14–17 Oct, 2019


Sridhar Alla (Blue Whale)
Any Business big or small depends on analytics whether the goal is revenue generation, churn reduction or sales/marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. In this talk, we will present some techniques used to evaluate the the quality of data and the means to detect the anomalies in the data.
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We'll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our machine learning models.
Julien Simon (AWS)
n this session, we’ll start with a quick introduction to word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). Then, we’ll run several demos showing you how to solve typical NLP problems by either computing embeddings or reusing pre-trained ones.
earn how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.
Rajib Biswas (Ericsson)
This talk is about application of AI algorithm like GAN to solve Natural language synthesis tasks. Learn how AI can accomplish complex tasks like Machine translation, writing poem with style, read a novel and answering your questions !
Tom Sabo (SAS)
Explore 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 is an exponential growth in the number of Internet-enabled devices on modern smart buildings. In this talk, the speakers show how they built anomaly detection models using federated learning for a) data quality and b) cyber security. 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. In this talk, we propose 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. This talk describe how to build & deploy an ensemble model to classify 26 complaint classes (originally 56) utilizing Machine Learning over Deep Learning. The talk also touches on business case, Data Product Development & GDPR.
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
In this session, we will cover the newly released github repository for recommended ways to train and deploy models on Azure. It 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 as well as the 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. In this talk, you will learn 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)
This talk will introduce differential privacy and its use cases, discuss the new component of the TensorFlow Privacy library that allows users to train differntially private lofistic regression and support vecotr machines. The talk will then offer real-world scenarios and demonstrations for how to apply the tools.
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley), Eric Liang (UC Berkeley RISELab)
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)
Imagine you're building a high-volume, expensive, robot-driven warehouse. Your pick, place, and packing 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 yet, and you need to start writing the logic and training them. Use a simulation to do it. Learn how.
Ilya Feige (Faculty)
Overview of AI Safety concerns — Explainability, Fairness, and Robustness — relevant for machine-learning models in use today. Demonstration of tools developed at Faculty to ensure black-box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data. Presentation will 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. In this session, we’ll discuss common architecture blueprints and popular technologies used to integrate AI into your existing infrastructures, and how you can build a production-ready containerized platform for deep learning.
Yi Zhang (Rulai & University of California Santa Cruz)
This tutorial gives you the practical methodology and tools to design and build sophisticated AI virtual assistants. The tutorial is based on Conversational AI Design Certificate Course jointly created by Professors from University of California Santa Cruz, Carnegie Mellon University, University of Washington and UX Specialists from Rulai.
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. In this talk, you’ll learn optimization, pruning, and compression techniques that keep app sizes small and inference speeds high.
Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
Performing image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model effectively captures how parameters such as Temperature and Density evolves across space and time. By analysing the patterns found in simulation data the model learns existing Physics relations implicitly defined within the data.
Siddha Ganju (Nvidia), Meher Kasam (Square)
Optimizing deep neural nets to run efficiently on mobile devices.
Thomas Phelan (BlueData), Nanda Vijaydev (BlueData (recently acquired by HPE))
Today organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning. These new technologies come with their own challenges. In this session we will demonstrate the deployment of Tensorflow, Horovod and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.
Ana Hocevar (The Data Incubator)
PyTorch is a machine learning library for Python that allows users 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. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets.
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move 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)
Probabilistic Modeling with Tensorflow Probability in cancer prediction.
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
In this talk, we focus on methodology, practice, and tools around deploying machine learning models on the Edge. We offer a step-by-step guide to creating a machine learning (ML) model using Python, packaging it in a Docker container, and deploying it as a local service on an Edge device. We will also discuss the consideration of deployment on GPU enabled Edge device.
Developing perception algorithms for autonomous vehicles is incredibly difficult as they need to operate in thousands of driving conditions and locations. This talk will explore the challenges involved in data collection, processing, and management, as well as model development and validation. It will also provide an overview of the necessary hardware and software infrastructure.
Katharine Jarmul (KIProtect)
Curious about how far we have come in implementing privacy within machine learning systems? In this talk, I will cover recent advances in privacy measurements and how this changed the approach of privacy in machine learning. We will discuss new techniques including differentially private data collection, federated learning and homomorphic techniques.
Bahman Bahmani (Rakuten)
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. This talk focuses on these strategies and best practices and provides insights into their successful execution.
Rebecca Gu (Baringa Partners LLP)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. We demonstrate how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace. We discuss 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 rapid changing world of AI, adopting the right design principles is key. At IBM Watson, Ethical AI as well as user-centered design principles are applied from the beginning and are leveraged throughout the product development cycle. From data scientists, business users to our clients end-users, we put ML and NLP to the service of the people, always seeking 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. We will explore ways to adapt the learnings from full-stack software engineering to create autonomous machine learning teams which ship and learn faster.
Ujwal Kayande (Melbourne Business School), Anastasia Kouvela (A.T. Kearney ), Bharath Thota (A.T. Kearney)
The Analytics Impact Index gives organisations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Speaker’s from A.T. Kearney and Melbourne Business School will talk through the 2019 results and showcase the analytics journey of leading global organizations; empowering companies to develop a case for change.
