Space is the final frontier for understanding how extreme environments affect human physiology. Following twin astronauts, one of which spent a year-long mission on the International Space Station, Garrett-Bakelman et al. examined molecular and physiological traits that may be affected by time in space (see the Perspective by Löbrich and Jeggo). Sequencing the components of whole blood revealed that the length of telomeres, which is important to maintain in dividing cells and may be related to human aging, changed substantially during space flight and again upon return to Earth. Coupled with changes in DNA methylation in immune cells and cardiovascular and cognitive effects, this study provides a basis to assess the hazards of long-term space habitation. Science, this issue p. eaau8650; see also p. 127 To date, 559 humans have been flown into space, but long-duration ( 300 days) missions are rare (n 8 total). Long-duration missions that will take humans to Mars and beyond are planned ...

In science news around the world, Sydney Brenner, the Nobel laureate who made seminal discoveries in genetics and developmental biology, dies at age 92. Japan's Hayabusa2 mission continues its novel exploration of the asteroid Ryugu by blowing a crater in it so the spacecraft can collect samples; scientists are eager to study material from beneath the surface that has not undergone eons of space weathering. The U.S. National Institutes of Health has begun to restrict visitors from certain countries at its campus in Bethesda, Maryland, for national security reasons, sparking concerns from its staff scientists. Only 10 days after Google created an eight-member external advisory council on the ethics of artificial intelligence research, the company pulled the plug last week amid controversy over the views and affiliations of the council's members. The use of chemicals to disperse oil spills largely doesn't create a mixture more toxic than the oil itself, the U.S. National Academy of Sciences reports.

The year of the gig economy IPO continues, when Uber Thursday made public its first bit of official paperwork with the Securities and Exchange Commission--a sign that the tech company is preparing to list its shares on the New York Stock Exchange. The filing shows a sprawling transportation business with operations stretching into 63 countries and over 700 cities, providing 5.2 billion rides in 2018: roughly one for every person in Europe and Asia. Uber pulled in $11.3 billion in revenue in 2018, a 42 percent jump over the year previous. And though its operating losses are still heavy--$3 billion in 2018--the company has managed to stem them, at least a bit, bringing operating losses down from $4.1 billion in 2017. Uber had 91 million active users at the end of 2018, 23 million more than a year earlier.

Democratic presidential candidate Andrew Yang appeared on "Fox and Friends" Friday morning to defend his campaign's key proposal of giving $12,000 to each American adult every year and criticized Democrats for their newfound support for the abolition of the Electoral College. Yang, former ambassador of global entrepreneurship in the Obama administration and a long-shot candidate for the party's nomination, was grilled by the show hosts and the audience about his universal basic income program, dubbed "Freedom Dividend," and his other views. "You have to look up who are going to be the biggest winners from artificial intelligence and self-driving cars and trucks and new technologies. The American people are gonna see very little of the gains in the innovation," said Yang. "The American people are gonna see very little of the gains in the innovation." He added that due to an increasing automation, "most of us" won't work at Amazon or other companies, leaving the rest of the people at a disadvantage because their source of income will disappear.

In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of "Over the Rainbow." If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever "map" of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

While there are innumerable cybersecurity threats, the end goal for many attacks is data exfiltration. Much has been said about using machine learning to detect malicious programs, but it's less common to discuss how machine learning can aid in identifying other types of notable threats. Critically, machine learning can be key in detecting one of the most insidious types of malicious actors – one with legitimate access to your network and systems. When properly trained, machine-learning algorithms can be used to identify insider threats and frauds before they become dangerous. When people hear the term "insider threat," many of them imagine an employee gone rogue, a disgruntled member of your team committing corporate espionage and leaking sensitive data or documents to competitors or criminals.

Lawmakers want to make sure the algorithms companies use to target ads, recruit employees and make other decisions aren't inherently biased against certain people. Sens. Ron Wyden, D-Ore., and Cory Booker, D-N.J., on Wednesday introduced legislation that would require organizations to assess the objectivity of their algorithms and correct any issues might unfairly skew their results. As society depends on tech to make increasingly consequential decisions, the Algorithmic Accountability Act aims to create a level playing field for people of all backgrounds. Rep. Yvette Clarke, D-N.Y., introduced a companion bill in the House. Under the act, the Federal Trade Commission would compel companies to test both their algorithms and training data for any shortcomings that could lead to biased, inaccurate, discriminatory or otherwise unfair decisions.

Mr Eduardo Belinchon de la Banda (Digital Innovation Manager, foraus - Swiss Forum on Foreign Policy) briefly introduced foraus, its goals and activities. Foraus is a Swiss think-tank on foreign policy. He explained that the main goal of the session would be to discuss means of developing inclusive Artificial Intelligence (AI). He highlighted the large scale and intensity with which AI might change modern society in comparison to other disrupting technologies. According to him, many countries have developed strategies, principles and guidelines for the ethical development of AI and nearly all included provisions on the matter of inclusion in AI.

BRUSSELS (Reuters) - Companies working with artificial intelligence need to install accountability mechanisms to prevent its being misused, the European Commission said on Monday, under new ethical guidelines for a technology open to abuse. AI projects should be transparent, have human oversight and secure and reliable algorithms, and they must be subject to privacy and data protection rules, the commission said, among other recommendations. The European Union initiative taps in to a global debate about when or whether companies should put ethical concerns before business interests, and how tough a line regulators can afford to take on new projects without risking killing off innovation. "The ethical dimension of AI is not a luxury feature or an add-on. It is only with trust that our society can fully benefit from technologies," the Commission digital chief, Andrus Ansip, said in a statement.

The problem of explaining the behavior of deep neural networks has gained a lot of attention over the last years. While several attribution methods have been proposed, most come without strong theoretical foundations. This raises the question of whether the resulting attributions are reliable. On the other hand, the literature on cooperative game theory suggests Shapley values as a unique way of assigning relevance scores such that certain desirable properties are satisfied. Previous works on attribution methods also showed that explanations based on Shapley values better agree with the human intuition. Unfortunately, the exact evaluation of Shapley values is prohibitively expensive, exponential in the number of input features. In this work, by leveraging recent results on uncertainty propagation, we propose a novel, polynomial-time approximation of Shapley values in deep neural networks. We show that our method produces significantly better approximations of Shapley values than existing state-of-the-art attribution methods.