Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Schedule: Implementing AI sessions

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9:00am–12:30pm Monday, September 18, 2017
Location: Yosemite A Level: Advanced
Secondary topics:  Data science and AI
Average rating: ****.
(4.75, 4 ratings)
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to everything from big data analytics to robotics and AI. Vikash Mansinghka surveys the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
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9:00am–12:30pm Monday, September 18, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Algorithms, Data science and AI, Transportation and autonomous vehicles
Marcos Campos (Bonsai)
Average rating: **...
(2.33, 9 ratings)
Marcos Campos offers an overview of reinforcement learning, walking you through the various classes of reinforcement learning algorithms, the types of problems that can be solved with this technique, and how to build and train AI models using reinforcement learning and reward functions. Read more.
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9:00am–12:30pm Monday, September 18, 2017
Location: Imperial A Level: Intermediate
Secondary topics:  Deep learning, Tools and frameworks
Yufeng Guo (Google), Amy Unruh (Google)
Average rating: ***..
(3.33, 6 ratings)
Yufeng Guo and Amy Unruh walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng and Amy take you from conceptual overviews all the way to building complex classifiers and explain how you can apply deep learning to complex problems in science and industry. Read more.
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9:00am–12:30pm Monday, September 18, 2017
Location: Imperial B Level: Intermediate
Secondary topics:  Case studies, Enterprise adoption
Jana Eggers (Nara Logics)
Average rating: ***..
(3.43, 14 ratings)
Now is the time for us to define roles and capabilities for AI in business. Jana Eggers demonstrates how to deliver on an AI project for business, walking you through defining your project, setting expectations, assembling your team, hunting for data, assessing capabilities, implementing it, and rinsing and repeating. Read more.
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9:00am–12:30pm Monday, September 18, 2017
Location: Nob Hill 2 & 3 Level: Intermediate
Secondary topics:  Algorithms, Case studies
Bruno Gonçalves (New York University)
Average rating: ****.
(4.50, 2 ratings)
Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture. Read more.
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1:30pm–5:00pm Monday, September 18, 2017
Location: Yosemite A Level: Advanced
Secondary topics:  Data science and AI, Tools and frameworks
Gunnar Carlsson (Ayasdi)
Average rating: *****
(5.00, 1 rating)
Topological data analysis (TDA) is a framework for machine learning that synthesizes and combines machine learning algorithms to identify the shape of data. The technique is responsible for several major breakthroughs in our understanding of science and business. Gunnar Carlsson offers an overview of TDA's mathematical underpinnings and its practical application through software. Read more.
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1:30pm–5:00pm Monday, September 18, 2017
Location: Imperial A Level: Intermediate
Secondary topics:  Algorithms, Open source, Transportation and autonomous vehicles
Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley)
Average rating: ****.
(4.57, 7 ratings)
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
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1:30pm–5:00pm Monday, September 18, 2017
Location: Imperial B Level: Non-technical
Secondary topics:  Enterprise adoption, Tools and frameworks
Kristian Hammond (Narrative Science)
Average rating: ****.
(4.68, 22 ratings)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making. Read more.
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11:05am–11:45am Tuesday, September 19, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Anomaly detection, Data science and AI
Ira Cohen (Anodot)
Average rating: ****.
(4.33, 3 ratings)
The best practice in machine learning is to define a clear performance measurement for each model. However, when multiple models are deployed in parallel or feed into each other, it is infeasible to manually monitor them. Ira Cohen explains how Anodot devised a way to intelligently monitor the performance of its highly complex unsupervised machine learning models. Read more.
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11:05am–11:45am Tuesday, September 19, 2017
Location: Franciscan AB Level: Non-technical
Secondary topics:  Algorithms, Bots, Branding and marketing, Case studies, Decision making, Enterprise adoption, Law, ethics and governance (including AI safety), Organizational best practices, Smart Bot
Susan Etlinger (Altimeter Group)
Average rating: ***..
(3.60, 5 ratings)
Drawing on her report The Conversational Business: How Chatbots Will Reshape Digital Experiences, Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Data science and AI, Tools and frameworks
Ion Stoica (UC Berkeley)
Average rating: ***..
