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.
Advancements in deep learning are enabling enterprise companies to make meaningful impacts to bottom-line profits. Enterprises capture thousands of hours of customer phone call recordings per day. This voice data is extremely valuable because it contains insights that the business can use to improve customer experience and operations. We'll follow Deepgram CEO Dr. Scott Stephenson's path from working in a particle physics lab two miles underground to founding a deep learning company for voice understanding. We'll describe applications of cutting-edge AI techniques to make enterprise voice datasets mineable for valuable business insights. Companies today use these insights to drive the bottom line.
Has your team developed an AI proof-of-concept with promising metrics? Next step is to broaden the scope to impact larger areas of the enterprise. With its unique challenges and complexities, scaling POCs across multiple business units is a significant part of any company''s AI roadmap. This session will look at best practices, insights and success, rooted in Element AI''s experience with enterprise customers.
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.
Get the latest information on how the proliferation of mobile, cloud, and IoT devices has brought us into a new era: The Extreme Data Economy. There''s a greater variety of data than ever before, and exponentially more of it, streaming in real time. Across industries, companies are turning data into an asset, above and beyond any product or service they offer. But unprecedented agility is required to keep business in motion and succeed in this post-big data era. To enable this level of agility, companies are turning to instant insight engines that are powered by thousands of advanced GPU cores, bringing unparalleled speed, streaming data analysis, visual foresight, and machine learning to break through the old bottlenecks. Learn about new data-powered use cases you''ll need to address, as well as advances in computing technology, particularly accelerated parallel computing, that will translate data into instant insight to power business in motion.
We'll review three practical use cases of applying AI and deep learning in the marketing and retail industries. For each use case, we'll cover business situations, discuss potential approaches, and describe final solutions from both the AI and infrastructural points of view. Attendees will learn about applications of AI and deep learning in marketing and advertising; AI readiness criteria; selecting the right AI and deep learning methods, infrastructure, and GPUs for specific use cases; and avoiding potential risks.
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.
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex manipulations tasks in real-world settings. Our model was trained on 44,000 video sequences, where the manipulator autonomously pushes various objects. Using the model, the robot is capable of moving objects that were not seen during training to desired locations, handling multiple objects and pushing objects around obstructions. Unlike other methods in robotic learning, video-prediction does not require any human labels. Our experiments show that the method achieves a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning. This session is for attendees that possess a basic understanding of convolutional and recurrent neural networks.
Reinforcement learning aims to determine a mapping from observations to actions that maximize a reward criterion. The agent starts off exploring the environment for rewards with random search, which is only likely to succeed in all but simplest of settings. Furthermore, measuring and designing reward functions for real-world tasks is non-trivial. Inspired by research in developmental psychology, in this talk I will discuss how reinforcement learning agents might use curiosity and knowledge accumulated from experience for efficient exploration. I will present results illustrating an agent learning to play the game of Mario and learning to navigate without rewards, a study quantifying the kinds of prior knowledge used by humans for efficient exploration and some robotic manipulation experiments including the use of an anthropomorphic hand for grasping objects.
To convert phonemes of telephone conversations and responses at meetings into texts in real time, pass the text to the computational model created by DGX-1, label with a learning without teacher, and add the clusters, we are developing a system which compares objects and analyzes meaning of conversation and profiles of interlocutors. With this technology, customers can receive appropriate responses at the beginning of a conversation with a help desk, and patients can receive correspondence during a remote diagnosis with a doctor based solely off of their dialogue and examination results. By using TensorFlow as a platform and running the K-Means method, Word2vec, Doc2Vec, etc. in DGX-1 clustered environment on DGX-1, the result of arithmetic processing is found at high speed conversation. Even if the amount of sentences is increased, the learning effect increases linearly, demonstrating that the proportion of validity can be raised without taking grammar of languages ??other than English (e.g. Japanese) into account.
We'll explain the concept and the importance of audio recognition, which aims to understand literally all the information contained in the audio, not limiting its scope to speech recognition. It includes the introduction of various types of non-verbal information contained in the audio such as acoustic scenes/events, speech, and music. This session is helpful to the people who are not familiar with audio processing but are interested in the context-aware system. Also, it might be inspiring for someone who develops AI applications such as AI home assistant, a humanoid robot, and self-driving cars. It also covers the potential use-cases and creative applications, including a video demonstration of the audio context-aware system applied to media-art performance for real-time music generation.
