TRAIN AI 2018 Presentations

From Competition to Collaboration, the Human-Machine Future

Presented by Garry Kasparov, Chairman of the Human Rights Foundation, Author, Speaker, 13th World Chess Champion

In 1997, Garry Kasparov became the first knowledge worker to be surpassed by an intelligent machine—at least that is one way to look at the world chess champion’s famous match loss to the IBM supercomputer Deep Blue. Instead of becoming the cognitive John Henry, Kasparov has spent the past 20 years pursuing his fascination with how humans and increasingly powerful AIs can work together. In this talk, he will also discuss the role business must play in moving AI from the laboratory into the mainstream and how the new generation of machine learning can create knowledge that contributes to real insight and understanding, not merely efficiency. Most of all, Kasparov wants us to be optimistic and ambitious about the reality and potential of intelligent machines, what he calls a self-fulfilling prophecy.

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Real World Human-in-the-Loop Machine Learning

Presented by Robert Munro, Chief Technology Officer, Figure Eight

Figure Eight’s CTO, Robert Munro, will talk about 20 years experience combining Human and Machine Intelligence. From simple early interactions in games and search engines, to complex modern tasks like autonomous vehicles, medical imaging, agriculture, translation, art and music, he will cover how AI is increasingly incorporated into our everyday lives.

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A Gif is Worth a 1000 Words: Animated Thoughts About the Future of Enterprise Intelligence

Presented by Anthony Johnson, CTO, GIPHY

This talk covers the many deep learning/machine learning and big data problems underlying searching for video at scale, and why despite the fact that GIFs are the furthest thing you can imagine from the enterprise, what we do today is probably how you are going to access data in the enterprise in a few years.

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Challenges & Design Patterns for Conversational AI

Presented by Peter Skomoroch, Co-Founder and CEO, SkipFlag (acquired by Workday)

Enterprise and consumer applications increasingly apply machine learning to create conversational interfaces. Adding a conversational UX presents a number of challenges for machine learning practicioners attempting to build intelligent applications. This session will describe some lessons learned from the recent wave of bots and from building SkipFlag, an intelligent knowledge base that integrated with Slack. Should you develop your own algorithms or make use of NLP as a service? How should you plan to include humans in the loop? To what degree do you need to specialize your models for the industry you’re working in? We’ll cover these questions and more.

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Customer-Focused Machine Learning for the Creative Landscape

Presented by Alex Filipkowski, Product Manager, Applied Science and Machine Learning, Adobe

At Adobe, we’ve started a new applied science team tasked with using research-inspired methods to solve real-world user problems. We train our models with native content and custom annotations, which enables us to build deep learned machinery tailored specifically to the use case at hand.

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Is Science Just a Game?

Adrien Treuille, VP of Simulation, Zoox

This talk describes Foldit and EteRNA, a series of scientific discovery games we have developed to solve real scientific problems. These games lead us to wonder: how many unknown “Kasparovs” are out there on the Internet — potential experts at tasks they never knew existed? Is this the future of expertise?

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Notes from the Field: The Platform, People and Processes of Agile Data Science

Presented by Sarah Aerni, Director of Data Science , Salesforce

As data scientists, our business users ask us to produce the perfect model to supercharge their products. While platforms and processes evolved to address challenges in software development, for data science this is still very nascent. In this talk I will share how Salesforce tackles the challenge of making data science an agile partner to over 100,000 customers without compromising on high quality AI solutions. I will cover both the process, and the platform that enables experimentation and deployment to enable each of our customers to build custom models with a few simple clicks.

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Modeling AI Advances Through Data

Presented by Jack Clark, Strategy & Communications Director, OpenAI

Using real data (and lots of it), this session measures the real progress of AI through the years showcasing AI adoption, the AI Index and multiple additional data sets that truly benchmark real world usage of AI

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Productizing Deep Learning for the Enterprise

Presented by Hanlin Fang, Director of Machine Learning Product Management, Workday

When consumers experience AI/ML benefit from various sources in our daily life, enterprises are facing challenges when applying similar AI/ML techniques to transform business. In this session, we will share how Workday (Enterprise SaaS company on HCM and FIN) has identified specific business problem for ML to solve, collected enough data to prototype, and deployed the solution as part of Workday Application product available to all Workday customers in less than 18months. We will also share lesson learned from legal, privacy, and security aspect with Human-in-the-loop approach which is a critical part of enterprise ML product development journey.

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Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI

Presented by Alyssa Simpson Rochwerger, VP of Product , Figure Eight

AI is like sex in high school – everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.

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Arterys - Figure Eight Customer Showcase

Presented by Daniel Golden, Director of Machine Learning, Arterys

Always a highlight of Train AI, the customer showcase is a rapid-fire session featuring some of the most innovative use-cases of AI.

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TalkIQ - Figure Eight Customer Showcase

Presented by Etienne Manderscheid, Head of Data Science and Co-Founder, TalkIQ

Always a highlight of Train AI, the customer showcase is a rapid-fire session featuring some of the most innovative use-cases of AI.

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Home Depot - Figure Eight Customer Showcase

Presented by Estelle Afshar, Senior Manager, Data Science Online, Home Depot

Always a highlight of Train AI, the customer showcase is a rapid-fire session featuring some of the most innovative use-cases of AI.

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Blue River Technology - Figure Eight Customer Showcase

Presented by Chris Padwick, Deep Learning Team Lead,

Always a highlight of Train AI, the customer showcase is a rapid-fire session featuring some of the most innovative use-cases of AI.

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AI Ethics Panel Discussion: Beyond Killer Robots and Trolley Problems

Presented by Blair Hanley Frank, Principal Analyst, ISG | Ruchir Puri, IBM Fellow and Chief Architect, IBM Watson | Lisha Li, Principal, Amplify Partners | Radha Basu, CEO, iMerit Technology Services | Tarin Ziyaee, Founder, "Stealth"

The long-term existential threats of AI have taken over our popular consciousness, but what about the everyday issues practitioners and the public face right now? This panel will explore the current ethical challenges in the AI field, with an eye towards how they might impact the future.

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From PhD to Product: Panel Discussion

Presented by Robert Munro, Chief Technology Officer, Figure Eight | Anima Anandkumar, Principal Scientist at Amazon AI and Bren Professor at Caltech, Amazon Web Services | Kapil Gupta, Data Science Lead, Airbnb | Olya Gurevich, Independent Natural Language Scientist

Leaders in AI applications will talk about their personal paths from being research-focused grad students to results-focused product leaders. They will share lessons learned from which parts of academia did (and did not) carry over to making AI work in the real-world, and provide guidance to people pursuing a similar path.

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Reinventing Businesses with AI

Ruchir Puri, IBM Fellow and Chief Architect, IBM Watson

Cognitive systems such as IBM Watson are transforming numerous industries from Customer care and Security, to Healthcare, that are of fundamental importance to our society. This has unleashed a torrent of innovation for businesses that leverage Data and Machine Learning/AI technologies. In this keynote, we will expand on the opportunities and challenges of AI for business, focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises to bridge the gap between data science and developers.

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Building the Software 2.0 Stack

Presented by Andrej Karpathy, Director of AI, Tesla

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