September 26-27, 2016
New York, NY
 

List by type

  • Keynotes
  • Sessions
  • River Pavilion B
    River Pavilion B
    9:00am Tuesday opening remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable)
    9:05am Obstacles to progress in AI Yann LeCun (Facebook)
    9:20am Minds and brains and the route to smarter machines Gary Marcus (Geometric Intelligence)
    9:35am Thor’s hammer Jim McHugh (NVIDIA)
    9:45am Lessons on building data products at Google Aparna Chennapragada (Google)
    10:00am Deep learning at scale and use cases Naveen Rao (Intel)
    10:15am Why AI needs emotion Rana el Kaliouby (Affectiva)
    10:30am Closing remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable)
    11:00am TensorFlow for mobile poets Pete Warden (TensorFlow)
    1:30pm Progress of delivering real AI workloads Xuedong (XD) Huang (Microsoft Research)
    3:45pm Chainer: A flexible and intuitive framework for complex neural networks Orion Wolfe (Preferred Networks), Shohei Hido (Preferred Networks)
    3D08
    11:00am Interactive learning systems: Why now and how? Alekh Agarwal (Microsoft Research)
    11:50am AI is not a matter of strength but of intelligence Francisco Webber (Cortical.io)
    1:30pm Making AI a reality for the enterprise and the physical world Aman Naimat (Demandbase), MARK PATEL (McKinsey & Company)
    3:45pm Leveraging artificial intelligence in creative technology Jennifer Rubinovitz (DBRS Innovation Lab), Amelia Winger-Bearskin (DBRS Innovation Lab)
    4:35pm Combining statistics and expert human judgement for better recommendations Jianqiang (Jay) Wang (Stitch Fix), Jasmine Nettiksimmons (Stitch Fix)
    3D09
    11:00am How to scope an AI project Jana Eggers (Nara Logics)
    11:50am Building an AI startup: Realities and tactics Matt Turck (FirstMark Capital), Peter Brodsky (HyperScience)
    2:20pm Genetic architect: Investigating the structure of biology with machine learning Laura Deming (The Longevity Fund), Sasha Targ (UCSF Institute for Human Genetics)
    4:35pm Bayesian program learning for the enterprise Ben Vigoda (Gamalon)
    8:00am Morning Coffee Service Sponsored by SAP | 8:15 - 8:45 Speed Networking | Room: River Pavilion A
    10:30am Morning Break | Room: River Pavilion A
    12:30pm Lunch Sponsored by NVIDIA | Room: River Pavilion A
    3:00pm Break | Room: River Pavilion A
    9:00am-9:05am (5m)
    Tuesday opening remarks
    Ben Lorica (O'Reilly Media), Roger Chen (Computable)
    Program chairs Ben Lorica and Roger Chen open the second day of keynotes.
    9:05am-9:20am (15m)
    Obstacles to progress in AI
    Yann LeCun (Facebook)
    The essence of intelligence is the ability to predict. Prediction, perception, planning/reasoning, attention, and memory are the pillars of intelligence. Yann LeCun describes several projects at FAIR and NYU on unsupervised learning, question answering with a new type of memory-augmented network, and various applications for vision and natural language understanding.
    9:20am-9:35am (15m)
    Minds and brains and the route to smarter machines
    Gary Marcus (Geometric Intelligence)
    Keynote by Gary Marcus
    9:35am-9:45am (10m)
    Thor’s hammer
    Jim McHugh (NVIDIA)
    We are entering a new computing paradigm—an era where software will write software. This is the biggest and fastest transition since the advent of the Internet. Big data and analytics brought us information and insight; AI and deep learning turn that insight into superhuman knowledge and real-time action. Jim McHugh shares real-world examples of companies solving problems once thought unsolvable.
    9:45am-10:00am (15m)
    Lessons on building data products at Google
    Aparna Chennapragada (Google)
    Aparna Chennapragada explores building data products at Google.
