Put AI to Work
April 15-18, 2019
New York, NY
New York, NY
Monday, 04/15/2019
8:00am
8:00am–9:00am Monday, 04/15/2019
Location: 2nd Floor Promenade
Morning Coffee
(1h)
9:00am
SOLD OUT
Average rating:
(4.33, 3 ratings)
Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
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Training
Location: Madison
Ana Hocevar (The Data Incubator)
PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Ana Hocevar introduces the PyTorch workflow and demonstrates how to use it to build deep learning models using real-world datasets.
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SOLD OUT
Average rating:
(2.33, 3 ratings)
Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
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Training
Location: Sutton Center
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs.
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SOLD OUT
Dylan Bargteil (The Data Incubator)
Average rating:
(2.00, 3 ratings)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications.
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Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.
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10:30am
10:30am–11:00am Monday, 04/15/2019
Location: Sutton Foyer
Morning Break
(30m)
12:30pm
12:30pm–1:30pm Monday, 04/15/2019
Location: Mercury Ballroom
Lunch
(1h)
3:00pm
3:00pm–3:30pm Monday, 04/15/2019
Location: Sutton Foyer
Afternoon Break
(30m)
Tuesday, 04/16/2019
8:00am
8:00am–9:00am Tuesday, 04/16/2019
Location: Sutton Foyer and 3rd Floor Promenade
Morning Coffee
(1h)
9:00am
Mo Patel (Independent)
Average rating:
(1.50, 2 ratings)
Mo Patel leads a deep dive into all aspects of the PyTorch lifecycle via hands-on examples such as image classification, text classification, and linear modeling. Along the way, you'll explore other aspects of machine learning such as transfer learning, data modeling, and deploying to production with immersive labs.
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Gunnar Carlsson (Ayasdi)
Gunnar Carlsson explains how to use topological data analysis to describe the functioning and learning of a neural network in a compact and understandable way—resulting in material speedups in performance (training time and accuracy) and enabling data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all datasets.
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Chris Butler (IPsoft)
Average rating:
(4.80, 5 ratings)
Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.
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Rachel Bellamy (IBM Research),
Kush Varshney (IBM Research),
KARTHIKEYAN NATESAN RAMAMURTHY (IBM Research),
Michael Hind (IBM Research AI)
Average rating:
(5.00, 1 rating)
Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.
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Kristian Hammond (Northwestern Computer Science)
Average rating:
(4.50, 8 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. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.
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Tutorial
Location: Trianon Ballroom
Garrett Hoffman (StockTwits)
Average rating:
(5.00, 5 ratings)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.
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10:30am
10:30am–11:00am Tuesday, 04/16/2019
Location: Sutton Foyer and 3rd Floor Promenade
Morning Break
(30m)
12:30pm
12:30pm–1:45pm Tuesday, 04/16/2019
Location: Americas Hall 2
Lunch (Sponsored by Intel AI)
(1h 15m)
1:45pm
Average rating:
(2.50, 2 ratings)
Alex Siegman and Kabir Seth walk you through the steps necessary to appropriately leverage AI in a large organization. This includes ways to identify business opportunities that lend themselves to AI as well as best practices on everything from data intake and manipulation to model selection, output analysis, development, and deployment, all while navigating a complex organizational structure.
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Justina Petraityte (Rasa)
Average rating:
(5.00, 2 ratings)
Justina Petraityte offers a hands-on walk-through of developing intelligent AI assistants based entirely on machine learning and using only the open source tools Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and best practices for developing AI assistants that scale and learn from real conversational data.
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1:45pm–5:15pm Tuesday, 04/16/2019
Location: Mercury Ballroom
TUTORIAL TBC
Location: Grand Ballroom West
Brigitte Alexander (Intel),
Eric Gudgion (H2O.ai),
Ajay Balakrishnan (Mphasis),
Yoav Einav (GigaSpaces),
Vladimir Starostenkov (Altoros),
Siarhei Sukhadolski (Altoros Development),
Suresh Vadakath (DataRobot),
Shioulin Sam (Cloudera Fast Forward Labs),
Dan Klein (Valtech),
Fabrizio Del Maffeo (AAEON Technology Europe),
David Talby (Pacific AI),
Sumit Sanyal (Minds.ai),
Sunil Baliga (Wipro),
Sundar Varadarajan (Wipro),
Rubayat Mahmud (Mobiliya Technologies (A Quest Global Company)),
Rachel Jordan (Anaconda),
Nanda Vijaydev (BlueData),
Tony Sandoval (Avaamo),
Brigitte Alexander (Intel)
Learn how to deploy enterprise AI solutions with Intel and its partner ecosystem. This event features offerings for banking and financial services, retail, and healthcare as well as cross-industry AI solutions.
