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, …
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
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)
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)
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
Deeply active learning: Approximating human learning with smaller datasets combined with human assistance - Binh Han (Arimo)
Combining statistics and expert human judgement for better recommendations - Jianqiang (Jay) Wang (Stitch Fix) and Jasmine Nettiksimmons (Stitch Fix)
Sponsored
Deploying AI-based services in the data center for real-time responsive experiences - Sanford Russell (NVIDIA)
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)
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)