by O'Reilly Media, Inc. | Released April 2019 | ISBN: 9781492050544

Why did companies like Intuit, JP Morgan Chase, MasterCard, and BuzzFeed deploy AI and what business advantages have they already reaped from those deployments? How have the toolsets recently developed at Google (BERT) and Microsoft (Project Brainwave) opened up AI as a mainstream business reality? How does IBM's AI Fairness 360 toolkit combat the very real problem of unwanted bias in AI applications? You'll find the answers to these questions and many more in this video compilation of the best talks from AI New York 2019. Containing hours of material to explore at your own pace, this video compilation provides an insider's view of the latest developments in AI.Highlights include:M/p> Complete video recordings of the talks delivered at AI NY 2019 by 170 of the world's top AI experts.Keynote addresses from AI's best thinkers, such as MIT's Aleksander Madry, Intuit's Desiree Gosby, Princeton University's Olga Troyanskaya, Netflix's Tony Jebara, Stanford University's Christopher Ré, Facebook's Kim Hazelwood, Carnegie Mellon University's Martial Hebert, and Primer's Sean Gourley, plus a look at Dell's "Sophia", the world's first robot citizen.All of the Executive Briefings and detailed case studies from the exclusive AI Business Summit, including Kristian Hammond's (Northwestern Computer Science) day long tutorial offering a practical framework for bringing AI into your company; Adam Cheyer's (Samsung) look at how AI enables a totally new form of software development where humans and machines work collaboratively together; and Jennifer Fernick's (NCC Group) learned predictions of the industries that will benefit from the coming intersection of quantum computing, machine learning, and AI.Tutorials by AI's most experienced practitioners, including Gunnar Carlsson (Stanford University) on using topological data analysis to understand, build, and improve neural networks; Bruno Goncalves (JPMorgan Chase) on using recurrent neural networks for time series analysis; and Mo Patel (Independent) on how to build machine learning models in PyTorch.Sessions focused on machine learning, including Alina Matyukhina's (Canadian Institute for Cybersecurity) reveal of the methods dishonest actors use ML to mimic the coding style of software developers in open source projects; Chakri Cherukuri's (Bloomberg LP) discussion of how to apply machine learning and deep learning techniques in quantitative finance; and Cibele Montez Halasz's (Twitter) description of timeline ranking and how it's used to implement ML at production scale.Sessions devoted to implementing AI, including Yu Dong (Facebook) on how to build a production-scale ML platform; Sanjian Chen (Alibaba Group) on the best practices for designing machine learning algorithms for retail data analysis; and Magnus Hyttsten (Google) on how to run distributed TensorFlow on CPUs, GPUs, and TPUs with Keras and estimator APIs.Sessions illuminating new AI models and methods, including Ameet Talwalkar (Carnegie Mellon University) on NAS (neural architecture search); Lise Getoor's (University of California, Santa Cruz) exploration into SRL (statistical relational learning); and Siwei Lyu (University of Albany) on recent advances in techniques used to detect AI-generated DeepFake videos. Show and hide more

1. Keynotes
   • Checking in on AI Tools - Roger Chen (Computable), Ben Lorica (O'Reilly Media) 00:09:07
   • Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale - Gadi Singer (Intel) 00:14:54
   • Is AI human-ready? - Aleksander Madry (MIT) 00:12:52
   • Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft) - Danielle Dean (Microsoft) 00:10:35
   • Teaching a computer to read - Desiree Gosby (Intuit) 00:12:22
   • Toward ethical AI: Inclusivity as a messy, difficult, but promising answer (sponsored by Dataiku) - Kurt Muehmel (Dataiku) 00:05:23
   • Machine learning for personalization - Tony Jebara (Netflix) 00:12:23
   • Data Fueling AI of the Future (sponsored by Dell Technologies) - Thomas Henson (Dell Technologies) 00:03:16
   • 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) 00:05:30
   • AI and the robotics revolution - Martial Hebert (Carnegie Mellon University) 00:14:06
   • Computational propaganda - Sean Gourley (Primer) 00:13:43