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Tutorial
Location: Petit Trianon
Robert Nishihara (University of California, Berkeley),
Philipp Moritz (University of California, Berkeley),
Ion Stoica (University of California, Berkeley),
Eric Liang (University of California, Berkeley, RISELab)
Average rating:
(5.00, 1 rating)
Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.
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Bruno Goncalves (Data For Science)
Average rating:
(3.50, 6 ratings)
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.
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3:00pm
3:00pm–3:30pm Tuesday, 04/16/2019
Location: Sutton Foyer and 3rd Floor Promenade
Afternoon Break
(30m)
7:30pm
Event
Location: Various Locations
Average rating:
(2.00, 1 rating)
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine New York City has to offer.
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Wednesday, 04/17/2019
8:00am
8:00am–8:50am Wednesday, 04/17/2019
Location: 3rd Floor Promenade
Morning Coffee (Sponsored by NetApp)
(50m)
8:15am
Event
Location: 3rd Floor Promenade
Average rating:
(5.00, 1 rating)
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
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8:45am
Keynote
Location: Grand Ballroom West
Average rating:
(2.50, 4 ratings)
Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the first day of keynotes.
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8:55am
Keynote
Location: Grand Ballroom West
Gadi Singer (Intel)
Average rating:
(3.17, 6 ratings)
Gadi Singer explores four real-world AI deployments at enterprise scale.
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9:10am
Keynote
Location: Grand Ballroom West
Aleksander Madry (MIT)
Average rating:
(4.56, 9 ratings)
Aleksander Madry discusses major roadblocks that prevent current AI frameworks from having a broad impact and outlines approaches to addressing these issues and making AI frameworks truly human-ready.
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9:25am
Keynote
Location: Grand Ballroom West
Danielle Dean (iRobot)
Average rating:
(4.17, 6 ratings)
Automated ML is at the forefront of Microsoft’s push to make Azure ML an end-to-end solution for anyone who wants to build and train models that make predictions from data and then deploy them anywhere. Join Danielle Dean for a surprising conversation about a data scientist’s dilemma, a researcher’s ingenuity, and how cloud, data, and AI came together to help build automated ML.
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9:35am
Keynote
Location: Grand Ballroom West
Desiree Gosby (Intuit)
Average rating:
(4.56, 9 ratings)
Desi Gosby dedicated years to developing technology that applies advanced machine learning capabilities to translate images and characters into an easy-to-use digital experience. Desi shares unique technical challenges faced and lessons learned while applying computer vision to seeing and reading complex financial documents, as well as what is next for the future of computer vision.
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9:50am
Keynote
Location: Grand Ballroom West
Kurt Muehmel (Dataiku)
Average rating:
(2.75, 4 ratings)
AI technologists must consider the ethical implications of what we're building. Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration is a necessary answer.
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9:55am
Keynote
Location: Grand Ballroom West
Thomas Henson (Dell Technologies)
Average rating:
(1.00, 2 ratings)
Keynote by Thomas Henson
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10:00am
Keynote
Location: Grand Ballroom West
Tony Jebara (Columbia University | Netflix)
Average rating:
(4.50, 6 ratings)
For many years, the main goal of the Netflix recommendation system has been to get the right titles in front of each member at the right time. Tony Jebara details the approaches Netflix uses to recommend titles to users and discusses how the company is working on integrating causality and fairness into many of its machine learning and personalization systems.
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10:15am
Keynote
Location: Grand Ballroom West
Joleen Liang (Squirrel AI Learning)
Average rating:
(3.00, 5 ratings)
Keynote by Joleen Liang
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10:20am
Keynote
Location: Grand Ballroom West
Martial Hebert (First Child Designs)
Average rating:
(5.00, 2 ratings)
Martial Hebert offers a brief overview of current challenges in AI for robotics and a glimpse of the exciting developments emerging in current research.
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10:35am
10:35am–11:05am Wednesday, 04/17/2019
Location: Expo Hall
Morning Break
(30m)
11:05am
Average rating:
(5.00, 2 ratings)
As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. The company first built publishing tools that let people work more efficiently, then built artificial intelligence tools that let people work more intelligently. Join Lucy Wang and Swara Kantaria to learn more about this evolution.
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Pamela Vagata (Stripe)
Average rating:
(4.00, 1 rating)
Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance.
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Siwei Lyu (University of Albany)
Average rating:
(5.00, 1 rating)
Siwei Lyu reviews the evolution of techniques behind the generation of fake media and discusses several projects in digital media forensics for the detection of fake media, with a special focus on recent work on detecting AI-generated fake videos (DeepFakes).
