Sep 9–12, 2019
 
Expo Hall 3
Add Semisupervised machine learning, the next frontier in AI to your personal schedule
11:55am Semisupervised machine learning, the next frontier in AI Vinay Rao (RocketML), Santi Adavani (RocketML)
Add PyTorch at scale for translation and NLP to your personal schedule
1:45pm PyTorch at scale for translation and NLP Stef Nelson-Lindall (Facebook)
230 A
Add Reference architectures for AI and machine learning to your personal schedule
11:05am Reference architectures for AI and machine learning Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)
Add Artificial and human intelligence in healthcare to your personal schedule
11:55am Artificial and human intelligence in healthcare Maithra Raghu (Cornell University | Google Brain)
Add Industrialized capsule networks for text analytics to your personal schedule
1:45pm Industrialized capsule networks for text analytics Vijay Srinivas Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
230 B
Add Language inference in medicine to your personal schedule
11:05am Language inference in medicine Chaitanya Shivade (IBM Research)
Add Container orchestrator to DL workload, Bing's approach: FrameworkLauncher to your personal schedule
4:00pm Container orchestrator to DL workload, Bing's approach: FrameworkLauncher Kai Liu (BING) (Microsoft), Yuqi Wang (Microsoft), Bin Wang (Microsoft)
230 C
Add Running large-scale machine learning experiments in the cloud to your personal schedule
1:45pm Running large-scale machine learning experiments in the cloud Shashank Prasanna (Amazon Web Services)
LL21 A/B
Add Executive Briefing: Ethical considerations for AI  to your personal schedule
11:55am Executive Briefing: Ethical considerations for AI Raj Minhas (PARC, a Xerox Company)
Add Executive Briefing: Rigorous application of domain insights in AI projects to your personal schedule
1:45pm Executive Briefing: Rigorous application of domain insights in AI projects Jike Chong (LinkedIn | Tsinghua University), Yue Cathy Chang (TutumGene)
Add Executive Briefing: Unpacking AutoML to your personal schedule
4:00pm Executive Briefing: Unpacking AutoML Paco Nathan (derwen.ai)
Add Executive Briefing: An age of embeddings to your personal schedule
4:50pm Executive Briefing: An age of embeddings Mayank Kejriwal (USC Information Sciences Institute)
LL21 C/D
Add Data science without seeing the data: Advanced encryption to the rescue to your personal schedule
11:05am Data science without seeing the data: Advanced encryption to the rescue Tzvika Barenholz (Intuit), Induprakas Keri (Intuit)
Add Mozart in the box: Interacting with AI tools for music creation to your personal schedule
1:45pm Mozart in the box: Interacting with AI tools for music creation Alessandro Palladini (Music Tribe)
Add A practical guide toward explainability and bias evaluation in AI and machine learning to your personal schedule
2:35pm A practical guide toward explainability and bias evaluation in AI and machine learning Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Add Delivering AI vision ecosystem offers with Intel AI: In Production to your personal schedule
4:00pm Delivering AI vision ecosystem offers with Intel AI: In Production Lindsay Hiebert (Intel), Vikrant Viniak (Accenture)
Add Live coding a self-driving car (without a car) to your personal schedule
4:50pm Live coding a self-driving car (without a car) Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (Lonely Coffee)
LL21 E/F
Add A framework to bootstrap and scale a machine learning function to your personal schedule
11:55am A framework to bootstrap and scale a machine learning function Madhura Dudhgaonkar (Hiring) (Workday)
4:50pm
Santa Clara Room (Hilton)
4:50pm
Add Thursday Opening Welcome to your personal schedule
Hall 2
9:00am Thursday Opening Welcome Ben Lorica (O'Reilly), Roger Chen (Computable), Julie Shin Choi (Intel AI)
Add Accelerate with purpose to your personal schedule
9:30am Accelerate with purpose Ananth Sankaranarayanan (Intel)
Add Open-endedness: A new grand challenge for AI to your personal schedule
10:10am Open-endedness: A new grand challenge for AI Kenneth Stanley (Uber AI Labs | University of Central Florida)
Add Closing Remarks to your personal schedule
10:30am Closing Remarks
10:35am Morning Break | Room: Expo Hall
3:15pm Afternoon Break | Room: Expo Hall
8:45am
8:00am Morning Coffee (sponsored by PwC) | Room: Level 2 Concourse
Add Speed Networking to your personal schedule
8:15am Speed Networking | Room: Hall 2 Foyer
11:05am-11:45am (40m) Models and Methods Machine Learning, Temporal data and time-series
Using automated machine learning for hyperparameter optimization and algorithm selection
Francesca Lazzeri (Microsoft)
Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and hyperparameter tuning.
