Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Speaker slides & video

Presentation slides will be made available after the session has concluded and the speaker has given us the files. Check back if you don't see the file you're looking for—it might be available later! (However, please note some speakers choose not to share their presentations.)

If you are looking for slides and video from 2017, visit the AI Conference in New York 2017 site.

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Implementing AI
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.
Implementing AI, Models and Methods
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Ashwin Vijayakumar gives you a hands-on overview of Intel's Movidius Neural Compute Stick, a miniature deep learning hardware development platform that you can use to prototype, tune, and validate your AI programs (specifically deep neural networks).
Implementing AI, Models and Methods
Yacin Nadji (Georgia Institute of Technology)
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.
AI Business Summit, Impact of AI on Business and Society
David Barrett (Expensify )
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.
AI Business Summit, AI in the Enterprise
Jeetu Patel (Box)
AI will completely change and fundamentally power the way the world works together, so what does the future of AI in the enterprise look like? Jeetu Patel 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.
AI Business Summit, AI in the Enterprise
Andre Luckow (BMW Group)
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 the fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications.
Angie Ma (ASI)
2-Day Training Please note: to attend, your registration must include Training courses.
Angie Ma offers a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
Impact of AI on Business and Society
Brian Pearce (Wells Fargo)
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.
Impact of AI on Business and Society
Jake Porway (DataKind)
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.
Implementing AI, Models and Methods
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.
Implementing AI
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.
Implementing AI, Interacting with AI
Chris Benson (Honeywell)
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.
Implementing AI, Models and Methods
Ambika Sukla (Morgan Stanley)
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.
Models and Methods
Lindsey Zuloaga (HireVue)
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 work to establish best practices.
AI Business Summit, AI in the Enterprise
Robbie  Allen  (InfiniaML)
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.
AI Business Summit, AI in the Enterprise
Kristian Hammond (Narrative Science)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.
Keynotes
Comprehensive and sustainable wildlife monitoring technologies are key to maintaining biodiversity. Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that can rapidly analyze animal footprints to help map wildlife presence and scale conservation efforts around the world.
Implementing AI, Models and Methods
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.
Implementing AI, Models and Methods
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms.
Models and Methods
Steve J Rennie (Fusemachines)
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.
Implementing AI
Zhenxiao Luo (Uber)
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.
Implementing AI, Models and Methods
Yulia Tell (Intel), Maurice Nsabimana (World Bank Development Data Group)
Yulia Tell walks you through getting started with BigDL and explains how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia details a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Program chairs Ben Lorica and Roger Chen close the first day of keynotes.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Program chairs Ben Lorica and Roger Chen close the second day of keynotes.
AI Business Summit, Interacting with AI, Models and Methods
Emily Pavlini (Diffeo), Max Kleiman-Weiner (Diffeo)
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.
Models and Methods
Funda Gunes (SAS)
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.
Erika Menezes (Microsoft)
Erika Menezes shares a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.
Implementing AI
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.
2-Day Training Please note: to attend, your registration must include Training courses.
Learn how to build, implement, and deploy a deep learning solution using optimized IA hardware and software. You'll dive in to dataset wrangling and training before learning how to deploy your model for inference on various architectures.
AI Business Summit, AI in the Enterprise
Radhika Dutt (Radical Product), Geordie Kaytes (Fresh Tilled Soil), Nidhi Aggarwal (Radical Product)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.
AI Business Summit, AI in the Enterprise
Michael Li (The Data Incubator), Len Usvyat (Fresenius), Glenn Hofmann (New York Life), madhu tadikonda (AIG), Saar Golde (Via Transportation), Lori Bieda (Bank of Montreal)
What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, and Len Usvyat—to answer these questions.
AI in the Enterprise, Impact of AI on Business and Society
Enhao Gong (Stanford University | Subtle Medical), Greg Zaharchuk (Stanford University)
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.
Rich Ott (The Data Incubator)
2-Day Training Please note: to attend, your registration must include Training courses.
BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, detailing the components of BigDL and explaining how to implement machine learning algorithms, with a focus on neural networks.
Dana Mastropole (The Data Incubator)
2-Day Training Please note: to attend, your registration must include Training courses.
TensorFlow is an increasingly popular tool for deep learning. Dana Mastropole offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Dana covers several real-world deep learning applications, including machine vision, text processing, and generative networks.
Implementing AI
Mark Hammond (Bonsai)
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.
