Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.

Deep neural networks have been widely deployed in various machine learning tasks. However, recent works have demonstrated that they are vulnerable to adversarial examples: carefully crafted small perturbations to cause misclassification by the network. In this work, we propose a novel defense mechanism called Boundary Conditional GAN to enhance the robustness of deep neural networks against adversarial examples. Boundary Conditional GAN, a modified version of Conditional GAN, can generate boundary samples with true labels near the decision boundary of a pre-trained classifier. These boundary samples are fed to the pre-trained classifier as data augmentation to make the decision boundary more robust. We empirically show that the model improved by our approach consistently defenses against various types of adversarial attacks successfully. Further quantitative investigations about the improvement of robustness and visualization of decision boundaries are also provided to justify the effectiveness of our strategy. This new defense mechanism that uses boundary samples to enhance the robustness of networks opens up a new way to defense adversarial attacks consistently.

Most deep learning models are based on deep neural networks with multiple layers between input and output. The parameters defining these layers are initialized using random values and are "learned" from data, typically using stochastic gradient descent based algorithms. These algorithms rely on data being randomly shuffled before optimization. The randomization of the data prior to processing in batches that is formally required for stochastic gradient descent algorithm to effectively derive a useful deep learning model is expected to be prohibitively expensive for in situ model training because of the resulting data communications across the processor nodes. We show that the stochastic gradient descent (SGD) algorithm can still make useful progress if the batches are defined on a per-processor basis and processed in random order even though (i) the batches are constructed from data samples from a single class or specific flow region, and (ii) the overall data samples are heterogeneous. We present block-random gradient descent, a new algorithm that works on distributed, heterogeneous data without having to pre-shuffle. This algorithm enables in situ learning for exascale simulations. The performance of this algorithm is demonstrated on a set of benchmark classification models and the construction of a subgrid scale large eddy simulations (LES) model for turbulent channel flow using a data model similar to that which will be encountered in exascale simulation.

Artificial intelligence is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

The United States builds a lot of weapons. Unless a lot of really bad stuff happens all at once, we build more weapons than we can possibly use, and since we keep inventing new ones that are better and doing what weapons do, all the old stuff tends to just pile up. These piles of old explosives aren't aging particularly well, leaving us with few options, which include forgetting about them for longer than is probably safe, or blowing them up. A third option is disassembly and recycling, but that's dangerous for humans, because these weapons can be very old, and very lethal. Sandia National Labs has been helping the Department of Defense deal with some of its stockpile of M26 rockets, which are packed full of tiny little grenades and need to be taken apart very carefully.

An NHS-backed medical app has been branded a'public health danger' after failing to suggest a 66-year-old woman's breast lump could be cancer. After watching a video of the interaction, Twitter users flocked to criticise Bablyon Health's'flawed' symptom-checking system. Someone claiming to be an NHS consultant told the artificial intelligence a painless breast lump – a hallmark of cancer – was their only symptom. Despite the made-up patient being over 66 years old, so being 10 years older than when the average menopause ends, the'utterly absurd' bot still asked whether she was pregnant or breastfeeding. And, after asking numerous questions, the app's best guess was that the patient had osteoporosis, a condition which weakens people's bones as they get older.

The Royal Society's artificial intelligence (AI) programme explores the frontiers of AI technologies, and their implications for individuals, communities, and society. As part of our programme of international science and policy dialogue about AI, last year we worked with the American Academy of Arts and Sciences to bring together leading researchers from across disciplines to consider the implications of AI for equality, transparency, and democracy. This blog gives some of the key points from discussions, which are summarised in more detail in the workshop discussions note (PDF). Today's AI technologies can help create highly accurate systems, which are able to automate sophisticated tasks. As these technologies progress, researchers and policymakers are grappling with questions about how well these technologies serve society's needs. Experience of previous waves of transformative technological change shows that, even when society has a good understanding of the issues new technologies present, it is challenging to create a common vision for the future, to set in place measures that align the present with that desired future, and to engage collective action in ways that help bring it into being.

A leading computer scientist has invented a way for artificial neural networks to recognize objects it sees, even if they're partially obscured. "Machines can readily accomplish tasks we think difficult, but they cannot yet accomplish among the simplest of tasks for humans," said Dr. Stuart H. Rubin of the Naval Information Warfare Center in San Diego. "That's where the game changer lies." On Tuesday, the U.S. Patent and Trademark Office granted Rubin U.S. Patent 10,217,023. The document, which can be downloaded below, describes Rubin's application of cameras connected to an artificial intelligence system, which uses arrays of spatial light modulators to repetitiously examine the camera's imagery and determine if it matches anything in its memory.

The algorithmic revolution is here, and nations are losing control of not only their understanding of the potential impact of artificial intelligence, but also the governance model that enforced accountability on the advances in science and technology over the years at all levels. While each new technology innovation claims its territory for the economic advances in the human ecosystem with significant ramifications across cyberspace, geospace and/or space (CGS), the rise of artificial intelligence (AI) has not only undermined governance, management and growth models, but it has also broken all barriers to boundaries defined by human decision makers. In addition, it is both blurring the boundaries between human intelligence and machine intelligence, and the boundaries between man and machine and real and fake. As a result, the power dynamics is shifting away from the select few across nations (and is moving away from humans entirely to algorithms)--re-defining the criteria upon which geopolitics was framed--and thereby threatening the foundations of global peace and security. Since the beginning of the technological age, each new idea, innovation and invention has helped humans across nations usher in a new era of economic growth, changing the fundamentals of respective nations and their security.

FedEx Corp. has today announced the "SameDay Bot" – an autonomous delivery service, designed to help retailers make same-day and last-mile deliveries to their customers. Using this machine, retailers will be able to accept orders from nearby customers and deliver items directly to homes or businesses the same day. FedEx is collaborating with companies such as AutoZone, Lowe's, Pizza Hut, Target, Walgreens and Walmart to help assess retailers' autonomous delivery needs. "The FedEx SameDay Bot is an innovation designed to change the face of local delivery and help retailers efficiently address their customers' rising expectations," said Brie Carere, executive vice president for FedEx. "The bot represents a milestone in our ongoing mission to solve the complexities and expense of same-day, last-mile delivery for the growing e-commerce market in a manner that is safe and environmentally friendly."