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AI is moving from its academic roots to the forefront of business and industry

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AI is moving from its academic roots to the forefront of business and industry

  1. 1. 1 HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING INDUSTRIES A SNEAK PEEK INTO THE FUTURE:
  2. 2. 2 TOPICS Where We Are with AI Today What Is Artificial Intelligence, ML and DL How Deep Learning Can Be Applied Industry Use Cases: Healthcare, Automotive, Finance, Retail How Do We Get Started?
  3. 3. 3 “Find where I parked my car” AI IS EVERYWHERE TOUCHING OUR LIVES “Find the bag I just saw in this magazine” “What movie should I watch next?”
  4. 4. 4Source: Gartner, “Architecting the On-Demand Digital Business”; Drue Reeves, Kyle Hilgendorf, Kirk Knoernschild, August 16, 2016
  5. 5. 5 DEFINITIONS
  6. 6. 6 GPU DEEP LEARNING IS A NEW COMPUTING MODEL TRADITIONAL APPROACH Requires domain experts Time consuming Error prone Not scalable to new problems Algorithms that learn from examples DEEP LEARNING APPROACH Learn from data Easily to extend Speedup with GPUs Expert Written Computer Program Car Vehicle Coupe Car Vehicle Coupe Deep Neural Network
  7. 7. 7 HEALTHCARE
  8. 8. 8 Every day, pathologists are tasked with providing definitive cancer diagnosis to guide patient treatment. However, keeping pace with the massive volume of data and the variety of analysis methods makes reliable predictions difficult. By combining GPU deep learning and CUDA with traditional pathology, PathAI’s approach is able to reduce error rates by 85% in breast cancer diagnosis. AI: HELPING DOCTORS DIAGNOSE BREAST CANCER
  9. 9. 9 AI SEES THE UNSEEN – COULD REDUCE THE NEED FOR BRAIN BIOPSIES Brain tumors can be spotted by today’s MRIs, but determining the right way to treat them requires information about the tumor’s genomic makeup — data that can only come from highly invasive brain biopsies. Researchers at the Mayo Clinic may have found another way. Using AI, Mayo discovered that the same genomic data can be found in the MRIs themselves, hidden from traditional analysis methods. Mayo used GPU-accelerated deep learning with CUDA to train its systems where to look and how to extract the information. The new system has greater than 90% accuracy and has the potential to greatly reduce the need for brain biopsies.
  10. 10. 10 RETAIL
  11. 11. 11 THE MODERN WAREHOUSE BUILT ON AI Worldwide retail e-commerce sales are expected to reach $2 trillion in 2016, according to eMarketer. With thousands of orders placed every hour, data scientists at Zalando, Europe’s leading online fashion retailer, applied deep learning and GPUs to develop the Optimal Cart Pick algorithm. Applying the algorithm resulted in an 11% decrease in workers’ travel time per item picked. The work is a good example of the efficiencies that AI can discover for e-commerce, manufacturing and other large-systems-based industries.
  12. 12. 12 AI-DRIVEN SMART SHOPPING According to Forrester E-Commerce was a $390B market in 2016 and is expected to double by 2024. E-commerce company Jet.com (acquired by Walmart) partners with multitudes of suppliers with different offerings at different prices. Jet uses GPU-accelerated AI to drive its smart cart solution that fulfills orders at the lowest prices though the smart bundling of supplier offers. The platform finds the ideal merchant and warehouse combination to lower the total order cost. The bigger the shopping cart, the greater the savings that can be generated.
  13. 13. 13 FINANCIAL SERVICES
  14. 14. 14 AI-DRIVEN ASSET MANGEMENT AI has led to break-through innovations across all industries and the finance industry is no exception. qplum, an online asset management firm, uses quantitative trading techniques and invests using data and GPU-powered deep learning. qplum blends the mathematics of data-driven decision-making, the science of behavioral economics, and the art of effective communications. In the speed trade category, qplum has been an innovation leader having started with a $10,000 risk limit and, over the last 10 years, making more than $1.4B in profits.
  15. 15. 15 AUTOMOTIVE
  16. 16. 16 Autonomous vehicles can reduce accidents, improve the productivity of trucks and taxis, and enable new mobility services — transforming the $10 trillion transportation industry. WEpods is piloting an autonomous shuttle that leverages GPUs to compute data and build a complete picture of the environment, enabling it to safely navigate traffic and other obstacles. It’s a revolutionary new kind of transportation that offers the convenience of a personal vehicle, without the hassles of car ownership. REVOLUTIONIZING TRANSPORTATION WITH AI
