Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
Sessions
Learn new skills and techniques at from experts and leading practitioners. Artificial Intelligence sessions take place Tuesday, May 1-Wednesday, May 2.
Tuesday, May 1
11:05am–11:45am Tuesday, May 1, 2018
Location: Grand Ballroom East
Zoubin Ghahramani (Uber | University of Cambridge)
Average rating:
(4.20, 5 ratings)
Zoubin Ghahramani explores the foundations of the field of probabilistic, or Bayesian, machine learning and details current areas of research, including Bayesian deep learning, probabilistic programming, and the Automatic Statistician. Zoubin also explains how Uber organizes AI research and where probabilistic machine learning fits in.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Grand Ballroom West
Srinivasa Karlapalem (Intel)
Srinivasa Karlapalem demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Sutton North/Center
William Benton (Red Hat)
Intelligent applications learn from data to provide improved functionality to users. William Benton examines the confluence of two development revolutions: almost every exciting new application today is intelligent, and developers are increasingly deploying their work on container application platforms. Join William to learn how these two revolutions benefit one another.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Sutton South
Faizan Buzdar (Box)
Average rating:
(1.50, 2 ratings)
AI will fundamentally change (and power) the way the world works together. So what does the future of AI in the enterprise look like? Faizan Buzdar explains how intelligence is being applied to enterprise content in practical ways that will revolutionize the most important business processes for companies of all sizes and across all industries.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Nassau East/West
Scott Zoldi (FICO)
Average rating:
(4.75, 4 ratings)
Scott Zoldi discusses innovations in explainable AI, such as Reason Reporter, which explains the workings of neural network models used to detect fraudulent payment card transactions in real time, and offers a comparative study with local interpretable model-agnostic explanations (LIME) that demonstrates why the former are better at providing explanations.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Regent Parlor
David Kiron (MIT Sloan Management Review)
Average rating:
(3.50, 4 ratings)
Few organizations have mastered integrating AI technology into their business processes and offerings, and many who want to don’t fully understand the work that lies ahead. David Kiron shares surprising insights about businesses’ appetite for and approach to AI, drawn from global collaborative research conducted by MIT Sloan Management Review and The Boston Consulting Group.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Concourse A
Taniya Mishra (Affectiva)
Drawing on Affectiva's experience building a multimodal emotion AI that can detect human emotions from face and voice, Taniya Mishra outlines various deep learning approaches for building multimodal emotion detection. Along the way, Taniya explains how to mitigate the challenges of data collection and annotation and how to avoid bias in model training.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Beekman Parlor
Average rating:
(4.00, 1 rating)
Erika Menezes and Serina Kaye share a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.
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11:05am–11:45am Tuesday, May 1, 2018
Location: Morgan
Ben Vigoda (Gamalon)
Average rating:
(5.00, 1 rating)
Ben Vigoda offers an overview of idea learning, a new approach to deep learning that has been funded since 2013 as one of DARPA's largest investments in next-generation machine learning. Ben details the process of teaching machines with ideas instead of labeled data and demonstrates use cases with state-of-the-art performance on applications in unstructured enterprise data.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Danny Lange (Unity Technologies)
Average rating:
(5.00, 2 ratings)
Danny Lange offers an overview of deep reinforcement learning—an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life—and explains how the crossroads between machine learning and gaming offers innovations that are applicable in other fields of technology, such as the robotics and automotive industries.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Mustafa Kabul (SAS)
Average rating:
(4.00, 3 ratings)
Forecasting the long-term values of time series data is crucial for planning. But how do you make use of a recurrent neural network when you want to compute an accurate long-term forecast? How can you capture short- and long-term seasonality or discover small patterns from the data that generate the big picture? Mustafa Kabul shares a scalable technique addressing these questions.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Sutton North/Center
Average rating:
(5.00, 1 rating)
Deep learning has fueled the emergence of many practical applications and experiences. Meanwhile, container technologies have been maturing, allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean as they walk you through using the Cognitive Toolkit (CNTK) with Kubernetes clusters.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Sutton South
