The 2018 GTC opening keynote is delivered by the NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing.
Many examples of AI are reported daily that enhance traditional products and servicesÃ¢â¬â but the benefits have only just begun to scratch the surface. Entire industries will be transformed and massive benefits will be realized in the next wave of AI deployment. Learn about the next generation of AI and how it will add over 60M jobs and $13 trillion to the global economy if policy makers, businesses and the AI community adopt the right strategies, initiatives and platforms to harness this incredible new technology.
The GTC Europe 2018 opening keynote delivered by NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing.
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.
Opening Keynote Speech
The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. With an annual budget of $7.5 billion, NSF awards grants to nearly 2,000 colleges, universities and other institutions in all 50 states. Hear how NSF is advancing discovery and technological innovation in all fields, including artificial intelligence, to keep the United States at the forefront of global science and engineering leadership.
In this keynote, we'll show how the Cancer Moonshot Task Force under Vice President Biden is unleashing the power of data to help end cancer as we know it. We'll discuss global efforts inspired by the Cancer Moonshot that will empower A.I. and deep learning for oncology with larger and more accessible datasets.
What New Results in Visual Question Answering Have to Say about Old AI
Don't miss GTC's opening keynote address from NVIDIA CEO and co-founder Jensen Huang. He'll discuss the latest breakthroughs in visual computing, including how NVIDIA is fueling the revolution in deep learning.
Over the past few years, we have built large-scale computer systems for training neural networks, and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas, and our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. In this talk, I''ll highlight some of the distributed systems and algorithms that we use in order to train large models quickly. I''ll then discuss ways in which we have applied this work to a variety of problems in Google''s products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in visual computing, and preview disruptive technologies and exciting demonstrations across industries.
A fundamental challenge of modern society is the development of effective approaches to enhance brain function and cognition in both healthy and impaired individuals. For the healthy, this serves as a core mission of our educational system and for the cognitively impaired this is a critical goal of our medical system. Unfortunately, there are serious and growing concerns about the ability of either system to meet this challenge. I will describe an approach developed in our lab that uses custom-designed video games to achieve meaningful and sustainable cognitive enhancement (e.g., Anguera, et al. Nature 2013), as well the next stage of our research program, which uses video games integrated with technological innovations in software (e.g., brain computer interface algorithms, GPU computing) and hardware (e.g., virtual reality headsets, mobile EEG, transcranial electrical brain stimulation) to create a novel personalized closed loop system. I will share with you a vision of the future in which high-tech is used as an engine to enhance our brain''s information processing systems, thus reducing our reliance on non-specific drugs to treat neurological and psychiatric conditions and allowing us to better target our educational efforts. This keynote will be preceded by naming the winner of the CUDA Center of Excellence Achievement Award, winner for Best Poster, and the new CUDA Fellows, followed by the launch announcement of the Global Impact Award. (Award ceremony duration approximately 15 minutes).
This presentation will show how Pixar uses GPU technology to empower artists in the animation and lighting departments. By providing our artists with high-quality, interactive visual feedback, we enable them to spend more time making creative decisions. Animators interactively pose characters in order to create a performance. When features like displacement, fur, and shadows become critical for communicating the story, it is vital to be able to represent these visual elements in motion at interactive frame rates. We will show Presto, Pixar''s proprietary animation system, which uses GPU acceleration to deliver real-time feedback during the character animation process, using examples from Pixar''s recent films. Lighting artists place and adjust virtual lights to create the mood and tone of the scene as well as guide the audience''s attention. A physically-based illumination model allows these artists to create visually-rich imagery using simpler and more direct controls. We will demonstrate our interactive lighting preview tool, based on this model, built on NVIDIA''s OptiX framework, and fully integrated into our new Katana-based production workflow.
Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations across industries.
The human genome is a sequence of 3 billion chemical letters inscribed in a molecule called DNA. Famously, short stretches (~10 letters, or a-base pairs) of DNA fold into a double helix. But what about longer pieces? How does a 2 meter long macromolecule, the genome, fold up inside a 6 micrometer wide nucleus? And, once packed, how does the information contained in this ultra-dense structure remain accessible to the cell? This talk will discuss how the human genome folds in three dimensions, a folding enables the cell to access and process massive quantities of information in parallel. To probe how genomes fold, we developed Hi-C, together with collaborators at the Broad Institute and UMass Medical School. Hi-C couples proximity-dependent DNA ligation and massively parallel sequencing. To analyze our data and reconstruct the underlying folds, we, too must engage in massively parallel computation. I will describe how we use NVIDIA's CUDA technology to analyze our results and simulate the physical processes of genome folding and unfolding.