(3.75, 4 ratings)
Ion Stoica offers an overview of Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Location: Imperial A Level: Intermediate
Secondary topics:  Deep learning, Technical best practices
Stephen Merity (Salesforce Research)
Average rating: ****.
(4.62, 8 ratings)
Deep learning is used broadly at the forefront of research, achieving state-of-the-art results across a variety of domains. However, that doesn't mean it's a fit for all tasks—especially when the constraints of production are considered. Stephen Merity investigates what tasks deep learning excels at, what tasks trigger a failure mode, and where current research is looking to remedy the situation. Read more.
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1:45pm–2:25pm Tuesday, September 19, 2017
Location: Yosemite A Level: Beginner
Secondary topics:  Deep learning, IoT (including smart cities, manufacturing, smart homes/buildings)
Siddha Ganju (Deep Vision)
Average rating: ***..
(3.33, 3 ratings)
Deep learning is necessary to bring intelligence and autonomy to the edge. Siddha Ganju offers an overview of Deep Vision's solution, which optimizes both the hardware and the software, and discusses the Deep Vision embedded processor, which is optimized for deep learning and computer vision and offers 50x higher performance per watt than existing embedded GPUs without sacrificing programmability. Read more.
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1:45pm–2:25pm Tuesday, September 19, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Data and training, Medicine
Avesh Singh (Cardiogram), Brandon Ballinger (Cardiogram)
Average rating: ****.
(4.40, 5 ratings)
Deep learning is fueled by large labeled datasets, but in domains like medicine, each label represents a human life at risk. Avesh Singh and Brandon Ballinger offer an overview of autoencoders, heuristic training, and few-shot learning, with an emphasis on practical tips to create high-performing models utilizing hundreds of thousands of unlabeled data points and only thousands of labeled points. Read more.
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1:45pm–2:25pm Tuesday, September 19, 2017
Location: Franciscan AB Level: Intermediate
Secondary topics:  Data and training, Visualization and Interfaces
Matt McIlwain (Madrona Venture Group), Carlos Guestrin (Apple | The University of Washington)
Average rating: ***..
(3.75, 4 ratings)
Matt McIlwain interviews Carlos Guestrin. Drawing on his experience as an AI pioneer, Carlos discusses the intelligent applications powered by data and data science that are being built and deployed at a rapid pace, including on smartphones and edge devices, and shares consumer, commercial, and embedded examples. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Yosemite BC Level: Beginner
Secondary topics:  Enterprise adoption, Healthcare
Todd Stewart (Mercy), Lonny Northrup (Intermountian)
Average rating: ****.
(4.00, 1 rating)
Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Imperial A Level: Beginner
Secondary topics:  Deep learning, Tools and frameworks
Danny Lange (Unity Technologies)
Average rating: ****.
(4.00, 2 ratings)
Game development is a difficult and time-consuming pursuit that requires highly skilled labor to succeed. Drawing on his experience at Unity, Danny Lange demonstrates how deep learning and deep reinforcement learning can help developers at various stages in the development process create awesome digital experiences in gaming, VR, and AR. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Imperial B Level: Intermediate
Secondary topics:  Data science and AI, Deep learning, Tools and frameworks
Jason Dai (Intel), Ding Ding (Intel)
Jason Dai and Ding Ding offer an overview of BigDL, an open source distributed deep learning framework built for big data platforms. By leveraging the cluster distribution capabilities in Apache Spark, BigDL successfully unleashes the power of large-scale distributed training in deep learning, providing good performance, efficient scaling on large clusters, and good convergence results. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Grand Ballroom Level: Intermediate
Secondary topics:  Algorithms, Data science and AI
Kenny Daniel (Algorithmia)
Average rating: ****.
(4.00, 1 rating)
Kenny Daniel explains why AI and machine learning are a natural fit for serverless computing and shares a general architecture for scalable and serverless machine learning in production. Along the way, Kenny discusses the issues Algorithmia ran into when implementing its on-demand scaling over GPU clusters and outlines one possible vision for the future of cloud-based machine learning. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Franciscan CD Level: Intermediate
Secondary topics:  Decision making, Deep learning, Robotics, Transportation and autonomous vehicles
Li Erran Li (Uber)
Average rating: ***..
