September 26-27, 2016
New York, NY
 

List by type

  • Keynotes
  • Sessions
  • River Pavilion B
    Add Monday opening remarks to your personal schedule
    River Pavilion B
    9:00am Monday opening remarks Ben Lorica (O'Reilly Media), Roger Chen
    Add Why we'll never run out of jobs to your personal schedule
    9:30am Why we'll never run out of jobs Tim O'Reilly (O'Reilly Media)
    Add Artificial intelligence: Making a human connection to your personal schedule
    9:45am Artificial intelligence: Making a human connection Genevieve Bell (Intel Corporation)
    Add Closing remarks to your personal schedule
    10:25am Closing remarks Ben Lorica (O'Reilly Media), Roger Chen
    Add The future of AI to your personal schedule
    11:50am The future of AI Oren Etzioni (Allen Institute for Artificial Intelligence)
    Add How advances in deep learning and computer vision can empower the blind community to your personal schedule
    1:30pm How advances in deep learning and computer vision can empower the blind community Anirudh Koul (Microsoft), Saqib Shaikh (Microsoft)
    Add Deep reinforcement learning for robotics to your personal schedule
    2:20pm Deep reinforcement learning for robotics Pieter Abbeel (OpenAI / UC Berkeley)
    Add End-to-end learning for autonomous driving to your personal schedule
    3:45pm End-to-end learning for autonomous driving Urs Muller (NVIDIA)
    Add High-level APIs for scalable machine learning to your personal schedule
    4:35pm High-level APIs for scalable machine learning Martin Wicke (Google)
    3D08
    Add Practical AI product development to your personal schedule
    11:50am Practical AI product development Hilary Mason (Fast Forward Labs)
    Add Transforming your industry with cognitive computing to your personal schedule
    1:30pm Transforming your industry with cognitive computing Guruduth Banavar (Cognitive Computing, IBM)
    Add Benefits of scaling machine learning to your personal schedule
    2:20pm Benefits of scaling machine learning Reza Zadeh (Stanford | Matroid)
    Add The need for speed: Benchmarking deep learning workloads to your personal schedule
    4:35pm The need for speed: Benchmarking deep learning workloads Greg Diamos (Baidu), Sharan Narang (Baidu)
    3D09
    Add How to make robots empathetic to human feelings in real time to your personal schedule
    11:00am How to make robots empathetic to human feelings in real time Pascale Fung (The Hong Kong University of Science and Technology)
    Add Building and applying emotion recognition to your personal schedule
    11:50am Building and applying emotion recognition Anna Roth (Microsoft), Cristian Canton (Microsoft Technology and Research)
    Add Probabilistic programming for augmented intelligence to your personal schedule
    2:20pm Probabilistic programming for augmented intelligence Vikash Mansinghka (MIT), Richard Tibbetts (MIT)
    Add The future of natural language generation, 2016–2026 to your personal schedule
    3:45pm The future of natural language generation, 2016–2026 Robbie Allen (Automated Insights, Inc.)
    Add The new artificial intelligence frontier to your personal schedule
    4:35pm The new artificial intelligence frontier Babak Hodjat (Sentient Technologies)
    3D10
    Add AI on IA to your personal schedule
    11:00am AI on IA Vin Sharma (Intel)
    Add What I learned by replacing middle-class manufacturing jobs with ML and AI to your personal schedule
    2:20pm What I learned by replacing middle-class manufacturing jobs with ML and AI Eduardo Arino de la Rubia (Domino Data Lab)
    Add Deep learning: Modular in theory, inflexible in practice to your personal schedule
    3:45pm Deep learning: Modular in theory, inflexible in practice Diogo Moitinho de Almeida (Enlitic)
    8:00am Morning coffee service Sponsored by Capital One | Room: River Pavilion A
    10:30am Morning Break | Room: River Pavilion A
    12:30pm Lunch Sponsored by Intel | Room: River Pavilion A
    3:00pm Afternoon Break | Room: River Pavilion A
    Add Attendee Reception to your personal schedule
    5:15pm Attendee Reception | Room: River Pavilion A
    9:00am-9:05am (5m)
    Monday opening remarks
    Ben Lorica (O'Reilly Media), Roger Chen (.)
