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New Updates to the NVIDIA Deep Learning SDK Now Helps Accelerate Inference

New Updates to the NVIDIA Deep Learning SDK Now Helps Accelerate Inference

New Updates to the NVIDIA Deep Learning SDK Now Helps Accelerate Inference

The latest update to the NVIDIA Deep Learning SDK includes the NVIDIA TensorRT deep learning inference engine (formerly GIE) and the new NVIDIA Deep Stream SDK.

TensorRT delivers high performance inference for production deployment of deep learning applications. The latest release delivers up to 3x more throughput, using 61% less memory with new INT8 optimized precision.

TensorRT charts

New Updates to the NVIDIA Deep Learning SDK Now Helps Accelerate Inference

NVIDIA DeepStream SDK simplifies development of high performance video analytics applications powered by deep learning. Using a high-level C++ API and high performance runtime, developers can use the SDK to rapidly integrate advanced video inference capabilities including optimized precision and GPU-accelerated transcoding to deliver faster, more responsive AI-powered services such as real-time video categorization.

Learn more about the NVIDIA Deep Learning SDK >

Deep Learning to Unlock Mysteries of Parkinson’s Disease

Deep Learning to Unlock Mysteries of Parkinson’s Disease

Deep Learning to Unlock Mysteries of Parkinson’s Disease

Researchers at The Australian National University are using deep learning and NVIDIA technologies to better understand the progression of Parkinson’s disease.

Currently it is difficult to determine what type of Parkinson’s someone has or how quickly the condition will progress.
The study will be conducted over the next five years at the Canberra Hospital in Australia and will involve 120 people suffering from the disease and an equal number of non-sufferers as a controlled group.

“There are different types of Parkinson’s that can look similar at the point of onset, but they progress very differently,” says Dr Deborah Apthorp of the ANU Research School of Psychology. “We are hoping the information we collect will differentiate between these different conditions.”

Researchers Alex Smith (L) and Dr Deborah Anthrop (R) work with Parkinson’s disease sufferer Ken Hood (middle).

Researchers Alex Smith (L) and Dr Deborah Anthrop (R) work with Parkinson’s disease sufferer Ken Hood (middle).

Dr Apthorp said the research will measure brain imaging, eye tracking, visual perception and postural sway.

From the data collected during the study, the researchers will be using a GeForce GTX 1070 GPU and cuDNN to train their deep learning models to help find patterns that indicate degradation of motor function correlating with Parkinson’s.

The researchers plan to incorporate virtual reality into their work by having the sufferers’ wear head-mounted displays (HMDs), which will help them better understand how self-motion perception is altered in Parkinson’s disease, and use stimuli that mimics the visual scene during self-motion.

“Additionally, we would like to explore the use of eye tracking built into HMDs, which is a much lower cost alternative to a full research eye tracking system and reduces the amount of equipment into a highly portable and versatile single piece of equipment,” says researcher Alex Smith.

GPUs Help Cut Siri’s Error Rate by Half

GPUs Help Cut Siri’s Error Rate by Half

GPUs Help Cut Siri’s Error Rate by Half

To make Siri great, Apple employed several artificial intelligence experts three years ago to apply deep learning to their intelligent mobile smart assistant.

The team began training a neural net to replace the original Siri. “We have the biggest and baddest GPU farm cranking all the time,” says Alex Acero, who heads the speech team.

“The error rate has been cut by a factor of two in all the languages, more than a factor of two in many cases,” says Acero. “That’s mostly due to deep learning and the way we have optimized it.”

Apple Siri GPU

Besides Siri, Apple’s adoption of deep learning and neural nets are now found all over their products and services — including fraud detection on the Apple store, facial recognition and locations in your photos, and to help identify the most useful feedback from thousands of reports from beta testers.

“The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product,” says Phil Schiller, senior vice president of worldwide marketing at Apple. “The most exciting [instances] are so subtle that you don’t even think about it until the third time you see it, and then you stop and say, How is this happening?”

Facial Recognition Software Helping Caterpillar Identify Sleepy Operators

Facial Recognition Software Helping Caterpillar Identify Sleepy Operators

Facial Recognition Software Helping Caterpillar Identify Sleepy Operators

Operator fatigue can potentially be a fatal problem for Caterpillar employees driving the massive mine trucks on long, repetitive shifts throughout the night.

