Put AI to Work
April 15-18, 2019
New York, NY
 
Grand Ballroom West
Add Opening remarks and keynote to your personal schedule
Grand Ballroom West
8:45am Opening remarks and keynote Ben Lorica (O'Reilly), Roger Chen (Computable), Alexis Crowell Helzer (Intel)
Add Is AI human-ready? to your personal schedule
9:10am Is AI human-ready? Aleksander Madry (MIT)
Add Teaching a computer to read to your personal schedule
9:35am Teaching a computer to read Desiree Gosby (Intuit)
Add Data Fueling AI of the Future (sponsored by Dell Technologies) to your personal schedule
9:55am Data Fueling AI of the Future (sponsored by Dell Technologies) Thomas Henson (Dell Technologies)
Add Machine learning for personalization to your personal schedule
10:00am Machine learning for personalization Tony Jebara (Columbia University | Netflix)
Add  AI and the robotics revolution to your personal schedule
10:20am AI and the robotics revolution Martial Hebert (First Child Designs)
Add The unreasonable effectiveness of structure to your personal schedule
11:05am The unreasonable effectiveness of structure Lise Getoor (University of California, Santa Cruz)
Add Random search and reproducibility for neural architecture search to your personal schedule
1:00pm Random search and reproducibility for neural architecture search Ameet Talwalkar (Carnegie Mellon University | Determined AI)
Add A software accelerator for machine learning to your personal schedule
1:50pm A software accelerator for machine learning Vinay Rao (RocketML), Santi Adavani (RocketML)
Add The curse of generality: Deep reinforcement learning in the wild  to your personal schedule
2:40pm The curse of generality: Deep reinforcement learning in the wild Sanjay Krishnan (University of Chicago)
Add Automatic concept learning to your personal schedule
4:05pm Automatic concept learning Haizi Yu (University of Illinois at Urbana-Champaign)
Add How to build privacy and security into deep learning models to your personal schedule
4:55pm How to build privacy and security into deep learning models Yishay Carmiel (IntelligentWire)
Sutton North/Center
Add Media meets AI: How we give superpowers to BuzzFeed's social curators to your personal schedule
11:05am Media meets AI: How we give superpowers to BuzzFeed's social curators Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed)
Add Decentralized governance of data to your personal schedule
1:00pm Decentralized governance of data Roger Chen (Computable)
Add Leveraging data science in asset management to your personal schedule
2:40pm Leveraging data science in asset management Andrew Chin (AllianceBernstein), Celia Chen (AllianceBernstein)
Add Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA to your personal schedule
4:05pm Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA Kyle Hoback (WorkFusion), James Lawson (WorkFusion)
Add Best practices for scaling modeling platforms to your personal schedule
4:55pm Best practices for scaling modeling platforms Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments)
Sutton South
Add Fraud detection without feature engineering to your personal schedule
11:05am Fraud detection without feature engineering Pamela Vagata (Stripe)
Add ML at Twitter: A deep dive into Twitter's timeline to your personal schedule
1:00pm ML at Twitter: A deep dive into Twitter's timeline Cibele Halasz (Apple), Satanjeev Banerjee (Twitter)
Add Industrialized capsule networks for text analytics to your personal schedule
2:40pm Industrialized capsule networks for text analytics Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Add Leveraging AI for social good to your personal schedule
4:55pm Leveraging AI for social good Jack Dashwood (Intel), Anna Bethke (Intel)
Mercury Ballroom
Add Executive Briefing: From cutting-edge AI research to business impact to your personal schedule
1:00pm Executive Briefing: From cutting-edge AI research to business impact Larry Carin (Infinia ML), Michael Eagan (Korn Ferry)
Add Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019 to your personal schedule
2:40pm Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019 Anna Gressel (Debevoise & Plimpton LLP), Jim Pastore (Debevoise & Plimpton LLP), Anwesa Paul (American Express)
Add Executive Briefing: Agile AI to your personal schedule
4:05pm Executive Briefing: Agile AI Sarah Aerni (Salesforce Einstein)
Regent Parlor
Add Turn devices into data scientists—at the edge to your personal schedule