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Deepashri Varadharajan (CB Insights)
Average rating:
(3.80, 5 ratings)
CB Insights tracks over 3,000 AI startups across 25+ verticals. While every vertical has benefited from deep learning and better hardware processing, the bottlenecks and opportunities are unique to each sector. Deepashri Varadharajan explores what's driving AI applications in different verticals like healthcare, retail, and security and analyzes emerging business models.
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Lise Getoor (University of California, Santa Cruz)
Average rating:
(4.50, 4 ratings)
Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. Lise Getoor discusses AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Along the way, Lise explores the benefit of utilizing structure—and the inherent risk of ignoring structure.
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40-minute session
Location: Beekman
Sarah Bird (Microsoft)
Average rating:
(4.00, 6 ratings)
Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
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40-minute session
Location: Petit Trianon
Will Nowak (Dataiku)
Average rating:
(3.00, 1 rating)
AI and machine learning are top priorities for nearly every company. Despite this, "productionalizing" machine learning processes is an underappreciated problem, and as a result, businesses often find themselves failing to maximize ROI from their data initiatives. Will Nowak identifies best practices and common pitfalls in bringing machine learning and AI models to production.
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Average rating:
(3.33, 3 ratings)
Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster.
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Joshua Gordon (Google)
Average rating:
(4.83, 6 ratings)
Josh Gordon shares the very latest in TensorFlow, focusing on TensorFlow 2.0 and its easy-to-use eager execution. Josh also covers how to use TensorFlow's revised high-level API and details pitfalls and tricks to get better performance on accelerator hardware.
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11:50am
Event
Location: Americas Hall 2
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
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1:00pm
40-minute session
Location: Sutton North/Center
Roger Chen (Computable)
Data remains a linchpin of success for machine learning yet too often is a scarce resource. And even when data is available, trust issues arise about the quality and ethics of collection. Roger Chen explores new models for generating and governing training data for AI applications.
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Average rating:
(5.00, 1 rating)
Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient.
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Danny Lange (Unity Technologies)
Average rating:
(5.00, 7 ratings)
Join Danny Lange to learn how to create artificially intelligent agents that act in the physical world (through sense perception and some mechanism to take physical actions, such as driving a car). You'll discover how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices.
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Average rating:
(2.44, 9 ratings)
Larry Carin, one of the world’s most published machine learning researchers, discusses the state of the art in machine learning and how it translates to business impact. Along the way, Larry shares examples of how modern machine learning is transforming business in several sectors, including healthcare delivery, security, and back-office business processing.
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Ameet Talwalkar (Carnegie Mellon University | Determined AI)
Average rating:
(5.00, 1 rating)
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. Ameet Talwalkar shares work that aims to help ground the empirical results in this field and proposes new NAS baselines.
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40-minute session
Location: Beekman
Richard Tong (Squirrel AI Learning)
One of the most critical issues of traditional education is the lack of high-quality teachers for the personalized attention of individual student need. AI technology, especially the AI adaptive technology can enable the new generation of teachers to teach student much more effectively and improve the efficiency of the education industry.
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Zongjie Diao (Cisco)
Zongjie Diao outlines the key ML challenges deployment companies face and examines the root causes. More importantly, Zongjie explores solutions in depth to demystify ML development in the enterprise.
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Average rating:
(4.75, 8 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency.
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Catherine Ordun (Booz Allen Hamilton)
Average rating:
(4.67, 3 ratings)
While building machine learning models for most large projects, data scientists typically design dozens of models using different combinations of hyperparameters, data configurations, and training settings. Catherine Ordun describes how to build your own machine learning model tracking leaderboard in Keras.
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1:50pm
40-minute session
AI Business Summit,
Executive Briefing/Best PracticesLocation: Sutton North/Center
David Talby (Pacific AI)
Average rating:
(3.00, 3 ratings)
New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.
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Maryam Jahanshahi (TapRecruit)
Average rating:
(5.00, 6 ratings)
Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
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Simon Crosby (SWIM.AI)
Average rating:
(4.67, 3 ratings)
Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head.
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Anand Rao (PwC)
Average rating:
(4.00, 4 ratings)
Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. Anand Rao synthesizes the current research in FAT (fairness, accountability, and transparency) into a step-by-step methodology to address these issues—illustrated with case studies from the financial services and healthcare industries.
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Average rating:
(5.00, 2 ratings)
The AI industry needs new software architectures for distributed systems to solve critical problems. Vinay Rao and Santi Adavani explain why software architectures will lead the next generation of machine learning approaches and how RocketML has built logistic regression models on the KDD12 dataset with ~150 million samples on an eight-Intel Xeon-node cluster in under a minute.
