Talking about cyber scams might make AI sound scary, but Murphy said it's important to remember that legitimate organizations use it for positive purposes, too. "AI can be both good and bad," he said. "From a cybersecurity perspective, we're using AI to help us develop tools and techniques to protect our own systems. We find patterns and create defenses that are more predictive and proactive, rather than being reactive." Murphy said many organizations use AI to teach their computers to detect when other computers are trying to penetrate their cybersecurity measures.

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for cross-language information retrieval and outlines some open research questions.

In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also developed an application for dataset visualization using a new programming language called Ring, our experiments demonstrates that this language have simple design like Python while integrates RAD tools like Visual Basic which is good for GUI development in the open-source world

Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational Inference. We formulate the problem of density propagation through layers of a DNN and solve it using an Ensemble Density Propagation (EnDP) scheme. The EnDP approach allows us to propagate moments of the variational probability distribution across the layers of a Bayesian DNN, enabling the estimation of the mean and covariance of the predictive distribution at the output of the model. Our experiments using MNIST and CIFAR-10 datasets show a significant improvement in the robustness of the trained models to random noise and adversarial attacks.

Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning algorithms difficult to deploy on edge devices. Typically, one has to partially sacrifice accuracy in favor of an increased performance quantified in terms of reduced memory usage and energy consumption. Current methods compress the networks by reducing the precision of the parameters or by eliminating redundant ones. In this paper, we propose a new insight into network compression through the Bayesian framework. We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network. Our experimental results show that the network architecture can be successfully compressed by deleting parameters identified by the network itself while retaining the same level of accuracy.

Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of an auto-encoder and a graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning a discriminative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the discriminative information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments validate that our method consistently outperforms state-of-the-art methods on six benchmark datasets. Especially, our method improves the ARI by more than 18.14% over the best baseline.

The world of scientists and experts is still at odds with what we know about artificial intelligence. Some scientists think that the AIs will turn their famous movie "The Terminator" into reality, while others think they will be our faithful companions. But today we see AI exists more than ever before and the question of giving them real human rights or not has become a matter of time. All around us, there are applications using AI technologies to improve our lives. Whether it be the services provided by Google, Facebook, or even self-driving cars, they can all be conceptualized as various forms of artificial intelligence.

Georgiana Strait is a Digital Marketing Manager at Link Labs with 6 years of experience in the technology and software industries, with specialized interest and expertise in the IoT field. She has helped market emerging technologies such as Machine Learning/Artificial Intelligence, analytics programs, virtual assistants, software for regulatory compliance needs, and much more. Prior to her professional career as a marketer, Georgiana served in the United States Military in the Active Duty Army as a Chemical Biological Radiological Nuclear Specialist. She was stationed in El Paso, Texas at Fort Bliss and had one deployment overseas during her time in the Army.

A bipartisan group of House lawmakers has introduced legislation that would give people more control over the algorithms that shape their online experience. If passed, the Filter Bubble Transparency Act would require companies like Meta to offer a version of their platforms that runs on an "input-transparent" algorithm that doesn't pull on user data to generate recommendations. The bill would not do away with "opaque" recommendation algorithms altogether but would make it a requirement to include a toggle that allows people to switch that functionality off. Additionally, platforms that continue to use recommendation algorithms need to have a notification that informs people those recommendations are based on inferences generated by their personal data. The prompt can be a one-time notice, but it would need to be presented in a "clear, conspicuous manner," according to the proposed bill. The legislation was introduced by Representatives Ken Buck (R-CO), David Cicilline (D-RI), Lori Trahan (D-MA) and Burgess Owens (R-UT).

"Surprise: Top US soldier clears US soldiers of murder" That should have been the headline attached to any story written about the "findings" of a recent "probe" into the massacre of an Afghan family, including seven children, obliterated by a US "Hellfire" missile in late August. Of course, not one editor – as far as I can gather – opted to tell that simple, blunt truth. Instead, most trotted out the usual pallet of euphemisms in effect to absolve US soldiers of the murders of an Afghan humanitarian worker, Zemari Ahmadi, three of his children, Zamir, 20, Faisal, 16, and Farzad, 13, as well as his cousin, Ahmad, 30, and three of Ahmadi's nephews, Arwin, seven, Benyamin, six, and Hayat, two and two three-year-old girls, Malika and Somaya. So, editors wrote lots of headlines like this one to summarise the predictable "conclusions" of a report authored by US Air Force Lieutenant General Sami Said: "Watchdog Finds No Misconduct in Mistaken Afghan Airstrike." The Pentagon could not have penned a more agreeable precis of Lieutenant General Said's "investigation" into the summary execution of Ahmadi and his family.