Ralph Gilles, senior vice president of Product Design and president and CEO of SRT (Street and Racing Technology) Brand and Motorsports at Chrysler Group LLC and the mind behind some of the company most innovative products, will provide a behind-the-scenes look at the auto industry. Gilles will review how GPUs are used to advance every step of the automobile development process from the initial conceptual designs and engineering phases through product assembly and marketing. He will also discuss and how Chrysler Group utilizes GPUs and the latest technologies to build better, safer cars and reduce time to market.
Do not miss the opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA. Hear about what's next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries. Jen-Hsun co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of directors.
Collective behavior is one of the most pervasive features of the natural world. Our brains are composed of billions of interconnected cells communicating with chemical and electrical signals. We are integrated in our own human society. Elsewhere in the natural world a fish school convulses, as if one entity, when being attacked by a predator. How does individual behavior produce dynamic group-level properties? Do animal groups -or even cells in a tumor- function as some form of collective mind? How does socially contagious behavior spread through natural human crowds? In his keynote address, Prof. Iain D. Couzin, Professor of Ecology and Evolutionary Biology at Princeton University, will demonstrate how GPU computing has been pivotal in the study of collective behavior, helping reveal how collective action emerges in a wide range of groups from plague locusts to human crowds, and the critical role that uninformed, or weakly-opinionated, individuals play in democratic consensus decision-making.
Do not miss the day 3 keynote, featuring Part-Time Scientists Robert Boehme and Wes Faler. Boehme and Faler are part of a team of international scientists and engineers who want to send a rover to the moon before the end of the year 2013. In this presentation, they will discuss their goals, recent accomplishments and milestones, and how GPUs have help in unexpected ways.
Do not miss this opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what’s next in gpu computing, and preview disruptive technologies and exciting demonstrations from across industries. Jensen Huang co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of directors.
The opening keynote, features Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries.
How does the H1N1 "Swine Flu" virus avoid drugs while attacking our cells? What can we learn about solar energy by studying biological photosynthesis? How do our cells read the genetic code? What comes next in computational biology? Computational biology is approaching a new and exciting frontier: the ability to simulate structures and processes in living cells. Come learn about the "computational microscope," a new research instrument that scientists can use to simulate biomolecules at nearly infinite resolution. The computational microscope complements the most advanced physical microscopes to guide today's biomedical research. In this keynote address, computational biology pioneer Dr. Klaus Schulten of the University of Illinois, Urbana-Champaign, will introduce the computational microscope, showcase the widely used software underlying it, and highlight major discoveries made with the aid of the computational microscope ranging from viewing protein folding, translating the genetic code in cells, and harvesting solar energy in photosynthesis. He will also look towards a future when cell tomography and computing will establish atom-by-atom views of entire life forms.
What really causes accidents and congestion on our roadways? How close are we to fully autonomous cars? In his keynote address, Stanford Professor and Google Distinguished Engineer, Dr. Sebastian Thrun, will show how his two autonomous vehicles, Stanley (DARPA Grand Challenge winner), and Junior (2nd Place in the DARPA Urban Challenge) demonstrate how close yet how far away we are to fully autonomous cars. Using computer vision combined with lasers, radars, GPS sensors, gyros, accelerometers, and wheel velocity, the vehicle control systems are able to perceive and plan the routes to safely navigate Stanley and Junior through the courses. However, these closed courses are a far cry from everyday driving. Find out what the team will do next to get one step closer to the "holy grail" of computer vision, and a huge leap forward toward the concept of fully autonomous vehicles.
High-Throughput Science How did the universe start? How is the brain wired? How does matter interact at the quantum level? These are some of the great scientific challenges of our times, and answering them requires bigger scientific instruments, increasingly precise imaging equipment and ever-more complex computer simulations. In his keynote address, Harvard professor, researcher and computing visionary Hanspeter Pfister will discuss the computational obstacles scientists face and how commodity high-throughput computing can enable high-throughput science, in which massive data streams are processed and analyzed rapidly -- from the instrument through to the desktop. Finally Professor Pfister will survey several groundbreaking projects at Harvard that leverage GPUs for high- throughput science, ranging from radio astronomy and neuroscience to quantum chemistry and physics.
Games and interactive media have long been the beneficiaries of cutting edge GPU technology and it has not gone unnoticed in the world of feature film production. To date the visual effects industry had been a sideline observer of these advances while awaiting technology to reach maturity. At Lucasfilm, research and development has been on-going for some time and this past summer Industrial Light & Magic employed this technology in two of its summer blockbuster films. Lucasfilm CTO, Richard Kerris, will show a brief history of their computer graphics for film, and will then pull back the curtain on how they are now using GPU technology to advance the state of the art in visual effects and provide a glimpse of what's on the horizon for GPU's in future and how it will impact filmmaking.
