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Monday, March 6


Grand Welcome and Opening Remarks
Dr. Alexy Khrabrov, the founder and organizer of By the Bay family of conferences, opens the conference and outlines the three-day exploration of AI to follow.

avatar for Alexy Khrabrov

Alexy Khrabrov

Chief Scientist, Cicero.AI
Dr. Alexy Khrabrov is a founder and organizer of Open-Source communities and conferences on Data Engineering, Artificial Intelligence, and Data Science. He is the Chief Scientist of Cicero.AI, a community-based research institute for AI. Dr. Khrabrov runs seven meetups in San Francisco: bay.area.ai, sfscala.org, sfspark.org, sfhadoop.org, reactive.community, sfgraphql.org, and saltops.org. He founded a series of technical conferences By the... Read More →

Monday March 6, 2017 8:40am - 8:50am


IBM: Host Sponsor Welcome
IBM is the Host Sponsor of AI By the Bay, enabling cognitive computing on global scale.

avatar for Joel Horwitz

Joel Horwitz

Vice President, Ecosystem & Partnership Development, IBM
Joel Horwitz is the Vice President, Ecosystem and Partnership Development, IBM. He graduated from the University of Washington in Seattle with a Masters in Nanotechnology with a focus in Molecular Electronics. He also hails from the University of Pittsburgh with an International MBA in Product Marketing and Financial Management. Joel designed, built, and launched new products at Intel and Datameer resulting in breakthrough innovations. He set and... Read More →

Monday March 6, 2017 8:50am - 9:00am


Machine Learning, The Right Way
Getting started with data science and Machine Learning is a chicken and egg thing. Do I have enough data to even do data science? Which use cases should I start with? It can be intimidating. Vitaly Gordon, VP of Engineering and Data Science at Salesforce Einstein, will give you the tips you need to get a right start with the right people - at the right time. He will discuss Machine Learning technology but also tell you what data you need, and what business use cases you should start with.

avatar for Vitaly Gordon

Vitaly Gordon

VP of Engineering and Data Science, Salesforce Einstein
VP of Engineering and Data Science at Salesforce Einstein

Monday March 6, 2017 9:00am - 9:40am


Deep Learning with the GPUs in Production
avatar for Adam Gibson

Adam Gibson

CTO, Skymind

Monday March 6, 2017 9:50am - 10:30am


Attention and Memory in Deep Learning Networks
Attention and memory in Neural Networks.

avatar for Stephen Merity

Stephen Merity

Senior Research Scientist, Salesforce Research
Stephen Merity is a senior research scientist at MetaMind, part of Salesforce Research, where he works on researching and implementing deep learning models for vision and text, with a focus on memory networks and neural attention mechanisms for computer vision and natural language processing tasks.

Monday March 6, 2017 10:40am - 11:20am


Deep Learning is Like Water (it's Everywhere!)
Deep Learning with H2O.ai, the most performant distributed system for Machine Learning

avatar for Arno Candel

Arno Candel

Chief Architect, H2O.ai
Dr. Arno Candel is the CTO at H2O.ai, the makers of the distributed and scalable open-source machine-learning platform H2O. Arno is also the main author of H2O’s Deep Learning and key contributor to H2O's GBM and DRF algorithms. Arno spent the last 5+ years designing and implementing high-performance machine-learning algorithms. Previously, he spent a decade in high-performance computing and ran his code on the world’s largest... Read More →

Monday March 6, 2017 11:30am - 12:10pm


Deploying and Scaling Spark ML and Tensorflow AI Models
In this talk, I will train, deploy, and scale Spark ML and Tensorflow AI Models in a distributed, hybrid-cloud and on-premise production environment. I will use 100% open source tools including Tensorflow, Spark ML, Jupyter Notebook, Docker, Kubernetes, and NetflixOSS Microservices. This talk discusses the trade-offs of mutable vs. immutable model deployments, on-the-fly JVM byte-code generation, global request batching, miroservice circuit breakers, and dynamic cluster scaling - all from within a Jupyter notebook. All code and docker images are 100% open source and available from Github and DockerHub at http://pipeline.io.

