The confluence of smart sensors and Artificial Intelligence (AI) is set to redefine the industrial world. Experts believe a wave of new technologies is creating the fourth industrial revolution or Industry 4.0. Defined by the trend of large industrial enterprises adopting automation, machine learning (ML) and real-time insights and configuration, Industry 4.0 will make global industrial operations smart and efficient. Industry 4.0 comprises of four key pillars: Industry 4.0 will lead to the creation of Smart Factories with machines that can communicate with each other. Backed by AI and a wealth of insights from raw data, these machines could be empowered to configure production processes and make modifications in real-time to optimize operations.

ABU DHABI – Japan and its top trade partner in the Middle East, the United Arab Emirates, agreed on Monday to expand economic, political and defense cooperation. Tokyo and Abu Dhabi also signed an investment protection agreement, capping off a two-day visit by Prime Minister Shinzo Abe to the oil-rich Gulf state. Abe arrived late Sunday on the first leg of a Middle East tour that will also take him to Jordan, Israel and the Palestinian territories. In a joint statement, the two countries praised growing trade between them. They "stressed the importance of further enhancing trade, investments, and business such as renewable energy, sustainable water desalination … artificial intelligence, health care and medical equipment," the statement said.

Alibaba Group Holding Ltd announced three partnerships on Monday with leading carmakers on artificial intelligence-backed connected vehicle services, cementing its commitment to the auto sector. Under the cooperation, owners of Daimler, Audi and Volvo cars in China will be able to remotely access information about their vehicles, such as location, engine status and fuel checks, using Alibaba's voice assistant service. AliGenie, the AI platform powering Alibaba's iconic smart speaker Tmall Genie, enables voice enquiries that unlock doors and turn on air conditioning before the driver even reaches the car, said Chen Lijuan, head of Alibaba AI Labs, an in-house AI research unit. The tie-up will also help enrich the in-car infotainment portfolio based on Alibaba's content offerings from access to video site Youku and music streaming service Xiami. "You can check the best route via Tmall Genie at home and send that information to your car," Chen told a media event in Beijing, citing the example of turning on the car's heater in winter as soon as the owner wakes up.

Hold the narrative about self-aware artificial intelligence wiping out the human race, at least for now. We've got more pressing issues. According to a study published last week, the United States is quickly falling behind other developed nations in preparing workers for a future driven by AI and automation. The Automation Readiness Index looks at 25 advanced economies to determine which is making the greatest strides in preparing their workforce for an automated future. Researchers broke it down into three main categories: innovation environment (money spent on research and development, and investment in the space), school policies (early education and lifelong curricula), and public workforce development (government-led programs, re-training of workers).

Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, a new bootstrapping method that protects against such contamination by highly effective confidence assessment. This is achieved by using entity and template seeds jointly (as opposed to just one as in previous work), by expanding entities and templates in parallel and in a mutually constraining fashion in each iteration and by introducing higherquality similarity measures for templates. Experimental results show that BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the art for four relationships.

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.

While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. This paper proposes a solution by introducing a family of safe mutation (SM) operators that aim within the mutation operator itself to find a degree of change that does not alter network behavior too much, but still facilitates exploration. Importantly, these SM operators do not require any additional interactions with the environment. The most effective SM variant capitalizes on the intriguing opportunity to scale the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks (which tend to be particularly brittle to mutation), including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution.

This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.

The deal, which could be announced early next week, would mark the first step for a potential broader alliance between the world's biggest auto maker by sales and the global leader in ride-hailing, as China races to get ahead in mobility services such as car sharing. It is also highlights that even as China begins to loosen requirements on joint ventures, foreign manufacturers are wary of going it alone and want a strong local partner to boost their chances of success in the country. "The joint venture with Didi is not just about ride-hailing. We want to explore mobility projects as well as autonomous driving and robo-taxis," said Weiming Soh, board member in charge of strategy at Volkswagen's China subsidiary. A spokeswoman for Didi said: "The parties are still exploring details of the cooperation. Potentially, both parties will focus on building together a fleet operation business, and look into other potential areas such as designing new car models for ride-hailing."

JERUSALEM – Israel's prime minister on Monday unveiled what he said was a "half ton" of Iranian nuclear documents collected by Israeli intelligence, claiming the trove of information proved that Iranian leaders covered up a nuclear weapons program before signing a deal with the international community in 2015. In a speech delivered in English and relying on his trademark use of visual aids, Prime Minister Benjamin Netanyahu claimed that the material showed that Iran cannot be trusted, and encouraged President Donald Trump to withdraw from the deal next month. "Iran lied big time," Netanyahu declared. Netanyahu's presentation, delivered on live TV from Israeli military headquarters in Tel Aviv, was his latest attempt to sway international opinion on the nuclear deal. The agreement offered Iran relief from crippling sanctions in exchange for curbs on its nuclear program.