In this tutorial, we will learn how to apply Computer Vision, Deep Learning, and OpenCV to identify potential child soldiers through automatic age detection and military fatigue recognition. Military service is something of personal importance to me, something I consider honorable and admirable. That's precisely the reason why this project, leveraging technology to identify child soldiers, is something I feel strongly about -- nobody should be forced to serve, and especially young children. You see, the military has always been a big part of my family growing up, even though I did not personally serve. Even outside my direct family, the military was still part of my life and community. I went to high school in a rural area of Maryland. If you didn't want to become a farmer or work in agriculture, that really only left two options -- go to college or join the military. If I'm recalling correctly, before I graduated from high school, at least 10 kids from my class enlisted, some of whom I knew personally and had classes with.

Over the past years, progress in Artificial Intelligence and Neuroscience has made possible brain activity interaction with computers and other devices. In particular, the advancement of various signal processing methodologies such as Electroencephalogram (EEG), combined with AI-powered algorithms, have enabled us to delve into the world of Brain-Computer Interfaces and to talk about a new era of human factor design and research. Brain-Computer Interfaces refer to devices that allow users to interact with computers, measuring brain activity through EEG, which recognizes the energy and frequency patterns of the brain. There are currently two types of Brain-Computer interfaces: invasive and non-invasive, and although both have their benefits, in this article we will focus on the non-invasive BCIs. By combining knowledge from Artificial Intelligence and specifically Machine Learning, Brain-Computer Interfaces have become a vital tool in aiding the accuracy and reliability of usability testing and user experience research, allowing us to talk about a new era of human factor design.

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

Artificial Neural Networks (NN) are widely used for solving complex problems from medical diagnostics to face recognition. Despite notable successes, the main disadvantages of NN are also well known: the risk of overfitting, lack of explainability (inability to extract algorithms from trained NN), and high consumption of computing resources. Determining the appropriate specific NN structure for each problem can help overcome these difficulties: Too poor NN cannot be successfully trained, but too rich NN gives unexplainable results and may have a high chance of overfitting. Reducing precision of NN parameters simplifies the implementation of these NN, saves computing resources, and makes the NN skills more transparent. This paper lists the basic NN simplification problems and controlled pruning procedures to solve these problems. All the described pruning procedures can be implemented in one framework. The developed procedures, in particular, find the optimal structure of NN for each task, measure the influence of each input signal and NN parameter, and provide a detailed verbal description of the algorithms and skills of NN. The described methods are illustrated by a simple example: the generation of explicit algorithms for predicting the results of the US presidential election.

Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset. Yet in some scientific disciplines, obtaining an abundance of data is an impractical luxury, however; there is an explicit model of the domain based upon previous scientific discoveries. Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model. We show its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration. Our accuracy surpasses the current state-of-the-art by over $20\%.$ Our approach thus demonstrates how artificial intelligence can be further enhanced by "standing on the shoulders of giants."

Explainable Artificial Intelligence (XAI), i.e., the development of more transparent and interpretable AI models, has gained increased traction over the last few years. This is due to the fact that, in conjunction with their growth into powerful and ubiquitous tools, AI models exhibit one detrimential characteristic: a performance-transparency trade-off. This describes the fact that the more complex a model's inner workings, the less clear it is how its predictions or decisions were achieved. But, especially considering Machine Learning (ML) methods like Reinforcement Learning (RL) where the system learns autonomously, the necessity to understand the underlying reasoning for their decisions becomes apparent. Since, to the best of our knowledge, there exists no single work offering an overview of Explainable Reinforcement Learning (XRL) methods, this survey attempts to address this gap. We give a short summary of the problem, a definition of important terms, and offer a classification and assessment of current XRL methods. We found that a) the majority of XRL methods function by mimicking and simplifying a complex model instead of designing an inherently simple one, and b) XRL (and XAI) methods often neglect to consider the human side of the equation, not taking into account research from related fields like psychology or philosophy. Thus, an interdisciplinary effort is needed to adapt the generated explanations to a (non-expert) human user in order to effectively progress in the field of XRL and XAI in general.

Carnegie Mellon University students who designed and built a small, boxy robot, called Iris, have achieved a major milestone: their robot passed its critical design review by NASA and is on track to land on the moon in the fall of 2021. "We are moving forward … we're going to the moon," a triumphant project manager, Raewyn Duvall, told Iris team members during a Zoom meeting following the review. Officials at NASA and Astrobotic Inc., whose Peregrine lander will deliver the robot to the lunar surface, performed the review. Duvall, a Ph.D. student in the Electrical and Computer Engineering Department, said the process resulted in a few small design revisions, which the team is now incorporating. The team will replace prototype parts with flight components this summer, as they test the robot to prove that it can withstand the trip to the moon without causing problems for Peregrine or other payloads aboard the lunar lander.

Artificial Intelligence (AI) is driving most of the economies towards an innovative future. Several countries and cities have emerged as AI leaders to produce great tech talents and nurture the potentials of the technology. Big-scale investments by the governments, citizens' likeliness to adapt AI-centric lifestyle, well-educated and well-versed workforce, the contribution of significant world-class universities and low cost for business establishment are among some of the major catalyzers that make following cities a potential AI hub, to begin with, your startup. Here are some of the most advanced cities to explore your chances to be the next AI entrepreneur. Austin has been named under the best places to start a business in the US.

The UK's job retention scheme, which pays 80 per cent of furloughed employees' wages up to £2500 a month, will be extended for four months until October. Rishi Sunak, the chancellor of the exchequer, said that from August employees will be allowed to work part-time while furloughed, but the government will require companies to shoulder some of the costs of furlough payments. The scheme currently covers the salaries of 7.5 million workers, a quarter of the UK's workforce, and costs the UK government about £14 billion a month. Head teachers have warned that the government's plan to reopen schools for some year groups in England on 1 June is not feasible. Paul Whiteman, head of the National Association for Head Teachers, told MPs that it wouldn't be possible to comply with the government's new guidance recommending a maximum class size of 15 pupils. Northern Ireland has unveiled a five-stage plan for easing coronavirus restrictions, which includes advice for specific job sectors and is ...

On April 3, the U.S. Centers for Disease Control and Prevention (CDC) recommended "wearing cloth face coverings in public settings where other social distancing measures are difficult to maintain (e.g., grocery stores and pharmacies) especially in areas of significant community-based transmission" of COVID-19. Despite a plethora of health experts telling it to do so since then, the White House only complied with this health guidance Monday. It sent an email to staffers ordering all of them to wear face masks inside the building. White House staffers can take-off their masks while they're seated at their desks and are able to maintain six feet of distance from others. Incredibly, President Donald Trump is exempted from this order, aides told The Washington Post.