In a time where global newsrooms are becoming smaller due to enhanced technological advancement, robotics journalism has emerged as a threat to the fourth estate. Artificial Intelligence has introduced a new paradigm in present-day journalism and newsrooms across the globe are facing fears of staff cuts. Automated journalism has already made its way into newsrooms with automated news writing and distribution, without human supervision already a reality. 'Jia Jia'was the first humanoid robot journalist created by developers from the University of Science and Technology in China's Anhui province in April. She hit headlines when she reported for the country's news agency Xinhua and conducted a live interview with an editor of a popular tech magazine.

By Sanjeev Sharma India, the second most populous country in the world, runs a host of socially beneficial programs. Most of such programs aim to reach the millions of people who live in remote areas of our extensive nation. From providing mid-day meals for under-privileged school children to making modern healthcare accessible to residents of far-flung rural areas, our nation spends significant resources to ensure its policies reach the right people at the right time. Making sure government services reach all 1.3 billion citizens, including those residing in remote regions, is a daunting task. AI-powered systems can become leapfrog technologies for our nation, transforming the reach and effectiveness of public welfare programs. A study by consultancy firm Accenture showed AI could boost India's annual growth rate by 1.3 percentage points by 2035.

Scientists from Osaka University in Japan have developed artificial intelligence (AI) that can identify different types of cancers based on microscopy images of their cells. The AI was also able to determine whether the cancer cells were resistant to radiation, and further learned the differences between human and animal cancers. Since the accuracy and timeliness of traditional methods of identifying cancer cells are prone to delays and errors, an accurate and automated system for accomplishing this would be beneficial to cancer research and treatment overall. The results of the scientists' research were published in the December 2018 issue of Cancer Research. The type of AI developed for the cell identification is called a convolutional neural network (CNN); it's loosely based on the connectivity patterns used by neurons in the brain and primarily used for classifying images.

KAZINFORM - The Tokyo-based tech venture Groove X on Tuesday unveiled an artificial-intelligence equipped robot pet that can sense people's emotions and communicate with them, Kyodo News reports. The doll-like robot named "LOVOT" will be shipped in the autumn of 2019 as a set of two with a price tag of 598,000 yen ($5,300) plus a monthly service fee expected to be around 20,000 yen. A single LOVOT will be available in 2020 and will cost 349,000 yen plus a monthly fee of around 10,000 yen. The big-eyed robot, featuring a bevy of sensors, recognizes its owners' facial expressions and obeys commands, Groove X said. Weighing 3 kilograms and standing 43 centimeters tall, the mechanical pet has an internal camera and learns the layout of its household so it can move around without crashing into furniture.

If we learned anything in 2018 it's that algorithms and social platforms are under regulated, can easily be weaponized and that cybersecurity is an accelerating threat. We learned that Huawei, a state sponsored technology company in China is considered a national security risk by the United States, Australia, New Zealand, Japan, Germany and maybe Canada and the United Kingdom. We also learned that Google and Microsoft sell their services to the U.S. government, many times against the wishes of their own employees and best ethical judgement. The trend of putting profits over ethics and integrity is very dangerous for humanity. Instead of thinking about the hype of AI, we need to be more skeptical about where AI could be heading us as a civilization.

Decades ago, small family operations dominated Japan's agriculture industry, but today, the gradual shift to large-scale farming is creating many new challenges. Facing a labor shortage and a rapidly aging workforce, the industry is trying to make its business more attractive to newcomers, attempting to cast aside the deep-rooted image that farming is all hard work. To bring in new blood, farms are reforming work practices, nurturing an entrepreneurial spirit, automating with robotic innovation and taking advantage of evolving information technology. "I can take a day off, as well as a longer holiday, so it's like working at an ordinary company," said Shizuka Ninomiya, 24, who has worked for over a year at a tomato farm in Saitama Prefecture. The farm is one of those run by Aeon Agri Create Co., based in Chiba Prefecture, that grows vegetables for retail giant Aeon Co., its holding company, and group stores.

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.

The world's AI superpower is at it again. This time they are using AI technology to gain an upper hand in the fight against truancy. Many Chinese schools were fed up with students leaving school without permission or skipping class. So they decided to do the only thing they believed would work. They created so called "smart" uniforms to keep students in line.

One of the most important tasks in network management is identifying different types of traffic flows. As a result, a type of management service, called Network Traffic Classifier (NTC), has been introduced. One type of NTCs that has gained huge attention in recent years applies deep learning on packets in order to classify flows. Internet is an imbalanced environment i.e., some classes of applications are a lot more populated than others e.g., HTTP. Additionally, one of the challenges in deep learning methods is that they do not perform well in imbalanced environments in terms of evaluation metrics such as precision, recall, and $\mathrm{F_1}$ measure. In order to solve this problem, we recommend the use of augmentation methods to balance the dataset. In this paper, we propose a novel data augmentation approach based on the use of Long Short Term Memory (LSTM) networks for generating traffic flow patterns and Kernel Density Estimation (KDE) for replicating the numerical features of each class. First, we use the LSTM network in order to learn and generate the sequence of packets in a flow for classes with less population. Then, we complete the features of the sequence with generating random values based on the distribution of a certain feature, which will be estimated using KDE. Finally, we compare the training of a Convolutional Recurrent Neural Network (CRNN) in large-scale imbalanced, sampled, and augmented datasets. The contribution of our augmentation scheme is then evaluated on all of the datasets through measurements of precision, recall, and F1 measure for every class of application. The results demonstrate that our scheme is well suited for network traffic flow datasets and improves the performance of deep learning algorithms when it comes to above-mentioned metrics.