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
 
Grand Ballroom East
Add Deep dive into probabilistic machine learning  to your personal schedule
11:05am Deep dive into probabilistic machine learning Zoubin Ghahramani (Uber | University of Cambridge)
Add Democratizing deep reinforcement learning to your personal schedule
11:55am Democratizing deep reinforcement learning Danny Lange (Unity Technologies)
Add Distributed DNN training: Infrastructure, challenges, and lessons learned to your personal schedule
1:45pm Distributed DNN training: Infrastructure, challenges, and lessons learned Kaarthik Sivashanmugam (Microsoft), Wee Hyong Tok (Microsoft)
Add Scalable deep learning to your personal schedule
2:35pm Scalable deep learning Ameet Talwalkar (Determined AI)
Add Using AI to solve complex economic problems to your personal schedule
4:00pm Using AI to solve complex economic problems Ashok Srivastava (Intuit)
Grand Ballroom West
Add Deep learning and AI is making clinical neuroimaging faster, safer, and smarter to your personal schedule
2:35pm Deep learning and AI is making clinical neuroimaging faster, safer, and smarter Enhao Gong (Stanford University | Subtle Medical), Greg Zaharchuk (Stanford University)
Add The cognitive IoT and eldercare to your personal schedule
4:50pm The cognitive IoT and eldercare David C Martin (IBM Watson)
Sutton North/Center
Add Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters to your personal schedule
11:55am Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Add Machine learning meets DevOps: Paying down the high-interest credit card to your personal schedule
2:35pm Machine learning meets DevOps: Paying down the high-interest credit card . . (Comcast NBCUniversal), Nabeel Sarwar (Comcast NBCUniversal)
Sutton South
Add AI and the future of work to your personal schedule
11:05am AI and the future of work Faizan Buzdar (Box)
Add How Comcast uses AI to reinvent the customer experience to your personal schedule
2:35pm How Comcast uses AI to reinvent the customer experience Jan Neumann (Comcast), Dominique Izbicki (Comcast)
Add Three examples of computer vision in action to your personal schedule
4:50pm Three examples of computer vision in action Ophir Tanz (GumGum)
Regent Parlor
Add Executive Briefing: Why AI needs human-centered design to your personal schedule
4:00pm Executive Briefing: Why AI needs human-centered design James Guszcza (Deloitte Consulting)
Add Executive Briefing: GDPR—Implications for AI to your personal schedule
4:50pm Executive Briefing: GDPR—Implications for AI Kayvaun Rowshankish (Mr), Alexis Trittipo (McKinsey & Company)
Nassau East/West
Add AI in personal finance: More than just chatbots to your personal schedule
11:55am AI in personal finance: More than just chatbots Brian Pearce (Wells Fargo)
Add Predicting the stock market using LSTMs to your personal schedule
1:45pm Predicting the stock market using LSTMs Aurélien Géron (Kiwisoft)
Add Automatic financial econometrics with AI to your personal schedule
2:35pm Automatic financial econometrics with AI Ambika Sukla (Morgan Stanley)
Concourse A
Add Deploying deep learning in the cloud to your personal schedule
11:55am Deploying deep learning in the cloud Alex Jaimes (Nauto)
Add Adversarial ML: Practical attacks and defenses against graph-based clustering to your personal schedule
2:35pm Adversarial ML: Practical attacks and defenses against graph-based clustering Yacin Nadji (Georgia Institute of Technology)
Add GPU-accelerating AI for cyberthreat detection to your personal schedule
4:00pm GPU-accelerating AI for cyberthreat detection Joshua Patterson (NVIDIA), Aaron Sant-Miller (Booz Allen Hamilton)
Add AI: A force for good to your personal schedule
4:50pm AI: A force for good Jake Porway (DataKind)
Beekman Parlor
Add Computational creativity: Making music with AI technologies (sponsored by Microsoft) to your personal schedule
11:05am Computational creativity: Making music with AI technologies (sponsored by Microsoft) Erika Menezes (Microsoft), Serina Kaye (Microsoft)
Add Don't get stung by extreme data (or honey bees) (sponsored by Kinetica) to your personal schedule
1:45pm Don't get stung by extreme data (or honey bees) (sponsored by Kinetica) Daniel Raskin (Kinetica), Jonathan Greenberg (Kinetica)
Add Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) to your personal schedule
4:00pm Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) William Griffith (Think Big Analytics, A Division of Teradata), Ben MacKenzie (Teradata)
Morgan
Add When machines have ideas to your personal schedule
11:05am When machines have ideas Ben Vigoda (Gamalon)
Add How artificial intelligence helps advance day-to-day quality and maintenance decisions to your personal schedule
1:45pm How artificial intelligence helps advance day-to-day quality and maintenance decisions Jacob Graham (Intel), Mallika Fernandes (Accenture)
Add Making business Bayesian: From uncertainty to action to your personal schedule
2:35pm Making business Bayesian: From uncertainty to action Richard Tibbetts (Empirical Systems)
Add AI building blocks: Speech technologies to your personal schedule
4:50pm AI building blocks: Speech technologies Omar Tawakol (Voicera)
Add Tuesday opening remarks to your personal schedule
Grand Ballroom
8:45am Tuesday opening remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Add Increasing business results through AI in the entertainment industry to your personal schedule
8:50am Increasing business results through AI in the entertainment industry Fiaz Mohamed (Intel AI Products Group), Justin Herz (Warner Bros.)
