Thanks to advances in artificial intelligence (AI), society is now facing a unique challenge: how do we regulate the usage of human faces and voices? Facial recognition is the ability of computer systems to identify and us by our faces. Voice recognition is the ability of computer systems to do the same for our words. Both are powered by AI, and both create benefits for consumers and citizens. These technologies also raise difficult questions about privacy and personal rights.

A new report from the Duke-Margolis Center for Health Policy explores some of the policy changes that should be made to enable safer and more effective deployment of artificial intelligence in healthcare. As AI and machine learning become de facto ingredients in many key clinical technologies, a better understanding of how they can best be leveraged for optimal analytics and decision support is the goal of the study, "Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care." WHY IT MATTERS The Duke report takes stock of the existing legal and regulatory landscape for algorithm-based CDS and diagnostic support software, and lays out some essential priorities to work toward in the years ahead to ensure safe deployment of AI in clinical settings. AI and ML are making inroads all over healthcare, of course, and current legislation and regulatory policy – whether it's the massive 21st Century Cures Act or FDA's new updates to the Software Pre-Cert Pilot Program – are adequate but still not optimal for a future that promises to evolve at a dizzying pace. The Duke-Margolis paper, meant as a "resource for developers, regulators, clinicians, policy makers, and other stakeholders as they strive to effectively, ethically, and safely incorporate AI as a fundamental component in diagnostic error prevention and other types of CDS," looks at some of the major challenges and opportunities facing AI in the years ahead.

This is the second of three blogs on the state of the AI chip market and what's to come in 2019. The year will be a festival of new chips and benchmark battles, led by the large companies I mentioned in the first blog ( Intel, Google, AMD, Xilinx, Apple, Qualcomm), and joined by dozens of Silicon Valley startups and Chinese Unicorns sporting valuations in excess of a billion US dollars. In this section, I will cover the most prominent, or at least the loudest, of the startups in the West and in China, where the government is intent on creating an indigenous AI chip industry. We will start with Wave, which appears to be the first to market with silicon for training. Wave Computing had an eventful 2018, taping out its first DataFlow Processing Unit, acquiring MIPS, creating MIPS Open, and shipping its first early systems to a few lucky customers.

Embattled Chinese telecom giant Huawei has some new problems. The US Department of Justice on Monday unsealed a 13-count indictment against Huawei and its CFO, Meng Wanzhou, alleging the company misled banking partners about violations of US sanctions against Iran. The charges include bank fraud, wire fraud, money laundering, and obstruction of justice. Meng, who is also the daughter of Huawei founder Ren Zhengfei, was arrested in Canada last month and is awaiting extradition to the US. In a separate case, the DOJ indicted Huawei for stealing intellectual property related to a cell-phone-testing robot from T-Mobile in 2012.

The devices are kicking off debate over regulating the latest frontier of automation in the state, even as online retail giant Amazon has already begun testing them. A bill up for a hearing Monday would restrict the weight of the robots, limit them to sidewalks and crosswalks, and require both insurance and active monitoring by a human, filling in what the bill's sponsor called a gray area in state law.

Miles of contaminated pipe must be measured, foot by foot, as part of the decommissioning effort at deactivated gaseous diffusion enrichment facilities. The current method requires cutting away asbestos-lined thermal enclosures and performing repeated, elevated operations to manually measure pipe from the outside. The RadPiper robot, part of the Pipe Crawling Activity Measurement System (PCAMS) developed by Carnegie Mellon University and commissioned for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, automatically measures U-235 in pipes from the inside. This improves certainty, increases safety, and greatly reduces measurement time. The heart of the RadPiper robot is a sodium iodide scintillation detector in an innovative disc-collimated assembly. By measuring from inside pipes, the robot significantly increases its count rate relative to external through-pipe measurements. The robot also provides imagery, models interior pipe geometry, and precisely measures distance in order to localize radiation measurements. Data collected by this system provides insight into pipe interiors that is simply not possible from exterior measurements, all while keeping operators safer. This paper describes the technical details of the PCAMS RadPiper robot. Key features for this robot include precision distance measurement, in-pipe obstacle detection, ability to transform for two pipe sizes, and robustness in autonomous operation. Test results demonstrating the robot's functionality are presented, including deployment tolerance tests, safeguarding tests, and localization tests. Integrated robot tests are also shown.

