Every decade seems to have its technological buzzwords: we had personal computers in 1980s; Internet and worldwide web in 1990s; smart phones and social media in 2000s; and Artificial Intelligence (AI) and Machine Learning in this decade. The 1950-82 era saw a new field of Artificial Intelligence (AI) being born, lot of pioneering research being done, massive hype being created, and AI going into hibernation when this hype did not materialize, and the research funding dried up [56]. During 1983 and 2010, research funding ebbed and flowed, and research in AI continued to gather steam although "some computer scientists and software engineers would avoid the term artificial intelligence for fear of being viewed as wild-eyed dreamers" [43]. During 1980s and 90s, researchers realized that many AI solutions could be improved by using techniques from mathematics and economics such as game theory, stochastic modeling, classical numerical methods, operations research and optimization. Better mathematical descriptions were developed for deep neural networks as well as evolutionary and genetic algorithms, which matured during this period.

A new broad oncology deep learning suite from the cloud-based medical imaging software solutions company Arterys Inc. was approved on Thursday, Feb. 15 for 501(k) clearance by the FDA, according to a report by Business Insider. The clearance is for Aterys new Oncology AI software aimed to advance medical imaging accuracy and consistency, according to the report. This is the fifth FDA clearance Arterys has received for its deep learning cloud-based software. Specifically, the deep learning oncology software will be able to help clinicians measure and track tumors or potential cancers in solely liver and lung magnetic resonance imaging (MRI) and computed tomography (CT) scans and apply radiological standards with ease. "The evaluation of primary and metastatic disease in the lung and liver are among the most valuable contributions of radiologists to the care of patients with cancer," said radiologist and Arterys co-founder Albert Hsiao, MD, PhD, in a prepared statement.

A successful autonomous system needs to not only understand the visual world but also communicate its understanding with humans. To make this possible, language can serve as a natural link between high level semantic concepts and low level visual perception. We'll discuss recent work in the domain of vision and language, covering topics such as image/video captioning and retrieval, and question-answering. ABOUT THE SPEAKER: Sanja Fidler is an assistant professor at the Department of Computer Science, University of Toronto. Previously, Sanja was a research assistant professor at TTI-Chicago, a philanthropically endowed academic institute located in the campus of the University of Chicago.

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist. Deep Learning is the modern buzzword for artificial neural networks, one of many concepts and algorithms in machine learning to build analytics models. A neural network works similar to what we know from a human brain: You get non-linear interactions as input and transfer them to output. A neural network is a supervised algorithm in most cases, which uses historical data sets to learn correlations to predict outputs of future events, e.g. for cross selling or fraud detection.

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.

Hypernetworks are neural networks that transform a random input vector into weights for a specified target neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We show that this formulation naturally arises as a relaxation of an optimistic probability distribution objective for the generated networks, and we explain how it is related to variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by weight sharing. We perform experiments on a four layer convolutional target network which classifies MNIST images, and show that the generated weights are diverse and have interesting distributions.

More and more companies are building and applying deep learning models in their business. Several practical issues should be taken into consideration before these models are put into production. Consider this scenario: you may build a model that works perfectly with training and validation data, but it doesn't perform well after deploying the model in real scenarios. Or, you may struggle with getting better performance compared to traditional machine learning models. While the latter case will make you rethink whether to invest more resourcing on this, the former situation is more risky and you may not realize it until you put your models into production.

Deep Learning Software: Deep Learning is a branch of machine learning for learning about multiple levels of representation and abstraction to make sense of the data such as images, sound, and text. It is a set of algorithms in machine learning which typically uses artificial neural networks to learn in multiple levels, corresponding to different levels of abstraction. The levels in these learned statistical models correspond to distinct levels of concepts, where higher level concepts are defined from lower level ones, and the same lower level concepts can help to define many higher level concepts. Deep learning architectures are Deep neural networks, Deep belief networks, Convolutional neural networks, Convolutional Deep Belief Networks, Deep Boltzmann Machines, Stacked Auto Encoders, Deep Stacking Networks, Tensor Deep Stacking Networks (T-DSN), Spike-and-Slab RBMs (ssRBMs), Compound Hierarchical-Deep Models, Deep Coding Networks and Deep Kernel Machines. Deep Learning applications are automatic speech recognition, image recognition and natural language processing.

You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard datasets that are well understood and widely used so that you can compare your results to see if you are making progress. In this post, you will discover a suite of standard datasets for natural language processing tasks that you can use when getting started with deep learning. I have tried to provide a mixture of datasets that are popular for use in academic papers that are modest in size.

A new technology uses artificial intelligence to generate synthetic images that can pass as real. The technology was developed by a team led by Hiroshi Ishikawa, a professor at Japan's Waseda University. It uses convolutional neural networks, a type of deep learning, to predict missing parts of images. The technology could be used in photo-editing apps. It can also be used to generate 3-D images from real 2-D images.