1:50pm Turn devices into data scientists—at the edge Simon Crosby (SWIM.AI)
Add Using AI to create interactive digital actors to your personal schedule
4:05pm Using AI to create interactive digital actors Kevin He (DeepMotion)
Add Game engines and machine learning to your personal schedule
4:55pm Game engines and machine learning Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (Lonely Coffee)
Rendezvous
Add TensorFlow 2.0: Machine learning for you to your personal schedule
11:05am TensorFlow 2.0: Machine learning for you Joshua Gordon (Google)
Add Developing your own model tracking leaderboard in Keras to your personal schedule
1:00pm Developing your own model tracking leaderboard in Keras Catherine Ordun (Booz Allen Hamilton)
Add Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues to your personal schedule
1:50pm Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues Jeremy Lewi (Google), Hamel Husain (GitHub)
Add Building intelligent conversational agents with transfer learning and cognitive automation to your personal schedule
2:40pm Building intelligent conversational agents with transfer learning and cognitive automation Sumeet Vij (Booz Allen Hamilton), Matt Speck (Booz Allen Hamilton)
Add Toward self-aware, resilient systems and ethical artificial intelligence to your personal schedule
4:05pm Toward self-aware, resilient systems and ethical artificial intelligence Pradip Bose (IBM T. J. Watson Research Center)
Add Responsible AI practices: A technical demonstration to your personal schedule
4:55pm Responsible AI practices: A technical demonstration Andrew Zaldivar (Google)
Trianon Ballroom
Add Deploying deep learning models on GPU-enabled Kubernetes clusters to your personal schedule
11:05am Deploying deep learning models on GPU-enabled Kubernetes clusters Mathew Salvaris (Microsoft), Fidan Boylu Uz (Microsoft)
Add Building an AI engine for time series data analytics to your personal schedule
1:00pm Building an AI engine for time series data analytics JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Add Building a production-scale ML platform to your personal schedule
1:50pm Building a production-scale ML platform YU DONG (Facebook)
Add Unifying Twitter around a single ML platform to your personal schedule
2:40pm Unifying Twitter around a single ML platform Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
Add Fast (and cheap) AI accelerated on FPGAs to your personal schedule
4:55pm Fast (and cheap) AI accelerated on FPGAs Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
Beekman
2:40pm
Add How leaders are tackling their most pressing AI challenges (sponsored by SAS) to your personal schedule
4:05pm How leaders are tackling their most pressing AI challenges (sponsored by SAS) Tom Roehm (SAS), Alexis Crowell Helzer (Intel)
Petit Trianon
10:35am Morning Break | Room: Expo Hall
3:20pm Afternoon Break (Sponsored by Dataiku) | Room: Expo Hall
Add Attendee Reception to your personal schedule
5:35pm Attendee Reception | Room: Expo Hall
8:00am Morning Coffee (Sponsored by NetApp) | Room: 3rd Floor Promenade
Add Speed Networking to your personal schedule
8:15am Speed Networking | Room: 3rd Floor Promenade
Add Wednesday Topic Tables at Lunch (sponsored by Microsoft) to your personal schedule
11:50am Lunch Wednesday Topic Tables at Lunch (sponsored by Microsoft) | Room: Americas Hall 2
Add AI at Night to your personal schedule
7:00pm AI at Night | Room: Bryant Park Grill
8:45am-8:55am (10m)
Opening remarks and keynote
Ben Lorica (O'Reilly), Roger Chen (Computable), Alexis Crowell Helzer (Intel)
Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the first day of keynotes.
8:55am-9:10am (15m)
Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale
Gadi Singer (Intel)
Gadi Singer explores four real-world AI deployments at enterprise scale.
9:10am-9:25am (15m)
Is AI human-ready?
Aleksander Madry (MIT)
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.
9:25am-9:35am (10m)
Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft Azure)
Danielle Dean (iRobot)
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.
9:35am-9:50am (15m)
Teaching a computer to read
Desiree Gosby (Intuit)
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.
9:50am-9:55am (5m)
Toward ethical AI: Inclusivity as a messy, difficult, but promising answer (sponsored by Dataiku)
Kurt Muehmel (Dataiku)
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.
