O'Reilly Artificial Intelligence Conference 2016 - New York, NY

Video description

The O'Reilly Artificial Intelligence Conference provided compelling evidence that 2016 is the year artificial intelligence moved from the province of university labs to being a critical part of the software developer's toolkit and a focus for mainstream companies.

Whether you’re a data scientist or software engineer looking to keep up with the latest developments; a CO in analytics, data, information, innovation or technology investigating AI trends; or a VC or corporate strategist evaluating new business opportunities, you'll find new information and insight in these videos. The compilation includes all keynotes and sessions.

Watch rapid-fire keynotes from Intel’s Genevieve Bell on the meaning of intelligence within the context of machines; O’Reilly’s founder Tim O’Reilly on the reasons why society must not fear artificial intelligence; Microsoft’s Lily Cheng on the success of Xiaoice, the company’s Chinese AI-driven chatbot); and many other visionaries.

Conference sessions include: Automated Insights’ Robbie Allen on the future of natural language generation over the next 10 years; Intel’s Vin Sharma on the company’s investment in open AI solutions for the autonomous driving, healthcare, and financial services industries; UC Berkeley’s Pieter Abbeel on reinforcement learning in robotics; Preferred Networks’ Shohei Hido on a Python framework for complex neural networks; Google’s Martin Wicke on the TensorFlow-based APIs that will democratize machine learning; and Cortical.io’s Francisco Webber on semantic folding, an alternative to the big data machine learning approach to AI.

O'Reilly Artificial Intelligence Conference

  • Total access to each of the 13 keynotes and 42 sessions delivered at AI NY 2016
  • Energized discourse by 66 AI experts from 39 of the world’s top AI companies and research groups
  • High-level briefings from MIT, HKUST, UCB, Stanford, and the Allen Institute for Artificial Intelligence
  • Demos of Capital One’s CI tool for cybersecurity and Intel’s Xeon Phi machine learning product line
  • Strategic advisories from FirstMark Capital, HyperScience, McKinsey, and The Longevity Fund
  • Deep learning updates from TensorFlow, Enlitic, Algorithmia, and Baidu’s Silicon Valley AI Lab
  • Demos of NVIDIA’s neural network tool DIGITS and x.ai’s AI personal assistant "Amy"
  • Insider looks at Microsoft’s Project Malmo and the deep learning toolkit CNTK
  • Overviews of breakthroughs in CNN based image, speech and emotion recognition

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Table of contents

  1. O'Reilly Artificial Intelligence Keynotes
    1. Software engineering of systems that learn in uncertain domains - Peter Norvig (Google)
    2. Why we'll never run out of jobs - Tim O'Reilly (O'Reilly Media, Inc.)
    3. Artificial intelligence: Making a human connection - Genevieve Bell (Intel Corporation)
    4. Humanizing AI development: Lessons from China and Xiaoice at Microsoft - Lili Cheng (Microsoft Research)
    5. How AI is propelling driverless cars, the future of surface transport - Shahin Farshchi (Lux Capital)
    6. Obstacles to progress in AI - Yann LeCun (Facebook)
    7. Minds and brains and the route to smarter machines - Gary Marcus (Geometric Intelligence)
    8. Thor’s hammer - Jim McHugh (NVIDIA)
    9. Lessons on building data products at Google - Aparna Chennapragada (Google)
    10. Deep learning at scale and use cases - Naveen Rao (Nervana)
    11. Why AI needs emotion - Rana El Kaliouby (Affectiva)
  2. Impact on business society
    1. What I learned by replacing middle-class manufacturing jobs with ML and AI - Eduardo Arino de la Rubia (Domino Data Lab)
    2. The future of natural language generation, 2016–2026 - Robbie Allen (Automated Insights, Inc.)
    3. The new artificial intelligence frontier - Babak Hodjat (Sentient Technologies)
    4. The future of AI - Oren Etzioni (Allen Institute for Artificial Intelligence)
  3. Implementing AI
    1. How to make robots empathetic to human feelings in real time - Pascale Fung (The Hong Kong University of Science and Technology)
    2. How advances in deep learning and computer vision can empower the blind community - Anirudh Koul (Microsoft) and Saqib Shaikh (Microsoft)
    3. Growing up: Continuous integration for machine-learning models - Zachary Hanif (Capital One)
    4. Deep learning: Modular in theory, inflexible in practice - Diogo Almeida (Enlitic)
    5. Unlock the power of AI: A fundamentally different approach to building intelligent systems - Mark Hammond (Bonsai)
    6. A peek at x.ai’s data science architecture - Angela Zhou (x.ai)
    7. How to scope an AI project - Jana Eggers (Nara Logics)
    8. AI is not a matter of strength but of intelligence - Francisco Webber (Cortical.io)
    9. Managing the deep learning computer-vision pipeline with DIGITS - Jon Barker (NVIDIA)
    10. Intel's new processors: A machine-learning perspective - Amitai Armon (Intel)
    11. Lessons learned from deploying the top deep learning frameworks in production - Kenny Daniel (Algorithmia)
    12. The identities of bots: A learning architecture for conversational software - Suman Roy (betaworks)
    13. Deep neural network model compression and an efficient inference engine - Song Han (Stanford University)
    14. Benefits of scaling machine learning - Reza Zadeh (Stanford | Matroid)
    15. Building an AI startup: Realities and tactics - Matt Turck (FirstMark Capital) and Peter Brodsky (HyperScience)
  4. Interacting with AI
    1. Deeply active learning: Approximating human learning with smaller datasets combined with human assistance - Binh Han (Arimo)
    2. Combining statistics and expert human judgement for better recommendations - Jianqiang (Jay) Wang (Stitch Fix) and Jasmine Nettiksimmons (Stitch Fix)
  5. Sponsored
    1. Deploying AI-based services in the data center for real-time responsive experiences - Sanford Russell (NVIDIA)
  6. Verticals applications
    1. End-to-end learning for autonomous driving - Urs Muller (NVIDIA)
    2. Making AI a reality for the enterprise and the physical world - Aman Naimat (Demandbase), Mark Patel (McKinsey Company)
    3. Achieving precision medicine at scale: Building medical AI to predict individual disease evolution in real time - Ash Damle (Lumiata)
    4. Leveraging artificial intelligence in creative technology - Jennifer Rubinovitz (DBRS Innovation Lab) and Amelia Winger-Bearskin (DBRS Innovation Lab)
    5. Deep reinforcement learning for robotics - Pieter Abbeel (OpenAI / UC Berkeley)
    6. Interactive learning systems: Why now and how? - Alekh Agarwal (Microsoft Research)
  7. Tools methods
    1. Transforming your industry with cognitive computing - Guruduth Banavar (Cognitive Computing, IBM)
    2. The need for speed: Benchmarking deep learning workloads - Greg Diamos (Baidu) and Sharan Narang (Baidu)
    3. TensorFlow for mobile poets - Pete Warden (TensorFlow)
    4. Progress of delivering real AI workloads - Xuedong (XD) Huang (Microsoft Research)
    5. Unlocking AI: How to enable every human in the world to train and use AI - Matt Zeiler (Clarifai, Inc.)
    6. Chainer: A flexible and intuitive framework for complex neural networks - Shohei Hido (Preferred Networks) and Orion Wolfe (Preferred Networks)

Product information

  • Title: O'Reilly Artificial Intelligence Conference 2016 - New York, NY
  • Author(s): O'Reilly Media, Inc.
  • Release date: September 2016
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781491973905