Andre Luckow (BMW Group)
Average rating:
(5.00, 1 rating)
AI delivers value to many facets of the automotive value chain, including smart manufacturing, supply chain management, and customer engagement. Andre Luckow discusses how to assess AI technologies, validate use cases, and foster fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Regent Parlor
Kathryn Hume (integrate.ai)
Average rating:
(3.40, 5 ratings)
Large enterprises struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. AI requires a radical shift. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Nassau East/West
brian pearce (Wells Fargo)
Average rating:
(3.40, 5 ratings)
Chatbots are having a moment, and banks across the world are utilizing them for everything from basic customer service to assisting internal IT support. But chatbots only skim the AI landscape. Brian Pearce explains how AI helps Wells Fargo use data in a smarter way, from developing custom experiences to uncovering new insights—with customers and employees at the center of it all.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Concourse A
Alex Jaimes (Dataminr)
Average rating:
(4.00, 2 ratings)
Alex Jaimes explains how the cloud can be used effectively to deploy deep learning and the factors that allow you to do so cost effectively. Along the way, Alex shares examples of when and how to deploy deep learning in the cloud as well as the corresponding benefits, challenges, and opportunities.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Morgan
Tatiana Mejia (Adobe)
AI will change—and in some ways already is changing—the way we work, live, and play at a scale the world has never experienced. Join Tatiana Mejia to see how marketers, designers, and creative professionals can gain huge benefits in productivity, content scale, and workflow efficiencies while unleashing expanded career opportunities for workers in these industries.
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11:55am–12:35pm Tuesday, May 1, 2018
Location: Beekman Parlor
Randall Hunt (Amazon Web Services)
Average rating:
(4.00, 2 ratings)
Amazon SageMaker is a fully managed machine learning platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models in the cloud, at any scale. Randall Hunt offers an overview of SageMaker and demonstrates an end-to-end machine learning workflow by building an ML-powered Twitter bot that you can interact with in real time.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Manas Ranjan Kar (Episource)
Episource is building a scalable NLP engine to help summarize medical charts and extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. Manas Ranjan Kar offers an overview of the wide variety of deep learning algorithms involved and the complex in-house training-data creation exercises that were required to make it work.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Sutton North/Center
Arshak Navruzyan (Sentient Technologies)
Average rating:
(3.50, 2 ratings)
Data scientists and machine learning professionals face a quandary of choices when trying to figure out how to scale their data science experiments. Arshak Navruzyan details the landscape of available options and explains how to make best use of the free and open source tools available.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Sutton South
David Barrett (Expensify )
Average rating:
(3.00, 5 ratings)
Expensify is using AI to streamline and improve customer service, reducing customer wait time from 15 hours to 3 minutes. David Barrett leads a deep dive into the process of building Concierge, a hybrid machine learning-driven chatbot, covering the challenges faced, results to date, and what he sees for the future of AI and customer service.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Regent Parlor
Susan Etlinger (Altimeter Group)
Average rating:
(4.00, 2 ratings)
Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots, covering the risks and opportunities of conversational interfaces, the strategic implications for customer experience, business models, brand strategy, and recent innovations.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Nassau East/West
Aurélien Géron (Kiwisoft)
Average rating:
(3.83, 6 ratings)
The stock market is well known to be extremely random, making investment decisions difficult, but deep learning can help. Drawing on a concrete financial use case, Aurélien Géron explains how LSTM networks can be used for forecasting.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Concourse A
Jamie Irza (Raytheon)
Average rating:
(3.00, 1 rating)
Activity-based intelligence (ABI) is the art and science of understanding normal patterns of life to enhance the ability of a system to detect anomalous behavior (e.g., to identify cases of credit card fraud). Jamie Irza demonstrates how machine learning can be used to implement ABI for detecting threatening behavior from unmanned aerial systems, commonly known as drones.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Beekman Parlor
Daniel Raskin and Jonathan Greenberg explain what the extreme data economy is about and how machine learning advances along with accelerated parallel computing will play a key role in translating data into instant insight to power business in motion.
