Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
 
Grand Ballroom East
Add Session by Zoubin Ghahramani to your personal schedule
11:05am Session by Zoubin Ghahramani Zoubin Ghahramani (Uber | University of Cambridge)
Add Democratizing deep reinforcement learning to your personal schedule
11:55am Democratizing deep reinforcement learning Danny Lange (Unity Technologies)
Add Distributed DNN training: Infrastructure, challenges, and lessons learned to your personal schedule
1:45pm Distributed DNN training: Infrastructure, challenges, and lessons learned Kaarthik Sivashanmugam (Microsoft), Wee Hyong Tok (Microsoft)
Add Scalable deep learning to your personal schedule
2:35pm Scalable deep learning Ameet Talwalkar (Determined AI)
Add Using AI to solve complex economic problems to your personal schedule
4:00pm Using AI to solve complex economic problems Ashok Srivastava (Intuit)
4:50pm
Grand Ballroom West
Add Deep learning and AI is making clinical neuroimaging faster, safer, and smarter to your personal schedule
2:35pm Deep learning and AI is making clinical neuroimaging faster, safer, and smarter Enhao Gong (Stanford University | Subtle Medical), Greg Zaharchuk (Stanford University)
Add Imputing medical conditions based on a patient's medical history with deep learning to your personal schedule
4:00pm Imputing medical conditions based on a patient's medical history with deep learning Julie Zhu (Optum), Dima Rekesh (United Healthcare Group - Optum Tech)
Add The cognitive IoT and eldercare to your personal schedule
4:50pm The cognitive IoT and eldercare David C Martin (IBM Watson)
Sutton North/Center
Add Using Cognitive Toolkit (CNTK) with Kubernetes clusters to your personal schedule
11:55am Using Cognitive Toolkit (CNTK) with Kubernetes clusters Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Add Machine learning meets DevOps: Paying down the high-interest credit card to your personal schedule
2:35pm Machine learning meets DevOps: Paying down the high-interest credit card Sameer Wadkar (Comcast NBCUniversal), Nabeel Sarwar (Comcast NBCUniversal)
Sutton South
Add AI and the future of work to your personal schedule
11:05am AI and the future of work Jeetu Patel (Box)
Add How Comcast uses AI to reinvent the customer experience to your personal schedule
2:35pm How Comcast uses AI to reinvent the customer experience Jan Neumann (Comcast), Jeanine Heck (Comcast)
Regent Parlor
Add Executive Briefing: Why AI needs human-centered design to your personal schedule
4:00pm Executive Briefing: Why AI needs human-centered design James Guszcza (Deloitte Consulting)
Add Executive Briefing: Achieving sustainability with GDPR to your personal schedule
4:50pm Executive Briefing: Achieving sustainability with GDPR Kayvaun Rowshankish (McKinsey & Company)
Nassau East/West
Add AI in personal finance: More than just chatbots to your personal schedule
11:55am AI in personal finance: More than just chatbots Brian Pearce (Wells Fargo)
Add Predicting the stock market using LSTMs to your personal schedule
1:45pm Predicting the stock market using LSTMs Aurélien Géron (Kiwisoft)
Add Automatic financial econometrics with AI to your personal schedule
2:35pm Automatic financial econometrics with AI Ambika Sukla (Morgan Stanley)
Add Explaining machine learning for consumer loans to your personal schedule
4:50pm Explaining machine learning for consumer loans Mike Ruberry (ZestFinance)
Concourse A
11:55am
Add Adversarial ML: Practical attacks and defenses against graph-based clustering to your personal schedule
2:35pm Adversarial ML: Practical attacks and defenses against graph-based clustering Yacin Nadji (Georgia Institute of Technology)
Add GPU-accelerating AI for cyber threat detection to your personal schedule
4:00pm GPU-accelerating AI for cyber threat detection Joshua Patterson (NVIDIA), Michael Balint (NVIDIA)
Add AI: A force for good to your personal schedule
4:50pm AI: A force for good Jake Porway (DataKind)
Beekman Parlor
Add Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) to your personal schedule
4:00pm Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) Simon Moss (Teradata), Ben MacKenzie (Think Big Analytics)
Add Tuesday opening remarks to your personal schedule
Grand Ballroom
8:45am Tuesday opening remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Add Bringing AI into the wild (sponsored by SAS) to your personal schedule
9:05am Bringing AI into the wild (sponsored by SAS) Mary Beth Ainsworth (SAS)
Add Program Chair keynote  to your personal schedule
9:10am Program Chair keynote Ben Lorica (O'Reilly Media)
Add Keynote by Manuela Veloso to your personal schedule