    10:00am-10:15am (15m)
    Deep learning at scale and use cases
    Naveen Rao (Intel)
    Deep learning has made a major impact in the last three years. Imperfect interactions with machines, such as speech or image processing, have been made robust by deep learning that finds usable structure in large datasets. Naveen Rao outlines deep learning challenges and explores how changes to the organization of computation and communication can lead to advances in capabilities.
    10:15am-10:30am (15m)
    Why AI needs emotion
    Rana el Kaliouby (Affectiva)
    Highly connected, interactive artificial intelligence systems surround us daily, but as smart as these systems are, they lack the ability to truly empathize with us humans. Rana El Kaliouby explores why emotion AI is critical to accelerating adoption of AI systems, how emotion AI is being used today, and what the future will look like.
    10:30am-10:30am (0m)
    Closing remarks
    Ben Lorica (O'Reilly Media), Roger Chen (Computable)
    Program chairs Ben Lorica and Roger Chen offer closing remarks on the last day of keynotes.
    11:00am-11:40am (40m)
    TensorFlow for mobile poets
    Pete Warden (TensorFlow)
    Pete Warden shows you how to train an object recognition model on your own images and then integrate it into a mobile application. Drawing on concrete examples, Pete demonstrates how to apply advanced machine learning to practical problems without the need for deep theoretical knowledge or even much coding.
    11:50am-12:30pm (40m) Implementing AI
    Managing the deep learning computer-vision pipeline with DIGITS
    Jon Barker (NVIDIA)
    The process for deploying an effective neural network is iterative. Before an effective neural network is reached, many parameters must be evaluated and their impact on performance assessed. Jon Barker offers an overview of DIGITS, a deep learning GPU-training system designed to provide a real-time interactive user interface targeted toward accelerating the development process.
    1:30pm-2:10pm (40m)
    Progress of delivering real AI workloads
    Xuedong (XD) Huang (Microsoft Research)
    Progress in enterprise AI workloads, particularly in deep learning, big data, and computing infrastructure, will profoundly impact productivity for users. XD Huang outlines enterprise AI examples to illustrate the collective efforts and exciting opportunities modern AI technologies are making possible.
    2:20pm-3:00pm (40m)
    Unlocking AI: How to enable every human in the world to train and use AI
    Matt Zeiler (Clarifai)
    Fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is “taught” and make both teaching and using AI accessible to anyone in the world.
    3:45pm-4:25pm (40m)
    Chainer: A flexible and intuitive framework for complex neural networks
    Orion Wolfe (Preferred Networks), Shohei Hido (Preferred Networks)
    Open source software frameworks are the key for applying deep learning technologies. Orion Wolfe and Shohei Hido introduce Chainer, a Python-based standalone framework that enables users to intuitively implement many kinds of other models, including recurrent neural networks, with a lot of flexibility and comparable performance to GPUs.
    4:35pm-5:15pm (40m) Implementing AI
    The identities of bots: A learning architecture for conversational software
    Suman Roy (betaworks)
    The recent explosion of bots on communication platforms has rekindled the hopes of conversational AI. However, building intelligent and customizable bots is not just bottlenecked by NLP and speech recognition. Our biggest limitation is the inability to modularize the goals of human bot interconnection. Suman Roy explains why we need a layered architecture for bots to learn about us from data.
    11:00am-11:40am (40m)
    Interactive learning systems: Why now and how?
    Alekh Agarwal (Microsoft Research)
    Alekh Agarwal explains why interactive learning systems that go beyond the routine train/test paradigm of supervised machine learning are essential to the development of AI agents. Along the way, Alekh outlines the novel challenges that arise at both the systems and learning side of things in designing and implementing such systems.
    11:50am-12:30pm (40m) Implementing AI
    AI is not a matter of strength but of intelligence
    Francisco Webber (Cortical.io)
    Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype—it's a production-grade enterprise technology.
    1:30pm-2:10pm (40m) Verticals and applications
    Making AI a reality for the enterprise and the physical world
    Aman Naimat (Demandbase), MARK PATEL (McKinsey & Company)
    Aman Naimat and Mark Patel present an analysis of the current adoption of AI in industry based on a systematic study of the entire business Internet at over 500,000 companies. Drawing on this data, Aman and Mark offer a new economic framework to discover, measure, and motivate future use cases for AI.
