Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Schedule: Machine Learning sessions

A.I. systems involve much more than deep learning. In these sessions we will highlight other emerging methods, tools, and techniques, including reinforcement learning, evolutionary algorithms, HTM, probabilistic machine learning, and human-in-the-loop systems.

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9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton North Level: Advanced
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Probabilistic inference is a widely-used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain and/or incomplete. It has become central to multiple fields, from big data analytics to robotics and AI. This class will survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to non-experts. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton North Level: Intermediate
Arthur Juliani (University of Oregon)
In the past few years computers have been able to learn to play Atari games, Go, and recently First Person Shooters at a superhuman level. Underlying all these accomplishments has been Deep Reinforcement Learning (Deep RL). This tutorial will cover RL from the basics using lookup tables and gridworld all the way to solving complex 3D tasks such as First-Person shooters with deep neural networks. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Beekman Level: Intermediate
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft)
We will be introducing the Cognitive Toolkit from Microsoft, which is native on both Windows and Linux, and offers flexible symbolic graph, friendly Python API, almost linear scalability across multi-GPU and multiple machines. We strongly believe the audiences will benefit learning and using our toolkit to speed up their experiments and find better deep learning algorithms. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Impact of AI on business and society
Location: Sutton Center Level: Beginner
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Businesses have spent decades trying to make better decisions by collecting & analyzing structured data. New AI technologies are beginning to transform this process. This talk will focus on AI that (i) guides business analysts to ask statistically sensible questions and (ii) lets junior data scientists answer questions in minutes that previously took hours for trained statisticians. Read more.
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10:10am10:30am Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom
Building machines that learn and think like people Read more.
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11:55am12:35pm Wednesday, June 28, 2017
Implementing AI
Location: Murray Hill E/W Level: Beginner
Ben Vigoda (Gamalon)
This session will focus on how Bayesian Program Synthesis (BPS) will provide significant advantages over deep learning technologies and remove some of the biggest obstacles preventing AI from being adopted in the enterprise. Read more.
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1:45pm2:25pm Wednesday, June 28, 2017
Interacting with AI
Location: Murray Hill E/W Level: Beginner
Anmol Jagetia (Media.Net)
In this talk we will cover the use of OpenAi's, Gym and Universe which can interact with external computer programs like games etc and design bots that can learn to defeat the game or become extremely smart using Reinforcement Learning. This talk aims at introducing beginners to AI and Machine Learning in a fun and interesting way, by designing bots that can play their favourite retro games! Read more.
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2:35pm3:15pm Wednesday, June 28, 2017
Implementing AI
Location: Murray Hill E/W Level: Intermediate
Philipp Moritz (UC Berkeley), Robert Nishihara (UC Berkeley)
AI applications are increasingly dynamic, interactive, and real-time. These properties impose new requirements on the distributed systems that support them. We will describe Ray, a new system designed to support these emerging applications. Read more.
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2:35pm3:15pm Wednesday, June 28, 2017
Verticals and applications
Location: Gramercy East/West Level: Intermediate
Ron Bodkin (Think Big Analytics), Nadeem Gulzar (Danske Bank Group)
Fraud in banking is an arms race with criminals using machine learning to improve their attack effectiveness. Ron Bodkin and Jens Christian Ipsen explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way. Read more.
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2:35pm3:15pm Wednesday, June 28, 2017
Verticals and applications
Location: Grand Ballroom West Level: Non-technical
Rana el Kaliouby (Affectiva)
Our interactions with technology is becoming conversational and perceptual. Emotion AI is a branch of artificial intelligence that brings emotional intelligence to these interfaces and AI systems. This talk will review the state of Emotion AI, the commercial applications as well as the underlying deep learning methods and research roadmap such as multi-modal emotion recognition and emotion chip. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Qirong Ho (Petuum, Inc.)
Petuum, Inc. builds software that lets enterprises develop AI solutions in multiple programming languages, and deploy them at scale and with high performance to internal, private computing resources that include a heterogeneous mix of workstations, clusters, CPUs and GPUs. We discuss the architectural design choices and technical foundation needed to achieve these targets. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Murray Hill E/W Level: Beginner
Matthew Taylor (Numenta)
Today's wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical Temporal Memory (HTM) is a realistic biologically-constrained model of the pyramidal neuron reflecting today's most recent neocortical research. This talk will describe and visualize core HTM concepts like sparse distributed representations, spatial pooling and temporal memory. Read more.
