The Chinese government has continued to enhance its military capabilities and in recent years, it has steadily invested to transform the People's Liberation Army (PLA) into a true world-class fighting force. Earlier this year, the People's Liberation Army Navy (PLAN) became the largest naval force in the world, and China has steadily been working to develop cutting edge aircraft, missiles and even small arms. Another area where Beijing has focused is in cyber warfare, and last month, the National Security Agency (NSA) issued a cybersecurity advisory that warned of Chinese state-sponsored activities targeting American companies, including those that work closely with the U.S. government. The PLA had even operated a special unit, known as PLA Unit 61398, which was believed to have conducted a series of cyber attacks against western companies at the behest of Beijing. In May 2014, the U.S. Department of Justice (DoJ) announced that a Federal grand jury returned an indictment of five 61398 officers for the theft of confidential business information and intellectual property from U.S. commercial firms.

Earlier this month, YouTube said it would remove videos containing misinformation about COVID-19 vaccines and would expand its current rules against falsehoods and conspiracy theories about the Pandemic. It also revealed it's removed over 200,000 videos containing dangerous or misleading COVID-19 information since early February. No wonder the World Health Organisation says the world isn't just fighting a pandemic, but an'infodemic' as well. As The New York Times recently put it, we are facing "the mass distortion of truth and overwhelming waves of speech from extremists that smear and distract". The problem, allege citizen data scientists: the infodemic isn't just crazy people talking to each other online, which in 2020 is basically BAU.

The cognitive technologies AI & ML also have quite a hot measurement on the Scoville pepper scale. AI & ML are not necessarily new innovations, but they are ones that still have yet to reach full potential. In 2020, both AI & ML started to flourish -- and it will continue to do so throughout 2021. At its core, AI & ML are really about data integration, quality (image definition) and collection and processing of that data that allows for meaningful analytics. Applications for AI are increasing in variety and capability (especially automation) and are now being applied to almost every industry vertical, including finance, healthcare, energy, transportation, and cybersecurity. Most intriguing, but only in the earliest stages is AL/ML neural human augmentation. Neuromorphic technologies, and human/computer interface will extend our human brain capacities, memories and capabilities.

A primary benefit of using AI and machine learning to monitor threats is that it loses little time between identifying the problem and reacting to it. This reduces the damage and disruption to businesses.

We are seeing overwhelming growth in AI/ML systems to process oceans of data that are being generated in the new digital economy. However, with this growth, there is a need to seriously consider the ethical and legal implications of AI. As we entrust increasingly more sophisticated and important tasks to AI systems, such as automatic loan approval, for example, we must be absolutely certain that these systems are responsible and trustworthy. Reducing bias in AI has become a massive area of focus for many researchers and has huge ethical implications, as does the amount of autonomy that we give these systems. The concept of Responsible AI is an important framework that can help build trust in your AI deployments.

New York City is trying to rein in the use of algorithms used to screen job applicants. It's one of the first cities in the U.S. to try to regulate what is an increasingly common -- and opaque -- hiring practice. The city council is considering a bill that would require potential employers to notify job candidates about the use of these tools, referred to as "automated decision systems." Companies would also have to complete an annual audit to make sure the technology doesn't result in bias. The move comes as the use of artificial intelligence in hiring skyrockets, increasingly replacing human screeners.

Coronavirus has killed hundreds of thousands of people and has strained health systems around the world, but for Tony Young there may be a patch of a silver lining. The pandemic is accelerating use of technology to radically advance medicine and save lives in the future. "There are so many fantastic examples of the way in which technology is empowering our patients and our professionals," says Prof Young, a surgeon and national clinical lead for NHS England. Having launched his own medical-technology start-ups, he is helping to introduce innovations across the UK health service. Digital tools, whether for data management and drug development or enhanced diagnosis and treatment, have sharply improved the response to the threat of infection and all sorts of disease.

Sikhanda Satapathy, from DEVCOM ARL, and Prof. K.T. Ramesh, director of the Hopkins Extreme Materials Institute, will lead the research activities. To launch these projects, the partners held a joint virtual kickoff meeting Nov. 4."This collaborative agreement will enable and accelerate intelligent design of materials for extreme dynamic environments to support our Soldiers and address Army's future material needs," Satapathy said.Over the next two years, researchers will explore the use of artificial intelligence and machine learning to accelerate materials development. One of the projects is focused using artificial intelligence techniques to accelerate the processing and characterization of new materials."This The experimentally and computationally generated data will be used to train neural networks which will be used to accelerate the materials design process.Another project will incorporate machine learning of acoustic emission measurements to characterize materials deformation mechanisms. These measurements are non-trivial and require expertise in both instrumentation and data analysis."This