14–17 Oct 2019

Presentations

Sridhar Alla (BlueWhale)
Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge.
Julien Simon (Amazon Web Services)
Many natural language processing (NLP) tasks require each word in the input text to be mapped to a vector of real numbers. Julien Simon explores word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). You'll get to see how to solve typical NLP problems through several demos by either computing embeddings or reusing pretrained ones.
Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.
Rajib Biswas (Ericsson)
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.
Don't miss AI at Night, happening on Wednesday after the Attendee Reception.
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. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.
Angie Ma (Faculty)
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. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.
Tom Sabo (SAS)
Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. Tom Sabo explores text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response.
Tuhin Sharma (Binaize Labs), Bargava Subramanian (Binaize Labs)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning for data quality and cybersecurity. Federated learning is privacy preserving and doesn't require data to be moved to the cloud.
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.
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.
Adithya Hrushikesh (Vodafone )
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 business case, data product development, and GDPR.
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
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.
AI assistants are getting a great deal of attention from the industry and research. However, the majority of assistants built to this day are still developed using a state machine and a set of rules. That doesn’t scale in production. Justina Petraityte explores how to build AI assistants that go beyond FAQ interactions using machine learning and open source tools.
Chang Liu (Georgian Partners ), Ji Chao Zhang (Georgian Partners)
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 and its use cases, the component of the TensorFlow privacy library that allows users to train differentially private logistic regression and support vector machines, and real-world scenarios and demonstrations for how to apply the tools.
Robert Nishihara (UC Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (UC Berkeley), Eric Liang (University of California, Berkeley, RISELab)
Building AI applications is challenging, and building the next generation is even more challenging. Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it.
O'Reilly AI program chairs close the first day of keynotes.
O'Reilly AI program chairs close the second day of keynotes.
Bruno Wassermann (IBM Research)
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.
Ilya Feige (Faculty)
Ilya Feige explores AI safety concerns—explainability, fairness, and robustness—relevant for machine learning (ML) models in use today. You'll see Ilya demonstrate tools developed at Faculty to ensure black box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data with a focus on concepts and examples.
Antje Barth (MapR)
Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML/AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and how you can build a production-ready containerized platform for deep learning.
Jameson Toole (Fritz)
Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high.
Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
Vignesh Gopakumar explores image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model captures how parameters such as temperature and density evolve across space and time. By analyzing the patterns found in simulation data, the model learns the existing physics relations implicitly defined within the data.
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices.
Thomas Phelan (BlueData), Nanda Vijaydev (BlueData)
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 and Nanda Vijaydev demonstrate the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Ana Hocevar to get the knowledge you need to build deep learning models using real-world datasets and PyTorch.
Ana Hocevar (The Data Incubator)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Ana Hocevar to get the knowledge you need to build deep learning models using real-world datasets and PyTorch.
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.
Biraja Ghoshal (Tata Consultancy Service)
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.
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
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.
Steve Flinter (Mastercard Labs), Ahmed Menshawy (Mastercard Labs)
Steve Flinter and Ahmed Menshaw explore 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.
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.
Katharine Jarmul (KIProtect)
Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques.
Bahman Bahmani (Rakuten)
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.
Rebecca Gu (Baringa Partners)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu explores how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.
Ariadna Font Llitjós (IBM Watson, Data and AI)
In the rapidly changing world of AI, adopting the right design principles is key. Ariadna Font Llitjós examines how at IBM Watson, ethical AI and user-centered design principles are applied from the beginning and leveraged throughout the product development cycle. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities.
Helen Ngo (Dessa)
The first artificial intelligence teams for the enterprise are being built right now. However, shipping machine learning-powered products requires knowledge across the technology stack from data pipelines to model debugging to production. Helen Ngo explores ways to adapt the insights from full stack software engineering to create autonomous machine learning teams that ship and learn faster.
Ujwal Kayande (Melbourne Business School), Anastasia Kouvela (A.T. Kearney ), Bharath Thota (A.T. Kearney)
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. Ujwal Kayande, 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.
