IBM Developer Day (Includes 6 sessions)

Agenda:

• 11:00 - General Welcome (Host: Dilek Sezgün)
• 11.15 - #AI4Good on PowerAI: How can your coding skills help others? (Hackathon) Speaker: Carmen Recio
• 12:00 - Watson-Studio: Putting AI to Work for Business: Umit Mert Cakmak
• 13:00 - Virtualization is real: Cloud-enabled CAD (Cloud session) Speaker:Alex Hudak, Charlie Dawson (IMSCAD)
• 14:00 - Integrated IBM AI session (Systems & Cloud) Speaker: Florin Manaila, Alex Hudak
• 15:00 - Fuel Pipeline for AI (Storage Session),Daniel Reiberg
• 16:00 - Accelerate and Scale High Performance Computing with IBM Cloud and Rescale, Speaker: Jerry Gutierrez, IBM & Joris Poort, Rescale

Join us for this event to hear from NVIDIA, key experts, technology leaders, researchers, and engineer on the latest innovations and solutions. See how they’re pushing the limits of technology to create the next generation of autonomous machines--from robotics and research to aerospace and disaster relief.

The GTC Europe 2018 opening keynote delivered by NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing.

Data is the lifeblood of the modern city, AI is the key to turning this information into insight. Deep Stream & NVIDIA AI solutions help capture, inspect and analyze data, to benefit everything from traffic and parking management to law enforcement and city services. AGENDA: We'll be addressing the biggest challenges in developing Intelligent Video Analytics (IVA) applications at scale and how NVIDIA SDKs and advances in AI can help solve them, quickly building solutions to process images and video for applications such as public safety, retail, traffic and transportation. We'll also offer a glimpse into what's to come in the future

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. Learn how to segment MRI images to measure parts of the heart by: 

· Comparing image segmentation with other computer vision problems 
· Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API
· Learning to implement effective metrics for assessing model performance 

Upon completion, you’ll be able to set up most computer vision workflows using deep learning.

Prerequisites: Basic experience with neural networks

Get started with an introduction to object detection and image segmentation. You'll explore the shift from traditional computer vision techniques to innovative methods based on deep learning and convolution neural networks (CNNs) for object detection. You'll learn how to:

· Leverage CNNs to iteratively improve performance and expand image understanding capabilities
· Use Microsoft Common Object in Contexts dataset and the Google object detection API in TensorFlow
· Determine accuracy vs. performance trade-offs between Single Shot Multibox Detectors (SSDs), Faster R-CNN with residual networks, and Mask R-CNN

Upon completion, you'll understand how to implement object detection networks with the API in TensorFlow.

Prerequisites: None

Anomaly detection is critical in many industries, especially cybersecurity, finance, healthcare, retail and telecom. Variational autoencoders can outperform traditional techniques for anomaly detection. In this mini course, you'll learn how to: 

· Use Bayesian inference roots of variational autoencoders and their implementation
· Define anomalies as the probability of being generated from a given model below a certain threshold and set thresholds that are specific to industry or use case
· Use variational autoencoders to detect anomalies from all data points

Upon completion, you'll know how to train a variational autoencoder to detect anomalies within the data.

Prerequisites: Experience with CNNs

In pushing the limits of throughput of floating-point operations, GPUs have become a unique technology. During this session, we'll explore the current state of affairs from an application perspective. For this, we'll consider different computational science areas including fundamental research on matter, materials science, and brain research. Focusing on key application performance characteristics, we review current architectural and technology trends to derive an outlook towards future GPU-accelerated architectures.

As many have argued, the rise of artificial intelligence could be seen as the second machine age. The exponential growth in computing power, memory capacity, distributional modules, and most importantly the data (structured and unstructured) created, all require algorithmic innovations to dig out meaningful business insights. Machine learning, especially deep learning, is undoubtedly a crucial key to future. The latest development in computer vision, NLU, and virtual assistants, could easily improve / alter our current insurance structures. Generative models have the potential to reveal deep insights of clients, which hence lead to deep underwriting / customized healthcare strategy.

