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GTC On-Demand

Deep Learning and AI
Presentation
Media
Opening Keynote
Don't miss this keynote from NVIDIA's Chief Scientist, Bill Dally, as he speaks on the future of computing.
Don't miss this keynote from NVIDIA's Chief Scientist, Bill Dally, as he speaks on the future of computing.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8101
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AI in Healthcare
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Using Deep Learning to Predict Activity and Drug Response of Unknown Mutations
By applying deep learning to images of mutated cells interacting with drugs, we can predict how cells with unknown mutations respond to these drugs. State-of-the-art cancer treatment starts with sequencing a tumor biopsy. Alas, we don't know how mos ...Read More
By applying deep learning to images of mutated cells interacting with drugs, we can predict how cells with unknown mutations respond to these drugs. State-of-the-art cancer treatment starts with sequencing a tumor biopsy. Alas, we don't know how most mutations act or respond to drugs. Therefore, the treating physician cannot integrate this data into the treating protocol. To solve this, we synthesize patients' mutated genes from the sequencing data and transfect the mutated genes into live-cells. The cells express the mutated genes, and we scan the images under the microscope. We use our dataset of images of over 8 billion cells expressing specific mutations to train a deep learning network to classify a mutation and predict its level of activity. The model classifies the images and predicts the mutations' response to different drugs.  Back
 
Keywords:
AI in Healthcare, Deep Learning and AI, GTC Israel 2018 - ID SIL8108
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Project Clara: AI Deployment in Healthcare
Although deep learning models have already demonstrated great capabilities when applied to medical image analysis tasks, their deployment and usage in clinical workflows is often inefficient and cumbersome. In this talk we discuss NVIDIA's solution ...Read More
Although deep learning models have already demonstrated great capabilities when applied to medical image analysis tasks, their deployment and usage in clinical workflows is often inefficient and cumbersome. In this talk we discuss NVIDIA's solution to this issue: Project Clara. Clara is a platform built to facilitate deployment of DL algorithms in an efficient, scalable, and safe manner. We have designed Clara to tackle healthcare specific tasks and therefore ensure reliability and compatibility with current medical systems. In a joint effort with Nuance, our partner in this development, we have built an AI marketplace powered by Clara which allows developers to make their algorithms accessible and doctors to process medical data in a simple and straightforward manner. Clara implements the "PLASTER" paradigm which sets the standard for programmability, latency, accuracy, size, throughput, energy efficiency and rate of learning of modern inference systems and therefore is the ideal tool to deploy healthcare oriented AI methods.  Back
 
Keywords:
AI in Healthcare, GTC Israel 2018 - ID SIL8128
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AI Powered Revolution in Cancer Diagnostics
Cancer diagnostics, in many ways, has not changed much in the past century. Tissue biopsies are manually inspected under the microscope by Pathologists, performing "Pattern Recognition" with their eyes on microscopic images equivalent in si ...Read More
Cancer diagnostics, in many ways, has not changed much in the past century. Tissue biopsies are manually inspected under the microscope by Pathologists, performing "Pattern Recognition" with their eyes on microscopic images equivalent in size to 1000 X-ray images. Even a small cluster of unnoticed cancer cells can lead to a possibly fatal misdiagnosis. This talk will describe how cancer and other diseases are diagnosed today and how Nucleai is transforming the current state using Machine Learning algorithms.  Back
 
Keywords:
AI in Healthcare, GTC Israel 2018 - ID SIL8147
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Deep Learning in Medical Imaging: Solving the Data Augmentation Challenge for Enhanced-Value Radiology Reporting
In this talk, Hayit will give an overview of the Deep Learning computer-aided detection and diagnosis tools they are developing, which can support the detection, segmentation and the characterization tasks of the radiologist. Examples will be present ...Read More
In this talk, Hayit will give an overview of the Deep Learning computer-aided detection and diagnosis tools they are developing, which can support the detection, segmentation and the characterization tasks of the radiologist. Examples will be presented in Chest Xray pathology identification, CT liver analysis, as well as MRI brain lesion segmentation. Obtaining large-scale annotated datasets is a key challenge in the medical domain. She will present novel methods they are developing to solve these data challenges. Hayit will conclude with an overview of possible translations of these tools towards augmented radiology reports and advanced radiologist workflows.  Back
 
