Last Wednesday, Oct. 17, traders on the floor of the New York Stock Exchange (NYSE) were treated to a first-ever experience: A collaborative robot (cobot) rang the day's closing bell. The cobot was Universal Robots' UR5e with a two-finger gripper from Robotiq. This particular ceremony celebrated the five-year anniversary of ROBO Global, the first-ever robotics, automation, and AI index. Having launched in October 2013, it currently invests into more than 80 companies across the globe, including 12 subsectors ranging from manufacturing to healthcare. Despite the sudden departure of Rethink Robotics, which was one of the leaders in the cobot market, the cobot industry remains the fastest-growing sector of the industrial automation market.

MUMBAI: More than half of generic work profiles are facing the risk of disruption over the next two years due to automation, according to an estimate by Teamlease Services. However, this does not necessarily imply job losses as automation would throw up new job profiles for those getting substituted, experts said. A Teamlease Services study, based on data from secondary sources, estimates 52-69% of repetitive and predictive roles in sectors including IT, financial services, manufacturing, transportation, packaging and shipping to get exposed to the risk of automation in the next couple of years. "The data implies risk of automation. However, that does not mean it would lead to a job loss necessarily," said Rituparna Chakraborty, co-founder of Teamlease Services, discrediting the oft-quoted premise that automation would lead to job loss.

It's only been almost a quarter since Qualcomm launched its Snapdragon 670 mid-range chipset, but today, the company is already bringing out a slightly beefier Snapdragon 675, which is clearly designed with three smartphone trends in mind: Gaming optimization, multiple cameras and AI-enhanced features (including face unlock). Announced at the 4G/5G Summit in Hong Kong, the Snapdragon 675 features Qualcomm's brand new fourth-generation Kryo CPU -- the Kryo 460 with two high-power 2GHz cores and six low-power 1.7GHz cores -- which apparently gives a notable boost to its everyday performance and gaming performance. For instance, compared to the Snapdragon 670, the new Snapdragon 675 is claimed to launch games 30 percent faster and offers 35 percent faster web browsing. In the case of gaming, the Snapdragon 675 also leverages from further software optimization for smoother gameplay, along with prioritized connectivity to ensure minimal interruption. Qualcomm listed PUBG and King of Honor being two of the handful of games that already benefit from these features, with the latter title even able to run at up to 60 fps -- not bad given that the GPU is just a mid-range Adreno 612, though we shall see whether it runs just as well outside of the lab, especially when this chipset is fabricated using an 11nm LPP process instead of the finer 10nm.

The Next Economy will be driven by 10 emerging technologies; artificial intelligence underpins them all. And the race is on for the crown of having the Holy Grail of computing, the countries that are leading in the adoption of AI are China and the United States. These countries are way ahead of the rest of the planet; including Latin America. Endeavor, a non-profit organization that helps entrepreneurs, published a report on The State of Artificial Intelligence Adoption in LATAM (spanish). The report highlights the obstacles and future of the 240 companies that participated in the report; insights were captured using surveys and interviews from 70 projects in Argentina, Brazil, Chile, Colombia, Mexico and Peru.

Like it or not, artificial intelligence (AI) is going to take over the luxury industry, and some brands are already using AI in some of their new digital marketing campaigns. Brands need to anticipate this revolution and adapt their strategies as soon as possible, because the industry will crown winners and losers as quickly as the technology grows. But AI is not simply a yellow brick road to financial success. There will be some key challenges for companies wanting to use AI, specifically ones that threaten the way they've been doing business for decades. CHALLENGE #1 – Artificial intelligence could be an "emotion killer" Emotions play an essential role in luxury buying.

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.

Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99% detection accuracy and categorization accuracy of 93.6%, with the inability to categorize some attacks. Further, we argue that such categorization can be applied to multi-cloud environments using the same machine learning techniques.

Cloud computing is gaining significant attention, however, security is the biggest hurdle in its wide acceptance. Users of cloud services are under constant fear of data loss, security threats and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning techniques. However, the major challenge in these methods is obtaining real-time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purpose in the simulated or closed experimental environments which may lack comprehensiveness. Machine learning models trained with such a single dataset generally result in a semantic gap between results and their application. There is a dearth of research work which demonstrates the effectiveness of these models across multiple datasets obtained in different environments. We argue that it is necessary to test the robustness of the machine learning models, especially in diversified operating conditions, which are prevalent in cloud scenarios. In this work, we use the UNSW dataset to train the supervised machine learning models. We then test these models with ISOT dataset. We present our results and argue that more research in the field of machine learning is still required for its applicability to the cloud security.

In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering. We compare our work against a state-of-the-art baseline model on different experimental datasets and show that this method can lead to a good and fast approximation of the baseline model performance.