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Katherine Taylor (SAS)
Average rating:
(5.00, 1 rating)
The whole of AI is greater than the sum of its parts, but achieving the best analytics edge often requires a mixture of technologies—chaining together AI technologies to build smart end-to-end processes. Katie Taylor explores use cases within key industries to uncover how companies are succeeding with AI through a layered technology stack.
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40-minute session
Location: Petit Trianon
Bill Roberts (Deloitte Consulting LLP)
Average rating:
(4.33, 3 ratings)
Bill Roberts discusses artificial intelligence for strategic business insight and for the solution of new business problems using advanced cognitive algorithms. Along the way, he highlights the importance of using the right algorithm for a given business challenge, using real-world examples.
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YU DONG (Facebook)
Average rating:
(3.50, 2 ratings)
Yu Dong offers an overview of the why, what, and how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions.
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Average rating:
(4.00, 1 rating)
Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.
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2:40pm
Average rating:
(5.00, 2 ratings)
Andrew Chin and Celia Chen offer an overview of data science applications within the asset management industry, covering use cases on using ML to derive better investment insights and improve client engagement.
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Average rating:
(3.00, 1 rating)
Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.
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Matt Zeiler (Clarifai)
At the core of today's problems with image classification and deep learning lies one fundamental truth: most AI systems operate by choosing the path of least resistance, not the path of highest long-term quality. Matt Zeiler discusses Clarifai's approach to closing the loop on AI and the techniques it employs to counter the AI quality regression phenomenon.
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Anna Gressel (Debevoise & Plimpton LLP),
Jim Pastore (Debevoise & Plimpton LLP),
Anwesa Paul (American Express)
Average rating:
(5.00, 2 ratings)
Anna Gressel, Jim Pastore, and Anwesa Paul lead a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and the California Consumer Privacy Act. They also explore key lawsuits challenging AI in US courts and unpack the implications for companies going forward, helping you mitigate legal and regulatory risks and position your AI products for success.
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Sanjay Krishnan (University of Chicago)
Average rating:
(3.00, 2 ratings)
Drawing on his work building and deploying an RL-based relational query optimizer, a core component of almost every database system, Sanjay Krishnan highlights some of the underappreciated challenges to implementing deep reinforcement learning.
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2:40pm–3:20pm Wednesday, 04/17/2019
Location: Beekman
TBC
40-minute session
Location: Petit Trianon
Pankaj Goyal (HPE)
Regardless of your AI, ML, or DL needs, HPE has the best-in-class people, technology, and partners to ensure you’re ready for the projects and challenges of today and tomorrow. Join Pankaj Goyal to hear about HPE’s latest AI offerings and discover how HPE can help you unlock your data with AI.
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Average rating:
(4.00, 3 ratings)
Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.
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Sumeet Vij (Booz Allen Hamilton),
Matt Speck (Booz Allen Hamilton)
Average rating:
(3.50, 2 ratings)
Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You'll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation.
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3:20pm
3:20pm–4:05pm Wednesday, 04/17/2019
Location: Expo Hall
Afternoon Break (Sponsored by Dataiku)
(45m)
4:05pm
Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant anti-money laundering (AML) and "know your customer" (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. Kyle Hoback walks you through a series of case studies that utilize AI-powered RPA that address AML and KYC.
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40-minute session
Location: Sutton South
Tom Sabo (SAS)
Average rating:
(3.00, 2 ratings)
Sources of international human trafficking data contain a wealth of textual information that is laborious to assess using manual methods. Tom Sabo demonstrates text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response.
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Kevin He (DeepMotion)
Average rating:
(4.00, 2 ratings)
Digital character interaction is hard to fake, whether it’s between two characters, between users and characters, or between a character and its environment. Nevertheless, interaction is central to building immersive XR experiences, robotic simulation, and user-driven entertainment. Kevin He explains how to use physical simulation and machine learning to create interactive character technology.
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Sarah Aerni (Salesforce Einstein)
Average rating:
(5.00, 1 rating)
How does Salesforce make data science an Agile partner to over 100,000 customers? Sarah Aerni shares the nuts and bolts of the platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology.
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Haizi Yu (University of Illinois at Urbana-Champaign)
Average rating:
(2.67, 3 ratings)
Can an AI learn the laws of music theory from sheet music in the same human-interpretable form as a music theory textbook? How little prior knowledge is needed to do so? Haizi Yu considers questions like these as he walks you through developing a general framework for automatic concept learning.
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40-minute session
Location: Beekman
Average rating:
(2.50, 2 ratings)
Drawing on case studies and recent survey insights, Tom Roehm and Alexis Crowell Helzer offer a front-row view into how companies are taking on everything from trust in AI to its impact on jobs, oversight, and ethics.