9:55am-10:00am (5m)
Data Fueling AI of the Future (sponsored by Dell Technologies)
Thomas Henson (Dell Technologies)
Keynote by Thomas Henson
10:00am-10:15am (15m)
Machine learning for personalization
Tony Jebara (Columbia University | Netflix)
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.
10:15am-10:20am (5m)
How AI Adaptive Technology can Upgrade Education Industry - Using MIBA, MCM, Deep Learning and NKC for AI + Adaptive Education (sponsored by Squirrel AI Learning)
Joleen Liang (Squirrel AI Learning)
Keynote by Joleen Liang
10:20am-10:35am (15m)
AI and the robotics revolution
Martial Hebert (First Child Designs)
Martial Hebert offers a brief overview of current challenges in AI for robotics and a glimpse of the exciting developments emerging in current research.
11:05am-11:45am (40m) Models and Methods Models and Methods
The unreasonable effectiveness of structure
Lise Getoor (University of California, Santa Cruz)
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.
1:00pm-1:40pm (40m) Machine Learning, Models and Methods Automation in machine learning and AI, Deep Learning and Machine Learning tools, Models and Methods
Random search and reproducibility for neural architecture search
Ameet Talwalkar (Carnegie Mellon University | Determined AI)
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.
1:50pm-2:30pm (40m) Models and Methods Models and Methods, Platforms and infrastructure
A software accelerator for machine learning
Vinay Rao (RocketML), Santi Adavani (RocketML)
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.
2:40pm-3:20pm (40m) Machine Learning, Models and Methods Automation in machine learning and AI, Models and Methods, Reinforcement Learning, Reliability and Safety
The curse of generality: Deep reinforcement learning in the wild
Sanjay Krishnan (University of Chicago)
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.
4:05pm-4:45pm (40m) Models and Methods
Automatic concept learning
Haizi Yu (University of Illinois at Urbana-Champaign)
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.
4:55pm-5:35pm (40m) Models and Methods Ethics, Privacy, and Security, Models and Methods
How to build privacy and security into deep learning models
Yishay Carmiel (IntelligentWire)
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.
11:05am-11:45am (40m) AI Business Summit, Case Studies AI case studies, Interfaces and UX, Media, Marketing, Advertising
Media meets AI: How we give superpowers to BuzzFeed's social curators
Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed)
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.
1:00pm-1:40pm (40m)
Decentralized governance of data
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.
1:50pm-2:30pm (40m) AI Business Summit, Executive Briefing/Best Practices AI case studies, Text, Language, and Speech
What you must know to build AI systems that understand natural language
David Talby (Pacific AI)
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.
2:40pm-3:20pm (40m) AI Business Summit, Case Studies AI case studies, Financial Services
Leveraging data science in asset management
Andrew Chin (AllianceBernstein), Celia Chen (AllianceBernstein)
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.
4:05pm-4:45pm (40m) AI Business Summit, Case Studies AI case studies, AI in the Enterprise, Financial Services, Text, Language, and Speech
Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA
Kyle Hoback (WorkFusion), James Lawson (WorkFusion)
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.
4:55pm-5:35pm (40m) AI Business Summit, Case Studies AI case studies, Automation in machine learning and AI, Financial Services, Platforms and infrastructure
Best practices for scaling modeling platforms
Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments)
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.
11:05am-11:45am (40m) Case Studies, Machine Learning AI case studies, Financial Services
Fraud detection without feature engineering
Pamela Vagata (Stripe)
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.
1:00pm-1:40pm (40m) Case Studies, Machine Learning AI case studies, Media, Marketing, Advertising, Platforms and infrastructure, Text, Language, and Speech
ML at Twitter: A deep dive into Twitter's timeline
Cibele Halasz (Apple), Satanjeev Banerjee (Twitter)
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.
1:50pm-2:30pm (40m) Case Studies, Machine Learning AI in the Enterprise, Text, Language, and Speech
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills
Maryam Jahanshahi (TapRecruit)
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.
2:40pm-3:20pm (40m) Case Studies, Machine Learning Models and Methods, Text, Language, and Speech
Industrialized capsule networks for text analytics
Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
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.