Tim Daines (QuantumBlack), Daniel First (QuantumBlack)
UX and DS can collaborate effectively when built with advanced analytics. Tim Daines and Daniel First detail best practices (via a case study for building an optimization algorithm for natural resource production) of how data science and design work in tandem to create adoptable data-driven products that feel intuitive to users, as well as deliver powerful insights into business operations.
Voiced-based AI continue to gain popularity among customers, businesses, and brands but it’s important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Ricardo Baeza-Yates examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.
Ted Malaska (Capital One)
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 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.
Mark Madsen (Teradata)
The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibility—the ability to get the same results given the same information—is a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management.
Paco Nathan (derwen.ai)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way.
Charlotte Han (NVIDIA)
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 the what 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.
Arun Verma (Bloomberg)
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 illustrates the use of NLP, AI, and ML techniques that help extract derived signals that have significant trading alpha or risk premium and lead to profitable trading strategies.
Martin Benson (Jaywing)
Machine learning has been used in credit scoring for three decades. Martin Benson explores 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 more about how machine learning systems are evolving and how to bypass the challenges to adoption.
Alex Ingerman (Google)
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.
Carlos Rodrigues (Siemens)
An evolving landscape of cyber threats demands innovation. It's time to bring AI to the fight. Carlos Rodrigues explains why it's mandatory to use bleeding-edge AI in production to improve threat detection in a worldwide company such as Siemens. The corporate network has more than 500,000 endpoint and more than 370,000 employees. The attack vectors are endless; thus, legacy approaches don't scale.
Ira Cohen (Anodot)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.
If you had five minutes on stage, what would you say? What if you only got 20 slides, and they rotated automatically after 15 seconds? Would you pitch a project? Launch a website? Teach a hack? We’ll find out at our Ignite event at AI London.
Holger Kyas (Helvetia Insurances Basel, University of Applied Sciences)
Holger Kyas details the AI multicloud broker, which is triggered by Amazon Alexa and mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), and Watson Tone Analyzer (IBM) to compare and analyze sentiment. The extended AI part generates new sentences (e.g., marketing slogans) with a recurrent neural network (RNN).
Vijay Srinivas Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Vijay Srinivas Agneeswaran and Abhishek Kumar outline 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.
Tony Xing (Microsoft), Bixiong Xu (Microsoft), Congrui Huang (Microsoft), Qun Ying (Microsoft)
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 how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.
Details to come.
Details to come.
Kim Hazelwood (Facebook)
AI plays a key role in achieving the Facebook 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 takes an end-to-end look at how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.
Weifeng Zhong (Mercatus Center at George Mason University)
Weifeng Zhong explores a novel method to learn structural changes embedded in unstructured texts based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong. He explains how an off-the-shelf application of deep learning—with an important twist—can help you detect structural breakpoints in time series text data.
Zhe Zhang (LinkedIn)
From people you may know (PYMK) to economic graph research, machine learning is the oxygen to power how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIn’s typical machine learning pipelines complemented with key types of ML use cases.
Tobias Martens (Universal Namespace)
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.
Alasdair Allan (Babilim Light Industries)
The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time.
Delip Rao (AI Foundation)
With large volumes of data exchanged as text, NLP techniques are indispensable to modern intelligent applications. Delip Rao explores natural language processing with deep learning. He walks you through neural network architectures and NLP tasks and examines how to apply these architectures for those tasks.
With large volumes of data exchanged as text, NLP techniques are indispensable to modern intelligent applications. Delip Rao explores natural language processing with deep learning. He walks you through neural network architectures and NLP tasks and examines how to apply these architectures for those tasks.
Manas Ranjan Kar (Episource)
Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. It’s also crucial to understand to diagnose an NLP model performance and identify possible gaps.
Ted Dunning (MapR)
Evaluating machine learning models is surprisingly hard, but it gets even harder because these systems interact in very subtle ways. Ted Dunning breaks the problem apart into operational and function and shows you how each can be done without unnecessary pain and suffering. You'll also get to see some exciting visualization techniques to help make the differences strikingly apparent.
