Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults. We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD. Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote. This algorithm uses $32\times$ less communication per iteration than full-precision, distributed SGD. Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct. We model adversaries as those workers who may compute a stochastic gradient estimate and manipulate it, but may not coordinate with other adversaries. Aggregating sign gradients by majority vote means that no individual worker has too much power. We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially. On the practical side, we built our distributed training system in Pytorch. Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.

Uploads 0:39 Welcome to Lucid Thoughts: A Video Series on AI - Duration: 39 seconds. AI, Machine Learning, and Deep Learning concepts explained in simple language you'll understand. Uploads 0:39 Welcome to Lucid Thoughts: A Video Series on AI - Duration: 39 seconds. AI, Machine Learning, and Deep Learning concepts explained in simple language you'll understand. AI, Machine Learning, and Deep Learning concepts explained in simple language you'll understand.

Behind most of today's artificial intelligence technologies, from self-driving cars to facial recognition and virtual assistants, lie artificial neural networks. Though based loosely on the way neurons communicate in the brain, these "deep learning" systems remain incapable of many basic functions that would be essential for primates and other organisms. However, a new study from University of Chicago neuroscientists found that adapting a well-known brain mechanism can dramatically improve the ability of artificial neural networks to learn multiple tasks and avoid the persistent AI challenge of "catastrophic forgetting." The study, published in Proceedings of the National Academy of Sciences, provides a unique example of how neuroscience research can inform new computer science strategies, and, conversely, how AI technology can help scientists better understand the human brain. When combined with previously reported methods for stabilizing synaptic connections in artificial neural networks, the new algorithm allowed single artificial neural networks to learn and perform hundreds of tasks with only minimal loss of accuracy, potentially enabling more powerful and efficient AI technologies.

Breast cancer affects one in eight women in the US. There are multiple factors involved in developing the disease, but one issue is dense breast tissue. Some 40 percent of US women have dense breast tissue, which alone increases the risk of breast cancer, and can make mammogram screening more difficult. Now, researchers from MIT and Massachusetts General Hospital (MGH) have developed an automated model that assesses dense breast tissue in mammograms as reliably as expert radiologists. Breast density assessments have traditionally relied on subjective human exams and calculations, but the deep-learning model -- trained on tens of thousands of digital mammograms -- is able to distinguish different types of breast tissue, from fatty to extremely dense, with 90 percent correlation to radiologists' diagnosis.

If you want to carve out a career in machine learning then knowing where to start can be daunting. Not only is the technology built on college-level math, jobs in the field typically ask for a Master's degree in a related technical field. Yet if you're willing to work at it, it's never been easier to learn about machine learning, and getting started doesn't even require much mathematical knowledge. Here's five tips for breaking into the field from senior data scientists and machine-learning engineers, speaking to TechRepublic at the AI Conference presented by O'Reilly and Intel AI. If you plan to start tweaking the machine-learning models used then you'll need need a reasonably deep knowledge of math, spanning linear algebra, calculus and statistics.

Kate Saenko had a problem. Her AI algorithms tended to identify scientists as men and kitchen workers as women, and she didn't know why. An associate professor at Boston University's Department of Computer Science, Kate had been using deep learning to automate the captioning of images and videos. And to be true, the results were spectacular. Neural networks, the software structure that underlies deep learning, proved to be very good at generating human-like descriptions of digital imagery.

As soon as it was possible to scan and load medical images into a computer, researchers have attempted to built system to automate the analysis of such images. Initially, from 1970s to 1990s, medical image analysis was done using sequential application of low level pixel processing(edge and line detector filters) and mathematical modeling to construct a rule-based system that could solve only particular task. At the same time there were some agents based on if-else rules, popular in field of Artificial Intelligence commonly known as GOFAI (Good Old Fashioned Artificial Intelligence) agent. Towards the end of 1990s, supervised techniques were becoming popular in which training data was used to train models and they were becoming increasingly popular in the field of medical image analysis. Examples may include active shape model, atlas method.

Artificial Intelligence is making it easier for people to do things every day. You can schedule your day, search for photos of loved ones, type emails on the go, or get things done with the virtual assistant. AI also provides innovative ways of tackling existing problems, from healthcare to advancing scientific discovery. According to Gartner's Top 10 Strategic Technology Trends for 2018, the next few years will see every app, application, and service incorporating AI at some level. With major companies like Google, Amazon, IBM investing in AI and incorporating AI in their products, this statement, instead of a prediction is becoming a fact.

Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads involved in accidents are required to submit an accident report to the Federal Railroad Administration (FRA). These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident. Although these narratives provide more information than a fixed field entry, the terminologies used in these reports are not easy to understand by a non-expert reader. Therefore, providing an assisting method to fill in the primary cause from such domain specific texts(narratives) would help to label the accidents with more accuracy. Another important question for transportation safety is whether the reported accident cause is consistent with narrative description. To address these questions, we applied deep learning methods together with powerful word embeddings such as Word2Vec and GloVe to classify accident cause values for the primary cause field using the text in the narratives. The results show that such approaches can both accurately classify accident causes based on report narratives and find important inconsistencies in accident reporting.

Abstract-- Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneousaware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy. Index Terms--Big data, bi-layered parallel computing, convolutional neural networks, deep learning, distributed computing. Convolutional Neural Network (CNN) algorithm is an important branch of DL.