A school robotics team made up of five students from The Gambia has joined Afghanistan's all-girl robotics team in having their one-week visas rejected by the U.S. in order to attend the First Global Challenge robotics competition in Washington D.C. The five Gambian students on the robotics team will have their robot shipped from the West African country to the First Global Challenge competition in the U.S., which takes place from July 16-18. But the teens will not be able to attend the prestigious international event themselves, Al-Jazeera reports. Despite saying they worked "rigorous shifts" of seven hours during Ramadan to complete the robot, they will instead have to settle for presenting it via Skype. Director of The Gambia's ministry of higher education, research, science and technology, Moktar Darboe, told Al Jazeera that the students are "very disappointed" they won't be able to attend the competition.

Samsung is working on a standalone virtual reality headset that can work without a smartphone or connected computer. A prototype of the VR headset has now appeared, and it comes with eye and hand tracking technology. The prototype VR headset by Samsung is labeled as Exynos VR III, and it comes with a 10nm hexa-core processor with an ARM Mali G71MP20 graphics, according to Liliputing. This seems to be an Exynos 9 Series chip, specifically the Exynos 8890 SoC. This setup will be able to run dual WQHD displays (2,560 x 1,440 resolution, or close to it) at 90Hz, or a single 4K display running at 75Hz.

On the day U.S.-backed forces made a major breakthrough in the battle for Islamic State group's (ISIS) operation capital Raqqa, reports said the terror group's poster girl Sally Jones, also known as Mrs. Terror, is desperate to return to the U.K. This was revealed by the wife of a former ISIS militant who is now living in a refugee camp in Syria, in an interview to Sky News. The woman, known as Aisha, said of Jones: "She was crying and wants to get back to Britain. She told me she wish [sic] to go to her country. Aisha said Umma Hussain al Britani, the name adopted by Jones, was distraught and crying as her plea had been denied by ISIS leaders on the basis she considered a military wife. READ: Who Is Sally Jones? Jones is originally from from Chatham, Kent, became the leading female recruitment officer for ISIS after moving to Raqqa and marrying a now-dead jihadist in 2004. She is now the most wanted woman in the world after climbing to the top of the CIA assassination list, the Sun reported. Jones, 47, has remained at large since her husband, ISIS recruit Junaid Hussain, was killed in a U.S. drone strike in 2015. The couple are thought to have been behind at least a dozen murderous attacks both in the Middle East and abroad. On September 28, 2015, the United Nations sanctioned Jones as an agent operating on behalf of a terrorist organization, the Mirror reported. Jones is believed to have enticed scores of would-be European jihadis to join the self-declared caliphate through her influential recruitment network called the "Raqqa 12," the Sun reported. Jones has been attributed with the recruitment and training of young girls in Syria. The Express quoted a counter extremism website as saying Jones's activity online was in line with her role as leader of the secret "Anwar al-Awlaki" battalion's female wing. "In this role, Jones is responsible for training all European female recruits, or'muhajirat', in the use of weapons and tactics.

Simon Sinek is by far one of the greatest thought leaders of our time. Almost religiously and on numerous occasions, I've implemented his "start with why" model and received positive results in doing so. When introduced, it challenged new and existing businesses to think differently, and pull on the emotions of consumers rather than their purse strings. It offered us the challenge of inventing a new way of thinking; outside of the box and with creative tact. Since the release of his now viral TED talk, we continue to see praise for the Sinek model, and it has been a for certain positive addition to education curriculums far and wide.

The challenge we face today is not a "world without work" but a world with rapidly changing work. The pace of progress in AI and machine learning is accelerating rapidly. In the past month alone, these are just a few of the news items I've seen: Deep learning and neural networks have dramatically improved in effectiveness and impact, leading to human-level performance in many aspects of vision, conversational speech, and problem-solving. As a result, industries are in the midst of a major transformation and more is on the way. Median income in America is lower now than in the past 15 years, and wealth is concentrated at the highest levels.

Five teenage pupils from The Gambia who built a robot for a prestigious international competition in the United States will not be able to accompany their invention to the event after being denied a visa. The Gambian pupils become the second team of students to be refused entry to attend the FIRST Global robotics event in Washington, DC on July 16-18. On Saturday, it was reported that an all-girls team from Afghanistan were also denied a visa to travel to the US to showcase their creation at the same competition. We're not giving up, despite the challenges we face, we still continue to work hard. Moktar Darboe, director of The Gambia's ministry of higher education, research, science and technology, told Al Jazeera that the team, made up of high school pupils aged 17-18, were "very disappointed".

Robots could take over the human job of milking a scorpion for its venom, potentially making the task a lot safer for everyone involved. The Society for Experimental Biology described the robot as clamping onto a scorpion's tail and using electrical stimulation to get it to release venom that can be safely collected. The robot is also portable and lightweight, and can be operated by a single person. According to SEB, which recently held its annual main meeting in Gothenburg, Sweden, the technology can have different settings for different species of scorpion and can be programmed to remember them. "The extraction of scorpion venom is a very difficult task and usually takes at least two experimenters," robot designer Mouad Mkamel said in the society's statement.

A'scorpion-milking' robot has been developed to extract venom from the arachnids faster and more safely for use in cancer research. Scorpion venom is normally milked by toxicologists manually, a dangerous procedure where one wrong move can prove deadly. The new machine allows researchers to strap scorpions into an extractor, reducing their contact time with the beasts and making venom extraction safer. A'scorpion-milking' robot (pictured) has been developed to extract venom from the arachnids faster and more safely for use in cancer research. Current scorpion-milking methods can be dangerous both for the animals, due to punctures made to the venom gland or damage to the abdomen, and to the researchers, due to electric shocks from the equipment.

SHORTLY after 9/11, the US deployed a new form of high-tech warfare: sending drones into foreign airspace to kill terror suspects. At first the strikes were restricted to Afghanistan, but soon they were extended into Pakistan, Yemen and Somalia. The strategy has been escalated by presidents Obama and Trump. Initially the US had a virtual monopoly on drone technology, but commentators pointed out that this would only be temporary. Legal scholars also warned that the strikes were of dubious international legality. The implication was clear: if the US could strike with impunity, what was there to stop others from doing the same?

Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a significant amount of human insight and preparation is required prior to executing machine learning algorithms. For example, when creating deep neural networks, the number of parameters must be selected in advance and furthermore, a lot of these choices are made based upon pre-existing knowledge of the data such as the use of a categorical cross entropy loss function. Humans are able to study a dataset and decide whether it represents a classification or a regression problem, and consequently make decisions which will be applied to the execution of the neural network. We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem. We test API on 16 different classification, regression and sentiment analysis datasets with up to 10,000 features and up to 17,000 unique target values. API achieves an average accuracy of $96.3\%$ in identifying the problem type without hardcoding any insights about the general characteristics of regression or classification problems. For example, API successfully identifies classification problems even with 1000 target values. Furthermore, the algorithm recommends which loss function to use and also recommends a neural network architecture. Our work is therefore a step towards fully automated machine learning.