    Program chairs Ben Lorica and Roger Chen open the first day of keynotes.
    9:05am-9:30am (25m)
    Software engineering of systems that learn in uncertain domains
    Peter Norvig (Google)
    Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities).
    9:30am-9:45am (15m)
    Why we'll never run out of jobs
    Tim O'Reilly (O'Reilly Media)
    There are many who fear that in the future, AI will do more and more of the jobs done by humans, leaving us without meaningful work. To believe this is a colossal failure of the imagination. Tim O'Reilly explains why we can't just use technology to replace people; we must use it to augment them so that they can do things that were previously impossible.
    9:45am-9:55am (10m) Sponsored
    Artificial intelligence: Making a human connection
    Genevieve Bell (Intel Corporation)
    Genevieve Bell explores the meaning of “intelligence” within the context of machines and its cultural impact on humans and their relationships. Genevieve interrogates AI not just as a technical agenda but as a cultural category in order to understand the ways in which the story of AI is connected to the history of human culture.
    9:55am-10:10am (15m)
    Humanizing AI development: Lessons from China and Xiaoice at Microsoft
    Lili Cheng (Microsoft Research)
    Keynote by Lili Cheng
    10:10am-10:25am (15m)
    How AI is propelling driverless cars, the future of surface transport
    Shahin Farshchi (Lux Capital)
    Keynote by Shahin Farshchi
    10:25am-10:30am (5m)
    Closing remarks
    Ben Lorica (O'Reilly Media), Roger Chen (.)
    Program chairs Ben Lorica and Roger Chen close the first day of keynotes.
    11:00am-11:40am (40m)
    Deep neural network model compression and an efficient inference engine
    Song Han (Stanford University)
    Neural networks are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. Song Han explains how deep compression addresses this limitation by reducing the storage requirement of neural networks without affecting their accuracy and proposes an energy-efficient inference engine (EIE) that works with this model.
    11:50am-12:30pm (40m)
    The future of AI
    Oren Etzioni (Allen Institute for Artificial Intelligence)
    Oren Etzioni offers his perspective on the future of AI, based on cutting-edge research at the Allen Institute for AI on projects such as Aristo and Semantic Scholar. This future reflects the institute's mission: AI for the common good.
    1:30pm-2:10pm (40m) Implementing AI
    How advances in deep learning and computer vision can empower the blind community
    Anirudh Koul (Microsoft), Saqib Shaikh (Microsoft)
    Anirudh Koul and Saqib Shaikh explore cutting-edge advances at the intersection of computer vision, language, and deep learning that can help describe the physical world to the blind community. Anirudh and Saqib then explain how developers can utilize this state-of-the-art image description, as well as visual question answering and other computer-vision technologies, in their own applications.
    2:20pm-3:00pm (40m)
    Deep reinforcement learning for robotics
    Pieter Abbeel (OpenAI / UC Berkeley)
    Pieter Abbeel explores deep reinforcement learning for robotics.
    3:45pm-4:25pm (40m) Verticals and applications
    End-to-end learning for autonomous driving
    Urs Muller (NVIDIA)
    Urs Muller presents the architecture and training methods used to build an autonomous road-following system. A key aspect of the approach is eliminating the need for hand-programmed rules and procedures such as finding lane markings, guardrails, or other cars, thereby avoiding the creation of a large number of “if, then, else” statements.
    4:35pm-5:15pm (40m) Implementing AI
    High-level APIs for scalable machine learning
    Martin Wicke (Google)
    TensorFlow is a system for scalable machine learning. However, using raw TensorFlow and profiling, optimizing, and debugging large-scale models can be daunting for novice and expert users alike. Martin Wicke explores new APIs based on TensorFlow that aim to make building complex models easier and allow users to scale quickly.
