SAN JOSE, CA--(Marketwired - Apr 5, 2016) - GPU Technology Conference -- NVIDIA (NASDAQ: NVDA) today announced that it is a founding technology partner of the MGH Clinical Data Science Center, which aims to advance healthcare by applying the latest artificial intelligence techniques to improve the detection, diagnosis, treatment and management of diseases. Massachusetts General Hospital -- which conducts the largest hospital-based research program in the United States, and is the top-ranked hospital on this year's US News and World Report "Best Hospitals" list -- recently established the MGH Clinical Data Science Center in Boston. The center will train a deep neural network using Mass General's vast stores of phenotypic, genetics and imaging data. The hospital has a database containing some 10 billion medical images. To process this massive amount of data, the center will deploy the NVIDIA DGX-1 -- a server designed for AI applications, launched earlier today at the GPU Technology Conference -- and deep learning algorithms created by NVIDIA engineers and Mass General data scientists.

The field of artificial intelligence has experienced a striking spurt of progress in recent years, with software becoming much better at understanding images, speech, and new tasks such as how to play games. Now the company whose hardware has underpinned much of that progress has created a chip to keep it going. On Tuesday Nvidia announced a new chip called the Tesla P100 that's designed to put more power behind a technique called deep learning. This technique has produced recent major advances such as the Google software AlphaGo that defeated the world's top Go player last month (see "Five Lessons from AlphaGo's Historic Victory"). Deep learning involves passing data through large collections of crudely simulated neurons.

The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a "maxout" network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. This paper shows that adversarial training has a regularizing effect also in networks with logistic, hyperbolic tangent and rectified linear units. A simple extension to the back-propagation method is proposed, that adds an adversarial gradient to the training. The extension requires an additional forward and backward pass to calculate a modified input sample, or mini batch, used as input for standard back-propagation learning. The first experimental results on MNIST show that the "adversarial back-propagation" method increases the resistance to adversarial examples and boosts the classification performance. The extension reduces the classification error on the permutation invariant MNIST from 1.60% to 0.95% in a logistic network, and from 1.40% to 0.78% in a network with rectified linear units. Results on CIFAR-10 indicate that the method has a regularizing effect similar to dropout in fully connected networks. Based on these promising results, adversarial back-propagation is proposed as a stand-alone regularizing method that should be further investigated.

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

As artificial intelligence sweeps across the technology landscape, NVIDIA unveiled today at its annual GPU Technology Conference a series of new products and technologies focused on deep learning, virtual reality and self-driving cars.

The sophisticated neural networks underlying systems like Google's Deep Dream and all manner of interesting experiments require a great deal of computing power. NVIDIA proposes to put all that horsepower in a single box, specially engineered to meet the needs of AI researchers. NVIDIA already has GPUs specializing in deep learning applications, so this was a logical next step. It's called the DGX-1, and it's basically a fancy enclosure for an 8-GPU supercomputing cluster. There are 8 Tesla P100 cards in there with 16 GB of RAM each, plus 7 TB of storage for all the raw data you'll be training your networks on.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer that was announced today. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care. Massachusetts General Hospital runs the largest hospital-based research program in the United States, and is the top-ranked hospital on this year's U.S. News and World Report's "Best Hospitals" list. The center will train a deep neural network using Mass General's vast stores of phenotypic, genetics, and imaging data. The hospital has a database containing some 10 billion medical images. To do this, it will use the Nvidia DGX-1 -- a supercomputer designed for AI applications. Using AI, physicians can compare a patient's symptoms, tests, and history with insight from a vast population of other patients. Initially, the MGH Clinical Data Science Center will focus on the fields of radiology and pathology -- which are particularly rich in images and data -- and then expand into genomics and electronic health records. "We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation," said Keith J. Dreyer, vice chairman of Radiology at Mass General and executive director of the center, in a statement. "Guided by precision healthcare, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.

Were you able to attend this year's South by Southwest (SXSW)? As always, the annual convergence of anything relevant and compelling has ignited some amazing conversation. THE FUTURE OF BIG DATA AND AI, by far one of our favorite panels, brought to light persuasive commentary on the applications of AI and how data has renewed these functions. Being the retail aficionados that we are, we couldn't help but tie those topics right back to retail and eCommerce. Amongst the four panelist, Dr. Doug Lenant, CEO of Cycorp, affirmed that deep learning technology has been around for about 30 years.

Salesforce has acquired MetaMind, an AI startup company. With the two coming together, they will be able to offer customers AI solutions with capabilities that further automate and personalize customer support, marketing, and other business processes. Salesforce's data science capabilities will be extended by embedding deep learning within its platform.

The first GTC took place in a set of hotel ballrooms a few blocks away. That's up from 4,000 last year, a growth rate that's tracked pretty steady since the start of the show. The stage is about five feet off the ground. And on the vast screen is an NVIDIA-green moving image that, as it scans looks like a multi-level rendering of the brain's neural network. With some electronics thrown in between. A great many of those here, though, are scientists and analysts of the computational sort -- those who rely on NVIDIA GPUs to help them crunch the rising sea of data that's engulfing us. A lot are associated with universities, close to 200 of them. Virtually every one of the top 100 university comp sci departments are here. There are also hundreds of companies represented--certainly the dozens of major web-services companies that use artificial intelligence. But also industrials, oil and gas, retail. Err, less so this time. But folks don't seem to mind.