Learn how Gensler is using the latest technology in virtual reality across all aspects of the design process for the AEC industry. We'll cover how VR has added value to the process when using different kinds of VR solutions. Plus we'll talk about some of the challenges Gensler has faced with VR in terms of hardware, software, and workflows. Along with all of this, NVIDIA's latest VR visualization tools are helping with the overall process and realism of our designs.
This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.
A wide area and city surveillance system solution for running real-time video analytics on thousands of 1080p video streams will be presented. System hardware is an embedded computer cluster based on NVIDIA TX1/TX2 and NXP iMX6 modules. A custom designed system software manages job distribution, resulting in collection and system wide diagnostics including instantaneous voltage, power and temperature readings. System is fully integrated with a custom designed video management software, IP cameras and network video recorders. Instead of drawing algorithm results on the processed video frames, re-encoding and streaming back to the operator computer for display, only the obtained metadata is sent to the operator computer. Video management software streams video sources independently, and synchronizes decoded video frames with the corresponding metadata locally, before presenting the processed frames to the operator.
Businesses of all sizes are increasingly recognizing the potential value of AI, but few are sure how to prepare for the transformational change it is sure to bring to their organizations. Danny Lange rolled out company-wide AI platforms at Uber and Amazon; now, through Unity Technologies, he's making AI available to the rest of us. He'll also share his thoughts for the most exciting advances that AI will bring over the next year. His insights will help you understand the true potential of AI, regardless of your role or industry.
What is Deep Learning? In what fields is it useful? How does it relate to artificial intelligence? We'll discuss deep learning and why this powerful new technology is getting so much attention, learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results.
We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including 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. Training scales with data size beyond multi-nodes. With these methodologies, one takes only 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.
Join our presentation on the first application of deep learning to cybersecurity. Deep learning is inspired by the brain's ability to learn: once a brain learns to identify an object, its identification becomes second nature. Similarly, as a deep learning-based artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, the most evasive and unknown cyber-attacks are immediately detected and prevented. We'll cover the evolution of artificial intelligence, from old rule-based systems to conventional machine learning models until current state-of-the-art deep learning models.
We'll introduce a novel approach to digital pathology analytics, which brings together a powerful image server and deep learning based image analysis on a cloud platform. Recent advances in AI and Deep Learning in particular show great promise in several fields of medicine, including pathology. Human expert judgement augmented by deep learning algorithms has the potential to speed up the diagnostic process and to make diagnostic assessments more reproducible. One of the major advantages of the novel AI-based algorithms is the ability to train classifiers for morphologies that exhibit a high level of complexity. We will present examples on context-intelligent image analysis applications, including e.g. fully automated epithelial cell proliferation assay and tumor grading. We will also present other examples of complex image analysis algorithms, which all run on-demand on whole-slide images in the cloud computing environment. Our WebMicroscope® Cloud is sold as a service (SaaS) approach, which is extremely easy to set up from a user perspective, as the need for local software and hardware installation is removed and the solution can immediately be scaled to projects of any size.
Long term goal of any financial institution is achieve the ability to address users with utmost experience within the boundaries of resources. It could only be a possibility when financial institutions adapt to intelligent systems. The success of such systems depends heavily on the intelligence. Deep Learning has provided a huge opportunity for financial institutions to start building and planning for such large scale intelligent systems which are multi-functional and adapt. In this talk, we will discuss about how we used Deep Learning, Vega as the platform and GPUs to build high scale automation use cases in Fraud detection to complex process automation in both banking and insurance.
Innovation can take many forms, and led by varying stakeholders across an organization. One successful model is utilizing AI for Social Good to drive a proof-of-concept that will advance a critical strategic goal. The Data Science Bowl (DSB) is an ideal example, launched by Booz Allen Hamilton in 2014, it galvanizes thousands of data scientists to participate in competitions that will have have far reaching impact across key industries such as healthcare. This session will explore the DSB model, as well as look at other ways organizations are utilizing AI for Social Good to create business and industry transformation.
From healthcare to financial services to retail, businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will look at how Accenture as an enterprise is optimizing itself in the age of AI, as well as how it guides its customers to success. A look at best practices, insights, and measurement to help the audience inform their AI roadmap and journey.
