Oreilly - O'Reilly Artificial Intelligence Conference 2017

Oreilly - O'Reilly Artificial Intelligence Conference 2017 - New York, New York

by O'Reilly Media, Inc. | Released June 2017 | ISBN: 9781491976272

https://www.oreilly.com/library/view/oreilly-artificial-intelligence/9781491976289/

What grabbed the attention of the thousands who attended AI New York 2017? The speakers: more than 120 of the world's best from the fields of deep learning, machine learning, natural language processing, and the cloud. This group included more than 40 PhDs and 20 founders of early stage AI companies, the heads of AI related research at some of the world's most prestigious universities (MIT, Carnegie Mellon, UC Berkeley, University of Cambridge, NYU, George Washington, Stanford, Johns Hopkins, etc.), and the leaders of AI product development from many of the world's top technology companies (IBM, Intel, Google, Amazon, Facebook, NVIDIA, Microsoft, Salesforce, SAP, etc.). This video compilation is a record of the 90+ presentations made by this extraordinary group. It's packed with case studies and tutorials that demonstrate AI's 2017 leap from the lab to the "real" world. AI is here and it has been put to work. Here is a sample of what you'll find in the compilation:Inspiring and illuminating keynotes from the top innovators in applied AI and intelligence engineering, including Google's Peter Norvig, IBM Watson's Damion Heredia, Carnegie Mellon's Tuomas Sandholm, NVIDIA's Jim McHugh, Elemental Cognition's David Ferrucci, UC Berkeley's Anca Dragan, and more.Eight tutorials, including MIT's Vikash Mansinghka survey of the emerging field of probabilistic programming; Microsoft's Anusua Trivedi, Barbara Stortz, and Patrick Buehler's examination of scalable deep learning using Microsoft's Cognitive Toolkit; and Narrative Science's Kristian Hammond's (founder of the University of Chicago's AI Lab) revelations on the best ways to evaluate emerging AI strategies and vendors. 40+ sessions on the tools, frameworks, algorithms, and approaches used to build practical AI technology, including Eric Greene (Wells Fargo Digital Innovation Labs) on deep learning and predictive payments; Rana el Kaliouby (Affectiva) on real life applications of artificial emotional intelligence; Jana Eggers (Nara Logics) on how to translate abstract AI into real numbers for business; Matt Shobe (Mighty AI) on building training data for autonomous driving; Jan Neumann (Comcast) on how AI powers the Comcast X1 voice interface; Pau Carré (Gilt) on deep learning in the fashion industry; Paco Nathan (O'Reilly Media) on the use of AI in publishing; and Ron Bodkin (Teradata) and Nadeem Gulzar (Danske Bank Group) on using AI to fight financial fraud at Danske Bank.25+ sessions on deep learning, including Richard Socher (Salesforce) on the limits of deep learning; Risto Miikkulainen (Sentient.ai) on using AI to build AI; Guy Ernest (Amazon Web Services) on scalable deep learning using Apache MXNet; Soumith Chintala (Facebook) on dynamic deep learning's paradigm shift in AI research and tools; and Joseph Bradley and Xiangrui Meng (Databricks) on integrating deep learning libraries with Apache Spark.20+ sessions on machine learning, including Josh Tenenbaum (MIT) on building machines that learn and think like people; Jason Laska (Clara Labs, Inc.) on strategies for integrating people and machine learning in online systems; and Matt Zeiler (Clarifai) on the risks and hidden costs of machine-learning technical debt.15+ sessions on natural language, including Jonathan Mugan (DeepGrammar) on how to add meaning to natural language processing; Yishay Carmiel (Spoken Communications) on developing conversational