We share new announcements and research in the field of deep learning.

Machine learning, deep learning and artificial intelligence (ML, DL and AI) are related technologies that are changing the face of how many industries manage themselves and make decisions. Clearly, they are very important and complex processes that are evolving very quickly. It is important to understand the differences between them. Unfortunately, you almost need to use one of them to do so. The map was laid out well earlier this week by Hope Reese at TechRepublic.

Much of the artificial intelligence talk at Mobile World Congress is at the device level -- services based on voice recognition, in particular. But AI has a role deeper in the infrastructure, of course, and Ericsson touched on that to kick off the conference Monday morning. In Ericsson's traditional MWC opener for press and analysts, Ericsson CEO Börje Ekholm understandably focused on 5G. But AI and machine learning were the second topic of his overview, an indication of how deeply Ericsson expects these technologies to change the network. Nokia is in the process of acquiring Deepfield, a deep learning startup, to help with traffic flows in software-defined networking (SDN).

Today we are routinely awed by the promise of machine learning (ML) and artificial intelligence (AI). Our phones speak to us and our favorite apps can ID our friends and family in our photographs. We didn't get here overnight, of course. Enhancements to the network itself – deep, convolutional neural networks executing advanced computer science techniques – brought us to this point. Now one of the primary beneficiaries of our super-connected world will be the very networks we have come to rely on for information, communication, commerce, and entertainment.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.

Summary: Count yourself lucky if you're not in one of the regulated industries where regulation requires you to value interpretability over accuracy. This has been a serious financial weight on the economy but innovations in Deep Learning point a way out. As Data Scientists we tend to take as gospel that more accuracy is better. There are some practical limits to this. It may not be profitable to continue to work a model for many days or weeks when the improvement to be had is minor.

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AI is mainstream these days. The attention it gets and the feelings it provokes cover the whole gamut: from hands-on technical to business, from social science to pop culture, and from pragmatism to awe and bewilderment. Data and analytics are a prerequisite and an enabler for AI, and the boundaries between the two are getting increasingly blurred. Many people and organizations from different backgrounds and with different goals are exploring these boundaries, and we've had the chance to converse with a couple of prominent figures in analytics and AI who share their insights. The Internet of Things is creating serious new security risks.

Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we present a matrix-based implementation of belief propagation algorithms on CRBMs, which is easily scalable to tens of thousands of visible and hidden units. We demonstrate that, in both maximum likelihood and max-margin learning, training conditional RBMs with BP as the inference routine can provide significantly better results than current state-of-the-art CD methods on structured prediction problems. We also include practical guidelines on training CRBMs with BP, and some insights on the interaction of learning and inference algorithms for CRBMs.

Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.