9:20am Keynote by Manuela Veloso Manuela Veloso (Carnegie Mellon University)
9:35am
Add Fireside chat with Peter Norvig and Kavya Kopparapu to your personal schedule
9:45am Fireside chat with Peter Norvig and Kavya Kopparapu Peter Norvig (Google), Kavya Kopparapu (GirlsComputingLeague)
10:00am
Add Keynote by Zoubin Ghahramani to your personal schedule
10:05am Keynote by Zoubin Ghahramani Zoubin Ghahramani (Uber | University of Cambridge)
Add Closing remarks to your personal schedule
10:20am Closing remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Add Tuesday Topic Tables at lunch to your personal schedule
12:35pm Lunch sponsored by Microsoft Tuesday Topic Tables at lunch | Room: America's Hall
Add Speed Networking to your personal schedule
8:00am Speed Networking | Room: TBD
Add AI at Night to your personal schedule
7:00pm AI at Night | Room: TBD
10:35am Morning Break sponsored by SAS | Room: Sponsor Pavilion
3:15pm Afternoon Break | Room: Sponsor Pavilion
Add Sponsor Pavilion Reception to your personal schedule
5:30pm Sponsor Pavilion Reception | Room: Sponsor Pavilion
11:05am-11:45am (40m)
Session by Zoubin Ghahramani
Zoubin Ghahramani (Uber | University of Cambridge)
Session by Zoubin Ghahramani
11:55am-12:35pm (40m) AI in the Enterprise
Democratizing deep reinforcement learning
Danny Lange (Unity Technologies)
Danny Lange offers an overview of deep reinforcement learning—an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life—and explains how the crossroads between machine learning and gaming offers innovations that are applicable in other fields of technology, such as the robotics and automotive industries.
1:45pm-2:25pm (40m) Implementing AI
Distributed DNN training: Infrastructure, challenges, and lessons learned
Kaarthik Sivashanmugam (Microsoft), Wee Hyong Tok (Microsoft)
Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure.
2:35pm-3:15pm (40m) Models and Methods
Scalable deep learning
Ameet Talwalkar (Determined AI)
While deep learning has enjoyed widespread empirical success, fundamental bottlenecks exist when attempting to develop deep learning applications at scale. Ameet Talwalkar shares research on addressing two core scalability bottlenecks: tuning the knobs of deep learning models (i.e., hyperparameter optimization) and training deep models in parallel environments.
4:00pm-4:40pm (40m) AI in the Enterprise
Using AI to solve complex economic problems
Ashok Srivastava (Intuit)
Entrusted with the financial data of 42 million customers, Intuit is in a unique position to take advantage of AI to solve some of its customers’ biggest financial pains. Ashok Srivastava discusses technology’s role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world.
4:50pm-5:30pm (40m)
Session
11:05am-11:45am (40m) Implementing AI, Models and Methods
High-throughput single-shot multibox object detection on edge devices using FPGAs
Yamini Nimmagadda (Intel)
Yamini Nimmagadda demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance.
11:55am-12:35pm (40m) Implementing AI, Models and Methods
Long-term time series forecasting with recurrent neural networks
Mustafa Kabul (SAS)
Forecasting the long-term values of a time series data is crucial for planning. But how do you make use of a recurrent neural network when you want to compute an accurate long-term forecast? How can you capture short- and long-term seasonality or discover small patterns from the data that generate the big picture? Mustafa Kabul shares a scalable technique addressing these questions.
1:45pm-2:25pm (40m) Implementing AI, Models and Methods
Building a healthcare decision support system for ICD10/HCC coding through deep learning
Manas Ranjan Kar (Episource)
Episource is building a scalable NLP engine to help summarize medical charts and extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. Manas Ranjan Kar offers an overview of the wide variety of deep learning algorithms involved and the complex in-house training-data creation exercises that were required to make it work.
2:35pm-3:15pm (40m) AI in the Enterprise, Impact of AI on Business and Society
Deep learning and AI is making clinical neuroimaging faster, safer, and smarter
Enhao Gong (Stanford University | Subtle Medical), Greg Zaharchuk (Stanford University)
What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making.
4:00pm-4:40pm (40m) Implementing AI
Imputing medical conditions based on a patient's medical history with deep learning
Julie Zhu (Optum), Dima Rekesh (United Healthcare Group - Optum Tech)
Julie Zhu shares a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras.