Keywords:
AI in Healthcare, Deep Learning and AI, GTC Israel 2018 - ID SIL8153
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Medical Imaging and Deep Learning: Creating Technologies for Assisting Radiologists (Presented by IBM)
In this talk we will focus on how AI based healthcare support-systems can assist radiologists in analyzing medical imaging and clinical data. We will present a state-of-the-art DL based system for detection and prediction of Breast Cancer using mammo ...Read More
In this talk we will focus on how AI based healthcare support-systems can assist radiologists in analyzing medical imaging and clinical data. We will present a state-of-the-art DL based system for detection and prediction of Breast Cancer using mammography images and medical history and show how it can be used to improve cancer detection rate and optimize physician decision making.  Back
 
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AI in Healthcare, Deep Learning and AI, GTC Israel 2018 - ID SIL8156
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Autonomous Vehicles
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The Missing Sense - Enabling Autonomous Vehicles to "feel" the Road using Tactile AI
Like people, Autonomous Vehicles (Level 2 through 5) need to "see" and "feel" the road to best perform (in an enjoyable, efficient and safe way). To date, this "feel" aspect has been under-served. In this session, w ...Read More

Like people, Autonomous Vehicles (Level 2 through 5) need to "see" and "feel" the road to best perform (in an enjoyable, efficient and safe way). To date, this "feel" aspect has been under-served. In this session, we will list novel methods for applying Artificial Intelligence to vehicle sensors in order to provide vehicles with advanced tactile sensing capabilities and augment the vastly-used visual sensors. We will discuss ways to leverage Big Data Analysis in the cloud, where data originating from vehicles equipped with Tactile Sensing Fusion and AI capabilities could be analyzed. These methods can also be leveraged to create Tactile Maps for roads based on crowd mapping. Additionally, we will describe cases in which the same data could be analyzed in the cloud to create continuously updated vehicle mechanical and health profiles. These profiles enable advanced predictive maintenance and other use cases that could be relevant to fleet managers and vehicle manufacturers.

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Keywords:
Autonomous Vehicles, Deep Learning and AI, GTC Israel 2018 - ID SIL8113
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Deep Learning Infrastructure for Autonomous Vehicles
We'll introduce deep learning infrastructure for building and maintaining autonomous vehicles. This includes techniques for managing the lifecycle of deep learning models from definition, training and deployment to reloading and life-long learning. ...Read More
We'll introduce deep learning infrastructure for building and maintaining autonomous vehicles. This includes techniques for managing the lifecycle of deep learning models from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. With these methodologies, one takes data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.  Back
 
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Autonomous Vehicles, GTC Israel 2018 - ID SIL8114
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Pushing the Boundaries - Simulation vs. Real Life
The greatest challenge regarding simulation is finding the matrix that compares it to real life. In this session, Danny will present a mathematical matrix that defines this relation and show how a proper simulation can be constructed based on deep le ...Read More
The greatest challenge regarding simulation is finding the matrix that compares it to real life. In this session, Danny will present a mathematical matrix that defines this relation and show how a proper simulation can be constructed based on deep learning techniques. He will also supply live examples of the Cognata simulation platform.  Back
 
Keywords:
Autonomous Vehicles, Deep Learning and AI, VR and Simulation, GTC Israel 2018 - ID SIL8122
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Reinforcement Learning with A* and a Deep Heuristic
Inspired by the recent achievements of methods which combine trees and DNNs (e.g. AlphaZero), this study demonstrates how to effectively combine AI with a deep heuristic represented by a DNN. AI is a highly popular and successful path-planning algori ...Read More
Inspired by the recent achievements of methods which combine trees and DNNs (e.g. AlphaZero), this study demonstrates how to effectively combine AI with a deep heuristic represented by a DNN. AI is a highly popular and successful path-planning algorithm, but it's usability is limited to only those domains where a good heuristic is known. This new algorithm, which we call Aleph-0, enables us to replicate the success of AI in new domains, including that of autonomous vehicles.  Back
 
