DLTK is an open source library that makes deep learning on medical images easier. DLTK comes with introduction tutorials and basic sample applications, including scripts to download data.

At the beginning of 2017, Chinese tech company Baidu, the largest provider of Chinese language internet search as well as other digital products and services, committed to emerging business sectors such as artificial intelligence (AI) and machine learning. Since China has 731 million internet users, almost twice the U.S. population, Baidu's data set is capable of fueling AI algorithms to make them even better. With this focus on artificial intelligence, Baidu is exploring some very intriguing applications for artificial intelligence and machine learning including in their offices where facial recognition technology makes standard ID cards unnecessary and allows you to order tea from a vending machine. They have also recruited top AI talent including one of the world's most notable AI pioneers Lu Qi, who was previously a Microsoft executive before he became Baidu's COO in January 2017. Qi will step down in July 2018 for personal reasons.

As of today, artificial intelligence (commonly referred to as AI) affects many aspects we experience. The way we work and the way we live are significantly changed with the involvement of artificial intelligence. Over the past decade or so, several Sci-Fi movies helped us gaining an idea about what artificial intelligence, machine learning, and deep learning can do together. Artificial Intelligence is the concept of developing comprehensive computer systems that can demonstrate intelligence by themselves instead of depending on natural intelligence exists on humans or animals. Generally, such a system can make decisions, translate languages, recognize speech, identify visuals and perform various other tasks alone.

The researchers tackled this with a "red dot" approach--one that's been around for more than four decades and traditionally involves marking abnormal plain film with circular, red stickers. The method has historically achieved 78 percent sensitivity and 91 percent specificity across pooled chest and abdominal exams, Yates et al. said, but it doesn't relieve the issue of backlog in the reporting room. "Existing machine learning approaches have typically focused on formal reporting of pathology or multi-label classification, rather than prioritization based on abnormality," the authors wrote. "The aim of the present study was to explore the ability of a deep learning, computer vision approach in binary normality classification of plain film chest radiographs to serve as a rapid screening tool." Yates' team applied the red dot system to machine learning using deep convolutional neural networks (CNNs), according to the study.

Artificial intelligence has the potential to dramatically re-arrange our relationship with technology, hearkening a new era of human productivity, leisure, and wealth. But none of that good stuff is likely to happen unless AI practitioners can deliver on one simple request: Explain to us how the algorithms got their answers. Businesses have never relied more heavily on machine learning algorithms to guide decision-making than they do right now. Buoyed by the rise of deep learning models that can act upon huge masses of data, the benefits of using machine learning algorithms to automate a host of decisions is simply too great to pass up. Indeed, some executives see it as a matter of business survival.

A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN-PCA method is inspired by recent developments in computer vision using deep learning. CNN-PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN-PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN-PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN-PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN-PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN-PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN-PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN-PCA method is extended to a more complex non-stationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images.

Numerous pattern recognition applications can be formed as learning from graph-structured data, including social network, protein-interaction network, the world wide web data, knowledge graph, etc. While convolutional neural network (CNN) facilitates great advances in gridded image/video understanding tasks, very limited attention has been devoted to transform these successful network structures (including Inception net, Residual net, Dense net, etc.) to establish convolutional networks on graph, due to its irregularity and complexity geometric topologies (unordered vertices, unfixed number of adjacent edges/vertices). In this paper, we aim to give a comprehensive analysis of when work matters by transforming different classical network structures to graph CNN, particularly in the basic graph recognition problem. Specifically, we firstly review the general graph CNN methods, especially in its spectral filtering operation on the irregular graph data. We then introduce the basic structures of ResNet, Inception and DenseNet into graph CNN and construct these network structures on graph, named as G_ResNet, G_Inception, G_DenseNet. In particular, it seeks to help graph CNNs by shedding light on how these classical network structures work and providing guidelines for choosing appropriate graph network frameworks. Finally, we comprehensively evaluate the performance of these different network structures on several public graph datasets (including social networks and bioinformatic datasets), and demonstrate how different network structures work on graph CNN in the graph recognition task.

AI agents continue to rack up wins in the video game world. Last week, OpenAI's bots were playing Dota 2; this week, it's Quake III, with a team of researchers from Google's DeepMind subsidiary successfully training agents that can beat humans at a game of capture the flag. As we've seen with previous examples of AI playing video games, the challenge here is training an agent that can navigate a complex 3D environment with imperfect information. DeepMind's researchers used a method of AI training that's also becoming standard: reinforcement learning, which is basically training by trial and error at a huge scale. Agents are given no instructions on how to play the game, but simply compete against themselves until they work out the strategies needed to win. Usually this means one version of the AI agent playing against an identical clone.