The paradigm for robot programming is changing with the adoption of the deep learning approach in the field of robotics. Instead of hard coding a complex sequence of actions, tasks are acquired by the robot through an active learning procedure. This introduces new challenges that have to be solved to achieve effective training. We'll show several issues that can be encountered while learning a close-loop DNN controller aimed at a fundamental task like grasping, and their practical solutions. First, we'll illustrate the advantages of training using a simulator, as well as the effects of choosing different learning algorithms in the reinforcement learning and imitation learning domains. We'll then show how separating the control and vision modules in the DNN can simplify and speed up the learning procedure in the simulator, although the learned controller hardly generalizes to the real world environment. Finally, we'll demonstrate how to use domain transfer to train a DNN controller in a simulator that can be effectively employed to control a robot in the real world.
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.
Using only randomized simulated images, we'll present a system to infer and simply execute a human-readable robotic program after watching a real-world task demonstration. The system is comprised of a series of deep neural network modules, each learned entirely in simulation. During training, images are generated in a gaming engine and made transferable to the real world by domain randomization. After training, the system is straightforwardly deployed on a real robot with no retuning of the neural networks and having never previously seen a real image. We demonstrate the system on a Baxter robot performing block tower construction tasks.
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 present a multi-node distributed deep learning framework called ChainerMN. 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, ChainerMN was developed and built on top of Chainer. We''ll first introduce the basic approaches to distributed deep learning and then explain the design choice, basic usage, and implementation details of Chainer and ChainerMN. To demonstrate the scalability and efficiency of ChainerMN, we''ll discuss the remarkable results from training ResNet-50 classification model on ImageNet database using 1024 Tesla P100 GPUs and our in-house cluster, MN-1.
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.
Learn how VUE.ai''s model generator uses conditional GANs to produce product-specific images suitable for replacing photographs in catalogs. We''ll present networks that generate images of fashion models wearing specific garments, using an image of the garment as a conditioning variable. Network architecture variants, training, and manipulation of latent variables to control attributes such as model pose, build, or skin color will be addressed.
We'll discuss the development of a novel model for video prediction and analysis -- the parallel multi-dimensional long short-term memory (PMD-LSTM). PMD-LSTM is a general model for learning from higher dimensional data such as images, videos, and biomedical scans. It is an extension of the popular LSTM recurrent neural networks to higher dimensional data with a rearrangement of the recurrent connections to dramatically increase parallelism. This gives the network the ability to compactly model the effect of long-range context in each layer, unlike convolutional networks, which need several layers to cover a larger input context. We'll discuss the blind spot problem in recent work on video prediction, and show how PMD-LSTM based models are fully context-aware for each predicted pixel. These models outperform comparatively complex state-of-the-art approaches significantly in a variety of challenging video prediction scenarios such as car driving, human motion, and diverse human actions.
We'll showcase how you can apply a wealth of unlabeled image data to significantly improve accuracy and speed of single-shot object-detection (SSD) techniques. Our approach, SSD++, advances the state-of-the-art of single shot multibox-based object detectors (such as SSD, YOLO) by employing a novel combination of convolution-deconvolution networks to learn robust feature maps, thus making use of unlabeled dataset, and the fresh approach to have confluence of convolution and deconvolution features to combine generic as well as semantically rich feature maps. As a result, SSD++ drastically reduces the requirement of labeled datasets, works on low-end GPUs, identifies small as well as large objects with high fidelity, and speeds up inference process by decreasing the requirement of default boxes. SSD++ achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Through ablation study, we'll explain the effectiveness of different components of our architecture that help us achieve improved accuracy on the above datasets. We'll further show a case study of SSD++ to identify shoppable objects in fashion, home decor, and food industry from images in the wild.
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?
Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints, diverse environments and in the presence of distractors. In robotics, this ability is referred to as visual servoing. Standard visual servoing approaches have limited generalization as they typically rely on manually designed features and calibrated camera. We exhibit generalizable visual servoing in the context of robotic manipulation and navigation tasks learned through visual feedback and by deep reinforcement learning (RL) without needing any calibrated setup. By highly randomizing our simulator, we train policies that generalize to novel environments and also to the challenging real world scenarios. Our domain randomization technique addresses the high sample complexity of deep RL, avoids the dangers of trial-and-error and also provides us with the liberty to learn recurrent vision-based policies for highly diverse tasks where capturing sufficient real robot data is impractical. An example of such scenario is learning view-invariant robotic policies which leads into learning physical embodiment and self-calibration purely through visual feedback.
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.
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.