4:05pm-4:45pm (40m)
An artificial intelligence framework to counter international human trafficking
Tom Sabo (SAS)
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.
4:55pm-5:35pm (40m)
Leveraging AI for social good
Jack Dashwood (Intel), Anna Bethke (Intel)
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.
11:05am-11:45am (40m) AI Business Summit, Executive Briefing/Best Practices AI in the Enterprise, Data and Data Networks, Ethics, Privacy, and Security, Health and Medicine, Retail and e-commerce
Executive Briefing: New business models in the age of artificial intelligence
Deepashri Varadharajan (CB Insights)
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.
1:00pm-1:40pm (40m) AI Business Summit, Executive Briefing/Best Practices AI in the Enterprise
Executive Briefing: From cutting-edge AI research to business impact
Larry Carin (Infinia ML), Michael Eagan (Korn Ferry)
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.
1:50pm-2:30pm (40m) AI Business Summit, Executive Briefing/Best Practices Ethics, Privacy, and Security, Financial Services, Health and Medicine, Reliability and Safety
Executive Briefing: Responsible AI—An approach to and case studies for building fair, interpretable, safe AI
Anand Rao (PwC)
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.
2:40pm-3:20pm (40m) AI Business Summit, Executive Briefing/Best Practices AI in the Enterprise, Ethics, Privacy, and Security
Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019
Anna Gressel (Debevoise & Plimpton LLP), Jim Pastore (Debevoise & Plimpton LLP), Anwesa Paul (American Express)
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.
4:05pm-4:45pm (40m) AI Business Summit, Executive Briefing/Best Practices AI in the Enterprise, Automation in machine learning and AI, Platforms and infrastructure
Executive Briefing: Agile AI
Sarah Aerni (Salesforce Einstein)
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.
4:55pm-5:35pm (40m) AI Business Summit, Executive Briefing/Best Practices Financial Services
Executive Briefing: AI changes everything. . .except in investment management
Angelo Calvello (Rosetta Analytics)
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.
11:05am-11:45am (40m) Models and Methods Computer Vision, Ethics, Privacy, and Security, Models and Methods
Seeing is deceiving: The rise of fake media and how to fight back
Siwei Lyu (University of Albany)
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).
1:00pm-1:40pm (40m) Machine Learning, Models and Methods Data and Data Networks, Models and Methods, Reinforcement Learning
Learning from multiagent emergent behaviors in a simulated environment
Danny Lange (Unity Technologies)
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.
1:50pm-2:30pm (40m) Implementing AI Edge computing and Hardware
Turn devices into data scientists—at the edge
Simon Crosby (SWIM.AI)
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.
2:40pm-3:20pm (40m) Interacting with AI Computer Vision, Deep Learning and Machine Learning tools
Closing the loop on AI: How to maintain quality long-term AI results
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.
4:05pm-4:45pm (40m) Interacting with AI Media, Marketing, Advertising, Models and Methods, Reinforcement Learning
Using AI to create interactive digital actors
Kevin He (DeepMotion)
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.
4:55pm-5:35pm (40m) Interacting with AI Deep Learning and Machine Learning tools, Reinforcement Learning
Game engines and machine learning
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (Lonely Coffee)
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.
11:05am-11:45am (40m) Implementing AI Deep Learning and Machine Learning tools
TensorFlow 2.0: Machine learning for you
Joshua Gordon (Google)
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.
1:00pm-1:40pm (40m) Implementing AI Deep Learning and Machine Learning tools
Developing your own model tracking leaderboard in Keras
Catherine Ordun (Booz Allen Hamilton)
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.
1:50pm-2:30pm (40m) Implementing AI Platforms and infrastructure, Text, Language, and Speech
Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues
Jeremy Lewi (Google), Hamel Husain (GitHub)
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.
2:40pm-3:20pm (40m) Implementing AI AI in the Enterprise, Models and Methods, Text, Language, and Speech
Building intelligent conversational agents with transfer learning and cognitive automation
Sumeet Vij (Booz Allen Hamilton), Matt Speck (Booz Allen Hamilton)
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.
