Digitalisation World, an online publication covering key technologies that underpin the digital revolution, highlighted IPsoft's partnership with Aruma, one of Australia's leading disability service providers, in a recent article. Initially, the partnership will use IPsoft's industry-leading digital colleague Amelia to assist staff with administrative tasks such as reporting and scheduling. Amelia will also capture data and build a knowledgebase about Aruma's practices so staff can provide optimized support to customers. Click here to explore ways that conversational AI can be used to enhance the lives of the disabled and elderly communities. "Part of the Aruma's strategic innovation initiative is identifying and working with like-minded people and companies who want to learn with us. That is why we are thrilled to work with IPsoft to bring Amelia into the disability sector in Australia," Mark Doro, Aruma's Chief Transformation Officer, said in the article.

Using Artificial Intelligence, researchers are now able to identify the back of the eye images. Scientists have utilized Artificial intelligence (AI) to produce a far more accurate and in-depth method for analyzing images of the rear of this eye, a prior which may help ophthalmologists better identify and monitor eye diseases as glaucoma, and age-related macular degeneration. In the study, released in the Scientific journal report, the scientists looked for a new way of analyzing images from a state-of-the-art instrument known as the Optical Coherence Tomography (OCT). The scientists, together with those from the Queensland Faculty of Technology (QUT) found Australia, explored a range of machine learning strategies to analyze OCT pictures. The retina and the choroid are the two main tissue layers at the back of the eye and researchers tried extracting images from these two layers.

There's a reason why security experts picked 2019 as the year in which the first artificial intelligence hack takes place. Peter Bailey explains how hackers and defenders are arming themselves. With cybercrime as much a business as any other, albeit one on the wrong side of the law, hackers are already sizing up the potential for artificial intelligence (AI) to further their goals. It's the flip side of a coin: on the one side IT professionals are using AI to help identify and eliminate threats more effectively, and even anticipate attacks before they happen. On the other, intelligent malware offers the potential of adapting its payload and evading detection.

For those who are new to AI, Machine Learning, and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed, such as the need for improved sharing of data by healthcare participants including hospitals and ensuring the highest quality in the preparation of data, are entirely valid and I take the view that the need for access to data and sharing of data by hospitals may need to become a matter of political and regulatory concern.

The Social Internet of Things (SIoT), integration of Internet of Things and Social networks paradigms, has been introduced to build a network of smart nodes which are capable of establishing social links. In order to deal with misbehavioral service provider nodes, service requestor nodes must evaluate their trustworthiness levels. In this paper, we propose a novel trust management mechanism in the SIoT to predict the most reliable service provider for a service requestor, that leads to reduce the risk of exposing to malicious nodes. We model an SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and requestors), then build the corresponding social network among service requestor nodes, using Hellinger distance. After that, we develop a social trust model, by using nodes' centrality and similarity measures, to extract behavioral trust between the network nodes. Finally, a matrix factorization technique is designed to extract latent features of SIoT nodes to mitigate the data sparsity and cold start problems. We analyze the effect of parameters in the proposed trust prediction mechanism on prediction accuracy. The results indicate that feedbacks from the neighboring nodes of a specific service requestor with high Hellinger similarity in our mechanism outperforms the best existing methods. We also show that utilizing social trust model, which only considers the similarity measure, significantly improves the accuracy of the prediction mechanism. Furthermore, we evaluate the effectiveness of the proposed trust management system through a real-world SIoT application. Our results demonstrate that the proposed mechanism is resilient to different types of network attacks and it can accurately find the proper service provider with high trustworthiness.

Computing expected predictions has many interesting applications in areas such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined by an arbitrary generative model has been proven to be hard in general. In fact, the task is intractable even for simple models such as logistic regression and a naive Bayes distribution. In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations of the latter with respect to the former, as well as moments of any order, in case of regression. Specifically, we consider expressive probabilistic circuits with certain structural constraints that support tractable probabilistic inference. Moreover, we exploit the tractable computation of high-order moments to derive an algorithm to approximate the expectations, for classification scenarios in which exact computations are intractable. We evaluate the effectiveness of our exact and approximate algorithms in handling missing data during prediction time where they prove to be competitive to standard imputation techniques on a variety of datasets. Finally, we illustrate how expected prediction framework can be used to reason about the behaviour of discriminative models.

W e investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor-intensive, thus making it impractical as a first-response tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role --left, news feed, right-- by using features extracted from social media, i.e., Twitter, in two scenarios: ( i) in a traditional supervised learning scenario, where labels for trolls are available, and ( ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.

ABSTRACT We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-true, or false. Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection. Index T erms-- deception detection, fact-checking, fake news, disinformation, computational paralinguistics, multi-modality, political debates. 1. INTRODUCTION Traditionally, news media have been the gate keepers of information, as they carefully selected what was appropriate to present to the public after double-checking it.

This book was designed around major ideas and methods that are directly relevant to machine learning algorithms. There are a lot of things you could learn about probability, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials. I designed the tutorials to focus on how to get things done with probability. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you less than one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

Osaro, a San Francisco startup developing AI-based solutions for industrial robots, today announced that it's closed a $16 million series B funding round led by King River Capital (KRC), with participation from Alpha Intelligence Capital, Founders Fund, Fenox Venture Capital, GiTV Fund, and existing and strategic investors. It brings the startup's total raised to $29.3 million coming after a $10 million series A in April 2017, which cofounder and CEO Derik Pridmore said will bolster Osaro's hiring, international deployment, and R&D efforts. Alongside the funding round, Osaro revealed that Applied Digital Access, Mahi Networks, and Calix vereran Kevin Pope has joined as VP of engineering. "A key element of our competitive advantage is Osaro's … deep learning algorithms," said Pridmore, an MIT computer science and electrical engineering graduate who cofounded Osaro in 2015 with a team hailing from UC Berkeley, Stanford, and the University of Massachusetts. "These algorithms generalize picking tasks with minimal training data and no SKU registration for quick, scalable solutions. In addition, as a software company, we support a wide array of commodity hardware and robotic arms which lets our customers select options that best fit their needs."