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 (Sponsored) Level: Advanced
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton North (Sponsored) Level: Intermediate
Arthur Juliani (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments has been deep reinforcement learning. Arthur Juliani covers RL from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks 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), Patrick Buehler (Microsoft)
Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Impact of AI on business and society
Location: Sutton Center (Sponsored) Level: Beginner
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions 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
Josh Tenenbaum explains how to build 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)
Ben Vigoda offers an overview of Bayesian program synthesis (BPS), outlines the significant advantages it provides over deep learning technologies, and explains how it removes 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)
Anmol Jagetia explains how to use OpenAI's Gym and Universe to design bots that can become extremely smart using reinforcement learning. You'll create a bot that uses reinforcement learning to beat games and learn how to reuse code to beat a set of games that includes Atari classics (Pac-Man or Pong), a Candy Crush clone, and a racing game. 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 and interactive and work in real time. These properties impose new requirements on the distributed systems that support them. Philipp Moritz and Robert Nishihara offer an overview of 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 (Teradata), 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 Nadeem Gulzar 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)
Emotion AI is a branch of artificial intelligence that brings emotional intelligence to AI systems. Rana el Kaliouby reviews the state of emotion AI, its commercial applications, its underlying deep learning methods, and the research roadmap, which includes multimodal emotion recognition and the idea of an 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. Qirong Ho outlines 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. Matthew Taylor offers an overview of core HTM concepts, including 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 (B12)
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. B12 is building infrastructure that celebrates humans where they’re best while bringing machines in for the rest. Adam Marcus offers an overview of human-assisted AI and demonstrates how it is already changing 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
Matt Shobe (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? Matt Shobe shares lessons Mighty AI has learned while 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)
Precision medicine is largely a big data and systems problem, especially with many different types of "siloed" health care information, such as lab results, genetic tests, IoT and wearables data, and insurance information. Michael Nova explains why cognitive computing and artificial intelligence that can dynamically learn using any healthcare data will dramatically impact precision health care. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Implementing AI
Location: Beekman Level: Intermediate
Jason Laska (Clara Labs, Inc.)
Clara Labs is fusing 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. Jason Laska explores the challenges of building a real-time(ish) knowledge workforce, how to integrate automation, and key strategies Clara Labs 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. Join in to learn approaches for building 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)
AI-powered machine-learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning. 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)
Nikita Lytkin offers an overview of personalized digital advertising and explains how Facebook uses modern supervised machine-learning methods, such as factorization machines and deep neural networks, to recommend over a billion products to nearly two billion people. 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)
Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Beginner
Xiaofan Xu (Intel), Cormac Brick (Intel, Movidius Group)
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, effectively creating a “data wall” blocking smaller companies. David Moloney and Xiaofan Xu explore the problem of the data wall and offer a solution: 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)
Jonathan Mugan surveys the field of natural language processing (NLP), both from a symbolic and a subsymbolic perspective, arguing that the current limitations of NLP stem from computers having a lack of grounded understanding of our world. Jonathan then outlines 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 (WUNDER.ai)
70% of consumers do NOT feel that online offers resonate with their personal interests and needs. Rupert Steffner explains how cognitive AI can help 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.