Keywords:
Autonomous Vehicles, Deep Learning and AI, GTC Israel 2018 - ID SIL8124
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Deep Learning and AI
Presentation
Media
Deep Reinforcement Learning Leading Industry 4.0
Deep Reinforcement Learning (DRL) is an emerging technology that is now allowing optimization of business processes that were not possible before, increasing efficiency and process output in the process. DRL opens a door into automation and optimizat ...Read More
Deep Reinforcement Learning (DRL) is an emerging technology that is now allowing optimization of business processes that were not possible before, increasing efficiency and process output in the process. DRL opens a door into automation and optimization of systems that were considered chaotic, and that did not show the potential for clear ROI using classic Machine Learning and Deep Learning methods. In many unsolved or un-adequately solved challenges, DRL can be the missing link between the business question and the insights generated by the "technology." In this session, we will explore a few real-world use-cases of DRL applied to industrial applications and together we will further understand the potential of DRL to impact our business use-cases positively. DRL can be successfully applied to many use cases: natural resources / mining, finance, insurance, manufacturing and chemicals, and many more.  Back
 
Keywords:
Deep Learning and AI, GTC Israel 2018 - ID SIL8119
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Deep Learning for Image Understanding
In this session, Amir will describe deep learning models that can use context to make predictions. Specifically, he will describe recent models for image annotation and text categorization. The models we consider iteratively refine their prediction b ...Read More
In this session, Amir will describe deep learning models that can use context to make predictions. Specifically, he will describe recent models for image annotation and text categorization. The models we consider iteratively refine their prediction by considering how each prediction components is influenced by other components. For example, in image annotation, there exists strong correlations between the identity of the objects in the image, and we show how our model can capture these effects. Our model produces state of the art results on the challenging problem of annotating images with objects and relations.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8135
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Deep Learning and Beyond
Attend this session to learn how Deep Learning is delivering breakthrough results across a wide range of industries and applications. We'll review the most effective neural networks for a variety of use cases, the latest GPU-accelerated algorithms, ...Read More
Attend this session to learn how Deep Learning is delivering breakthrough results across a wide range of industries and applications. We'll review the most effective neural networks for a variety of use cases, the latest GPU-accelerated algorithms, and powerful application development tools and workflows. We'll also cover several best practices for managing data, effectively training models, and optimizing applications for high performance deployment environments.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8142
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OpenSeq2Seq: A Deep learning Toolkit for Speech Recognition, Speech Synthesis, and NLP
OpenSeq2Seq is an open-source, TensorFlow-based toolkit, which supports a wide range of off-the-shelf models for Natural Language Translation (GNMT, Transformer, ConvS2S), Speech Recognition (Wave2Letter, DeepSpeech2), Speech Synthesis (Tacotron ...Read More

OpenSeq2Seq is an open-source, TensorFlow-based toolkit, which supports a wide range of off-the-shelf models for Natural Language Translation (GNMT, Transformer, ConvS2S), Speech Recognition (Wave2Letter, DeepSpeech2), Speech Synthesis (Tacotron 2), Language Modeling and transfer learning for NLP tasks. OpenSeq2Seq is optimized for latest GPUs. It supports multi-GPU and mixed-precision training. Benchmarks on machine translation and speech recognition tasks show that models built using OpenSeq2Seq give state-of-the-art performance at 1.5-3x faster training time.

