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."

Climate action is the latest buzzword among industry circles since the many International Panel on Climate Change (IPCC) reports and the recent UN Climate Summit in New York City. Greta Thunberg grabbed the headlines, but industrialists are all wondering: How can we move swiftly and effectively to reduce carbon emissions? How can we use AI and other exponential technologies to do the job better, faster and cheaper? As a business strategist and urban planner, I advise companies to focus on cities since they consume 80% of energy and emit 70% of carbon, so we'll win or lose the carbon battle in the cities. Fortunately, cities can move faster than national governments and, as energy buyers, they can directly negotiate energy types and pricing, giving them enormous economic clout.

In Part 1 of this 3-part miniseries, we discussed the origins of artificial intelligence (AI), and we considered some low-hanging AI-enabled fruit in the form of speech recognition, voice control, and machine vision. In Part 2, we noted some of the positive applications of AI, like recognizing skin cancer, identifying the source of outbreaks of food poisoning, and the early detection of potential pandemics. In fact, there are so many feel-good possibilities for the future that they can make your head spin. In a moment, we'll ponder a few more of these before turning our attention to the dark side. Another topic we considered in Part 2 was the combination of mediated reality (MR) and AI, where mediated reality encompasses both augmented reality (AR) and deletive reality (DR). In the case of AR, information is added to the reality we are experiencing.

Researchers from The University of Western Australia and the University of California have developed a new machine-learning tool that will improve the management, restoration and irrigation of rangeland areas used for grazing. Associate professor Sally Thompson from the UWA School of Engineering and UWA Institute of Agriculture says the tool was suited to environments where the amount of rainfall exceeded the absorption capacity of the soil, resulting in the excess water flowing over the land. The new machine learning tool models surface water flows in dry environments with patchy vegetation cover. The findings have significant implications for agricultural and natural systems in Australia and worldwide. "The research could help environmental designers limit soil erosion in rangeland environments and agricultural systems, minimising degradation risks in drylands," says Sally. "It also has applications in urban settings, where waterproof surfaces, like pavement, generate runoff and flood risks."