   • Making real-world distributed deep learning easy with Nauta - Carlos Humberto Morales (Intel) 00:10:03
   • Artificial Intelligence - the “refinery” for Data (sponsored by Dell Technologies) - Nick Curcuru (Mastercard) 00:05:48
   • Applied machine learning at Facebook - Kim Hazelwood (Facebook) 00:11:05
   • Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) - Ruchir Puri (IBM) 00:52:31
   • Software 2.0 & Snorkel - Christopher Ré (Stanford University | Apple) 00:09:06
   • Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) - Rajendra Prasad (Accenture) 00:35:27
   • Decoding the human genome with deep learning - Olga Troyanskaya (Princeton University) 00:13:38
2. AI Business Summit
   • Media meets AI: How we give superpowers to BuzzFeed's social curators - Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed) 00:39:07
   • Executive Briefing: New business models in the age of artificial intelligence - Deepashri Varadharajan (CB Insights) 00:31:23
   • Executive Briefing: From cutting-edge AI research to business impact - Larry Carin (Infinia ML) and Michael Eagan (Korn Ferry) 00:40:12
   • What you must know to build AI systems that understand natural language - David Talby (Pacific AI) 00:38:38
   • Executive Briefing: Responsible AI—An approach to and case studies for building fair, interpretable, safe AI - Anand Rao (PwC) 00:46:07
   • Leveraging data science in asset management - Andrew Chin (AllianceBernstein), Celia Chen 00:42:14
   • Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA - Kyle Hoback (WorkFusion) 00:37:39
   • Executive Briefing: Agile AI - Sarah Aerni (Salesforce Einstein) 00:41:19
   • Best practices for scaling modeling platforms - Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments) 00:37:49
   • Executive Briefing: AI changes everything. . .except in investment management - Angelo Calvello (Rosetta Analytics) 00:48:00
   • The evolution of software development and conversational assistants - Adam Cheyer (Samsung) 00:40:49
   • Maintaining human control of artificial intelligence - Joanna Bryson (University of Bath) 00:40:03
   • Executive Briefing: 5 key questions to kick off your AI implementation - Vinay Seth Mohta (Manifold) 00:41:15
   • How deep learning can improve medical outcomes now - Eric Oermann (Mount Sinai Health System), Katie Link (Allen Institute for Brain Science) 00:38:23
   • Building enterprise data products - Hilary Mason (Cloudera) 00:43:56
   • Using AI to transform high-volume, confidential, disparate data for the United States Patent Office - Tammy Bilitzky (DCL) 00:21:03
   • Executive Briefing: Overview of data governance - Paco Nathan (derwen.ai) 00:46:42
   • Executive Briefing: The hidden data in AI IP - Thomas G Marlow (Black Hills IP) 00:44:32
   • Ethical AI: Separating fact from fad - Sheldon Fernandez (DarwinAI) 00:40:42
3. Sponsored
   • Deploy machine learning for real impact: Bridge the gap between data scientists and IT (sponsored by Cisco) - Zongjie Diao (Cisco) 00:39:02
   • Unlocking AI value at scale: 3 building blocks and 1 massive mistake to avoid (sponsored by MapR) - Jack Norris (MapR Technologies) 00:38:11
   • Synergize your tech stack to realize AI’s full potential (sponsored by SAS) - Katie Taylor (SAS) 00:27:30
   • From prediction to prescription: Optimizing AI (sponsored by DataRobot) - Suresh Vadakath (DataRobot) 00:43:08
   • How leaders are tackling their most pressing AI challenges (sponsored by SAS) - Tom Roehm (SAS), Alexis Crowell Helzer (Intel) 00:42:50
   • Using artificial intelligence and machine learning for risk modeling in financial services (sponsored by IBM Watson) - Marcelo Labre (Morgan Stanley) 00:42:20
   • Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData) - Nanda Vijaydev (BlueData (recently acquired by HPE)) 00:40:23
   • Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) - Ruchir Puri (IBM) 00:52:31
   • Deployment considerations and best practices for your AI workloads from Mastercard (sponsored by Dell EMC) - Nick Curcuru (Mastercard) 00:44:16
   • Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) - Rajendra Prasad (Accenture) 00:35:27
4. Models and Methods
   • Seeing is deceiving: The rise of fake media and how to fight back - Siwei Lyu (University of Albany) 00:41:26