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4:50pm5:30pm Wednesday, June 28, 2017
Verticals and applications
Location: Sutton South/Regent Parlor Level: Beginner
Francisco Webber (Cortical.io)
Financial industries are under increased pressure due to regulations that demand extended information management capabilities. Information largely consists of text data, which forces companies to increase headcount to keep up with the growing workload. Francisco Webber demonstrates how Cortical.io’s semantic folding, a neuroscience-based approach to NLU, helps automate these uses cases. Read more.
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11:05am11:45am Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Intermediate
Adam Marcus (Unlimited Labs)
AI has a way to go before it replaces the jobs we know today. But long before AI automates away jobs, it will elevate expertise. At B12, we’re building infrastructure that celebrates humans where they’re best while bringing machines in for the rest. In this talk, we’ll explain human-assisted AI, and show how even today it changes incredibly creative and fundamentally human fields like design. Read more.
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11:05am11:45am Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Beginner
Daryn Nakhuda (Mighty AI)
Autonomous vehicles must recognize objects in context, no matter the weather, time of day or season. What does a cat in the road look like on a sunny summer day? How about on a snow-covered road at night? This session will highlight Mighty AI’s learnings from creating a training dataset for autonomous driving, including workflow tips and guidance for engineers building computer vision models. Read more.
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11:05am11:45am Thursday, June 29, 2017
Verticals and applications
Location: Gramercy East/West Level: Beginner
Michael Nova (Pathway Genomics)
In general, precision medicine is a big data and systems problem, especially with many different types of "siloed" healthcare information such as lab results, genetic tests, IoT/wearables, and insurance information. The use of Cognitive Computing and artificial intelligence (A.I.) that can dynamically learn using any healthcare data to will dramatically impact precision healthcare. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Intermediate
Jason Laska (Clara Labs, Inc.)
At Clara Labs, we've fused machine learning (ML) with distributed human labor for natural language tasks. The result is a virtuous cycle: ML predictions improve workers’ efficiency, and workers help improve prediction models. This session will focus on the challenges of building a real-time-ish knowledge-workforce, how to integrate automation, and key strategies we've learned that enable scale. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Intermediate
Mark Hammond (Bonsai)
As interactive and autonomous systems make their way into nearly every aspect of our lives, it is crucial to gain more trust in intelligent systems. Mark Hammond explores the latest techniques and research in building explainable AI systems. Learn how various approaches can be applied to build explainability into control and optimization tasks including robotics, manufacturing and logistics. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Matt Zeiler (Clarifai, Inc.)
AI-powered machine learning technologies bring a higher and more complex level of technical debt to applications especially if the AI and machine learning system has been built from the ground up. In this talk, Clarifai's CEO and renowned machine learning expert Matt Zeiler discusses best practices for companies hoping to build AI into their businesses. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Impact of AI on business and society
Location: Murray Hill E/W Level: Intermediate
Nikita Lytkin (Facebook)
Using the context of personalized digital advertising, this talk will showcase an application of modern supervised machine learning methods such as Factorization Machines and Deep Neural Networks for recommending hundreds of millions of products to nearly two billion people on Facebook. This talk will be of great interest to business leaders and machine learning practitioners. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Verticals and applications
Location: Gramercy East/West Level: Non-technical
Paco Nathan (O'Reilly Media)
How does Media employ AI? Obvious: chat bots, case studies about other firms. Less obvious: apply AI methods to show the structure of content in detail; enhance search and recommendations; guide editors for gap analysis, assessment, pathing, etc. Approaches explored include: vector embedding search, summarization, TDA for content gap analysis, speech-to-text to index video, etc. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Beginner
David Moloney (Intel (through acquisition of Movidius)), Xiaofan Xu (Intel)
Data is the “oxygen” of the AI revolution, but access to data on a large scale remains a luxury of an elite group of tech companies. The result of this is a “data wall” facing smaller companies. Thankfully, there are some innovative solutions around the data wall through synthetic datasets. This talk will outline the problem of the data wall, and how it can be addressed through synthetic datasets. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Jonathan Mugan (DeepGrammar)
The talk will survey the field of natural language processing (NLP), both from a symbolic and a sub-symbolic perspective. It will argue that the current limitations of NLP stem from computers having a lack of grounded understanding of our world, and it will point to ways that computers can achieve that understanding. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Interacting with AI
Location: Murray Hill E/W Level: Intermediate
Rupert Steffner (WUNDERAI GmbH)
According to market research 70% of consumers do NOT feel that online offers resonate with their personal interests and needs. With cognitive AI there’s a way to create Deep Shopping bots based on true personal relevance. This shift in the shopping paradigm is built upon Deep Symbolic Reinforcement Learning, the Psychometry of Shopping, a new breed of playful UI and cognified Product metadata. Read more.