    11:00am-11:40am (40m) Impact on business and society
    Only humans need apply: Adding value to the work of very smart machines
    Tom Davenport (Babson College, MIT)
    The automation of decisions and actions now threatens even knowledge-worker jobs. Tom Davenport describes both the threat of automation and the promise of augmentation—combining smart machines with smart people—and explores five roles that individuals can adopt to add value to AI, as well as what these roles mean for businesses.
    11:50am-12:30pm (40m) Implementing AI
    Practical AI product development
    Hilary Mason (Fast Forward Labs)
    Hilary Mason explores a framework for applied AI research, with a focus on algorithmic capabilities that are useful for building real-world products today. Drawing on real-world examples, Hilary outlines a system for thinking about which AI capabilities are ready to transition from pure research to applied products and how to make the transition from research paper to a working product.
    1:30pm-2:10pm (40m)
    Transforming your industry with cognitive computing
    Guruduth Banavar (Cognitive Computing, IBM)
    In the last decade, the availability of massive amounts of new data, the development of new AI techniques, and the availability of scalable computing infrastructure have given rise to a new class of machine capabilities we call cognitive computing. Guruduth Banavar offers an overview of the technological breakthroughs that are enabling this trend.
    2:20pm-3:00pm (40m)
    Benefits of scaling machine learning
    Reza Zadeh (Stanford | Matroid)
    Machine learning is evolving to utilize new hardware, such as GPUs and large commodity clusters. Reza Zadeh presents two projects that have benefitted greatly through scaling: obtaining leading results on the Princeton ModelNet object recognition task and matrix computations and optimization in Apache Spark.
    3:45pm-4:25pm (40m) Implementing AI
    Unlock the power of AI: A fundamentally different approach to building intelligent systems
    Mark Hammond (Bonsai)
    Mark Hammond explains how Bonsai’s platform enables every developer to add intelligence to their software or hardware, regardless of AI expertise. Bonsai’s suite of tools—a new programming language, AI engine, and cloud service—abstracts away the lowest-level details of programming AI, allowing developers to focus on concepts they want a system to learn and how those concepts can be taught.
    4:35pm-5:15pm (40m)
    The need for speed: Benchmarking deep learning workloads
    Greg Diamos (Baidu), Sharan Narang (Baidu)
    Greg Diamos and Sharan Narang discuss the impact of AI on applications within Baidu, including autonomous driving and speech recognition, offering a brief introduction to the challenges in training deep learning algorithms as well as the different workloads that are used in various deep learning applications.
    11:00am-11:40am (40m) Implementing AI
    How to make robots empathetic to human feelings in real time
    Pascale Fung (The Hong Kong University of Science and Technology)
    Pascale Fung describes an approach to enable an interactive dialogue system to recognize user emotion and sentiment in real time and explores CNN models that recognize emotion from raw speech input without feature engineering and sentiments. These modules allow otherwise conventional dialogue systems to have “empathy” and answer users while being aware of their emotion and intent.
    11:50am-12:30pm (40m) Implementing AI
    Building and applying emotion recognition
    Anna Roth (Microsoft), Cristian Canton (Microsoft Technology and Research)
    Anna Roth and Cristian Canton walk you through building a system to recognize emotions by inferring them from facial expressions. Cristian and Anna explain how they trained their emotion recognition CNN from noisy data and how to approach labeling subjective data like emotion with crowdsourcing before showing a demo of this work in action, as it is exposed in Microsoft’s Emotion API.
    1:30pm-2:10pm (40m) Interacting with AI
    Deeply active learning: Approximating human learning with smaller datasets combined with human assistance
    Christopher Nguyen (Arimo), Binh Han (Arimo)
    Natural-language assistants are the emergent killer app for AI. Getting from here to there with deep learning, however, can require enormous datasets. Christopher Nguyen and Binh Han explain how to shorten the time to effectiveness and the amount of training data that's required to achieve a given level of performance using human-in-the-loop active learning.
