Georgia Tech researchers are using deep learning to identify early signs of heart failure. In a paper published by the Journal of the American Medical Informatics Association (JAMIA), Georgia Tech's School of Computational Science and Engineering Associate Professor Jimeng Sun and Ph.D. student Edward Choi present a pioneering method for analyzing vast amounts of personal health record data that addresses temporality in the data – something previously ignored by conventional machine learning models in health care applications. The new research, funded by the National Institutes of Health in collaboration with Sutter Health, uses a deep learning model to enable earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, Sun and Choi use a recurrent neural network (RNN) to model temporal relations among events in electronic health records. Temporal relationships communicate the ordering of events or states in time. This type of relation is traditionally used in natural language processing.

Nando de Freitas is a "machine learning professor at Oxford University, a lead research scientist at Google DeepMind, and a Fellow of the Canadian Institute For Advanced Research (CIFAR) in the Neural Computation and Adaptive Perception program." Above, you can watch him teach an Oxford course on Deep Learning, a hot subfield of machine learning and artificial intelligence which creates neural networks--essentially complex algorithms modeled loosely after the human brain--that can recognize patterns and learn to perform tasks. To complement the 16 lectures you can also find lecture slides, practicals, and problems sets on this Oxford web site. If you'd like to learn about Deep Learning in a MOOC format, be sure to check out the new series of courses created by Andrew Ng on Coursera. Oxford's Deep Learning course will be added to our list of Free Online Computer Science Courses, part of our meta collection, 1,300 Free Online Courses from Top Universities.

Lately, I have spent large swaths of my time focused on Deep Learning and Neural Networks (either with customers or in our lab). One of the most common questions that I get is around underperforming model training with regard to "wall clock time." This has more to do with focusing on only one aspect of their architecture, say GPUs. As such, I will spend a little time writing about the 3 fundamental tenets for a successful Deep Learning architecture. These fundamental tenants are: compute, file access, and bandwidth.

In December 2017, a user named "DeepFakes" posted realistic looking explicit videos of famous celebrities on Reddit. He generated these fake videos using deep learning, the latest in AI, to insert celebrities' faces into adult movies. In the following weeks, the internet exploded with articles about the dangers of face swapping technology: harassing innocents, propagating fake news, and hurting the credibility of video evidence forever. In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications. DeepFakes offers the ability to swap one face for another in an image or a video.

Interestingly enough you might want to look into some RL research for game specific applications. It's usually done using a value iteration methodology to track rewards over time in a game. A successful RL agent will have to have some predictive ability about the environment/opponent's strategies, so even though it's not exactly what you are looking for, with some manipulation to the ideas in that literature you might be able to come up with something that is of use to you. As an aside, LSTMs while incredibly powerful, may not be a good starting point. Perhaps it would be more functional to start off simpler and ramp into an LSTM if you feel that it is necessary.

Leading-edge techniques like deep learning are quickly gaining traction as today's enterprises attempt to extract real-time insights from massive data volumes. However, many businesses are looking to get started with deep learning and may be unsure of how to acquire the tools and expertise required for success. New Centers of Excellence (CoEs) from Hewlett Packard Enterprise (HPE) and NVIDIA are addressing these key challenges and providing access to the technological tools and skills that will help customers in every industry better utilize these key innovations. Many businesses today are striving to fully leverage all of their data as a rapidly expanding'Internet of Things' generates a massive amount of data every day. It's become quite a task to analyze, classify, recognize, and categorize such large data volumes, not to mention convert it into actionable intelligence that can be used to drive competitive advantage.

In the coming year, more and more companies will make the move to deploy some type of AI solution--whether it is a chatbot, machine learning functionality, or deep learning application. You may have educated yourself on the latest innovations in AI, reviewed different solutions on the market and educated your company on how AI can improve your decision making, automate key processes and improve efficiency. But despite all this planning, the million-dollar question remains: Is your data ready for AI? Last May, the Economist stated that "the most valuable resource is no longer oil, but data." All applications of AI require lots of data to create, test and train algorithms.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Many organizations are slow at adopting progressive methods. IT professionals need to prepare themselves for substantial change and a threat to jobs. This is because there is an accelerating and disruptive digital technology transformation in progress. It is referred to as the "digital revolution" which includes artificial intelligence. It can potentially adversely impact an organization's competitiveness and will be replacing employee jobs with computers.