Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World San Francisco
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This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline. Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
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Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World San Francisco
- 1. Session Title Name Title Company Social Media (LinkedIn / Twitter) #H2OWORLD Data Engineering Lifecycle on IA Meg Mude Solutions Architecture, Intel @megmyname www.Linkedin.com/in/megmude Software.intel.com
- 3. Today is a big day... Announcing Blue Danube https://www.prnewswire.com/news-releases/h2oai-teams-up-with-intel-to-drive-an-ai-transformation-in-the-enterprise-300789659.html
- 4. 46% of Chief Information Officers (CIOs) have developed plans to implement AI, but only 4%have implemented AI so far. According to a recent Gartner survey… Ai adoption is nascent Source: Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence. February 2018 (https://www.gartner.com/newsroom/id/3856163)
- 5. Consider how the brain processes data, Endless, enormous quantities of data… and delivers useful insights.
- 6. 4 TButonomous vehicle 5 TBNECTED AIRPLANE 1 PBSmart Factory 1.5 GBrage internet user 750 pBoud video Provider Daily By 2020 Source: Amalgamation of analyst data and Intel analysis. Business Insights Operational Insights Security Insights The deluge of data
- 7. Artificial I ntelligence is the ability of machines to learn from experience, without explicit programming, in order to perform cognitive functions associated with the human mind Artificial Intelligence Machine learning Algorithms whose performance improve as they are exposed to more data over time Deep learning Subset of machine learning in which multi-layered neural networks learn from vast amounts of data Analytics
- 8. Consumer Health Finance Retail Governme nt Energy Transport Industrial Other Smart Assistants Chatbots Search Personalization Augmented Reality Robots Enhanced Diagnostics Drug Discovery Patient Care Research Sensory Aids Algorithmic Trading Fraud Detection Research Personal Finance Risk Mitigation Support Experience Marketing Merchandising Loyalty Supply Chain Security Defense Data Insights Safety & Security Resident Engagement Smarter Cities Oil & Gas Exploration Smart Grid Operational Improvement Conservation Autonomous Cars Automated Trucking Aerospace Shipping Search & Rescue Factory Automation Predictive Maintenance Precision Agriculture Field Automation Advertising Education Gaming Professional & IT Services Telco/Media Sports Source: Intel forecast AI will transform…everything
- 9. The AI lifecycle 2. Approach Team breaks down the defined business problem into workable steps to translate the right data to achieve results 3. Expertise A team of management sponsors, data scientists, data engineers, solution architects, and domain experts identifies the right data and works to translate the data to achieve results 4. Philosophy Team embraces fail-fast continuous improvement practices to evaluate their success in translating data to achieve results 5. Source Data Team understands and obtains the right data that explains the business problem to achieve results 6. Infrastructure Organization secures hardware and software infrastructure that supports data processing in a timely manner 7. Organization Organization embraces data insights, sponsors properly resourced teams, and prioritizes analytic development work 1. Define the Challenge
- 10. hardwareMulti-purpose to purpose-built AI compute from cloud to device solutions Partner ecosystem to facilitate AI in finance, health, retail, industrial & more Intel analytics ecosystem to get your data ready Data Driving AI forward through R&D, investments and policy Future tools Software to accelerate development and deployment of real solutions Bring Your AI Vision to Life Using Our Extensive Portfolio
- 11. Edge Device ARTIFICIAL INTELLIGENCE Platforms Finance Healthcare Energy Industrial Transport Retail Home More… Data Center TOOLKIT S App Developers libraries Data Scientists foundatio n Library Developers * * * * FOR * * * * Hardware IT System Architects Solution s Solution Architects AI Solutions Catalog (Public & Internal) DEEP LEARNING ACCELERATORS Inference DEEP LEARNING DEPLOYMENT OpenVINO™ † Intel® Movidius™ SDK Open Visual Inference & Neural Network Optimization toolkit for inference deployment on CPU, processor graphics, FPGA & VPU using TF, Caffe* & MXNet* Optimized inference deployment for all Intel® Movidius™ VPUs using TensorFlow* & Caffe* DEEP LEARNING FRAMEWORKS Now optimized for CPU Optimizations in progress TensorFlow* MXNet* Caffe* BigDL/Spark* Caffe2* PyTorch* PaddlePaddle* DEEP LEARNING Intel® Deep Learning Studio‡ Open-source tool to compress deep learning development cycle MACHINE LEARNING LIBRARIES Python R Distributed •Scikit-learn •Pandas •NumPy •Cart •RandomF orest •e1071 •MlLib (on Spark) •Mahout ANALYTICS, MACHINE & DEEP LEARNING PRIMITIVES Python DAAL MKL-DNN Intel distribution optimized for machine learning Intel® Data Analytics Acceleration Library (for machine learning) Open-source deep neural network functions for CPU, processor graphics DEEP LEARNING GRAPH COMPILER Intel® nGraph™ Compiler (Alpha) Open-sourced compiler for deep learning model computations optimized for multiple devices (CPU, GPU, NNP) using multiple frameworks (TF, MXNet, ONNX) AI FOUNDATION A R T I F I C I A l I N T E L L I G E n C e NNP L-1000 * * * * Ai.intel.com † Formerly the Intel® Computer Vision SDK *Other names and brands may be claimed as the property of others. All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