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1:45pm–2:25pm Tuesday, May 1, 2018
Location: Morgan
Jacob Graham (Intel),
Mallika Fernandes (Accenture)
Average rating:
(3.50, 2 ratings)
In manufacturing, software development, and aerospace, tech-op teams need to make critical decisions on the spot with very little information. In this session, presented by Intel Saffron, the speakers share actual use cases of cognitive AI-based applications helping technical professionals make more confident decisions to solve the pressing issues in their day-to-day work.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Ameet Talwalkar (Carnegie Mellon University | Determined AI)
Average rating:
(4.50, 2 ratings)
While deep learning has enjoyed widespread empirical success, fundamental bottlenecks exist when attempting to develop deep learning applications at scale. Ameet Talwalkar shares research on addressing two core scalability bottlenecks: tuning the knobs of deep learning models (i.e., hyperparameter optimization) and training deep models in parallel environments.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Grand Ballroom West
What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Sutton North/Center
Average rating:
(3.50, 2 ratings)
Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Sutton South
Average rating:
(5.00, 2 ratings)
Jan Neumann and Jeanine Heck explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers' questions or resolve their concerns.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Regent Parlor
Tolga Kurtoglu (PARC)
Average rating:
(4.00, 1 rating)
Tolga Kurtoglu walks you through the advanced technology needed to implement cyberphysical systems, covering the right hardware to sense the right data, explainable AI, and designing security for trustworthy operations. Along the way, Tolga shares case studies and examples of advanced tech deployments.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Nassau East/West
Ambika Sukla (Morgan Stanley)
Average rating:
(4.00, 2 ratings)
Financial econometric models are usually handcrafted using a combination of statistical methods, stochastic calculus, and dynamic programming techniques. Ambika Sukla explains how recent advancements in AI can help simplify financial model building by carefully replacing complex mathematics with a data-driven incremental learning approach.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Concourse A
Yacin Nadji (Georgia Institute of Technology)
Average rating:
(5.00, 2 ratings)
The adversarial nature of security makes applying machine learning complicated. If attackers can evade signatures and heuristics, what is stopping them from evading ML models? Yacin Nadji evaluates, breaks, and fixes a deployed network-based ML detector that uses graph clustering. While the attacks are specific to graph clustering, the lessons learned apply to all ML systems in security.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Beekman Parlor
Mary Beth Ainsworth (SAS)
Indigenous trackers all over the world can look at a single footprint in the dirt and intuitively know what animal species that print belongs to. Mary Beth Ainsworth explains how biologists, zoologists, machine learning and computer vision experts have come together to develop, automate, and scale a noninvasive approach to monitoring endangered wildlife by analyzing where animals have walked.
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2:35pm–3:15pm Tuesday, May 1, 2018
Location: Morgan
Richard Tibbetts (Tableau)
Average rating:
(3.00, 2 ratings)
New technologies make Bayesian inference and generative modeling more accessible to business analysts, but this also creates new communications challenges. Richard Tibbetts shares techniques for capturing domain knowledge and making findings actionable for decision makers utilizing the explanatory powers of transparent AI.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Ashok Srivastava (Intuit)
Average rating:
(2.50, 2 ratings)
Entrusted with the financial data of 42 million customers, Intuit is in a unique position to take advantage of AI to solve some of its customers’ biggest financial pains. Ashok Srivastava discusses technology’s role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Julie Zhu (Optum),
Dima Rekesh (Optum)
Julie Zhu and Dima Rekesh share a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Sutton North/Center
Zhenxiao Luo (Twitter)
Average rating:
(3.00, 1 rating)
From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Zhenxiao Luo explains how Uber tackles data caching in large-scale machine learning, exploring Uber's machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speed up AI jobs.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Sutton South
Robbie Allen (InfiniaML)
Average rating:
(4.12, 8 ratings)
Drawing on his experience leading two successful AI companies that implemented machine learning and NLP solutions in over a hundred organizations, Robbie Allen details patterns and characteristics of successful machine learning implementations (and those that predict failure) and explains how to build and cultivate ML talent within your organization in an increasingly competitive job market.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Regent Parlor
James Guszcza (Deloitte Consulting)
Average rating:
(4.80, 5 ratings)
AI is about more than automating tasks; it's about augmenting and extending human capabilities. James Guszcza discusses principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-centered design has been crucial to the economic success of an AI project.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Nassau East/West
Megan Yetman (Capital One)
Average rating:
(5.00, 3 ratings)
Pensieve is a natural language processing (NLP) project that classifies reviews for their sentiment, reason for sentiment, high-level content, and low-level content. Megan Yetman offers an overview of Pensieve as well as ways to improve model reporting and the ability for continuous model learning and improvement.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Concourse A
Average rating:
(5.00, 1 rating)
Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Aaron Sant-Miller explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Beekman Parlor
Average rating:
(4.67, 3 ratings)
Analytic techniques leveraging artificial intelligence can result in dramatic improvements in crime detection and interdiction across diverse attack modalities. Will Griffith and Ben MacKenzie share AI models and operational techniques they’ve used with major banking clients to substantially strengthen and accelerate their responses to criminal attacks.