11:55am-12:35pm (40m) Models and Methods Data, Data Networks, Data Quality
Semisupervised machine learning, the next frontier in AI
Vinay Rao (RocketML), Santi Adavani (RocketML)
Current deep learning approaches require large amounts of labeled data. The creation of labeled data is expensive, error prone, and time consuming. Vinay Rao and Santi Adavani walk you through an effective learning method with minimum labelled data and human intervention.
1:45pm-2:25pm (40m) Models and Methods Text, Language, and Speech
PyTorch at scale for translation and NLP
Stef Nelson-Lindall (Facebook)
PyText is a research to production platform that Facebook has leveraged to quickly develop state-of-the-art natural language processing (NLP) systems and deploy them to critical production use cases. Stef Nelson-Lindall explores several challenges with developing, training, and deploying real production systems with Torch, how to deal with them in NLP use cases, and more.
2:35pm-3:15pm (40m) Implementing AI Deep Learning tools
Swift for TensorFlow: A next-generation framework for differential programming
Brennan Saeta (Google)
Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. Brennan Saeta leads you through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects.
11:05am-11:45am (40m) Implementing AI Deep Learning tools, Machine Learning
Reference architectures for AI and machine learning
Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)
Join Danielle Dean, Mathew Salvaris, and Angus Taylor to learn best practices and reference architectures (which have been validated in real-world AI and ML projects for customers globally) for implementing AI. They detail lessons distilled from working with large global customers on AI and ML projects and the challenges that they overcame.
11:55am-12:35pm (40m) Interacting with AI Computer Vision, Deep Learning, Health and Medicine, Machine Learning
Artificial and human intelligence in healthcare
Maithra Raghu (Cornell University | Google Brain)
With the fundamental breakthroughs in artificial intelligence and the significant increase of digital health data, there's been enormous interest in AI for healthcare applications. Maithra Raghu examines how to more effectively develop AI algorithms for these settings and the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts.
1:45pm-2:25pm (40m) Implementing AI Deep Learning, Health and Medicine, Machine Learning, Text, Language, and Speech
Industrialized capsule networks for text analytics
Vijay Srinivas Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Vijay Agneeswaran and Abhishek Kumar explore multilabel text classification problems, where multiple tags or categories have to be associated with a given text or documents. Multilabel text classification occurs in numerous real-world scenarios, for instance, in news categorization and bioinformatics (such as the gene classification problem).
2:35pm-3:15pm (40m) Case Studies Data, Data Networks, Data Quality, Deep Learning, Health and Medicine, Machine Learning
Improving revenue cycle management with deep learning: A healthcare case study
Sanji Fernando (Optum)
Sanji Fernando explores his experience building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including business alignment, data preparation, model development, model selection, deployment, and operations. Sanji also details key knowledge and opportunities for improvement with deep learning models in healthcare.
4:00pm-4:40pm (40m) Models and Methods Computer Vision, Deep Learning, Machine Learning
Using deep learning models to extract the most value from 360-degree images
Shourabh Rawat (Zillow)
Lately, 360-degree images have become ubiquitous in industries from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses to direct viewers to the most important parts of the scene. Shourabh Rawat walks you through how to identify and extract engaging static 2-D images using specific algorithms and deep learning methods.
4:50pm-5:30pm (40m) Implementing AI Deep Learning, Machine Learning, Temporal data and time-series
Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs
Alex (Tianchu) Liang (American Tire Distributors)
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.
11:05am-11:45am (40m) R&D and Innovation Data, Data Networks, Data Quality, Health and Medicine, Machine Learning, Text, Language, and Speech
Language inference in medicine
Chaitanya Shivade (IBM Research)
Using deep learning models to perform natural language inference (NLI) is a fundamental task in natural language processing. Chaitanya Shivade introduces a recently released dataset, MedNLI, for this task in the clinical domain, describes state-of-the-art models, explores how to adapt these into the healthcare domain, and details applications that can leverage these models.
11:55am-12:35pm (40m) R&D and Innovation Hardware
Software toolchain for the hybrid digital-analog, memristor-based accelerator for machine learning
Dejan Milojicic (Hewlett Packard Laboratories)
Dejan Milojicic examines a software stack designed for the special-purpose machine learning accelerator. The software stack improves usability and programmability of the accelerator, making it accessible from common machine learning frameworks. The software toolchain also exposes the intricacies of the parallelism of the accelerator while hiding its complexities.