   • A software accelerator for machine learning - Vinay Rao (RocketML), Santi Adavani (RocketML) 00:40:30
   • How to build privacy and security into deep learning models - Yishay Carmiel (IntelligentWire) 00:37:44
   • The curse of generality: Deep reinforcement learning in the wild - Sanjay Krishnan (University of Chicago) 00:39:34
   • Learning from multiagent emergent behaviors in a simulated environment - Danny Lange (Unity Technologies) 00:44:15
   • Random search and reproducibility for neural architecture search - Ameet Talwalkar (Carnegie Mellon University | Determined AI) 00:40:46
   • Decentralized governance of data - Roger Chen (Computable) 00:36:13
   • Chargrid: Understanding 2D documents - Anoop Katti (SAP) 00:46:06
   • Deep learning for recommender systems; Or, How to compare pears with apples - Marcel Kurovski (inovex) 00:41:51
   • Using AutoML to automate selection of machine learning models and hyperparameters - Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft) 00:42:33
   • Deep learning for time series data - Arun Kejariwal (Independent), Ira Cohen (Anodot) 00:42:29
   • Privacy-preserving machine learning in TensorFlow with TF Encrypted - Morten Dahl (Dropout Labs) 00:33:23
   • nGraph: Unlocking next-generation performance with deep learning compilers - Adam Straw (Intel), Adam Procter (Intel), Robert Earhart (Intel) 00:44:17
   • Sailing with Nauta - Adam Marek (Intel) 00:36:30
5. Implementing AI
   • AutoML in the Chatbot Builder Framework - Jaewon Lee (Naver/LINE), Sihyeung Han (Naver/LINE) 00:39:35
   • Open source tools for machine learning model and dataset versioning - Dmitry Petrov (Iterative AI), Ivan Shcheklein (Iterative AI) 00:42:07
   • Turn devices into data scientists—at the edge - Simon Crosby (SWIM.AI) 00:35:33
   • ImageNet for satellite imagery: Opportunities and risks - Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL) 00:36:51
   • Distributed TensorFlow with distribution strategies - Magnus Hyttsten (Google) 00:39:55
   • TensorFlow 2.0: Machine learning for you - Joshua Gordon (Google) 00:36:57
   • Designing a machine learning operating platform - Diego Oppenheimer (Algorithmia) 00:38:31
   • Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues - Jeremy Lewi (Google), Hamel Husain (GitHub) 00:39:03
   • Developing your own model tracking leaderboard in Keras - Catherine Ordun (Booz Allen Hamilton) 00:35:28
   • Building a production-scale ML platform - Yu Dong (Facebook) 00:24:11
   • Distributed AI at scale - Mohamed Fawzy (Facebook) 00:39:03
   • Understanding and integrating Intel Deep Learning Boost (Intel DL Boost) - Banu Nagasundaram (Intel) 00:43:11
   • GAIA: The Global AI Allocator - Aric Whitewood (WilmotML) 00:42:04
6. Interacting with AI
   • Game engines and machine learning - Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee) 00:41:37
   • Using AI to create interactive digital actors - Kevin He (DeepMotion) 00:38:47
   • Sooner than you think: Neural interfaces are finally here - Patrick Kaifosh (CTRL-labs) 00:45:51
   • An active learning framework to optimize training of deep models with human in the loop - Humayun Irshad (Figure Eight) 00:37:47
   • Artists and supercomputers: Creative collaborations in AI - Jeff Thompson (Stevens Institute of Technology) 00:36:33
   • Manipulating and measuring model interpretability - Forough Poursabzi-Sangdeh (Microsoft Research NYC) 00:39:42
   • Leveraging AI for social good - Jack Dashwood (Intel), Anna Bethke (Intel) 00:40:25
   • An artificial intelligence framework to counter international human trafficking - Tom Sabo (SAS) 00:39:06
7. Case Studies
   • Fraud detection without feature engineering - Pamela Vagata (Stripe) 00:40:11
   • ML at Twitter: A deep dive into Twitter's timeline - Cibele Montez Halasz (Twitter), Satanjeev Banerjee (Twitter) 00:40:44
   • Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills - Maryam Jahanshahi (TapRecruit) 00:37:57
   • Industrialized capsule networks for text analytics - Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient) 00:41:30
   • Regularization of RNNs through Bayesian networks - Vishal Hawa (Vanguard) 00:44:23
   • Deep learning for third-party risk identification and evaluation at Dow Jones - Yulia Zvyagelskaya (Dow Jones), Victor Llorente (Dow Jones) 00:43:56