4:50pm-5:30pm (40m) Interacting with AI
The cognitive IoT and eldercare
David C Martin (IBM Watson)
David Martin explores cognitive function in conjunction with edge computing and IoT sensors and actuators for eldercare scenarios—specifically the identification of individuals, daily activity monitoring, and aberration detection performed on-premises using HomeAssistant, the Intu open source project, and IBM's Watson cognitive services.
11:05am-11:45am (40m) Implementing AI
Containers and the intelligent application revolution
William Benton (Red Hat)
Intelligent applications learn from data to provide improved functionality to users. William Benton examines the confluence of two development revolutions: almost every exciting new application today is intelligent, and developers are increasingly deploying their work on container application platforms. Join William to learn how these two revolutions benefit one another.
11:55am-12:35pm (40m) Implementing AI
Using Cognitive Toolkit (CNTK) with Kubernetes clusters
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Deep learning has fueled the emergence of many practical applications and experiences. Meanwhile, container technologies have been maturing, allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean as they walk you through using the Cognitive Toolkit (CNTK) with Kubernetes clusters.
1:45pm-2:25pm (40m) Models and Methods
Scaling your data science experiments from Jupyter notebooks to 6,000 GPUs
Arshak Navruzyan (Sentient Technologies)
Data scientists and machine learning professionals face a quandary of choices when trying to figure out how to scale their data science experiments. Arshak Navruzyan details the landscape of available options and explains how to make best use of the free and open source tools available.
2:35pm-3:15pm (40m) Implementing AI
Machine learning meets DevOps: Paying down the high-interest credit card
Sameer Wadkar (Comcast NBCUniversal), Nabeel Sarwar (Comcast NBCUniversal)
Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments.
4:00pm-4:40pm (40m) Implementing AI
Caching big data for machine learning platform at Uber
Zhenxiao Luo (Uber)
From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Zhenxiao Luo explains how Uber tackles data caching in large-scale machine learning, exploring Uber's machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speed up AI jobs.
4:50pm-5:30pm (40m) Implementing AI, Interacting with AI
Artificial intelligence strategy: Delivering deep learning
Chris Benson (Honeywell)
Deep learning is the driving force behind the current AI revolution and will impact every industry on the planet. However, success requires an AI strategy. Chris Benson walks you through creating a strategy for delivering deep learning into production and explores how deep learning is integrated into a modern enterprise architecture.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise
AI and the future of work
Jeetu Patel (Box)
AI will completely change and fundamentally power the way the world works together, so what does the future of AI in the enterprise look like? Jeetu Patel explains how intelligence is being applied to enterprise content in practical ways that will revolutionize the most important business processes for companies of all sizes and across all industries.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
AI applications, best practices, and lessons learned in the automotive domain
Andre Luckow (BMW Group)
AI delivers value to many facets of the automotive value chain, including smart manufacturing, supply chain management, and customer engagement. Andre Luckow discusses how to assess AI technologies, validate use cases, and foster the fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications.
1:45pm-2:25pm (40m) AI Business Summit, Impact of AI on Business and Society
AI and the future of customer service: Meet Expensify’s new AI-assistant, Concierge
David Barrett (Expensify )
Expensify is using AI to streamline and improve customer service, reducing customer wait time from 15 hours to 3 minutes. David Barrett leads a deep dive into the process of building Concierge, a hybrid machine learning-driven chatbot, covering the challenges faced, results to date, and what he sees for the future of AI and customer service.
2:35pm-3:15pm (40m) AI Business Summit, Implementing AI
How Comcast uses AI to reinvent the customer experience
Jan Neumann (Comcast), Jeanine Heck (Comcast)
Jan Neumann and Jeanine Heck explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers' questions or resolve their concerns.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise
How artificial intelligence is transforming traditional industries, from property insurance to agriculture
Ryan Kottenstette (Cape Analytics)
There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries like insurance, finance or agriculture. Ryan Kottenstette shares lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture.
4:50pm-5:30pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Three ways to put computer vision to work today
Ophir Tanz (GumGum)
Advancements in computer vision are creating new opportunities across business verticals, from programs that help the visually impaired to extracting business insights from socially shared pictures, but the benefits of applied AI in computer vision are only beginning to emerge. Ophir Tanz explores the tools and image technology utilizing AI that you can apply to your business today.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: The adoption of artificial intelligence in business—Why leaders forge ahead and laggards fall behind
David Kiron (MIT Sloan Management Review)
Few organizations have mastered integrating AI technology into their business processes and offerings, and many who want to don’t fully understand the work that lies ahead. David Kiron shares surprising insights about businesses’ appetite for and approach to AI, drawn from global collaborative research conducted by MIT Sloan Management Review and the Boston Consulting Group.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: Building an AI-first enterprise culture
Kathryn Hume (integrate.ai)
Large enterprises struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. AI requires a radical shift. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture.
