The news: Customers in China who buy SIM cards or register new mobile-phone services must have their faces scanned under a new law that came into effect yesterday. China's government says the new rule, which was passed into law back in September, will "protect the legitimate rights and interest of citizens in cyberspace." A controversial step: It can be seen as part of an ongoing push by China's government to make sure that people use services on the internet under their real names, thus helping to reduce fraud and boost cybersecurity. On the other hand, it also looks like part of a drive to make sure every member of the population can be surveilled. How do Chinese people feel about it? It's hard to say for sure, given how strictly the press and social media are regulated, but there are hints of growing unease over the use of facial recognition technology within the country.

Public-sector CIOs can use this list of strategic technology trends as inspiration and factor them into their organizations' strategic plans. Forward-looking government officials know that, in a digital society, "Policy is the technology and technology is the policy." Any government service delivered at scale is underpinned by a host of technologies. If the success of these business projects is compromised by poor implementation of technology, then the political objectives are compromised too. Implementing a digital government strategy is a journey that will span multiple budget cycles and political administrations.

Frost & Sullivan's has published a new report, Artificial Intelligence (AI)-based Security Industry Guide. The report examines smarter security frameworks, use cases of AI-based security solutions and profiling of AI-/ML-driven and AI-/ML-centric cybersecurity companies in the Asia-Pacific market. Artificial Intelligence (AI) and Machine Learning (ML) have been increasingly adopted across a wide range of industries. The multifaceted benefits of the technologies span predictive outcomes to advanced data analytics. AI-based cybersecurity has the potential to augment the capabilities of staff and help organizations better manage cyber threats. Digital transformation is a priority for a majority of enterprises in the Asia-Pacific region.

The Australian Government released its artificial intelligence (AI) technology roadmap during Australia's inaugural AI summit Techtonic, held recently in Canberra. As reported, 'Artificial Intelligence: Solving problems, growing the economy and improving our quality of life' was developed by CSIRO, Australia's national science agency. The roadmap outlines the importance of action for Australia to capture the benefits of AI, which is estimated to be worth AU$ 22.17 trillion to the global economy by 2030. It was developed for the Australian Government in consultation with industry, government and academia. The roadmap is intended to help guide future investment in AI and machine learning, and accompanies Artificial Intelligence: Australia's Ethics Framework, a discussion paper prepared by CSIRO's Data61 and published by the Australian Government in April 2019.

We've been told a million times that texting (or tweeting) while driving is dangerous. However, a lot of folks still do it; putting their and other people's lives in danger. Now, Australia is set out to catch them with a new AI-based system. The government of New South Wales state is setting up cameras specifically made for catching drivers using mobile phones. The state's transport department tested the system with cameras on two spots, and it's now spending $88 million to install them on 45 spots.

Today at RSNA, IBM Watson Health is announcing two new products, and showcasing a variety of partnerships and clients that are using our advanced technologies to improve the way radiologists deliver care. We are delighted to announce these collaborations at RSNA highlighting our advancements in medical imaging globally," said Anne Le Grand, General Manager, Imaging, Life Sciences, and Oncology, IBM Watson Health. "From helping clinicians to identify potential missed findings to seeing a summary view of patient records quickly, our innovative technologies are at the forefront of Watson Health's mission to help enable clinicians to more effectively respond to the world's most pressing health challenges." Clinical Review 3.0, a tool recently launched in the UK that analyzes medical imaging studies and their associated reports to identify potentially missed findings, facilitating higher quality and more comprehensive care for the patient. IBM Watson Health Imaging has recently engaged with Fortrus Ltd, to grow upon the reseller's strong relationship with the UK public sector, which includes a single supplier outcome-based Managed Services framework. The Imaging AI Marketplace is a single-source solution designed to help simplify the complex process of locating, purchasing, deploying and managing the vast array of AI imaging applications. The Imaging AI Marketplace is carefully curated and contains only FDA-cleared solutions alongside Watson Health developed AI solutions. Guerbet, a global specialist in contrast agents and solutions for diagnostic and interventional imaging, also recently signed an exclusive joint development agreement to develop an artificial intelligence software solution to support prostate cancer diagnostics and monitoring, utilizing MR imaging. This deal extends their earlier collaboration regarding liver cancer signed in January 2018. In addition, 4ways, a fast-growing private teleradiology network in the UK that enables UK-based radiologists to work remotely over a leading technology platform, has committed to underpin its ambitious growth strategy with IBM Watson Health's Merge PACS 8.0 platform, upgrading its current platform to support their business growth. Merge PACS is a workflow platform that is designed to help simplify physicians' reading activities and can empower IT leaders with advanced control of the flow of studies throughout the enterprise. "We're committed to constantly investing in and upgrading our IT provision to be able to offer our clients and partners the very best service.

Much has been said about the power of AI and how tomorrow's CISO won't be able to provide efficient cybersecurity without it. The hype surrounding AI is based on both the quickening pace of natural language capability development and the current deficiency of capable and competent cybersecurity professionals. A quick clarification of what AI is and to what extent it exists today may be useful before explaining the legal recognition it has today, including in the world of cybersecurity. The term "artificial intelligence" is rather vague from a legal standpoint--and in the legal world, words tend to have a strong impact. For French people (and for most people around the world), the official definition of AI is as follows: "A theoretical and practical interdisciplinary field whose purpose is to understand the mechanisms of cognition and reflection, and their imitation by a material and software device, for purposes of assistance or substitution to human activities".

The Chinese have a very public, very-deep, extremely well-funded commitment to AI. Air Force General VeraLinn Jamieson says it plainly: "We estimate the total spending on artificial intelligence systems in China in 2017 was $12 billion. We also estimate that it will grow to at least $70 billion by 2020." According to the Obama White House Report in 2016, China publishes more journal articles on deep learning than the US and has increased its number of AI patents by 200%. China is determined to be the world leader in AI by 2030.

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach to learn discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are also applicable when effects of special relativity and general relativity are important. The illustrated advantages of discrete field theories relative to continuous theories in terms of machine learning compatibility are consistent with Bostrom's simulation hypothesis.

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.