Add Understanding automation to your personal schedule
9:00am Understanding automation Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Add Bringing AI into the wild (sponsored by SAS) to your personal schedule
9:10am Bringing AI into the wild (sponsored by SAS) Mary Beth Ainsworth (SAS)
Add Autonomy and human-AI interaction to your personal schedule
9:15am Autonomy and human-AI interaction Manuela Veloso (Carnegie Mellon University)
Add Intel AI for the enterprise ecosystem to your personal schedule
9:40am Intel AI for the enterprise ecosystem Fiaz Mohamed (Intel AI Products Group)
Add Fireside chat with Peter Norvig and Kavya Kopparapu to your personal schedule
9:50am Fireside chat with Peter Norvig and Kavya Kopparapu Peter Norvig (Google), Kavya Kopparapu (GirlsComputingLeague)
Add The frontiers of machine learning and AI to your personal schedule
10:10am The frontiers of machine learning and AI Zoubin Ghahramani (Uber | University of Cambridge)
Add Closing remarks to your personal schedule
10:25am Closing remarks Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Add Tuesday Topic Tables at lunch to your personal schedule
12:35pm Lunch sponsored by Microsoft Tuesday Topic Tables at lunch | Room: America's Hall
Add Speed Networking to your personal schedule
8:00am Speed Networking | Room: 3rd Floor Promenade
Add AI at Night to your personal schedule
6:30pm AI at Night | Room: Brasserie 8 1/2
10:35am Morning Break sponsored by SAS | Room: Sponsor Pavilion
3:15pm Afternoon Break - sponsored by Amazon Web Services | Room: Sponsor Pavilion
Add Attendee Reception to your personal schedule
5:30pm Attendee Reception | Room: Sponsor Pavilion
11:05am-11:45am (40m)
Deep dive into probabilistic machine learning
Zoubin Ghahramani (Uber | University of Cambridge)
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.
11:55am-12:35pm (40m) AI in the Enterprise
Democratizing deep reinforcement learning
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.
1:45pm-2:25pm (40m) Implementing AI
Distributed DNN training: Infrastructure, challenges, and lessons learned
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.
2:35pm-3:15pm (40m) Models and Methods
Scalable deep learning
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.
4:00pm-4:40pm (40m) AI in the Enterprise
Using AI to solve complex economic problems
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.
4:50pm-5:30pm (40m)
Build deep learning-powered big data solutions with BigDL
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.
11:05am-11:45am (40m) Implementing AI, Models and Methods
High-throughput single-shot multibox object detection on edge devices using FPGAs
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.
11:55am-12:35pm (40m) Implementing AI, Models and Methods
Long-term time series forecasting with recurrent neural networks
Mustafa Kabul (SAS)
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.
1:45pm-2:25pm (40m) Implementing AI, Models and Methods
Building a healthcare decision support system for ICD10/HCC coding through deep learning
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.
2:35pm-3:15pm (40m) AI in the Enterprise, Impact of AI on Business and Society
Deep learning and AI is making clinical neuroimaging faster, safer, and smarter
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.
4:00pm-4:40pm (40m) Implementing AI
Imputing medical conditions based on a patient's medical history with deep learning
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.
4:50pm-5:30pm (40m) Interacting with AI
The cognitive IoT and eldercare
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.
11:05am-11:45am (40m) Implementing AI
Containers and the intelligent application revolution
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.
11:55am-12:35pm (40m) Implementing AI
Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters
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.
1:45pm-2:25pm (40m) Models and Methods
Scaling your data science experiments from Jupyter notebooks to 6,000 GPUs
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.