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field programmable gate array (FPGA) systems. Conversely, the deep learning revolution has been fueled by GPUs and the ease with which they may be programmed for highly parallel computations. The next generation RF and wireless technology will require wide-band systems capable of real-time machine learning with GPUs. Working with strategic partners, we''ve designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and machine learning with a Jetson TX2. We discuss system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we''ll present data demonstrating applications in the field of RF machine learning and deep learning.
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are time-consuming and the results vary by individuals, automatic defect detection and classification has the potential to reduce the cost of quality assurance significantly. In this talk, we will demonstrate how to utilize deep learning algorithms, i.e., Fully Convolutional Neural Network to build a general defect inspection and classification model. We will also share experiences on how to effectively collect labelling data, deal with imbalance data, and also how to optimize the model in terms of latency and throughput with TensorRT before deploy the model to the production line.
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.
Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augmentation techniques, which do not violate the physics of ultrasound imaging. Explore the possibilities of using raw ultrasound data to increase performance. Ultrasound images collected from two different commercial machines are used to train an algorithm to segment suspicious breast with a mean dice coefficient of 0.82. The algorithm is shown to perform at par with conventional seeded algorithm. However, a drastic reduction in computation time is observed enabling real-time segmentation and detection of breast masses.
Fast, inexpensive and safe, ultrasound imaging is the modality of choice for the first level of medical diagnostics. The emerging solutions of portable and hand-held 2/3D scanners, advanced imaging algorithms, and deep learning promise further democratization of this technology. During the session, we will present an overview of ultrasound imaging techniques in medical diagnostics, explore the future of ultrasound imaging enabled by GPU processing, as well as set out the path to the conception of a portable 3D scanner. We will also demonstrate our hardware developments in ultrasound platforms with GPU-based processing. Having started with one large research scanner, we have begun our migration towards more commercially-viable solutions with a small hand-held unit built on the mobile GPU NVidia Tegra X1.
How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is fundamentally driven by machine learning in general and computer vision and deep learning in particular. With the help of PAIGE.AI we are building a clinical-grade AI at Memorial Sloan Kettering Cancer Center. The models are trained based on petabytes of image and clinical data on top of the largest DGX-1 V100 cluster in pathology. The goal is not only to automated cumbersome and repetitive tasks, but to impact diagnosis and treatment decisions in the clinic. This talk will focus on our recent advances in deep learning for tumor detection and segmentation, on how we train these high capacity models with annotations collected from pathologists, and how the resulting systems are implemented in the clinic.
In this talk I will describe the research and development work on medical imaging, done at PingAn Technology and Google Cloud, covering five different tasks. I'll present the technical details of the deep learning approaches we have developed, and share with the audiences the research direction and scope in the medical fields at PingAn technology and PingAn USA Lab.
Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.
Radiological diagnosis and interpretation should not take place in a vacuum -- but today, it does. One of the greatest challenges the radiologist faces when interpreting studies is understanding the individual patient in the context of the millions of patients who have come previously. Without access to historical data, radiologists must make clinical decisions based only on their memory of recent cases and literature. Arterys is working to empower the radiologist with an intelligent lung nodule reference library that automatically retrieves historical cases that are relevant to the current case. The intelligent lung nodule reference library is built on top of our state-of-the-art deep learning-based lung nodule detection, segmentation and characterization system.
As deep learning techniques have been applied to the field of healthcare, more and more AI-based medical systems continue to come forth, which are accompanied by new heterogeneity, complexity and security risks. In the real-world we've seen this sort of situation lead to demand constraints, hindering AI applications development in China's hospitals. First, we'll share our experience in building a unified GPU accelerated AI engine system to feed component-based functionality into the existing workflow of clinical routine and medical imaging. Then, we'll demonstrate in a pipeline of integrating the different types of AI applications (detecting lung cancer, predicting childhood respiratory disease and estimating bone age) as microservice to medical station, CDSS, PACS and HIS system to support medical decision-making of local clinicians. On this basis, we'll describe the purpose of establishing an open and unified, standardized, legal cooperation framework to help AI participants to enter the market in China to build collaborative ecology.
