September 26-27, 2016
New York, NY

O'Reilly AI Conference 2016 Sessions

Monday, September 26

11:00am–11:40am Monday, 09/26/2016
Location: 3D08 Level: Non-technical
Tom Davenport (Babson College, MIT)
Average rating: ***..
(3.83, 6 ratings)
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. Read more.
11:00am–11:40am Monday, 09/26/2016
Location: River Pavilion B
Song Han (Stanford University)
Average rating: *****
(5.00, 5 ratings)
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. Read more.
11:00am–11:40am Monday, 09/26/2016
Location: 3D09 Level: Advanced
Pascale Fung (The Hong Kong University of Science and Technology)
Average rating: ****.
(4.33, 3 ratings)
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. Read more.
11:00am–11:40am Monday, 09/26/2016
Location: 3D10
Vin Sharma (Intel)
Average rating: *....
(1.00, 1 rating)
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. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: 3D08 Level: Beginner
Hilary Mason (Cloudera Fast Forward Labs)
Average rating: ****.
(4.78, 9 ratings)
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. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: River Pavilion B
Oren Etzioni (Allen Institute for Artificial Intelligence)
Average rating: ****.
(4.00, 2 ratings)
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. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: 3D09 Level: Intermediate
Anna Roth (Microsoft), Cristian Canton (Microsoft Technology and Research)
Average rating: **...
(2.00, 1 rating)
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. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: 3D10
Sanford Russell (NVIDIA)
Average rating: ***..
(3.00, 1 rating)
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. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: River Pavilion B Level: Beginner
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. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: 3D08
Guruduth Banavar (Cognitive Computing, IBM)
Average rating: **...
(2.75, 4 ratings)
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. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: 3D09 Level: Intermediate
Christopher Nguyen (Arimo), Binh Han (Arimo)
Average rating: ***..
(3.00, 1 rating)
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. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: 3D10
Zachary Hanif (Capital One)
Average rating: ***..
(3.50, 4 ratings)
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. Read more.
2:20pm–3:00pm Monday, 09/26/2016
Location: River Pavilion B
Pieter Abbeel (OpenAI / UC Berkeley)
Average rating: ****.
(4.67, 3 ratings)
Pieter Abbeel explores deep reinforcement learning for robotics. Read more.
2:20pm–3:00pm Monday, 09/26/2016
Location: 3D08
Reza Zadeh (Matroid | Stanford)
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. Read more.
2:20pm–3:00pm Monday, 09/26/2016
Location: 3D09 Level: Beginner
Average rating: ****.
(4.83, 6 ratings)
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. Read more.
2:20pm–3:00pm Monday, 09/26/2016
Location: 3D10 Level: Beginner
Eduardo Arino de la Rubia (Domino Data Lab)
Average rating: ****.
(4.00, 5 ratings)
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. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: River Pavilion B Level: Beginner
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. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: 3D08 Level: Intermediate
Mark Hammond (Microsoft)
Average rating: *....
(1.00, 1 rating)
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. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: 3D09 Level: Non-technical
Robbie Allen (InfiniaML)
Average rating: *****
(5.00, 1 rating)
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. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: 3D10 Level: Advanced
Average rating: *****
(5.00, 3 ratings)
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. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: River Pavilion B Level: Intermediate
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. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: 3D08 Level: Intermediate
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. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: 3D09 Level: Non-technical
Babak Hodjat (Sentient Technologies)
Average rating: ****.
(4.00, 2 ratings)
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. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: 3D10 Level: Beginner
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. Read more.

Tuesday, September 27

11:00am–11:40am Tuesday, 09/27/2016
Location: River Pavilion B
Pete Warden (TensorFlow)
Average rating: ****.
(4.50, 2 ratings)
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. Read more.
11:00am–11:40am Tuesday, 09/27/2016
Location: 3D08
Alekh Agarwal (Microsoft Research)
Average rating: **...
(2.50, 2 ratings)
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. Read more.
11:00am–11:40am Tuesday, 09/27/2016
Location: 3D09 Level: Non-technical
Jana Eggers (Nara Logics)
Average rating: ****.
(4.50, 2 ratings)
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. Read more.
11:50am–12:30pm Tuesday, 09/27/2016
Location: River Pavilion B Level: Intermediate
Jon Barker (NVIDIA)
Average rating: *****
(5.00, 1 rating)
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. Read more.
11:50am–12:30pm Tuesday, 09/27/2016
Location: 3D08 Level: Beginner
Francisco Webber (Cortical.io)
Average rating: ****.
(4.20, 5 ratings)
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. Read more.
11:50am–12:30pm Tuesday, 09/27/2016
Location: 3D09
Matt Turck (FirstMark Capital), Peter Brodsky (HyperScience)
Average rating: *****
(5.00, 3 ratings)
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. Read more.
1:30pm–2:10pm Tuesday, 09/27/2016
Location: River Pavilion B
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. Read more.
1:30pm–2:10pm Tuesday, 09/27/2016
Location: 3D08 Level: Non-technical
Aman Naimat (Demandbase), MARK PATEL (McKinsey & Company)
Average rating: *****
(5.00, 3 ratings)
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. Read more.
1:30pm–2:10pm Tuesday, 09/27/2016
Location: 3D09 Level: Beginner
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. Read more.
2:20pm–3:00pm Tuesday, 09/27/2016
Location: River Pavilion B
Matt Zeiler (Clarifai)
Average rating: ****.
(4.00, 1 rating)
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. Read more.
2:20pm–3:00pm Tuesday, 09/27/2016
Location: 3D08 Level: Intermediate
Kenny Daniel (Algorithmia)
Average rating: ****.
(4.33, 3 ratings)
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. Read more.
2:20pm–3:00pm Tuesday, 09/27/2016
Location: 3D09 Level: Intermediate
Laura Deming (The Longevity Fund), Sasha Targ (UCSF Institute for Human Genetics)
Average rating: *****
(5.00, 1 rating)
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. Read more.
3:45pm–4:25pm Tuesday, 09/27/2016
Location: River Pavilion B
Orion Wolfe (Preferred Networks), Shohei Hido (Preferred Networks)
Average rating: ****.
(4.00, 1 rating)
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. Read more.
3:45pm–4:25pm Tuesday, 09/27/2016
Location: 3D08 Level: Beginner
Jennifer Rubinovitz (DBRS Innovation Lab), Amelia Winger-Bearskin (DBRS Innovation Lab)
Average rating: **...
(2.50, 2 ratings)
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. Read more.
3:45pm–4:25pm Tuesday, 09/27/2016
Location: 3D09 Level: Beginner
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. Read more.
4:35pm–5:15pm Tuesday, 09/27/2016
Location: River Pavilion B Level: Intermediate
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. Read more.
4:35pm–5:15pm Tuesday, 09/27/2016
Location: 3D08 Level: Intermediate
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. Read more.
4:35pm–5:15pm Tuesday, 09/27/2016
Location: 3D09 Level: Non-technical
Ben Vigoda (Gamalon)
Average rating: ****.
(4.80, 5 ratings)
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. Read more.