The 4th industrial revolution is going to be disruptive and become mainstream because courageous startups bring their ideas to market. These innovations span industries, use cases, and applications. From building a wearable device that understands the visual world for the blind, to weather forecasting accurate to the minute, these small and agile teams are breaking new ground every day.
Come meet the luminaries of the deep learning startup community, the NVIDIA Inception Program. Attend our award presentations and shake some hands in the exhibition hall at our Startup Square. The brightest minds in AI startups are here at GTC.
Your chance to network with other brilliant and determined minds in your field. Experts, press, analysts, and VCs are attending in force.
Hangout sessions where you can whiteboard technical questions, ask business leaders advice, or ask an expert on go-to-market strategies.
At the awards night, we will feature a few of you and you’ll have a chance to win. Some will be mentioned on our website. Some will be featured in blogs. Others will be featured on @NVIDIAAI.
We will be hosting an exclusive awards night to highlight the most brilliant startups in our program. Three lucky startups will walk away with major prizes.
Come to our startup square to try demos and talk directly with startups in our program
Are you a VC focused on AI and self-driving cars? We have a special event for you. Email us if you are interested.
CosmiQ Works, CTO
CosmiQ Works explores how the U.S. government can leverage new commercial space capabilities against national security problems.
The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. SpaceNet's objective is to release remote sensing data (for example, satellite imagery) to the public to enable developers and data scientists.
ABOUT THE SPEAKER: Todd Stavish is a co-founder and CTO of CosmiQ Works, a division of In-Q-Tel Labs. CosmiQ Works's mission is to help the intelligence community leverage new and emerging commercial space capabilities against mission problems. At CosmiQ Works, Todd leads the SpaceNet Challenge, a corpus of commercial satellite imagery and associated algorithm design competitions. The goal of SpaceNet is to foster innovation in the development of computer vision to automatically extract information from remote sensing data. Before working at CosmiQ, Todd was the technical lead on In-Q-Tel's big data, geospatial, and commercial space investments. He spent his early career working in Silicon Valley startups.
HARRIS CORPORATION, Program Manager
From ocean to orbit and everywhere in between, Harris solutions connect, inform, and protect the world.
We'll walk through several use cases Harris has developed to illustrate the benefit of harnessing multiple input sources for deep learning using NVIDIA GPUs and discuss the implications of this research in the wider remote sensing community. In the realm of remote sensing, deep learning has been applied to automatic feature extraction for a variety of individual data types such as electro-optical panchromatic and multi-spectral imagery, point clouds, and video. One of the less explored benefits of deep learning application to remote sensing data is the ability to incorporate multiple streams of data into the same neural network. When leveraging multiple modalities in the same model, a synergistic decision can be made from the data that reveals more information than either of the individual data types can provide alone.
ABOUT THE SPEAKER: Will Rorrer has worked with the Harris Corporation for over 15 years providing management and guidance to key business units. These areas include the Night Vision operations team, the Jagwire program for streaming, cataloging and analyzing full-motion video, and leading research and development on deep learning tools and applications. Throughout his career, Will has been honored to support the National Geospatial Intelligence Agency and other parts of the Department of Defense in using high-tech capabilities for solving global security problems.
BRIEFCAM LTD., CTO
Briefcam develops software-based solutions that enable reviewing hours of video in just minutes while analyzing object interaction and generating statistical data.
Law enforcement and enterprise security personnel are increasingly being drowned by the sheer volume of video stream data. We'll discuss Briefcam's general purpose video analysis engine that tackles this problem by using GPUs and deep learning to break live or archived video into structured data with rich metadata. From this metadata, a wide range of applications are possible to manage the barrage of video, such as rapid video review, video search, statistics, and alerts.
ABOUT THE SPEAKER: Tom Edlund joined BriefCam in 2011 as director of the Software Engineering Group and was promoted to vice president of R&D in 2012. Under his leadership, BriefCam's R&D team has expanded in scope, developing professional, consumer, and mobile versions of the company's core Video Synopsis technology. Tom graduated from Chalmers University of Technology, Gothenburg, Sweden, with an M.S. in physics, mathematics and computer science. He's an avid hiker and a volunteer at Tech Career, a unique student mentoring program, where he holds weekly C#, ASP .NET, and HTML5 lessons with Ethiopian-Israeli young adults to prepare and integrate them into Israel's thriving high-tech sector.
Scalable Distributed Deep Learning with Chainer
Deep Learning for Retail Analytics and Reference Data Management
Director of Machine Learning
Cloud-Based Deep Learning as the Radiologist's Best Friend
PREFERRED NETWORKS, Researcher
We'll present ChainerMN, a multi-node distributed deep learning framework, together with the basics of distributed deep learning. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better scalability and productivity, it is paramount to accelerate the training process by using multiple GPUs. To enable high-performance and flexible distributed training, we developed ChainerMN, built on top of Chainer. We'll first introduce the basic approaches to distributed deep learning. Then, we'll explain the design choice, basic usage, and implementation details of Chainer and ChainerMN. We'll report benchmark results and discuss the future directions of distributed deep learning.
ABOUT THE SPEAKER: Takuya Akiba is a researcher at Preferred Networks, Inc., working on research and development for making deep learning faster and more scalable. He received a Ph.D. in information science and technology from the University of Tokyo, Japan, in 2015.
