NUTLEY, NEW JERSEY – Authorities say a New Jersey student accused of making an online threat against his high school posted a video created in the popular video game Minecraft showing a shooting at a replica of the school. NJ.com reports that Joseph Rafanello's avatar can be seen walking through the virtual school, complete with lockers and the sound of gunshots in the background. The new information was discussed Wednesday during a detention hearing for the 18-year-old Nutley High School student. A judge rejected prosecutors' request to keep him in custody until his trial, ordering that he instead be placed on home detention. Rafanello was charged with creating a false public alarm for a video that spurred the closure of Nutley schools Feb. 16.

Machine learning and AI are some of the biggest topics in the tech world right now, and Google is looking to make those fields more accessible to more people with its new Learn with Google AI website. Google has been pursuing AI education for a while, both with advanced projects like TensorFlow and more playful projects like cat doodles and a machine vision experiment meant to showcase AI projects in more practical ways. Google envisions the Learn with Google AI site serving as a repository for machine learning and AI, and it's meant to be a hub for anyone looking to "learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems." The site will apparently cater to all levels of AI enthusiasts, from researchers looking for advanced tutorials to beginners. The site also features a free course called Machine Learning Crash Course (MLCC).

This paper presents a novel formulation and solution of orbit determination over finite time horizons as a learning problem. We present an approach to orbit determination under very broad conditions that are satisfied for n-body problems. These weak conditions allow us to perform orbit determination with noisy and highly non-linear observations such as those presented by range-rate only (Doppler only) observations. We show that domain generalization and distribution regression techniques can learn to estimate orbits of a group of satellites and identify individual satellites especially with prior understanding of correlations between orbits and provide asymptotic convergence conditions. The approach presented requires only visibility and observability of the underlying state from observations and is particularly useful for autonomous spacecraft operations using low-cost ground stations or sensors. We validate the orbit determination approach using observations of two spacecraft (GRIFEX and MCubed-2) along with synthetic datasets of multiple spacecraft deployments and lunar orbits. We also provide a comparison with the standard techniques (EKF) under highly noisy conditions.

Although many contextual bandit algorithms have similar theoretical guarantees, the characteristics of real-world applications oftentimes result in large performance dissimilarities across algorithms. We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits: the way exploration is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning. We show that contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We propose new contextual bandit designs, combining parametric and non-parametric statistical estimation methods with causal inference methods in order to reduce the estimation bias that results from adaptive treatment assignment. We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data.

Why do we need Machine Learning? Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve. Let's look at these 2 examples: Then comes the Machine Learning Approach: Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job.

Paul Allen puts a premium on common sense – so much so that he's investing $125 million to teach it to computers. The Microsoft co-founder said Wednesday he would commit the money over the next three years to the Seattle-based Allen Institute for Artificial Intelligence, known as AI2. The funds will go toward multiple AI2 projects, but specifically will be used for the new "Project Alexandria" that will try to bring together various technology elements used in artificial intelligence, with the goal of creating a system imbued with good sense and judgment. Currently, AI systems can scan and "read" text, interpret some pictures and play board games. But they can't react to unexpected situations or tell you, say, which way water would flow on a hill.

A drone taxi using 5G technology is displayed at the Mobile World Congress on Feb. 27, 2018. Visitors try out Virtual 5G technology during the Mobile World Congress on Feb. 27, 2018. A 5G antenna is displayed at the Deutsche Telekom stand on the first day of the Mobile World Congress. Docomo 5G Robot remote humanoid assistant draws some'sumi-e' style drawings on the first day of the Mobile World Congress. Visitors look at a US company Qualcomm stand announcing '5G' technology at the Mobile World Congress (MWC) in Barcelona, Spain, 26 February 2018 (Photo: EPA-EFE/ALBERTO ESTEVEZ) People walk by a 5G stand at the Mobile World Congress (MWC), the world's biggest mobile fair, on February 26, 2018 in Barcelona.

With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself.

As ATD marks its 75th year, we want to take time to talk to industry luminaries about where the field of talent development has been, where it is going, and what professionals need to succeed. There is no better place to start than a conversation with Elaine Biech, whom many consider an industry treasure. As president of ebb associates inc, a strategic implementation, leadership development, and experiential learning consulting firm, Elaine has helped organizations develop their talent and navigate change. She has presented at dozens of national and international conferences, is the author of more than 80 books, and has been featured in such publications as The Wall Street Journal, Harvard Management Update, Investors Business Daily, and Fortune. A long-time volunteer for ATD, she has served on its National Board of Directors and been the recipient of numerous awards. More importantly, Elaine has led the charge in the evolution of training and talent development--helping it transform from an order-taking function to a fully realized profession and strategic partner that businesses need to excel. However, I still didn't know there was a profession called "training," and I'd never heard of Malcolm Knowles, Don Kirkpatrick, or of the other thought leaders in our field.

Following the school shooting in Parkland, Florida, responsible for the loss of 17 lives, Donald Trump held a meeting at the White House. Seemingly intended to disabuse the nation of the imminent threat of semi-automatic weapons, the president shifted attention to other possible culprits: violent video games. He said: "I'm hearing more and more people say the level of violence on [sic] video games is really shaping young people's thoughts." Considering he couldn't maintain focus on violent games for a full speech, let alone a news cycle, it's a challenge to muster concern about what Trump's bluster means for the future of the medium. Nor is the fate of the video game industry as pressing as the fate of the nation's populace, whose lives will remain in real peril, so long as Trump and his supporters continue to turn the conversation away from dramatic change in the commercial gun industry.