- 12. • Typical HPC Cluster • Typical Cloud setup One Example - Intel® Xeon® Based Clusters
- 13. Customer Testimonials “Thanks to Intel OpenVino toolkit, that Learning Factory can now deliver the expected SLA of less than 1 second for inferencing all 3 X-ray models that we were targeting! I can’t believe, all the above happened in almost a month since this effort got started! – Aruna Narayanan, GE Healthcare AI DL Platform” – Aug 2018 (1) “Taboola ended up sticking with Intel for reasons of speed and cost, said Ariel Pisetzky, the company's vice president of information technology. Nvidia's chip was far faster, but time spent shuffling data back and forth to the chip negated the gains, Pisetzky said. Second, Intel dispatched engineers to help Taboola tweak its computer code so that the same servers could handle more than twice as many requests. – Ariel Pisetzky, VP Taboola IT” July 2018 (3) Intel and Philips achieved a speed improvement of 188 times for the bone-age-prediction model, and a 38 times speed improvement for the lung-segmentation model over the baseline measurements. Vijayananda J., chief architect and fellow, Data Science and AI at Philips HealthSuite Insights July 2018 (4) “With PaddlePaddle now optimized for Intel Xeon Scalable processors, developers and data scientists can now use the same hardware that powers the world’s data centers and clouds to advance their AI algorithms.” – Jul 2018 (5) The CERN team demonstrated that AI-based models have the potential to act as orders-of-magnitude-faster replacements for computationally expensive tasks in simulation, while maintaining a remarkable level of accuracy. Dr. Federico Carminati, Gul Rukh Khattak, and Dr. Sofia Vallecorsa at CERN, as well as Jean-Roch Vlimant at Caltech. The work is part of a CERN openlab project in collaboration with Intel Corporation, who partially funded the endeavor through the Intel Parallel Computing Center (IPCC) program” – Aug 2018 (2) “The collaboration team with representatives from Novartis and Intel have shown more than 6X improvement in the time to process a dataset of 10K images for training. Using the Broad Bio-image Benchmark Collection* 021 (BBBC-021) dataset, the team has achieved a total processing time of 31 minutes with over 99 percent accuracy.” May 2018 (6) 1) https://newsroom.intel.com/articles/solve-healthcare-intel-partners-demonstrate-real-uses-artificial-intelligence-healthcare/ 2) https://www.hpcwire.com/2018/08/14/cern-incorporates-ai-into-physics-based-simulations/ 3) https://www.reuters.com/article/us-nvidia- intel/as-nvidia-expands-in-artificial-intelligence-intel-defends-turf-idUSKBN1L2051 4) https://venturebeat-com.cdn.ampproject.org/c/s/venturebeat.com/2018/08/14/intel-and-philips-use-xeon-chips-to-speed-up-ai-medical-scan-analysis/amp/ 5) https://newsroom.intel.com/news/intel-ai-baidu-create-ai-camera-fpga-based-acceleration-xeon-scalable-optimizations-deep-learning/ 6) https://newsroom.intel.com/news/using-deep-neural-network-acceleration-image-analysis-drug-discovery/ (7) For more information, see http://aidc.gallery.video/detail/videos/china:-keynotes/video/5977039606001/large-scale-deep-learning-applications-at-baidu-and-open-source-ai-framework-paddlepaddle?autoStart=true “Machine learning is a big part of our heritage. IT works on GPUs today, but it also works on instances powered by highly customized Intel Xeon Processors” – Bratin Saha, VP & GM Machine Learning Platforms, Amazon AI - Amazon “Inference is one thing we do, but we do lots more. That’s why flexibility is really essential” – Kim Hazelwood, Head of AI Infrastructure Foundation, Facebook Public Philips “We rely heavily on Intel Xeon processors for deep learning training and inference workloads at Baidu” – Dianhai Yu, Tech leader of Baidu PaddlePaddle (7) Baidu Customers Internal Baidu
- 14. Intel® Confidential. For Internal Use ONLY Intel works with customers across the entire AI lifecycle TIME- TO- SOLUTI ON Opportunity Hypotheses Data Modeling Deployment Iteration Evaluation 15% 15% 23% 15% 15% 8% 8% Experiment with Topologies Tune Hyper- parameters Share ResultsLabel Data Load Data Augment Data Support Inference Compute-intensiveLabor-intensive Labor-intensive Proof of concept Training Source Data Scale & Deploy Inference Scale & Deploy inference within broader application 15% 15% 23% 15% 15% 8% 8% Dev Cycle … Build, Deploy & Scale AI customer example The complete analytics pipeline