Join the RAPIDS data science workshop, where industry leadership and data science luminaries including Wes McKinney, Travis Oliphant, Peter Wang, Tim Hunter, and others will get together to discuss the NEW RAPIDS release. Learn how NVIDIA and the ecosystem are advancing data science using open-source strategies to enable more innovative solutions.

AGENDA:

1:30 – 1:45 pm Introduction - Clément Farabet, VP of AI Infrastructure, NVIDIA

1:45 – 2:30 pm RAPIDS: The Platform Inside and Out – Josh Patterson, Director of AI Infrastructure, NVIDIA

2:30 – 3:15 pm Open Source Data Science and Where It’s Going: Panel

Panelists:

• Wes McKinney, Managing Director of Ursa Labs
• Travis Oliphant, Founder and CEO of Quansight
• Peter Wang, CTO Anaconda

Designing an autonomous machine are about much more than just the AI. Electrical, Mechanical, Connectivity, and Security are just a few of the disciplines where you will require expertise. Not all companies will have complete expertise in all these areas. In this session, we will provide examples followed by design considerations, strategies and solutions to begin to address these challenges.

Explore an introduction to Numba, a just-in-time function compiler that allows developers to utilize the CUDA platform in their Python applications. You'll learn how to:

· Decorate Python functions to be compiled by Numba
· Use Numba to GPU accelerate NumPy ufuncs

Upon completion, you'll be able to use Numba to GPU-accelerate NumPy ufuncs in your Python code, and will be ready to learn how to write custom CUDA kernels in Python.

Prerequisites: Basic Python and Numpy Competency

See how deep neural networks are trained to perform tasks with super-human accuracy and will explore which deep neural network models are best-suited for a variety of tasks.

Porsche's view on ADAS, with a focus on the specific profile that Porsche sees for sports cars. Porsche's experience with advanced systems like Innodrive as well as topics like the demands on V&V and data driven development.

Vive X is HTC's global program to build the XR ecosystem by investing in startups. With over 80 portfolio companies and local hubs in the US and Asia, the program is now active in Europe, where over 150 startups have been looked at in the last six months. This talk will share the insights gained from this activity as well as discussing the latest investment trends in the sector generally. Who is investing in AR and VR and what types of ventures are most successful in getting funded? Where do the biggest opportunities lie and what are some key challenges that still need to be addressed? This talk is for you whether you're a startup founder, investor or just interested in the latest innovations happening in XR.

The NVIDIA Docker plugin makes it possible to containerize production-grade deep learning workflows using GPUs. Learn to reduce host configuration and administration by:

· Learning to work with Docker images and manage the container lifestyle
· Accessing images on the public Docker image registry—DockerHub—for maximum reuse in creating composable lightweight containers
· Training neural networks using both TensorFlow and MXNet frameworks

Upon completion, you’ll be able to containerize and distribute pre-configured images for deep learning.

Prerequisites: Basic experience with a bash terminal

When a trained neural network is tasked to find the answer on new data inputs, it is referred to as deployment. TensorRT is the primary tool for deployment, with various options to improve inference performance of neural networks. In this mini course, you'll:

· Learn how to use giexec to run inferencing
· Use mixed precision INT8 to optimize inferencing
· Leverage custom layers API for plugins 

Upon completion, you'll know how to use TensorRT to accelerate inferencing performance for neural networks.

Prerequisites: Basic experience with CNNs and C++

Get a hands-on practical introduction to deep learning for radiology and medical imaging. You'll learn how to:

· Collect, format, and standardize medical image data
· Architect and train a convolutional neural network (CNN) on a dataset
· Use the trained model to classify new medical images

Upon completion, you’ll be able to apply CNNs to classify images in a medical imaging dataset.

Prerequisites: None

This panel will showcase how management teams can implement new AI/DL solutions quickly and effectively, by developing talented teams successfully. It will also discuss how POCs can be moved seamlessly into productive use.

