Owura Kwadwo Hottish illustrates a window of Microsoft Word using colored chalk on a blackboard. He uses it to teach computer skills to students at the Betenase M/A Junior High School in Kumasi, Ghana. Owura Kwadwo Hottish illustrates a window of Microsoft Word using colored chalk on a blackboard. He uses it to teach computer skills to students at the Betenase M/A Junior High School in Kumasi, Ghana. Could you teach computer class without a computer?

The robotic platform heaves, as if breathing. Atop it stretches a piece of white gauze with a blue line painted down the middle. Along this line another robot snips with little surgical scissors, waiting for the platform to come to a brief rest before making a cut. This could be you one day. Not that you'll turn into a robot--you may go under the knife of a machine working as a surgical assistant.

YouTube's new moderation team appears to have mistakenly removed a number of right-wing political channels from the site. The video-streaming giant - owned, like Google, by Alphabet - is currently stepping up its efforts to remove offensive content as the US gun control debate heats up following the Florida high school massacre on 14 February in which ex-student Nikolas Cruz killed 17 people with an AR-15 assault rifle. Over 10,000 new staff were hired in December as the company sought to address fake news and inappropriate clips being uploaded, following criticism of its hosting contentious material without challenge. However, YouTube appears to have encountered teething problems with its new censorship body. Pulling accounts entirely would have marked a major policy change for YouTube - one that it has not publicly announced.

One year back, I was a newbie to the world of Machine Learning. I used to get overwhelmed by small decisions, like choosing the language to code with, choosing the right online courses, or choosing the correct algorithms. So, I have planned to make it easier for folks to get into Machine Learning. I'll assume that many of us are starting from scratch on our Machine Learning journey. Let's find out how current professionals in the field reached their destination, and how we can emulate them on our journey. I will illustrate how you can learn Data Science by drawing a parallel between how Rafael Nadal learned to play tennis, and how you can learn Machine Learning.

Here are four projects led by 5th-year engineering students majoring in Computer Science, Big Data and Connected Objects and Financial Engineering which resorted to Machine Learning. "Weather Learning" is a project which aims at developing a tool allowing to predict the weather report, anywhere in France. It is based on modern methods of Machine learning and Deep learning. This project does not use physico-chemical modellings but the history of the weather data. After identifying meteorological sources from which data from more than 10 years ago could be extracted, students developed algorithms allowing to extract the meteorological data. MongoDB was chosen as a database for its capacity to manage geographical coordinates.

A combination of breathless excitement and near-paralysing fear are driving the debate along with countless – often ill-informed – predictions about how the technology which will revamp our future lives. While it is far too early in AI's evolution to understand its true potential or how quickly it will have a fundamental impact, it is already penetrating our lives. From smartphones and dating sites to web searches and driverless vehicles; it is becoming part of the fabric of life. Over the next few years, businesses, homes and schooling could be completely different with AI on the scene. At its core, AI is a type software or hardware that learns – and it could become programmed to learn mostly about us, its users.

During college, while doing a geophysics internship aboard an oil rig, I realized that software was the future--so I switched my major to computer science. After more than a decade working at Google, I had a similar moment where I realized that AI is the future of computer science. Today, I lead Google's machine learning education effort, in the hope of making AI and its benefits accessible to everyone. AI can solve complex problems and has the potential to transform entire industries, which means it's crucial that AI reflect a diverse range of human perspectives and needs. That's why part of Google AI's mission is to help anyone interested in machine learning succeed--from researchers, to developers and companies, to students like Abu.

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.