2:35pm-3:15pm (40m) Implementing AI
Machine learning meets DevOps: Paying down the high-interest credit card
. . (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.
4:00pm-4:40pm (40m) Implementing AI
Caching big data for machine learning platform at Uber
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.
4:50pm-5:30pm (40m) Implementing AI, Interacting with AI
Artificial intelligence strategy: Delivering deep learning
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.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise
AI and the future of work
Faizan Buzdar (Box)
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.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
AI applications, best practices, and lessons learned in the automotive domain
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 fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications.
1:45pm-2:25pm (40m) AI Business Summit, Impact of AI on Business and Society
AI and the future of customer service: Meet Expensify’s new AI-assistant, Concierge
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.
2:35pm-3:15pm (40m) AI Business Summit, Implementing AI
How Comcast uses AI to reinvent the customer experience
Jan Neumann (Comcast), Dominique Izbicki (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.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise
Best practices for machine learning 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.
4:50pm-5:30pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Three examples of computer vision in action
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.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: The adoption of artificial intelligence in business—Why leaders forge ahead and laggards fall behind
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.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: Building an AI-first enterprise culture
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.
1:45pm-2:25pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: The conversational business—Use cases and best practices for chatbots in financial services and media
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.
2:35pm-3:15pm (40m) AI Business Summit, Impact of AI on Business and Society
Executive Briefing: Making reliable and trustworthy AI systems a reality
Tolga Kurtoglu (PARC)
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.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: Why AI needs human-centered design
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.
4:50pm-5:30pm (40m) AI Business Summit
Executive Briefing: GDPR—Implications for AI
Kayvaun Rowshankish (Mr), Alexis Trittipo (McKinsey & Company)
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.
11:05am-11:45am (40m) Implementing AI, Interacting with AI
Innovations in explainable AI in the context of real-world business applications
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.
11:55am-12:35pm (40m) Impact of AI on Business and Society
AI in personal finance: More than just chatbots
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.
1:45pm-2:25pm (40m) Implementing AI, Interacting with AI, Models and Methods
Predicting the stock market using LSTMs
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.
2:35pm-3:15pm (40m) Implementing AI, Models and Methods
Automatic financial econometrics with AI
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.
4:00pm-4:40pm (40m) Models and Methods
Using NLP, neural networks, and reporting metrics in production for continuous improvement in text classifications
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.
4:50pm-5:30pm (40m) Implementing AI, Interacting with AI, Models and Methods
Explaining machine learning for consumer loans
Sean Kamkar
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.
11:05am-11:45am (40m) Models and Methods
Humanizing technology: Emotion detection from face and voice
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.
11:55am-12:35pm (40m)
Deploying deep learning in the cloud
Alex Jaimes (Nauto)
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.
1:45pm-2:25pm (40m) Implementing AI
Using machine learning to enhance activity-based intelligence
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.
2:35pm-3:15pm (40m) Implementing AI, Models and Methods
Adversarial ML: Practical attacks and defenses against graph-based clustering
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.
4:00pm-4:40pm (40m) Implementing AI, Interacting with AI, Models and Methods
GPU-accelerating AI for cyberthreat detection
Joshua Patterson (NVIDIA), Aaron Sant-Miller (Booz Allen Hamilton)
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.
4:50pm-5:30pm (40m) Impact of AI on Business and Society
AI: A force for good
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.
11:05am-11:45am (40m) Sponsored
Computational creativity: Making music with AI technologies (sponsored by Microsoft)
Erika Menezes (Microsoft), Serina Kaye (Microsoft)
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.
11:55am-12:35pm (40m) Sponsored
Building scalable machine learning workflows with Amazon SageMaker (sponsored by Amazon Web Services)
Randall Hunt (Amazon Web Services)
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.
1:45pm-2:25pm (40m) Sponsored
Don't get stung by extreme data (or honey bees) (sponsored by Kinetica)
Daniel Raskin (Kinetica), Jonathan Greenberg (Kinetica)
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.
2:35pm-3:15pm (40m) Sponsored
Improving wildlife conservation with artificial intelligence (sponsored by SAS)
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.
4:00pm-4:40pm (40m) Sponsored
Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata)
William Griffith (Think Big Analytics, A Division of Teradata), Ben MacKenzie (Teradata)
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.
4:50pm-5:30pm (40m) Implementing AI
Building conversational AI in-house in the Fortune 500
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.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
When machines have ideas
Ben Vigoda (Gamalon)
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.
11:55am-12:35pm (40m) Sponsored
AI in the digital age: Friend, not foe (brought to you by Adobe)
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.
