It's not by chance that Angela Merkel is devoting a whole day to the city of Shenzhen during her two-day visit to China. The southeastern Chinese metropolis is one of the most developed cities in the world -- it is modern, connected and astonishingly clean by China's standards thanks to an almost entirely e-mobile bus system. Like no other city, Shenzhen embodies the Chinese dream to become a technological global power. China wants to lead the world in key areas such as space travel, e-mobility and industrial robotics by 2025. That's the government's plan and it has already achieved its goal with regard to high-speed trains.

Machines giving suggestions to doctors about treating their patients, a scene straight from a sci-fi movie and a fantasy of the healthcare community, is becoming a reality now. In a first of its kind move, the US Food and Drug Administration (FDA) recently gave green signal to market IDx-DR, a medical device to detect diabetic retinopathy, a disease in which high blood sugar damages blood vessels in the retina of eyes and leads to the vision loss. Made by the US-based IDx LLC, the software analyses images of the eye taken by a camera and tells the doctor accurately about the extent of the disease, called as'diabetic retinopathy'. Approved last month, this is the first approval in the area of artificial intelligence that can potentially replace a specialized doctor to interpret medical imagery and decide on the medical outcome. This week, the FDA gave go ahead to Boston-based Beta Bionics, which has a partnership with Novo Nordisk, to test its autonomous bionic pancreas that employs artificial intelligence to vary hormone doses in adults and children with Type 1 diabetes.

Alibaba Holding Group Ltd. has teamed up with SenseTime Group Ltd., the world's most valuable artificial intelligence startup, to set up a new artificial intelligence lab in Hong Kong in a bid to promote AI innovation between mainland China and the special administrative region. The Hong Kong Artificial Intelligence Laboratory is the first major move following the Chinese government's announcement of new policies to develop Hong Kong into a global innovation base, online news journal Leiphone reported. Government-backed tech incubator Hong Kong Science and Technology Parks Corp. is also a partner in the new project. The HKAI Lab will support local research talent and share more of the mainland's smart computing technologies and application scenarios with them to provide a platform for interdisciplinary exchange. It kicked off operations by launching an accelerator program designed to cultivate more AI startups in Hong Kong.

A vehicle drives by the spot where an Uber self-driving vehicle struck and killed a pedestrian earlier this year in Tempe, Ariz. The National Transportation Safety Board released a preliminary report Thursday on the collision. A vehicle drives by the spot where an Uber self-driving vehicle struck and killed a pedestrian earlier this year in Tempe, Ariz. The National Transportation Safety Board released a preliminary report Thursday on the collision. The Uber self-driving vehicle that struck and killed a pedestrian two months ago in Tempe, Ariz., took note of the victim with its sensors, but its software did not engage the car's brakes to prevent the collision, according to a preliminary report released Thursday by the National Transportation Safety Board.

Three and half years ago, I stood on the corner of West Street and gasped as two window washers clung to life at the end of a rope a thousand feet above. By the time rescue crews reached the men on the 69th floor of 1 World Trade they were close to passing out from dangling upside down. Ramone Castro, a window washer of three decades, said it best, "It is a very dangerous job. It is not easy going up there. You can replace a machine but not a life."

With strawberry picking season well under way - but migrant labour in short supply in several countries - we look at the various robots being developed around the world to help producers harvest this most popular fruit. Next time you buy strawberries take a look a good look in the punnet. Do the berries still have the stem attached or has it been plucked off leaving only the green hat of leaves called the calyx? You may not think that matters, but it's a key consideration for growers as they contemplate the merits of a range of robotic prototypes that promise to pick strawberries as fast and as carefully as humans. Whether the berry is plucked or whether the stalk is snipped through and kept attached is one critical difference between the concepts that Spanish, Belgian, British and US engineers are testing, ready to roll out in fields as soon as next year.

Mass surveillance by government agencies is increasing, especially in the United States, whistleblower Chelsea Manning told a Montreal audience on Thursday as she called for limits on the development of artificial intelligence. 'Ten years ago, I was working in military intelligence and I could feel the power, and could see how technology is implemented,' Manning, once jailed for leaking classified information, said at the C2 Montreal business conference. Manning said she is stunned now by the'dramatic change in policing style, and aggressive (government) surveillance.' Manning said she is stunned now by the'dramatic change in policing style, and aggressive (government) surveillance It is'getting worse, especially in the United States,' she added. She urged programmers and computer scientists working on artificial intelligence and machine learning'to consider the ethical implications of the technology that they are building and developing.'

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.

In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detecting deceptive reviews. Unlike standard GAN models which have a single Generator and Discriminator model, FakeGAN uses two discriminator models and one generative model. The generator is modeled as a stochastic policy agent in reinforcement learning (RL), and the discriminators use Monte Carlo search algorithm to estimate and pass the intermediate action-value as the RL reward to the generator. Providing the generator model with two discriminator models avoids the mod collapse issue by learning from both distributions of truthful and deceptive reviews. Indeed, our experiments show that using two discriminators provides FakeGAN high stability, which is a known issue for GAN architectures. While FakeGAN is built upon a semi-supervised classifier, known for less accuracy, our evaluation results on a dataset of TripAdvisor hotel reviews show the same performance in terms of accuracy as of the state-of-the-art approaches that apply supervised machine learning. These results indicate that GANs can be effective for text classification tasks. Specifically, FakeGAN is effective at detecting deceptive reviews.

If you listen at all to industry news, you can't escape the onslaught of marketing hype surrounding Artificial Intelligence (AI) and Machine Learning (ML). Is SkyNet ready to take over the world? Despite the optimism and promise, AI and ML largely remain in an immature stage where they suffer mistakes and biases from classic programming issues, which we will explore below: garbage-in, garbage-out and poor quality control. Still, with the potential for huge benefits on the horizon, we cannot simply reject security tools that have tried to incorporate these advances. Instead, we need to create a strategy for adoption that provides a safety net for failure through overlapping technology – just as we do for any other security product. AI will be eventually be good.