Interpretability of deep neural networks has focused on the analysis of individual samples. This can distract our attention from patterns originating at the distribution of the dataset itself. We broaden the scope of interpretability analysis, from individual images to entire datasets, and found that some high-performing classifiers use less than half the information contained in any given sample. While the learned features are more intuitive to visualize for image-centric neural networks, in time-series it is much more complicated as there is no direct interpretation of the filters and inputs as compared to image modality. In this talk we are presenting two approaches to analyze the behavior of networks with respect to used input signal, which pave the way to go beyond simple layer stacking and towards a more principled design of neural networks.

The presentation gives a deep insight into the virtual development stages at Audi design check. It shows how we react to new customer wishes and how we virtually design, conceptualize and develop the interior of the future. In the past we have focused on 2d power walls. Today we have the possibility to move freely in interactive rooms and to make the car experienceable. To drive on virtual roads or to communicate in collaborative spaces by using 3d data. Starting from the concept phase to the start of production. An impressive leap forward. Vorsprung durch Technik.

Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache Spark 

Data is the key ingredient to building high-quality, production AI applications. It comes in during the training phase, where more and higher-quality training data enables better models, as well as during the production phase, where understanding the model’s behavior in production and detecting changes in the predictions and input data are critical to maintaining a production application. However, so far most data management and machine learning tools have been largely separate. In this presentation, we’ll talk about several efforts from Databricks, in Apache Spark, as well as other open source projects, to unify data and AI by using accelerated hardware such as GPUs. Through a demo, we will show how this project significantly simplifies AI applications at scale.

AI makes it possible for robots to perceive and interact with their environments, enabling them to take on tasks that were unthinkable--until now. NVIDIA ISAAC accelerates development and deployment with an advanced SDK and tools for enhanced robotics simulation. Claire Delaunay VP of Engineering at NVIDIA will present on the ISAAC SDK and GEMS, while Dieter Fox will discuss Robotics research projects

Learn how to accelerate your C/C++ or Fortran application using OpenACC to harness the massively parallel power of NVIDIA GPUs. OpenACC is a directive-based approach to computing where you provide compiler hints to accelerate your code, instead of writing the accelerator code yourself. Get started on the four-step process for accelerating applications using OpenACC:

· Characterize and profile your application
· Add compute directives
· Add directives to optimize data movement
· Optimize your application using kernel scheduling

Upon completion, you will be ready to use a profile-driven approach to rapidly accelerate your C/C++ applications using OpenACC directives.

Prerequisites: Basic experience with C/C++

Since 2009, our deep learning artificial neural networks have won numerous contests in pattern recognition and machine learning. Today, they are used billions of times per day by the world's most valuable public companies. True AI, however goes far beyond slavishly imitating teachers through deep learning. That's why we have also focused, since 1990, on unsupervised AIs that invent their own goals and experiments to figure out how the world works and what can be done in it. Many of them model the world through a recurrent neural network that learns to predict the consequences of their action sequences. Without a teacher, they derive rewards from continually creating and solving their own, new, previously unsolvable problems, a bit like playing kids do, to become more and more like general problem solvers in the process. Relevant buzzwords include "artificial curiosity" (since 1990) and PowerPlay (since 2011). I will also briefly outline how AIs that set their own goals will eventually colonise the entire universe and make it intelligent.

Unlike other industries, architecture has still a lot of frontiers to adopt digital tools, impacting productivity by the use of limited and disjointed solutions. While being used by big architecture and automotive companies, Dassault Systèmes 3DEXPERIENCE has recently expanded its breadth to Cloud licensed users, giving a much wider access to the latest CATIA features including improved visualization on large datasets and native VR integration with NVIDIA GPU. This talk will present how SHoP leverages CATIA for engineering, wireframing and building systems automation but also their early investigations with latest R2018x built-in VR immersive experiences for design validation and collaborative decision workflow.