Models and Methods
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.
AI Business Summit, Impact of AI on Business and Society
Paul Nemitz (European Commission)
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.
AI in the Enterprise
Danny Lange (Unity Technologies)
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.
Implementing AI, Interacting with AI, Models and Methods
Greg Werner (3Blades)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Greg Werner walks you through using MXNet and TensorFlow to train deep learning models and deploy them using the leading serverless compute services in the market: AWS Lambda, Google Cloud Functions, and Azure Functions. You'll also learn how to monitor and iterate upon trained models for continued success using standard development and operations tools.
Simon Moss (Teradata), Ben MacKenzie (Think Big Analytics)
Analytic techniques leveraging artificial intelligence can result in dramatic improvements in crime detection and interdiction across diverse attack modalities. Simon Moss 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.
AI Business Summit, Impact of AI on Business and Society
Chris Butler (Philosophie)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that AI. Chris Butler leads you through exercises that borrow from the principals of design thinking to help you create more impactful solutions and better team alignment.
Models and Methods
John Hebeler (Lockheed Martin)
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 that combined further improve performance.
Implementing AI
Kaarthik Sivashanmugam (Microsoft), Wee Hyong Tok (Microsoft)
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.
Implementing AI
Alasdair Allan (Babilim Light Industries)
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.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Rachel Silver (MapR Technologies)
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.
AI Business Summit
Kayvaun Rowshankish (McKinsey & Company)
The session will explore the extent to which firms have addressed the GDPR regulation (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
John Sumser (TwoColorHat)
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.
AI Business Summit, AI in the Enterprise
Jana Eggers (Nara Logics)
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.
AI Business Summit, AI in the Enterprise
Kathryn Hume (integrate.ai)
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.
AI Business Summit, AI in the Enterprise
Shane Lewin (Lumiata)
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.
AI Business Summit, Impact of AI on Business and Society
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.
AI Business Summit, AI in the Enterprise
David Kiron (MIT Sloan Management Review)
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.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Susan Etlinger (Altimeter Group)
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.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
James Guszcza (Deloitte Consulting)
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.
Implementing AI, Interacting with AI, Models and Methods
Mike Ruberry (ZestFinance)
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. Mike Ruberry shares a framework for evaluating, explaining, and managing these more complex methods.
Keynotes
Peter Norvig (Google), Kavya Kopparapu (GirlsComputingLeague)
Fireside chat with Peter Norvig and Kavya Kopparapu
Interacting with AI, Models and Methods
Andrew Ilyas (Massachusetts Institute of Technology), Logan Engstrom (Massachusetts Institute of Technology), Anish Athalye (Massachusetts Institute of Technology)
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.
Implementing AI
Mridu Narang (Microsoft)
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.
AI Business Summit, Implementing AI, Interacting with AI
Ian Beaver (Next IT - Verint), Cynthia Freeman (Next IT)
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.
Implementing AI
Anand Rao (PwC)
There are a number of enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially in new product or market entry—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.
Implementing AI, Models and Methods
Amy Unruh (Google)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Yufeng Guo walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you'll gain insight into deep learning and how it can apply to complex problems in science and industry.
Implementing AI, Interacting with AI, Models and Methods
Joshua Patterson (NVIDIA), Michael Balint (NVIDIA)
Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Michael Balint 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.
Implementing AI, Models and Methods
Yamini Nimmagadda demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance.
AI Business Summit, AI in the Enterprise
Ryan Kottenstette (Cape Analytics)
There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries like insurance, finance or agriculture. Ryan Kottenstette shares lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture.
AI Business Summit, Implementing AI
Jan Neumann (Comcast), Jeanine Heck (Comcast)
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.
Implementing AI, Models and Methods
Raghav Ramesh (DoorDash)
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.
AI in the Enterprise
Ofer Ronen (Chatbase)
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.
Models and Methods
Taniya Mishra (Affectiva)
Drawing on Affectiva's experience building a multimodal emotion AI that can detect human emotions from face and voice, Taniya Mishra discusses how to build multimodal emotion detection using various deep learning approaches. Along the way, Taniya explains how to mitigate the challenges of data collection and annotation and how to avoid bias in model training.
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.
Implementing AI
Julie Zhu (Optum), Dima Rekesh (United Healthcare Group - Optum Tech)
Julie Zhu shares a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras.
Implementing AI, Interacting with AI
Scott Zoldi (FICO)
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.
