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
11:05am Fairness and bias in computer vision Olga Russakovsky (Princeton University)
11:55am MLPerf: A benchmark suite for machine learning from an academic-industry cooperative David Patterson (UC Berkeley), Greg Diamos (Baidu), Cliff Young (Google), Peter Mattson (Google), Peter Bailis (Stanford University), Gu-Yeon Wei (Harvard University)
1:45pm Building winning AI technology: The anatomy of a champion Steve J Rennie (Fusemachines)
4:00pm Deep sentiment analysis across language boundaries Gerard de Melo (Rutgers University)
Grand Ballroom West
11:55am Learned index structures Tim Kraska (MIT)
1:45pm Revolutionizing aviation with AI Carolina Sanchez Hernandez (NATS)
2:35pm Classifying images in Spark Yulia Tell (Intel), Maurice Nsabimana (World Bank Development Data Group)
4:00pm The quest for a new visual search beyond language Mike Ranzinger (Shutterstock)
4:50pm A reliable and robust classification pipeline for protein crystallization imaging Chris Watkins (Commonwealth Scientific and Industrial Research Organisation)
Sutton North/Center
11:55am Serving billions of personalized news feeds with AI Meihong Wang (facebook)
Sutton South
11:55am The vital role of failure in machine learning Scott Weller (SessionM)
1:45pm Data, AI, and innovation in the enterprise Michael Li (The Data Incubator), Len Usvyat (Fresenius), Saar Golde (Via Transportation), Sassoon Kosian (New York Life), LORI BIEDA (Bank of Montreal)
Regent Parlor
1:45pm Executive Briefing: A new taxonomy of machine learning Rachel Silver (MapR Technologies)
4:00pm Collaborative machine intelligence: Accelerating human knowledge Emily Pavlini (Diffeo), Max Kleiman-Weiner (Diffeo)
Nassau East/West
11:55am Racial bias in facial recognition software Stephanie Kim (Algorithmia)
4:00pm Model evaluation in the land of deep learning Pramit Choudhary (h2o.ai)
4:50pm AI at the edge with Intel Movidius technology Kathleen Kallot (Intel), Augustin MARTY (Deepomatic)
Concourse A
11:05am Determining normal (and abnormal) using deep learning John Hebeler (Lockheed Martin)
11:55am Fooling neural networks in the physical world Andrew Ilyas (Massachusetts Institute of Technology), Logan Engstrom (Massachusetts Institute of Technology), Anish Athalye (Massachusetts Institute of Technology)
4:00pm Do-it-yourself artificial intelligence Alasdair Allan (Babilim Light Industries)
Beekman Parlor
1:45pm Democratizing AI (sponsored by Google Cloud) Drew Hodun (Google Cloud)
4:50pm Using Artificial Intelligence in the field of Diagnostics Neeyanth Kopparapu (Girls Computing League)
Morgan
Grand Ballroom
8:45am Wednesday opening remarks Ben Lorica (O'Reilly), Roger Chen (Computable)
9:05am AI4ALL: AI will change the world, but who will change AI? Olga Russakovsky (Princeton University)
10:05am Hybrid bio-opto-electronics for AI George Church (Harvard University)
10:20am Closing remarks Ben Lorica (O'Reilly), Roger Chen (Computable)
12:35pm Lunch sponsored by IBM Watson Wednesday Topic Tables at lunch | Room: America's Hall
10:35am Morning Break - sponsored by Digitate | Room: Sponsor Pavilion
3:15pm Afternoon Break - sponsored by Google Cloud | Room: Sponsor Pavilion
8:00am Speed Networking | Room: Rendezvous Trianon
11:05am-11:45am (40m) Models and Methods
Fairness and bias in computer vision
Olga Russakovsky (Princeton University)
Session with Olga Russakovsky
11:55am-12:35pm (40m)
MLPerf: A benchmark suite for machine learning from an academic-industry cooperative
David Patterson (UC Berkeley), Greg Diamos (Baidu), Cliff Young (Google), Peter Mattson (Google), Peter Bailis (Stanford University), Gu-Yeon Wei (Harvard University)
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.
1:45pm-2:25pm (40m) Models and Methods
Building winning AI technology: The anatomy of a champion
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.
2:35pm-3:15pm (40m) Implementing AI
From answering questions to questioning answers: Challenges of large-scale QnA systems
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.
4:00pm-4:40pm (40m) Models and Methods
Deep sentiment analysis across language boundaries
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.
4:50pm-5:30pm (40m) Implementing AI, Models and Methods
How DoorDash leverages AI in its world-class on-demand logistics engine
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.
11:05am-11:45am (40m) Implementing AI
Deep reinforcement learning’s killer app: Intelligent control in real-world systems
Mark Hammond (Microsoft)
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.
