Austin AI Weekend at Data Day Texas

For the 8th annual Data Day Texas (2018), we will be hosting a sub-conference focusing on Artificial Intelligence, Deep Learning, and related topics - Austin AI Weekend. Except for a few special workshops, all AI content will be included in your Data Day Texas ticket. No need to buy a separate ticket. There will also be pre and post conference AI events open to all Data Day Texas ticket holders.

Confirmed Sessions

KEYNOTE: Deep Learning in the Real World

Lukas Biewald - Crowdflower

Deep Learning has made some incredible advances in the past few years. I've watched hundreds of organizations build and deploy machine learning algorithms in the past few years and I've seen it make a huge impact on many different applications. But deep learning isn't magic and it takes real work to make it effective. Everyone talks about algorithms, but that's rarely the biggest problem. This talk is about real machine learning from beginning to end, collecting training data, setting expectations, handling errors, dealing with potential adversaries and explaining why the model did what it did. It will cover a variety of use cases from medical diagnosis to sentiment analysis to self driving cars.

Machine Learning: From The Lab To The Factory

John Akred - Silicon Valley Data Science

When data scientists are done building their models, there are questions to ask:
* How do the model results get to the hands of the decision makers or applications that benefit from this analysis?
* Can the model run automatically without issues and how does it recover from failure?
* What happens if the model becomes stale because it was trained on data that is no longer relevant?
* How do you deploy and manage new versions of that model without breaking downstream consumers?
This talk will illustrate the importance of these questions and provide a perspective on how to address them. John will share experiences deploying models across many enterprises, some of the problems we encountered along the way, and what best practice is for running machine learning models in production.

Here and now: Bringing AI into the enterprise

Kristian Hammond - Narrative Science

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. On the plus side, technology we thought was decades away seems to be showing up at our doorstep with increasing frequency. However, little effort has been made to clearly explain the value and genuine business utility of this technology.
Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making, focusing on how they can be used, the requirements for doing so, and the expectations for their effectiveness. Kris starts with a lecture outlining this functional framework and ends with hands-on exercises so you can practice using it in the real world when evaluating data, requirements and opportunities. You’ll leave with greater knowledge of the space and the skills to apply that knowledge to your businesses, ensuring that as you build, evaluate, and compare different systems, you’ll understand and be able to articulate how they work and the resulting impact.

Lexicon Mining for Semiotic Squares: Exploding Binary Classification

Jason Kessler - CDK Global

A common task in natural language processing is category-specific lexicon mining, or identifying words and phrases that are associated with the presence or absence of a specific category. For example, lists of words associated with positive (vs. negative) product reviews may be automatically discovered from labeled corpora.
In the 1960s, the semanticists A. J. Greimas and F. Rastier developed a framework for turning two opposing categories into a network of 10 semantic classes. This talk introduces an algorithm for discovering lexicons associated with those semantic classes given a corpus of categorized documents. This algorithm is implemented as part of Scattertext, and the output can be viewed in an interactive browser-based visualization.

Understanding the development of visual focus of attention in infants using computer vision tools

Qazaleh Mirsharif - CrowdFlower

Head cameras enable developmental scientists to have access to infant’s visual field from his/her own point of view. The head camera can be mounted on infant and collect his/her momentary visual experience about how they visually recognize objects, interact with their social partners and assign names to those objects. Analysis of such videos requires frame by frame human observation of infant’s behavior and high-level expertise. Computer vision have been emerging in this field recently to help the developmental scientists further their understanding of the development of visual focus of attention in infants by providing tools to process these videos in terms of objects and analyze motion that generates visual attention. Such computer vision tools reveal patterns in the developmental process of visual focus of attention in infants which cannot be estimated by humans as the head camera is in constant motion due to infant’s large and random head movements.

Vital Role of Humans in Machine Learning

Lynn Pausic - Expero / Chris LaCava - Expero

It doesn’t take much effort to stumble across high profile stories faulting “automated technology” for misguided decisions made by courts of law, medical professionals, financial institutions and other important establishments. Upon further examination, these “technology failures” are often attributed to a lack of human oversight or aiming the intelligence at ill-defined problems rather than some critical flaw in the algorithms per se. While the relationship between humans and machine learning (ML) is still in its infancy, one thing is clear - humans play a symbiotic if not vital role in augmenting intelligent technology. For example, training algorithms requires continuous curation, ML outcomes often need human counterparts who can sensibly apply them to real world contexts and any organization utilizing ML should routinely review the moral implications of decisions made using intelligent technology. Join us for a fun and engaging talk where we’ll demonstrate how the same ML can yield from good to very bad outcomes based key aspects of human involvement.

Generating Natural-Language Text with Neural Networks

Jonathan Mugan - Deep Grammar

Automatic text generation enables computers to summarize text, to have conversations in customer-service and other settings, and to customize content based on the characteristics and goals of the human interlocutor. Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. We will also discuss existing work that specifies how the content of generated text can be determined by manipulating a latent code. The talk will conclude with a discussion of current challenges and shortcomings of neural text generation.