avatar for Chris Fregly

Chris Fregly

Research Scientist, PipelineAI
Chris Fregly is a Research Scientist at PipelineIO - a Machine Learning and Artificial Intelligence Startup in San Francisco. | | Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Advanced Spark and TensorFlow Meetup, Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Deploying and Scaling Distributed TensorFlow in Production. | | Previously, Chris was a... Read More →

Monday March 6, 2017 1:00pm - 1:30pm


The Advantages of Deep Learning with Recurrent Neural Networks for Real Time Anomaly Detection
In this session we will go through several tactics for anomaly detection with sequential and non-sequential data sets. We will review the advantages and disadvantages of these tactics, analyze an overall end to end strategy for real time anomaly detection, and explore the benefits of leveraging RNN architecture as a part of this strategy.

avatar for Mike Tamir

Mike Tamir

Chief Data Science Officer, Takt

Monday March 6, 2017 1:40pm - 2:10pm


AI at Stitch Fix
I'll review applied deep learning techniques we use at Stitch Fix to understand our client's personal style. Interpretable deep learning models are not only useful to scientists, but lead to better client experiences -- no one wants to interact with a black box virtual assistant. We do this in several ways. We've extended factorization machines with variational techniques, which allows us to learn quickly by finding the most polarizing examples. And by enforcing sparsity in our models, we have RNNs and CNNs that reveal how they function. The result is a dynamic machine that learns quickly and challenges our clients' styles.

Chris Moody came from a Physics background from Caltech and UCSC, and is now a scientist at Stitch Fix. He has an avid interest in NLP, has dabbled in deep learning, variational methods, and Gaussian Processes. He's contributed to the Chainer deep learning library (http://chainer.org/), the super-fast Barnes-Hut version of t-SNE to scikit-learn (http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) and written (one of the few!) sparse tensor factorization libraries in Python (https://github.com/stitchfix/ntflib). Lately he's been working on lda2vec (https://lda2vec.readthedocs.org/en/latest/) and variational forms of t-SNE. 

Monday March 6, 2017 2:20pm - 2:50pm


BachBot: Composing Bach Chorales using Deep Learning
Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date. Our results show that the average listener can distinguish BachBot from real Bach only 5% better than random guessing, suggesting that algorithmic composition of Bach chorales is more closed (as a result of BachBot) than open a problem.

avatar for Feynman Liang

Feynman Liang

Engineering Manager, Gigster
Feynman is the engineering manager at Gigster and a statistics PhD student at UC Berkeley. His research lies at the intersection between industry and academia, focusing on distributed machine learning and practical systems for deploying machine learning in production. He is a contributor to Apache Spark and a recreational producer of electronic music. During his MPhil degree at Cambridge University, he collaborated with Microsoft Research... Read More →

Monday March 6, 2017 3:00pm - 3:30pm


Developing and Deploying Models at Scale — without Spark!
Looking for a break after an endless stream of talks about Spark? We'll do a live demo of developing and deploying a predictive model using an easy-to-use, Spark-less platform. We'll show how standard cloud compute resources let you train models using terabytes of RAM, and we'll demo deploying a model and serving requests at web scale, with horizontal scalability and high availability. Benefits of the approach we'll demonstrate include: (a) flexibility to use arbitrary R and Python development without shoehorning work into Spark; (b) simpler infrastructure management by avoiding distributed computing; (c) better performance for problems that are merely "medium data" rather than legitimately "big" data.

avatar for Eduardo Ariño de la Rubia

Eduardo Ariño de la Rubia

Chief Data Scientist in Residence, Domino Data Lab
Eduardo Arino de la Rubia is Chief Data Scientist at Domino Data Lab. Eduardo is a lifelong technologist with a passion for data science who thrives on effectively communicating data-driven insights throughout an organization. He is a graduate of the MTSU Computer Science department, General Assembly’s Data Science program, and the Johns Hopkins Coursera Data Science specialization. Eduardo is currently pursuing a master’s degree in... Read More →

