Artificial Intelligence Conference 2019 - New York, New York

Table of contents

1. Keynotes
   1. Checking in on AI Tools - Roger Chen (Computable), Ben Lorica (O'Reilly Media)
   2. Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale - Gadi Singer (Intel)
   3. Is AI human-ready? - Aleksander Madry (MIT)
   4. Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft) - Danielle Dean (Microsoft)
   5. Teaching a computer to read - Desiree Gosby (Intuit)
   6. Toward ethical AI: Inclusivity as a messy, difficult, but promising answer (sponsored by Dataiku) - Kurt Muehmel (Dataiku)
   7. Machine learning for personalization - Tony Jebara (Netflix)
   8. Data Fueling AI of the Future (sponsored by Dell Technologies) - Thomas Henson (Dell Technologies)
   9. How AI Adaptive Technology can Upgrade Education Industry - Using MIBA, MCM, Deep Learning and NKC for AI + Adaptive Education (sponsored by Squirrel AI Learning) Joleen Liang (sponsored by Squirrel AI)
   10. AI and the robotics revolution - Martial Hebert (Carnegie Mellon University)
   11. Computational propaganda - Sean Gourley (Primer)
   12. Making real-world distributed deep learning easy with Nauta - Carlos Humberto Morales (Intel)
   13. Artificial Intelligence - the “refinery” for Data (sponsored by Dell Technologies) - Nick Curcuru (Mastercard)
   14. Applied machine learning at Facebook - Kim Hazelwood (Facebook)
   15. Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) - Ruchir Puri (IBM)
   16. Software 2.0 Snorkel - Christopher Ré (Stanford University | Apple)
   17. Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) - Rajendra Prasad (Accenture)
   18. Decoding the human genome with deep learning - Olga Troyanskaya (Princeton University)
2. AI Business Summit
   1. Media meets AI: How we give superpowers to BuzzFeed's social curators - Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed)
   2. Executive Briefing: New business models in the age of artificial intelligence - Deepashri Varadharajan (CB Insights)
   3. Executive Briefing: From cutting-edge AI research to business impact - Larry Carin (Infinia ML) and Michael Eagan (Korn Ferry)
   4. What you must know to build AI systems that understand natural language - David Talby (Pacific AI)
   5. Executive Briefing: Responsible AI—An approach to and case studies for building fair, interpretable, safe AI - Anand Rao (PwC)
   6. Leveraging data science in asset management - Andrew Chin (AllianceBernstein), Celia Chen
   7. Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA - Kyle Hoback (WorkFusion)
   8. Executive Briefing: Agile AI - Sarah Aerni (Salesforce Einstein)
   9. Best practices for scaling modeling platforms - Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments)
   10. Executive Briefing: AI changes everything. . .except in investment management - Angelo Calvello (Rosetta Analytics)
   11. The evolution of software development and conversational assistants - Adam Cheyer (Samsung)
   12. Maintaining human control of artificial intelligence - Joanna Bryson (University of Bath)
   13. Executive Briefing: 5 key questions to kick off your AI implementation - Vinay Seth Mohta (Manifold)
   14. How deep learning can improve medical outcomes now - Eric Oermann (Mount Sinai Health System), Katie Link (Allen Institute for Brain Science)
   15. Building enterprise data products - Hilary Mason (Cloudera)
   16. Using AI to transform high-volume, confidential, disparate data for the United States Patent Office - Tammy Bilitzky (DCL)
   17. Executive Briefing: Overview of data governance - Paco Nathan (derwen.ai)
   18. Executive Briefing: The hidden data in AI IP - Thomas G Marlow (Black Hills IP)
   19. Ethical AI: Separating fact from fad - Sheldon Fernandez (DarwinAI)
3. Sponsored
   1. Deploy machine learning for real impact: Bridge the gap between data scientists and IT (sponsored by Cisco) - Zongjie Diao (Cisco)
   2. Unlocking AI value at scale: 3 building blocks and 1 massive mistake to avoid (sponsored by MapR) - Jack Norris (MapR Technologies)
   3. Synergize your tech stack to realize AI’s full potential (sponsored by SAS) - Katie Taylor (SAS)
   4. From prediction to prescription: Optimizing AI (sponsored by DataRobot) - Suresh Vadakath (DataRobot)
