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

Sessions

Tuesday, September 19

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11:05am–11:45am Tuesday, September 19, 2017
Location: Yosemite A
Joshua Joseph (Alpha Features)
Average rating: ****.
(4.00, 4 ratings)
As artificial intelligence (and specifically machine learning) firmly takes hold in industry, there has been a significant increase in the amount of AI snake oil being developed, pitched, and sold. Joshua Joseph shares a practical guide for detecting AI products of questionable value or benefit, whether intentional or not. 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: Imperial A Level: Intermediate
Secondary topics:  Case studies, Deep learning
Xiaofang Chen (Pinterest), Derek Cheng (Pinterest)
Average rating: ****.
(4.62, 8 ratings)
Pinterest’s power is grounded in its personalization systems. Over the years, these recommender systems have evolved through different types of models. Xiaofang Chen and Derek Cheng explore Pinterest's recent transition from a GBDT system to one based in neural networks powered by TensorFlow, covering the challenges and solutions to providing recommendations to over 160M monthly active users. Read more.
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11:05am–11:45am Tuesday, September 19, 2017
Location: Imperial B
Vijay Pande (Andreessen Horowitz)
Average rating: **...
(2.67, 3 ratings)
Expanding on his keynote, Vijay Pande explains how machine learning techniques together with the recent explosion in data are leading to a new approach to prevention, allowing us to more effectively tackle some of the deadliest and costliest health challenges, including heart disease, cancer, and Type 2 diabetes. 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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11:05am–11:45am Tuesday, September 19, 2017
Location: Franciscan CD
Ruchir Puri (IBM)
Ruchir Puri expands on his keynote address, exploring the opportunities and challenges of AI for business and focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Location: Yosemite A Level: Non-technical
Secondary topics:  Media
Paco Nathan (O'Reilly Media)
Average rating: ****.
(4.00, 4 ratings)
Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video. 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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11:55am–12:35pm Tuesday, September 19, 2017
Location: Imperial B Level: Beginner
Secondary topics:  Biopharmaceuticals, Deep learning
Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
Average rating: **...
(2.00, 1 rating)
Blake Borgeson and Nan Li offer a technical overview of how Recursion—a company that applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states—leverages cellular phenotyping for drug discovery. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Location: Franciscan AB Level: Intermediate
Secondary topics:  Algorithms, Finance
Andy Steinbach (NVIDIA)
Average rating: ***..
(3.43, 7 ratings)
Andy Steinbach shares case studies and applications in artificial intelligence that are having an impact on financial markets. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Location: Franciscan CD
Bruce Horn (Intel)
Average rating: ****.
(4.33, 3 ratings)
Deep learning needs cognitive memory and vice versa. In complementary learning, both forms work together to build a more complete AI system. Bruce Horn explores Intel's Saffron's cognitive approach, which provides one-shot learning using associative and episodic memories and is more appropriate for individual and dynamic patterns. 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: Imperial A Level: Beginner
Secondary topics:  Data science and AI
Jeremy Stanley (Instacart)
Average rating: *****
(5.00, 2 ratings)
In the on-demand economy, if something doesn’t happen in real time, it’s too late. The secret ingredient that makes this possible? Data science. Jeremy Stanley explains how Instacart uses deep learning to enable its shoppers to become the most efficient shoppers ever, putting the company at the top of the food chart in the on-demand economy. Read more.
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1:45pm–2:25pm Tuesday, September 19, 2017
Location: Imperial B
Otavio Good (Google)
Average rating: ****.
(4.67, 9 ratings)
Otavio Good demonstrates how Word Lens (part of Google Translate) uses machine learning to detect and translate printed text and explores various other machine learning concepts and their significance. 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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1:45pm–2:25pm Tuesday, September 19, 2017
Location: Franciscan CD
Bill Jenkins (Intel)
Field-programmable gate arrays (FPGAs) provide deterministic low latency and highly efficient implementations with various levels of precision due to their customizable architecture.​ Bill Jenkins shares Intel's deep learning accelerator library, which offers a variety of primitives and architectures highly optimized for FPGAs and allows seamless integration into the Intel ecosystem. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Location: Yosemite A
Yinyin Liu (Intel Nervana)
Average rating: ***..