11:55am-12:35pm (40m) Implementing AI, Interacting with AI, Models and Methods
Learned index structures
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 (i.e., learned indexes).
1:45pm-2:25pm (40m) Implementing AI
Revolutionizing aviation with AI
Carolina Sanchez Hernandez (NATS)
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.
2:35pm-3:15pm (40m) Implementing AI, Models and Methods
Classifying images in Spark
Yulia Tell (Intel), Maurice Nsabimana (World Bank Development Data Group)
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.
4:00pm-4:40pm (40m) Models and Methods
The quest for a new visual search beyond language
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.
4:50pm-5:30pm (40m) Implementing AI
A reliable and robust classification pipeline for protein crystallization imaging
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.
11:05am-11:45am (40m) Implementing AI, Models and Methods
Scaling up deep learning-based superresolution models more efficiently using the cloud
Xiaoyong Zhu (Microsoft)
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.
11:55am-12:35pm (40m)
Serving billions of personalized news feeds with AI
Meihong Wang (facebook)
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.
1:45pm-2:25pm (40m) Implementing AI
Gamifying strategy: Enterprise AI use cases on agent-based simulation and learning
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.
2:35pm-3:15pm (40m) Implementing AI
An open extensible AI platform implementing four use cases for the enterprise
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.
4:00pm-4:40pm (40m)
Neural interfaces: Connecting humans and artificial intelligence
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.
4:50pm-5:30pm (40m) Implementing AI
The long and winding road to AI: Lessons from implementing cognitive AI
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.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise
Using artificial intelligence to enhance the digital experience
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.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
The vital role of failure in machine learning
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.
1:45pm-2:25pm (40m) AI Business Summit, AI in the Enterprise
Data, AI, and innovation in the enterprise
Michael Li (The Data Incubator), Len Usvyat (Fresenius), Saar Golde (Via Transportation), Sassoon Kosian (New York Life), LORI BIEDA (Bank of Montreal)
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.
2:35pm-3:15pm (40m) AI Business Summit, AI in the Enterprise
How artificial intelligence is transforming traditional industries, from property insurance to agriculture
Ryan Kottenstette (Cape Analytics)
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.
4:00pm-4:40pm (40m) AI Business Summit, AI in the Enterprise
Lessons learned building an AI company from the ground up
Justin Fier (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.
4:50pm-5:30pm (40m) AI Business Summit, Impact of AI on Business and Society
Democracy, human rights, and the rule of law by design for artificial intelligence
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.
11:05am-11:45am (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: AI in human resources—Use cases and ethical issues
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.
11:55am-12:35pm (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: Building a learning organization is AI's hat trick
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.
1:45pm-2:25pm (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Executive Briefing: A new taxonomy of machine learning
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.
2:35pm-3:15pm (40m) AI Business Summit, AI in the Enterprise
Executive Briefing: Lean AI product development (and common pitfalls)
John Lewin (Microsoft)
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.
4:00pm-4:40pm (40m) AI Business Summit, Interacting with AI, Models and Methods
Collaborative machine intelligence: Accelerating human knowledge
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.
4:50pm-5:30pm (40m) AI Business Summit, Implementing AI, Interacting with AI
From here to "Her": Evolving chatbot interactions to meet the relational needs of humans
Ian Beaver (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.
11:05am-11:45am (40m) Models and Methods
Avoiding biased algorithms: Lessons from the hiring space
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 explains how to establish best practices.
11:55am-12:35pm (40m) Implementing AI
Racial bias in facial recognition software
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.
1:45pm-2:25pm (40m) Models and Methods
Combining well-established statistical techniques with modern machine learning algorithms
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.
2:35pm-3:15pm (40m) Models and Methods
Recurrent neural networks for recommendations and personalization
Nick Pentreath (IBM)
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.
4:00pm-4:40pm (40m) Implementing AI, Interacting with AI
Model evaluation in the land of deep learning
Pramit Choudhary (h2o.ai)
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.
4:50pm-5:30pm (40m)
AI at the edge with Intel Movidius technology
Kathleen Kallot (Intel), Augustin MARTY (Deepomatic)
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.
11:05am-11:45am (40m) Models and Methods
Determining normal (and abnormal) using deep learning
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, which when combined further improve performance.
11:55am-12:35pm (40m) Interacting with AI, Models and Methods
Fooling neural networks in the physical world
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.
1:45pm-2:25pm (40m) Implementing AI, Interacting with AI, Models and Methods
TensorFlow Lite: How to accelerate your Android and iOS app with AI
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 to bring the latest AI technology to production mobile apps and embedded systems.
