Presented By O'Reilly and Cloudera
Make Data Work
March 28–29, 2016: Training
March 29–31, 2016: Conference
San Jose, CA

A.I. conference sessions

4:30pm–5:00pm Tuesday, 03/29/2016
Mike Cafarella (University of Michigan)
Dark data is the great mass of data buried in text, tables, figures, and images that lacks structure and so is essentially unprocessable by existing software. DeepDive is a system that extracts value from dark data. Mike Cafarella offers an introduction to DeepDive, exploring the key technical innovations that enable DeepDive to produce statistical inference at massive scale.
11:50am–12:30pm Wednesday, 03/30/2016
Eric Colson (Stitch Fix)
Recommender systems use machine-learning algorithms to surface relevant products to consumers. While they are extremely effective, they cannot fully replace human interpretation. The two have very different capabilities that are additive. Eric Colson shows what's possible when the unique contributions of machines are combined with those of human experts to create a truly personalized experience.
9:25am–9:40am Thursday, 03/31/2016
Adam Cheyer (Samsung)
As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems solving real-world problems for hundreds of millions of users. Drawing on specific examples, Adam reveals some of the techniques he uses to maximize the impact of the AI technologies he employs.
4:20pm–5:00pm Wednesday, 03/30/2016
Brandon Ballinger (Cardiogram), Johnson Hsieh (Cardiogram)
Each year, 15 million people suffer strokes, and at least a fifth of those are due to atrial fibrillation, the most common heart arrhythmia. Brandon Ballinger reports on a collaboration between UCSF cardiologists and ex-Google data scientists that detects atrial fibrillation with deep learning.
2:00pm–2:30pm Tuesday, 03/29/2016
Faisal Farooq (IBM Watson Health), Balaji Krishnapuram (IBM Watson Health)
Faisal Farooq and Balaji Krishnapuram explore how data science 3.0 is empowering domain experts in industry verticals—due to the industrialized division of labor with standardized processes and user tools—and offer an example from the healthcare industry that substantially reduced costs for a healthcare entity and improved clinical outcomes in patients.
11:50am–12:30pm Thursday, 03/31/2016
Jeremy Howard ( | USF | and
In his 20+ years of applying machine learning and data analysis to a wide range of industries, Jeremy Howard never felt that his work really changed anyone's life in a deep and positive way, so he spent a year researching ways he might effect real change. Jeremy outlines the impact that deep learning is going to make on the world and explains how you too can make a difference.
5:10pm–5:50pm Wednesday, 03/30/2016
Josh Patterson (Skymind), Dave Kale (Skymind), Zachary Lipton (University of California, San Diego)
Time series data is increasingly ubiquitous with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using recurrent neural networks (RNNs).
11:30am–12:00pm Tuesday, 03/29/2016
Naveen Rao (Intel)
Naveen Rao discusses deep learning, a form of machine learning loosely inspired by the brain. Naveen explores the benefits of deep learning over other machine-learning techniques, recent advances in the field, the deep learning workflow, challenges in developing and deploying deep learning-based solutions, and the need for standardized tools for building and scaling deep learning solutions.
12:00pm–12:30pm Tuesday, 03/29/2016
Stephen Merity (Salesforce Research), Caiming Xiong (Metamind)
Stephen Merity, Richard Socher, and Caiming Xiong discuss their recent work on extending the dynamic memory network (DMN) to question answering in both the textual and visual domains and explore how memory networks and attention mechanisms can allow for better interpretability of deep learning models.
4:40pm–5:00pm Tuesday, 03/29/2016
Michael Ludden (IBM Watson)
Artificial intelligence is quickly moving from science fiction to science fact. But how should industries harness AI to extract business insight from data? Drawing on a number of real-world examples from mobile apps, healthcare, and education, Michael Ludden explains how machine learning can be democratized, augmenting human understanding and knowledge with early-stage AI.
4:20pm–5:00pm Thursday, 03/31/2016
Sreeni Iyer (quadanalytix), Anurag Bhardwaj (Quad Analytix)
Typically, 8–10% of product URLs in ecommerce sites are misclassified. Sreeni Iyer and Anurag Bhardwaj discuss a machine-learning-based solution that relies on an innovative fusion of classifiers that are both text- and image-based, along with human touch to handle edge cases, to automatically classify product URLs according to a canonical taxonomic organization with a high F-score.
10:00am–10:30am Tuesday, 03/29/2016
James Crawford (Orbital Insight)
Big data is exploding in space. Constellations of satellites are being launched to monitor the world in all wavelengths—tracking everything from ships to corn harvests. James Crawford explains how machine vision lets us see vast areas at once, while machine learning lets us analyze these images trillions of pixels at a time to recognize patterns that can help with world-changing projects
3:30pm–4:10pm Wednesday, 03/30/2016
Christopher Nguyen (Arimo), Anh Trinh (Arimo, Inc.)
Christopher and Anh are happy to answer questions about Distributed DataFrame (The DDF Project), visual DDFs and their role in collaborative data visualization, and distributed deep learning on Spark/DDF.
1:50pm–2:30pm Wednesday, 03/30/2016
Eric Colson (Stitch Fix)
Stop by to talk to Eric about the (current) limits to machine learning and find out when you should include a human in the loop, how to set up an organization to leverage both machine-learning and human-learning algorithms, how to gain insights into human decision making, and the various domains where machines and humans can be combined to produce new capabilities and value.
4:20pm–5:00pm Wednesday, 03/30/2016
Rajat Monga (Google), Amy Unruh (Google), Kaz Sato (Google)
Googlers Rajat, Amy, and Kazunori can answer all your TensorFlow questions, including how to use TensorFlow to utilize interactive queries on petabyte-sized datasets, empower large-scale distributed training of neural networks, and train and deploy machine-learning models.
11:00am–11:30am Tuesday, 03/29/2016
Kanu Gulati (Zetta Venture Partners)
Hardware-accelerated solutions are ready to meet challenges in data analytics with regard to data I/O, computational capacity, and interactive visualization. Data analytics and HPC evolution must go hand in hand. Kanu Gulati offers an overview of the advances in hardware acceleration and discusses the HPC applications enabling the next major wave of analytics innovation.
4:20pm–5:00pm Wednesday, 03/30/2016
Alex Ingerman (Amazon Web Services)
Alex Ingerman explains how several AWS services, including Amazon Machine Learning, Amazon Kinesis, AWS Lambda, and Amazon Mechanical Turk, can be tied together to build a predictive application to power a real-time customer-service use case.
9:55am–10:10am Wednesday, 03/30/2016
Jana Eggers (Nara Logics)
We hear about AI almost every day now. Opinions seem split between impending doom side and "superintelligence will save the human race." Jana Eggers offers the real deal on AI, explaining what's hype and what isn't and what we can do about it.
4:00pm–4:30pm Tuesday, 03/29/2016
Rajat Monga (Google)
TensorFlow is an open source software library for numerical computation with a focus on machine learning. Rajat Monga offers an introduction to TensorFlow and explains how to use it to train and deploy machine-learning models to make your next application smarter.
10:10am–10:25am Wednesday, 03/30/2016
Alyosha Efros (UC Berkeley)
Alyosha Efros discusses using computer vision to understand big visual data.