   5. How leaders are tackling their most pressing AI challenges (sponsored by SAS) - Tom Roehm (SAS), Alexis Crowell Helzer (Intel)
   6. Using artificial intelligence and machine learning for risk modeling in financial services (sponsored by IBM Watson) - Marcelo Labre (Morgan Stanley)
   7. Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData) - Nanda Vijaydev (BlueData (recently acquired by HPE))
   8. Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) - Ruchir Puri (IBM)
   9. Deployment considerations and best practices for your AI workloads from Mastercard (sponsored by Dell EMC) - Nick Curcuru (Mastercard)
   10. Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) - Rajendra Prasad (Accenture)
4. Models and Methods
   1. Seeing is deceiving: The rise of fake media and how to fight back - Siwei Lyu (University of Albany)
   2. A software accelerator for machine learning - Vinay Rao (RocketML), Santi Adavani (RocketML)
   3. How to build privacy and security into deep learning models - Yishay Carmiel (IntelligentWire)
   4. The curse of generality: Deep reinforcement learning in the wild - Sanjay Krishnan (University of Chicago)
   5. Learning from multiagent emergent behaviors in a simulated environment - Danny Lange (Unity Technologies)
   6. Random search and reproducibility for neural architecture search - Ameet Talwalkar (Carnegie Mellon University | Determined AI)
   7. Decentralized governance of data - Roger Chen (Computable)
   8. Chargrid: Understanding 2D documents - Anoop Katti (SAP)
   9. Deep learning for recommender systems; Or, How to compare pears with apples - Marcel Kurovski (inovex)
   10. Using AutoML to automate selection of machine learning models and hyperparameters - Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft)
   11. Deep learning for time series data - Arun Kejariwal (Independent), Ira Cohen (Anodot)
   12. Privacy-preserving machine learning in TensorFlow with TF Encrypted - Morten Dahl (Dropout Labs)
   13. nGraph: Unlocking next-generation performance with deep learning compilers - Adam Straw (Intel), Adam Procter (Intel), Robert Earhart (Intel)
   14. Sailing with Nauta - Adam Marek (Intel)
5. Implementing AI
   1. AutoML in the Chatbot Builder Framework - Jaewon Lee (Naver/LINE), Sihyeung Han (Naver/LINE)
   2. Open source tools for machine learning model and dataset versioning - Dmitry Petrov (Iterative AI), Ivan Shcheklein (Iterative AI)
   3. Turn devices into data scientists—at the edge - Simon Crosby (SWIM.AI)
   4. ImageNet for satellite imagery: Opportunities and risks - Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL)
   5. Distributed TensorFlow with distribution strategies - Magnus Hyttsten (Google)
   6. TensorFlow 2.0: Machine learning for you - Joshua Gordon (Google)
   7. Designing a machine learning operating platform - Diego Oppenheimer (Algorithmia)
   8. Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues - Jeremy Lewi (Google), Hamel Husain (GitHub)
   9. Developing your own model tracking leaderboard in Keras - Catherine Ordun (Booz Allen Hamilton)
   10. Building a production-scale ML platform - Yu Dong (Facebook)
   11. Distributed AI at scale - Mohamed Fawzy (Facebook)
   12. Understanding and integrating Intel Deep Learning Boost (Intel DL Boost) - Banu Nagasundaram (Intel)
   13. GAIA: The Global AI Allocator - Aric Whitewood (WilmotML)
6. Interacting with AI
   1. Game engines and machine learning - Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
   2. Using AI to create interactive digital actors - Kevin He (DeepMotion)
   3. Sooner than you think: Neural interfaces are finally here - Patrick Kaifosh (CTRL-labs)
   4. An active learning framework to optimize training of deep models with human in the loop - Humayun Irshad (Figure Eight)
   5. Artists and supercomputers: Creative collaborations in AI - Jeff Thompson (Stevens Institute of Technology)
   6. Manipulating and measuring model interpretability - Forough Poursabzi-Sangdeh (Microsoft Research NYC)
   7. Leveraging AI for social good - Jack Dashwood (Intel), Anna Bethke (Intel)
   8. An artificial intelligence framework to counter international human trafficking - Tom Sabo (SAS)
7. Case Studies
   1. Fraud detection without feature engineering - Pamela Vagata (Stripe)
   2. ML at Twitter: A deep dive into Twitter's timeline - Cibele Montez Halasz (Twitter), Satanjeev Banerjee (Twitter)