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Deep Learning and AI, GTC Israel 2018 - ID SIL8152
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The Deep Learning Approach to Natural Language Processing
Deep Learning is revolutionizing Natural Language Processing (NLP). Written text is available everywhere, from text messages and social media posts and all the way to lengthy emails and blog posts. As an organization, being able to extract informatio ...Read More
Deep Learning is revolutionizing Natural Language Processing (NLP). Written text is available everywhere, from text messages and social media posts and all the way to lengthy emails and blog posts. As an organization, being able to extract information from your customers' communications has the potential to give you a great advantage. In this talk, we'll describe the Deep Learning approach to NLP, its elements, and how to apply it. We'll see some relevant architectures that can be used for these types of problems and discuss their operations. Finally, we'll bring it all together to optimize and deploy a working model.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8106
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Load-And-Go GPU Analytics: Overcoming the I/O Bottleneck for AI
In this session, learn how to efficiently arrange data for consumption by GPUs for analytics. We'll take a look at how combining several modular components and ideas can deliver fast AI performance by limiting the effect of I/O on data-intense queri ...Read More
In this session, learn how to efficiently arrange data for consumption by GPUs for analytics. We'll take a look at how combining several modular components and ideas can deliver fast AI performance by limiting the effect of I/O on data-intense queries and models. We'll show how arranging data for the GPU, combined with fast GPU compression, metadata mapping, and several other techniques, can accelerate real database physical operators. We'll also show real examples of how GPU databases, powered by energy-efficient NVIDIA Tesla GPUs, can compete with much larger and more expensive tailored hardware MPP and distributed solutions. See how tools like TensorFlow, Spark MLib, R, and IBM's Watson interact with GPU Databases and benefit from analyzing and training on larger data sets than ever before.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8127
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Revolutionizing a 4 Trillion Dollar Industry using Sub-Millimeter Imagery and AI
In this session, CEO Ofir Schlam will talk about the huge opportunity in agriculture, a 4 Trillion dollar industry that saw little disruption in recent decades. Another unbelievable number is 35% - the amount of crop losses every farmer in the world ...Read More
In this session, CEO Ofir Schlam will talk about the huge opportunity in agriculture, a 4 Trillion dollar industry that saw little disruption in recent decades. Another unbelievable number is 35% - the amount of crop losses every farmer in the world faces annually by weeds, diseases, insects, and fertilizer deficiencies. Ofir will explain Taranis' unique AI challenges in developing AgroBrain our AI agronomist and AgroSet the ImageNet for Ag our proprietary data set. Taranis, a precision agriculture intelligence platform, helps Ag Retailers, input manufacturers and large farms increase their yields and cut costs by giving them a way to effectively monitor their fields, make informed decisions, and then act on them. The system uses sophisticated computer vision, data science, and deep learning algorithms to detect early symptoms of weeds, uneven emergence, nutrient deficiencies, disease/insect infestations, water damage, equipment problems and more so that farmers can address issues quickly and understand the impact on yield and cost of production.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8133
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GPU Accelerated Data Science
Data Science/Data Mining is the exploration of data to extract novel knowledge and insight. That discovery process often involves a considerable amount of trial and error, after all, if you know what you are looking for you are not doing discovery. T ...Read More
Data Science/Data Mining is the exploration of data to extract novel knowledge and insight. That discovery process often involves a considerable amount of trial and error, after all, if you know what you are looking for you are not doing discovery. The Python programming language has grown in popularity amount data scientists for its flexibility, ease of programming, and readability. However, Python is not known for performance, which has not been an issue in the past. Unfortunately, today, a large amount of science is driven through the exploration of large volumes of data. Combined with the ever-increasing need for more complex algorithms and analytics, data scientists have had to turn more and more of their attention away from the problems they're trying to solve and instead towards implementing their hypotheses in less friendly, "more performant" systems. Luckily, work being done in the GPU Open Analytics Initiative (GOAI) and the NEW RAPIDS platform are pushing to make GPU-accelerated Data Science in Python a first class citizen and driving performance to be on par with the other languages, including GPU-accelerated C/C++.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8136
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GPU Accelerated Machine Learning
Artificial Intelligence is a wide field where different types of learning models serve different kinds of tasks. Deep neural networks (DL), for example, are great for data with spatial-temporal locality like images, audio, and text. However, for data ...Read More
Artificial Intelligence is a wide field where different types of learning models serve different kinds of tasks. Deep neural networks (DL), for example, are great for data with spatial-temporal locality like images, audio, and text. However, for data without inherent locality, non-neural network machine learning algorithms, such as random forest or gradient boosted trees, are often used instead. In this session, we will discuss our how RAPIDS machine learning libraries are optimized for non-neural-network based ML algorithms for latest GPU architectures.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8137
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Simplifying AI Infrastructure: Lessons in Scaling on DGX Systems
While every enterprise is on a mission to infuse its business with deep learning, few know how to build the infrastructure to get them there. Short-sighted approaches to data center design can lead to long-term consequences that make the ROI of AI el ...Read More
While every enterprise is on a mission to infuse its business with deep learning, few know how to build the infrastructure to get them there. Short-sighted approaches to data center design can lead to long-term consequences that make the ROI of AI elusive. NVIDIA has distilled the insights and best practices learned from deep learning deployments around the globe, into prescriptive guidance that every organization can leverage to shorten deployment timeframes, improve developer productivity and streamline operations. Attend this session to learn: the common challenges and pitfalls associated with deep learning platform selection and infrastructure design; design principles for a deep learning data center that can meet the unique demands of GPU-accelerated workloads; and how leading technologies and solutions have come together to simplify and accelerate your deep learning deployment.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8139
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Super-Resolution for Aliased Images
In this talk we discuss different techniques for training the super resolution algorithm with the focus on producing high quality results on the highly aliased and noisy data. Though such artifacts are not often present in the real images, they frequ ...Read More
In this talk we discuss different techniques for training the super resolution algorithm with the focus on producing high quality results on the highly aliased and noisy data. Though such artifacts are not often present in the real images, they frequently appear in gaming and animation images and videos. To solve this problem, we have introduced a special downsampling procedure that allows the network to simultaneously do anti-aliassing, denoising and super-resolution. Our experiments show that even though our model was trained solely on natural images, it is able to achieve significant improvement for aliased and noisy animation images.  Back
 
