Schedule

Steve Flinter and Ahmed Menshaw explore the work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow. They demonstrate how the pipeline can be defined, configured, connected to a data streaming service, and used to train and deploy a model, which can be exposed for inference via an API.

Real business usage of most advanced methods for financial time series forecasting (based on winning methods from M4 competition) and assets portfolio optimization (based on Monte Carlo Tree Search with neural networks - Alpha Zero approach). Complete investments platform with the AI workflow and real time integration with the brokers. Real usage demo.

On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks in tandem with the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud.

Every day Careem’s platform relies on machine learning (ML) in production to enable the movement of millions of its users. Ahmed Kamal outlines the challenges Careem faced while productionizing ML on scale and explains how to build an in-house ML platform that facilitates development and fast deployment of scalable ML services and accelerates the impact of ML everywhere.

Demand for AI compute is doubling every three months. Alexis Crowell Helzer explains why the way we compute AI has to be completely rethought so it can evolve to enable the promise of global business transformation.

Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure.

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.

Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been used for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting.

Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML and AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and explains how you can build a production-ready containerized platform for deep learning.

Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.

Imagine there's a new version of your complex machine learning pipeline, but you need to make sure it doesn't negatively impact the performance of large numbers of existing customer models in production. Bruno Wassermann explains how IBM Research tackled the challenge for the natural language understanding layer of the IBM Watson Assistant service and demonstrates a new tool called Clue.

Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and explain how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.

Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis, but knowing how much confidence there is in a prediction is essential for gaining clinicians' trust. Biraja Ghoshal explores probabilistic modeling with TensorFlow Probability in cancer prediction.

Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system.

Abhishek Kumar outlines how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow.

AI-powered market research is performed by indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases). These approaches are more scalable, authentic, and suitable for real-time consumer insights. Gianmario Spacagna proposes a novel algorithm of audience projection able to provide consumer insights over multiple domains.

Statistical approaches alone are not sufficient to tackle the complexity of AI challenges today. Being smarter with the data we already have is critical to achieving machine understanding of any complex domain. James Fletcher explains how knowledge graph convolutional networks (KGCNs) demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach.

Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated.

The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy—the component of the TensorFlow Privacy library that allows users to train differentially private logistic regression and support vector machines—along with real-world use cases and demonstrations for how to apply the tools.

Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.

To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. Arun Verma shares NLP, AI, and ML techniques that help extract derived signals that have significant trading alpha or risk premium and lead to profitable trading strategies.

Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions.

Amid fears of sentient killing robots and a freezing AI winter, AI has a true potential to transform the enterprise. Actualizing this potential requires a well-informed organizational strategy and consistent execution of best practices regarding people, processes, and platforms. Bahman Bahmani examines these strategies and best practices and provides insights into their successful execution.

While at a big tech conference on AI, it's important to reflect on the human components. Ted Malaska walks you through scenarios and strategies to help different groups work together and explains how to evaluate success and sniff out trouble areas. You'll look at every part of the pipeline to see who's involved and how to optimize the interaction points throughout the pipeline—and how to have fun.

In the rapidly changing world of AI, adopting the right design principles is key. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities. Ariadna Font Llitjós examines how IBM Watson applies ethical AI and user-centered design principles from the beginning and leverages them throughout the product development cycle.

Data scientists feel naturally comfortable with the language of mathematics, while designers think in the language of human empathy. Creating a bridge between the two was essential to the success of a recent project at an energy company. Tim Daines and Philip Pilgerstorfer detail what they learned while creating these bridges, showcasing techniques through a series of “aha” moments.

The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Anastasia Kouvela and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change.

According to research by AI2, China is poised to overtake the US in the most-cited 1% of AI research papers by 2025. The view that China is a copycat but not an innovator may no longer be true. Charlotte Han explores what the implications of China's government funding, culture, and access to massive data pools mean to AI development and how the world could benefit from such advancement.

Every day, millions of Vodafone Germany customers reach out through various social media channels about issues related to mobile, internet, signal issues, etc. Adithya Hrushikesh details how to build and deploy an ensemble model to classify 26 (originally 56) complaint classes using machine learning over deep learning. He also touches on the business case, data product development, and GDPR.

Machine learning has been used in credit scoring for three decades. Martin Benson discusses the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and learn how machine learning systems are evolving and how to bypass the challenges to adoption.

Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and explains how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure.

More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable.

Danielle Deibler examines an approach to detecting bias, fine-grained emotional sentiment, and misinformation through the detection of political narratives in online media. As building blocks, the methodology uses human-in-the-loop, alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election.

Ritika Gunnar explores why you need to focus on your organization’s culture and build a data-first approach to shape a strong, AI-ready organization.

As machine learning and deep learning techniques reach mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly as you mature beyond small-scale sandbox and POC environments. Thomas Henson walks you through eliminating I/O bottlenecks to keep your GPU-powered AI rocket ship fueled with data.

Brett Phaneuf outlines how similar types of AI can fit into your company solutions and how technologies like containers, deep learning, cloud, machine learning, and more all fit together to drive innovation for the "new world" of the future.

Sunil Mallya walks you through building complex ML-enabled products using reinforcement learning (RL), explores hardware design challenges and trade-offs, and details real-life examples of how any developer can up-level their RL skills through autonomous driving.

Lyndon Leggate walks you through a step-by-step demonstration of how you can up level your reinforcement learning (RL) skills through autonomous driving.

Your company has a large amount of data locked into thousands or millions of scanned paper documents. You'd like to extract and analyze it, but you first have to prove that your algorithm works and brings business value. Ciprian Tomoiaga explains how to start.

Advances in artificial intelligence have meant that it's now more accessible than ever before—and this accessibility means that it can be both the hunter and the hunted. In the race to ensure cybersecurity, AI is an essential tool to protect your most sensitive assets. Join Matt Armstrong-Barnes to find out how this new dimension is changing the threat landscape and how to make AI your friend.

Join Thomas Phelan to learn whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.

Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.

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 Wednesday.

Program chairs Ben Lorica, Roger Chen, and Alexis Helzer open the first day of keynotes.

Details to come.

The AI revolution is poised to scale both machine and human knowledge. To generate that knowledge, companies must think differently about AI and how to deploy it. Alexis will cover the three “Be’s”, and how to approach AI systematically to truly harness knowledge at scale.

Ritika Gunnar explores why you need to focus on your organization’s culture and build a data-first approach to shape a strong, AI-ready organization.

If you've ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques to train a reinforcement learning agent on decades of randomized control trials. You'll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many.

As we look toward more demanding applications of artificial intelligence to unlock value from data, it's increasingly essential to develop a sustainable big data strategy and to efficiently scale artificial intelligence initiatives. Arash Ghazanfari covers the fundamental principles that need to be considered in order to achieve this goal.

AI plays a key role in achieving Facebook's mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Kim Hazelwood and Mohamed Fawzy explain how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.

Entity resolution—determining “who is who” and “who is related to whom”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster.

O'Reilly AI program chairs close the first day of keynotes.

Don't miss AI at Night, happening on Wednesday after the Attendee Reception.

Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.