Caterpillar recognized this and joined forces with Seeing Machines to install their fatigue detection software in thousands of mining trucks worldwide. Using NVIDIA TITAN X and GTX 1080 GPUs along with the cuDNN-accelerated Theano, TensorFlow and Caffe deep learning frameworks, the Australian-based tech company trained their software for face tracking, gaze tracking, driver attention region estimation, facial recognition, and fatigue detection.

On-board the truck, a camera, speaker and light system are used to monitor the driver and once a potential “fatigue event” is detected, an alarm sounds in the truck and a video clip of the driver is sent to a 24-hour “sleep fatigue center” at the Caterpillar headquarters.

Facial Recognition Software Helping Caterpillar Identify Sleepy Operators

Facial Recognition Software Helping Caterpillar Identify Sleepy Operators

“This system automatically scans for the characteristics of microsleep in a driver,” Sal Angelone, a fatigue consultant at the company, told The Huffington Post, referencing the brief, involuntary pockets of unconsciousness that are highly dangerous to drivers. “But this is verified by a human working at our headquarters in Peoria.”

In the past year, Caterpillar referenced two instances – in one, a driver had three fatique events within four hours and he was contacted onsite and forced to take a nap. In another, a night shift truck driver who experienced a fatique event realized it was a sign of sleep disorder and asked his management for medical assistance.

It’s a matter of time before this technology is incorporated into every car on the road.

Open-Access Visual Search Tool for Satellite Imagery

Open-Access Visual Search Tool for Satellite Imagery

Open-Access Visual Search Tool for Satellite Imagery

A new project by Carnegie Mellon University researchers provides journalists, citizen scientists, and other researchers with the ability to quickly scan large geographical regions for specific visual features.

Simply click on a feature in the satellite imagery – a baseball diamond, cul-de-sac, tennis court – and Terrapattern will find other things that look similar in the area and pinpoint them on the map.

Using a deep learning neural network trained for five days on an NVIDIA GeForce GPU, their model will look at small squares of the landscape and, comparing those patterns to a huge database of tagged map features from OpenStreetMap, it learned to associate them with certain concepts.

Open-Access Visual Search Tool for Satellite Imagery

Open-Access Visual Search Tool for Satellite Imagery

Currently, Terrapattern is limited to Pittsburgh, San Francisco, New York City and Detroit, but access to more cities is coming soon.

Facebook and CUDA Accelerate Deep Learning Research

Facebook and CUDA Accelerate Deep Learning Research

Facebook and CUDA Accelerate Deep Learning Research

Last Thursday at the International Conference on Machine Learning (ICML) in New York, Facebook announced a new piece of open source software aimed at streamlining and accelerating deep learning research. The software, named Torchnet, provides developers with a consistent set of widely used  deep learning functions and utilities. Torchnet allows developers to write code in a consistent manner speeding development and promoting code re-use both between experiments and across multiple projects.

Torchnet sits atop the popular Torch deep learning framework benefits from GPU acceleration using CUDA and cuDNN.

Torchnet sits atop the popular Torch deep learning framework benefits from GPU acceleration using CUDA and cuDNN.

Torchnet sits atop the popular Torch deep learning framework benefits from GPU acceleration using CUDA and cuDNN. Further, Torchnet has built-in support for asynchronous, parallel data loading and can make full use of multiple GPUs for vastly improved iteration times. This automatic support or multi-GPU training helps Torchnet take full advantage of powerful systems like the NVIDIA DGX-1 with its eight Tesla P100 GPUs.

Facebook and CUDA Accelerate Deep Learning Research

Facebook and CUDA Accelerate Deep Learning Research

According to the Torchnet research paper, its modular design makes it easy to re-use code in a series of experiments. For instance, running the same experiments on a number of different datasets is accomplished simply by plugging in different dataloaders. And the evaluation criterion can be changed easily by plugging in a different performance meter.

Torchnet adds another powerful tool to data scientists’ toolkit and will help speed the design and training of neural networks, so they can focus on their next great advancement.

