Vancouver, British Columbia--(Newsfile Corp. - December 4, 2019) - HEYBRYAN MEDIA INC. (CSE: HEY) ("HeyBryan") an app that connects home-maintenance Experts to homeowners for help with small tasks around the home, today reported a significant increase in the number of completed tasks in the month of November, leading to increased revenue. These impressive results were fuelled by last month's rapid growth in new Expert acquisition across multiple task categories and geographies. Expert acquisition grew by 167% in November, thanks to HeyBryan's AI-enabled, data-driven programmatic marketing campaign. Using AI-driven platform HeyBryan is able to predictably understand the outcomes of its Facebook and Instagram campaigns, and the results speak for themselves. As HeyBryan continues to add investment to its strategic marketing campaigns, these numbers will continue on this trend.

The success of deep neural networks is clouded by two issues that largely remain open to this day: the abundance of adversarial attacks that fool neural networks with small perturbations and the lack of interpretation for the predictions they make. Empirical evidence in the literature as well as theoretical analysis on simple models suggest these two seemingly disparate issues may actually be connected, as robust models tend to be more interpretable than non-robust models. In this paper, we provide evidence for the claim that this relationship is bidirectional. Viz., models that are forced to have interpretable gradients are more robust to adversarial examples than models trained in a standard manner . With further analysis and experiments, we identify two factors behind this phenomenon, namely the suppression of the gradient and the selective use of features guided by high-quality interpretations, which explain model behaviors under various regularization and target interpretation settings.

In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that will satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.

Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of- the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian. 1 Introduction Ethnic classification from facial images has been studied for the past two decades with the purpose of understanding how humans perceive and determine an ethnic group from a given image. The motivation stems, for example, from the fact that (gender and) ethnicity play an important role in face-related applications, such as advertising, social insensitive-based systems, etc. Furthermore, while facial features are subject to change (due to aging, for example), ethnicity is of interest due to its invariance over time. Recent works on demographic classification are divided conceptually into appearancebased methods (using, e.g., eigenface methods, fisherface methods, etc.) and geometry-based methods (relying, e.g., on geometric parameters, such as the distance between the eyes, face width and length, nose thickness, etc.). One of the main challenges of automatic demographic classification is to avoid any "noise", such as illumination, background distortion, and a subject's pose. In this paper, we introduce a deep learning-based method, that achieves state-of-the-art results for facial image representations and classification for the four ethnic groups: African, Asian, Caucasian, and Indian. 2 Related Work 2.1 Traditional ML-Based Techniques During the past two decades, there has been enormous progress on the topic of ethnic group classification, using various classical Machine Learning methods.

Adversarial attacks represent a serious menace for learning algorithms and may compromise the security of future autonomous systems. A theorem by Khoury and Hadfield-Menell (KH), provides sufficient conditions to guarantee the robustness of active learning algorithms, but comes with a caveat: it is crucial to know the smallest distance among the classes of the corresponding classification problem. We propose a theoretical framework that allows us to think of active learning as sampling the most promising new points to be classified, so that the minimum distance between classes can be found and the theorem KH used. The complexity of the quantum active learning algorithm is polynomial in the variables used, like the dimension of the space $m$ and the size of the initial training data $n$. On the other hand, if one replicates this approach with a classical computer, we expect that it would take exponential time in $m$, an example of the so-called `curse of dimensionality'.

Artificial intelligence has had a profound impact on finance. In the span of a few decades, it has made finance faster, more accessible, more profitable, and more efficient in many ways. Despite all the significant benefits made possible by financial artificial intelligence, it also presents serious risks and implications for law, business, and society. My recent article, 'Artificial Intelligence, Finance, and the Law', published in the Fordham Law Review, offers a study of those risks and implications. It provides a broad examination of the inherent risks and larger implications of financial artificial intelligence.

Lord Drayson seems a man on the move. As an amateur racing driver, it is perhaps an innate charateristic, and in the current debate around health data his foot is very much on the gas. I meet the former Labour government science and defence minister shortly after he lays out a bold new vision for the NHS at FutureScot's recent Digital Health & Care conference in Glasgow. Urbane and well-connected, Drayson is also a keen student of policy and how the arguments around big tech and health data are shaping up. For background, there is an intensifying argument that the NHS needs to make much more use of a still largely untapped goldmine of data, which could herald a new dawn in the way we diagnose, treat and manage disease – not to mention save billions of pounds annually.

Artificial intelligence and AI are transformative advancements that level many playing fields, such a large number of in reality that a small country can militarily contend with extraordinary military power, similar to the US. The Chinese have an open, exceptionally profound, amazingly well-subsidized pledge to AI. Aviation based armed forces General VeraLinn Jamieson says it evidently: "We gauge the complete spending on artificial intelligence frameworks in China in 2017 was $12 billion. We likewise gauge that it will develop to at any rate $70 billion by 2020." Andrew Yang, during a Democratic Candidates banter, expressed that the US is losing the AI weapons contest to China. Barely a year back, I contended something very similar.

As wildfire season raged in California this fall, a startup a few states away used artificial intelligence to pinpoint the location of blazes there within minutes -- in some cases far faster than these fires might otherwise be noticed by firefighters or civilians. Santa Fe-based Descartes Labs, which uses AI to analyze satellite imagery, launched its U.S. wildfire detector in July. The company's AI software pores over images coming in roughly every few minutes from two different U.S. government weather satellites, in search of any changes -- the presence of smoke, a shift in thermal infrared data showing hot spots -- that could indicate a fire has ignited. Descartes is testing its detector by sending alerts to select forestry officials in its home state of New Mexico and told CNN Business its wildfire detector has spotted about 6,200 total thus far. The company says it can often detect these fires when they're just about 10 acres in size.

SpaceX launched a 3-ton cargo payload to the International Space Station (ISS) on Thursday, which included barley grains for a beer experiment, mice for muscle-building research and a robot designed to show empathy. The Falcon 9 rocket carrying the recycled Dragon capsule filled with the goodies lifted off from Cape Canaveral, Fla., around 12:30 p.m. The capsule is expected to arrive at the station housing six astronauts -- three Americans, two Russians and one Italian -- on Sunday. SpaceX recovered the new booster on a barge just off the coast in the Atlantic several minutes following liftoff so that it could be reused. SpaceX employees in Southern California cheered when the booster landed, and again a few minutes later when the capsule reached orbit.