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4:00pm–4:40pm Tuesday, May 1, 2018
Location: Morgan
Sumeet Vij (Booz Allen Hamilton)
Drawing on his experience bringing AI to the public sector, Sumeet Vij offers perspectives on public sector AI trends, dispelling myths around barriers to entry and sharing approaches and opportunities as he highlights examples of successful AI adoptions.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Sergey Ermolin (Intel)
Sergey Ermolin details the latest features, real-world use cases, and what's in store for 2018 for BigDL on Intel Xeon processor-based data center and cloud deployments.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Grand Ballroom West
David C Martin (IBM Watson)
David Martin explores cognitive function in conjunction with edge computing and IoT sensors and actuators for eldercare scenarios—specifically the identification of individuals, daily activity monitoring, and aberration detection performed on-premises using HomeAssistant, the Intu open source project, and IBM's Watson cognitive services.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Sutton North/Center
Chris Benson (Lockheed Martin)
Average rating:
(3.00, 2 ratings)
Deep learning is the driving force behind the current AI revolution and will impact every industry on the planet. However, success requires an AI strategy. Chris Benson walks you through creating a strategy for delivering deep learning into production and explores how deep learning is integrated into a modern enterprise architecture.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Sutton South
Ophir Tanz (GumGum)
Advancements in computer vision are creating new opportunities across business verticals, from programs that help the visually impaired to extracting business insights from socially shared pictures, but the benefits of applied AI in computer vision are only beginning to emerge. Ophir Tanz explores the tools and image technology utilizing AI that you can apply to your business today.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Regent Parlor
Average rating:
(3.00, 3 ratings)
Kayvaun Rowshankish and Alexis Trittipo explore the extent to which firms have addressed the EU's General Data Protection Regulation (GDPR) (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Nassau East/West
Average rating:
(4.00, 2 ratings)
Historically, the consumer loan industry has restricted itself to using relatively simple machine learning models and techniques to accept or deny loan applicants. However, more powerful (but also more complicated) methods can significantly improve business outcomes. Sean Kamkar shares a framework for evaluating, explaining, and managing these more complex methods.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Concourse A
Jake Porway (DataKind)
Average rating:
(5.00, 3 ratings)
Jake Porway explores AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges you may face in using AI to tackle complex humanitarian issues.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Morgan
Omar Tawakol (Voicera)
Average rating:
(3.50, 2 ratings)
Regardless of industry, every executive is concerned with the same thing: their customers. Omar Tawakol details the building blocks of speech technologies, including natural language processing, automatic speech recognition, and neural networks, that are necessary to implement voice-activated artificial intelligence and more importantly, enable a customer-centric enterprise.
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4:50pm–5:30pm Tuesday, May 1, 2018
Location: Beekman Parlor
Alan Nichol (Rasa)
Fortune 500 companies are building conversational AI in-house to create a competitive edge. Alan Nichol shares a case study of a successful customer acquisition chatbot built by a large corporation and demonstrates how to build a useful, engaging conversational AI bot based entirely on machine learning using Rasa NLU and Rasa Core, the leading open source libraries for building conversational AI.