Making Magic with Keras and Shiny

Nicholas Strayer - Vanderbilt University

The web-application framework Shiny has opened up enormous opportunities for data scientists by giving them a way to bring their models and visualizations to the public in interactive applications with only R code. Likewise, the package keras has simplified the process of getting up and running with deep-neural networks by abstracting away much of the boiler-plate and book-keeping associated with writing models in a lower-level library such as tensorflow. In this presentation, I will demo and discuss the development of a shiny app that allows users to cast 'spells' simply by waving their phone around like a wand. The app gathers the motion of the device using the library shinysense and feeds it into a convolutional neural network which predicts spell casts with high accuracy. A supplementary shiny app for gathering data will be also be shown. These applications demonstrate the ability for shiny to be used at both the data-gathering and model-presentation steps of data science.

Confirmed Speakers

Keynote Lukas Biewald (SF Bay) @l2k

Lukas Biewald (Wikipedia / LinkedIn / GitHub) is the founder and CEO of CrowdFlower. Founded in 2007, CrowdFlower provides Labor-on-Demand to help companies outsource high-volume, repetitive tasks to a massively-distributed global workforce.
Before founding CrowdFlower, Lukas was a senior scientist and manager within the Ranking and Management Team at Powerset, Inc., acquired by Microsoft in 2008. He led the Search Relevance Team for Yahoo! Japan after graduating from Stanford University with a B.S. in Mathematics and an M.S. in Computer Science. Recently, Lukas won the Netexplorateur Award for GiveWork – a collaboration with Samasource that brings digital work to refugees worldwide. Lukas is also an expert level Go player.
Check out Lukas' recent interview with Ben Lorica for the O'Reilly Data Show
Lukas will be giving the Data Day / AI Weekend presentation: Deep Learning in the Real World

John Akred (SF Bay) @BigDataAnalysis

John Akred is the Founder and CTO of Silicon Valley Data Science. In the business world, John Akred likes to help organizations become more data driven. He has over 15 years of experience in machine learning, predictive modeling, and analytical system architecture. His focus is on the intersection of data science tools and techniques; data transport, processing and storage technologies; and the data management strategy and practices that can unlock data driven capabilities for an organization. A frequent speaker at the O'Reilly Strata Conferences, John is host of the perennially popular workshop: Building A Data Platform.
John will be giving the following AI Weekend presentation: Machine Learning: From The Lab To The Factory

Chris LaCava @uxchrislacava


Chris LaCava has spent the past two decades defining, designing and building software for a variety of industry verticals. He has experience as a usability engineer, interaction designer, front-end developer as well as product manager for both consulting and product-oriented organizations. Chris leads Expero's efforts in defining visualization for graph datasets.
Chris LaCava will co-present the following session: Vital Role of Humans in Machine Learning .

Kristian Hammond (Chicago) @kj_hammond

Kristian Hammond (LinkedIn) is chief scientist at Narrative Science and professor of computer science and journalism at Northwestern University. Previously, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.
Kristian will be giving the following AI Weekend presentation: Here and now: Bringing AI into the enterprise

Jason Kessler (Seattle) @jasonkessler

Jason Kessler (LinkedIn) is a lead data scientist at CDK Global, where he analyzes language use and consumer behavior in the online auto-shopping ecosystem. Prior to joining CDK, Jason was the founding data scientist at PlaceIQ and worked as a research scientist for JD Power and Associates. He has published peer-reviewed papers on algorithms and corpora for sentiment and belief analysis and has sat on program committees and reviewed for several AI and NLP conferences. Most recently, he has conducted research on identifying persuasive and influential language and the visualization of differing corpora.
Jason will be giving the following presentation: Lexicon Mining for Semiotic Squares: Exploding Binary Classification

Jonathan Mugan (Austin) @jmugan

Jonathan Mugan (Linkedin) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day Texas conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.
Jonathan will be giving the following AI Weekend presentation: Generating Natural-Language Text with Neural Networks

Lynn Pausic (Austin) @lynnpausic

As head of the Design team at Expero, co-principal and business strategist, Lynn Pausic takes multitasking to the next level. By combining expertise in strategy, innovation and design, Lynn brings the breadth and depth of complex problems to light and figures out how to break them down into useful, usable and manageable pieces that form a holistic experience.
Lynn’s extensive background in user experience ranges from designing user interfaces for wearable devices, to creating enterprise software solutions and mobile UIs, to innovating scenarios beyond the 2D screen. She has ever-growing expertise with timely topics such as Big Data, the Internet of Things, UI Design Pattern Libraries and High-Performance Computing, in industries as varied and diverse as Austin itself. Lynn’s recent clients are in agronomy, enterprise management, energy, biotechnology and other verticals.
Prior to founding Expero, Lynn earned a B.S. from Carnegie Mellon University and worked as a Director of Product Management, a Consulting Manager and a Director of Human-Computer Interaction (HCI). At Trilogy, she led the HCI team and established user-centered design as an integral part of the company’s software development process.
Lynn often speaks on user experience and design, including at Nielsen Norman Group conferences around the world. Lynn created the popular tutorial “Complex Applications & Websites” (which she co-presents with John Morkes). Lynn also has presented at Carnegie Mellon University’s HCI Institute, Cornell University’s Media Lab and ACM’s SIGCHI conference.
Lynn will co-present the following session: Vital Role of Humans in Machine Learning .

Some shots from previous Data Day Texas events:


Emil Eifrem of Neo4j speaking at the most recent Data Day Texas.


Chris Moody of StitchFix speaking at the most recent Data Day Texas.


Jonathan Morgan of Deep Grammar speaking at the most recent Data Day Texas.