Understanding the needs of a variety of distinct user groups is vital in designing effective, desirable dialogue systems that will be adopted by the largest possible segment of the population. Despite the increasing popularity of dialogue systems in both mobile and home formats, user studies remain relatively infrequent and often sample a segment of the user population that is not representative of the needs of the potential user population as a whole. This is especially the case for users who may be more reluctant adopters, such as older adults. In this paper we discuss the results of a recent user study performed over a large population of age 50 and over adults in the Midwestern United States that have experience using a variety of commercial dialogue systems. We show the common preferences, use cases, and feature gaps identified by older adult users in interacting with these systems. Based on these results, we propose a new, robust user modeling framework that addresses common issues facing older adult users, which can then be generalized to the wider user population.

Citizen engagement and technology usage are two emerging trends driven by smart city initiatives. Governments around the world are adopting technology for faster resolution of civic issues. Typically, citizens report issues, such as broken roads, garbage dumps, etc. through web portals and mobile apps, in order for the government authorities to take appropriate actions. Several mediums -- text, image, audio, video -- are used to report these issues. Through a user study with 13 citizens and 3 authorities, we found that image is the most preferred medium to report civic issues. However, analyzing civic issue related images is challenging for the authorities as it requires manual effort. Moreover, previous works have been limited to identifying a specific set of issues from images. In this work, given an image, we propose to generate a Civic Issue Graph consisting of a set of objects and the semantic relations between them, which are representative of the underlying civic issue. We also release two multi-modal (text and images) datasets, that can help in further analysis of civic issues from images. We present a novel approach for adversarial training of existing scene graph models that enables the use of scene graphs for new applications in the absence of any labelled training data. We conduct several experiments to analyze the efficacy of our approach, and using human evaluation, we establish the appropriateness of our model at representing different civic issues.

A generative model is an unsupervised learning approach that is able to learn a domain by processing a large amount of data from it and then generate new data like it (Hinton and Ghahramani, 1997;Yu et al., 2018). VAE, together with Generative Adversarial Networks (Goodfellow et al., 2016) and Deep Autoregressive Networks(Gregor et al., 2013), are amongst the most powerful and popular generative model techniques. VAE has been successfully applied in many domains, such as image processing (Pu et al., 2016), natural language processing (Semeniuta etal., 2017), and cybersecurity (Chandy et al., 2019). A VAE works by maximizing a variational lower bound of the likelihood of the data (Kingma and Welling, 2013). A VAE has two halves: a recognition model (an encoder) and a generative model(a decoder). The recognition model learns a latent representation of the input data, and the generative model learns to transform this representation back into the original data. The recognition and generative models are jointly trained by optimizing theprobability of the input data using stochastic gradient ascent. Application of the VAE involves selection of an approximate posterior distribution for the latent variables.

Case studies, such as Kay et al., 2015 have shown that in image summarization, such as with Google Image Search, the people in the results presented for occupations are more imbalanced with respect to sensitive attributes such as gender and ethnicity than the ground truth. Most of the existing approaches to correct for this problem in image summarization assume that the images are labelled and use the labels for training the model and correcting for biases. However, these labels may not always be present. Furthermore, it is often not possible (nor even desirable) to automatically classify images by sensitive attributes such as gender or race. Moreover, balancing according to the labels does not guarantee that the diversity will be visibly apparent - arguably the only metric that matters when selecting diverse images. We develop a novel approach that takes as input a visibly diverse control set of images and uses this set to produce images in response to a query which is similarly visibly diverse. We implement this approach using pre-trained and modified Convolutional Neural Networks like VGG-16, and evaluate our approach empirically on the Image dataset compiled and used by Kay et al., 2015. We compare our results with the Google Image Search results from Kay et al., 2015 and natural baselines and observe that our algorithm produces images that are accurate with respect to their similarity to the query images (on par with that of the Google Image Search results), but significantly outperforms with respect to visible diversity as measured by their similarity to our diverse control set.