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Wednesday, May 2
11:05am–11:45am Wednesday, May 2, 2018
Location: Grand Ballroom East
Olga Russakovsky (Princeton University)
Average rating:
(4.00, 1 rating)
Session with Olga Russakovsky
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11:05am–11:45am Wednesday, May 2, 2018
Location: Grand Ballroom West
Mark Hammond (Microsoft)
Average rating:
(2.00, 2 ratings)
Reinforcement learning is a powerful machine learning technique for solving problems in dynamic and adaptive environments. Mark Hammond dives into two real-world case studies and demonstrates how to build and deploy deep reinforcement learning models for industrial applications.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Sutton North/Center
Xiaoyong Zhu (Microsoft)
Average rating:
(3.00, 1 rating)
Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. Xiaoyong Zhu shares the latest academic progress in superresolution using deep learning and explains how it can be applied in various industries, including healthcare. Along the way, Xiaoyong demonstrates how the training can be done in a distributed fashion in the cloud.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Sutton South
Ron Bodkin (Google)
Average rating:
(4.33, 6 ratings)
Ron Bodkin explains how Google is using AI internally to enhance understanding and experiences for its digital customers and enabling external businesses, such as Spotify and Netflix, to do the same. Along the way, Ron shares examples of deep learning use cases that enable improved recommendations, help companies better understand their customers, and drive engagement in the customer lifecycle.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Nassau East/West
Lindsey Zuloaga (HireVue)
Average rating:
(5.00, 1 rating)
We're all familiar with the highly publicized stories of algorithms displaying overtly biased behavior toward certain groups, but what actually happens behind the scenes, and how can these situations be avoided? Lindsey Zuloaga shares experiences and lessons learned in the hiring space to help others prevent unfair modeling and explains how to establish best practices.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Regent Parlor
John Sumser (TwoColorHat)
Average rating:
(5.00, 3 ratings)
AI and its related subtechnologies are being introduced into operational decision making throughout the enterprise. The most promising and risky experiments involve the way people are selected and utilized, but the use of AI in HR raises the specter of software product liability. John Sumser offers an overview of the available use case solutions and the accompanying ethical issues.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Concourse A
John Hebeler (Lockheed Martin)
Average rating:
(3.50, 2 ratings)
Determining abnormal conditions depends on maintaining a useful definition of normal. John Hebeler offers an overview of two deep learning methods to determine normal behavior, which when combined further improve performance.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Morgan
Chad Meley (Teradata)
Average rating:
(4.83, 6 ratings)
AI has already begun to demonstrate its value in large enterprises, even outside of Silicon Valley and other West Coast digital giants. Fortune 500 companies in industries like finance, manufacturing, travel, transportation, and pharmaceuticals have begun to leverage its power. Chad Meley shares insights from real-world client engagements using deep learning.
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11:05am–11:45am Wednesday, May 2, 2018
Location: Beekman Parlor
Dario Gil (IBM)
Average rating:
(5.00, 1 rating)
Over the last five years, AI has become more capable thanks to the availability of data, algorithms, and models. Companies are exploring ways to leverage these advances, and soon AI technology will touch every industry worldwide. Dario Gil explores the challenges faced by companies building AI solutions for enterprise applications and areas of research required to drive this field forward.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Grand Ballroom East
David Patterson (UC Berkeley),
Greg Diamos (Baidu),
Cliff Young (Google),
Peter Mattson (Google),
Peter Bailis (Stanford University),
Gu-Yeon Wei (Harvard University)
Average rating:
(4.00, 1 rating)
Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Tim Kraska (MIT)
Average rating:
(5.00, 2 ratings)
Tim Kraska explains how fundamental data structures can be enhanced using machine learning with wide-reaching implications even beyond indexes, arguing that all existing index structures can be replaced with other types of models, including deep learning models (i.e., learned indexes).
