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
Publisher resources
Table of contents
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O'Reilly Artificial Intelligence Keynotes
- Software engineering of systems that learn in uncertain domains - Peter Norvig (Google)
- Why we'll never run out of jobs - Tim O'Reilly (O'Reilly Media, Inc.)
- Artificial intelligence: Making a human connection - Genevieve Bell (Intel Corporation)
- Humanizing AI development: Lessons from China and Xiaoice at Microsoft - Lili Cheng (Microsoft Research)
- How AI is propelling driverless cars, the future of surface transport - Shahin Farshchi (Lux Capital)
- Obstacles to progress in AI - Yann LeCun (Facebook)
- Minds and brains and the route to smarter machines - Gary Marcus (Geometric Intelligence)
- Thor’s hammer - Jim McHugh (NVIDIA)
- Lessons on building data products at Google - Aparna Chennapragada (Google)
- Deep learning at scale and use cases - Naveen Rao (Nervana)
- Why AI needs emotion - Rana El Kaliouby (Affectiva)
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Impact on business society
- What I learned by replacing middle-class manufacturing jobs with ML and AI - Eduardo Arino de la Rubia (Domino Data Lab)
- The future of natural language generation, 2016–2026 - Robbie Allen (Automated Insights, Inc.)
- The new artificial intelligence frontier - Babak Hodjat (Sentient Technologies)
- The future of AI - Oren Etzioni (Allen Institute for Artificial Intelligence)
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Implementing AI
- How to make robots empathetic to human feelings in real time - Pascale Fung (The Hong Kong University of Science and Technology)
- How advances in deep learning and computer vision can empower the blind community - Anirudh Koul (Microsoft) and Saqib Shaikh (Microsoft)
- Growing up: Continuous integration for machine-learning models - Zachary Hanif (Capital One)
- Deep learning: Modular in theory, inflexible in practice - Diogo Almeida (Enlitic)
- Unlock the power of AI: A fundamentally different approach to building intelligent systems - Mark Hammond (Bonsai)
- A peek at x.ai’s data science architecture - Angela Zhou (x.ai)
- How to scope an AI project - Jana Eggers (Nara Logics)
- AI is not a matter of strength but of intelligence - Francisco Webber (Cortical.io)
- Managing the deep learning computer-vision pipeline with DIGITS - Jon Barker (NVIDIA)
- Intel's new processors: A machine-learning perspective - Amitai Armon (Intel)
- Lessons learned from deploying the top deep learning frameworks in production - Kenny Daniel (Algorithmia)
- The identities of bots: A learning architecture for conversational software - Suman Roy (betaworks)
- Deep neural network model compression and an efficient inference engine - Song Han (Stanford University)
- Benefits of scaling machine learning - Reza Zadeh (Stanford | Matroid)
- Building an AI startup: Realities and tactics - Matt Turck (FirstMark Capital) and Peter Brodsky (HyperScience)
- Interacting with AI
- Sponsored
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Verticals applications
- End-to-end learning for autonomous driving - Urs Muller (NVIDIA)
- Making AI a reality for the enterprise and the physical world - Aman Naimat (Demandbase), Mark Patel (McKinsey Company)
- Achieving precision medicine at scale: Building medical AI to predict individual disease evolution in real time - Ash Damle (Lumiata)
- Leveraging artificial intelligence in creative technology - Jennifer Rubinovitz (DBRS Innovation Lab) and Amelia Winger-Bearskin (DBRS Innovation Lab)
- Deep reinforcement learning for robotics - Pieter Abbeel (OpenAI / UC Berkeley)
- Interactive learning systems: Why now and how? - Alekh Agarwal (Microsoft Research)
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Tools methods
- Transforming your industry with cognitive computing - Guruduth Banavar (Cognitive Computing, IBM)
- The need for speed: Benchmarking deep learning workloads - Greg Diamos (Baidu) and Sharan Narang (Baidu)
- TensorFlow for mobile poets - Pete Warden (TensorFlow)
- Progress of delivering real AI workloads - Xuedong (XD) Huang (Microsoft Research)
- Unlocking AI: How to enable every human in the world to train and use AI - Matt Zeiler (Clarifai, Inc.)
- 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):
- Release date: September 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491973905
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