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Artificial Intelligence and Machine Learning in Research

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Artificial Intelligence and Machine Learning in Research

Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.

Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.

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Artificial Intelligence and Machine Learning in Research

  1. 1. P U B L I C S E C T O R S U M M I T Washingt on, DC
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Artificial Intelligence and Machine Learning in Research Sanjay Padhi AWS Research Initiatives Amazon Web Services S e s s i o n I D 3 0 1 0 6 9 Cornelia Caragea Professor of Computer Science University of Illinois at Chicago Lise St. Denis Research Scientist UC Colorado Boulder, CIRES Earthlab
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Artificial Intelligence Amazon AI: https://aws.amazon.com/amazon-ai/ Amazon Machine Learning: https://aws.amazon.com/machine-learning/ A system or service which can perform tasks that usually require human intelligence
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Machine Learning Supervised Learning Unsupervised Learning Supervised Learning: - Learning from “labelled data” - Classification, Regression, Prediction, Function Approx. Unsupervised Learning: - Method to find similar groups in the data clusters - Groups that are similar to near clusters - Groups different far away from each other Reinforcement Learning
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T • Detecting Financial Market Manipulation: An Integrated Data- and Model-Driven Approach University of Michigan, Georgia Tech • Scalable and Interpretable machine learning: bridging mechanistic and data-driven modeling in the biological sciences University of California, Berkeley • Taming Big Networks via Embedding University of Virginia, University of Illinois at Urbana-Champaign • Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media Kansas State University, University of North Texas and Pennsylvania State University • Distributed Semi-Supervised Training of Deep Models and Its Applications in Video Understanding University of Central Florida Examples of Research projects funded by NSF with Big Data Awards on AWS “In today's era of data-driven science and engineering, we are pleased to work with the AWS Research Initiative via the NSF BIGDATA program, to provide cloud resources for our Nation’s researchers to foster and accelerate discovery and innovation." Dr. Jim Kurose, Assistant Director, CISE, National Science Foundation (NSF)
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T • Automating Analysis and Feedback to Improve Mathematics Teachers' Classroom University of Colorado, Boulder • Collaborative Research: Protecting Yourself from Wildfire Smoke: Big Data Driven Adaptive Air Quality Prediction Methodologies University of Nevada, Reno • Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data Emory University • Collaborative Research: Optimizing Log-Structured-Merge-Based Big Data Management Systems University of California, Riverside • Collaborative Research: Intelligent Solutions for Navigating Big Data from the Arctic and Antarctic Texas A&M University Corpus Christi Examples of Research projects funded by NSF with 2018 Big Data Award on AWS “This NSF big data award, coupled with AWS’s advanced computational and analytic services, is expected to help unlock the secrets of interactions among biomolecules that drive human and animal biological processes.” Dr. Bin Yu, Chancellor’s Professor at University of California,
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Big Data Market Manipulation Project • Collaboration between U.Michigan and Georgia Tech • Sponsored by NSF BIGDATA program, computational support from AWS • Interdisciplinary: Computer Science (AI/ML), Finance, Law & Public Policy Goal: New techniques for detecting and mitigating manipulation
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Market Manipulation
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Spoofing Definition: Practice of submitting large spurious orders to buy or sell some security to mislead other traders about market state True buy order True sell order Spoofing sell order Spoofing buy order Source: UK Financial Conduct Authority
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Approach Model spoofing and other manipulation strategies Develop new surveillance and audit algorithm based on signatures extracted from model-augmented data streams background trading model market data calibration spoofing strategy optimization signature extractor surveillance/audit algorithms machine learning Generate realistic financial order streams
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T An Agent-Based Model of Spoofing • Simulate financial markets as complex multi-agent systems. • Elucidate strategic choices faced by market participants. • Evaluate market performance and the impact of spoofing given agent interactions at Nash equilibrium.
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Machine Learning
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Generative Adversarial Network (GAN) Interleave training of two deep Neural Net
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Order Book Evolution Generator outputs next order, conditional on order book state and history
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Generator NN Architecture LSTM layer Noise input Convolutional layers after a single fully connected layer Pre-trained CDA network Time History xj of length