1:45pm-2:25pm (40m) Implementing AI Machine Learning
Introducing Kubeflow (with special guests TensorFlow and Apache Spark)
Holden Karau (Independent)
Modeling is easy—productizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karau—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.
2:35pm-3:15pm (40m) Implementing AI Machine Learning
Are we deployed yet? Turning AI research into a revenue engine
Manasi Vartak (Verta.ai)
Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue-driving functions due to a lack of efficient tooling. Manasi Vartak examines key methods that enterprise AI teams can leverage with regard to driving revenue, including A/B testing, data pipelines, and reproducibility.
4:00pm-4:40pm (40m) Implementing AI
Container orchestrator to DL workload, Bing's approach: FrameworkLauncher
Kai Liu (BING) (Microsoft), Yuqi Wang (Microsoft), Bin Wang (Microsoft)
Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.
4:50pm-5:30pm (40m) Implementing AI Machine Learning
The OS for AI: How serverless computing enables the next gen of machine learning
Jonathan Peck (GitHub)
ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.
11:05am-11:45am (40m) Implementing AI Data, Data Networks, Data Quality, Health and Medicine
Building and managing training datasets for ML with Snorkel
Alex Ratner (Snorkel)
Alex Ratner explores programmatic approaches to building, managing, and modeling training data for machine learning (ML) using the open source framework Snorkel. Training data is increasingly one of the key bottlenecks to using modern ML, and Alex outlines recent systems and algorithmic and theoretical advances in building and managing training data for ML.
11:55am-12:35pm (40m) Implementing AI Data, Data Networks, Data Quality, Deep Learning, Machine Learning, Reinforcement Learning
Data distribution search: Deep reinforcement learning to improvise input datasets
Vijay Gabale (Infilect)
Beyond computer games and neural architecture search, practical applications of deep reinforcement learning (DRL) to improve classical classification or detection tasks are few and far between. Vijay Gabale outlines a technique and some experiences of applying DRL on improving the distribution input datasets to achieve state-of-the-art performance, specifically on object-detection tasks.
1:45pm-2:25pm (40m) Implementing AI Deep Learning tools, Machine Learning
Running large-scale machine learning experiments in the cloud
Shashank Prasanna (Amazon Web Services)
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.
2:35pm-3:15pm (40m) Models and Methods Design, Interfaces, and UX, Machine Learning, Text, Language, and Speech
Transfer learning NLP: Machine reading comprehension for question answering
Anusua Trivedi (Microsoft)
Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models.
4:00pm-4:40pm (40m) Implementing AI Deep Learning, Ethics, Security, and Privacy, Machine Learning, Reinforcement Learning, Text, Language, and Speech
Building autonomous network operation using deep learning and AI
Jisheng Wang (Mist Systems)
Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also details some of his experiences and the lessons he learned applying ML, DL, and AI to the development of SaaS-based enterprise solutions.
4:50pm-5:30pm (40m) Models and Methods Ethics, Security, and Privacy, Machine Learning, Text, Language, and Speech
Can behavioral analytics for enterprise security benefit from approaches in NLP?
Ramsundar Janakiraman (Aruba)
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice.
11:05am-11:45am (40m) AI Business Summit, Culture and Organization, Executive Briefing/Best Practices
Executive Briefing: Similar but different—Delivering software with AI
Jana Eggers (Nara Logics)
Though Nara Logics doesn't always follow them, it has developed great best practices for designing, developing, and delivering great software. Jana Eggers is here to explore what happens when you start adding AI to great software by covering six key features of software development that are similar when adding AI, six that are different, and how to adjust.
11:55am-12:35pm (40m) AI Business Summit, Law and Ethics Ethics, Security, and Privacy
Executive Briefing: Ethical considerations for AI
Raj Minhas (PARC, a Xerox Company)
The use of AI is growing rapidly and expanding into applications that impact people’s lives. Raj Minhas explores how, while researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications.
1:45pm-2:25pm (40m) AI Business Summit
Executive Briefing: Rigorous application of domain insights in AI projects
Jike Chong (LinkedIn | Tsinghua University), Yue Cathy Chang (TutumGene)
Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.
2:35pm-3:15pm (40m) AI Business Summit Ethics, Security, and Privacy
Executive Briefing: Explaining machine learning models
Ankur Taly (Fiddler)
As machine learning (ML) models get deployed to high-stakes tasks like medical diagnosis, credit scoring, and fraud detection, an overarching question that arises is why the model made its prediction. Ankur Taly explores techniques for answering this question and applications of the techniques in interpreting, debugging, and evaluating machine learning models.
