Of all digital technologies, Artificial Intelligence (AI) is potentially the one that could have a deeper impact on the political, economic and social transformation process we are currently experiencing. Before this wide range of opportunities, there is a strong competition among world powers to dominate AI's technologic development and the EU should be mindful of what is really at stake. How is the EU framing its AI policies? Is the EU equipped to influence the global competition? What are the future scenarios?

Cybersecurity is a major concern for every business across all verticals. Software vulnerabilities and targeted attacks are two major cybersecurity concerns for today's modern business. The latest solutions in artificial intelligence (AI) and machine learning (ML) are being implemented to aid in preventing cybersecurity attacks and securing software vulnerabilities. According to market research by Mordor Intelligence, the global cybersecurity market was valued at USD 161.07 billion in 2019, and is expected to reach USD 363.05 billion by 2025, registering a compound annual growth rate of 14.5 percent during the period of 2020 to 2025. The research company says the popularity of the internet of things, bring your own device, artificial intelligence (AI), and machine learning (ML) in cybersecurity is increasing, leading to more vulnerabilities and an ever-growing need to secure networks and devices.

As the world unites in the fight against COVID-19, scientists and researchers around the world are studying the novel coronavirus and publishing their findings in peer-reviewed journals and pre-print servers. Scattered across these research papers might be the pieces of the puzzle that will unlock the cure or vaccine for COVID-19 or new ways to treat patients and prevent the spread of the virus. Unfortunately, no single person can go through tens of thousands of documents, and the thousands more that are being added every week. This is where the artificial intelligence community enters the scene. Among other efforts to help fight the coronavirus pandemic, AI researchers are fast busy developing tools that will help medical scientists navigate the fast-growing corpus of literature surrounding coronavirus. The concerted effort to process COVID-19 papers, which has brought together government agencies, tech giants, universities, and research labs, will be a measure of how useful our state-of-the-art AI algorithms have become.

Intel, along with the Georgia Institute of Technology (Georgia Tech) recently obtained a multimillion-dollar deal from the Defense Advanced Research Projects Agency (DARPA) in the US. As per the four-year contract, both will work on'Guaranteeing Artificial Intelligence (AI) Robustness against Deception' – or GARD – program for DARPA. According to Intel, it is the main contractor in the multimillion-dollar joint deal, which is targeted at improving cybersecurity defence support facing and spoofing attacks on machine learning (ML) systems. Spoofing attacks can alter and imperil the interpretation of data by the ML algorithms used in an autonomous system. Military systems are vulnerable to security attacks, which can pose risks to extremely sensitive information that can potentially harm military systems.

Ending the US coronavirus lockdown too early could lead to an explosion of new coronavirus cases, according to a study modelling the spread of the virus. Researchers from the Massachusetts Institute of Technology (MIT) created a model showing the spread of the deadly virus using publicly available data from Wuhan, Italy, South Korea and the USA. The authors say that any immediate or near-term relaxation of quarantine measures already in place in the US would lead to an'exponential explosion' in COVID-19 cases. It comes as President Donald Trump announced a new three-phase plan to reopen the country that'allows' governors to decide when their states should come out of lockdown measures. The plan provided only a general idea of how and when states would be able to reopen - shying away from specific details or a timeline. 'To preserve the health of our citizens we must also preserve the health and functioning of our economy,' said Trump.

Controversial facial recognition firm Clearview AI has been found to have extensive ties to far-right individuals and movements. Clearview AI has come under scrutiny for scraping billions of photos from across the internet and storing them in a database for powerful facial recognition services. Privacy activists criticise the practice as the people in those images never gave their consent. "Common law has never recognised a right to privacy for your face," Clearview AI lawyer Tor Ekeland said recently. "It's kind of a bizarre argument to make because [your face is the] most public thing out there."

Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challenging, due to the large size of the events. Events in these detectors are typically two orders of magnitude larger than images found in classical challenges, like recognition of handwritten digits contained in the MNIST database or object recognition in the ImageNet database. Ideally, training would occur on many instances of the entire event data, instead of many instances of cropped regions of interest from the event data. However, such efforts lead to extremely long training cycles, which slow down the exploration of new network architectures and hyperparameter scans to improve the classification performance. We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out on simulated events in the MicroBooNE detector. We emphasize that it is beyond the scope of this study to optimize networks or extract the physics from any results here. Institutional computing at Pacific Northwest National Laboratory and the SummitDev machine at Oak Ridge National Laboratory's Leadership Computing Facility have been used. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics and their attendant compute techniques onto the DOE Leadership Class Facilities. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High Energy Physics (HEP) program for the coming decades.

A yuru-chara is a mascot character created by local governments and companies for publicizing information on areas and products. Because it takes various costs to create a yuruchara, the utilization of machine learning techniques such as generative adversarial networks (GANs) can be expected. In recent years, it has been reported that the use of class conditions in a dataset for GANs training stabilizes learning and improves the quality of the generated images. However, it is difficult to apply class conditional GANs when the amount of original data is small and when a clear class is not given, such as a yuruchara image. In this paper, we propose a class conditional GAN based on clustering and data augmentation. Specifically, first, we performed clustering based on K-means++ on the yuru-chara image dataset and converted it into a class conditional dataset. Next, data augmentation was performed on the class conditional dataset so that the amount of data was increased five times. In addition, we built a model that incorporates ResBlock and self-attention into a network based on class conditional GAN and trained the class conditional yuru-chara dataset. As a result of evaluating the generated images, the effect on the generated images by the difference of the clustering method was confirmed.

The construction site of 2050 will be human-free. Robots will work in teams to build complex structures using dynamic new materials. Elements of the build will self-assemble. Drones flying overhead will scan the site constantly, inspecting the work and using the data collected to predict and solve problems before they arise, sending instructions to robotic cranes and diggers and automated builders with no need for human involvement. The role of the human overseer will be to remotely manage multiple projects simultaneously, accessing 3D and 4D visuals and data from the on-site machines, ensuring the build is proceeding to specification. The very few people accessing the site itself will wear robotically enhanced exoskeletons and will use neural-control technology to move and control machinery and other robots on site.

A planet with a similar surface temperature and size to the Earth has been discovered 300 light years away orbiting in its star's habitable zone. Kepler-1649c was discovered hidden away in data collected from the Kepler space telescope two years after it was retired by NASA and in seven-year-old observations. It's been described as'the most similar planet to Earth ever found' in data from the Kepler space telescope observations by the Search for Extra Terrestrial Intelligence. The rocky planet is just 1.06 times larger than the Earth and gets about 75 per cent of the starlight from its star that the Earth receives from the Sun. Astronomers from the University of Texas found Kepler-1649c while looking through old observations from 2013 - they found a computer algorithm had misidentified it.