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Ben Vigoda (Gamalon)
Average rating:
(3.25, 4 ratings)
How does customer experience and digital marketing know what customers are saying in human chat, bot chat, survey, or social interactions? The first step is to deeply analyze customer conversations. Ben Vigoda explains how a new generation of AI technology makes it possible to extract the ideas contained in text to summarize, organize, and display for analysis.
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Location: Trianon Ballroom
Banu Nagasundaram (Intel)
Banu Nagasundaram offers an overview of Intel's Deep Learning Boost (Intel DL Boost) technology, featuring integer vector neural network instructions targeting future Intel Xeon scalable processors. Banu walks you through the 8-bit integer convolution implementation made in the Intel MKL-DNN library to demonstrate how this new instruction is used in optimized code.
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Pradip Bose (IBM T. J. Watson Research Center)
Average rating:
(5.00, 1 rating)
Pradip Bose details a next-generation AI research project focused on creating "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field—in particular, cognitive bias and inaccurate decisions that are perceived as being unethical.
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4:55pm
Average rating:
(2.00, 1 rating)
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.
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40-minute session
Location: Sutton South
The hardware, software, and algorithms that automatically tag our images or recommend the next book to read can also improve medical diagnosis and protect our natural resources. Jack Dashwood and Anna Bethke discuss a variety of technical projects at Intel that have enabled social good organizations and provide guidance on creating or engaging in these types of projects.
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Paris Buttfield-Addison (Secret Lab),
Mars Geldard (University of Tasmania),
Tim Nugent (lonely.coffee)
Average rating:
(5.00, 9 ratings)
Games are wonderful contained problem spaces, making them great places to explore AI—even if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world.
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Angelo Calvello (Rosetta Analytics)
Average rating:
(5.00, 1 rating)
Angelo Calvello explains why asset managers will inevitably (but slowly and haltingly) incorporate AI into their investment processes in a meaningful manner and argues that this incorporation could be accelerated by the entrance of an external AI-based actor or the success of AI-based investment startups.
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Yishay Carmiel (IntelligentWire)
Average rating:
(5.00, 2 ratings)
In recent years, we've seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.
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Suresh Vadakath (DataRobot)
Average rating:
(2.33, 3 ratings)
Many companies want to influence the future by adjusting factors that they control. Suresh Vadakath covers practical ways to extend machine learning models via simulations and points out common pitfalls to avoid. Suresh then discusses a few applications in marketing, pricing, and operations to illustrate how this approach works in the real world.
Read more.
Jack Norris (MapR Technologies)
Average rating:
(1.00, 1 rating)
Jack Norris delves into the three building blocks and the one massive mistake to avoid for any organization looking to leverage AI.
Read more.
Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave.
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Andrew Zaldivar (Google)
Average rating:
(4.00, 1 rating)
The development of AI is creating new opportunities to improve the lives of all people. It's also raising new questions about ways to build fairness, interpretability, and other moral and ethical values into these systems. Using Jupyter and TensorFlow, Andrew Zaldivar shares hands-on examples that highlight current work and recommended practices toward the responsible development of AI.
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5:35pm
Event
Location: Expo Hall
Come enjoy delicious snacks and beverages with fellow AI Conference attendees, speakers, and sponsors at the Attendee Reception, happening immediately after the afternoon sessions on Wednesday.
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Thursday, 04/18/2019
8:00am
8:00am–9:00am Thursday, 04/18/2019
Location: 3rd Floor Promenade
Morning Coffee (Sponsored by Gamalon)
(1h)
8:15am
Event
Location: 3rd Floor Promenade
Average rating:
(5.00, 1 rating)
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
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9:00am
Keynote
Location: Grand Ballroom West
Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the second day of keynotes.
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9:05am
Keynote
Location: Grand Ballroom West
Sean Gourley (Primer)
Average rating:
(4.56, 9 ratings)
Keynote by Sean Gourley
Read more.
9:20am
Keynote
Location: Grand Ballroom West
Carlos Humberto Morales (Intel)
Average rating:
(2.50, 2 ratings)
Carlos Humberto Morales offers an overview of Nauta, a new open source multiuser platform that allows teams of data scientists to run complex deep learning models on shared hardware resources.
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9:30am
Keynote
Location: Grand Ballroom West
Nick Curcuru (Mastercard)
Average rating:
(4.00, 3 ratings)
Nick Curcuru, VP, Data Analytics and Cyber Security, will discuss Mastercard’s commitment to AI and its recent investments and developments.
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9:35am
Keynote
Location: Grand Ballroom West
Kim Hazelwood (Facebook)
Average rating:
(4.60, 5 ratings)
Applied Machine Learning at Facebook
Read more.
9:50am
Keynote
Location: Grand Ballroom West
Ruchir Puri (IBM)
Average rating:
(4.00, 3 ratings)
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks, accelerating enterprises' journey to AI.
