Adversarial training, especially projected gradient descent (PGD), has been the most successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs are meaningful and interpretable by humans. However, the concept of interpretability is not mathematically well established, making it difficult to evaluate it quantitatively. We define interpretability as the alignment of the model gradient with the vector pointing toward the closest point of the support of the other class. We propose a method for measuring this alignment for binary classification problems, using generative adversarial model training to produce the smallest residual needed to change the class present in the image. We show that PGD-trained models are more interpretable than the baseline according to our definition, and our metric presents higher alignment values than a competing metric formulation. We also show that enforcing this alignment increases the robustness of models without adversarial training.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Imagine this land-war scenario: An enemy fighter is several hundred yards away, another is attacking from one mile while a third fires from a nearby room in a close-quarters urban warfare circumstance, when U.S. Army soldiers apprehend, integrate, and quickly map the locations of multiple targets at once in 3D, all while knowing the range and distance of the enemy forces. How could something like this be possible, one might wonder, given the nuances in perspective, range, navigational circumstances and the limitations of a human eye? These complexities form the conceptual basis upon which the Army is fast-tracking its Integrated Visual Augmentation System, or IVAS, which is a soldier-worn combat goggle engineered with advanced sensors that are able to overcome some of the limitations of human vision and quickly organize target data.

As machine learning has evolved, so have best practices, especially in the wake of COVID-19. Join this VB Live event to learn from experts about how machine learning solutions are helping companies respond in these uncertain times – and the lessons learned along the way. Misinformation around COVID-19 is driving human behavior across the world. Here in the information age, sensationalized clickbait headlines are crowding out actual fact-based content, and, as a result misinformation spreads virally. Conversations within small communities become the epicenter of false information, and that misinformation spreads as people talk, both online and off.

Wading through waist-deep water, residents of the al-Shigla neighbourhood in Omdurman, twin city of Sudan's capital Khartoum, tried to rescue what was left of their possessions as they floated by. Others stood by in despair, observing the aftermath of days of torrential rains that brought record-breaking flash floods to the country where the Blue and White Niles join to become the Nile River. Pieces of furniture, broken tiles, damaged vehicles and more were washed away by this year's rain that fell profusely and continuously for nearly two weeks. The rain and flooding exceeded records set in 1946 and 1988, killing more than 100 people and forcing the government to declare a three-month state of emergency this week. To many Sudanese like Amna Ahmed, seasonal rains, in and of themselves, are nothing new.

It was the first down week in five for the markets, as technology shares finally started to show some weakness, trading lower Thursday and Friday last week. There was some positive economic data points, with the unemployment rate dropping to an impressive 8.4% versus expectations of 9.8%, amid a recovery that may not be V-shaped but looks likely to recover in time. This is all dependent on how quickly a virus can be manufactured and distributed across the globe, which there have been some promising developments of late. If you're looking for places to trade the market, Q.ai's deep learning algorithms have used Artificial Intelligence ("AI") technology to identify Unusual Movers for the last week. Sign up for the free Forbes AI Investor newsletter here to join an exclusive AI investing community and get premium investing ideas before markets open.

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce. Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life. This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies – not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie. When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background.

Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and reaching out to many areas of society, including the industrialized world. A common alternative to control the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices can be found. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this work, we present a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control is performed using a more natural, human-like way to communicate the instructions. Moreover, we implement an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. The conducted experiments involve participants giving voice commands to the drone in both languages in order to compare the effectiveness of each of them, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion have been applied to the voice commands in order to test the proposed approach when facing noisy input signals. The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speech-to-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions respectively, whereas using our phoneme matching approach the results are improved achieving 93.33% and 100.00% accuracy for English and Spanish languages.

CAIRO: With most credible information on COVID-19 and its symptoms supplied in English, Arabic-speaking populations have faced a significant barrier, falling prey to hysterical and inaccurate social media posts that come from questionable sources. This urgent and potentially life-threatening problem was quickly identified by the team at DxWand, an Egyptian startup providing conversational artificial intelligence (AI) solutions -- and they sought a fast and effective fix for it. "In late February, we, as a team, were struggling to find credible information about COVID-19, and we found that one needs certain access to find credible information," said Ahmed Mahmoud, co-founder of DxWand. "That made us think about others who would find it challenging to make a distinction between credible and false information. Even on official websites it was sometimes hard to find an answer to a specific question. Or worse -- you would get your information from social media."

The market research report on the global USA Machine Learning in Communication industry provides a comprehensive study of the various techniques and materials used in the production of USA Machine Learning in Communication market products. Starting from industry chain analysis to cost structure analysis, the report analyzes multiple aspects, including the production and end-use segments of the USA Machine Learning in Communication market products. The latest trends in the pharmaceutical industry have been detailed in the report to measure their impact on the production of USA Machine Learning in Communication market products. This report comes along with an added Excel data-sheet suite taking quantitative data from all numeric forecasts presented in the report. Research Methodology: The USA Machine Learning in Communication market has been analyzed using an optimum mix of secondary sources and benchmark methodology besides a unique blend of primary insights.

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks -- post-hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.