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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

Advanced Real-Time Visualization for Robotic Heart Surgery

Advanced Real-Time Visualization for Robotic Heart Surgery

Advanced Real-Time Visualization for Robotic Heart Surgery

Researchers at the Harvard Biorobotics Laboratory are harnessing the power of GPUs to generate real-time volumetric renderings of patients’ hearts. The team has built a robotic system to autonomously steer commercially available cardiac catheters that can acquire ultrasound images from within the heart. They tested their system in the clinic and reported their results at the 2016 IEEE International Conference on Robotics and Automation (ICRA) in Stockholm, Sweden.

The team used an Intracardiac Echocardiography (ICE) catheter, which is equipped with an ultrasound transducer at the tip, to acquire 2D images from within a beating heart. Using NVIDIA GPUs, the team was able to reconstruct a 4D (3D + time) model of the heart from these ultrasound images.

Generating a 4D volume begins with co-registering ultrasound images that are acquired at different imaging angles but at the same phase of the cardiac cycle. The position and rotation of each image with respect to the world coordinate frame is measured using electromagnetic (EM) trackers that are attached to the catheter body. This point cloud is then discretized to lie on a 3D grid. Next, infilling is performed to fill the gaps between the slices, generating a dense volumetric representation of the heart. Finally, the volumes are displayed to the surgeon using volume rendering via raycasting, leveraging the CUDA – OpenGL interoperability. The team accelerated the volume reconstruction and rendering algorithms using two NVIDIA TITAN GPUs.

“ICE catheters are currently seldom used due to the difficulty in manual steering,” said principal investigator Prof. Robert D. Howe, Abbott and James Lawrence Professor of Engineering at Harvard University. “Our robotic system frees the clinicians of this burden, and presents them with a new method of real-time visualization that is safer and higher quality than the X-ray imaging that is used in the clinic. This is an enabling technology that can lead to new procedures that were not possible before, as well as improving the efficacy of the current ones.”

Providing real-time procedure guidance requires the use of efficient algorithms combined with a high-performance computing platform. Images are acquired at up to 60 frames per second from the ultrasound machine. Generating volumetric renderings from these images in real-time is only possible using GPUs.

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.

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.

OpenAI Creates a Gym to Train Your AI

OpenAI Creates a Gym to Train Your AI

OpenAI Creates a Gym to Train Your AI

Open AI, a non-profit artificial intelligence research company backed by Elon Musk, launched a toolkit for developing and comparing reinforcement learning algorithms.

OpenAI Gym is a suite of environments that include simulated robotic tasks and Atari games as well as a website for people to post their results and share code.

OpenAI researcher John Schulman shared some details about his organization, why reinforcement learning is important and how the OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications.

Moodbox First Emotionally Intelligent Speaker Trained on GPUs

 Moodbox First Emotionally Intelligent Speaker Trained on GPUs

Moodbox First Emotionally Intelligent Speaker Trained on GPUs

Created by researchers at the Hong Kong University of Science and Technology, the MoodBox speaker is billed as the first ever high quality wireless speaker that senses human emotions.

Using NVIDIA Tesla GPUs and deep learning, the speaker operates with cutting edge sensory recognition technology named “Emi”. Emi collects and analyzes audio signals and music lyrics to provide efficient retrieval of millions of songs by genres, styles, mood, and artist. Emi not only converses, but also suggests appropriate music, adjusts the lighting to music, reports on weather conditions, and offers wake-up calls.

“We are bringing the latest R&D in speech, music and emotion recognition technology to people’s lives,” explains creator and emotional intelligence pioneer Pascale Fung, PhD. “When you speak to MoodBox, the predictive engine delineates emotional state from tone of voice and content of speech.”

With less than two weeks remaining of their Indiegogo campaign, the team already surpassed their $40,000 funding goal.



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About me

My name is Sayed Ahmadreza Razian and I am a graduate of the master degree in Artificial intelligence .
Click here to CV Resume page

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.

جهت نمایش صفحه معرفی و رزومه کلیک کنید

My Scientific expertise
  • Image processing
  • Machine vision
  • Machine learning
  • Pattern recognition
  • Data mining - Big Data
  • CUDA Programming
  • Game and Virtual reality

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