    2:20pm-3:00pm (40m) Implementing AI
    Lessons learned from deploying the top deep learning frameworks in production
    Kenny Daniel (Algorithmia)
    By building a marketplace for algorithms, Algorithmia gained unique experience with building and deploying machine-learning models using a wide variety of frameworks. Kenny Daniel shares the lessons Algorithmia learned through trial and error, the pros and cons of different deep learning frameworks, and the challenges involved with deploying them in production systems.
    3:45pm-4:25pm (40m) Verticals and applications
    Leveraging artificial intelligence in creative technology
    Jennifer Rubinovitz (DBRS Innovation Lab), Amelia Winger-Bearskin (DBRS Innovation Lab)
    Jennifer Rubinovitz and Amelia Winger-Bearskin offer an overview of how artificial intelligence researchers and artists at the DBRS Innovation Lab have collaborated on five different projects (and counting), ranging from composing modern classical music to visualizing deep neural networks in virtual reality.
    4:35pm-5:15pm (40m) Interacting with AI
    Combining statistics and expert human judgement for better recommendations
    Jianqiang (Jay) Wang (Stitch Fix), Jasmine Nettiksimmons (Stitch Fix)
    Jay Wang and Jasmine Nettiksimmons explore the business model of Stitch Fix, an emerging startup that uses artificial intelligence and human experts for a personalized shopping experience, and highlight the challenges encountered implementing Stitch Fix's recommendation algorithm and interacting AI with human stylists.
    11:00am-11:40am (40m) Implementing AI
    How to scope an AI project
    Jana Eggers (Nara Logics)
    Drawing on her experience implementing AI systems in large enterprises, Jana Eggers covers the dos and don'ts of scoping a project across time, money, and people and compares and contrasts AI projects with typical IT and data science projects to explore the new aspects you need to consider as you add AI to your tech portfolio.
    11:50am-12:30pm (40m)
    Building an AI startup: Realities and tactics
    Matt Turck (FirstMark Capital), Peter Brodsky (HyperScience)
    AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. Matt Turck and Peter Brodsky explain why the realities of building a startup are different and offer successful strategies and tactics that consider not just technical prowess but also thoughtful market positioning and business excellence.
    1:30pm-2:10pm (40m) Implementing AI
    Intel's new processors: A machine-learning perspective
    Amitai Armon (Intel)
    Intel has recently released new processors for the Xeon and Xeon Phi product lines. Amitai Armon discusses how these processors are used for machine-learning tasks and offers data on their performance for several types of algorithms in both single-node and multinode settings.
    2:20pm-3:00pm (40m) Verticals and applications
    Genetic architect: Investigating the structure of biology with machine learning
    Laura Deming (The Longevity Fund), Sasha Targ (UCSF Institute for Human Genetics)
    Each human genome is a 3 billion-base-pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. Laura Deming and Sasha Targ describe novel machine-learning search algorithms that allow us to find architectures suited to decode genomics.
    3:45pm-4:25pm (40m) Verticals and applications
    Achieving precision medicine at scale: Building medical AI to predict individual disease evolution in real time
    Ash Damle (Lumiata)
    AI in healthcare demands models that can handle the complexity of health data and implementation of automation, precision, speed, and transparency with minimal error. Drawing on Lumiata’s experience with building medical AI, Ash Damle discusses key considerations in dealing with high-dimensional data, deep learning, and how to apply practical AI in healthcare today.
    4:35pm-5:15pm (40m) Implementing AI
    Bayesian program learning for the enterprise
    Ben Vigoda (Gamalon)
    Benjamin Vigoda explains how Bayesian program learning can do things that other machine-learning approaches can't and why it's especially suited to enterprise data challenges.
    8:00am-9:00am (1h)
    Break: Morning Coffee Service Sponsored by SAP | 8:15 - 8:45 Speed Networking
    10:30am-11:00am (30m)
    Break: Morning Break
    12:30pm-1:30pm (1h)
    Break: Lunch Sponsored by NVIDIA
    3:00pm-3:45pm (45m)
    Break