- 15. 15 Results 188X & 38x increase Client: Philips, a worldwide leader in healthcare products for consumers, patients, providers and caregivers across the health continuum. Challenge: AI for medicalimagingischallenging becausetheinformationis often high-resolutionand multi-dimensional. Down-samplingimagesto lower resolutionsdueto memory constraintscan cause misdiagnoses.Philips’goalis to offerAI to its end customers withoutsignificantlyincreasingthe cost of the customers’systems,and withoutrequiring modificationsto the hardwaredeployed in the field. Solution: Philips and Intel tested two healthcare use cases for deep learning inference, models: one on X-rays of bones for bone-age-prediction modeling, and the other on CT scans of lungs for lung segmentation. The solution took advantage of efficient multi-core processing Intel Xeon® Scalable processors, along with the OpenVINO™ toolkit. Intel® Distribution for OpenVINO™ In inference performance over baseline (images per second) for a 2S Intel® Xeon® Scalable 8168 processor Bone age prediction model Lung segmentation model *Other names and brands may be claimed as the property of others. Configuration: 2-socket Intel® Xeon® Platinum 8168 processor, 2.70Ghz, HT OFF ,Total Memory 192 GB (2666 MHz), Ubuntu 18.04.1 LTS (GNU/Linux 4.15.0-29-generic x86_64*), BIOS: SE5C620.86B.0D.01.0010.072020182008, Intel Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) v0.14. Source: https://ai.intel.com/ai/wp-content/uploads/sites/69/Intel-PhilipsAIHealthcare-CaseStudy-FinalV2-withquote.pdf Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit http://www.intel.com/performance. Performance results are based on testing as of August 2018 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. White paper: https://ai.intel.com/ai/wp-content/uploads/sites/69/Intel-PhilipsAIHealthcare-CaseStudy-FinalV2-withquote.pdf Video: https://ai.intel.com/videos/philips/ Public
- 16. 16 LINKS: Blog: Montefiore Health System Improves Patient Outcomes and Healthcare Efficiency with Semantic Data Lake and Artificial Intelligence Powered by Intel Technologies Research brief: https://ai.intel.com/ai/wp-content/uploads/sites/69/montefiore-in-ai-case-study.pdf Client: Montefiore, a premier academic health system in the Bronx, NY, which has implemented a Patient-centered Analytical Machine Learning (PALM) platform Challenge: Risk stratification across a patient population. For example, determining which patients are at risk of respiratory failure, and subsequent intubation (which significantly diminishes the odds of a positive outcome). A robust and scalable intelligent healthcare system is needed, where models will need to be built on data coming from a variety of sources (traditional databases or in newer unstructured data stores), while still complying with privacy regulations. Solution: The PALM platform, which can tap into a myriad of data stores, regardless of where the information is located or how it is structured. PALM is powered by Intel® Xeon® Scalable Gold processors and Intel® Optane™ SSDs, and was first deployed help identify patients at risk for respiratory failure. This improved patient outcomes and lowered costs, and is already starting to apply PALM to a variety of other projects. Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate. Result “With Intel’s solutions for AI, all of [these AI capabilities] can occur on the same architecture already in use for so many other traditional enterprise activities, increasing efficiency and improving time to value.” Public
- 17. Intel® Confidential. For Internal Use ONLY Sample End-to-End Solution 7 Complementary Public Cloud/Private Cloud Sensors logs Messages Smart Machines Transaction logs Source Data Sourcing and Collection (Examples) Storage Processing + Analysis Kafka Sqoop Spark Streaming Storm/Heron Informatica, DataDtage Object Storage RDS In-memory Cache MPP DB k/v storage SAP HANA Elastic Search/Solr Spark/BigDL Impala/Presto ML/deep learning Consumable, Visualized and Syndicated Data / Information Add Arcadia Data • Apache spark and apache • * Native visualization stacks Post-processing Ingest
- 18. Intel® Confidential. For Internal Use ONLY 8 Examples of Pipelines
- 19. AI Is the driving force The path to deeper insight Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Cognitive Analytics Foresight What Will Happen, When, and Why Hindsight What Happened? Insight What Happened and Why? Forecast How Should I Proceed? Self-Learning How Do I Proceed?
- 20. Thank You! Learn more @: Software.intel.com Ai.intel.com Contact: meg.mude@intel.com; @megmyname
Editor's Notes
- AI adoption is nascent – just coming into existence, but demonstrating enormous potential moving forward. Forrester surveyed business and technology professionals and found that 58% of them were researching AI, but only 12% were using AI systems. This gap reflects growing interest in AI, but little actual use in practice. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. AI technologies will increasingly be rapidly assimilated into analytics practices, giving business users unprecedented access to powerful insights that drive action. In 2018 and beyond, expect the flood gates to open even further, driven by the business’ voracious appetite for deeper contextual insights.
