15Mar

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. The complete insideBIGDATA Guide to Deep Learning & Artificial Intelligence is available for download from the insideBIGDATA White Paper Library.

Deep Learning and AI Success Stories

This section highlights a number of compelling use case examples focusing on the use of AI and deep learning for the solution of important problems across a wide spectrum of domains. The examples illustrate how GPUs can be effectively combined with AI technology. The visualization below show the rapid growth in number of organizations engaged with AI and deep learning in just two years’ time.

AI-Powered Healthcare at Scale

A number of use case examples of AI can be seen in the healthcare field. The following examples demonstrate AI’s flexibility across many problem domains.

  • AI platform to accelerate cancer research – To speed advances in the fight against cancer, the Cancer Moonshot initiative unites the Department of  Energy (DOE), the National Cancer Institute (NCI) and other agencies with researchers at Oak Ridge, Lawrence Livermore, Argonne, and Los Alamos  National Laboratories. NVIDIA is collaborating with the labs to help accelerate their AI framework called CANDLE as a common discovery platform,  with the goal of achieving 10X annual increases in productivity for cancer researchers. This exascale framework will make it possible for scientists and  researchers to use deep learning as well as computational sciences to address the urgent challenges of fighting cancer. The new NVIDIA DGX  SATURNV supercomputer will help in this regard.
  • Accelerating drug discoveries with AI – New drugs typically take 12 to 14 years and $2.6 billion to bring to market. BenevolentAI is using GPU deep  learning to bring new therapies to market quickly and more affordably. They’ve automated the process of identifying patterns within large amounts of research data, enabling scientists to form hypotheses and draw conclusions quicker than any human researcher could. For example, using the NVIDIA DGX-1 AI supercomputer, two potential drug targets for Alzheimer’s were identified in less than one month.
  • AI advances the fight against breast cancer – Breast cancer is the second leading cause of cancer death for women worldwide. Genomic tests help  doctors determine a cancer’s aggressiveness so they can prescribe appropriate treatment. But testing is expensive, tissue-destructive, and takes 10 to  14 days. Case Western Reserve is using GPU deep learning to develop an automated assessment of cancer risk at 1/20 the cost of current genomic  tests.
  • AI predicts and prevents disease – GPU deep learning is giving doctors a life-saving edge by identifying high-risk patients before diseases are  diagnosed. Icahn School of Medicine at Mount Sinai built an AI-powered tool, “Deep Patient,” based on NVIDIA GPUs and the CUDA programming  model. Deep Patient can analyze a patient’s medical history to predict nearly 80 diseases up to 1 year prior to onset.

AI-Powered Weather Forecasting

Weather forecasting involves processing vast amounts of data to derive predictions that can save lives and protect property. Colorful Clouds is using GPU computing and AI to process, predict, and communicate weather and air-quality conditions quickly through a new generation forecasting and  reporting tool which, unlike traditional tools, provides individual location based real-time forecasts with extremely high accuracy. Moving from CPUs  to GPUs was able to speed the processing of data by 30-50x.

AI Accelerated Cyber Defense

Our daily life, economic vitality, and national security depend on a stable, safe and resilient cyberspace. But attacks on IT systems are becoming more  complex and relentless, resulting in loss of information and money and disruptions to essential services. Accenture’s dedicated cyber security lab uses  NVIDIA GPUs, CUDA libraries, and machine learning to accelerate the analysis and visualization of 200M-300M alerts daily so analysts can take timely action.

Defending the Planet with AI

The U.S. government’s Asteroid Grand Challenge seeks to identify asteroid threats to human populations. The team at NASA Frontier Development  Labs picked up the challenge by employing GPU powered AI & deep learning to identify threats and their unique characteristics. The resulting  “Deflector Selector” achieved a 98% success rate in determining which technology produced the most successful deflection.

Summary

AI is transforming the entire world of technology, but AI isn’t new. It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics technologies. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data.

In this guide, we’ve taken a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it  possible. We also explained the difference between AI, machine learning and deep learning, and examined the intersection of AI and HPC.

We presented the results of a recent insideBIGDATA survey, “insideHPC / insideBIGDATA AI/Deep Learning Survey 2016,” to see how well these new technologies are being received. The results showed that organizations are making significant inroads in using AI to solve real-life problems, especially in the enterprise.

Finally, we took a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. We strived to highlight real examples where people are getting impacted by AI, and the fact that early adopters have been very successful in what they set out to  achieve where it did not take a lot for this success to be attained. This is an incentive for organizations to try out AI and deep learning to solve their problems.

At first glance, when looking out over the global business landscape, some companies might be considered as “under-investing” in computer systems  for AI. But maybe this observation could be more of a long-term investment strategy, since you have to do a lot of work to make AI solutions more  productive. It is possible that some companies are still learning how to make more long-term rather than short-term investments in technology.

AI is an amazing tool set that is helping people create exciting applications and creating new ways to service customers, cure diseases, prevent security threats, and much more. Rapid progress continues to unlock more and more opportunities for enterprises and scientific research where AI can make a  big impact. Many believe that the real world potential for AI is highly promising. Speaking at a 2016 AI conference in London, Microsoft’s Chief  Envisioning Officer, Dave Coplin observed “This technology will change how we relate to technology. It will change how we relate to each other. I  would argue that it will even change how we perceive what it means to be human.” Apparently, the best is still to come.

The complete insideBIGDATA Guide to Deep Learning & Artificial Intelligence is available for download from the insideBIGDATA White Paper Library, courtesy of NVIDIA.

Photo Cred: www.rafaelgrossmann.com

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