(3.50, 4 ratings)
Deep learning is providing new opportunities for and solutions to natural language processing problems, enabling new approaches for text, language, and conversation-based use cases. Yinyin Liu shares the latest NLP advances, practices, and resources for data and explores enterprise NLP use cases using the Intel Nervana platform. 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: Franciscan AB Level: Non-technical
Secondary topics:  Healthcare, Law, ethics and governance (including AI safety)
Astrid Chow (IBM Watson Health), Amy Chenault (Insulet), Joel Wu (Children's Minnesota)
With great cognitive computing comes great responsibility. As AI becomes ubiquitous in our society, it's critical to discuss the ethical concerns of AI and ask the tough questions. This multidisciplinary roundtable opens a dialogue on how bioethical principles might be applied to everyday design practice within healthcare. 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: Imperial A Level: Beginner
Secondary topics:  Deep learning, Transportation and autonomous vehicles
Michael B. Henry (Mythic)
Average rating: **...
(2.67, 3 ratings)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Location: Imperial B
Reza Zadeh (Matroid & Stanford)
Average rating: ****.
(4.00, 1 rating)
Reza Zadeh presents a Kubernetes deployment on Amazon AWS that provides customized computer vision to a large number of users. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Location: Franciscan AB Level: Intermediate
Secondary topics:  Data and training, Visualization and Interfaces
John Whalen (Brilliant Experience)
Average rating: ****.
(4.25, 4 ratings)
John Whalen explores the concept of cognitive design, describing how humans structure their commands to AI systems (syntax, word usage, prosody) and how to measure human reactions to AI responses using biometrics (facial emotion recognition, heart rate, GSR). Along the way, John shares insights into how to optimally architect the customer experience. 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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4:00pm–4:40pm Tuesday, September 19, 2017
Location: Franciscan CD
Ron Bodkin (Teradata)
Average rating: *****
(5.00, 1 rating)
Tools, frameworks, access to high-value data, and practical approaches to deployment and integration with existing systems and applications are just some of the considerations facing companies adopting deep learning. Ron Bodkin explores tools, open source technology, frameworks, and strategies to cost-effectively achieve strategic results with deep learning in the enterprise. 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 A Level: Intermediate
Secondary topics:  Deep learning, IoT (including smart cities, manufacturing, smart homes/buildings)
Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
Average rating: *****
(5.00, 1 rating)
Recently, both deep learning and the IoT have attracted tremendous attention. Jisheng Wang shares firsthand experience in applying deep learning to solving some real-world enterprise IoT problems (e.g., IoT device identification and IoT security) and outlines some challenges for deep learning in enterprise applications, along with suggestions to overcome them. 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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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Franciscan AB Level: Intermediate
Secondary topics:  Law, ethics and governance (including AI safety), Organizational best practices
Daniel Guillory (Autodesk), Matthew Scherer (Littler Mendelson, PC )
Average rating: *****
(5.00, 2 ratings)
Diversity has many dimensions relevant to AI development. If designers don't consider and integrate diversity from the very beginning, they risk creating systems that are irrelevant to excluded groups and worse, make excluded groups irrelevant. Daniel Guillory and Matthew Scherer discuss the importance of ensuring diversity and inclusion when developing AI and share tips on how to do so. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Grand Ballroom
Secondary topics:  Transportation and autonomous vehicles
Timnit Gebru (Microsoft Research)
Average rating: ****.
(4.75, 4 ratings)
Targeted socioeconomic policies require an accurate understanding of a country’s demographics, and the US spends more than $1 billion a year gathering such data. Timnit Gebru shares a solution that leverages Google Street View images and a computer vision pipeline to predict income, carbon emission, crime rates, and other city attributes from a single source of publicly available data. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Franciscan CD
Jason Knight (Intel)
Average rating: ****.
