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AI Legend Gill Pratt of Toyota to Keynote at GPU Technology Conference

AI Legend Gill Pratt of Toyota to Keynote at GPU Technology Conference

AI Legend Gill Pratt of Toyota to Keynote at GPU Technology Conference

Gill Pratt, CEO of the Toyota Research Institute and one of the world’s leading figures in artificial intelligence will be a keynote speaker at the GPU Technology Conference in Silicon Valley on April 7.

Pratt joined Toyota in 2015 from the U.S. Defense Advanced Research Projects Agency where he led the DARPA Robotics Challenge. His move to the world’s largest automaker was widely covered and is a recent example the key role deep learning will be play in the future of the automotive industry – and GPUs will play a large part in the movement.

At Toyota, he will work on improving the safety of vehicles and make driving accessible to more people.

Watch his talk on the future of automobiles at the Toyota press conference during the 2016 Consumer Electronics Show in Las Vegas.

To register for the conference, visit GTC registration page.

About CUDA – More Than A Programming Model

The CUDA compute platform extends from the 1000s of general purpose compute processors featured in our GPU’s compute architecture, parallel computing extensions to many popular languages, powerful drop-in accelerated libraries to turn key applications and cloud based compute appliances. CUDA extends beyond the popular CUDA Toolkit and the CUDA C/C++ programming language, we invite you to explore the CUDA Ecosystem and learn how you can accelerate your applications.

Widely Used By Researchers

Since its introduction in 2006, CUDA has been widely deployed through thousands of applications and published research papers, and supported by an installed base of over 500 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers.

Real People – Real Success Stories

Many researchers and developers have used the CUDA Platform to push the state of the art of their work, read some of their stories in the CUDA In Action Spotlight Series.

Acceleration For All Domains

Learn more about GPU-accelerated applications available for astronomy, biology, chemistry, physics, data mining, manufacturing, finance, and more on the software solutions page and industry solutions page. Check out our dedicated Geo-Intelligence for Developers page. Read some real industrial application case studies.

How to get started

Software developers, scientists and researchers can add support for GPU acceleration in their own applications using one of  three simple approaches:

  • Drop in a GPU-accelerated library to replace or augment CPU-only libraries such as MKL BLAS, IPP, FFTW and other widely-used libraries
  • Automatically parallelize loops in Fortran or C code using OpenACC directives for accelerators
  • Develop custom parallel algorithms and libraries using a familiar programming language such as C, C++, C#, Fortran, Java, Python, etc.

Start accelerating your application today, learn how by visiting the Getting Started Page.

Learn More

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Google Research presents a new real-time approach to object detection that exploits the efficiency of cascade classifiers with the accuracy of deep neural networks. Pedestrian detectors is very important as it relates to a variety of applications including advanced driver assistance systems, or surveillance systems. The need for very high-accurate and real-time speed is crucial that can be relied on and are fast enough to run on systems with limited compute power.

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

The research team combined a fast cascade with a cascade of deep neural networks which is both very fast, running in real-time at 67 milliseconds on GPU per image or 15 frames per second. Their approach was trained using the publicly available ‘cuda-convnet2’ code  running on an NVIDIA Tesla K20 GPU.

Detecting Objects from Space with Artificial Intelligence

Detecting Objects from Space with Artificial Intelligence

Detecting Objects from Space with Artificial Intelligence

To reveal deeper insights into important activities taking place around the world, DigitalGlobe’s advanced satellite constellation collects nearly 4 million km2 of high-resolution earth imagery each day.

The company announced they will now rely on NVIDIA GPUs and deep learning to automatically identify objects such as airplanes, vehicles, and gray elephants, as well as to detect patterns from their massive volumes of big data satellite imagery.

Object recognition results from DigitalGlobe’s deep learning software

Object recognition results from DigitalGlobe’s deep learning software

This technology has a wide variety of uses within defense and intelligence, civil agencies, mapping and analysis, environmental monitoring, oil and gas exploration, infrastructure management, Internet portals, and navigation technology.

Diagnosing Cancer with Deep Learning and GPUs

Diagnosing Cancer with Deep Learning and GPUs

Diagnosing Cancer with Deep Learning and GPUs

Using GPU-accelerated deep learning, researchers at The Chinese University of Hong Kong pushed the boundaries of cancer image analysis in a way that could one day save physicians and patients precious time.

The team used a TITAN X GPU to win the 2015 Gland Segmentation Challenge held at the Medical Image Computing and Computer conference, the world’s leading conference on medical imaging.

Traditionally, pathologists diagnose cancer by looking for abnormalities in tumor tissue and cells under a microscope, but it’s a time-consuming process that is open to error.

An overview of the team’s proposed framework

An overview of the team’s proposed framework

The research team trained their deep convolutional neural network on a set of images of known abnormalities. They then used this training for segmenting individual glands from tissues to make it easier to distinguish individual cells, determine their size, shape and location relative to other cells. By calculating these measurements, pathologists can determine the likelihood of malignancy.

“Training with GPUs was 100 times faster than with CPUs,” said Hao Chen, a third-year Ph.D. student and member of the team that developed the solution. “That speed is going to become even more important as we advance our work.”