    2:20pm-3:00pm (40m) Interacting with AI
    Probabilistic programming for augmented intelligence
    Vikash Mansinghka (MIT), Richard Tibbetts (MIT)
    The next generation of AI systems will provide assisted intuition and judgment for everyday people trying to collaboratively solve hard problems. Vikash Mansinghka and Richard Tibbetts explore how AI will be used on problems like malnutrition, public health, education, and governance—complex, ambiguous areas of human knowledge where data is sparse and there are no rules.
    3:45pm-4:25pm (40m) Impact on business and society
    The future of natural language generation, 2016–2026
    Robbie Allen (Automated Insights, Inc.)
    Natural language generation, the branch of AI that turns raw data into human-sounding narratives, is coming into its own in 2016. Robbie Allen explores the real-world advances in NLG over the past decade and then looks ahead to the next. Computers are already writing finance, sports, ecommerce, and business intelligence stories. Find out what—and how—they’ll be writing by 2026.
    4:35pm-5:15pm (40m) Impact on business and society
    The new artificial intelligence frontier
    Babak Hodjat (Sentient Technologies)
    Babak Hodjat discusses the progress in AI, diving into how AI can offer unique solutions in verticals such as investment, medical diagnosis, and ecommerce. Babak details how using massively scaled distributed evolutionary computation, mimicking biological evolution, allows an AI to learn, adapt, and react faster to provide customers with the answers and decisions they need.
    11:00am-11:40am (40m) Sponsored
    AI on IA
    Vin Sharma (Intel)
    Vin Sharma explores how Intel is investing in artificial intelligence and using open source software platforms, frameworks, and libraries, as well as Intel's hardware to advance the future.
    11:50am-12:30pm (40m) Sponsored
    Deploying AI-based services in the data center for real-time responsive experiences
    Sanford Russell (NVIDIA)
    In the new era of artificial intelligence, every organization must examine how to extract intelligence from its data using deep learning. Sanford Russell explores how NVIDIA GPUs are deployed today to accelerate deep learning inference workloads in the data center.
    1:30pm-2:10pm (40m) Implementing AI
    Growing up: Continuous integration for machine-learning models
    Zachary Hanif (Capital One)
    Developing and validating frequently updated models is core to professional data science teams. Zachary Hanif discusses the adaptation of CI tools and practices to solve model governance and accuracy tracking concerns in a complex environment with adversarial and temporal data complications.
    2:20pm-3:00pm (40m) Impact on business and society
    What I learned by replacing middle-class manufacturing jobs with ML and AI
    Eduardo Arino de la Rubia (Domino Data Lab)
    Manufacturing in the United States is facing extreme pressures from globalization. Eduardo Arino de la Rubia synthesizes what he learned working side by side with the workers he was replacing with AI and ML, discussing their struggles, how they saw the technology the would take their jobs, the limitations of the technology, and what his real impact was in the face of globalization.
    3:45pm-4:25pm (40m) Implementing AI
    Deep learning: Modular in theory, inflexible in practice
    Diogo Moitinho de Almeida (Enlitic)
    The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. The reality isn't that simple, and the commonly used tools greatly limit what we are capable of doing. Diogo Almeida explains what we can do about it and offers a practical attempt at a deep learning library of the future.
    4:35pm-5:15pm (40m) Implementing AI
    A peek at x.ai’s data science architecture
    Angela Zhou (x.ai)
    In any human-machine interaction, you need a dialogue model: the machine must understand and be able to respond appropriately. Angela Zhou discusses x.ai's AI personal assistant, Amy Ingram, who schedules meetings for you, focusing on the way x.ai has approached both understanding and responding.
    8:00am-9:00am (1h)
    Break: Morning coffee service Sponsored by Capital One
    10:30am-11:00am (30m)
    Break: Morning Break
    12:30pm-1:30pm (1h)
    Break: Lunch Sponsored by Intel
    3:00pm-3:45pm (45m)
    Break: Afternoon Break
    5:15pm-6:15pm (1h)
    Attendee Reception
    Come enjoy delicious snacks and beverages with fellow O'Reilly AI attendees, speakers, and sponsors.