(3.80, 5 ratings)
Deep reinforcement learning has enabled artificial agents to achieve human-level performance across many challenging domains (for example, playing Atari games and Go). Li Erran Li shares several important algorithms, discusses major challenges, and explores promising results. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Algorithms, Architectures
Art Popp (ServiceNow)
Average rating: *....
(1.00, 1 rating)
Art Popp walks you through a “from scratch" implementation of two algorithms to demonstrate the tools available for original algorithm development, using both SIMD and SIMT designs, the leading hardware architectures of which are Xeon Phi and NVIDIA Cuda. Along the way, Art explores the performance per watt, performance per dollar (initial cost), and performance per dollar (TCO) of each. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Location: Grand Ballroom Level: Intermediate
Secondary topics:  Algorithms, Tools and frameworks
Jeremy Howard (fast.ai)
Average rating: ****.
(4.00, 1 rating)
Although most devs are aware of the benefits of GPU acceleration, many assume that the technique is only applicable to specialist areas like deep learning and that learning to program a GPU takes complex specialist knowledge. Jeremy Howard explains how to easily harness the power of a GPU using a standard clustering algorithm with PyTorch and demonstrates how to get a 20x speedup over NumPy. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Deep learning, Infrastructure
Bharadwaj Pudipeddi (NVXL Technology)
Bharadwaj Pudipeddi proposes a highly dense modular acceleration cluster completely disaggregated from generic servers in the data center that is specifically targeted for deep learning- and AI-related workloads. This cluster is scalable and lightweight (and devoid of Xeons) with the ability to run very deep neural networks through data and model parallelism for extreme performance. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Imperial B Level: Intermediate
Secondary topics:  Technical best practices, Tools and frameworks
Mary Wahl (Microsoft Corporation)
Average rating: ****.
(4.00, 1 rating)
Mary Wahl shares a cloud-based Hadoop ecosystem solution for deploying deep neural networks (DNNs) with scalable compute resources to accommodate changing workloads and demonstrates how to apply trained Microsoft CNTK and TensorFlow DNNs to a large image set in HDFS (Azure Data Lake Store) using the Python bindings for these deep learning frameworks and a Microsoft HDInsight Spark cluster. Read more.
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11:05am–11:45am Wednesday, September 20, 2017
Location: Imperial B Level: Beginner
Secondary topics:  Algorithms, Data and training
Ben Vigoda (Gamalon)
Average rating: ****.
(4.20, 5 ratings)
Ben Vigoda demonstrates new advances in AI technology that enable companies to accurately read millions of complex customer messages and take action. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Location: Imperial B Level: Beginner
Secondary topics:  Algorithms, Deep learning, Tools and frameworks, Transportation and autonomous vehicles
Kenneth Stanley (Uber AI Labs | University of Central Florida)
Average rating: ****.
(4.62, 8 ratings)
Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Yosemite A Level: Beginner
Secondary topics:  Deep learning, Tools and frameworks
Anirudh Koul (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in computer vision. Anirudh Koul explains how to bring the power of deep learning to memory- and power-constrained devices like smartphones. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Technical best practices, Transportation and autonomous vehicles
Lukas Biewald (CrowdFlower)
Average rating: ***..
(3.71, 7 ratings)
Making the best possible use of training data is essential for effective machine learning. Active learning can make your training data collection 10x–1,000x more efficient, while transfer learning opens up a world of new training data possibilities. Lukas Biewald explores the state of the art in training data, active learning, and transfer learning, especially as applied to deep learning. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Imperial A Level: Beginner
Secondary topics:  Data science and AI, Deep learning, IoT (including smart cities, manufacturing, smart homes/buildings), Transportation and autonomous vehicles
Average rating: ****.
(4.00, 1 rating)
Current driving policy models are limited to models trained using homogenous data from a small number of vehicles running in controlled environments. Bruno Fernandez-Ruiz offers an overview of a network of connected devices that is building an end-to-end driving policy to leverage the 10 trillion miles driven every year. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Algorithms, Enterprise adoption
Mark Hammond (Bonsai)
Average rating: ****.