1:45pm-2:25pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: The conversational business—Use cases and best practices for chatbots in financial services and media
Susan Etlinger (Altimeter Group)
Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots, covering the risks and opportunities of conversational interfaces, the strategic implications for customer experience, business models, brand strategy, and recent innovations.
2:35pm-3:15pm (40m) AI Business Summit, Impact of AI on Business and Society
Executive Briefing: Making reliable and trustworthy AI systems a reality
Tolga Kurtoglu (PARC)
Tolga Kurtoglu walks you through the advanced technology needed to implement cyberphysical systems, covering the right hardware to sense the right data, explainable AI, and designing security for trustworthy operations. Along the way, Tolga shares case studies and examples of advanced tech deployments.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: Why AI needs human-centered design
James Guszcza (Deloitte Consulting)
AI is about more than automating tasks; it's about augmenting and extending human capabilities. James Guszcza discusses principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-centered design has been crucial to the economic success of an AI project.
4:50pm-5:30pm (40m)
Executive Briefing: Achieving sustainability with GDPR
Kayvaun Rowshankish (McKinsey & Company)
The session will explore the extent to which firms have addressed the GDPR regulation (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.
11:05am-11:45am (40m) Implementing AI, Interacting with AI
Innovations in explainable AI in the context of real-world business applications
Scott Zoldi (FICO)
Scott Zoldi discusses innovations in explainable AI, such as Reason Reporter, which explains the workings of neural network models used to detect fraudulent payment card transactions in real time, and offers a comparative study with local interpretable model-agnostic explanations (LIME) that demonstrates why the former are better at providing explanations.
11:55am-12:35pm (40m) Impact of AI on Business and Society
AI in personal finance: More than just chatbots
Brian Pearce (Wells Fargo)
Chatbots are having a moment, and banks across the world are utilizing them for everything from basic customer service to assisting internal IT support. But chatbots only skim the AI landscape. Brian Pearce explains how AI helps Wells Fargo use data in a smarter way, from developing custom experiences to uncovering new insights—with customers and employees at the center of it all.
1:45pm-2:25pm (40m) Implementing AI, Interacting with AI, Models and Methods
Predicting the stock market using LSTMs
Aurélien Géron (Kiwisoft)
The stock market is well known to be extremely random, making investment decisions difficult, but deep learning can help. Drawing on a concrete financial use case, Aurélien Géron explains how LSTM networks can be used for forecasting.
2:35pm-3:15pm (40m) Implementing AI, Models and Methods
Automatic financial econometrics with AI
Ambika Sukla (Morgan Stanley)
Financial econometric models are usually handcrafted using a combination of statistical methods, stochastic calculus, and dynamic programming techniques. Ambika Sukla explains how recent advancements in AI can help simplify financial model building by carefully replacing complex mathematics with a data-driven incremental learning approach.
4:00pm-4:40pm (40m) Models and Methods
Using NLP, neural networks, and reporting metrics in production for continuous improvement in text classifications
Megan Yetman (Capital One)
Pensieve is a natural language processing (NLP) project that classifies reviews for their sentiment, reason for sentiment, high-level content, and low-level content. Megan Yetman offers an overview of Pensieve as well as ways to improve model reporting and the ability for continuous model learning and improvement.
4:50pm-5:30pm (40m) Implementing AI, Interacting with AI, Models and Methods
Explaining machine learning for consumer loans
Mike Ruberry (ZestFinance)
Historically, the consumer loan industry has restricted itself to using relatively simple machine learning models and techniques to accept or deny loan applicants. However, more powerful (but also more complicated) methods can significantly improve business outcomes. Mike Ruberry shares a framework for evaluating, explaining, and managing these more complex methods.
11:05am-11:45am (40m) Models and Methods
Humanizing technology: Emotion detection from face and voice
Taniya Mishra (Affectiva)
Drawing on Affectiva's experience building a multimodal emotion AI that can detect human emotions from face and voice, Taniya Mishra discusses how to build multimodal emotion detection using various deep learning approaches. Along the way, Taniya explains how to mitigate the challenges of data collection and annotation and how to avoid bias in model training.