Keywords:
Deep Learning and AI, Visualization, GTC Israel 2018 - ID SIL8154
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DDN A3I Storage Solutions: Extract More Answers From Your Data at Any Scale (Presented by DDN Storage)
DDN is a leader in storage solution for data intensive workflows at scale. This session will cover DDN's activity across the various markets of data intensive AI and Machine learning. The session is built from market coverage down to the underlaying ...Read More
DDN is a leader in storage solution for data intensive workflows at scale. This session will cover DDN's activity across the various markets of data intensive AI and Machine learning. The session is built from market coverage down to the underlaying technology that DDN provides and its advantages for NVIDIA DGX and GPU accelerated workflows. Topics to be covered include: Accelerate AI Performance, AI accelerated in various verticals, DDN Architecture Scale up & out, DDN A3I solutions \u2013 Accelerated At any Scale AI \u2013 for DGX & more, Parallel storage computing vs NFS, and AI Benchmarks numbers with reduced time to result.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8150
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Ubiquitous Machine Learning (Presented by Cisco)
Data is the lifeblood of an enterprise, and it's being generated everywhere. To overcome the challenges of data gravity, data analytics, including machine learning, is best done where the data is located. Come to this session to understand h ...Read More

Data is the lifeblood of an enterprise, and it's being generated everywhere. To overcome the challenges of data gravity, data analytics, including machine learning, is best done where the data is located. Come to this session to understand how to overcome the challenges of machine learning everywhere.

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Deep Learning and AI, GTC Israel 2018 - ID SIL8155
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Accelerating Research to Production with PyTorch 1.0 and ONNX (Presented by Facebook)
Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. Learn how ONNX and PyTorch 1.0 are helping to accelerate the path from research to producti ...Read More
Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. Learn how ONNX and PyTorch 1.0 are helping to accelerate the path from research to production by making AI development more seamless and interoperable. We'll share the latest on PyTorch 1.0, and also discuss Facebook's initiatives around ethical and responsible AI development.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8141
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Accelerating Deep Learning Applications (Presented by Mellanox Technologies)
Come join us and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We will briefly present the state of t ...Read More
Come join us and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We will briefly present the state of the art techniques for distributed machine learning and what special requirements they impose on the system, followed by an overview of interconnect technologies used to scale and accelerate distributed machine learning including RDMA, NVIDIA's GPUDirect technology, and in-network computing used to accelerate large scale deployments in HPC and artificial intelligence.  Back
 
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Deep Learning and AI, GTC Israel 2018 - ID SIL8145
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Intelligent Machines and IoT
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Media
AI Engine for Autonomous Shopping Carts
Tracxpoint is building the new Retail AI-Pipeline that guarantees an individual shopping experience with cashier-less on-card payment. Our system includes a unique combination of AI-Engines, IoT- & proprietary sensor fusion. We will present our p ...Read More
Tracxpoint is building the new Retail AI-Pipeline that guarantees an individual shopping experience with cashier-less on-card payment. Our system includes a unique combination of AI-Engines, IoT- & proprietary sensor fusion. We will present our progress towards recognizing one hundred thousand individual products in under a second on a NVIDIA Jetson installed on our next generation shopping cart. We call this game changer the AIC. To support this, we have invested in three critical enabling components: a fast and scalable semi-automatic pipeline for scanning and registering new inventory; a flexible and efficient NVIDIA GPU powered training pipeline for our custom in-house deep learning based product classification models; and a natural human-focused experience for highly variable real-world use case conditions. We will discuss various pitfalls and problems and how we have overcome them as well. We will also show a live demo using our latest shopping cart design.  Back
 