Monday March 6, 2017 3:30pm - 4:00pm


State of AI: Chatbots, Robots & Virtual Reality, Oh My!
Artificial Intelligence is quickly moving from science fiction to science fact, and IBM Watson is not alone in offering machine learning algorithms in easy to consume REST API forms for developers to consume. But in this nascent industry it can be hard to understand the differences between different approaches along with the value of "AI." Drawing on a number of real-world examples, Michael Ludden explains how "Artificial Intelligence" (in quotes for a reason) can and is already being leveraged by developers to reduce the need for teams of data scientists, create insane new applications and services, and allow for personalized experiences that border on creepy. More importantly we'll give a realistic look at the immediate future of the space and some helpful guidance as we rapidly begin to live the futurist novels we read last year.

avatar for Michael Ludden

Michael Ludden

IBM Watson Developer Labs Program Director, IBM
Michael Ludden is the IBM Watson Developer Labs Program Director and Senior Product Manager. Previously, Michael was Lead Developer Marketing Manager at Google, Head of Developer Marketing at Samsung, a Developer Evangelist at HTC, Global Director of Developer Relations at startups Quixey & Nexmo, and was involved at various times in development, product marketing, co-founding startups, tech show hosting, and even cruise-ship singing (don’t... Read More →

Monday March 6, 2017 4:10pm - 4:50pm


AI: from an Idea to the Customer Panel
The Idea to the Customer panel takes several companies through the journeys of data-driven business, and covers many aspects of a live AI strategy touching customers. How do you think of Machine Learning and AI being a key part of your business, and how do you execute on it? What do you find along the way? Our distinguished panelists will share their own experiences.

avatar for Alexy Khrabrov

Alexy Khrabrov

Chief Scientist, Cicero.AI
Dr. Alexy Khrabrov is a founder and organizer of Open-Source communities and conferences on Data Engineering, Artificial Intelligence, and Data Science. He is the Chief Scientist of Cicero.AI, a community-based research institute for AI. Dr. Khrabrov runs seven meetups in San Francisco: bay.area.ai, sfscala.org, sfspark.org, sfhadoop.org, reactive.community, sfgraphql.org, and saltops.org. He founded a series of technical conferences By the... Read More →

avatar for Sara Asher

Sara Asher

Director of Product at Salesforce Einstein, Salesforce
Sara is a Director of Product Management for Salesforce Einstein, where she creates products that let people build smarter applications with Salesforce and advanced AI. Prior to Salesforce, Sara worked at Alpine Data where she was chief product manager and founding director of Alpine Labs. Sara holds an AB in mathematics from Princeton University and a PhD in mathematics from Northwestern University.
avatar for Daniel Golden

Daniel Golden

Senior Image Scientist, Arterys, Inc.
Dan is the Director of Machine Learning at Arterys, a startup focused on streamlining the practice of medical image interpretation and post-processing. After receiving a PhD in Electrical Engineering from Stanford, he stuck around for a postdoc, focusing on using machine learning to predict outcomes and disease characteristics in cancer patients. From there, he joined CellScope, where he founded a machine learning team that used concepts from the... Read More →
avatar for Leon Katsnelson

Leon Katsnelson

Director & CTO, Analytic Platform Emerging Technologies, IBM
I am a serial Intrepreneur with a number of very successful projects to my credit. My responsibility is to push frontiers of technology to create next generation products in the area of Big Data, Data Analytics and Data Science. A technologist at heart, I focus on value creation using technology rather than technology itself. I am a relentless pursuer of product and market fit and value these above all else. As a Definite Optimist (concept by... Read More →
avatar for Gabor Melli

Gabor Melli

Senior Director of Machine Learning at Sony Interactive Entertainment
avatar for Xin Wei Ngiam

Xin Wei Ngiam

Director of Corporate Strategy, Grab, and Regional Head, GrabHitch, for Singapore, Kuala Lumpur (Malaysia) and Jakarta (Indonesia)

Monday March 6, 2017 5:00pm - 6:00pm