   3. Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills - Maryam Jahanshahi (TapRecruit)
   4. Industrialized capsule networks for text analytics - Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
   5. Regularization of RNNs through Bayesian networks - Vishal Hawa (Vanguard)
   6. Deep learning for third-party risk identification and evaluation at Dow Jones - Yulia Zvyagelskaya (Dow Jones), Victor Llorente (Dow Jones)
   7. Adversarial machine learning in digital forensics - Alina Matyukhina (Canadian Institute for Cybersecurity)
   8. Applied machine learning in finance - Chakri Cherukuri (Bloomberg LP)
8. AI in the Enterprise: The Intel® AI Builders Showcase Event
   1. Intel® AI Builders Showcase welcome - Brigitte Alexander (Intel)
   2. Predicting credit card payment default using H2O Driverless AI - Eric Gudgion (H2O.ai)
   3. A cognitive intelligence platform for faster and more effective data insights - Ajay Balakrishnan (Mphasis)
   4. Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory - Yoav Einav (GigaSpaces)
   5. Using machine learning to automate car damage assessment and document workflows - Vladimir Starostenkov (Altoros), Siarhei Sukhadolski (Altoros Development)
   6. Automated machine learning for the enterprise - Suresh Vadakath (DataRobot)
   7. Machine learning possibilities - Shioulin Sam (Cloudera Fast Forward Labs)
   8. Putting AI prototypes into production operation - Dan Klein (Valtech)
   9. AI on the edge - Fabrizio Del Maffeo (AAEON Technology Europe)
   10. Industrial-Grade, State-of-the-Art Language Understanding - David Talby (John Snow Labs)
   11. Addressing the market for AI-based controllers using high-accuracy multibody simulations - Sumit Sanyal (Minds.ai)
   12. Automated Medical X-ray Image Segmentation and Medical Image Classification - Sunil Baliga (Wipro), Sundar Varadarajan (Wipro)
   13. Introduction to Mobiliya (a QuEST Global company) and its capabilities in the AI space with Intel - Rubayat Mahmud (Mobiliya Technologies (A Quest Global Company))
   14. How Anaconda Enterprise enables CPU-optimized TensorFlow models - Rachel Jordan (Anaconda)
   15. One-click deployment for containerized ML and DL environments - Nanda Vijaydev (BlueData (recently acquired by HPE))
   16. Vertical AI for banking with NLU - Tony Sandoval (Avaamo)
   17. Intel® AI Builders Showcase closing remarks - Brigitte Alexander (Intel)
9. Tutorials
   1. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 1
   2. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 2
   3. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 3
   4. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 4
   5. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 1
   6. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 2
   7. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 3
   8. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 4
   9. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 5
   10. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 6
   11. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 7
   12. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 8
   13. Design thinking for AI - Chris Butler (IPSoft) - Part 1
   14. Design thinking for AI - Chris Butler (IPSoft) - Part 2
   15. Design thinking for AI - Chris Butler (IPSoft) - Part 4
   16. Design thinking for AI - Chris Butler (IPSoft) - Part 3
   17. Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 1
   18. Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 2
   19. Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 3
   20. Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 4
   21. Leveraging AI in a large organization - Alex Siegman (Dow Jones), Kabir Seth (Wall Street Journal)
   22. Getting started with PyTorch - Mo Patel (Independent) - Part 1
   23. Getting started with PyTorch - Mo Patel (Independent) - Part 2
   24. Getting started with PyTorch - Mo Patel (Independent) - Part 3
   25. Getting started with PyTorch - Mo Patel (Independent) - Part 4
   26. Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 1
   27. Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 2
   28. Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 3
   29. Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 4