Read more.
10:00am
10:15am
Keynote
Location: Grand Ballroom West
Rajendra Prasad (Accenture)
Average rating:
(1.00, 2 ratings)
After crossing the first AI implementation milestone, leaders often ask, "What’s next?" Based on experience implementing AI-led automation for more than 100 clients, Accenture has developed an easy-to-use methodology for scaling and sustaining reliable AI solutions. Rajendra Prasad (RP) explains how leaders and change makers in large enterprises can make AI adoption successful.
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10:20am
Keynote
Location: Grand Ballroom West
Olga Troyanskaya (Princeton University)
Average rating:
(4.50, 2 ratings)
How can machine learning decode the mysteries of life? Why are algorithms essential to enabling precision medical treatments? How do genomes encode the diversity of cells that make up humans and the signals predisposing us to diseases? Olga Troyanskaya discusses these and other questions through the prism of developing deep learning-based approaches for analysis of the human genome.
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10:35am
10:35am–11:05am Thursday, 04/18/2019
Location: Expo Hall
Morning Break (Sponsored by Dell Technologies)
(30m)
11:05am
Adam Cheyer (Samsung)
Average rating:
(4.50, 2 ratings)
We're entering a new age of software development, where humans and machines work collaboratively together, each doing what they do best. Adam Cheyer offers an overview of a freely downloadable development environment so that you can give this a try yourself and start monetizing your content and services through a new channel that will be backed by more than a billion devices in just a few years.
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vishal hawa (Vanguard)
While deep learning has shown significant promise for model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and overfit. Vishal Hawa explains how a combination of RNNs and Bayesian networks (PGM) can improve the sequence modeling behavior of RNNs.
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40-minute session
Location: Regent Parlor
Mohamed Fawzy (Facebook)
Average rating:
(3.50, 2 ratings)
Session with Mohamed Fawzy
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Thomas Marlow (Black Hills IP)
Average rating:
(5.00, 1 rating)
Three elements will control the AI market: technology, data, and IP rights. Leveraging rich patent data, Thomas Marlow uncovers the companies with the top patent holdings across the world in groundbreaking research and implementation technologies, surfacing insights into the sources and owners of AI technology as well as the hurdles and opportunities that those entering the field today face.
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40-minute session
Location: Grand Ballroom West
Average rating:
(4.50, 2 ratings)
The rapid growth of deep learning in demanding large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. Adam Straw, Adam Procter, and Robert Earhart offer a comprehensive overview of Intel's nGraph deep learning compiler.
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40-minute session
Location: Beekman
Ruchir Puri (IBM)
Average rating:
(2.50, 2 ratings)
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks, accelerating enterprises' journey to AI.
Read more.
Rajendra Prasad (Accenture)
Average rating:
(4.00, 1 rating)
After crossing the first AI implementation milestone, leaders often ask, "What’s next?" Based on experience implementing AI-led automation for more than 100 clients, Accenture has developed an easy-to-use methodology for scaling and sustaining reliable AI solutions. Rajendra Prasad (RP) explains how leaders and change makers in large enterprises can make AI adoption successful.
Read more.
Patrick Kaifosh (CTRL-labs)
Average rating:
(4.00, 1 rating)
Following the launch of CTRL-labs’s developer kit, CTRL-kit (neural interface device), Patrick Kaifosh paints a picture of a world with neural interfaces, explaining how this technology will change our lives. Patrick outlines a future where we'll be looking up at the world instead of down at our phones—and leads a live demo of CTRL-kit in action.
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Diego Oppenheimer (Algorithmia),
Brendan Collins (Algorithmia)
Average rating:
(4.00, 1 rating)
Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science.
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11:50am
Event
Location: Americas Hall 2
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
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1:00pm
Joanna Bryson (University of Bath)
Although not a universally held goal, maintaining human-centric artificial intelligence is necessary for society’s long-term stability. Joanna Bryson discusses why this is so and explores both the technological and policy mechanisms by which it can be achieved.
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Average rating:
(2.00, 1 rating)
Companies have a strong need for complying with anti-money laundering, antibribery, corruption, and economic sanctions regulation in mitigating third-party risk. Yulia Zvyagelskaya and Victor Llorente highlight how Dow Jones Risk & Compliance uses deep learning and NLP for efficient compliance solutions.
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Chang Ming-Wei (Google)
Average rating:
(5.00, 4 ratings)
Ming-Wei Chang offers an overview of a new language representation model called BERT (Bidirectional Encoder Representations from Transformers). Unlike recent language representation models, BERT is designed to pretrain deep bidirectional representations by jointly conditioning on both left and right context in all layers.