(4.00, 3 ratings)
With the chaotic and rapidly evolving landscape around deep learning, we need deep learning-specific compilers to enable maximum performance in a wide variety of use cases on a wide variety of hardware platforms. Jason Knight offers an overview of the Intel Nervana Graph project, which was designed to solve this problem. Read more.

Wednesday, September 20

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11:05am–11:45am Wednesday, September 20, 2017
Location: Yosemite A Level: Intermediate
Secondary topics:  Security, Technical best practices
Aaron Goldstein (Cylance)
Average rating: *****
(5.00, 3 ratings)
The current threat landscape is in a state of evolution that poses a significant risk to organizations' assets, reputations, and identities. Aaron Goldstein explores new and existing threats (and why traditional defenses fail to address them) and explains how leveraging AI techniques can improve the speed and efficiency of incident response tactics, even when combating the toughest threat actors. Read more.
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11:05am–11:45am Wednesday, September 20, 2017
Location: Yosemite BC
Shahin Farshchi (Lux Capital), Ashu Rege (Zoox)
Join Shahin Farshchi in conversation with Ashu Rege, who is reinventing the automobile from scratch at Zoox to offer consumers an unforgettable, autonomous transportation experience. They'll discuss early challenges that turned out to be straightforward, easy problems that turned out to be very hard, and the obstacles that lie ahead. Read more.
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11:05am–11:45am Wednesday, September 20, 2017
Location: Imperial A
Alyosha Efros (UC Berkeley)
Average rating: ****.
(4.86, 7 ratings)
Alyosha Efros shares several case studies exploring the paradigm of self-supervised learning and discusses several ways of defining objective functions in high-dimensional spaces. Alyosha also covers the applications of this technology for image synthesis, including automatic colorization, image-to-image translation, curiosity-based exploration, and, terrifyingly, #edges2cats. 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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11:05am–11:45am Wednesday, September 20, 2017
Location: Franciscan AB Level: Non-technical
Secondary topics:  Case studies, Enterprise adoption
Jana Eggers (Nara Logics)
Average rating: ****.
(4.50, 2 ratings)
Having spent the last three years working with Global 200 customers to get AI systems into production, Jana Eggers can tell you that the technology is (finally) ready—but the organization is not. Jana discusses the top five reasons orgs struggle—data silos, the tech-business gap, driving innovation, resistance to change, and the hype-reality gap—and shares ideas on how to overcome them. Read more.
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11:05am–11:45am Wednesday, September 20, 2017
Location: Franciscan CD
Anusua Trivedi (Microsoft)
Average rating: ****.
(4.00, 1 rating)
Anusua Trivedi offers an overview of Microsoft’s Cognitive Toolkit, also known as CNTK. CNTK has unique advantages over other toolkits, especially in speed and scalability. Anusua compares five well-known toolkits to demonstrate how CNTK achieves almost linear scalability, which is far superior to all the other well-known toolkits. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Location: Yosemite A Level: Non-technical
Secondary topics:  Data science and AI, IoT (including smart cities, manufacturing, smart homes/buildings)
David Rogers (Sight Machine)
Average rating: ***..
(3.33, 3 ratings)
Artificial intelligence in manufacturing has been around for a long time, but are you aware of how it can make your operations more efficient and profitable? David Rogers explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Location: Yosemite BC
Ashwin Ram (Amazon)
Average rating: ****.
(4.00, 4 ratings)
Conversational AI in Amazon Alexa Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Location: Imperial A
Alex Kurakin (Google)
Average rating: ***..
(3.50, 2 ratings)
Adversarial machine learning session by Alex Kurakin 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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11:55am–12:35pm Wednesday, September 20, 2017
Location: Franciscan AB Level: Non-technical
Secondary topics:  Data science and AI, Deep learning
Tim Estes (Digital Reasoning)
As AI moves from concept to reality, debates about ethics are evolving into excitement and the desire to learn more about AI and its promise of a better world. Tim Estes discusses two customer use cases at Digital Reasoning: Nasdaq, which found a way to use AI to help safeguard financial markets, and Thorn, which found a way to use AI to combat human trafficking and rescue children. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Location: Franciscan CD
Magnus Hyttsten (Google)
Average rating: ****.
