'Sophia' the life-size social robot speaks at the United Nations, telling the audience she is'a year-and-a-half old and I can see you, have a full conversation, make thousands of facial expressions and understand speech and meaning behind words'. It adds: 'And I just got these new hands – check this out' before moving its fingers. United Nations deputy secretary general Amina J Mohammed asks the robot a question about how the UN can help those without basic needs such as electricity. 'Sophia' quotes William Gibson before talking about how artificial intelligence is more efficient and can be used to better distribute resources. The robot thanks the audience before attempting a slightly forced smile.

While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? For example, the drone can't fly "Beyond-Line-of-Sight" (BLOS), meaning it must not move away from the drone pilot more than 100 meters, except if its weight is under 2 kg (4,4 lb) or if you have a special exemption. As we map thousands of hectares thanks to solar energy to address conservation issues, we are directly progressing towards SDGs 7, 9 and 15 (cf.: We are currently raising funds and starting the drone marketing abroad, in Africa and Brazil.

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.

We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The method is based on the invariances or stability of predicted results when known data is represented as expansions in terms of various stochastic functions. The algorithm predicts the classification/regression values of new data by combining (via voting) the outputs of these numerous linear expansions in randomly chosen functions. The few parameters (typically only one parameter is used in all studied examples) that this model has may be automatically optimized. The algorithm has been tested on 10 diverse training data sets of various types and feature space dimensions. It has been shown to consistently exhibit high accuracy and readily allow for optimization of parameters, while simultaneously avoiding pitfalls of existing algorithms such as those associated with class imbalance. We very briefly speculate on whether spatial coordinates in physical theories may be viewed as emergent "features" that enable a robust machine learning type description of data with generic low order smooth functions.

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to privacy concerns, patients often desire to withhold sensitive information. This self-censorship can impede proper diagnosis and treatment, which may lead to serious health complications and even death over time. In this paper, we present privacy distillation, a mechanism which allows patients to control the type and amount of information they wish to disclose to the healthcare providers for use in statistical models. Meanwhile, it retains the accuracy of models that have access to all patient data under a sufficient but not full set of privacy-relevant information. We validate privacy distillation using a corpus of patients prescribed to warfarin for a personalized dosage. We use a deep neural network to implement privacy distillation for training and making dose predictions. We find that privacy distillation with sufficient privacy-relevant information i) retains accuracy almost as good as having all patient data (only 3\% worse), and ii) is effective at preventing errors that introduce health-related risks (only 3.9\% worse under- or over-prescriptions).

Four out of 10--that's how many Americans the National Cancer Institute estimates will be diagnosed with cancer at some point. While 33 percent of those patients won't live longer than five years, giving them precious little time to find effective treatments, it takes over a decade to bring new cancer drugs to market. The process involves animal testing, human trials and regulatory review--a gantlet through which less than 7 percent of experimental medicines successfully pass. Is it any wonder, then, that there are less than 2,000 Food and Drug Administration-approved pharmaceuticals on the market? Insilico Medicine, a Baltimore-based biotech research company, hopes to revolutionize drug development by slashing the time necessary for research with the help of artificial intelligence (AI). In a study published in the medical journal Oncotarget, a team led by Insilico Medicine details their approach.

We are moving rapidly towards a world where robots and artificial intelligence (AI) systems are connected to and influenced by social media, the Internet of Things (IoT) and big data. Technological developments are moving fast, and AI has many governments concerned. Given the pace of technological advancement, how do rule-makers set legislation for AI while allowing the safe evolution of technology? Who thinks about and enforces these guidelines, and what work is being done, or should be done, with governments to craft AI policy? Moves by the European Parliament to consider granting some form of legal status to AI have revived questions of liability and responsibility.

To help keep tabs on the safety of driverless cars rolling around U.S. cities, the federal government last year, and again last month, suggested that tech firms and car companies submit safety checklists. None of the companies have done it. Waymo, a self-driving car project spun off from Google, submitted a 43-page safety report to the U.S. Department of Transportation on Thursday, offering the most detailed description yet of how it -- or any other company -- equips and trains vehicles to avoid the range of mundane and outrageous problems that are part of U.S. driving. "We've staged people jumping out of canvas bags or porta-potties on the side of the road, skateboarders lying on their boards, and thrown stacks of paper in front of our sensors," says the report, which describes how company engineers use a 91-acre California Central Valley test facility mocked up like a city, as well as computer simulations covering hundreds of thousands of variations of possible road scenarios. The National Highway Traffic Safety Administration has suggested a set of 28 "behavioral competencies," or basic things an autonomous vehicle should be able to do.

The front-line of the stuggle to take out ISIS' most deadly fighters and recruiters like the infamous'White Widow' Sally Jones isn't waged on the battle fields of Syria or Iraq but from an air-conditioned trailer in the middle of the Nevada desert. Seven thousand miles from the war in Syria, pilots sit in a sun-bleached desert base 45 miles north of the bright lights of Las Vegas guiding the lethal Predator drones that have revolutionized modern combat. From cool, dark booths at Creech Air Force Base in Indian Springs, Nevada, pilots and sensor operators work closely with large teams of intelligence analysts who sift streams of real-time data transmitted by the drones on the other side of the planet. The front-line of the stuggle to take out ISIS' most deadly fighters and recruiters isn't waged on the battle fields of Syria or Iraq but from an air-conditioned trailer (pictured) in the middle of the Nevada desert And it is a pilot at this base who is said to have remotely taken out the British ISIS recruiter Sally Jones. Jones, from Chatham in Kent, fled to Syria with her son in 2013 before becoming a recruiter for the terror group.

Ukrainian military officials have unveiled an adaptable war robot that can switch up its mode of travel, and even the type of weapons it carries. The Phantom ground robot can be fitted with tank-like treads, or move about on six wheels – and, it can carry anti-tank weapons, grenade launchers, or machine guns, according to DefenseOne. The robot was demonstrated on Monday at the Association of the US Army show in Washington, D.C., and could hit the battlefield as soon as next year. Ukrainian military officials have unveiled an adaptable war robot that can switch up its mode of travel, and even the type of weapons it carries. Phantom, also sometimes referred to as Fantom, can drive up to 20 kilometers (12.4 miles) at a time, at a maximum speed of 38 kilometers per hour.