4:00pm-4:40pm (40m) AI Business Summit
Executive Briefing: Unpacking AutoML
Paco Nathan (derwen.ai)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way.
4:50pm-5:30pm (40m) AI Business Summit, Executive Briefing/Best Practices Machine Learning, Text, Language, and Speech
Executive Briefing: An age of embeddings
Mayank Kejriwal (USC Information Sciences Institute)
Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy.
11:05am-11:45am (40m) R&D and Innovation Ethics, Security, and Privacy
Data science without seeing the data: Advanced encryption to the rescue
Tzvika Barenholz (Intuit), Induprakas Keri (Intuit)
Tzvika Barenholz and Induprakas Keri detail Intuit’s efforts to deploy fully homomorphic encryption (FHE) in production, which allows models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. You'll take a sneak peak at some of the optimizations and tricks that make FHE practical.
11:55am-12:35pm (40m) Implementing AI Machine Learning, Temporal data and time-series
Talking to the machines: Monitoring production machine learning systems
Ting-Fang Yen (DataVisor)
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems.
1:45pm-2:25pm (40m) Interacting with AI
Mozart in the box: Interacting with AI tools for music creation
Alessandro Palladini (Music Tribe)
Alessandro Palladini explores the role of experts and creatives in a world dominated by intelligent machines by bridging the gap between the research on complex systems and tools for creativity, examining what he believes to be the key design principles and perspectives on making intelligent tools for creativity and for experts in the loop.
2:35pm-3:15pm (40m) Models and Methods Ethics, Security, and Privacy, Machine Learning
A practical guide toward explainability and bias evaluation in AI and machine learning
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge.
4:00pm-4:40pm (40m) Executive Briefing/Best Practices Computer Vision
Delivering AI vision ecosystem offers with Intel AI: In Production
Lindsay Hiebert (Intel), Vikrant Viniak (Accenture)
Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings.
4:50pm-5:30pm (40m) Interacting with AI Computer Vision, Machine Learning, Reinforcement Learning
Live coding a self-driving car (without a car)
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (Lonely Coffee)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines.
11:05am-11:45am (40m) AI Business Summit, Case Studies Machine Learning, Reinforcement Learning
Advancing our understanding of deep reinforcement learning with community-driven insights
Danny Lange (Unity Technologies)
This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Then, they invited the public to try to solve the challenge. Danny Lange reveals what Unity learned from the contest and the real-world impact of observing the behaviors of multiple AI agents in a simulated virtual environment.
11:55am-12:35pm (40m) AI Business Summit, Implementing AI Machine Learning, Temporal data and time-series
A framework to bootstrap and scale a machine learning function
Madhura Dudhgaonkar (Hiring) (Workday)
Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset.
1:45pm-2:25pm (40m) AI Business Summit, Case Studies Computer Vision, Health and Medicine, Machine Learning, Mobile Computing, IoT, Edge
Environmental AI: Using machine learning to address mosquito-borne diseases
Leslie De Jesus (Wovenware)
Leslie De Jesus examines a machine learning solution enabling the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika and dengue fever. She outlines the challenges, strategy, and technologies used, the results achieved to date, and the implications of the AI project in helping to address a global threat.
2:35pm-3:15pm (40m) AI Business Summit, Implementing AI Design, Interfaces, and UX
Data science + design thinking: A perfect blend to achieve the best user experience
Michael Radwin (Intuit)
Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.
4:00pm-4:40pm (40m) AI Business Summit, Culture and Organization
Repeatable AI-driven digital transformation: Insights from 1,000 projects
Debo Olaosebikan (Gigster)
As the gap between technology giants and the rest of the enterprise widens, AI-driven transformation has become essential and urgent. From the lens of over 1,000 projects delivered and a broad view across real use cases in multiple industries, Debo Olaosebikan examines an organizational and technical framework for using AI to drive business impact regardless of where an organization starts from.
4:50pm-5:30pm (40m)
Session
11:05am-11:45am (40m) Sponsored
Framing business problems as machine learning (ML) problems (sponsored by Amazon Web Services)
Carlos Escapa (Amazon Web Services)
Carlos Escapa takes a deep dive into how to identify use cases for ML, acquire cutting-edge best practices to frame problems in a way that key stakeholders and senior management can understand and support, and set the stage for delivering successful ML-based solutions for your business.