1:45pm-2:25pm (40m) Implementing AI
How artificial intelligence helps advance day-to-day quality and maintenance decisions
Jacob Graham (Intel), Mallika Fernandes (Accenture)
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.
2:35pm-3:15pm (40m) AI Business Summit, Impact of AI on Business and Society
Making business Bayesian: From uncertainty to action
Richard Tibbetts (Empirical Systems)
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.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
AI for the public sector: Benefitting citizens through cognitive solutions
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.
4:50pm-5:30pm (40m) AI Business Summit, AI in the Enterprise
AI building blocks: Speech technologies
Omar Tawakol (Voicera)
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.
8:45am-8:50am (5m)
Tuesday opening remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Artificial Intelligence program chairs Ben Lorica and Roger Chen open the first day of keynotes.
8:50am-9:00am (10m)
Increasing business results through AI in the entertainment industry
Fiaz Mohamed (Intel AI Products Group), Justin Herz (Warner Bros.)
In this fireside chat, Justin Herz and Fiaz Mohammed discuss how artificial intelligence can improve content discovery and monetization. In collaboration with Intel AI technologies, Warner Bros. is just scratching the surface of what’s possible.
9:00am-9:10am (10m)
Understanding automation
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Keynote by program chairs Ben Lorica and Roger Chen
9:10am-9:15am (5m) Sponsored
Bringing AI into the wild (sponsored by SAS)
Mary Beth Ainsworth (SAS)
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.
9:15am-9:30am (15m)
Autonomy and human-AI interaction
Manuela Veloso (Carnegie Mellon University)
Autonomy—consisting of extensive data processing, decision making and execution, and learning from experience—creates the need for a new interaction between humans and AI. Manuela Veloso delves into the roles humans can have in such interactions, as well as the underlying challenges to AI, in particular in terms of collaboration and interpretability.
9:30am-9:40am (10m) Sponsored
Using machine learning, the IoT, drones, and networking to reduce world hunger (sponsored by Microsoft)
Jennifer Marsman (Microsoft)
Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, machine learning, and networking research in television whitespaces and discusses the AI for Earth grant program, which supports similar work in the areas of clean water, agriculture, biodiversity, and climate change.
9:40am-9:50am (10m)
Intel AI for the enterprise ecosystem
Fiaz Mohamed (Intel AI Products Group)
The Intel AI portfolio includes hardware and software solutions that span use cases and edge-to-cloud implementations, rooted in extensive expertise in data science and research. Fiaz Mohamed explains how Intel AI solves today’s business problems and how Intel’s partner ecosystem is accelerating the adoption of solutions built on Intel technology.
9:50am-10:05am (15m)
Fireside chat with Peter Norvig and Kavya Kopparapu
Peter Norvig (Google), Kavya Kopparapu (GirlsComputingLeague)
Fireside chat with Peter Norvig and Kavya Kopparapu
10:05am-10:10am (5m) Sponsored
Rapid AI experimentation and innovation on Amazon Web Services (sponsored by Amazon Web Services)
Dan Romuald Mbanga (Amazon Web Services)
For more than 20 years, Amazon has invested in experimenting and deploying AI at scale. Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go and how developers and data scientists from startups to large-scale enterprises have benefited from this innovation.
10:10am-10:25am (15m)
The frontiers of machine learning and AI
Zoubin Ghahramani (Uber | University of Cambridge)
Zoubin Ghahramani discusses fundamental concepts and recent advances in artificial intelligence, highlighting research on the frontiers of deep learning, probabilistic programming, Bayesian optimization, and AI for data science. Zoubin concludes by considering the societal implications of this work.
10:25am-10:35am (10m)
Closing remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Program chairs Ben Lorica and Roger Chen close the first day of keynotes.
12:35pm-1:45pm (1h 10m)
Tuesday Topic Tables at lunch
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
8:00am-8:30am (30m)
Speed Networking
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Tuesday and Wednesday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
6:30pm-9:00pm (2h 30m)
AI at Night
Don't miss AI at Night, happening on Tuesday after the Attendee Reception.
10:35am-11:05am (30m)
Break: Morning Break sponsored by SAS
3:15pm-4:00pm (45m)
Break: Afternoon Break - sponsored by Amazon Web Services
5:30pm-6:30pm (1h)
Attendee Reception
Come enjoy delicious snacks and beverages with fellow AI Conference attendees, speakers, and sponsors at the Attendee Reception, happening immediately after the afternoon sessions on Tuesday.