- The business imperative for AI is firmly rooted in data. Data is the currency of the future. By 2020, we expect over 50 billion devices and 200 billion sensors to join the internet, and this hug explosion of smart & connected devices will lead to a ton of data being generated. Even just by 2020, as you can see in this slide, we’re talking about huge volumes of data. This data contains extremely valuable insights, in business, operations and security that we really want to extract. In order to extract that data, we need help, and analytics & AI are tools in our toolbag that will help us extract value from this treasure trove of data. ------ BACKUP INFO BELOW ------ The “People Devices” we’re all familiar with, PCs, tablets, phones, will remain an important part of the Intel and we will continue to invest and maximize returns in these businesses Moving forward, these “people devices” are welcoming billions and billions of things to the internet By 2020, 50B devices and 212B sensors will join the internet At this point, 47% of total devices and connection will be Machine to Machine Truly the rise of the machines… These “things” will generate tremendous amounts of data Consider this… In 2020, it is expected that the average internet user will generate ~1.5 GB of traffic per day (Up from ~650MB in 2015) Certainly a huge amount of data… until you consider the machines… A Smart Hospital will generate 3,000 GB/day Self-driving cars are generating over 4,000 GB/day… each A connected plane will generate 5,000 gigabytes per day A connected factory will generate 1 million gigabytes per day This data, will need to be analyzed and interpreted in real time Intel’s technology makes this possible, in order to unlock: Operational insight – optimized efficiencies can lead to lower operational costs and higher quality Business insishgt – understanding market needs/drivers can lead to more predictable outcomes and new opportunities Security insight – recognizing behaviors and predicting vulnerabilities can lead to better protected IP and security planning http://www.cisco.com/c/en/us/solutions/service-provider/vni-network-traffic-forecast/infographic.html http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.html https://datafloq.com/read/self-driving-cars-create-2-petabytes-data-annually/172 http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.html http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.html
- The definition of “Artificial Intelligence” is continually evolving, but at its core, AI is about machines mimicking (and/or exceeding) cognitive functions associated with the human mind. In the universe of AI, which includes many different approaches, data-centric machine learning has emerged as a leader due to its increasing ability to tackle the three main AI sub-tasks: perception, planning/reasoning and control. Ultimately, AI is achieved through the fusion of multiple approaches to deliver ever more intelligent machines, and the nexus of AI developments in the near-future is centered on deep learning, with other approaches all playing important roles – depending on the dataset, problem, and unique requirements.
- Which industries are the earliest adopters of AI? Generally, those segments with clear use cases, high purchasing power, and high rewards for making decisions quickly and/or more accurately will adopt AI fastest. Here are the segments that we believe will lead AI through 2020, ordered roughly by market opportunity (earliest at left). Consumer Smart Assistants – personal assistant that anticipates, optimizes, automates daily life (e.g. Amazon Alexa, Apple Siri, Google Assistant, Microsoft Cortana, Facebook Jarvis home automation, X.ai virtual assistant Amy) Chatbots – 24/7/365 no waiting access to an informative or helpful agent (e.g. WeChat, Bank of America, Uber, Pizza Hut, Alaska Airlines, Amtrak, etc.) Search – ability to more intelligently search more data types including image, video, context, etc (e.g. Improved Google search, Google Photos, ReSnap) Personalization – ability to automatically adjust content/recommendations to suit individuals (e.g. Entefy, Netflix recommendation engine, Amazon personalized shopping recommendations) Augmented Reality – overlay information on our field of view in real-time to identify interesting or undesirable things (e.g. Intel Project Alloy, Google Translate using smartphone camera) Robots – personal robots that are able to perform household, yard, or other chores (e.g. Jibo robot for day-to-day functions, Roomba follow-ons) Health (SME: Kristina Kermanshahche, Ketan Paranjape) Enhanced Diagnosis – a tool for doctors to augment their own diagnosis with more data, experience, precision and accuracy (e.g. radiology image analysis, Journal of American Medicine Association paper on retina scan for diabetic retinopathy, skin lesion classification to recognize melanoma with 98% accuracy, medical history scraping, treatment outcome prediction) Drug Discovery – computational drug discovery that intelligently hones in on the most promising treatments (e.g. speeding pharma drug development) Patient Care – machines that aid with monitoring, treatment, and/or recovery of patients (e.g. visual patient monitoring, autonomous robotic surgery, friendly medication and/or physical therapy robots) Research – instantly sifting through hundreds of new research papers and clinical trials that are published each day to make new connections (e.g. AI at University of North Carolina’s Lineberger Comprehensive Cancer Center) Sensory Aids – filling in for various senses that are absent or challenged (e.g. visual aid, audio aid) Finance (SME: Robert Geva) Algorithmic Trading – augment rule-based algorithmic trading models and data sources using AI (e.g. Kensho analysis of myriad data to predict stock movement) Fraud Detection – ability to identify fraudulent transactions and/or claims (e.g. USAA identifies insurance fraud) Research – ability to intelligently assemble, parse, and extract meaning from troves of data that influence asset prices (e.g. Quid, FSI firm reducing