AGENDA (2017)





Train AI Opening Reception @The Village

Check-In, Breakfast and AI Solutions Showcase

Breakfast and coffee served for all Train AI attendees on the 2nd Floor

KEYNOTE: Creating Software with Machine Learning: Challenges and Promise

Peter Norvig, Director of Research, Google

Traditionally, software is built by programmers who consider the possible situations and write rules to deal with them. But recently, many applications have been created by machine learning: the programmer is replaced by a trainer, who shows the computer examples until it learns to complete the task. This shift in the way software is built is opening up exciting new possibilities and posing new challenges.

KEYNOTE: Computer Vision: helping machines see and understand the world

Lukas Biewald, Founder & Executive Chairman, CrowdFlower

The biggest change to CrowdFlower’s business in the last year has come from deep learning and computer vision applications. The use cases range from health care to self-driving cars to drones to animal conservation. CrowdFlower has been hard at work building tools to support our customers labeling and human in the loop workflows.

KEYNOTE: What we learned from the 2 million plus model submissions to Kaggle

Anthony Goldbloom, Co-Founder & CEO, Kaggle

Kaggle is platform for machine learning competitions, where companies post very difficult problems and data scientists compete to submit the best answer.. We have worked on problems ranging from predicting when somebody will have a seizure (based on EEG readings) to using algorithms to grade high school essays. This talk will draw on Kaggle’s experience to survey what’s possible at the limits of machine learning.

Morning Break + AI Solutions Showcase (2nd Level)

Machine Learning for Digital Creativity

Scott Prevost, VP Engineering, Search, Adobe

Adobe is changing the world of digital creativity through Adobe Sensei, a unified AI and machine learning framework that harnesses trillions of content and data assets, from high-resolution images to customer clicks. Sensei delivers deep understanding of creative content, from semantics to aesthetics, and increasingly drives the process of creating digital experiences. From inspiration, to discovery, to re-imagination and beyond, learn how Sensei is changing the world of digital creativity.

Revisiting Talking Robots and AI's Progress

Robert Munro, Principal Product Manager, Amazon AI

Five years ago, I gave a series of talks called “Where’s My Talking Robot”, which focused on the progress towards conversational AI, and the ways in which AI is already making more decisions about our lives than many people realize. That presentation made predictions about what the state of AI would be today, and so I will revisit AI in areas including the media, self-driving cars, education, health, and military defense. I will talk about how current technologies are supporting the development of conversational AI in a number of ways that directly impact people’s lives in many areas, and also about what the next five years might look like as technology grows.

Crucial questions for success of AI Applications

Barney Pell, PhD, Pioneer, Entrepreneur, Investor

In theory, AI algorithms can be applied to a wide set of problems of importance to humanity. In practice, however, technology itself is only a small portion of the overall success of an application. This talk discusses crucial issues that must be addressed in most successful applications of Artificial Intelligence. Key issues include quality, robustness, usability, optimal mix of humans and automation, trust, evolution, and economics.

Amplifying machine learning with human intelligence

Toby Segaran, Director of Engineering, Reddit

The most common approaches to using machine learning often use humans to initially label a dataset for training and afterwards involve humans to check for quality. In this talk, Toby Segaran, engineering director at Reddit and author of “Programming Collective Intelligence” will explore ways that human intelligence can be used during the process of optimization, topic modeling and entity resolution to improve algorithmic results as they’re happening.

Lunch + AI Solutions Showcase (2nd Level)

CrowdFlower Customer Showcase

Moderator: Nick Gaylord, Sr. Data Scientist, CrowdFlower

Maria Sumner, Research Scientist, Ozlo

Sid Dixit, Director, Product Program Management, Planet Labs

Ryan Keiser, Head of Machine Learning, Descartes Labs

James Rubinstein, Director, Global Product Testing, Lexis Nexis

Henriette Cramer, Sr. Research Lead, Spotify

Paul Ogilvie, Engineering Manager, Machine Learning Algorithms Team, LinkedIn

Presentations from six of CrowdFlower’s Customers on making AI work in the real-world.

Cognitive Toolkit and Language Processing

Xuedong Huang, Technical Fellow, Microsoft

Microsoft recently achieved a historical human parity milestone to recognize conversational speech on the switchboard task. Microsoft Cognitive Toolkit (CNTK) is the secret weapon that enabled this historical breakthrough. This talk will explain the story behind the scene how Microsoft did this with CNTK.

Afternoon Break + AI Showcase (2nd level)

Machine Intelligence Landscape and Overview

James Cham, Partner, Bloomberg Beta

Shivon Zilis, Partner, Bloomberg Beta

Bloomberg Beta’s Shivon Zilis and James Cham review their latest Machine Intelligence landscape—what successful MI companies are doing and what problems are open for startups.

Data Science: Past, Present, Future

Monica Rogati, Data Science Advisor

Drawing from decades in data science, Monica will share war stories from the past, best practices for the present, and wild speculations about the future of data and machine learning.

The Role of Machine Learning in HD Mapping

Sanjay Sood, VP, Highly Automated Driving (HAD), HERE Technologies

A key ability of a high definition, “self-healing” map is being able to reflect changes in the real world, in real-time given the environment the vehicle is in. Leveraging machine learning technology, HERE can better provide vehicles on the road real-time change detection in the world, by comparing a vehicles environment model with HERE’s reference cloud based model to determine inconsistencies and thus determine changes in the real-world.

Reaching unparalleled quality with human-in-the-loop

Maran Nelson, Co-Founder & CEO, Clara Labs

A popular goal of AI is to replace the need for human intervention and energy expense at any given task, but is this the most optimal model? The Kasparov approach, more commonly known as human-in-the-loop, increasingly seems like the best pathway to reach a high-achieving system. This talk discusses the implementation of these hybrid structures that take what works best in AI and humans, and create outperforming products.

AI and Medicine: A Symbiotic Future

Dan Ruderman, PhD, Professor of Research Medicine, Ellison Institute for Transformative Medicine of USC

AI has proven value in image analysis and other data intensive tasks relevant to medicine. With digital clinical imagery becoming ever more pervasive, AI is poised to improve the accuracy and efficiency of medical practice. A cooperative, rather than a competitive relationship with healthcare providers will be the key to adoption.

KEYNOTE: Tackling The Limits of Deep Learning

Richard Socher, Chief Scientist, Salesforce

Deep learning has made great progress in a variety of language tasks. However, there are still many practical and theoretical problems and limitations. In this talk I will introduce solutions to some of these:  How to predict previously unseen words at test time.  How to have a single input and output encoding for words.  How to grow a single model for many tasks.  How to use a single end-to-end trainable architecture for question answering.

Closing Remarks

Lukas Biewald, Founder and Executive Chairman, CrowdFlower

Robin Bordoli, CEO, CrowdFlower,

Train AI Party