Deep Learning Helps Robot Learn to Walk the Way Humans Do

Deep Learning Helps Robot Learn to Walk the Way Humans Do

Deep Learning Helps Robot Learn to Walk the Way Humans Do

University of California, Berkeley researchers are using deep learning and NVIDIA GPUs to create a new generation of robots that adapt to changing environments and new situations without a human reprogramming them.

Their robot “Darwin” learned how to keep his balance on an uneven surface – and GPUs were essential for learning of this complexity.

“If we did the training on CPU, it would have required a week. With a GPU, it ended up taking three hours,” said Igor Mordatch, who is now using GPUs hosted in the Amazon Web Services cloud.

Without being taught, the deep learning robot rises from the floor to a standing position.

This type of humanoid robots could one day tackle dangerous tasks like handling rescue efforts or cleaning up disaster areas.

New GPU Computing Model for Artificial Intelligence

New GPU Computing Model for Artificial Intelligence

New GPU Computing Model for Artificial Intelligence

Yann LeCun, Director of Facebook AI Research, invited NVIDIA CEO Jen-Hsun Huang to speak at “The Future of AI” symposium at NYU, where industry leaders discussed the state of AI and its continued advancement.

Jen-Hsun published a blog on his talk that coverstopics such as how deep learning is a new software model that needs a new computing model; why AI researchers have adopted GPU-accelerated computing; and NVIDIA’s ongoing efforts to advance AI as we enter into its exponential adoption. And why, after all these years, AI has taken off.

In just two years, the number of companies NVIDIA collaborates with on deep learning has jumped nearly 35x to over 3,400 companies.

In just two years, the number of companies NVIDIA collaborates with on deep learning has jumped nearly 35x to over 3,400 companies.

Google AI Algorithm Masters Ancient Game of Go

GPU accelerated GoFor the first time, a computer has beaten a human professional at the game of Go — an ancient board game that has long been viewed as one of the greatest challenges for Artificial Intelligence.

Google DeepMind’s GPU-accelerated AlphaGo program beat Fan Hui, the European Go champion, five times out of five in tournament conditions.

Demis Hassabis, who oversees DeepMind, mentioned in a recent article that DeepMind’s deep learning system works pretty well on a single computer equipped with a decent number of GPU accelerators, but for the match against Fan Hui, the researchers used a larger network of computers that spanned about 170 GPUs. This larger computer network both trained the system and played the actual game, drawing on the results of the training.

The team confirmed they will use the same setup when they take on the Go world champion in South Korea.

Rémi Coulom, the French researcher behind what was previously the world’s top artificially intelligent Go player, has spent the past decade trying to build a system capable of beating the world’s best players, and now, he believes that system is here. “I’m busy buying some GPUs,” he says.

Using Machine Learning to Optimize Warehouse Operations

With thousands of orders placed every hour and each order assigned to a pick list, Europe’s leading online fashion retailer Zalando is using GPU-accelerated deep learning to identify the shortest route possible to products in their 1.3 million-square-foot distribution center.

Two schematics of a rope ladder warehouse zone with picks. The blue shelves denote shelves with items to be picked, so the goal is to find the shortest possible route that allows a worker to visit all blue shelves while starting and ending at the depot.

Two schematics of a rope ladder warehouse zone with picks. The blue shelves denote shelves with items to be picked, so the goal is to find the shortest possible route that allows a worker to visit all blue shelves while starting and ending at the depot.

Calvin Seward, a Data Scientist focused on warehouse logistics, shares how his team is using the Caffe deep learning framework and Tesla K80 GPUs to train their deep neural network to greatly accelerate a processing bottleneck, which in turn enabled the company to more efficiently split work between workers.

How GPUs are Revolutionizing Machine Learning

How GPUs are Revolutionizing Machine Learning

How GPUs are Revolutionizing Machine Learning

NVIDIA announced that Facebook will accelerate its next-generation computing system with the NVIDIA Tesla Accelerated Computing Platform which will enable them to drive a broad range of machine learning applications.

Facebook is the first company to train deep neural networks on the new Tesla M40 GPUs – introduced last month – this will play a large role in their new open source “Big Sur” computing platform, Facebook AI Research’s (FAIR) purpose-built system designed specifically for neural network training.

How GPUs are Revolutionizing Machine Learning---Open Rack V2 compatible 8-GPU server. Big Sur is two times faster than Facebook’s existing system and will enable the company to train twice as many neural networks which in return will help develop more accurate neural network models and new classes of advanced applications.

How GPUs are Revolutionizing Machine Learning—Open Rack V2 compatible 8-GPU server. Big Sur is two times faster than Facebook’s existing system and will enable the company to train twice as many neural networks which in return will help develop more accurate neural network models and new classes of advanced applications.

Training the sophisticated deep neural networks that power applications such as speech translation and autonomous vehicles requires a massive amount of computing performance.

With GPUs accelerating the training times from weeks to hours, it’s not surprising that nearly every leading machine learning researcher and developer is turning to the Tesla Accelerated Computing Platform and the NVIDIA Deep Learning software development kit.

A recent article on WIRED explains how GPUs have proven to be remarkably adept at deep learning and how large web companies like Facebook, Google and Baidu are shifting their computationally intensive applications to GPUs.

The artificial intelligence is on and it’s powered by GPU-accelerated machine learning.



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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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It’s the possibility of having a dream come true that makes life interesting.

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The fear of death is the most unjustified of all fears, for there’s no risk of accident for someone who’s dead.

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