(4.00, 1 rating)
Magnus Hyttsten explains how Google is pushing the boundaries of machine learning with TensorFlow and Google Cloud, sharing some of the latest models Google teams have been working on and the technical challenges they've encountered, new APIs in TensorFlow, how the Tensor Processing Unit (TPU) works, and how Google Cloud can be used to train extremely large models. 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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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Imperial B
Greg Diamos (Baidu)
Average rating: ****.
(4.50, 2 ratings)
Accuracy scales with data and compute, transforming some difficult AI problems into problems of computational scale. Greg Diamos covers challenges to further improving performance and outlines a plan of attack for tearing down the remaining obstacles standing in the way of strong scaling deep learning to the largest machines in the world. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Franciscan AB Level: Beginner
Secondary topics:  Bots, Deep learning
Paul Tepper (Nuance Communications)
Average rating: ****.
(4.67, 3 ratings)
Many industries are now exploring chatbots powered by artificial intelligence as a source for improved insights and better understanding of customer preferences. Paul Tepper explores the unique challenges chatbots present, shares available solutions, and outlines a number of critical factors in building successful chatbots and virtual assistants. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Location: Franciscan CD
Average rating: ****.
(4.00, 1 rating)
Banu Nagasundaram and Akhilesh Kumar offer an overview of the architectural features of the latest Intel Xeon scalable processor, outline the changes from previous generations, and discuss the architectural benefits that favor AI workloads. Along the way, Banu and Akhilesh explore AI workload performance for data center CPUs. 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 A
Ameet Talwalkar (Determined AI)
Average rating: ****.
(4.50, 2 ratings)
Ameet Talwalkar offers an overview of Hyperband, a novel algorithm for hyperparameter optimization that is simple, flexible, theoretically sound, and an order of magnitude faster than leading competitors, and shares research aimed at understanding the underlying landscape of training deep learning models in parallel and distributed environments. 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: Franciscan AB Level: Beginner
Secondary topics:  Algorithms, Enterprise adoption
Gang Wang (Intuit)
Average rating: ****.
(4.75, 4 ratings)
Taxes are one of consumers' most complex financial transactions, thanks to a tax code that is 80,000 pages long. Gang Wang explains how Intuit built the industry’s only Tax Knowledge Engine, a constraint-based engine that encodes changing financial regulations and provides the foundation for a host of artificial intelligence technologies that save customers time and money. 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.
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4:50pm–5:30pm Wednesday, September 20, 2017
Location: Imperial A
Derik Pridmore (Osaro)
Average rating: ****.
(4.00, 1 rating)
There continues to be a gap between the most advanced papers and the reality of deployed industrial robots. Derik Pridmore explores the most recent advances in deep and reinforcement learning for robotics, the current state of industrial robotics, and how Osaro is working to bridge the gap. Read more.
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4:50pm–5:30pm Wednesday, September 20, 2017
Location: Franciscan AB Level: Non-technical
Secondary topics:  Finance, Healthcare
Average rating: *****
(5.00, 2 ratings)
Low-level task-based AI gets commoditized quickly, and more general AI is decades off. While most of the machine learning talent works in big tech companies, massive, timely problems lurk in every major industry outside tech. Bradford Cross explains how vertical AI startups leverage subject-matter expertise, AI, and unique data to deliver their product's core value proposition. Read more.
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4:50pm–5:30pm Wednesday, September 20, 2017
Location: Grand Ballroom Level: Intermediate
Secondary topics:  Data science and AI, Deep learning
Nikita Lytkin (Facebook)
Average rating: ****.
(4.50, 2 ratings)
Nikita Lytkin explains how Facebook uses machine learning technologies developed by its ads ranking, applied machine learning, and AI research teams to enable personalized ecommerce that recommends a vast diversity of products to nearly two billion people. Read more.