AI at large scale; and Kristian Hammond (Narrative Science) on advanced natural language generation.9 sessions on AI and the cloud, including Reza Zadeh (Stanford) on scaling computer vision in the cloud, and Yonghua Lin (IBM Research) on AI Vision and how it enables deep learning-based visual analysis in edge and cloud environments.Multiple sessions on the impact of AI on business and society, including Chuck Howell and Lashon Booker (MITRE) on "Fairness Cases" as an accelerant and enabler for AI adoption, and Katy George's (McKinsey & Company) talk on whether or not AI will automate jobs faster than we create them.This video compilation offers you a great way to see the sessions you missed—or to revisit the ones you attended. Just download the video to view whatever you choose at your own pace. Show and hide more

Keynotes
- Tackling the limits of deep learning - Richard Socher (Salesforce) 00:13:42
- AI Now. For Every Industry. (sponsored by NVIDIA) - Jim McHugh (NVIDIA) 00:09:59
- Can machines spot diseases faster than expert humans? - Suchi Saria (Johns Hopkins University) 00:15:26
- The future of AI is now (sponsored by IBM) - Damion Heredia (IBM Watson and Cloud Platform ) 00:04:31
- Machines as thought partners - David Ferrucci (Elemental Cognition) 00:18:15
- Building machines that learn and think like people - Josh Tenenbaum (MIT) 00:20:10
- Magenta: Machine learning and creativity - Doug Eck (Google Brain) 00:15:42
- Cars that coordinate with people - Anca Dragan (UC Berkeley) 00:14:28
- Machine learning on Google Cloud Platform (sponsored by Google) - Amy Unruh (Google) 00:05:28
- Superhuman AI for strategic reasoning: Beating top pros in heads-up no-limit Texas hold’em - Tuomas Sandholm (Carnegie Mellon University) 00:16:59
- Evolve AI (sponsored by Intel Nervana) - Naveen Rao (Intel) 00:10:31
- Artificial intelligence in the software engineering workflow - Peter Norvig (Google) 00:17:36
Impact of AI on business and society
- Harnessing the power of artificial intelligence to diagnose diseases - Kavya Kopparapu (GirlsComputingLeague) 00:25:14
- Planning for the social impact of AI - Madeleine Elish (Data & Society) 00:38:11
- "Fairness cases" as an accelerant and enabler for AI adoption - Chuck Howell (MITRE), Lashon Booker (MITRE) 00:38:44
- AI's legal history and some notions of the future - Aileen Nielsen (One Drop) 00:45:36
- Interpretable AI: Not just for regulators - Patrick Hall (H2O.ai | George Washington University), Sri Satish (H2O.ai) 00:40:20
- The AI-powered newsroom - Codruta Gamulea (Bakken & Bæck) 00:30:27
- Will we automate jobs faster than we create them? - Katy George (McKinsey & Company) 00:38:13
Sponsored
- Machine learning with TensorFlow and Google Cloud (sponsored by Google) - Vijay Reddy (Google Cloud) 00:38:09
- The future of AI is now (sponsored by IBM) - Damion Heredia (IBM Watson and Cloud Platform ), Bjorn Austraat (IBM) 00:33:12
- Live and let die: The need for an AI-enabled enterprise (sponsored by Arago) - Rene Buest (Arago) 00:34:44
- Accelerating deep learning (sponsored by NVIDIA) - Ryan Olson (NVIDIA) 00:42:10
- AI: What makes it hard (and fun) (sponsored by Intel) - Pradeep Dubey (Intel Corporation) 00:40:31
- Bring AI to BI: The benefits of using a GPU database for machine learning and deep learning (sponsored by Kinetica) - Karthik Lalithraj (Kinetica) 00:37:41
- Embedding machine learning into the fabric of enterprise apps (sponsored by SAP) - Erik Marcade (SAP) 00:37:47
- The practitioner’s guide to AI with Intel Nervana (sponsored by Intel Nervana) - Hanlin Tang (Intel) 00:39:09
Verticals and applications
- Bayesian deep learning - Yarin Gal (University of Cambridge) 00:38:46