2:35pm-3:15pm (40m) AI in the Enterprise
Things nobody told you about building conversational UIs
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.
4:00pm-4:40pm (40m) Implementing AI
Do-it-yourself artificial intelligence
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.
4:50pm-5:30pm (40m) Implementing AI, Models and Methods
Online and active learning for recommender systems
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.
11:05am-11:45am (40m) Sponsored
AI and quantum computing for business (sponsored by IBM Watson)
Dario Gil (IBM)
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.
11:55am-12:35pm (40m) Sponsored
Removing complexity for workload automation with machine learning (sponsored by Digitate)
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.
1:45pm-2:25pm (40m) Sponsored
Democratizing AI (sponsored by Google Cloud)
Drew Hodun (Google Cloud)
Drew Hodun explores the progress Google is making to decrease the amount of work needed to go from "zero to AI."
2:35pm-3:15pm (40m) Sponsored
People, process, and platforms deliver AI (sponsored by Deloitte Analytics)
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.
4:00pm-4:40pm (40m) Implementing AI
Why data management for deep learning computer vision is challenging
Moses Guttmann (Seematics)
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.
4:50pm-5:30pm (40m) Impact of AI on Business and Society
Using Artificial Intelligence in the field of Diagnostics
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.
11:05am-11:45am (40m) Sponsored
How AI produces high-impact business outcomes in the finance, manufacturing, travel, transportation, and pharmaceutical industries (sponsored by Teradata)
Chad Meley (Teradata)
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.
11:55am-12:35pm (40m) Implementing AI, Models and Methods
An end-to-end video analytics solution for surveillance and securing high-value assets
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.
1:45pm-2:25pm (40m) Sponsored
Evolving your enterprise with AI: How to create transformative business impact (sponsored by Deeplearni.ng)
Stephen Piron (DeepLearni.ng)
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.
2:35pm-3:15pm (40m) Implementing AI, Interacting with AI, Models and Methods
Conversational AI: What we’ve learned from millions of AI conversations with thousands of customers
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.
4:00pm-4:40pm (40m) Sponsored
How to improve, enhance, and automate your business processes with Watson offerings for Salesforce and Box (sponsored by IBM Watson)
Marc Nehme (IBM Watson)
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.
8:45am-8:50am (5m)
Wednesday opening remarks
Ben Lorica (O'Reilly), Roger Chen (Computable)
Artificial Intelligence program chairs Ben Lorica and Roger Chen open the second day of keynotes.
8:50am-9:00am (10m)
Machine learning just ate algorithms in one large bite
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.
9:00am-9:05am (5m) Sponsored
Using machine learning in workload automation (sponsored by Digitate)
Abhijit Deshpande (Digitate)
We live in a world of constantly changing business environments across various business units, limited end-to-end visibility, and high alerts. Abhijit Deshpande details how to use machine learning to identify root causes of problems in minutes instead of hours or days to free up valuable time by automating routine tasks without scripting or preprogramming.
9:05am-9:20am (15m) Impact of AI on Business and Society
AI4ALL: AI will change the world, but who will change AI?
Olga Russakovsky (Princeton University)
Keynote with Olga Russakovsky
9:20am-9:30am (10m) Sponsored
The physics of AI (sponsored by IBM Watson)
Dario Gil (IBM)
The extraordinary progress in AI over the last few years has been enabled, in part, by modern advancements in computing. Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.
9:30am-9:45am (15m)
Serving billions of personalized news feeds with AI
Meihong Wang (facebook)
Everyone's Facebook news feed experience is unique and highly personalized. 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.
9:45am-10:00am (15m)
Neural interfaces: Connecting humans and artificial intelligence
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 they touch.
10:00am-10:05am (5m) Sponsored
WTT: What the tensor? (sponsored by Google Cloud)
Ron Bodkin (Google)
Ron Bodkin explains WTF a tensor is and why you should care. Along the way, Ron details some real AI products from Google. No cats or dogs.
10:05am-10:20am (15m)
Hybrid bio-opto-electronics for AI
George Church (Harvard University)
The IARPA MICrONS project aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. George Church offers an overview of this work and explains how his team has accelerated in vitro growth of many brain architectures, which might enable us to build new hybrid bio-opto-electronic artificial computational platforms.
10:20am-10:35am (15m)
Closing remarks
Ben Lorica (O'Reilly), Roger Chen (Computable)
Program chairs Ben Lorica and Roger Chen close the second day of keynotes.
12:35pm-1:45pm (1h 10m)
Wednesday 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.
10:35am-11:05am (30m)
Break: Morning Break - sponsored by Digitate
3:15pm-4:00pm (45m)
Break: Afternoon Break - sponsored by Google Cloud
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.