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Sutton North/Center
Meihong Wang (facebook)
Average rating:
(3.67, 3 ratings)
Everyone's Facebook news feed experience is unique and highly personalized. In this extension of his keynote, Meihong Wang explains how Facebook solves the personalization problem with machine learning techniques and offers an overview of its large-scale machine learning system that models every user and delivers them the most relevant content in real time.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Sutton South
Scott Weller (SessionM)
Average rating:
(2.33, 3 ratings)
In video games, players learn by failing, sometimes “dying” hundreds of times before learning how to succeed. By enabling us to simulate scenarios and predict outcomes, AI has essentially made the world like a game. Scott Weller explores the role of failure in machine learning, explaining how to set realistic expectations and sharing examples of good and bad AI deployments in the wild.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Regent Parlor
Jana Eggers (Nara Logics)
Average rating:
(4.50, 2 ratings)
AI scores points for providing better answers to your company's challenges and for requiring you to get your data house in order. Jana Eggers explains why AI's hat trick is how it can transform your company into a learning organization. Jana reviews the benefits of a learning org and details how to build an AI program that can support you in achieving those benefits.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Nassau East/West
Stephanie Kim (Algorithmia)
Stephanie Kim discusses the basics of facial recognition and the importance of having diverse datasets when building out a model. Along the way, she explores racial bias in datasets using real-world examples and shares a use case for developing an OpenFace model for a celebrity look-alike app.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Concourse A
Andrew Ilyas (Massachusetts Institute of Technology),
Logan Engstrom (Massachusetts Institute of Technology),
Anish Athalye (Massachusetts Institute of Technology)
Average rating:
(5.00, 2 ratings)
Andrew Ilyas, Logan Engstrom, and Anish Athalye share an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Morgan
Harsh Kumar (Intel)
Harsh Kumar explains one way the energy industry is using AI and computer vision for security surveillance: a video analytics solution that can be optimized for the functional safety of workers in the loading and unloading zone of an oil and gas offshore rig.
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11:55am–12:35pm Wednesday, May 2, 2018
Location: Beekman Parlor
Jayanti Murty (Digitate)
Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms and struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Drawing on real-world examples, Jayanti Murty explains how ignio can reduce operational risks and outages, enabling you to more quickly adapt to change.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Steve J Rennie (Fusemachines)
Average rating:
(4.00, 2 ratings)
Over the last year, Steve Rennie and his colleagues have significantly advanced the state of the art in performance on two flagship challenges in AI: the Switchboard Evaluation Benchmark for Automatic Speech Recognition and the MSCOCO Image Captioning Challenge. Steve shares the innovations in deep learning research that have most advanced performance on these and other benchmark AI tasks.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Carolina Sanchez Hernandez (NATS)
Average rating:
(3.00, 1 rating)
New technologies have the potential to revolutionize the aviation industry. Airports in particular are perfect candidates for AI and machine learning concepts. Carolina Sanchez Hernandez discusses how National Aviation Technical Services (NATS) is collaborating with several companies and institutes to change the way that data is captured and processed to transform airport operations.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Sutton North/Center
Anand Rao (PwC)
There are many enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially for new products or when entering new markets—there are very few successful use cases. Anand Rao presents four successful use cases on gamifying strategy and applying agent-based simulation in the auto, payments, medical devices, and airlines industries.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Sutton South
Michael Li (The Data Incubator),
Len Usvyat (Fresenius),
Saar Golde (Via Transportation),
Sassoon Kosian (New York Life),
LORI BIEDA (Bank of Montreal)
Average rating:
(4.00, 1 rating)
What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Regent Parlor
Rachel Silver (MapR Technologies)
Average rating:
(3.67, 3 ratings)
With all the buzz around machine learning, it can be difficult to distinguish what is disruptive from what is merely a marginal improvement. Rachel Silver shares a new taxonomy of machine learning approaches that categorizes both models and learning algorithms with respect to technical complexity and explains how to use it to identify approaches that provide compelling competitive advantage.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Nassau East/West
Funda Gunes (SAS)
Average rating:
(4.67, 3 ratings)
As machine learning algorithms and artificial intelligence continue to progress, we must take advantage of the best techniques from various disciplines. Funda Gunes demonstrates how combining well-proven methods from classical statistics can enhance modern deep learning methods in terms of both predictive performance and interpretability.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Concourse A
Kaz Sato (Google)
Average rating:
(4.00, 2 ratings)
TensorFlow Lite—TensorFlow’s lightweight solution for Android, iOS, and embedded devices—enables on-device machine learning inference with low latency and a small binary size. Kazunori Sato walks you through using TensorFlow Lite, helping you overcome the challenges to bring the latest AI technology to production mobile apps and embedded systems.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Morgan
Stephen Piron (DeepLearni.ng)
Average rating:
(1.00, 2 ratings)
Is your enterprise striving to build AI applications that produce transformative business value? Stephen Piron shares real-world examples of AI applications that are evolving the way enterprises work from the ground up as well as a framework for enterprise leaders to use to ensure their team’s AI initiatives lay the foundation for genuine business impact.
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1:45pm–2:25pm Wednesday, May 2, 2018
Location: Beekman Parlor
Drew Hodun (Google Cloud)
Average rating:
(4.00, 1 rating)
Drew Hodun explores the progress Google is making to decrease the amount of work needed to go from "zero to AI."
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Mridu Narang (Microsoft)
Average rating:
(3.00, 3 ratings)
In a world of information overload and manipulation, knowledge acquisition techniques are expected to provide instant, precise, and succinct answers. Question-answering (QnA) systems must serve answers with high accuracy and be backed by strong verification techniques. Mridu Narang offers an overview of the challenges of and approaches taken by large-scale QnA systems.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Yulia Tell and Maurice Nsabimana walk you through getting started with BigDL and explain how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia and Maurice detail a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Sutton North/Center
Murali Kaundinya (Independent)
Murali Kaundinya outlines an InnerSource model to curate and operationalize machine learning and deep learning algorithms with a common workflow and engaging user experience. Focusing on patterns and practices, Murali then shares lessons learned implementing four enterprise scale use cases: optical character recognition, release engineering, virtual customer assistants, and data unification.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Sutton South
Ryan Kottenstette (Cape Analytics)
Average rating:
(5.00, 2 ratings)
There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries. Ryan Kottenstette shares lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Regent Parlor
John Lewin (Microsoft)
Average rating:
(5.00, 2 ratings)
Great AI products are more than technology; they are built on a clear (computationally tractable) model of customer success. Getting that model right can be more challenging than building the AI models themselves; and getting it wrong is very expensive. Shane Lewin outlines common pitfalls in defining AI products and explains how to organize teams to solve them.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Nassau East/West
Nick Pentreath (IBM)
Average rating:
(4.25, 4 ratings)
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Concourse A
Ofer Ronen (Chatbase)
Average rating:
(5.00, 1 rating)
Chatbots are expected to make machine communication feel human, but high-quality bot experiences are very hard to build. Ofer Ronen explores the challenges in optimizing chatbots and shares ways for developers to address them quickly and efficiently.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Beekman Parlor
Brian Ray (Deloitte)
Average rating:
(4.00, 1 rating)
Brian Ray unveils the secrets behind the execution of Deloitte's framework for AI summarized in "Artificial Intelligence for the Real World," recently published in the January–February 2018 issue of Harvard Business Review. Join in to learn how to go from data to delivering real and measurable predictive value.
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2:35pm–3:15pm Wednesday, May 2, 2018
Location: Morgan
Dr. Sid J. Reddy (Conversica)
Sid Reddy shares Conversica's artificial intelligence approach to creating, deploying, and continuously improving an automated sales assistant that engages in a genuinely human conversation at scale with every one of an organization’s sales leads.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Gerard de Melo (Rutgers University)
Across the globe, people are voicing their opinion online. However, sentiment analysis is challenging for many of the world's languages, particularly with limited training data. Gerard de Melo demonstrates how to exploit large amounts of surrogate data to learn advanced word representations that are custom-tailored for sentiment and shares a special deep neural architecture to use them.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Mike Ranzinger (Shutterstock)
Mike Ranzinger shares his research on composition-aware search and explains how the research led to the launch of AI technology that allows Shutterstock’s users to more precisely find the image they need within the company's collection of more than 150 million images.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Sutton North/Center
Thomas Reardon (CTRL-Labs)
Expanding his keynote, Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs's transformative and noninvasive neural interface approach. Along the way, he discusses the near-term opportunities for practical applications that will soon revolutionize daily life and the industries they touch.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Sutton South
Justin Fier (Darktrace)
Average rating:
(4.00, 4 ratings)
Although AI technology seems to be everywhere, implementing AI in practice is a real challenge. The technology needs to be scalable, trusted by the humans that use it, and easily accessible for those with limited AI expertise. Nicole Eagan shares the unique insights on building practical and successful AI applications Darktrace has gained from its 4,000+ deployments.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Regent Parlor
Recent advances have made machines more autonomous, but much work remains for AI to collaborate with people. Emily Pavlini and Max Kleiman-Weiner share new insights inspired by the way humans accumulate knowledge and naturally work together that enable machines and people to work and learn as a team, discovering new knowledge in unstructured natural language content together.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Nassau East/West
Pramit Choudhary (h2o.ai)
Average rating:
(4.67, 3 ratings)
Predicting the target label for computer vision machine learning problems is not enough. You must also understand the why, what, and how of the categorization process. Pramit Choudhary offers an overview of ways to faithfully interpret and evaluate deep neural network models, including CNN image models to understand the impact of salient features in driving categorization.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Concourse A
Alasdair Allan (Babilim Light Industries)
Average rating:
(3.00, 1 rating)
The AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan explains how to set up and build the kits and how to use the Python SDK to use machine learning both in the cloud and locally on the Raspberry Pi.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Beekman Parlor
Moses Guttmann (Seematics)
Average rating:
(5.00, 1 rating)
One of the most important aspects of deep learning is the quality and quantity of the data used in the learning process. Moses Guttmann explores the problem and offers approaches to solve it.
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4:00pm–4:40pm Wednesday, May 2, 2018
Location: Morgan
Marc Nehme (IBM Watson)
Average rating:
(4.00, 1 rating)
Marc Nehme demonstrates how you can quickly and easily use Watson with CRM solutions like Salesforce and cloud storage solutions like Box to improve and enhance your business processes.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Raghav Ramesh (DoorDash)
Average rating:
(5.00, 1 rating)
DoorDash is a last-mile delivery platform, and its logistics engine powers fulfillment of every delivery on its three-sided marketplace of consumers, Dashers, and merchants. Raghav Ramesh highlights AI techniques used by DoorDash to enhance efficiency and quality in its marketplace and provides a framework for how AI can augment core operations research problems like the vehicle routing problem.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Chris Watkins (Commonwealth Scientific and Industrial Research Organisation)
The achievement of human-level accuracy in image classification through the use of modern AI algorithms has renewed interest in its application to automated protein crystallization imaging. Christopher Watkins explores the development of the deep tech pipeline required for the robust operation of an online classification system in CSIRO's GPU cluster and shares lessons learned along the way.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Sutton North/Center
Rupert Steffner (WUNDER)
The road to real-world AI is long and winding. All we've heard from reputable experts turned out to be true, including the need for better data, a new UX, and new ways of learning. To help you along the way, Rupert Steffner highlights lessons learned implementing cognitive AI applications to help consumers find the products they love.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Sutton South
Paul Nemitz (European Commission)
Average rating:
(4.67, 3 ratings)
The rise of AI has shown the importance of implementing the basic rules of democracy, human rights, and the rule of law into the innovation process and the programs of artificial intelligence by design and default. Paul Nemitz outlines justice-oriented AI development processes and shares a model for globally sustainable development and deployment of artificial intelligence in the future.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Regent Parlor
Average rating:
(4.60, 5 ratings)
Conversation is emerging as the next great human-machine interface. Ian Beaver and Cynthia Freeman outline the challenges faced by the AI industry to relate to humans in the way they relate to each other and highlight findings from a recent study to demonstrate relational strategies used by humans in conversation and explain how virtual assistants must evolve to communicate effectively.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Nassau East/West
Kathleen Kallot and Augustin Marty explain how Intel Movidius technology is reducing the complexity of developing custom circuit boards and allowing developers and companies to prototype AI applications with the Intel Movidius Neural Compute Stick. They also demonstrate how the newly announced Intel AI: In Production program makes it easier to bring these designs to market.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Concourse A
Jorge Silva (SAS)
Average rating:
(4.00, 2 ratings)
Recommender systems suffer from concept drift and scarcity of informative ratings. Jorge Silva explains how SAS uses a Bayesian approach to tackle both problems by making the learning process online and active. Active learning prioritizes the most informative users and items by quantifying uncertainty in a principled, probabilistic framework.
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4:50pm–5:30pm Wednesday, May 2, 2018
Location: Beekman Parlor
Neeyanth Kopparapu (Girls Computing League)
With the improvement of medical devices in the technological era, doctors have access to an enormous amount of unharnessed medical data. Artificial Intelligence is a tool that can be used to process this data to solve problems that are considered hard or impossible as a doctor. These AI tools is what Neeyanth used to help the field of diagnostics enter the digital age.
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