World’s First Real-Time 3D Oil Painting Simulator

World’s First Real-Time 3D Oil Painting Simulator

World’s First Real-Time 3D Oil Painting Simulator

The painting and drawing tools most people use are 2D, but now a new project gives artists the ability to choose any brush they like, a limitless array of paint colors, and use the same natural twists and turns of the brush to create the rich textures of oil painting, all on a digital canvas.

Delivering such a realistic, physically-based painting tool requires some heavy-duty computational power, so Adobe Research collaborated with NVIDIA to create the world’s first real-time simulation-based 3D painting system with bristle-level interactions entirely with CUDA. Adobe Researchers Zhili Chen and Byungmoon Kim originally developed Project Wetbrush in 2015, but and have collaborated with NVIDIA software experts to optimize their application performance, allowing them to add even more GPU-accelerated features to the system.

This is just the beginning for the project. Using deep learning, some of the most computationally challenging physical simulations could potentially be added to create more responsive and realistic brush dynamics, or the system could even learn from itself.

World’s First Real-Time 3D Oil Painting Simulator

World’s First Real-Time 3D Oil Painting Simulator

Easily Build Your First Movie Recommender System

Easily Build Your First Movie Recommender System

Easily Build Your First Movie Recommender System

Recommender systems are being deployed everywhere to deliver personalized experiences.

Siraj Raval, a former software engineer at Meetup and CBS Interactive, recently launched an entertaining yet informative YouTube channel called Sirajology aimed to inspire and equip developers to build the future.

His recent tutorial video explains how you can create a recommender system in just 10 lines of C++ code using Amazon’s DSSTNE deep learning framework and GPUs in the Amazon Web Services cloud.

Interested in deep learning? Check out these other GPU-accelerated deep learning projects.

“Fitbit for Cows” Uses Deep Learning to Provide Insights for Dairy Farmers

“Fitbit for Cows” Uses Deep Learning to Provide Insights for Dairy Farmers

“Fitbit for Cows” Uses Deep Learning to Provide Insights for Dairy Farmers

To meet the demand of the world’s growing population, farmers need to improve the productivity of their herds.

Amsterdam-based Connecterra recently raised nearly $2 million to further develop their GPU-accelerated deep learning solution that consists of a wearable device that monitors each animal in the herd and transmits the data to a cloud platform for analysis and prediction of behavioral patterns.

“A little over a year ago, we started with a vision to contribute to solving the problems that impact the future of our planet by combining sensors and machine learning technologies,” said Yasir Khokhar, CEO of Connecterra. “Today we are turning that vision into reality by bringing usable technology to farmers, helping them increase productivity and keep the herd healthier, while reducing the impact on the environment.”

The startup expects to make the service commercially available in the first half of 2016.

“Fitbit for Cows” Uses Deep Learning to Provide Insights for Dairy Farmers

“Fitbit for Cows” Uses Deep Learning to Provide Insights for Dairy Farmers

Amazon Releases Open-Source Deep Learning Software

Amazon Releases Open-Source Deep Learning Software

Amazon Releases Open-Source Deep Learning Software

With hundreds of millions of customers shopping daily on Amazon, it’s critical they help them discover the right product from their massive catalog of products.

Amazon’s Deep Scalable Sparse Tensor Network Engine (DSSTNE or “Destiny”) is a deep learning framework built from the ground up to help researchers develop search and recommendation systems. With multi-GPU support, DSSTNE can automatically distribute computational workloads across all available GPUs, speeding up training of larger models without a lot of effort. As a result, DSSTNE can be used to build recommendations systems that model ten million unique products instead of being limited to hundreds of thousands possible with other solutions.

Amazon says 35 percent of its product sales result from product recommendations

Amazon says 35 percent of its product sales result from product recommendations

The DSSTNE roadmap includes support for the types of convolutional layers used in image processing, and additional recurrent layers needed for many natural language understanding and speech recognition tasks.



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My name is Sayed Ahmadreza Razian and I am a graduate of the master degree in Artificial intelligence .
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Related topics such as image processing, machine vision, virtual reality, machine learning, data mining, and monitoring systems are my research interests, and I intend to pursue a PhD in one of these fields.

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  • Data mining - Big Data
  • CUDA Programming
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