Sign in to report inappropriate content. Dorabot's Robot for recycling, can identify, pick, and sort recyclable items such as plastic bottles, glass bottles, paper, cartons, and aluminum cans. The robot has deep learning-based computer vision and dynamic planning to select items in a moving conveyor belt. It also includes customized and erosion resistant grippers to pick irregularly shaped items, which results in a cost-effective integrated solution. Follow us on Twitter: https://twitter.com/dorabot_inc

The Chinese robotics market is growing strong, but not without its own pains. Trade tensions and a global economic slowdown, particularly in automotive manufacturing, have affected demand in the Chinese robotics market. However, interest in supply chain automation and political support of domestic innovation could encourage growth in 2020. This is Part 2 of The Robot Report's Q&A with Georg Stieler, managing director for Asia at international consulting firm STM Stieler. In Part 1, he discussed the state of the robotics market in China, looking at causes for the current slowdown and what types of robots are in demand. Here, Stieler continues his analysis with a look ahead.

If I had to pick one platform that has single-handedly kept me up-to-date with the latest developments in data science and machine learning – it would be GitHub. The sheer scale of GitHub, combined with the power of super data scientists from all over the globe, make it a must-use platform for anyone interested in this field. Can you imagine a world where machine learning libraries and frameworks like BERT, StanfordNLP, TensorFlow, PyTorch, etc. weren't open sourced? GitHub has democratized machine learning for the masses – exactly in line with what we at Analytics Vidhya believe in. This was one of the primary reasons we started this GitHub series covering the most useful machine learning libraries and packages back in January 2018.

One in eight women will be diagnosed with breast cancer throughout their lives. In an effort to help with quicker detection, researchers have trained a deep-learning algorithm to spot breast cancer in screening scans as accurately or better than a radiologist. While still at an early stage, the research could eventually help reduce incorrect results in the US and help alleviate the shortage of radiologists in the UK. As early detection is key to treatment, women over the age of 50 are tested in the US and UK even if they don't show signs of the disease. False negatives, when cancer is present but not spotted, can prove deadly, while false positives can be distressing.

Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static node-link diagrams, which illustrate only the structure of a network, rather than the behavior. Motivated by this observation, this paper presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real time. The networks were trained on an augmented version of MNIST, so they excel at categorizing centred upright numbers.

Due to sufficient support suitable for exploration and introduction to parallel programming, actuator interface, Linux-based programming, deep learning, and artificial intelligence application development, the Jetson Nano developer kit is definitely suitable for a maker to get started with exciting advanced projects in robotics, computer vision, and IoT. The Nvidia community also provides a platform to share various Jetson projects from the community. Nvidia provides the JetPack SDK which is a collection of libraries, software, and OS options optimized for the Jetson boards. The Linux4Tegra OS is a GNU Linux special distribution by Nvidia for their Tegra series processors used in the Jetson boards. CUDA for GPU acceleration, TensorRT, cuDNN, and OpenCV are few of the widely used packages pre-installed on the OS image.

QuEST Global, a global product engineering and lifecycle services company, will demonstrate Deep Learning driven Advanced Driver Assistance Systems (ADAS) at CES (Consumer Electronic Show) 2020. The deep learning models developed by QuEST Global aim to enhance ADAS by improving the accuracy in detection of traffic signs, pedestrians and traffic. This enhanced ADAS will be demonstrated at Booth # 1909, Westgate Pavilion. The ADAS demo has been developed by training deep learning models using synthetic data representing various environmental conditions and terrains. Such deep learning driven ADAS are 25% more accurate than the ones developed using classic image processing techniques.

Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequence by explicitly learning/extracting the trend, seasonality, and event components. The trend is learned via a 1D and 2D temporal CNN and LSTM hierarchical neural net. The CNN-LSTM architecture can (i) seamlessly leverage the dependency among multiple correlated time series in a natural way, (ii) extract the weighted differencing feature for better trend learning, and (iii) memorize the long-term sequential pattern. The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets, such as forecasts of Amazon AWS Simple Storage Service (S3) and Elastic Compute Cloud (EC2) billings, and the closing prices for corporate stocks in the same category.

Abstract--T umblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blo gs and 53.3 million daily posts However, it is a challenging task t o target specific demographic groups for ads, since T umblr doe s not require user information like gender and ages during the ir registration. Hence, to promote ad targeting, it is essenti al to predict user's demography using rich content such as posts, images and social connections. In this paper, we propose gra ph based and deep learning models for age and gender prediction s, which take into account user activities and content feature s. For graph based models, we come up with two approaches, network embedding and label propagation, to generate connection fe atures as well as directly infer user's demography. Experimental results on real T umblr daily dataset, with hun dreds of millions of active users and billions of following relati ons, demonstrate that our approaches significantly outperform t he baseline model, by improving the accuracy relatively by 81% for age, and the AUC and accuracy by 5% for gender . Online social media has become a ubiquitous part of our daily life, which allows us to easily share ideas/contents w ith other users, discuss social events/activities, and get con nected with friends. The rich content including text, images, and videos, provide great opportunities for advertisers to champion th eir products to specific groups. In particular, Tumblr offers "n ative advertisement" that allows advertisers to present their sp on-sored posts on the users" interface. Native advertising has gained over 3 billion paid ad impressions in 2015 since it was started in 2012 [1].