- AI-powered natural language understanding applications in the financial industry - Francisco Webber (Cortical.io) 00:49:23
- Fighting financial fraud at Danske Bank with artificial intelligence - Ron Bodkin (Teradata), Nadeem Gulzar (Danske Bank Group) 00:46:59
- AI within O'Reilly Media - Paco Nathan (O'Reilly Media) 00:42:16
- AI-assisted computational chemistry: Predicting chemical properties with minimal expert knowledge - Garrett Goh (Pacific Northwest National Lab) 00:35:29
- Transforming an investment firm with AI: A case study - Aida Mehonic (ASI Data Science) 00:47:45
- Cognitive mobile healthcare for the patient and physician - Michael Nova (Pathway Genomics) 00:38:57
- AI for manufacturing: Today and tomorrow - David Rogers (Sight Machine) 00:33:58
Implementing AI
- Benchmarking deep learning inference - Sharan Narang (Baidu) 00:35:36
- Bayesian deep learning in PyMC3 - Thomas Wiecki (Quantopian) 00:39:53
- Beyond the state of the art in reading comprehension - Jennifer Chu-Carroll (Elemental Cognition) 00:40:24
- Dynamic deep learning: A paradigm shift in AI research and tools - Soumith Chintala (Facebook) 00:35:53
- Ray: A distributed execution framework for emerging AI applications - Philipp Moritz (UC Berkeley), Robert Nishihara (UC Berkeley) 00:37:54
- Software and hardware breakthroughs for deep neural networks at the edge - Michael B. Henry (Mythic) 00:41:28
- Algorithms for hire - Lindsey Zuloaga (HireVue) 00:35:37
- Human-assisted AI at B12: 10 lessons in giving humans superpowers - Adam Marcus (B12) 00:35:22
- Deep learning applied to consumer transactions with Think Big Analytics - Eric Greene (Think Big Analytics) 00:42:09
- Programming your way to explainable AI - Mark Hammond (Bonsai) 00:41:27
- Building training data for autonomous driving - Matt Shobe (Mighty AI) 00:41:36
- Running TensorFlow at scale in the cloud - Yufeng Guo (Google) 00:42:20
- How AI powers the Comcast X1 voice interface - Jan Neumann (Comcast), Ferhan Ture (Comcast), Shahin Sefati (Comcast) 00:37:48
- Rules of machine learning verification: From data-driven bugs to explainable AI - Suman Roy (Betaworks) 00:40:44
- Risks, hidden costs, and how to escape the black hole of machine learning technical debt - Matt Zeiler (Clarifai) 00:41:24
- Anaerobic AI: Developing in a data-starved environment - Xiaofan Xu (Intel), Cormac Brick (Intel) 00:36:33
- Deep learning in the fashion industry - Pau Carré (Gilt) 00:39:42
- Customizing state-of-the-art deep learning models for new computer vision solutions - Timothy Hazen (Microsoft) 00:37:23
- Beyond the hype: Real AI contributions in industry and engineering - Christoph Peylo (Bosch Center for Artificial Intelligence) 00:42:06
- From ∞ to 8: Translating abstract AI into real numbers for business - Jana Eggers (Nara Logics) 00:43:34
- Adding meaning to natural language processing - Jonathan Mugan (DeepGrammar) 00:39:16
- AI for smartphones: Running neural networks locally on phones for real-time use of the camera as a sensor - Alberto Rizzoli (Aipoly) 00:40:38
- How Amy, an artificial intelligence capable of scheduling meetings, understands human intents - Rakesh Chada (x.ai) 00:36:57
- AI Vision: Enable deep learning-based visual analysis in edge and cloud environments - Yonghua Lin (IBM Research) 00:42:16
- AI building AI: How evolutionary algorithms are revolutionizing deep learning - Risto Miikkulainen (Sentient.ai) 00:41:54
- Strategies for integrating people and machine learning in online systems - Jason Laska (Clara Labs, Inc.) 00:43:49
- Software architectures for building enterprise AI - Qirong Ho (Petuum, Inc.) 00:45:19
- The biological path toward strong AI - Matthew Taylor (Numenta) 00:47:59