11:55am-12:35pm (40m)
Getting the AI you want on the infrastructure you know: 2 deep-dive case studies of AI on CPU
Kushal Datta (Intel)
Kushal Datta specializes in optimizing AI applications on CPUs; hear two of his latest customer success stories and get the details behind the technical collaboration that led to incredible performance for AI on CPU.
1:45pm-2:25pm (40m) Sponsored
A practical guide to responsible AI (sponsored by PwC)
Anand Rao (PwC)
Anand Rao provides an overview from the practitioner’s perspective on addressing ethics within businesses. Anand explores PwC’s responsible AI toolkit, which enables businesses to identify and contextualize relevant ethical AI principles and provides tools for evaluating interpretability of systems. You'll see example applications that illustrate model interpretability.
2:35pm-3:15pm (40m) Sponsored
Automatic machine learning for the enterprise with H2O.ai Driverless AI (sponsored by H2O.ai)
Arno Candel (H2O.ai)
Driverless AI is H2O.ai’s latest flagship product for automatic machine learning for the enterprise. Arno Candel outlines Driverless AI, explores customer use cases, and performs a live demo with custom recipes to solve a specific machine learning problem.
4:00pm-4:40pm (40m) AI Business Summit
Artificial intelligence social influence model and migration paths: Implications to institutions, governments, and businesses
Loretta Cheeks (Strong TIES)
Loretta Cheeks provides the language and framework to talk to experts and executives. You'll gain insights into ways to use machine intelligence for shedding light on complex dynamic real-world issues and understanding the embedded biases that exist in news articles (unstructured text).
4:50pm-5:30pm (40m)
Session
9:00am-9:05am (5m)
Thursday Opening Welcome
Ben Lorica (O'Reilly), Roger Chen (Computable), Julie Shin Choi (Intel AI)
Program chairs Ben Lorica, Roger Chen, and Julie Choi open the second day of keynotes.
9:05am-9:20am (15m) Machine Learning
On gradient-based methods for finding game-theoretic equilibria
Michael Jordan (UC Berkeley)
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program.
9:20am-9:30am (10m) Sponsored
Safe and smarter driving, powered by AI (sponsored by Amazon Web Services)
Lei Pan (Nauto)
Lei Pan examines how Nauto uses Amazon SageMaker and other AWS services, including Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to continually evolve smarter data for driver behavior.
9:30am-9:40am (10m)
Accelerate with purpose
Ananth Sankaranarayanan (Intel)
Ananth Sankaranarayanan discusses three key shifts in the AI landscape—incredibly large models with billions of hyperparameters, massive clusters of compute nodes supporting AI, and the exploding volume of data meeting ever-stricter latency requirements—how to navigate them, and when to explore hardware acceleration.
9:40am-9:55am (15m) Machine Learning, Reinforcement Learning
Practical insights into deep reinforcement learning
Sahika Genc (Amazon)
Sahika Genc dives deep into the current state-of-the-art techniques in deep reinforcement learning (DRL) for a variety of use cases. Reinforcement learning (RL) is an advanced machine learning (ML) technique that makes short-term decisions while optimizing for a longer-term goal through trial and error.
9:55am-10:10am (15m) Ethics, Security, and Privacy
AI transparency: A brief overview of frameworks for transparent reporting of AI provenance, usage, and fairness-informed evaluation
Andrew Zaldivar (Google)
In an effort to encourage responsible transparent and accountable practices, Andrew Zaldivar details existing frameworks technologists can use for ethical decision making in AI.
10:10am-10:30am (20m) Machine Learning, Reinforcement Learning
Open-endedness: A new grand challenge for AI
Kenneth Stanley (Uber AI Labs | University of Central Florida)
We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continually—even endlessly.
10:30am-10:35am (5m)
Closing Remarks
O'Reilly AI program chairs close the first day of keynotes.
10:35am-11:05am (30m)
Break: Morning Break
12:35pm-1:45pm (1h 10m)
Lunch (sponsored by Amazon Web Services) and Thursday Topic Tables
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
12:35pm-1:45pm (1h 10m)
Thursday's AI Business Summit Lunch
Join us for a networking lunch with AI Business Summit attendees on Thursday. You’ll have the opportunity to network and discuss the AI technologies that are transforming business and industry with other attendees.
3:15pm-4:00pm (45m)
Break: Afternoon Break
8:45am-9:00am (15m)
Plenary
8:00am-9:00am (1h)
Break: Morning Coffee (sponsored by PwC)
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.
  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • H2O.ai
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit

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