  17. 17. 17 Deep neural networks require a huge amount of computational power and tremendous amounts of data, which is particularly true with safety critical systems, like self-driving cars, where detection accuracy requirements are extremely high. Zenuity is tackling this with the combined power of DGX-1 and FlashBlade, which is enabling them to make ground- breaking progress in reducing training run intervals, to the extent that they expect to be able to iterate on their models. DEVELOPING THE VEHICLES OF THE FUTURE
  18. 18. 18 AIRI: AI-READY INFRASTRUCTURE 18 • NVIDIA DGX-1 | 4x DGX-1 Systems | 4 PFLOPS • PURE FLASHBLADE™ | 15x 17TB Blades | 1.5M IOPS • ARISTA | 2x 100Gb Ethernet Switches with RDMA • NVIDIA GPU CLOUD DEEP LEARNING STACK | NVIDIA Optimized Frameworks • AIRI SCALING TOOLKIT | Multi-node Training Made Simple HARDWARE SOFTWARE Extending the power of DGX-1 at-scale in every enterprise
  19. 19. 19 HOW TO GET STARTED
  20. 20. 20 DO YOU HAVE ENOUGH LABELED DATA? The Achilles heel of deep learning: You need a lot of labeled data. Based on a presentation from Bryan Catanzaro Without a large dataset, deep learning isn’t likely to succeed. Labels:  Getting someone to decide the “right” answer can be hard (think about medical imaging)  If a dataset requires skilled labor to produce labels, this limits scale / affects the cost
  21. 21. 21 DO YOU HAVE ENOUGH LABELED DATA? “As of 2016, a rough rule of thumb is that a supervised deep learning algorithm will generally achieve acceptable performance with around 5,000 labeled examples per category, and will match or exceed human performance when trained with a dataset containing at least 10 million labeled examples.” Ian Goodfellow, Yoshua Bengio, Aaron Courville How much data is enough? Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
  22. 22. 22 WHAT LEVEL OF ACCURACY DO YOU NEED? How much accuracy you need? (mortgage risk calculation - high, celebrity portal - low) Aim for lowest acceptable for the product What is the measure: • Accuracy (% correct) • Coverage (% of examples processed) • Precision (% of detections that are right) • Recall (% of objects that are detected) • Amount of error (for regression problems) • What protective mechanisms to you need to safeguard the system from unavoidable prediction error? Defining and measuring accuracy Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
  23. 23. 23 BEST PRACTICE FOR STARTING A DL PROJECT Hypothesis for the business outcome you believe DL can solve Current, needed Data – enough to train? Current AI & DL skills People training plan Current IT Infrastructure (Cloud, On-premise) ASSESS DESIGN & SELECT LEARN DEPLOY Analyze data to train (e.g. text, video, images, structure) Plan research (Data Scientist) & deployment models (IT Architect) Select DNN Network, Libraries & Frameworks Begin training Feedback on outputs so the network can learn Achieve training state that provides actionable data for business decisions Performance monitoring Optimization of trained DNN for deployment performance Move trained outcomes to inferencing platform Begin inferencing (e.g. search, speak, translate, classify, segment, predict, recommend) Expand DL Training to adjacent areas Performance monitoring
  24. 24. 24 CLOUD, ON-PREMISE OR HYBRID? Cloud Pre-trained models Ease of integration into your app development Cloud scale & efficiency Cloud billing On – Premise Instant productivity Desktop to data center Tuned /optimized perf. Data security Hybrid Any compute environment Common software stack Flexibility (e.g. train local, inference in cloud)
  25. 25. 25 BE READY FOR THE RACE FOR TALENT • Freedom, flexibility and challenges attract talents • Provide great tools and infrastructure • Data Science + Business + IT have to partner together
  26. 26. 26 DEEP LEARNING INSTITUTE DLI Mission: Help the world to solve the most challenging problems using AI and deep learning We help developers, data scientists and engineers to get started in architecting, optimizing, and deploying neural networks to solve real-world problems in diverse industries such as autonomous vehicles, healthcare, robotics, media & entertainment and game development. https://www.nvidia.co.uk/deep-learning-ai/education/
  27. 27. Charlotte Han charlotteh@nvidia.com @sunsiren

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