  17. 17. Managed Distributed Training with Flexibility Training code • Matrix Factorization • Regression • Principal Component Analysis • K-Means Clustering • Gradient Boosted Trees • And More! Amazon provided Algorithms Bring Your Own Script (IM builds the Container) Bring Your Own Algorithm (You build the Container) I ML Training Service Fetch Training data Save Model Artifacts Fully managed – Secured – Amazon ECR Save Inference Image IM Estimators in Apache Spark CPU GPU HPO © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  18. 18. Results Fake means generated Order Results compare - Real and Fake Order distributions Training Simulated financial Market Thinly traded stock - PN, 20k orders/day Thickly traded stock: - GOOG, 230k order/day © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations M L S E R V I C E S Data labeling | Pre-built algorithms & notebooks | One-click training and deployment Build, train, and deploy machine learning models fast Easily add intelligence to applications without machine learning skills Flexibility & choice, highest-performing infrastructure Support for ML frameworks | Compute options purpose-built for ML © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E L E XR E K O G N I T I O N V I D E O Vision Speech Chatbots M L S E R V I C E S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E Language Forecasting Recommendations T E X T R A C T C O M P R E H E N D & C O M P R E H E N D M E D I C A L F O R E C A S T P E R S O N A L I Z E A M A Z O N S A G E M A K E R G R O U N D T R U T H N O T E B O O K S A W S M A R K E T P L A C E A L G O R I T H M S R E I N F O R C E M E N T L E A R N I N G T R A I N I N G O P T I M I Z A T I O N ( N E O ) D E P L O Y M E N T H O S T I N G © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  21. 21. Amazon SageMaker: Build, Train, and Deploy ML Models at Scale © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  22. 22. • Optical Nerve Damage & Macular Degeneration • Radiology Image Recognition • Readmission Risk • Clinical Decision Support (Diagnosis & Treatment) • Fitness Incentives • Hospital Stay Length & Re-admittance Predictions • Pharmaceutical Sales Force Optimization • Personalized Medicine • Structure-based Drug Design • Knowledge Curation for Drug Discovery • Cohort Selection • Etiology of Disease • Disease Onset Likelihood • Clinical Trial Intelligence • Outbreak Prediction • Anomaly Detection • Computational Drug Design • Pictogram Classification • Patient Condition Forecasting D i a g n o s i s & O u t c o m e s Health and Life Sciences D r u g D i s c o v e r y & O p s © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  23. 23. Stanford: Automatic Grading of Diabetic Retinopathy through Deep Learning using AWS © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  25. 25. FDA-Approved Medical Imaging © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  26. 26. HeartFlow creates personalized medical technology using deep learning to help diagnose heart disease. • Analyzes CT scans to create accurate 3D model of a patient’s heart and coronary arteries • Simulates the flow of blood in each vessel to provide details on blockages and blood flow with which physicians can base a diagnosis • The 100% non-invasive solution means 60% of patients can avoid an angiogram, reducing healthcare system costs by 25% Detecting Heart Disease with Deep Learning © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  27. 27. Computer Vision/NLP • Amazon Rekognition provides computer vision • Amazon Comprehend Medical provides natural language processing (NLP) • No algorithm development or model training required • Implemented in less than 100 lines of Python © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  28. 28. Medical Image Classification • Use machine learning to detect diseases shown by medical images • ML models created using Amazon SageMaker • Train a model on over 100,000 images in about 9 hours using large GPU instances • Generalized pattern for model training and threshold selection can be applied to any image modality © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  29. 29. Research for Social Good (Collaboration: AWS, NSF and University of Nevada) https://www.unr.edu/nevada-today/news/2019/big-data-wildfires © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  30. 30. Machine Learning for improving Disaster Management and Response usin Hurricane Irma predicted path Hurricane Irma real path Source: Weather Channel © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Deep Learning for Disaster Management and Response Cornelia Caragea Professor of Computer Science University of Illinois at Chicago S e s s i o n I D 3 0 1 0 6 9
  32. 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Acknowledgements NSF BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media
  33. 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Social Networking Sites • Connect people all around the world. • Have become part of our daily lives and everyday communication patterns.
  34. 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Big (Disaster) Data • The use of social media is on a rise, and so is the use of social media in disaster events. • Deadly disasters happen all the time -> source of Big (Disaster) Data
  35. 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Big (Disaster) Data – Hurricane Sandy
  36. 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Big (Disaster) Data - 2011 Tohoku Earthquake
  37. 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Importance of Social Media Data in Disasters • Micro-blogging data from crowds of non-professional participants during disasters are of significant value. Researchers assert that bystanders “on the ground are uniquely positioned to share information that may not yet be available elsewhere in the information space...” [Starbird et al., 2010].
