Kicking off the Washington edition of our GPU Technology Conference, Buck, NVIDIA's VP for accelerated computing, detailed a new generation of technologies that will help companies put modern AI to work. Buck also announced that the United States Postal Service -- the world's largest delivery service, with 146 billion pieces of mail processed and delivered annually -- is adopting end-to-end AI technology from NVIDIA. "The challenge is how do we take AI from innovation to actually applying AI," Buck told an audience of more than 3,500 developers, CIOs and federal employees at the three-day GTC DC. "Our challenge, NVIDIA's challenge, and my challenge is'How can I bring AI to industries and activate it.'" Over the course of his hour-long talk, Buck explained how modern AI is trained and deployed, and described how NVIDIA is adapting AI for the automotive, healthcare, robotics, and 5G industries, among others. The U.S. Postal Service offers a glimpse at what's possible. Buck said the U.S. Postal Service will roll out a deep learning solution based on NVIDIA EGX to 200 processing facilities that should be operational in 2020.

Language doesn't exist in a vacuum. It's based on having experiences in and knowledge of the real world. Douglas Hofstadter would either laugh in your faces or succumb to a deep depression if he only knew what you small-minded pretenders were working on.

As the final model release of GPT-2's staged release, we're releasing the largest version (1.5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models. While there have been larger language models released since August, we've continued with our original staged release plan in order to provide the community with a test case of a full staged release process. We hope that this test case will be useful to developers of future powerful models, and we're actively continuing the conversation with the AI community on responsible publication. Our partners at Cornell University surveyed people to assign GPT-2 text a credibility score across model sizes. People gave the 1.5B model a "credibility score" of 6.91 out of 10.

This directory contains files to build Docker images - encapsulated computational containers which enhance reproducibility for scientific research. They are similar in design philosophy to the excellent Jupyter Docker Stacks but with a focus on making it easy to get up and running with GPU-accelerated deep learning. The base image provides a Jupyter Lab (notebook) environment in a Docker container which has direct access to the host system's GPU(s). Additionally there is a custom directory with instructions and examples for building your own image. These are considered stable but may be moved to their own repositories in future. The instructions below refer to the combined (default) image mmrl/dl (based on the Keras Dockerfile) which contains ALL TEH THINGZ!!!

In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning. Last weekend I drove down to Maryland to visit my parents. As I pulled into their driveway I noticed something strange -- there was a car I didn't recognize sitting in my dad's parking spot. I parked my car, grabbed my bags out of the trunk, and before I could even get through the front door, my dad came out, excited and enlivened, exclaiming that he had just gotten back from the car dealership and traded in his old car for a brand new 2020 Honda Accord. Most everyone enjoys getting a new car, but for my dad, who puts a lot of miles on his car each year for work, getting a new car is an especially big deal.

While neurological diseases seem sudden, striking out of nowhere, many of them are actually progressive. The brain develops such conditions over time, and the symptoms can be barely noticeable until it is too late. The secret to helping such patients in time is befriending deep learning technology. Various types of image analysis software, mostly based on deep learning (a subfield of artificial intelligence) are being increasingly adopted in radiology due to their ability to automate image processing and segmentation, reducing the time on scan interpretation. According to a recent report, the forecast for the global medical image analysis market is set to reach $4.26 billion by 2025.

We have applied this idea to compute Margin Valuation Adjustments (MVA) for an interest rate swap book, in a use case for a major bank. Recall that computing margin valuation adjustment involves predicting future Initial Margin (IM) charged by a clearing house on (a portfolio of) derivatives, and these, in turn, are computed using the sensitivities of these derivatives to underlying factors (such as interest rates or volatilities). It is the precise calculation of these sensitivities that is usually cumbersome and time consuming. For example, numerical differentiation algorithms (the so-called "bump-and-revalue") are inefficient and inaccurate. The modern technique of Algorithmic Adjoint Differentiation (AAD) does significantly better on both fronts, but can be still quite time-consuming (and complex). So we generated a large number of interest rate curves scenarios (100 000) in the two-curves setting – we used the two-factor Hull-White model – and then trained a deep learning algorithm (Feed-Forward Neural Network) to approximate the sensitivities of interest rate derivatives to the swap rates as a function of maturity, volatility and other parameters.

Machine Learning is gaining exponential momentum every day and its applications are increasing in every domain whether it be a trivial stock price prediction in finance domain or a complex task like detection and segmentation of Objects in Computer Vision Domain. No domain is left untouched by the AI revolution and in some domains, the Machine Learning Algorithms are even surpassing human-level performance. For example, ImageNet challenge was organised every year for various Computer Vision tasks like Image Classification, Object Detection, Image Localization etc and each year the error rates of the best-performing algorithms kept on decreasing and in 2017, 29 of 38 competing teams had greater than 95% accuracy. Human top-5 classification error rate on the large scale ImageNet dataset has been reported to be 5.1% whereas the state of the art CNN achieves an accuracy of about 3.57%. With the increasing performance of Machine Learning Systems, the interpretability of the systems are gradually decreasing. This trend is seen more in Deep Learning Algorithms comprising of millions of parameters and hundreds of layers making it extremely difficult to interpret them as compared to basic machine learning algorithms like Linear Regression, K Nearest Neighbours, Decision Tree etc.

Generative Adversarial Networks (GANs) have revolutionized high-fidelity image generation, making global headlines with their hyperrealistic portraits and content-swapping, while also raising concerns with convincing deepfake videos. Now, DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis. Text-to-Speech (TTS) is a process for converting text into a humanlike voice output. One of the most commonly used TTS network architectures is WaveNet, a neural autoregressive model for generating raw audio waveforms. But because WaveNet relies on the sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers.

This article is part of a series. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7 and Part 8! You can also read this article in Русский, Tiếng Việt or 한국어. Giant update: I've written a new book based on these articles! It not only expands and updates all my articles, but it has tons of brand new content and lots of hands-on coding projects. Almost as long as programmers have been writing computer programs, computer hackers have been figuring out ways to exploit those programs.