time to insight for portfolio managers through smart knowledge management system) Personal Finance – smarter recommendations, lower risk lending, greater efficiency (e.g. active portfolio recommendations, quickly parsing more data before issuing loan, automatic reading of check scans, etc.) Risk Mitigation – detect risk factors and/or reduce the burden of regulation and minimize errors through automated compliance (e.g. IBM+Promontory Financial Group using natural language processing to detect excursions) Retail (SME: Janet Kerby, Chris Hunt) Support – bots providing shopping, ordering and support in lifelike interaction (e.g. My Starbucks Barista, KLM Dutch Airline customer support via social media, Nieman Marcus visual search, Pizza Hut order pizza via bot, Adobe Digital’s digital mirror that recommends clothes, intelligent phone menu routing based on NLP, ViSenze recommending similar items based on image, Adobe Digital’s digital mirror that recommends clothes) Experience – deliver winning consumer experiences in-store (e.g. Amazon Go checkout-free grocery store, Macy’s mobile shopping assistant, Lowes Lowebots that roam stores answering simple questions and tracking inventory) Marketing – precision marketing to consumers, promoting products and services how and where they want to hear (e.g. North Face “Expert Personal Shopper” on website) Merchandising – better planning through accelerated and expanded insight into consumer buying patterns (e.g. Stitch Fix virtual styling, Skechers.com analyzing clicks in real-time to bring similar catalog items forward, Wal-mart pairing products that sell together, Cosabella evolutionary website tweaks) Loyalty – transform the consumer experience through segmentation (e.g. Under Armour health app that constantly collects user data to deliver personalized fitness recommendations) Supply Chain – optimize the supply chain and inventory management for efficiency and innovate new business models (e.g. OnProcess technology’s use of predictive analytics for inventory management) Security – improve security of all consumer and business digital assets, such as real-time shoplifting/lifter detection, multi-factor identity verification, data breach detection (e.g. Mastercard pay with your face, Walmart facial recognition to catch shoplifters) Government (SME: Harris Joyce) Defense – drones, connected soldiers, defense strategy (e.g. military/surveillance drones, autonomous rescue vehicles, augmented connected soldier, real-time threat assessment and strategy recommendation) Data Insights – analyze massive amounts of data to identify opportunities/inefficiencies in bureaucracy, cybersecurity threats and more, to ultimately implement better systems and policies (e.g. MIT AI that detects cyber security threats) Crime Preventionusing AI to predict and help recover from disasters thanks to ability to quickly process large amounts of unstructured data and optimize limited resources (e.g. 1Concern, BlueLineGrid) Safety & Security – crowd analytics, behavioral/sentiment analytics, social media analytics, face/vehicle recognition, online identity recognition, real-time video analytics, using AI to predict and help recover from disasters thanks to ability to quickly process large amounts of unstructured data and optimize limited resources (e.g. police analyzing social media to adjust police presence, license plate readers in police cars, 1Concern, BlueLineGrid) Resident Engagement – new tools to facilitate citizen engagement like chatbots, at-risk citizen identification, (e.g. Amelia chatbot in North London Enfield council, North Carolina chatbot to help state employees with IT inquiries) Smarter Cities – traffic/pedestrian management, lighting management, weather management, energy conservation, services analytics (e.g. San Francisco and Pittsburgh using sensors and AI to optimize traffic flow) Energy (SME: Noe Garcia, Tonya Cosby) Oil & Gas Exploration – automated geophysical feature detection (e.g. oil & gas producers using AI to augment traditional modeling & simulation) Smart Grid – predictive and real-time intelligent generation, allocation, and storage of power to meet variable demand (e.g. GridSense, SoloGrid) Operational Improvement – safety and efficiency improvements through predictive and/or insightful AI (e.g. GE Oil and Gas using predictive analytics and AI to predict and preempt potential operational problems) Conservation – intelligent buildings, computing and appliances that reduce power consumption and are more efficient than producing another kWh of electricity (e.g. Google DeepMind datacenter energy reductions) Transport (SME: Len Klebba) Automated Cars – autonomous cars driving on the roadways (e.g. BMW, Google, Uber, many others) Automated Trucking – autonomous trucks driving on the roadways (e.g. Daimler) Aerospace – autonomous planes and other aerial vehicles (e.g. Boeing’s evolution of autopilot and drones) Shipping – autonomous package delivery via drone or other vehicle (e.g. Amazon package delivery drone) Search & Rescue – ability to deploy autonomous robot to search and rescue victims in potentially hazardous environments (e.g. war casualty extraction, miner rescue, firefighting, avalanche rescue) Industrial (SME: Mary Bunzel, Esther Baldwin) Factory Automation – highly-productive, efficient and safe factories with robots that can see, hear and adapt to their environment to produce goods with incredible quality and speed (e.g. assembly line) Predictive Maintenance – ability to detect patterns that indicate the likelihood of an upcoming fault that would require maintenance (e.g. airline being able to adjust schedule to perform preventive maintenance before a failure) Precision Agriculture – ability to deliver the precise amount of water, nutrients, sunlight, weed killer, etc to a particular crop or individual plant (e.g. farmer using visual weed search to zap only weeds with RoundUp, automated sorting of produce for market) Field Automation – ability to automate heavy equipment beyond the factory walls (e.g. mining, excavation, construction, road