  38. 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Challenges with Using Social Media Data • Scholars of disasters see hope in social media, and argue that can produce accurate results, often in advance of official communications. • Still, there has not been much uptake of message data by large-scale, disaster response organizations. • Response organizations operate in conditions of extreme uncertainty • With the exponential increase in social media data, so comes the increase in irrelevant data • Diminish people’s ability to find information that they need in order to organize relief efforts, find help and potentially save lives.
  39. 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T How To Identify Useful Content in Twitter? • Direct Twitter search: • Keyword-based search • “oklahoma tornado" • "oklahoma" or "tornado" • #oklahomatornado • Location-based search • collect postings containing geographical coordinates inside the affected areas Irrelevant tweet: “I’ve lived in Oklahoma since I was born.” • Manual selection: too time consuming • Hence, there is an increasing need to automatically extract appropriate information, which could make improvements in the response process.
  40. 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Deep Learning
  41. 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T • Use Twitter Streaming API to crawl tweets posted during crisis events • Parse the tweet JSON objects to extract tweet text, hashtags, media information, user information, and geo-location (when available) • Perform text classification, natural language processing and text analytics on the tweet text Data collection and analysis
  42. 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Tweet classification
  43. 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Classes of machine learning algorithms Supervised learning [Imran et al., 2013; Ashktorab et al., 2014; Caragea et al., 2014; Imran et al., 2018] • Labeled tweets needed, but not readily available for an emergent disaster Domain adaptation [Li et al., 2015; Li et al., 2017, Alam et al., 2018, Mazloom et al., 2018] • Knowledge from a prior source disaster is transferred to a target disaster Unsupervised learning, e.g., topic modeling [Resch et al., 2017] • Topic modeling can help associate topics/categories with tweets
  44. 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  45. 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T How it works
  46. 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Comprehend workflow Identify a tweet’s language Identify key phrases to help categorize tweets Identify topics in a collection of tweets • Use them to categorize tweets • Enhance situational awareness Identify entities in actionable tweets • The goal is to understand who, where, when, what, why Identify the sentiment of the tweets • Enhance situational awareness
  47. 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Example: topics Hurricane degenerates Hurricane regenerates Harvey degenerates into tropical wave RT Hurricane Hunter plane finds that the remnants of Harvey have not regenerated into tropical cyclone RT Harvey showing signs of regeneration over western Caribbean Sea amp will produce heavy rainfall storms this week hit RT Active tropics continue redvlpmt of Harvey likely as well as the potential for two new tropical cyclones next days on
  48. 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Deep Learning for Identifying Informative Tweets We study the effectiveness of deep neural networks in comparison with traditional machine learning classifiers for identifying informative messages from social media streams (Twitter).
  49. 49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Results
  50. 50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Using locations identified by Comprehend to track hurricane path Source: Wikipedia Source: Weather Channel
  51. 51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Mining Social Media During Disasters using AWS and Machine Learning Lise St. Denis Research Scientist University of Colorado Boulder, CIRES Earth Lab S e s s i o n I D : 3 0 1 0 6 9
  52. 52. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Social Media in Emergency Response
  53. 53. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T 2014 Carlton Complex Wildfire, Eastern Washington
  54. 54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T “In the thick of it …what I really want to know is what I don’t know.” Kris Eriksen PIO, Portland NIMO
  55. 55. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Filtering Concept
  56. 56. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Filtered Content
  57. 57. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  58. 58. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Data Collection Process • Real-time • Flexible • Integrated
  59. 59. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Future Data Collection • Independent collections • Multiple processes • Collection User Interface
  60. 60. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Account Classification
  61. 61. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Confusion Matrix: Account Classification
  62. 62. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Tweet Content Analysis: filtered out
  63. 63. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Tweet Content Analysis: prioritized • Life safety • Hazards • Criticism • Misinformation • Rumor • Information gaps • Situational awareness
  64. 64. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Next Steps • Collaborative effort • Early feedback* • Flexible framework
  65. 65. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T
  66. 66. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R S U M M I T Sanjay Padhi: sanpadhi@amazon.com Cornelia Caragea: cornelia@uic.edu Lise St. Denis: Lise.St.Denis@Colorado.edu

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