Each algorithm in the hierarchy applies a nonlinear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep. In traditional machine learning, the learning process is supervised and the programmer has to be very, very specific when telling the computer what types of things it should be looking for when deciding if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction and the computer's success rate depends entirely upon the programmer's ability to accurately define a feature set for "dog."

Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification. Our novel method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then we present a novel scheme for liver lesion classification using CNN. Finally, we train the CNN using classic data augmentation and our synthetic data augmentation and compare performance. In addition, we explore the quality of our synthesized examples using visualization and expert assessment. The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity. We believe that this approach to synthetic data augmentation can generalize to other medical classification applications and thus support radiologists' efforts to improve diagnosis.

In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible. In a setting where a small amount of labeled data as well as a large amount of unlabeled data is available, our method first learns the labeled data set. This initialization is followed by an expectation maximization algorithm, where further training reduces classification entropy on the unlabeled data by targeting a low entropy fit which is consistent with the labeled data. In addition the algorithm asks at a specified frequency an oracle for labels of data with entropy above a certain entropy quantile. Using this active learning component we obtain an agile labeling process that achieves high accuracy, but requires only a small amount of known labels. For the MNIST dataset we report an error rate of 2.06% using only 300 labels and 1.06% for 1,000 labels. These results are obtained without employing any special network architecture or data augmentation.

China wants to become a country of innovation, and lead the world in artificial intelligence in 2030. China is outdoing the US in some kinds of AI-related intellectual property, according to a report published in mid-February by US business research firm CB Insights. The number of patents with the words "artificial intelligence" and "deep learning" published in China has grown faster than those published in the US, particularly in 2017, the firm found. Publication is a step that comes after applications are filed but before a patent is granted. The firm looked at data from the European patent office.

The U.S. Air Force is deploying a new open architecture for its primary intelligence, surveillance and reconnaissance system. At the same time, Air Force researchers are developing deep learning capabilities that will allow the decades-old system to sort through reams of data more easily, enabling faster decision making on the battlefield and enhancing multidomain command and control. An Air Force fact sheet describes the Air Force Distributed Common Ground Station (AF DCGS) as a planning and direction, collection, processing and exploitation, analysis and dissemination weapon system. The system employs a global communications architecture that connects multiple intelligence platforms and sensors. Airmen assigned to AF DCGS produce actionable intelligence from data collected by various sensors on the U-2, RQ-4 Global Hawk, MQ-1 Predator, MQ-9 Reaper and other intelligence, surveillance and reconnaissance (ISR) platforms.

In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.

Intel unveiled "Intel AI: In Production," a new program that makes it easier for developers to bring prototypes of their artificial intelligence devices to market. Since its introduction last July, the Intel Movidius Neural Compute Stick (NCS) reportedly has gained a developer base in the tens of thousands. Once developers have a prototype, the next step is to take it into production, which can be challenging and costly for small companies and entrepreneurs. To make it easier, Intel selected AAEON Technologies, a leading manufacturer of advanced industrial and embedded computing platforms, as the first Intel AI: In Production partner. Through the program, AAEON provides two streamlined production paths for developers integrating the low-power Intel Movidius Myriad 2 Vision Processing Unit (VPU) into their product designs.

Deep machine learning can extract and quantify several risk factors for cardiovascular disease (CVD) from photographs of the retinal fundus, according to findings published online February 19 in Nature Biomedical Engineering. Traditional risk factors for CVD include age, sex, smoking status, blood pressure, body mass index, and blood glucose and cholesterol levels. However, a major limitation in considering these risk factors is that many people do not know all of their values, particularly serum cholesterol, for which body mass index is sometimes used as a substitute. However, another way to assess CVD risk may be from retinal images, which are easily obtained in an outpatient setting. Retinal anatomy may reveal cardiovascular status through the presence of cholesterol emboli, hypertensive retinopathy, and details of blood vessel caliber, bifurcation and further branching patterns, and tortuosity.

We've been running into performance issues when training an LSTM/RNN based Seq2Seq to create a multivariate, online trainable forecasting model - Does anyone here have experience with much harder (1000, n-dimensional) problems? From the paper, I feel like this might lead to a pretty dramatic improvement in performance.