Keynotes
George Church (Harvard University)
Keynote by George Church
Keynotes
Keynote by Intel
Keynotes
Keynote by Intel
Keynotes
Manuela Veloso (Carnegie Mellon University)
Keynote by Manuela Veloso
Keynotes
Meihong Wang (Facebook)
Keynote by Meihong Wang
Keynotes
Olga Russakovsky (Princeton University)
Keynote by Olga Russakovsky
Keynotes
Zoubin Ghahramani (Uber | University of Cambridge)
Keynote by Zoubin Ghahramani
Implementing AI, Interacting with AI, Models and Methods
Tim Kraska (MIT)
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, which we term learned indexes
AI Business Summit, AI in the Enterprise
Nicole Eagan (Darktrace)
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.
Implementing AI, Models and Methods
Forecasting the long-term values of a 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.
Keynotes
Tim Kraska (MIT)
Recent results show that machine learning has the potential to significantly alter the way basic data structures and algorithms are implemented and the performance they can provide. Tim Kraska explains the basic intuition behind learned data structures and outlines the potential consequences of this technology for industry.
Implementing AI
Sameer Wadkar (Comcast NBCUniversal), Nabeel Sarwar (Comcast NBCUniversal)
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.
Liran Zvibel (WekaIO)
Modern analytics platforms need to process large datasets to deliver the highest levels of accuracy to the training and analytics systems. Liran Zvibel explains how WekaIO’s parallel and distributed Matrix filesystem can easily saturate a GPU node and how the integrated cloud tiering scales to exabyte of capacity in a single namespace.
Implementing AI, Interacting with AI
Pramit Choudhary (DataScience.com)
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.
Delip Rao (R7 Speech Science)
2-Day Training Please note: to attend, your registration must include Training courses.
Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
Keynotes
Thomas Reardon (CTRL-Labs)
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 it touches.
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 it touches.
Tom Hanlon (Skymind), Josh Patterson (Skymind), Susan Eraly (Skymind), Dave Kale (Skymind)
2-Day Training Please note: to attend, your registration must include Training courses.
Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.
Implementing AI, Models and Methods
Jorge Silva (SAS)
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.
Brian Ray (Deloitte)
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.
Implementing AI, Interacting with AI, Models and Methods
Aurélien Géron (Kiwisoft)
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.
Keynotes
Ben Lorica (O'Reilly Media)
Keynote by program chair Ben Lorica
Implementing AI, Models and Methods
Mo Patel (Independent), Neejole Patel (Virginia Tech)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Computer vision has led the artificial intelligence renaissance, and pushing it further forward is PyTorch, a flexible framework for training models. Mo Patel and Neejole Patel offer an overview of computer vision fundamentals and walk you through PyTorch code explanations for notable objection classification and object detection models.
Implementing AI
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.
Models and Methods
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.
Implementing AI
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.
Models and Methods
Ameet Talwalkar (Determined AI)
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.
Implementing AI, Models and Methods
Xiaoyong Zhu (Microsoft)
Super-resolution 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 super-resolution 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.
Models and Methods
Arshak Navruzyan (Sentient Technologies)
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.
Meihong Wang (Facebook)
Session by Meihong Wang
Olga Russakovsky (Princeton University)
Session by Olga Russakovsky
Zoubin Ghahramani (Uber | University of Cambridge)
Session by Zoubin Ghahramani
Implementing AI, Interacting with AI, Models and Methods
Kaz Sato (Google)
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 for bringing the latest AI technology to production mobile apps and embedded systems.
Interacting with AI
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.
Implementing AI
Rupert Steffner (WUNDER.ai)
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 finding the products they love.
Models and Methods
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.
AI Business Summit, AI in the Enterprise
Scott Weller (SessionM)
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.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
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.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Artificial Intelligence program chairs Ben Lorica and Roger Chen open the first day of keynotes.
AI in the Enterprise
Ashok Srivastava (Intuit)
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.
AI Business Summit, AI in the Enterprise
Ron Bodkin (Google)
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.
Implementing AI
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
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.
Implementing AI
Jamie Irza (Raytheon)
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.
Models and Methods
Megan Yetman (Capital One)
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.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Artificial Intelligence program chairs Ben Lorica and Roger Chen open the second day of keynotes.
Implementing AI
Bruno Gonçalves (New York University)
Tutorial Please note: to attend, your registration must include Tutorials on Monday.
Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture.