September 26-27, 2016
New York, NY

Schedule: Implementing AI sessions

11:00am–11:40am Monday, 09/26/2016
Location: 3D09 Level: Advanced
Pascale Fung (The Hong Kong University of Science and Technology)
Average rating: ****.
(4.33, 3 ratings)
Pascale Fung describes an approach to enable an interactive dialogue system to recognize user emotion and sentiment in real time and explores CNN models that recognize emotion from raw speech input without feature engineering and sentiments. These modules allow otherwise conventional dialogue systems to have “empathy” and answer users while being aware of their emotion and intent. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: 3D08 Level: Beginner
Hilary Mason (Cloudera Fast Forward Labs)
Average rating: ****.
(4.78, 9 ratings)
Hilary Mason explores a framework for applied AI research, with a focus on algorithmic capabilities that are useful for building real-world products today. Drawing on real-world examples, Hilary outlines a system for thinking about which AI capabilities are ready to transition from pure research to applied products and how to make the transition from research paper to a working product. Read more.
11:50am–12:30pm Monday, 09/26/2016
Location: 3D09 Level: Intermediate
Anna Roth (Microsoft), Cristian Canton (Microsoft Technology and Research)
Average rating: **...
(2.00, 1 rating)
Anna Roth and Cristian Canton walk you through building a system to recognize emotions by inferring them from facial expressions. Cristian and Anna explain how they trained their emotion recognition CNN from noisy data and how to approach labeling subjective data like emotion with crowdsourcing before showing a demo of this work in action, as it is exposed in Microsoft’s Emotion API. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: River Pavilion B Level: Beginner
Anirudh Koul (Microsoft), Saqib Shaikh (Microsoft)
Anirudh Koul and Saqib Shaikh explore cutting-edge advances at the intersection of computer vision, language, and deep learning that can help describe the physical world to the blind community. Anirudh and Saqib then explain how developers can utilize this state-of-the-art image description, as well as visual question answering and other computer-vision technologies, in their own applications. Read more.
1:30pm–2:10pm Monday, 09/26/2016
Location: 3D10
Zachary Hanif (Capital One)
Average rating: ***..
(3.50, 4 ratings)
Developing and validating frequently updated models is core to professional data science teams. Zachary Hanif discusses the adaptation of CI tools and practices to solve model governance and accuracy tracking concerns in a complex environment with adversarial and temporal data complications. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: 3D08 Level: Intermediate
Mark Hammond (Microsoft)
Average rating: *....
(1.00, 1 rating)
Mark Hammond explains how Bonsai’s platform enables every developer to add intelligence to their software or hardware, regardless of AI expertise. Bonsai’s suite of tools—a new programming language, AI engine, and cloud service—abstracts away the lowest-level details of programming AI, allowing developers to focus on concepts they want a system to learn and how those concepts can be taught. Read more.
3:45pm–4:25pm Monday, 09/26/2016
Location: 3D10 Level: Advanced
Average rating: *****
(5.00, 3 ratings)
The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. The reality isn't that simple, and the commonly used tools greatly limit what we are capable of doing. Diogo Almeida explains what we can do about it and offers a practical attempt at a deep learning library of the future. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: River Pavilion B Level: Intermediate
Martin Wicke (Google)
TensorFlow is a system for scalable machine learning. However, using raw TensorFlow and profiling, optimizing, and debugging large-scale models can be daunting for novice and expert users alike. Martin Wicke explores new APIs based on TensorFlow that aim to make building complex models easier and allow users to scale quickly. Read more.
4:35pm–5:15pm Monday, 09/26/2016
Location: 3D10 Level: Beginner
Angela Zhou (x.ai)
In any human-machine interaction, you need a dialogue model: the machine must understand and be able to respond appropriately. Angela Zhou discusses x.ai's AI personal assistant, Amy Ingram, who schedules meetings for you, focusing on the way x.ai has approached both understanding and responding. Read more.
11:00am–11:40am Tuesday, 09/27/2016
Location: 3D09 Level: Non-technical
Jana Eggers (Nara Logics)
Average rating: ****.
(4.50, 2 ratings)
Drawing on her experience implementing AI systems in large enterprises, Jana Eggers covers the dos and don'ts of scoping a project across time, money, and people and compares and contrasts AI projects with typical IT and data science projects to explore the new aspects you need to consider as you add AI to your tech portfolio. Read more.
11:50am–12:30pm Tuesday, 09/27/2016
Location: River Pavilion B Level: Intermediate
Jon Barker (NVIDIA)
Average rating: *****
(5.00, 1 rating)
The process for deploying an effective neural network is iterative. Before an effective neural network is reached, many parameters must be evaluated and their impact on performance assessed. Jon Barker offers an overview of DIGITS, a deep learning GPU-training system designed to provide a real-time interactive user interface targeted toward accelerating the development process. Read more.
11:50am–12:30pm Tuesday, 09/27/2016
Location: 3D08 Level: Beginner
Francisco Webber (Cortical.io)
Average rating: ****.
(4.20, 5 ratings)
Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype—it's a production-grade enterprise technology. Read more.
1:30pm–2:10pm Tuesday, 09/27/2016
Location: 3D09 Level: Beginner
Amitai Armon (Intel)
Intel has recently released new processors for the Xeon and Xeon Phi product lines. Amitai Armon discusses how these processors are used for machine-learning tasks and offers data on their performance for several types of algorithms in both single-node and multinode settings. Read more.
2:20pm–3:00pm Tuesday, 09/27/2016
Location: 3D08 Level: Intermediate
Kenny Daniel (Algorithmia)
Average rating: ****.
(4.33, 3 ratings)
By building a marketplace for algorithms, Algorithmia gained unique experience with building and deploying machine-learning models using a wide variety of frameworks. Kenny Daniel shares the lessons Algorithmia learned through trial and error, the pros and cons of different deep learning frameworks, and the challenges involved with deploying them in production systems. Read more.
4:35pm–5:15pm Tuesday, 09/27/2016
Location: River Pavilion B Level: Intermediate
Suman Roy (betaworks)
The recent explosion of bots on communication platforms has rekindled the hopes of conversational AI. However, building intelligent and customizable bots is not just bottlenecked by NLP and speech recognition. Our biggest limitation is the inability to modularize the goals of human bot interconnection. Suman Roy explains why we need a layered architecture for bots to learn about us from data. Read more.
4:35pm–5:15pm Tuesday, 09/27/2016
Location: 3D09 Level: Non-technical
Ben Vigoda (Gamalon)
Average rating: ****.
(4.80, 5 ratings)
Benjamin Vigoda explains how Bayesian program learning can do things that other machine-learning approaches can't and why it's especially suited to enterprise data challenges. Read more.