Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition performance. Therefore, it is important to analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. This paper proposes a method that can automatically map the gaze fixations collected using a wearable eye-tracker to the predefined areas of interests. The proposed method detects these areas or objects (i.e., hazards) of interests through a computer vision-based segmentation technique and transfer learning. The mapped fixation data is then used to analyze the viewing behaviors of workers and compute their attention distribution. The proposed method is implemented on an under construction road as a case study to evaluate the performance of the proposed method.

The term neural networks refers to networks of neurons in the mammalian brain. Neurons are its fundamental units of computation. In the brain they are connected together in networks to process data. This can be a very complex task, and the dynamics of neural networks in the mammalian brain in response to external stimuli can therefore be quite intricate. Inputs and outputs of each neuron vary as functions of time, in the form of so-called spike trains, but also the network itself changes. We learn and improve our data-processing capacities by establishing reconnections between neurons. Neural-networkalgorithms are inspired by the architecture and the dynamics of networks of neurons in the brain. Yet the algorithms use neuron models that are highly simplified, compared with real neurons. Nevertheless, the fundamental principle is the same: artificial neural networks learn by reconnection.

In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motion is rarely treated explicitly; it is rather assumed that the controller learns the necessary motion representation from temporal stacks of frames implicitly. In this paper, we show that for continuous control tasks learning an explicit representation of motion improves the quality of the learned controller in dynamic scenarios. We demonstrate this on common benchmark tasks (Walker, Swimmer, Hopper), on target reaching and ball catching tasks with simulated robotic arms, and on a dynamic single ball juggling task. Moreover, we find that when equipped with an appropriate network architecture, the agent can, on some tasks, learn motion features also with pure reinforcement learning, without additional supervision. Further we find that using an image difference between the current and the previous frame as an additional input leads to better results than a temporal stack of frames.

The instance segmentation problem intends to precisely detect and delineate objects in images. Most of the current solutions rely on deep convolutional neural networks but despite this fact proposed solutions are very diverse. Some solutions approach the problem as a network problem, where they use several networks or specialize a single network to solve several tasks. A different approach tries to solve the problem as an annotation problem, where the instance information is encoded in a mathematical representation. This work proposes a solution based in the DCME technique to solve the instance segmentation with a single segmentation network. Different from others, the segmentation network decoder is not specialized in a multi-task network. Instead, the network encoder is repurposed to classify image objects, reducing the computational cost of the solution.

In 1933, a chicken keeper and amateur photographer decided to find the culprit who was stealing his eggs. Since its inception, security cameras are everywhere nowadays, most of the claimed "smart ones" work by streaming videos back to a monitor or a server so as someone or some software can analyze video frames and hopefully find some useful information from them. They consume a large amount of network bandwidth and power to stream videos even though ten image frames are all we need to know who was stealing the eggs. They are also facing a dilemma of out of service when the network is unstable, images cannot be analyzed and the "smart" becomes "dumb". Edge computing is a network model which enables data processing occurs at the edge of the network where the camera is located, eliminating the need to send videos to a central server for processing.

Intel announced Nauta, an open source deep learning project based on Kubernetes. The project comes with select open source components and Intel-developed custom applications, tools, and scripts for building deep learning models. According to Intel, Nauta provides a multi-user, distributed computing environment for running deep learning model training experiments on systems based on Intel Xeon processor. The software foundation for the distributed platform is built on Kubernetes – industry's leading container orchestration engine. Mainstream deep learning tools and frameworks such as TensorFlow, TensorBoard and Jupyter Hub are tightly integrated with the platform.

Along America's west coast, the world's most valuable companies are racing to make artificial intelligence smarter. Google and Facebook have boasted of experiments using billions of photos and thousands of high-powered processors. Late last year, a project in eastern Tennessee quietly exceeded the scale of any corporate AI lab. It was run by the US government. The record-setting project involved the world's most powerful supercomputer, Summit, at Oak Ridge National Lab.

Looking for the older 2018 courses?: This site covers the new 2019 deep learning course. The 2018 courses have been moved to: course18.fast.ai. Note that the 2019 edition of part 2 (Cutting Edge Deep Learning) is not yet available, so you'll need to use the 2018 course for now (the 2019 edition will be available in June 2019). If you're new to all this deep learning stuff, then don't worry--we'll take you through it all step by step. We do however assume that you've been coding for at least a year, and also that (if you haven't used Python before) you'll be putting in the extra time to learn whatever Python you need as you go.

In this tutorial, you will learn how to train a Convolutional Neural Network (CNN) for regression prediction with Keras. You'll then train a CNN to predict house prices from a set of images. Today's tutorial builds on last week's basic Keras regression example, so if you haven't read it yet make sure you go through it in order to follow along here today. By the end of this guide, you'll not only have a strong understanding of training CNNs for regression prediction with Keras, but you'll also have a Python code template you can follow for your own projects. To learn how to train a CNN for regression prediction with Keras, just keep reading!

Richard Socher is an adjunct professor at the Stanford Computer Science Department where he obtained his PhD working on deep learning with Chris Manning and Andrew Ng. He won the best Stanford CS PhD thesis award. He is now Chief Scientist at Salesforce where he leads the company's research efforts in artificial intelligence. He previously founded MetaMind, a deep learning AI platform that analyzes, labels and makes predictions on image and text data. Richard Socher is Chief Scientist at Salesforce and an adjunct professor at the Stanford Computer Science Department.