   • Adversarial machine learning in digital forensics - Alina Matyukhina (Canadian Institute for Cybersecurity) 00:36:15
   • Applied machine learning in finance - Chakri Cherukuri (Bloomberg LP) 00:45:27
8. AI in the Enterprise: The Intel® AI Builders Showcase Event
   • Intel® AI Builders Showcase welcome - Brigitte Alexander (Intel) 00:06:54
   • Predicting credit card payment default using H2O Driverless AI - Eric Gudgion (H2O.ai) 00:13:31
   • A cognitive intelligence platform for faster and more effective data insights - Ajay Balakrishnan (Mphasis) 00:13:30
   • Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory - Yoav Einav (GigaSpaces) 00:11:14
   • Using machine learning to automate car damage assessment and document workflows - Vladimir Starostenkov (Altoros), Siarhei Sukhadolski (Altoros Development) 00:09:41
   • Automated machine learning for the enterprise - Suresh Vadakath (DataRobot) 00:08:49
   • Machine learning possibilities - Shioulin Sam (Cloudera Fast Forward Labs) 00:14:53
   • Putting AI prototypes into production operation - Dan Klein (Valtech) 00:08:56
   • AI on the edge - Fabrizio Del Maffeo (AAEON Technology Europe) 00:05:57
   • Industrial-Grade, State-of-the-Art Language Understanding - David Talby (John Snow Labs) 00:09:27
   • Addressing the market for AI-based controllers using high-accuracy multibody simulations - Sumit Sanyal (Minds.ai) 00:10:56
   • Automated Medical X-ray Image Segmentation and Medical Image Classification - Sunil Baliga (Wipro), Sundar Varadarajan (Wipro) 00:08:14
   • Introduction to Mobiliya (a QuEST Global company) and its capabilities in the AI space with Intel - Rubayat Mahmud (Mobiliya Technologies (A Quest Global Company)) 00:10:00
   • How Anaconda Enterprise enables CPU-optimized TensorFlow models - Rachel Jordan (Anaconda) 00:06:46
   • One-click deployment for containerized ML and DL environments - Nanda Vijaydev (BlueData (recently acquired by HPE)) 00:14:06
   • Vertical AI for banking with NLU - Tony Sandoval (Avaamo) 00:11:46
   • Intel® AI Builders Showcase closing remarks - Brigitte Alexander (Intel) 00:02:59
9. Tutorials
   • Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 1 00:43:02
   • Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 2 00:48:10
   • Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 3 00:39:59
   • Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 4 00:49:45
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 1 00:40:22
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 2 00:48:42
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 3 00:35:37
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 4 00:54:25
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 5 00:37:46
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 6 00:41:16
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 7 00:58:13
   • Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 8 00:48:52
   • Design thinking for AI - Chris Butler (IPSoft) - Part 1 00:42:57
   • Design thinking for AI - Chris Butler (IPSoft) - Part 2 00:29:38
   • Design thinking for AI - Chris Butler (IPSoft) - Part 4 00:49:33
   • Design thinking for AI - Chris Butler (IPSoft) - Part 3 00:31:17
   • Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 1 00:39:51
   • Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 2 00:41:16
   • Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 3 00:46:26
   • Building AI assistants that scale using machine learning and open source tools - Justina Petraityte (Rasa) - Part 4 00:50:12
   • Leveraging AI in a large organization - Alex Siegman (Dow Jones), Kabir Seth (Wall Street Journal) 00:57:44
   • Getting started with PyTorch - Mo Patel (Independent) - Part 1 00:44:32
   • Getting started with PyTorch - Mo Patel (Independent) - Part 2 00:38:31
   • Getting started with PyTorch - Mo Patel (Independent) - Part 3 00:39:13
   • Getting started with PyTorch - Mo Patel (Independent) - Part 4 00:48:04
   • Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 1 00:32:07
   • Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 2 00:37:13
   • Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 3 00:36:39
   • Recurrent neural networks for time series analysis - Bruno Goncalves (JPMorgan Chase) - Part 4 00:40:15
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