- The road to affordable AI-capable products - Shaoshan Liu (PerceptIn) 00:43:18
- Integrating deep learning libraries with Apache Spark - Joseph Bradley (Databricks), Xiangrui Meng (Databricks) 00:43:13
Interacting with AI
- Tackling the fake news problem with AI - Delip Rao (Joostware) 00:43:47
- Demystifying AI hype - Kathryn Hume (integrate.ai) 00:40:22
- Inverse reward design - Anca Dragan (UC Berkeley) 00:37:48
- Top down versus bottom up: Computational creativity - Drew Silverstein (Amper Music), Cole Ingraham (Amper Music) 00:39:16
- XPRIZE Workshop: Using AI for Impact - Amir Banifatemi (Xprize), Balazs Kegl (CNRS) 00:36:38
- What, how, and why: The dynamic of advanced NLG - Kristian Hammond (Narrative Science) 00:41:01
- Deep shopping bots: Building machines that think and sell like humans - Rupert Steffner (WUNDER.ai) 00:41:00
- Building game bots using OpenAI’s Gym and Universe - Anmol Jagetia (Media.net) 00:39:24
- Bigger than bots: Machine reading and writing in enterprise - Mohamed Musbah (Maluuba Inc.) 00:41:24
- Conversational AI at large scale - Yishay Carmiel (Spoken Communications) 00:40:39
- Building conversational experiences - Brad Abrams (Google) 00:40:17
- Learning to recreate our visual world - Jun-Yan Zhu (Berkeley AI Research Lab) 00:41:18
- Rethinking design tools in the age of machine learning - Patrick Hebron (New York University) 00:30:04
- AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery - Abe Heifets (Atomwise) 00:41:06
- Teaching machines to reason and comprehend - Russ Salakhutdinov (Carnegie Mellon University) 00:44:18
- Tackling the limits of deep learning - Richard Socher (Salesforce) 00:40:58
Tutorials
- Deep reinforcement learning tutorial - Arthur Juliani (Unity Technologies) - Part 1 1:03:17
- Deep reinforcement learning tutorial - Arthur Juliani (Unity Technologies) - Part 2 00:45:27
- Deep reinforcement learning tutorial - Arthur Juliani (Unity Technologies) - Part 3 00:41:45
- Introduction to neural networks with Keras - Laura Graesser (New York University) - Part 1 00:44:09
- Introduction to neural networks with Keras - Laura Graesser (New York University) - Part 2 00:46:26
- Introduction to neural networks with Keras - Laura Graesser (New York University) - Part 3 00:44:11
- Introduction to neural networks with Keras - Laura Graesser (New York University) - Part 4 00:31:19
- Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 1 00:45:10
- Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 2 00:46:07
- Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 3 00:46:34
- Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 4 00:43:25
- BigDL: Distributed deep learning on Apache Spark - Yiheng Wang (Intel), Jennie Wang (Intel) - Part 1 00:39:35
- BigDL: Distributed deep learning on Apache Spark - Yiheng Wang (Intel), Jennie Wang (Intel) - Part 2 00:51:35
- BigDL: Distributed deep learning on Apache Spark - Yiheng Wang (Intel), Jennie Wang (Intel) - Part 3 00:31:35
- BigDL: Distributed deep learning on Apache Spark - Yiheng Wang (Intel), Jennie Wang (Intel) - Part 4 00:54:33
- How to gain business insights from unstructured data by leveraging NERs, graphs, and conversational interfaces - Galiya Warrier (Microsoft), Gary Short (Microsoft) - Part 1 00:39:14
- How to gain business insights from unstructured data by leveraging NERs, graphs, and conversational interfaces - Galiya Warrier (Microsoft), Gary Short (Microsoft) - Part 2 00:25:46
- Distributed deep learning on AWS using Apache MXNet - Joseph Spisak (Amazon), Sunil Mallya (Amazon Web Services) - Part 1 00:42:21
- Distributed deep learning on AWS using Apache MXNet - Joseph Spisak (Amazon), Sunil Mallya (Amazon Web Services) - Part 2 00:42:45