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
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, and 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.
Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
In many countries, policy decisions are disconnected from data and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.
Michael Mahoney (UC Berkeley)
An important practical challenge is developing theoretically principled tools to guide the use of production-scale neural networks. Michael Mahoney explores recent work from scientific computing and statistical mechanics to develop such tools, covering basic ideas and their use for analyzing production-scale neural networks in computer vision, natural language processing, and related tasks.
Emily Webber (Amazon Web Services)
Ever wondered if we could use artificial intelligence to inform public policy? Join us as we combine classic economic methods with artificial intelligence techniques, training a reinforcement learning agent on decades of randomized control trials. Learn about classic philosophical foundations for public policy decision making, and how these can be applied to solve problems that impact the many.
Jeff Jonas (Senzing)
Entity resolution—determining “who is who” and “who is related to who”—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.
Zaid Tashman (Accenture Labs)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries.
Jim Dowling (Logical Clocks), Ajit Mathews (AMD)
The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling outlines how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs.
Ahmed Kamal (Careem)
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 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.
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been leveraged 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.
Douglas Calegari (Independent)
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.
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.
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.
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.
Vijay Srinivas Agneeswaran (Walmart Labs), Pramod Singh (Publicis Sapient), Akshay Kulkarni (Publicis Sapient)
An estimated 80% of data generated is an unstructured format, such as text, image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0.
Robert Crowe (Google)
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.
Martin Goodson (Evolution AI)
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 how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure.
Alexander Adam (Faculty)
In just a few years, it will be possible to create synthetic videos that are indistinguishable to both eye and ear from reality. Whilst the debate around “fake news” often leads us to question the meaning of ‘truth’, this Deepfake technology is starting to pose a distinct and even more dramatic risk in the form of a new kind of political disinformation - how do we tackle this?
Casey Dugan (IBM Research), Zahra Ashktorab (IBM Research)
Casey Dugan and Zahra Ashktorab challenge you to guess the backdoor of a hacked classifier. Join them to learn more about novel AI technologies through the design and development of engaging games. Take a look at their latest research around improving the interactions between humans and AI systems from empathy building to feedback design.
Ihab Ilyas (University of Waterloo)
Details to come.
Sacha Arnoud (Waymo)
To navigate city streets, self-driving vehicles need a deep semantic understanding of the world around us. Sacha Arnoud explores how Waymo uses deep learning to unlock new capabilities and build safe autonomous vehicles and provides an overview of how Waymo is thinking about developing machine learning at scale as it expands to new cities and geographies.
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
Ben Lorica (O'Reilly Media), Roger Chen (Computable), Alexis Crowell Helzer (Intel)
Program chairs Ben Lorica, Roger Chen, and Alexis Helzer open the second day of keynotes.
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft), Aashish Bhateja (Microsoft)
Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.
Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.
Danielle Deibler (MarvelousAI)
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 utilizes human-in-the-loop alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election.
Danielle Dean (Microsoft), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.
Jewel James (Gojek), Mudit Maheshwari (Gojek)
GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari walk you through the story of how they prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history.
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services.
Marie Smith (Data 360)
Join Marie Smith to hear some key findings as illuminated by her career since 1998 of the rapid prototyping world of Silicon Valley and R&D and innovation projects from many large financial, insurance, health, real estate, retail, and entertainment companies.
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
Ben Lorica (O'Reilly Media), Roger Chen (Computable), Alexis Crowell Helzer (Intel)
Program chairs Ben Lorica, Roger Chen, and Alexis Helzer open the first day of keynotes.
Marta Kwiatkowska (Trinity College, University of Oxford)
Details to come.
James Fletcher (Grakn)
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.
Laurence Moroney (Google)
Laurence Moroney explores how to go from wondering what machine learning (ML) is to building a convolutional neural network to recognize and categorize images. With this, you'll gain the foundation to understand how to use ML and AI in apps all the way from the enterprise cloud down to tiny microcontrollers using the same code.

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