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Intelligent Machines and IoT, Deep Learning and AI, GTC Israel 2018 - ID SIL8120
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The Future of Robotics with NVIDIA Isaac
Artificial intelligence is the most powerful technology of our time. For robotics, it enables new levels of autonomy, giving machines the ability to perceive and navigate the world as well as seamlessly interact with people and handle objects. In thi ...Read More
Artificial intelligence is the most powerful technology of our time. For robotics, it enables new levels of autonomy, giving machines the ability to perceive and navigate the world as well as seamlessly interact with people and handle objects. In this talk, we'll discuss how AI is critical to driving robotics breakthroughs and do a technical dive into NVIDIA's Isaac robotics platform. This includes: Jetson AGX Xavier a system built to meet the large computational requirements for AI-powered robots, capable of delivering up to 32 TOPs, Isaac SDK and Isaac Gems - a software stack that enables developers to quickly develop and deploy robotics software, and Isaac Sim a virtual simulation environment to test and train robots. Discover how we're making it easy to bring AI capabilities to an entirely new class of robots for manufacturing, last mile delivery, logistics/retails and more and the important role you play in developing the next wave of intelligent machines.  Back
 
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Intelligent Machines and IoT, GTC Israel 2018 - ID SIL8121
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Solving Challenges in Future Mobility Solutions Using AI
Advances in AI and machine learning present new challenges in the form of supporting and operating the future mobility solutions. We need to actually train these future mobility solutions to overcome real-world challenges and operational workflow. Th ...Read More
Advances in AI and machine learning present new challenges in the form of supporting and operating the future mobility solutions. We need to actually train these future mobility solutions to overcome real-world challenges and operational workflow. The industry needs to ensure the reliable functionality for the increasing automatic and future mobility solutions. In this presentation, we will describe the steps we took to solve this problem, including deep learning models for representations of vehicles, similarity metrics, segmentation, and anomaly detection. We will explain how GPU's combines these models into a singular system that analyzes a vehicle in just a few seconds. We will also show how models trained for security purpose have great value in the automotive industry, whereby using similar systems helps detect various types of mechanical problems and damages to the exterior of any vehicle. By using such technologies for anomaly detection in vehicles in automotive/civilian context, we can enable and streamline predictive maintenance practices, and consequently ensure safe and reliable mobility.  Back
 
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Intelligent Machines and IoT, Deep Learning and AI, GTC Israel 2018 - ID SIL8125
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Debug and Approve your Deep Networks by Overcoming the Black Box Problem
Deep Learning AI may learn to perform tasks by cheating in unknown and unexpected ways, which may be a liability for the developer. Feedforward networks are the basis of artificial neural networks such as deep, convolution, recurrent, and even machin ...Read More
Deep Learning AI may learn to perform tasks by cheating in unknown and unexpected ways, which may be a liability for the developer. Feedforward networks are the basis of artificial neural networks such as deep, convolution, recurrent, and even machine learning regression methods. However the internal decision processes of feedforward networks are difficult to explain: they are known to be a "black-box". This is especially problematic in applications where consequences of an error can be severe, such as in medicine, banking, or self-driving cars. Optimizing Mind has developed a new type of feedback neural networks motivated by neuroscience that allows easier understanding of the internal decision process. Developers, regulators, and users can better understand their AI, reduce unexpected surprises, and liability by having feedforward networks converted to our Illuminated form to explain the internal decision processes. We'll demonstrate some of these benefits.  Back
 