repair) Other Advertising – interactive ads, adaptive ads, personalized ads, real-time ads (e.g. AdBrain, MetaMarkets, Proximic, RocketFuel) Education – virtual mentors, foreign language instruction, automated study sheets, personalized assignments, cheating detection, deliberate practice, machine-to-machine instruction (e.g. Intelligent Tutor Systems, Content Technologies Inc, PR2 robot from Cornell) Gaming – dynamic and interactive video game experiences (e.g. Xbox Kinect, Playstation Eye, Wii) Professional & IT Services – sales, marketing, legal research, accounting/tax, assisted counseling, customized IT recommendations (e.g. Pinsent Masons law firm that emulates human decision-making, Salesforce use of AI) Telco/Media – customized content/ads, network optimization, quality of service, mobile/home security (e.g. media company customizing tv show recommendations and ads, network operator ensuring efficient and high-quality delivery/repair, wireless company using multi-factor security) Sports – intelligent analytics for injury prevention and betting (e.g. Kinduct injury prevention, Microsoft Cortana predicting football games) Here is an even broader list of industries that will be impacted by AI: Advertising, Aerospace, Agriculture, Automotive, Building Automation, Business, Education, Fashion, Finance, Gaming, Government, Healthcare, IT, Investment, Legal, Life Sciences, Logistics, Manufacturing, Media & Entertainment, Oil/Gas/Mining, Real Estate, Retail, Sports & Fitness, Telecommunications, Transportation Sources: Intel forecast (IDC, GII Research, Tractica, Technavio, Market Research Store, Allied Market Research, BCC Research)
- Now, before we explore “what is AI”, it’s important to understand that implementing AI in your organization will be a journey. As we saw on the last slide, most businesses are at step 1 or 2 in this lifecycle, while the minority have gone full circle. Let’s step through it starting at the top and going clockwise… The first step in any analytics or AI journey is to define the challenge you want to go solve, through brainstorming what challenges you’re facing across your organization, and prioritizing them based on business value and how much it will cost to solve them. If you think of a 2x2 chart with increasing business value on the y-axis and decreasing cost to solve on the x-axis, naturally the most impactful challenges to tackle first are those in the upper righthand quadrant. Once you’ve identified some high potential opportunities to investigate, the next step is to figure out which AI (or other) approach is best-suited to each problem, which we’ll explore in the next slide and next section. The next step is to assess whether or not you have the expertise required to implement the solution, and whether those people embrace a fail-fast continuous improvement philosophy, since AI projects typically involve a lot more uncertainty, trial & error, and exploration than more traditional and deterministic software development projects. Once the human element is in place, the next step is to source data and prepare it for analysis, as well as stand up whatever technology infrastructure is required to tackle the problem. Last, but certainly not least, you can do all the heavy-lifting to use data to solve business challenges, but if your organization isn’t ready to accept data-driven insights, then all that work may have been for naught. A classic example is the initial resistance to data analytics in sports, where general managers and scouts scoffed at the idea of computer algorithms outsmarting their years of experience and tribal knowledge. Bottom line, if think about all these steps in the AI lifecycle, you’ll stand a much better chance of realizing the business value that you set out to deliver in the first place through AI. In the next few sections, we’ll unpack much of this AI lifecycle.
- Intel’s commitment to AI is simple: help our customers bring their AI visions to life using our Extensive Portfolio. The first step on your AI journey is getting your data ready. Intel and our partner ecosystem are ready to help you with one of multiple solutions to integrating, storing, processing and managing your data. The complexity of bringing AI from model to reality takes a mix of hardware solutions, and our multi-architecture approach optimizes a variety of computing for different purposes, enabling application designers to choose what works best. They can use their existing multi-purpose CPU resources to begin their AI journey – including breakthrough deep learning through scaling on Xeon – and if/when it makes sense, choose from the broadest (and best) deep learning acceleration portfolio to maximize ROI for their unique requirements. One level up this stack, software of course a critical requirement for AI, and we continue optimizing key open-source software like popular deep learning frameworks and working with our partners to bring tools to bear that reduce development time and overall time-to-solution. In addition, with the necessity of a multi-architecture approach to satisfy the demands of a wide variety of use cases, Intel is also in the process of developing tools to drive increasing harmony, reducing development and deployment complexity each step of the way. Beyond technology and tools, we’re taking also taking a solutions-driven approach in building a strong partner ecosystem in order to scale AI and enrich the lives of every person on the planet. This will include ready-built solutions through Intel and our partner ecosystem for many segments and verticals, including healthcare, finance, retail, government, energy, transport, industrial & more. Finally, Intel continues our push into the future by deepening our investments to push the forefront of AI computing into the next decade, including funding cutting-edge academic research, internal R&D, investments in leading innovators, and policy/ethics leadership. In the next few slides we’ll unpack these pillars in more detail, or feel free to skip ahead to whichever section you’re most interested in.