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Vinay Mohta (Manifold)
Average rating:
(4.00, 4 ratings)
The significant hype bubble building up around AI has convinced many executives that if they’re not already tech savvy, they might not be ready for AI’s “transformative power.” However, the reality is that AI is just another tool that can help your business, and you’re probably not that far behind. Vinay Seth Mohta explains how to evaluate AI as you would any other strategic investment.
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Average rating:
(4.17, 6 ratings)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is a fundamental shift in how organizations approach machine learning. Francesca Lazzeri and Wee Hyong Tok demonstrate how to use AutoML to automate the selection of machine learning models and automate tuning of hyperparameters.
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40-minute session
Location: Beekman
Average rating:
(5.00, 1 rating)
There are many different decisions to make when choosing the right solutions and infrastructure. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—Mastercard exercised to build and deploy a successful AI.
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40-minute session
Location: Petit Trianon
Suresh Vadakath (DataRobot)
Many companies want to influence the future by adjusting factors that they control. Suresh Vadakath covers practical ways to extend machine learning models via simulations and points out common pitfalls to avoid. Suresh then discusses a few applications in marketing, pricing, and operations to illustrate how this approach works in the real world.
Read more.
Jeff Thompson (Stevens Institute of Technology)
What's it like to be a mobile phone or to attach a wind sensor to a neural network? Jeff Thompson outlines several recent creative projects that push the tools of AI in new directions. Part technical discussion and part case study for embedding artists in technical institutions, this talk explores the ways that artists and scientists can collaborate to expand the ways that AI can be used.
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Evan Sparks (Determined AI)
Average rating:
(5.00, 1 rating)
Building deep learning applications is hard. Building them repeatably is harder. Maintaining high computational performance during a repeatable deep learning development process is borderline impossible. Evan Sparks describes the key pitfalls associated with fast, repeatable model development and details what practitioners can do to avoid them and maintain a supercharged AI development workflow.
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1:50pm
Average rating:
(5.00, 1 rating)
There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them.
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Label leakage is a pervasive problem in predictive modeling data, and it takes on monstrous proportions at enterprise companies, where the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann and Leah McGuire explain how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem.
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Forough Poursabzi-Sangdeh (Microsoft Research NYC)
Average rating:
(4.00, 1 rating)
Forough Poursabzi-Sangdeh argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users’ behavior.
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Jana Eggers (Nara Logics)
Average rating:
(4.67, 3 ratings)
Jana Eggers explores explainability and transparency as both required and unachievable goals for AI, with a focus on helping teams structure discussions about levels of explainability possible and needed for both user trust and regulatory requirements.
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Average rating:
(4.00, 3 ratings)
Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data.
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40-minute session
Location: Beekman
Average rating:
(5.00, 1 rating)
Successful financial institutions like Morgan Stanley are growing more committed to efficiency and investing heavily in tools to do so. Marcelo Labre explains how the computing power and AI-readiness of IBM Power Systems enables a new journey of exploration and new possibilities in AI/ML use cases in finance.
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1:50pm–2:30pm Thursday, 04/18/2019
Location: Petit Trianon
TBC
40-minute session
Location: Trianon Ballroom
Adam Marek (Intel)
Average rating:
(3.00, 2 ratings)
Adam Marek discusses the motivation for, architecture behind, and functionality of Nauta's offerings and explains how this solution differs from other OSS offerings in the deep learning space.
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40-minute session
Location: Rendezvous
Morten Dahl (Dropout Labs)
Morten Dahl reviews modern cryptographic techniques such as homomorphic encryption and multiparty computation, sharing concrete examples in TensorFlow using the open source library TF Encrypted. Join in to learn how to get started with privacy-preserving techniques today, without needing to master the cryptography.
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2:40pm
Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals.
Read more.
Alina Matyukhina (Canadian Institute for Cybersecurity)
Average rating:
(4.00, 2 ratings)
Machine learning models are often susceptible to adversarial deception of their input at test time, which leads to poorer performance. Alina Matyukhina investigates the feasibility of deception in source code attribution techniques in real-world environments and explores attack scenarios on users' identities in open source projects—along with possible protection methods.
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Behrooz Hashemian (VideaHealth)
Artificial intelligence has shown great potential to revolutionize clinical medicine and healthcare delivery. However, incorporating these algorithms into clinical workflows involves a big challenge: convincing clinicians and regulators to trust a “black box” solution. Behrooz Hashemian explains how he's helping make deep neural networks interpretable to provide evidence for clinical decisions.
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Average rating:
(4.80, 5 ratings)
Hilary Mason shares a process for repeatedly creating effective AI products, from idea through process to specific design considerations, and explains how architecture and algorithmic choices can support or hinder this process.