We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, from expert radiologists and deep learning experts. How Deep Learning/AI is changing clinical neuroimaging practice * How will deep learning be applied in radiology workflow right now and in the future * Practical concerns and perspectives from radiologists How Deep Learning assists smarter neuroimaging decision making * Multi-scale 3D network enables lesion outcome prediction for stroke * More accurate lesion segmentation in neuroimaging How Deep Learning enables safer and cheaper neuroimaging screening * Deep Learning and GAN enables >95% reduction in radiation for functional medical imaging * Deep Learning enables 90% reduction in chemical (Gadolinium) contrast agent usage in contrast enhanced MRI How Deep Learning accelerate neuroimaging * Further acceleration and improved MRI reconstruction using deep learning * Deep Generative Adversarial Network for Compressed Sensing
Discuss the difficulties in digital mammography, and the computational challenges we encountered while adapting deep learning algorithms, including GAN, to digital mammography. Learn how we address those computational issues, and get the information of our benchmarking results using both consumer and enterprise grade GPUs.
There is large promise in machine learning methods for the automated analysis of medical imaging data for supporting disease detection, diagnosis and prognosis. These examples include the extraction of quantitative imaging biomarkers that are related to presence and stage of disease, radiomics approaches for tumor classification and therapy selection, and deep learning methods for directly linking imaging data to clinically relevant outcomes. However, the translation of such approaches requires methods for objective validation in clinically realistic settings or clinical practice. In this talk, I will discuss the role of next generation challenges for this domain.
Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applications. We''ll first describe the types of medical imaging and the key clinical applications for deep learning for improving image interpretation. Next, we''ll describe recent developments of word-embedding methods to leverage narrative radiology reports associated with images to generate automatically rich labels for training deep learning models and a recent AI project that pushes beyond image classification and tackles the challenging problem of clinical prediction. We''ll also describe emerging methods to leverage multi-institutional data for creating AI models that do not require data sharing and recent innovative approaches of providing explanation about AI model predictions to improve clinician acceptance.
Dive in to recent work in medical imaging, where TensorFlow is used to spot cancerous cells in gigapixel images, and helps physicians to diagnose disease. During this talk, we''ll introduce concepts in Deep Learning, and show concrete code examples you can use to train your own models. In addition to the technology, we''ll cover problem solving process of thoughtfully applying it to solve a meaningful problem. We''ll close with our favorite educational resources you can use to learn more about TensorFlow.
Learn CAIDE Systems'' unique diagnosis system with highly accurate prediction and delineation of brain stroke lesion. We''ll present how we increase sensitivity in medical diagnosis system and how we develop a state-of-the-art generative deep learning model for acquiring segmented stroke lesion CT images, and demonstrate our market-ready product: a diagnostic tool as well as a medical deep learning platform. We trained our diagnostic system using CT image data from thousands of patients with brain stroke and tested to see commercial feasibility of use for hospitals and mobile ambulances.
The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding and DNA accessibility. For example, the ATAC-seq protocol identifies open genomic sites by sequencing open DNA fragments; genome-wide fragment counts provide a profile of DNA accessibility. Recent advances enable profiling from smaller patient samples than previously possible. To reduce sequencing cost, we developed a convolutional neural network that denoises data from a small number of DNA fragments, making the data suitable for various downstream tasks. Our platform aims to accelerate adoption of DNA sequencers by minimizing data requirements.
This talk will present the challenges and opportunities in developing a deep learning program for use in medical imaging. It will present a hands on approach to the challenges that need to be overcome and the need for a multidisciplinary approach to help define the problems and potential solutions. The role of highly curated data for training the algorithms and the challenges in creating such datasets is addressed. The annotation of data becomes a key point in training and testing the algorithms. The role of experts in computer vision, and radiology will be addressed and how this project can prove to be a roadmap for others planning collaborative efforts will be addressed Finally I will discuss the early results of the Felix project whose goal is nothing short of the early detection of pancreatic cancer to help improve detection and ultimately improve patient outcomes.
For more than a decade, GE has partnered with Nvidia in Healthcare to power our most advanced modality equipment, from CT to Ultrasound. Part 1 of this session will offer an introduction to the deep learning efforts at GEHC, the platform we're building on top of NGC to accelerate new algorithm development, and then a deep dive into a case study of the evolution of our cardiovascular ultrasound scanner and the underlying extensible software stack. It will contain 3 main parts as follows: (a) Cardiovascular ultrasound imaging from a user perspective. Which problems we need to solve for our customers. Impact of Cardiovascular disease in a global perspective (b) An introduction to the Vivid E95 and the cSound platform , GPU based real time image reconstruction & visualization. How GPU performance can be translated to customer value and outcomes and how this has evolved the platform during the last 2 ½ years. (c) Role of deep learning in cardiovascular ultrasound imaging, how we are integrating deep learning inference into our imaging system and preliminary results from automatic cardiac view detection.
Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where many discrete quantitative clinical elements like visual acuity can be tied to rich imaging datasets. In this session, we will explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer aided diagnoses.
Diabetic retinopathy, also known as diabetic eye disease, is a major complication of diabetes, which damage occurs to the retina due to diabetes mellitus and is a leading cause of blindness. AirDoc's product Dirctor, Emma Xu and Professor You Li of Shanghai Changzheng Hospital, will share how AirDoc, the leading Intelligent Medical startup in China, leverages Nvidia's GPU and Deep Learning to improve the DR diagnose with Automatic left/right eye recognition, Automatic detection of the location and numbers, Automatic DR staging, Fast recognition speed, Patient Information Management for real-time screening statistics and usage management.
In this talk, you will get a detailed yet accessible look at how AI is disrupting logistics. Firms have for years been using classical optimization algorithms to make decisions such as how to deliver goods to multiple clients in a city, place packages in a warehouse or route orders. Such algorithms are often built on heuristics which experts have designed to get reasonable solutions quickly. Recent advances in Deep Learning and Reinforcement learning are however making it possible to build AI systems that tackle these optimization problems from scratch. Through constant learning, a modern AI system can match and even beat existing optimization algorithms, or deliver faster solutions thanks to GPU parallel processing. Companies can now leverage these advances into significant efficiency gains for their operations.
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs. We introduce H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust python API that is a drop dead replacement for scikit-learn. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.
Road identification and route prediction in near real time remains a challenging problem for many geographic regions, particularly in the case of natural disasters or crisis situations. Existing methods such as manual road labeling or aggregation of mobile GPS track data are currently insufficient in dynamic scenarios. The frequent revisits of satellite imaging constellations may accelerate efforts to rapidly update road network and optimal path prediction, provided routing information can be extracted from imaging pixels. We'll demonstrate deep learning segmentation methods for identifying road center lines and intersections from satellite imagery, and inferring networks from these road segments. We'll also explore data quality requirements by comparing open source labels with-high precision labels created as part of the SpaceNet Roads challenge.
We'll present a complete open-source software stack for self-driving vehicles, called Autoware, and its open integration with the NVIDIA DRIVE platform. Autoware implements working modules of localization and 3D mapping with LiDAR and GNSS, object detection and traffic light recognition with deep learning, path planning with lattice and search methods, and vehicle dynamics control. Compute-intensive tasks of these modules are accelerated by using CUDA, and timing-aware tasks are protected by RTOS capabilities. We'll discuss the impact of CUDA acceleration on self-driving vehicles and its performance evaluation. Learn how Autoware enables any by-wire vehicles to become high-quality self-driving vehicles that can operate in real-world environments.
Pony.ai will share the key technological milestones it has achieved in the past several months of road testing in China, including the company's soft launch of China's first-ever autonomous vehicle robotaxi service. CEO James Peng will share the unique challenges posed by a Chinese road environment and how we leveraged deep learning and computational models to conquer those challenges. Pony.ai's mission is to build the safest and most reliable L4 autonomous driving technology. The startup was founded at the end of 2016 and is co-located in the heart of Silicon Valley and China.
Learn about our application of deep learning techniques for perception systems in autonomous driving, reinforcement learning for autonomous systems, label detection in warehouse inventory management, and undergraduate engagement in this research. In collaboration with Clemson University''s International Center for Automotive Research, we''ve developed a perception module that processes camera inputs to provide environmental information for use by a planning module to actively control the autonomous vehicle. We''re extending this work to include an unsupervised planning module for navigation with reinforcement learning. We''ve also applied these techniques to automate the job of warehouse inventory management using a deep neural network running on a mobile, embedded platform to automatically detect and scan labels and report inventory, including its location in the warehouse. Finally, we''ll discuss how we involve undergraduate students in this research.
Deep learning/artificial intelligence methods are increasingly being deployed to enable new avenues of big-data-driven discovery in key scientific application areas such as the quest to deliver Fusion Energy identified by the 2015 CNN "Moonshots for the 21st Century" series as one of 5 prominent modern grand challenges. Princeton University''s associated R&D methods have been successfully applied to accelerate progress in reliably predicting and avoiding large-scale losses (called "disruptions") of the thermonuclear plasma fuel in magnetically-confined devices the largest of which is the $25B international ITER device a burning plasma experiment under construction with the potential to exceed "breakeven" fusion power (i.e., "power out = power in") by a factor of 10 or more.