- Now that we’ve unpacked the Intel AI hardware portfolio, let’s build on top of that by looking at the important software and solutions stacks. Software: Intel is investing in AI tools that get the most out of, and streamline development across, each hardware option in our portfolio – in order to ultimately accelerate total time-to-solution. For application developers – those who deploy solutions using AI-based algorithms – Intel develops several tools to optimize performance and accelerate time-to-solution. For deep learning, the open-source OpenVINO™ (formerly the Intel® CV SDK and Deployment Toolkit) facilitates model deployment for inference, by converting & optimizing trained models for whichever hardware target is downstream, with support for TensorFlow, Caffe & MXNet on CPU, integrated GPU, VPU (Movidius Myriad 2 / Neural Compute Stick NCS) and FPGA. Similarly, the Intel® Movidius™ SDK supports inference deployment on TensorFlow & Caffe across the full range of VPUs. Intel is also in the process of developing the Intel® Deep Learning Studio (coming soon!) to help compress the end-to-end deep learning development cycle (including training). For data scientists – those who create AI-based algorithms – Intel contributes to and optimizes a set of open-source libraries that are widely used for machine and deep learning. There are a number of such machine learning libraries that get the most out of Intel hardware today, spanning Python, R and Distributed. For deep learning, Intel aims to ensure that all the major DL frameworks and topologies run well on Intel hardware, and customers are of course free to choose whichever framework(s) best suit their needs. We’ve been directly optimizing the most popular AI frameworks first, based on market demand, and producing huge speedups (>100x!!!). Today, we have many optimized topologies available for TensorFlow, MXNet, Caffe and BigDL on Spark, and you can download & install the optimized version of these frameworks by clicking on the links in this slide. Going forward, we intend to enable even more frameworks in the future through the Intel® nGraph™ Compiler. For library developers – those who develop and optimize API’s/libraries/frameworks to support new algorithms/topologies on the underlying hardware – Intel offers a host of foundational building blocks to get the most out of our hardware. Beginning on the left with the primitives category, the Data Analytics & Acceleration library (DAAL) and Intel Python distribution are important building blocks for machine learning. The ‘DNN’ (deep neural network) open source libraries contain CPU-optimized functions that are most relevant for, you guessed it, deep learning model development. On the right side of this row is a description of the Intel® nGraph™ Library (formerly the Nervana Graph), which takes the computational graph from each deep learning framework and creates an intermediate representation, which is executed by calling the math accelerator software libraries of each Intel hardware target. This compiler reduces the need for framework & model direct optimization for each hardware target using low-level software & math accelerator libraries. Today, it supports Xeon, GPU (CUDA) and the Crest family, with more hardware targets planned going forward. Solutions: Many business don’t want to start their AI journey from scratch and/or don’t have AI expertise or desire to build a core competency in it, but would still like to harness its benefits as quickly and efficiently as possible. Enter the Intel AI builders program, which is a one-stop-shop to find Intel AI technology-based solutions, be it ready-to-develop platforms or customized solutions that address particular problems. For the more do-it-yourself (DIY) crowd, Intel also publishes case studies, reference solutions and reference blueprints through the builders program, that you can leverage to scope and implement your own AI solutions. For more information about both technical services and reference solutions, visit our builders site at builders.intel.com/ai
- On the left is a typical HPC cluster showing underlying hardware to storage. On top is shown the OS, run time libraries, cluster and workload managers. The dark blue stack is AI workflow and brown stack is the HPC workflow. If the end user application is climate modeling, the HPC code will follow the brown stack, however if there is AI code as part of climate modeling for image recognition, it will follow the dark blue stack. On the right is a cloud cluster showing an abstracted vide of running Virtual Machines and Containers. The difference when you are running containers is that you abstract away the OS – the container holds application code and all dependencies enabling much better portability. Container – share host kernel; as far as libraries and run time environment – unique to each container. GCC/GlibC is unique to each container. So is HostOS/Kernel.
- We have been working with several customers to implement AI. Here are some examples of customers we’ve worked with and their testimonials.
