For the last several years, Russia has been steadily improving its ground combat robots. Just last year, Kalashnikov, the maker of the famous AK-47 rifle, announced it would build "a range of products based on neural networks," including a "fully automated combat module" that promises to identify and shoot at targets. According to Bendett, Russia delivered a white paper to the UN saying that from Moscow's perspective, it would be "inadmissible" to leave UAS without any human oversight. In other words, Russia always wants a human in the loop and to be the one to push the final button to fire that weapon. Worth noting: "A lot of these are still kind of far-out applications," Bendett said.

Artificial Intelligence in retail market is expected to grow at CAGR of 38.5% during the forecast period, 2019–2026. AI has become a cardinal element across various industry verticals for digitalization, especially in the retail segment. According to the World Economic Forum, e-commerce is on the verge of becoming the most important retail channel, driving 42% of consumption growth and 90% of the growth from mobile e-commerce. Thus, implementing advanced technologies in e-commerce, such as artificial intelligence, would offer better prospects for the retail industry in the coming years. AI is predicted to unleash a digital disruption in retail with prominent industry players ramping-up their businesses. AI-powered solutions are increasingly becoming a priority for the food & beverage industry and retailers.

In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL). In this framework, we first unify different tasks, goals, and constraints into a single formula for data poisoning attack in G-SSL, then we propose two specialized algorithms to efficiently solve two important cases --- poisoning regression tasks under $\ell_2$-norm constraint and classification tasks under $\ell_0$-norm constraint. In the former case, we transform it into a non-convex trust region problem and show that our gradient-based algorithm with delicate initialization and update scheme finds the (globally) optimal perturbation. For the latter case, although it is an NP-hard integer programming problem, we propose a probabilistic solver that works much better than the classical greedy method. Lastly, we test our framework on real datasets and evaluate the robustness of G-SSL algorithms. For instance, on the MNIST binary classification problem (50000 training data with 50 labeled), flipping two labeled data is enough to make the model perform like random guess (around 50\% error).

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/

It is only natural that businesses look within their own territory or market to learn about new technology. But chatbots are a global phenomenon, adopted far faster outside the west that most others, so taking a look at their growth, use and adoption globally provides some valuable insights. Both countries have populations of over 1.3 billion people, or over a third of the world's population. That makes them the global focus for customer service automation to battle the sheer volume of calls that even a small public business will receive. Banks, airlines and hotels are adopting chatbots at high speed to cope with the demand, with the likes of WeChat dominating platform usage with its 700 million users across Asia.

Recent innovations in agile aerospace have created unique offerings in high cadence satellite imagery. While this is of immense interest to imagery analysts, a significant portion of GIS professionals and geo-data scientists work less with raster data (AKA imagery) and more with point and vector data. Planet operates the world's largest constellation of earth observation satellites providing near-daily coverage of the entirety of Earth's landmass. Over the past couple of years, we have been working on bringing computer vision and spatiotemporal analysis to market to enable access and data transformations on this rich imagery archive. We recently announced the general availability of our analytic feeds and the launch of our building change detection analytics in private beta (sign up for info here).

The term'Artificial Intelligence' (AI) is currently trending as a revolutionary new technology that will change the face of business forever. The reality though is that AI is actually not new, and the phrase is being misused to cover a broad spectrum of different concepts. One of those is deep machine learning, which actually is new and fairly revolutionary. It's already being applied by organisations like Amazon and Google to perform complex analytics that previously was impossible. The trouble is, for the majority of businesses, it is just too expensive and too complicated to be of much use.

As the world enters the age of the fourth industrial revolution, marked by accelerating innovation and the adoption of automation, the future of work is a fundamental question for the Middle East. While some jobs will be lost and others will be created, nearly all jobs will be transformed. The new reality is one in which 45 per cent of jobs will be automatable by 2030. The automation potential will vary across sectors: jobs requiring repetitive routine work such as manufacturing, warehousing and transportation will see more than 50 per cent of its work done by smart devices. Jobs that require emotional intelligence and creativity such as the arts, health care and entertainment will only see a 29 to 37 per cent automation rate.

Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

Tuberculosis (TB) can now be identified in less than an hour thanks to a new blood test. The test procedure -- developed by The University of Queensland's Emeritus Professor Ian Riley in collaboration with researchers in Tanzania, India, Mexico and the Philippines -- is hoped to positively impact TB diagnosis in adults living in remote areas. "TB has been difficult to control because its symptoms are similar to many other diseases," Prof Riley said. "Other challenges include drug resistance to the disease and the high burden of HIV-positive cases in developing countries." Prof Riley explained that the discovery of the testing procedure came from using machine learning techniques to study three groups of adults who had a persistent cough for more than three weeks.