For enterprises daunted by the prospect of AI and investing in a new technology platform, the reality is that AI can leverage already-in-place big data and cloud strategies. This session will explore AI and deep learning use cases that are designed for ROI, and look at how success is being measured and optimized.
We'll introduce new concepts and algorithms that apply deep learning to radio frequency (RF) data to advance the state of the art in signal processing and digital communications. With the ubiquity of wireless devices, the crowded RF spectrum poses challenges for cognitive radio and spectral monitoring applications. Furthermore, the RF modality presents unique processing challenges due to the complex-valued data representation, large data rates, and unique temporal structure. We'll present innovative deep learning architectures to address these challenges, which are informed by the latest academic research and our extensive experience building RF processing solutions. We'll also outline various strategies for pre-processing RF data to create feature-rich representations that can significantly improve performance of deep learning approaches in this domain. We'll discuss various use-cases for RF processing engines powered by deep learning that have direct applications to telecommunications, spectral monitoring, and the Internet of Things.
We'll discuss training techniques and deep learning architectures for high-precision landmark localization. In the first part of the session, we'll talk about ReCombinator Networks, which aims at maintaining pixel-level image information, for high-accuracy landmark localization. This model combines coarse-to-fine features to first observe global (coarse) image information and then recombines local (fine) information. By using this model, we report SOTA on three facial landmark datasets. This model can be used for other tasks that require pixel-level accuracy (for example, image segmentation, image-to-image translation). In the second part, we'll talk about improving landmark localization in a semi-supervised setting, where less labeled data is provided. Specifically, we consider a scenario where few labeled landmarks are given during training, but lots of weaker labels (for example, face emotions, hand gesture) that are easier to obtain are provided. We'll describe training techniques and model architectures that can leverage weaker labels to improve landmark localization.
Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, motion, and change over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re3 simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS, while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.
We''ll explore how deep learning approaches can be used for perceiving and interpreting the driver''s state and behavior during manual, semi-autonomous, and fully-autonomous driving. We''ll cover how convolutional, recurrent, and generative neural networks can be used for applications of glance classification, face recognition, cognitive load estimation, emotion recognition, drowsiness detection, body pose estimation, natural language processing, and activity recognition in a mixture of audio and video data.
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don''t perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
The growth in density of housing in cities like London and New York has resulted in the higher demand for efficient smaller apartments. These designs challenge the use of space and function while trying to ensure the occupants have the perception of a larger space than provided. The process of designing these spaces has always been the responsibility and perception of a handful of designers using 2D and 3D static platforms as part of the overall building design and evaluation, typically constraint by a prescriptive program and functional requirement. A combination of human- and AI-based agents creating and testing these spaces through design and virtual immersive environments (NVIDIA Holodeck) will attempt to ensure the final results are efficient and best fit for human occupancy prior to construction.
Go beyond working with a single sensor and enter the realm of Intelligent Multi-Sensor Analytics (IMSA). We''ll introduce concepts and methods for using deep learning with multi-sensor, or heterogenous, data. There are many resources and examples available for learning how to leverage deep learning with public imagery datasets. However, few resources exist to demonstrate how to combine and use these techniques to process multi-sensor data. As an example, we''ll introduce some basic methods for using deep learning to process radio frequency (RF) signals and make it a part of your intelligent video analytics solutions. We''ll also introduce methods for adapting existing deep learning frameworks for multiple sensor signal types (for example, RF, acoustic, and radar). We''ll share multiple use cases and examples for leveraging IMSA in smart city, telecommunications, and security applications.
As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers'' identities and activities, as well as their cognitive and emotional states, to adapt and optimize their experience. AI capable of interpreting what we call "people analytics" captured through their facial and vocal expressions, and aspects of the context that surrounds them will power these advances. We''ll give an overview of our Emotion AI solution, and describe how we employ techniques like deep learning-based spatio-temporal modeling. By combining these techniques with a large-scale dataset, we can develop AI capable of redefining the in-cab experience.
Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing gradients. These shortcut connections have interesting properties that make ResNets behave differently from other typical network architectures. In this talk we will use these properties to design a network based on a ResNet but with parameter sharing and adaptive computation time, we call it IamNN. The resulting network is much smaller than the original network and can adapt the computational cost to the complexity of the input image. During this talk we will provide an overview of ways to design compact networks, give an overview of ResNets properties and discuss how they can be used to design compact dense network with only 5M parameters for ImageNet classification.
Want to get started using TensorFlow together with GPUs? Then come to this session, where we will cover the TensorFlow APIs you should use to define and train your models, and the best practices for distributing the training workloads to multiple GPUs. We will also look at the underlying reasons why are GPUs are so great to use for Machine Learning workloads?