One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents. Previously, researchers often relied on ad-hoc lab environments. There have been recent advances in virtual systems built with 3D physics engines and photo-realistic rendering for indoor and outdoor environments, but the embodied agents in those systems can only conduct simple interactions with the world (e.g., walking around, moving objects, etc.). Most of the existing systems also do not allow human participation in their simulated environments. In this work, we design and implement a virtual reality (VR) system, VRKitchen, with integrated functions which i) enable embodied agents powered by modern AI methods (e.g., planning, reinforcement learning, etc.) to perform complex tasks involving a wide range of fine-grained object manipulations in a realistic environment, and ii) allow human teachers to perform demonstrations to train agents (i.e., learning from demonstration). We also provide standardized evaluation benchmarks and data collection tools to facilitate a broad use in research on task-oriented learning and beyond.

In real-world machine learning applications, there is a cost associated with sampling of different features. Budgeted learning can be used to select which feature-values to acquire from each instance in a dataset, such that the best model is induced under a given constraint. However, this approach is not possible in the domain of online learning since one may not retroactively acquire feature-values from past instances. In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream. In this paper we introduce the issue of online budgeted learning and describe a general framework for addressing this challenge. We propose two types of feature value acquisition policies based on the multi-armed bandit problem: random and adaptive. Adaptive policies perform online adjustments according to new information coming from a data stream, while random policies are not sensitive to the information that arrives from the data stream. Our comparative study on five real-world datasets indicates that adaptive policies outperform random policies for most budget limitations and datasets. Furthermore, we found that in some cases adaptive policies achieve near-optimal results.

Many efforts have been put to use automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to picture comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. In this paper, we formally define and analyse the NLP model adaptation problem, particularly in phenotype identification tasks, and identify two types of common unnecessary or wasted efforts: duplicate waste and imbalance waste. A distributed representation approach is proposed to represent familiar language patterns for an NLP model by learning phenotype embeddings from its training data. Computations on these language patterns are then introduced to help avoid or reduce unnecessary efforts by combining both geometric and semantic similarities. To evaluate the approach, we cross validate NLP models developed for six physical morbidity studies (23 phenotypes; 17 million documents) on anonymised medical records of South London Maudsley NHS Trust, United Kingdom. Two metrics are introduced to quantify the reductions for both duplicate and imbalance wastes. We conducted various experiments on reusing NLP models in four phenotype identification tasks. Our approach can choose a best model for a given new task, which can identify up to 76% mentions needing no validation & model retraining, meanwhile, having very good performances (93-97% accuracy). It can also provide guidance for validating and retraining the model for novel language patterns in new tasks, which can help save around 80% of the efforts required in blind model-adaptation approaches.

Deep learning techniques are rapidly advanced recently, and becoming a necessity component for widespread systems. However, the inference process of deep learning is black-box, and not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we develop a simple analysis method which consists of 1) structural feature analysis: lists of the features contributing to inference process, 2) linguistic feature analysis: lists of the natural language labels describing the visual attributes for each feature contributing to inference process, and 3) consistency analysis: measuring consistency among input data, inference (label), and the result of our structural and linguistic feature analysis. Our analysis is simplified to reflect the actual inference process for high transparency, whereas it does not include any additional black-box mechanisms such as LSTM for highly human readable results. We conduct experiments and discuss the results of our analysis qualitatively and quantitatively, and come to believe that our work improves the transparency of neural networks. Evaluated through 12,800 human tasks, 75% workers answer that input data and result of our feature analysis are consistent, and 70% workers answer that inference (label) and result of our feature analysis are consistent. In addition to the evaluation of the proposed analysis, we find that our analysis also provide suggestions, or possible next actions such as expanding neural network complexity or collecting training data to improve a neural network.

Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the `persona' information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.

The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio-temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring the spatial properties in local or global scope from spatio-temporal graphs simultaneously. To tackle this problem, we design a novel multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), for graph-structured time series modeling. In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi-resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST-Unpool) restores the original structure of spatio-temporal graphs and resumes regular intervals within graph sequences. Experiments on spatio-temporal prediction tasks demonstrate that our model effectively captures comprehensive features in multiple scales and achieves substantial improvements over mainstream methods on several real-world datasets.

We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledge graphs, we obtain characterizations of which relations they are able to capture, as well as bounds on the minimal dimensionality and precision needed.

'Empathy robot' Reeti, made in France for use in health care. In a new book, Eric Topol wants to see medics themselves freed to provide compassion to patients.Credit: BSIP/UIG via Getty So much has been written about artificial intelligence (AI) that any new book on it can struggle to create a signal amid the noise. There are volumes hyping AI as the fourth industrial revolution, others decrying it as the greatest threat to modern society and many calling for AI to become less artificial and more intelligent. Now, Eric Topol, a cardiologist and director of the Scripps Research Translational Institute in La Jolla, California, adds his voice. Deep Medicine summarizes hype and threat, then takes us to a place where no one else has gone: a future in which AI helps to re-establish empathy and trust between doctors and patients.

The human brain has advanced over time in responding to survival instincts, harnessing intellectual curiosity, and managing demands of nature. When humans got an inkling about the dynamics of the environment, we began our quest to replicate nature. Artificial intelligence (AI) involves the development of computer systems able to perform activities that normally enlist human intelligence. Such tasks include visual perception, speech recognition and decision-making. AI systems share common attributes: the ability to ingest data; the ability to adapt and react to data in their environment; and the ability to project multiple steps into the future. Machine learning, a certain application of AI, enables computers to use algorithms give computers access to data to teach themselves.

Participants pass through an illuminated walk at the 31st annual Chaos Communication Congress. We'll be interacting with machines for the rest of our lives, as tech futurist and New York University professor Amy Webb told us recently. But who are those machines built by? And how does that affect their function? A recent study by the Georgia Institute of Technology revealed that if you're a person with dark skin, you may "be more likely than your white friends to get hit by a self-driving car," according to Vox.