Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the childrens affective states and engagement during robot-assisted autism therapy. The key to our approach is a novel shift from the traditional ML paradigm - instead of using 'one-size-fits-all' ML models, our personalized ML framework is optimized for each child by leveraging relevant contextual information (demographics and behavioral assessment scores) and individual characteristics of each child. We designed and evaluated this framework using a dataset of multi-modal audio, video and autonomic physiology data of 35 children with autism (age 3-13) and from 2 cultures (Asia and Europe), participating in a 25-minute child-robot interaction (~500k datapoints). Our experiments confirm the feasibility of the robot perception of affect and engagement, showing clear improvements due to the model personalization. The proposed approach has potential to improve existing therapies for autism by offering more efficient monitoring and summarization of the therapy progress.

Although there are many approaches to implement intrinsically motivated artificial agents, the combined usage of multiple intrinsic drives remains still a relatively unexplored research area. Specifically, we hypothesize that a mechanism capable of quantifying and controlling the evolution of the information flow between the agent and the environment could be the fundamental component for implementing a higher degree of autonomy into artificial intelligent agents. This paper propose a unified strategy for implementing two semantically orthogonal intrinsic motivations: curiosity and empowerment. Curiosity reward informs the agent about the relevance of a recent agent action, whereas empowerment is implemented as the opposite information flow from the agent to the environment that quantifies the agent's potential of controlling its own future. We show that an additional homeostatic drive is derived from the curiosity reward, which generalizes and enhances the information gain of a classical curious/heterostatic reinforcement learning agent. We show how a shared internal model by curiosity and empowerment facilitates a more efficient training of the empowerment function. Finally, we discuss future directions for further leveraging the interplay between these two intrinsic rewards.

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats Sub-Challenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single subsystem in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.

Not a week goes by that I don't see dozens of headlines blaming "AI" or "deep learning" for yet another ethical conundrum that will doom human society. Whether it is predictive policing or facial recognition or autonomous weapons, it seems nearly every facet of society is facing an "AI revolution" that will destroy humankind. If one peels back the breathless hype and viral buzzwords, however, is it really AI that we are worried about or is it the shift towards a data-centric society with or without deep learning advances? To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data. From a technical standpoint, however, these are two entirely distinct concepts.

The applications of image analytics are endless. Organizations are starting to realize the possibilities of how to extract value from unstructured data, such as images or video footage, to create a new and enticing customer experience within retail, entertainment, transportation and airport security, insurance claims, and more. Here are five image analytics applications that are unexpected, disruptive, and creative. On June 26th, I'll be talking about image analytics use cases at the Boston Area SAS Users Group in my Image Processing: Seeing the World through the Eyes of SAS Viya talk. Curious to know who attended the Royal Wedding?

Hysteria about the future of artificial intelligence (AI) is everywhere. There is no shortage of sensationalist news about how AI can cure diseases, accelerate human innovation and improve human creativity. From the headlines alone, you would think we already live in a future where AI has infiltrated every aspect of society. While AI has opened up a wealth of promising opportunities, it has also led to a mindset that can be best described as "AI solutionism". This is the attitude that, given enough data, machine learning algorithms can solve all of humanity's problems.

NEW DELHI: The government should drive cross-border collaboration on artificial intelligence research with countries like Japan, UK, Germany, Singapore, Israel and China to develop solutions that tackle social and economic challenges, a report said today. The Ministry of External Affairs and Department of Science and Technology (DST) may take the lead in developing such relationships, suggested the Assocham-PwC joint study. It observed that forming cooperative relationships with some of the front-runners such as Japan, the UK, Germany, Singapore, Israel and China to develop solutions that tackle social and economic challenges can aid and accelerate strategy formulation in artificial intelligence, machine learning and other new-age technologies in India. "Exchanging best practices and learning from prior initiatives is one way of strengthening cooperation," noted the study. The study also suggested that policy planning in artificial intelligence (AI) must be aimed at creating an ecosystem that is supportive of research, innovation and commercialisation of applications.

Pune, India -- (SBWIRE) -- 06/15/2018 -- In the solutions segment, the software tools solution is expected to have the largest market share, whereas platforms solution is expected to be the highest contributor during the forecast period. Software tools help in deploying AI enabled solutions in the finance sector to extract a large amount of data, as well as accurate and complete data on time. Financial companies were early adopters of a relational database and mainframe computers, moreover, the industry is one of the early integrators of artificial integration in their daily operation to enhance the customer experience and handle growing big data. The inclusion of artificial intelligence improves results through implementation of sophisticated algorithms and understanding consumer behavior. The ever-increasing number customers in the financial industry are giving tough times to accommodate and attend every service request humanely whereas Chabot, an automated chat box helps in monitoring and replying near a specific resolution to consumers is expected to have a positive impact on the market growth over the forecast period.

Artificial Intelligence has been the buzz word of 2017. Deborah Kay gives us 7 tools you can use. IBM Watson uses linguistic analytics and personality theory to infer personality insights from unstructured text like tweets (try it here!). Microsoft Cognitive Service's #HowOldRobot lets you upload a photo and uses facial recognition intelligence to predict your gender and your age (try it here!). These AI solutions are being used by large enterprises to predict shopping behaviour, assess risk potential or make hiring decisions.