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Army researchers develop A.I. tech that helps U.S. soldiers learn 13x faster than conventional methods

#artificialintelligence

A Phys.org article states that Army researchers are making huge strides in the field of artificial intelligence (AI) that can support U.S. soldiers on the battlefield. Their latest development is an affordable yet capable AI assistant that can reportedly help human troops learn more than 13 times faster than normal training methods. Featuring vastly improved machine learning capabilities, the AI will be installed upon the Army's future ground combat vehicles. It is intended to help a human soldier spot important clues, recognize the developing situation, and come up with a solution to the problem on the fly. The AI would reportedly help preserve American lives during the chaos of combat.


Three Impacts Of Artificial Intelligence On Society

#artificialintelligence

Over the next five years, we are about to witness the world we live in entirely disrupted by improvements in artificial intelligence (AI) and machine learning. Children today are growing up with AI assistants in their homes (Google Assistant, Siri and Alexa) -- to the point that you might consider their mere presence an extension of co-parenting. As voice and facial recognition continue to evolve, machine learning algorithms are getting smarter. More and more industries are being influenced by AI, and our society as we know it is transforming. The transportation industry looks like it will be the first to be completely disrupted by artificial intelligence.


Three Impacts Of Artificial Intelligence On Society

Forbes Technology

Over the next five years, we are about to witness the world we live in entirely disrupted by improvements in artificial intelligence (AI) and machine learning. Children today are growing up with AI assistants in their homes (Google Assistant, Siri and Alexa) -- to the point that you might consider their mere presence an extension of co-parenting. As voice and facial recognition continue to evolve, machine learning algorithms are getting smarter. More and more industries are being influenced by AI, and our society as we know it is transforming. The transportation industry looks like it will be the first to be completely disrupted by artificial intelligence.


Security: Using AI for Evil

#artificialintelligence

Artificial intelligence (AI) is positively impacting our world in previously unimaginable ways across many different industries. The use of AI is particularly interesting in the cybersecurity industry because of its unique ability to scale and prevent previously unseen, aka zero-day, attacks. But remember, similar to how drug cartels built their own submarines and cellphone towers to evade law enforcement, and the Joker arose to fight Batman, so too will cyber-criminals build their own AI systems to carry out malicious counter-attacks. An August 2017 survey commissioned by Cylance discovered that 62% of cybersecurity experts believe weaponized AI attacks will start occurring in 2018. AI has been heavily discussed in the industry over the past few years, but most people do not realize that AI is not just one thing, but that it is made up of many different subfields.


NVIDIAVoice: Building The AI Architecture To Train, Simulate And Test AI Self-Driving Cars

Forbes Technology

Developing an autonomous vehicle requires a massive amount of data. Before any AV can safely navigate on the road, engineers must first train the artificial intelligence (AI) algorithms that enable the car to drive itself. Deep learning, a form of AI, is used to perceive the environment surrounding the car and to make driving decisions with superhuman levels of performance and precision. This is an enormous big data challenge. A single test vehicle can generate petabytes of data a year.


Inside Yandex self-driving car: Here's what it's like to ride on Moscow's crazy roads ZDNet

#artificialintelligence

Video: Yandex's autonomous car hits Moscow's streets. Transportation is about to get a technology-driven reboot. The details are still taking shape, but future transport systems will certainly be connected, data-driven and highly automated. With harsh winters, drivers who constantly switch lanes, traffic jams and occasional crashes, the Russian capital of Moscow provides a challenging setting for testing autonomous cars. "In Moscow, the guys behind you honk the horn even before the traffic lights turn green," says Dmitry Polishchuk, head of Yandex's driverless car project.


MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model

arXiv.org Machine Learning

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning. Developing a MEBN model to support a given application is a challenge, requiring definition of entities, relationships, random variables, conditional dependence relationships, and probability distributions. When available, data can be invaluable both to improve performance and to streamline development. By far the most common format for available data is the relational database (RDB). Relational databases describe and organize data according to the Relational Model (RM). Developing a MEBN model from data stored in an RDB therefore requires mapping between the two formalisms. This paper presents MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We identify links between the two languages (RM and MEBN) and define four levels of mapping from elements of RM to elements of MEBN. These definitions are implemented in the MEBN-RM algorithm, which converts a relational schema in RM to a partial MEBN model. Through this research, the software has been released as a MEBN-RM open-source software tool. The method is illustrated through two example use cases using MEBN-RM to develop MEBN models: a Critical Infrastructure Defense System and a Smart Manufacturing System.


A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction

arXiv.org Machine Learning

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.


Inside Yandex self-driving car: Here's what it's like to ride on Moscow's crazy roads

ZDNet

Video: Yandex's autonomous car hits Moscow's streets. With harsh winters, drivers who constantly switch lanes, traffic jams and occasional crashes, the Russian capital of Moscow provides a challenging setting for testing autonomous cars. "In Moscow, the guys behind you honk the horn even before the traffic lights turn green," says Dmitry Polishchuk, head of Yandex's driverless car project. Polishchuk is taking me on a ride along Moscow's busy streets to show me how far the company's self-driving technology has evolved in the year and a half since it was officially announced. Since local legislation does not allow unmanned cars on public roads, one of his colleagues, Alex, is sitting behind the wheel hoping not to have to touch it.


Human-aided Multi-Entity Bayesian Networks Learning from Relational Data

arXiv.org Machine Learning

An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability to reason under uncertainty about the situation. Multi-Entity Bayesian Networks (MEBNs) combines First-Order Logic with Bayesian Networks for representing and reasoning about uncertainty in complex, knowledge-rich domains. MEBN goes beyond standard Bayesian networks to enable reasoning about an unknown number of entities interacting with each other in various types of relationships, a key requirement for the OODA process of an AI system. MEBN models have heretofore been constructed manually by a domain expert. However, manual MEBN modeling is labor-intensive and insufficiently agile. To address these problems, an efficient method is needed for MEBN modeling. One of the methods is to use machine learning to learn a MEBN model in whole or in part from data. In the era of Big Data, data-rich environments, characterized by uncertainty and complexity, have become ubiquitous. The larger the data sample is, the more accurate the results of the machine learning approach can be. Therefore, machine learning has potential to improve the quality of MEBN models as well as the effectiveness for MEBN modeling. In this research, we study a MEBN learning framework to develop a MEBN model from a combination of domain expert's knowledge and data. To evaluate the MEBN learning framework, we conduct an experiment to compare the MEBN learning framework and the existing manual MEBN modeling in terms of development efficiency.