Japan is launching multiple missions to explore the mysteries of the solar system in the coming years, joining hands with the European Union and countries such as India to compete with space superpowers such as the United States and Russia. The ultimate goal of space exploration is "to expand the areas of activities for humans and find another habitable planet. I believe there is a possibility that we can colonize Mars," said Hitoshi Kuninaka, a vice president of the Japan Aerospace Exploration Agency (JAXA). In 2018, Japan made history by landing two small rovers from the space probe Hayabusa2 on the surface of an asteroid 300 million kilometers from Earth. Hayabusa2's touchdown on the Ryugu asteroid is expected in late January this year.

The year 2019 marks 50 years since the first humans landed on the moon in 1969 as part of NASA's Apollo 11 lunar mission. In an interview with Managing Editor Sayuri Daimon, former Japanese astronaut Naoko Yamazaki shares her experience in space in 2010 and her views on space development in the coming years. Former astronaut Naoko Yamazaki hopes to open Asia's first spaceport, which will serve as a hub for space planes for travelers, in Japan as early as 2021. She believes that a new age of space tourism where ordinary people, not only astronauts, will be able to travel beyond Earth is just around the corner. In July, she co-founded the Space Port Japan Association, an organization to support efforts to open spaceports in Japan through collaboration with companies, groups and government institutions.

This year in science saw important developments, including a few surprises--but, many of 2018's most significant events were the products of ongoing social and political trends that have been in motion for years. The year 2018 saw renewed debate about the urgency and efficacy of policies to limit climate change, against a backdrop of severe weather events and at a time when the U.S. government continues to roll back environmental regulation. It was also a year when both CRISPR and artificial intelligence (AI) revealed their growing potential for innovation across many fields, even as researchers expressed concern that these technologies were developing at speeds too fast to assess and evaluate their impact. NASA continued its exploration of the solar system, and Americans had the opportunity to watch, in real time, the harrowing landing of the InSight probe on Mars. But, the public was also fixated on the launches of SpaceX's revolutionary, reusable rockets--the latest chapter in the rise of private space companies, as the Trump administration continues to look for ways to outsource and commercialize U.S. space exploration.

John Marinos was skeptical when he received an email at his United Nations office in Bangkok that a team from SAP was developing an AI-based reporting tool to help this organization better manage humanitarian aid. One year later he's convinced. The aptly named 4W-Wizard has turned thousands of lines of non-standard, confusing and misspelled information from numerous agencies on the ground into clean data that the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA) can use for streamlined disaster relief management. AI-based tool helps support UN's vision of a seamless system across the humanitarian universe of support.Getty Images "Since 90 percent of our challenges involve people-oriented problems, I never believed technology would help us to sort this out," said Marinos, information management officer at UN OCHA. "We've struggled with manual-based reporting that took hours to clean up before it was usable. The 4W-Wizard helps provide fast visibility into which organizations are providing what kind of help where and when. In less than an hour, we can immediately see where the gaps are and what's needed next."

We may remember 2018 as the year when technology's dystopian potential became clear, from Facebook's role enabling the harvesting of our personal data for election interference to a seemingly unending series of revelations about the dark side of Silicon Valley's connect-everything ethos. The list is long: High-tech tools for immigration crackdowns. YouTube algorithms that steer youths into extremism. Doorbells and concert venues that can pinpoint individual faces and alert police. Repurposing genealogy websites to hunt for crime suspects based on a relative's DNA.

The best hope for fighting computer-generated fake-porn videos might come from a surprising source: the artificial intelligence software itself. Technical experts and online trackers say they are developing tools that could automatically spot these "deepfakes" by using the software's skills against it, deploying image-recognition algorithms that could help detect the ways their imagery bends belief. The Defense Advanced Research Projects Agency, the Pentagon's high-tech research arm known as DARPA, is funding researchers with hopes of designing an automated system that could identify the kinds of fakes that could be used in propaganda campaigns or political blackmail. Military officials have advertised the contracts -- code-named "MediFor," for "media forensics" -- by saying they want "to level the digital imagery playing field, which currently favors the manipulator." The photo-verification start-up Truepic checks for manipulations in videos and saves the originals into a digital vault so other viewers -- insurance agencies, online shoppers, anti-fraud investigators -- can confirm for themselves.

The video showed the woman in a pink off-the-shoulder top, sitting on a bed, smiling a convincing smile. But it had been seamlessly grafted, without her knowledge or consent, onto someone else's body: a young pornography actress, just beginning to disrobe for the start of a graphic sex scene. A crowd of unknown users had been passing it around online. She felt nauseous and mortified: What if her co-workers saw it? Would it change how they thought of her? Would they believe it was a fake?

Interest in artificial intelligence technology, including in the association space, is picking up in a big way as we close out 2018 and move into the new year, and an array of recent studies on the topic shows just what we have to look forward to. Studies from Stanford University, Pew Research Center, and New York University examine the state of AI from different directions. They explore the field's global growth, its diversity, and its economic and ethical implications. As highlighted by the fact that numerous reports on the topic came out at around the same time, artificial intelligence is driving a lot of research momentum these days. According to Stanford's AI Index [PDF], growth in the number of artificial intelligence papers published in a given year has outpaced that of even computer science.

Data poisoning is an attack on machine learning models wherein the attacker adds examples to the training set to manipulate the behavior of the model at test time. This paper explores poisoning attacks on neural nets. The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data. They are also targeted; they control the behavior of the classifier on a specific test instance without degrading overall classifier performance. For example, an attacker could add a seemingly innocuous image (that is properly labeled) to a training set for a face recognition engine, and control the identity of a chosen person at test time. Because the attacker does not need to control the labeling function, poisons could be entered into the training set simply by leaving them on the web and waiting for them to be scraped by a data collection bot. We present an optimization-based method for crafting poisons, and show that just one single poison image can control classifier behavior when transfer learning is used. For full end-to-end training, we present a "watermarking" strategy that makes poisoning reliable using multiple ( 50) poisoned training instances. We demonstrate our method by generating poisoned frog images from the CIFAR dataset and using them to manipulate image classifiers.

Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can be disastrous and even potentially fatal as shown by the recent Tesla autopilot crashes. Thus, there is an urgent need for formal analysis systems that can rigorously check neural networks for violations of different safety properties such as robustness against adversarial perturbations within a certain L-norm of a given image. An effective safety analysis system for a neural network must be able to either ensure that a safety property is satisfied by the network or find a counterexample, i.e., an input for which the network will violate the property. Unfortunately, most existing techniques for performing such analysis struggle to scale beyond very small networks and the ones that can scale to larger networks suffer from high false positives and cannot produce concrete counterexamples in case of a property violation. In this paper, we present a new efficient approach for rigorously checking different safety properties of neural networks that significantly outperforms existing approaches by multiple orders of magnitude. Our approach can check different safety properties and find concrete counterexamples for networks that are 10 larger than the ones supported by existing analysis techniques. We believe that our approach to estimating tight output bounds of a network for a given input range can also help improve the explainability of neural networks and guide the training process of more robust neural networks.