Today at the 2017 GPU Technology Conference in San Jose, NVIDIA CEO Jen-Hsun Huang announced the new NVIDIA Tesla V100, the most advanced accelerator ever built. From recognizing speech to training virtual personal assistants to converse naturally; from detecting lanes on the road to teaching autonomous cars to drive; data scientists are taking on increasingly complex challenges with AI. Solving these kinds of problems requires training exponentially more complex deep learning models in a practical amount of time. HPC is a fundamental pillar of modern science. From predicting weather, to discovering drugs, to finding new energy sources, researchers use large computing systems to simulate and predict our world. AI extends traditional HPC by allowing researchers to analyze large volumes of data for rapid insights where simulation alone cannot fully predict the real world.

More overlooked machine learning and/or machine learning-related projects? Let's do without the wordy intro this time, and simply say that projects are selected by no criteria other than they have caught my eye over time; really, any objective set of criteria would not work in this case. The only requriements are that the projects are open source and have Github repositories. So here they are: 5 more... well, you know the rest. You may also remember that they are numbered not our of necessity related to relative ordering, but because my therapist suggests it's the best way to deal with my phobia of non-numbered lists.

You won't need to buy a rack of 400 servers if you have one high-powered Nvidia DGX-1 supercomputer with a Volta GPU sitting on your desktop. The DGX-1 supercomputer -- which looks like a regular rack server -- gets most of its computing power from eight Tesla V100 GPUs. The GPU, the first one based on the brand-new Volta architecture, was introduced at the company's GPU Technology Conference in San Jose, California, on Wednesday. "It comes out of the box, plug it in and go to work," said Nvidia's CEO Jen-Hsun Huang during a keynote speech. But the DGX-1 with Tesla V100 computer is expensive.

The first is an exciting new Volta-based GPU instance that we think will completely change the face of the AI developer world through a 3x speedup on LSTM training. Second, we are announcing plans to train 100,000 developers through the Deep Learning Institute (DLI) running on AWS. The third is the joint development of tools that enable large-scale deep learning for the broader developer community. AWS is also delivering sessions at GTC including using Apache MXNet training at scale on Amazon EC2 P2 instances and at the edge through the support of NVIDIA's Jetson TX2 platform. The Tesla V100, based on the Volta architecture and equipped with 640 Tensor Cores, provides breakthrough performance of 120 teraflops of mixed precision deep learning performance.

A few years ago, it was hard to think of a commonly used technology tool that used AI. In a few years, it will be hard to imagine any technology that doesn't tap into the power of AI. Thanks to the convergence of three major forces -- increased computing power in the cloud, powerful algorithms that run on deep neural networks and access to massive amounts of data -- we're finally able to realize the dream of AI. AI now has the potential to disrupt every single vertical industry, like banking or retail, and every single business process, from sales and marketing to HR and recruiting. Along the way, AI also promises to amplify our endless reserves of human ingenuity -- to augment our capabilities as people and help us be more productive.

I put together an in-depth tutorial to explain Transforms (Data Augmentation), the Dataset class, and the DataLoader class in Pytorch. I also show a ton of use cases for different transforms applied on Grayscale and Color images, along with Segmentation datasets where the same transform should be applied to both the input and target images. I show how to do Affine transforms (rotation, translation, shear, zoom), some awesome Image-based transforms (saturation, brightness, contrast, gamma, grayscale). These transforms can be applied with pre-determined settings or randomly sampled from a range of values. I also show some cool utility transforms like type casting, converting to tensors, and going from CHW to HWC.

Three of the most notable technological innovations facing enterprises today are artificial intelligence (AI), the Internet of Things (IoT), and blockchain. And while many organizations are awakening to these (and other) technological concepts and capabilities, few implementations are focused on the convergence between them. How will the convergence of emerging data analytics techniques, connected devices/infrastructure, and distributed database architectures manifest? What follows are four examples of companies exploring applications involving the intersection of these technologies. IBM is currently working on the intersection between AI, blockchain, and the IoT in projects and experimentations that combine these three areas for comprehensive device lifecycle management.

Language translation is important to Facebook's mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language -- all at the highest possible accuracy and speed. Today, the Facebook Artificial Intelligence Research (FAIR) team published research results using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems.1 Additionally, the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub so that other researchers can build custom models for translation, text summarization, and other tasks. Originally developed by Yann LeCun decades ago, CNNs have been very successful in several machine learning fields, such as image processing. However, recurrent neural networks (RNNs) are the incumbent technology for text applications and have been the top choice for language translation because of their high accuracy. Though RNNs have historically outperformed CNNs at language translation tasks, their design has an inherent limitation, which can be understood by looking at how they process information.

Deep learning is coming to 3D design. The company's 3DExperience Platform is used for everything from Tesla cars and Boeing airplanes to Procter & Gamble consumer products. "What people have done with deep learning for image and speech recognition, we're doing to transform how products are designed and experienced," said Patrick Johnson, vice president for corporate research at the 3D modeling software company. Traditionally, designers and engineers had to start each new product from scratch. With NVIDIA GPUs and deep learning, Dassault will soon be able to harness the history of previous designs.

Before putting the deep learning system into production recently, Twitter was using less computationally intensive machine learning methods such as decision trees and logistical regression, Twitter software engineers Nicolas Koumchatzky and Anton Andryeyev wrote in a blog post. For its 328 million monthly active users, the company is evaluating and scoring thousands of tweets per second to determine what's worth recommending in timelines, taking into consideration an increasing number of factors, including whether tweets contain images or videos, the number of retweets and likes, and your previous interactions with other account holders, Koumchatzky and Andryeyev wrote. "Online experiments have also shown significant increases in metrics such as Tweet engagement, and time spent on the platform," the engineers wrote. For the past two years Twitter stock has stayed below $40 per share after closing at $44.90 on its first day of trading in 2013. In the past year some members of Twitter's Cortex A.I. research group have left; in March the group's technology lead, Clément Farabet, took a job at Nvidia.