- Discussion on what compute means Let’s use an example to highlight the value that Intel brings to AI. The breakdown on this slide, including the proof-of-concept (POC) percentages, is from a real AI customer project focused on industrial defect detection. While other projects will differ in time breakdown, the steps are typically the same. At the bottom, overall time-to-solution is the complete AI journey, including steps from opportunity assessment, to development & deployment, and ultimately evaluation of the end result. In the middle, we zoom in on the develop & deploy portion of the overall solution, which includes sourcing data, proof-of-concept development, inference deployment, as well as integration into a broader application. At the top, we zoom in further on the proof-of-concept development itself, where you can see that data preparation took a majority of the development time, followed by model training, and testing plus documentation. The bottom line here is that while compute-intensive training (the slivers in yellow) dominate a lot of the discourse around deep learning, they’re a relatively small part of your overall time-to-solution, and Intel works with customers across the entire AI lifecycle to speedup overall time-to-solution. Thus, it’s important to think about how to spend your IT budget in a way that gets you to deployment fastest, rather than paying a premium to – now only marginally – accelerate one portion of your solution, when that acceleration may add data management and other headaches, then potentially sit idle collecting dust when it’s served its purpose.
- - Demacation of Visual and Consume layers the end2end big data solution includes data collection，data storage，data process&analyze and data visualize;each step includes a lot of different products and solutions; please select the proper solution according to your business requirements, we will further discuss the technical details of each solution in our 201 version.
- Analytics is a constantly evolving science that companies can leverage for insight, innovation, and competitive advantage. Analytics has changed over the years and continues to advance through five stages of increasing scale & maturity: descriptive, diagnostic, predictive, prescriptive, and cognitive. AI is its own category, applied to all phases of the analytics pipeline (especially more advanced analytics), and a vital tool for reaching higher maturity & scale data analytics. AI is now a reality because of three key factors: Data deluge: Our world of smart and connected devices has unleashed a data deluge, as the Internet of Things (IoT) joins apps in generating continuous streams of structured and unstructured data. The IoT will include a projected 200 billion smart-and-connected devices by 2020,8 and the data produced is expected to double every two years to total 40 zettabytes (40 trillion gigabytes) by 2020. These vast data stores are required to train many AI algorithms and are ripe to be mined for fresh insights. Compute breakthrough: Paved by Moore’s Law, compute capability and architectural innovation have progressed to the point where we’ve crossed the threshold required to support the intense demands of machine intelligence. For example, the concept of deep learning through artificial neural networks has existed for at least 20 years, but not until the past few years have computing advancements enabled the practical application of these intensive algorithms, thanks to greater accuracy and speed. Innovation surge: Of course, compute power and data are not enough on their own. The road to AI is also being driven by a surge of innovation that has pushed us over the tipping point from research to mainstream use. Each new AI algorithmic innovation and use case opens more eyes to the power of AI, leading more innovators to join the community and stimulating an ever-increasing demand for the technology. Neural network innovations in the 1990s renewed research into AI, but it was accuracy breakthroughs in both speech recognition and image recognition, in 2009 and 2012 respectively, that proved to be catalysts for today’s surge of innovation. In last year’s ImageNet Computer Vision contest, a neural network–based application even outperformed a human. As we progress, a plethora of unsolved AI challenges will continue to attract researchers and innovators around the world. ------------------ BACKUP ------------------ Descriptive identifies what happened in the past. It helped us understand, but it focuses on hindsight. In today’s competitive environment, hindsight is not a competitive position. Diagnostic offers more insight into what happened, by describing why it happened. That’s more insight, but not helpful to identify what we should do as a company going forward in a fast-paced competitive world. Predictive and Prescriptive analytics provides foresight, identifying potentials out of the many possibilities of forward pathways the business can go. It then points the business in the best directions to achieve desired outcomes. Use of many data sources and simulations help prescribe the best path forward. 40% of enterprises net-new investments in analytics will be in predictive + prescriptive analytics by 2020. (IDC, Big Data Forecast, November 2015) Cognitive leverages what we’re learning and developing in machine and deep learning, artificial intelligence, and high performance data analytics to automate decisions using a human-like analysis. “Traditional analytics” consists of Descriptive and Diagnostic analytics, while Artificial Intelligence plays an increasingly important role at the upper echelon of Diagnostic analytics and beyond, for Predictive, Prescriptive, and Cognitive analytics. While there is much interest and focus on AI esp. deep learning, it is worth noting that both advanced analytics such as Predictive and the field of AI have been around for decades. The availability of open source frameworks and platforms (such as Hadoop) and advancements in analytics technologies along with a downward push on compute and storage prices have opened up the field of AI and advanced analytics to the mainstream. Moreover, no single analytics tool or AI approach in itself is a silver bullet. For instance, not all workloads are well suited for deep learning. Successful advanced analytics and AI deployment are about layering the right analytic tools and approaches matched to the right workloads with proper scoping of projects.