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Intelligent Machines and IoT, AI in Healthcare, Deep Learning and AI, GTC Israel 2018 - ID SIL8146
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New Developer Tools
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Boost DNN Training Performance using NVIDIA Tools
Learn how to boost DNN training performance using NVIDIA Tools. See how NVIDIA experts profile DNN training applications using Nsight Systems to significantly reduce training time. NVIDIA's New Developer Tools transform the developer's profiling ex ...Read More
Learn how to boost DNN training performance using NVIDIA Tools. See how NVIDIA experts profile DNN training applications using Nsight Systems to significantly reduce training time. NVIDIA's New Developer Tools transform the developer's profiling experience from a "black box" model to a "white box" model, letting the developer see what's happening at the CUDA and GPU execution levels, to be able to optimize the deep learning framework usage.  Back
 
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New Developer Tools, Deep Learning and AI, GTC Israel 2018 - ID SIL8105
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Optimizing CUDA Applications for the Volta/Turing GPU Architecture
This session will cover details of performance features released in the latest version of CUDA, new features of Turing architecture alongside a wealth of optimization techniques, and in-depth information to get the most out of the Volta/Turing GPU ar ...Read More
This session will cover details of performance features released in the latest version of CUDA, new features of Turing architecture alongside a wealth of optimization techniques, and in-depth information to get the most out of the Volta/Turing GPU architecture.  Back
 
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New Developer Tools, GTC Israel 2018 - ID SIL8140
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The Path to GPU as a Service in Kubernetes
Kubernetes modern production patterns for Deep Learning applications and a deep dive into the Kubernetes GPU subsystem and its challenges (performance, scheduling, monitoring). Autonomous vehicles, face recognition, High Performance Computing, Virtua ...Read More
Kubernetes modern production patterns for Deep Learning applications and a deep dive into the Kubernetes GPU subsystem and its challenges (performance, scheduling, monitoring). Autonomous vehicles, face recognition, High Performance Computing, Virtual Reality, NVIDIA GPUs are enabling a new computer era with cloud computing at its center. With kubernetes being the next iteration in cloud technologies, the NVIDIA container team with the kubernetes community is driving the advances in GPU integration. During this talk we will review how to deploy a GPU enabled Kubernetes and the modern production patterns for deploying GPU enabled services and applications. We will also dive into the details of the Kubernetes device plugin (its GPU subsystem), the NVIDIA container stack and the limitations provided by the kubernetes infrastructure. We will finally be discussing the latest improvements in the device plugin subsystem of Kubernetes, and the challenges ahead of it such as NUMA, sharing and scheduling.  Back
 
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New Developer Tools, Deep Learning and AI, GTC Israel 2018 - ID SIL8143
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Visualization
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Optimizing NVIDIA GRID Virtual GPU for the Best VDI User Experience
Gartner states that user experience is the single most important predictor of VDI success. From evolving design and engineering workflows to the new performance challenges of Windows 10, traditional performance metrics can no longer capture true user ...Read More
Gartner states that user experience is the single most important predictor of VDI success. From evolving design and engineering workflows to the new performance challenges of Windows 10, traditional performance metrics can no longer capture true user experience. NVIDIA has taken standard testing to the next level, enabling customers to achieve successful enterprise-wide VDI deployments. Join this session to learn more about real user testing and scientific data that demonstrates the impact of NVIDIA virtual GPUs on the overall user experience. You will discover how we are executing and automating performance tests. Workflows, real test metrics, and conclusions will be shared with you. Use this information to help size vGPU environment today.  Back
 
Keywords:
Visualization, GTC Israel 2018 - ID SIL8103
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Practical Realtime Raytracing with RTX - From Concepts to Implementation
Bring real-time raytracing into your raster-based application using NVIDIA RTX and Microsoft DXR or Vulkan. This session will cover and connect the RTX principles with the implementation details to add raytracing from the ground up. You will learn al ...Read More
Bring real-time raytracing into your raster-based application using NVIDIA RTX and Microsoft DXR or Vulkan. This session will cover and connect the RTX principles with the implementation details to add raytracing from the ground up. You will learn all about setting up acceleration structures, raytracing pipelines and shader binding tables through simple and progressive additions. We will also cover the characteristics and interactions of the raytracing shaders: ray generation, miss and hit shaders. This talk will be complemented by online resources providing in-depth explanations and easy-to-integrate source code to make your integration of raytracing based on NVIDIA RTX and DirectX/Vulkan the smoothest experience.  Back
 
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Visualization, GTC Israel 2018 - ID SIL8149
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