4:05pm-4:45pm (40m) Implementing AI AI case studies, Deep Learning and Machine Learning tools, Reliability and Safety
Toward self-aware, resilient systems and ethical artificial intelligence
Pradip Bose (IBM T. J. Watson Research Center)
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.
4:55pm-5:35pm (40m) Implementing AI Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Responsible AI practices: A technical demonstration
Andrew Zaldivar (Google)
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.
11:05am-11:45am (40m) Implementing AI Deep Learning and Machine Learning tools, Edge computing and Hardware, Platforms and infrastructure
Deploying deep learning models on GPU-enabled Kubernetes clusters
Mathew Salvaris (Microsoft), Fidan Boylu Uz (Microsoft)
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.
1:00pm-1:40pm (40m) Implementing AI Edge computing and Hardware, Platforms and infrastructure, Reinforcement Learning, Retail and e-commerce, Temporal data and time-series
Building an AI engine for time series data analytics
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
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.
1:50pm-2:30pm (40m) Implementing AI Media, Marketing, Advertising, Platforms and infrastructure
Building a production-scale ML platform
YU DONG (Facebook)
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.
2:40pm-3:20pm (40m) Implementing AI Deep Learning and Machine Learning tools, Media, Marketing, Advertising, Platforms and infrastructure
Unifying Twitter around a single ML platform
Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
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.
4:05pm-4:45pm (40m)
Understanding and integrating Intel Deep Learning Boost (Intel DL Boost)
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.
4:55pm-5:35pm (40m) Implementing AI Computer Vision, Edge computing and Hardware, Platforms and infrastructure
Fast (and cheap) AI accelerated on FPGAs
Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
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.
11:05am-11:45am (40m)
Bringing your machine learning to production with ML Ops (sponsored by Microsoft)
Sarah Bird (Microsoft)
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.
1:00pm-1:40pm (40m)
The intersection between human learning and machine learning. How AI will fundamentally change teaching and learning. (sponsored by Squirrel AI Learning)
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.
1:50pm-2:30pm (40m) Sponsored
Synergize your tech stack to realize AI’s full potential (sponsored by SAS)
Katherine Taylor (SAS)
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.
2:40pm-3:20pm (40m)
Session
4:05pm-4:45pm (40m)
How leaders are tackling their most pressing AI challenges (sponsored by SAS)
Tom Roehm (SAS), Alexis Crowell Helzer (Intel)
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.
4:55pm-5:35pm (40m) Sponsored
From prediction to prescription: Optimizing AI (sponsored by DataRobot)
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.
11:05am-11:45am (40m)
Getting to ROI: Case studies in operationalizing machine learning (sponsored by Dataiku)
Will Nowak (Dataiku)
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.
1:00pm-1:40pm (40m) Sponsored
Deploy machine learning for real impact: Bridge the gap between data scientists and IT (sponsored by Cisco)
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.
1:50pm-2:30pm (40m)
Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting)
Bill Roberts (Deloitte Consulting LLP)
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.
2:40pm-3:20pm (40m)
Unlock your data with AI (sponsored by HPE)
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.
4:05pm-4:45pm (40m) Sponsored
Continuous improvement of chat, social, and survey interactions using AI “idea analysis” (sponsored by Gamalon)
Ben Vigoda (Gamalon)
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.
4:55pm-5:35pm (40m) Sponsored
Unlocking AI value at scale: 3 building blocks and 1 massive mistake to avoid (sponsored by MapR)
Jack Norris (MapR Technologies)
Jack Norris delves into the three building blocks and the one massive mistake to avoid for any organization looking to leverage AI.
10:35am-11:05am (30m)
Break: Morning Break
3:20pm-4:05pm (45m)
Break: Afternoon Break (Sponsored by Dataiku)
5:35pm-6:35pm (1h)
Attendee Reception
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.
8:00am-8:50am (50m)
Break: Morning Coffee (Sponsored by NetApp)
8:15am-8:45am (30m)
Speed Networking
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.
11:50am-12:50pm (1h)
Wednesday Topic Tables at Lunch (sponsored by Microsoft)
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
7:00pm-9:00pm (2h)
AI at Night
Don't miss AI at Night, happening on Wednesday after the Attendee Reception.