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Latest NVIDIA JetPack Developer Tools Will Double Your Deep Learning Performance

Latest NVIDIA JetPack Developer Tools Will Double Your Deep Learning Performance

Latest NVIDIA JetPack Developer Tools Will Double Your Deep Learning Performance

Today NVIDIA released a major update of the JetPack SDK with new developer tools and libraries that doubles the performance of deep learning applications on the Jetson TX1 Developer Kit, the world’s highest performance platform for deep learning on embedded systems.

JetPack 2.3 is available as a free download and is focused on making it easier for developers to add complex AI and deep learning capabilities to intelligent machines. This update includes the new TensorRT deep learning inference engine, the latest versions of CUDA 8 and cuDNN 5.1, and tighter camera and multimedia integration to easily add complex AI and deep learning abilities to intelligent machines.

JetPack 2.3 chart
See footnotes below.

In addition, NVIDIA announced a new partnership with Leopard Imaging Inc., a Jetson Preferred Partner that specializes in the creation of camera solutions. The new camera API included in the JetPack 2.3 release delivers enhanced functionality to ease developer integration.

Download JetPack 2.3 today.

Chart footnotes:

  • The efficiency was measured using the methodology outlined in the whitepaper.
  • Jetson TX1 efficiency is measured at GPU frequency of 691 MHz.
  • Intel Core i7-6700k efficiency was measured for 4 GHz CPU clock.
  • GoogLeNet batch size was limited to 64 as that is the maximum that could run with Jetpack 2.0. With Jetpack 2.3 and TensorRT, GoogLeNet batch size 128 is also supported for higher performance.
  • FP16 results for Jetson TX1 are comparable to FP32 results for Intel Core i7-6700k as FP16 incurs no classification accuracy loss over FP32.
  • Latest publicly available software versions of IntelCaffe and MKL2017 beta were used.
  • For Jetpack 2.0 and Intel Core i7, non-zero data was used for both weights and input images. For Jetpack 2.3 (TensorRT) real images and weights were used.

JetPack Latest NVIDIA JetPack Developer Tools Will Double Your Deep Learning Performance

NVIDIA Helping Developers Get Started with Deep Learning

NVIDIA Helping Developers Get Started with Deep Learning

From self-driving cars to medical diagnostics, deep learning powered artificial intelligence is impacting nearly every industry.

In 2015, NVIDIA’s Deep Learning Institute delivered more than 16,000 hours of training to help data scientists and developers master this burgeoning field of AI – and the need for deep learning training is rapidly growing.

In the next four months developers can take more than 80 instructor-led workshops and hands-on labs at one of the eight GPU Technology Conferences around the world – starting this week at GTC China.

“We want to share all our knowledge about deep learning with the world so others can create amazing things with it,” said Mark Ebersole, director of the institute.

Julie Bernauer, an NVIDIA Deep Learning Institute instructor, teaches a class on deep learning on GPUs.

Julie Bernauer, an NVIDIA Deep Learning Institute instructor, teaches a class on deep learning on GPUs.

The Deep Learning Institute has joined forces with three industry-leading organizations to train data scientists and developers interested in deep learning:

  • Teaming up with Coursera to create a series of courses on how deep learning is poised to transform healthcare
  • Collaborating with Microsoft on a hands-on workshop about how to use deep learning to create smarter robots
  • Partnering with Udacity to help developers learn how to build a self-driving car

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?”

Teaching an AI to Detect Key Actors in Multi-person Videos

Teaching an AI to Detect Key Actors in Multi-person Videos

Teaching an AI to Detect Key Actors in Multi-person Videos

Researchers from Google and Stanford have taught their computer vision model to detect the most important person in a multi-person video scene – for example, who the shooter is in a basketball game which typically contains dozens or hundreds of people in a scene.

Using 20 Tesla K40 GPUs and the cuDNN-accelerated Tensorflow deep learning framework to train their recurrent neural network on 257 NCAA basketball games from YouTube, an attention mask selects which of the several people are most relevant to the action being performed, then tracks relevance of each object as time proceeds. The team published a paper detailing more of their work.

The distribution of attention for the model with tracking, at the beginning of “free-throw success”. The attention is concentrated at a specific defender’s position. Free-throws have a distinctive defense formation, and observing the defenders can be helpful as shown in the sample images in the top row.

The distribution of attention for the model with tracking, at the beginning of “free-throw success”. The attention is concentrated at a specific defender’s position. Free-throws have a distinctive defense formation, and observing the defenders can be helpful as shown in the sample images in the top row.