(4.67, 3 ratings)
Mark Hammond explores how enterprises can move beyond games and leverage deep reinforcement learning and simulation-based training to build programmable, adaptive, and trusted AI models for their real-world applications. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Imperial A Level: Intermediate
Secondary topics:  Deep learning, Tools and frameworks
Rachel Thomas (fast.ai)
Average rating: **...
(2.00, 6 ratings)
If the math used in AI seems intimidating, this tutorial is for you. Rachel Thomas walks you through working with arrays of different dimensions and how broadcasting handles data dimensions. You'll also gain hands-on experience with PyTorch, the Python framework for GPU computing developed by Facebook. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Imperial B Level: Intermediate
Secondary topics:  Architectures, Deep learning
Nigel Toon (Graphcore)
Average rating: *****
(5.00, 1 rating)
Nigel Toon explains how new processing platforms will enable the next wave of machine intelligence beyond deep learning and how these machine learning innovations will impact businesses and improve competitiveness. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Franciscan AB Level: Beginner
Secondary topics:  Law, ethics and governance (including AI safety), Tools and frameworks
Nate Soares (MIRI)
The field of artificial intelligence has made major strides in recent years, but there is a growing movement to consider the implications of machines that can rival humans in general problem-solving abilities. Nate Soares outlines the underresearched fundamental technical obstacles to building AI that can reliably learn to be "aligned" with human values. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Grand Ballroom Level: Beginner
Secondary topics:  Deep learning
Yishay Carmiel (IntelligentWire)
Average rating: ****.
(4.00, 1 rating)
Today almost every achievement in language understanding is based on neural networks. Yishay Carmiel explains why analyzing conversational speech is still a challenging proposition despite all the recent breakthroughs in natural language processing and offers some potential solutions. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Location: Franciscan CD
Hanlin Tang (Intel)
Average rating: *****
(5.00, 2 ratings)
Training deep learning networks is often seen as a dark art. Hanlin Tang demystifies the process, sharing lessons learned from building AI algorithms across multiple verticals and tips and tricks for designing models. Hanlin also offers an overview of the Intel Nervana deep learning stack, which accelerates the iteration cycle for data scientists. Read more.
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4:00pm–4:40pm Wednesday, September 20, 2017
Location: Imperial B Level: Intermediate
Secondary topics:  New product development, Transportation and autonomous vehicles
Shaoshan Liu (PerceptIn)
Autonomous cars, like humans, need good eyes and a good brain to drive safely. Shaoshan Liu explains how PerceptIn designed and implemented its high-definition, stereo 360-degree camera sensors targeted for computer-vision-based autonomous driving. Read more.
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4:00pm–4:40pm Wednesday, September 20, 2017
Location: Yosemite BC Level: Intermediate
Secondary topics:  Algorithms, Data science and AI
Melanie Warrick (Google)
Average rating: ****.
(4.00, 4 ratings)
Reinforcement learning is a popular subfield in machine learning because of its success in beating humans at complex games like Go and Atari. The field’s value is in utilizing an award system to develop models and find more optimal ways to solve complex, real-world problems. This approach allows software to adapt to its environment without full knowledge of what the results should look like. Read more.
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4:00pm–4:40pm Wednesday, September 20, 2017
Location: Grand Ballroom Level: Beginner
Secondary topics:  Data science and AI, Deep learning
Wee Hyong Tok (Microsoft), Joy Qiao (Microsoft)
Average rating: *****
(5.00, 1 rating)
Joy Qiao and Wee Hyong Tok demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications. Along the way, Joy and Wee Hyong explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow. Read more.
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4:50pm–5:30pm Wednesday, September 20, 2017
Location: Yosemite BC Level: Beginner
Secondary topics:  Data and training, Deep learning
Sherry Moore (Google)
Average rating: ****.
(4.00, 2 ratings)
TensorFlow is the world's most popular machine learning framework. Google Brain team member Sherry Moore discusses the latest developments in TensorFlow and offers a dive into her research on evolving deep learning models using genetic algorithms. Read more.