11:55am-12:35pm (40m) Implementing AI, Models and Methods
Session
1:45pm-2:25pm (40m) Implementing AI
Using machine learning to enhance activity-based intelligence
Jamie Irza (Raytheon)
Activity-based intelligence (ABI) is the art and science of understanding normal patterns of life to enhance the ability of a system to detect anomalous behavior (e.g., to identify cases of credit card fraud). Jamie Irza demonstrates how machine learning can be used to implement ABI for detecting threatening behavior from unmanned aerial systems, commonly known as drones.
2:35pm-3:15pm (40m) Implementing AI, Models and Methods
Adversarial ML: Practical attacks and defenses against graph-based clustering
Yacin Nadji (Georgia Institute of Technology)
The adversarial nature of security makes applying machine learning complicated. If attackers can evade signatures and heuristics, what is stopping them from evading ML models? Yacin Nadji evaluates, breaks, and fixes a deployed network-based ML detector that uses graph clustering. While the attacks are specific to graph clustering, the lessons learned apply to all ML systems in security.
4:00pm-4:40pm (40m) Implementing AI, Interacting with AI, Models and Methods
GPU-accelerating AI for cyber threat detection
Joshua Patterson (NVIDIA), Michael Balint (NVIDIA)
Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Michael Balint explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools.
4:50pm-5:30pm (40m) Impact of AI on Business and Society
AI: A force for good
Jake Porway (DataKind)
Jake Porway explores AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges you may face in using AI to tackle complex humanitarian issues.
11:05am-11:45am (40m)
Computational creativity: Making music with AI technologies (sponsored by Microsoft)
Erika Menezes (Microsoft)
Erika Menezes shares a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.
2:35pm-3:15pm (40m)
Improving wildlife conservation with artificial intelligence (sponsored by SAS)
Mary Beth Ainsworth (SAS)
Indigenous trackers all over the world can look at a single footprint in the dirt and intuitively know what animal species that print belongs to. Mary Beth Ainsworth explains how biologists, zoologists, machine learning and computer vision experts have come together to develop, automate, and scale a noninvasive approach to monitoring endangered wildlife by analyzing where animals have walked.
4:00pm-4:40pm (40m)
Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata)
Simon Moss (Teradata), Ben MacKenzie (Think Big Analytics)
Analytic techniques leveraging artificial intelligence can result in dramatic improvements in crime detection and interdiction across diverse attack modalities. Simon Moss and Ben MacKenzie share AI models and operational techniques they’ve used with major banking clients to substantially strengthen and accelerate their responses to criminal attacks.
8:45am-8:50am (5m)
Tuesday opening remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Artificial Intelligence program chairs Ben Lorica and Roger Chen open the first day of keynotes.
8:50am-9:05am (15m)
Keynote by Intel
Keynote by Intel
9:05am-9:10am (5m)
Bringing AI into the wild (sponsored by SAS)
Mary Beth Ainsworth (SAS)
Comprehensive and sustainable wildlife monitoring technologies are key to maintaining biodiversity. Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that can rapidly analyze animal footprints to help map wildlife presence and scale conservation efforts around the world.
9:10am-9:20am (10m)
Program Chair keynote
Ben Lorica (O'Reilly Media)
Keynote by program chair Ben Lorica
9:20am-9:35am (15m)
Keynote by Manuela Veloso
Manuela Veloso (Carnegie Mellon University)
Keynote by Manuela Veloso
9:35am-9:45am (10m)
Plenary
9:45am-10:00am (15m)
Fireside chat with Peter Norvig and Kavya Kopparapu
Peter Norvig (Google), Kavya Kopparapu (GirlsComputingLeague)
Fireside chat with Peter Norvig and Kavya Kopparapu
10:00am-10:05am (5m)
Plenary
10:05am-10:20am (15m)
Keynote by Zoubin Ghahramani
Zoubin Ghahramani (Uber | University of Cambridge)
Keynote by Zoubin Ghahramani
10:20am-10:35am (15m)
Closing remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Program chairs Ben Lorica and Roger Chen close the first day of keynotes.
12:35pm-1:45pm (1h 10m)
Tuesday Topic Tables at lunch
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
8:00am-8:30am (30m)
Speed Networking
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Tuesday keynotes and during the afternoon for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
7:00pm-9:30pm (2h 30m)
AI at Night
Don't miss AI at Night happening on Tuesday after the Sponsor Pavilion Reception.
10:35am-11:05am (30m)
Break: Morning Break sponsored by SAS
3:15pm-4:00pm (45m)
Break: Afternoon Break
5:30pm-6:30pm (1h)
Sponsor Pavilion Reception
Come enjoy snacks and beverages with fellow AI Conference attendees, speakers, and sponsors.