Deep Learning for Retail Analytics and Reference Data Management
We'll show how state-of-the-art deep learning techniques can be applied to retail analytics. Namely, we'll show how one can retrieve various information about the product, including its category and ingredients, using a mixture of visual and textual information. We'll start with depicting the business scenario and operational needs of such a system, and then move into a technical and in-depth discussion of the underlying deep learning pipeline. The solution is based on an interplay of region-based convolutional neural networks and NLP techniques. This is a joint effort of Nielsen and deepsense.io.
ABOUT THE SPEAKER: Robert Bogucki is the chief science officer at deepsense.io, where he manages the R&D team and focuses on deep learning. Robert is also a successful Kaggle competitor. When tackling real-life problems, he particularly enjoys leveraging algorithms and computational power instead of, or in addition to, domain knowledge. His motivation to work in the IT Industry is to bring the theoretical ideas and concepts and put them to good use
ARTERYS, Director of Machine Learning
Cloud-Based Deep Learning as the Radiologist's Best Friend
Sad but true: most of radiology is mind-numbing tedium. Radiologists spend countless hours on tasks that are onerous and error-prone, resulting in high costs and frequent misdiagnoses. Our first product designed to address these deficiencies is Arterys Cardio DL, a web-based, zero-footprint cardiac MRI postprocessing suite. Arterys Cardio DL includes a deep learning-based contouring algorithm that vastly reduces the time required to diagnose heart disease in cardiac MRI. Arterys Cardio DL is the first technology ever to be cleared by the FDA that leverages cloud computing and deep learning in a clinical setting. We'll discuss the technology behind the software and how we proved its safety and efficacy to secure FDA clearance in the United States and the CE Mark in Europe.
ABOUT THE SPEAKER: Dan Golden is the director of machine learning at Arterys, a startup focused on streamlining the practice of medical image interpretation and post-processing. He joined Arterys in 2015 to found its machine learning team, after previously being with CellScope, where he founded a machine learning team that used concepts from the then-nascent field of deep learning to diagnose ear disease and streamline the process of recording ear exams at home. Dan received a Ph.D. in electrical engineering from Stanford, and the pursued a postdoc focusing on using machine learning to predict outcomes and disease characteristics in cancer patients.
DIGITAL GLOBE, Senior Data Scientist
Multi-Source Fusion Using Deep Learning
DigitalGlobe's satellite constellation collects millions of square kilometers of earth's imagery daily, yielding high resolution data of our planet. By employing DL algorithms & NVIDIA GPUs, DigitalGlobe processes imagery & detect objects at speeds orders of magnitude faster than ever before. Emergency responders require a multitude of information sources to support their mission. DigitalGlobe utilizes several methodologies of fusing disparate data sets together. Social media, weather, other sensor types (eg. RADAR/LIDAR) & Satellite Imagery can be fused together to help decision makers answer questions. By combining the data sets based on their location and common categories from the DL algorithms, emergency responders & analysts are able to automatically verify objects on the ground. Sign up for this session now >
ABOUT THE SPEAKER: Andrew Jenkins works for DigitalGlobe Inc. as a Senior Data Scientist focused on applying deep learning to multi-source spatial data such as satellite imagery, geo-tagged photos and videos. Andrew is currently a PhD candidate in the Department of Geography and Geographic Information Science at George Mason University. He holds a MS degree in Geoinformatics and a BS in Computer and Information Science. Andrew previously worked as a government researcher at the US Army Engineer Research and Development Center, and prior to that spent eight years in the military.
HERTA SECURITY, CEO
Implementing Deep Learning for Video Analytics on Tegra X1
The performance of Tegra X1 architecture opens the door to real-time evaluation and deployment of deep neural networks for video analytics applications. This session presents a highly-optimized low-latency pipeline to accelerate demographics estimation based on deep neural networks in videos. The proposed techniques leverage the on-die hardware video decoding engine and Maxwell GPU cores for conducting advanced video analytics such as gender or age estimation. Our results show that Tegra X1 is the right platform for developing embedded video analytics solutions. Sign up for this session now >
ABOUT THE SPEAKER: Dr. Javier Rodriguez Saeta is CEO of Herta Security, which he founded in 2009. He received M.S. and Ph.D. in electrical engineering from the Universitat Politecnica de Catalunya in 2000 and 2005, respectively. He received a B.A. in business administration from the Open University of Catalonia, and an MBA from ESADE Business School. In 2000, he worked for Robert Bosch, GmbH, in Hildesheim, Germany. In 2001, he joined Biometric Technologies, in Barcelona, Spain, where he was the R&D manager. He has published more than 20 papers indifferent magazines and workshops, and holds three patents. His main research interests include all issues related to innovation, security, and biometric systems and applications.
CARNEGIE MELON UNIVERSITY, Ph.D. Candidate
Listen, Attend and Spell
Most recently, Listen, Attend and Spell (LAS) was presented to directly transcribe speech utterances to characters. Unlike traditional DNN-HMM models, these models learn all the components of a speech recognizer jointly. The LAS model has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions between the characters. In this talk, we describe a distributed asynchronous training platform for training such an model on an array of GPUs. Sign up for this session now >
ABOUT THE SPEAKER: William Chan is a Ph.D. candidate at Carnegie Mellon University in the Department of Electrical and Computer Engineering. William graduated with an M.S. in electrical and computer engineering from Carnegie Mellon University in 2013, and a B.S. in computer engineering in 2011 from the University of Waterloo. His past industry experience includes internships at Google, Amazon, Intel, NVIDIA, AMD, and TD Securities. His current research crosses the fields of machine learning, deep learning, and speech recognition.