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Anoop Katti (SAP)
Average rating:
(4.60, 5 ratings)
Anoop Katti explores the shortcomings of the existing techniques for understanding 2D documents and offers an overview of the Character Grid (Chargrid), a new processing pipeline pioneered by data scientists at SAP.
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40-minute session
Location: Beekman
Nanda Vijaydev (BlueData)
Average rating:
(5.00, 1 rating)
Nanda Vijaydev shares practical examples of—and lessons learned from—ML/DL use cases in financial services, healthcare, and other industries. You'll learn how to quickly deploy containerized multinode environments for TensorFlow and other ML/DL tools in a multitenant architecture either on-premises, in the cloud, or in a hybrid environment.
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2:40pm–3:20pm Thursday, 04/18/2019
Location: Petit Trianon
TBC
Average rating:
(5.00, 1 rating)
While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. Ryan Mukherjee and Neil Fendley offer an overview of functional Map of the World (fMoW), an ImageNet for satellite imagery built to address this issue, and explain how you can attack or defend these deep learning models.
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Magnus Hyttsten (Google)
Average rating:
(5.00, 2 ratings)
Magnus Hyttsten explains how to use TensorFlow effectively in a distributed manner using best practices. Magnus covers using TensorFlow's new DistributionStrategies to get easy high-performance training with Keras models (and custom models) on multi-GPU setups as well as multinode training on clusters with accelerators.
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3:20pm
3:20pm–4:05pm Thursday, 04/18/2019
Location: Expo Hall
Afternoon Break (Sponsored by Accenture)
(45m)
4:05pm
Tammy Bilitzky (DCL)
Tammy Bilitzky shares a case study that details lights-out automation and explains how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Along the way, she outlines considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units.
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Chakri Cherukuri (Bloomberg LP)
Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.
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Aric Whitewood (WilmotML)
Average rating:
(5.00, 1 rating)
Aric Whitewood details WilmotML's research on the application of AI to investment management and offers an overview of the company's prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018.
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Paco Nathan (derwen.ai)
Average rating:
(4.33, 3 ratings)
Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
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Marcel Kurovski (inovex)
Average rating:
(5.00, 2 ratings)
Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for vehicle recommendations at Germany's biggest online vehicle market.
Read more.
4:05pm–4:45pm Thursday, 04/18/2019
Location: Beekman
TBC
4:05pm–4:45pm Thursday, 04/18/2019
Location: Petit Trianon
TBC
Jaewon Lee (Naver/LINE),
Sihyeung Han (Naver/LINE)
Jaewon Lee and Sihyeung Han walk you through implementing a self-trained dialogue model using AutoML and the Chatbot Builder Framework. You'll discover the value of AutoML, which allows you to provide better model, and learn how AutoML can be applied in different areas of NLP, not just for chatbots.
Read more.
Humayun irshad (Figure Eight)
Average rating:
(2.00, 1 rating)
Humayun Irshad offers an overview of an active learning framework that uses a crowdsourcing approach to solve parking sign recognition—a real-world problem in transportation and autonomous driving for which a large amount of unlabeled data is available. The solution generates an accurate model, quickly and cost-effectively, despite the unevenness of the data.
Read more.
4:55pm
Sheldon Fernandez (DarwinAI)
Sheldon Fernandez draws on his degrees in both engineering and theology to separate fact from fad in ensuring that artificial systems behave ethically.
Read more.
4:55pm–5:35pm Thursday, 04/18/2019
Location: Sutton South
TBC
Baohong Sun (Cheung Kong Graduate School of Business)
Andrew Caosun discusses a framework that unifies hidden Markov models and deep learn algorithms (RNN) with modeling components that consider long-term memory and semantics of music (LSTM and convolution). It takes users' original creations as input, modifies the raw scores, and generates musically appropriate melodies.
Read more.
Jennifer Fernick (NCC Group )
Quantum computers will enable us to efficiently compute things never thought possible, but how will this impact artificial intelligence? Jennifer Fernick explains how to filter signal from noise in discussions surrounding quantum machine learning by exploring how quantum computers work, what types of AI problems they may be good at, and which industries and use cases will (and won't) benefit.
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Maja Vukovic (IBM)
AI planning offers an opportunity to drive reasoning about action trajectories to help build automation. Maja Vukovic demos an application of AI planning for the migration of legacy infrastructure to the cloud, based on real-world examples and data, and discusses challenges in adopting AI planning solutions in the enterprise.
Read more.
4:55pm–5:35pm Thursday, 04/18/2019
Location: Beekman
TBC
4:55pm–5:35pm Thursday, 04/18/2019
Location: Petit Trianon
TBC
Matthew REYES (Technergetics)
Average rating:
(1.00, 1 rating)
Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations.
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Average rating:
(4.67, 3 ratings)
ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.
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