Over time the system can identify not only the most important actor, but potential important actors and the events with which they are associated – such as, the ability to understand the player going up for a layup could be important, but that the most important player is the one who then blocks the shot.

New Deep Learning Method Enhances Your Selfies

New Deep Learning Method Enhances Your Selfies

New Deep Learning Method Enhances Your Selfies

Researchers from Adobe Research and The Chinese University of Hong Kong created an algorithm that automatically separates subjects from their backgrounds so you can easily replace the background and apply filters to the subject.

Their research paper mentions there are good user-guided tools that support manually creating masks to separate subjects from the background, but the “tools are tedious and difficult to use, and remain an obstacle for casual photographers who want their portraits to look good.”

A highly accurate automatic portrait segmentation method allows many portrait processing tools to be fully automatic.

A highly accurate automatic portrait segmentation method allows many portrait processing tools to be fully automatic.

Using a TITAN X GPU and the cuDNN-accelerated Caffe deep learning framework, the researchers trained their convolutional neural network on 1,800 portrait images from Flickr. Their GPU-accelerated method was 20x faster than a CPU-only approach.

Portrait video segmentation is next on the radar for the researchers.

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.

World’s Fastest Commercial Drone Powered by Jetson TX1

World’s Fastest Commercial Drone Powered by Jetson TX1

World’s Fastest Commercial Drone Powered by Jetson TX1

Records were made to be broken, and drones are no exception. Teal Drones unveiled its first production product, Teal, a Jetson TX1-powered drone capable of flight speeds in excess of 70 mph. That makes Teal the world’s fastest production drone. 

But Teal is as much a flying supercomputer platform as it is “just” a drone. Set to ship with an SDK and its own operating system — Teal OS —Teal is designed with user- and developer-friendliness firmly in mind. The drone will ship fully assembled and ready to fly straight out of the box, with the SDK providing access to software and hardware development via onboard USB ports. Built for a wide variety of uses, from drone racing to industrial applications, Teal features an onboard camera for still image and video capture, integrated storage and a microSD expansion slot, and the full visual computing power of the NVIDIA Jetson ecosystem for machine learning, image recognition, and autonomous navigation applications.

George Matus’ startup has built the world’s fastest production drone.

George Matus’ startup has built the world’s fastest production drone.

The company’s backstory is nearly as compelling as its first product. Teal Drones was founded by George Matus, Jr. Matus built his first drone from scratch at age 14, was selected as a Thiel Fellow at 16, and as CEO of Teal Drones, built the fastest production drone in the world at the ripe old age of 18. To hear him tell it, the drone itself is just the beginning of his vision for Teal.

AI Build Smart Home Hub Smart Home Hub Brings Artificial Intelligence Into Your Home

Smart Home Hub Brings Artificial Intelligence Into Your Home

Smart Home Hub Brings Artificial Intelligence Into Your Home

A new AI-powered device will be able to replace all of your various smart home control apps, as well as being able to recognize specific people and respond to a range of emotions and gestures.

AI Build is a London-based startup focused on making your smart home more natural and intuitive. Powered by an NVIDIA Jetson TX1 and six cameras, the aiPort keeps track of your daily activities and uses this knowledge to get better at helping you. It learns your preferences, recognizes your body language, and adapts its actions with your comfort in mind.

The startup plans to launch a crowd-funding campaign later this year toand sell the device for about $1,000 each.

Autonomous Robot Starts Work as Office Manager

Autonomous Robot Starts Work as Office Manager

Autonomous Robot Starts Work as Office Manager

Programmed with the latest artificial intelligence software, Betty will spend the next two months working as an office manager at Transport Systems Catapult monitoring staff and check environmental conditions.

The robot, developed by engineers at the University of Birmingham, uses NVIDIA GPUs for various forms of computer vision — like feature extraction — and 3D image processing to create a map of the surrounding area. This allows Betty to identify desks, chairs and other objects that she must negotiate while moving around the office, and observe her colleague’s movement through activity recognition.

“For robots to work alongside humans in normal work environments it is important that they are both robust enough to operate autonomously without expert help, and that they learn to adapt to their environments to improve their performance,” said Dr Nick Hawes, from the School of Computer Science at the University of Birmingham. “Betty demonstrates both these abilities in a real working environment: we expect her to operate for two months without expert input, whilst using cutting-edge AI techniques to increase her understanding of the world around her.”

Betty is part of an EU-funded STRANDS project where robots are learning how to act intelligently and independently in real-world environments while understanding 3D space.

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.

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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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