the future of work. However, a recent study, "Implications of Artificial Intelligence on Business Schools and Lifelong Learning," shows that business schools remain cautious in adapting management education to address the changing needs of students, workers and organizations, writes Anne Trumbore in this opinion piece. Trumbore, one of the study's coauthors, is senior director of Wharton Online, a strategic digital learning initiative at the Wharton School of the University of Pennsylvania. In the past few weeks, COVID 19 has moved hundreds of millions of students around the globe from physical to online classes.