Tim Daines (QuantumBlack), Daniel First (QuantumBlack)
Attendees will hear how UX and DS collaborate effectively, when building advanced analytics. Best practices will be discussed (via a case study for building an optimisation algorithm for natural resource production) of how data science and design work in tandem to create adoptable data-driven products that felt intuitive to users, as well as deliver powerful insights into business operations.
Ted Malaska (Capital One)
In this session we will work through scenarios and strategies to help these different groups to work together. Defining how to evaluate success and sniff out trouble areas. We will look through out every part of the pipeline to get to AI and ML value. Who are the people involved and how to optimize the interaction points throughout the pipeline. Lastly how to have fun while we work together.
Mark Madsen (Teradata)
The growing complexity of data science leads to “black box” solutions that few people in an organization understand. The concept of reproducibility – our ability to get the same results given the same information – is a key element to build trust and grow data science use in the organization. And one of the foundational elements of reproducibility (and successful ML projects) is data management.
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. In this talk, we’ll explore 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.
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 non-traditional sources of data. We would illustrate 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)
Martin Benson, Head of Artificial Intelligence at Jaywing, explores the history of machine learning in Credit Scoring and the growing need for explainable and justified decisions made by machine learning systems. Is it possible to overcome the “black box” problem? Find out more about how machine learning systems are evolving and how the challenges to adoption can be overcome.
Alex Ingerman (Google)
Federated learning is the approach of training ML models across many devices, without collecting their data in a central location. Alex Ingerman introduces federated learning concepts and explores the use cases for decentralized machine learning, drawing on Google's real-world deployments. You will also learn how to build your first federated models today with the open-source TensorFlow Federated.
Carlos Rodrigues (Siemens)
An evolving landscape of cyber threats demand innovation. It is time to bring AI to the fight. Using bleeding edge AI in production to improve threat detection in a worldwide company such as Siemens is mandatory. 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)
The goal of the tutorial is to learn and experience what it takes to be a manage machine learning (ML ) based products. In the tutorial we will go through the cycle of developing machine learning based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.
Holger Kyas (University of Applied Sciences)
The “AI Multi-Cloud Broker” triggered by Amazon Alexa mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), Watson Tone Analyzer (IBM) to compare and analyse sentiment. The extended AI part generates new sentences (e.g. marketing slogans) with a Recurrent Neural Network (RNN). Finding those with very positive sentiment is the goal.
Vijay Srinivas Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics and tasks such as NLU or summarisation. 2. We provide a scalable, productionizable implementation of capsule networks over KuberFlow.
Tony Xing (Microsoft), Bixiong Xu (Microsoft), Congrui Huang (Microsoft), Qun Ying (Microsoft)
Anomaly Detection may sound old fashioned yet super important in many industry applications. How about doing this in a computer vision way? Come to our talk to learn a novel Anomaly Detection algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN), and how this novel method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.
Kim Hazelwood (Facebook)
Details to come.
Weifeng Zhong (Mercatus Center at George Mason University)
How to learn structural changes embedded in unstructured texts? This presentation introduces a novel method to do just that. Based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong, the speaker explains how an off-the-shelf applications of deep learning---with an important twist---can help us detect structural breakpoints in time series text data.
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. Using deep learning can be very energy-efficient, and allows us to make sense of sensor data in real time. This talk shows you how.
Tobias Martens (UNS.ai)
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. This presentation provides an overview of current issues in NLP interoperability. Applying Chomsky's theory of Universal "hard-wired" Grammar, it outlines a framework to make the human voice in AI universal accountable and computable.
Manas Ranjan Kar (Episource)
NLP is hard, even more so for clinical text. There are multiple challenges - varying range of doctor utterances, lack of punctuation, no singular POS tagger being most common. It becomes all the more important that we invest a fair amount of time on domain specific feature engineering. It’s also crucial to understand to diagnose a NLP model performance and identify possible gaps.
Ted Dunning (MapR)
Evaluating machine learning models is surprisingly hard. It gets even harder because these systems interact in very subtle ways. I will break the problem of evaluation apart into operational and function evaluation and show how each can be done without unnecessary pain and suffering. In particular, I will show some exciting visualization techniques that help make differences strikingly apparent.
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
On-device machine learning and artificial intelligence is the future for privacy-conscious, cloud-averse users of modern smartphones. This tutorial explores what's possible using CoreML, Swift, and associated frameworks, and the powerful ML-tuned silicon in modern Apple iOS hardware. We use Swift to demonstrate and create ML-/AI- features, and show how much you can do without touching the cloud.
Ganes Kesari (Gramener Inc), Soumya Ranjan (Gramener)
In many countries, policy decisions are disconnected from data and very few avenues exist to understand deeper demographic and socio-economic insights. Satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data like the national census, this can help answer very important questions, and also surface inconsistencies in survey data.
Jeff Jonas (Senzing)
In this talk, visionary technologist and entrepreneur Jeff Jonas will describe how a purpose-built real-time AI, created for general-purpose entity resolution, can be used by your organization to gain new insights and make better decisions faster.
Jim Dowling (Logical Clocks)
ROCm, the Radeon Open Ecosystem, is an open-source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch, and in this talk we we describe 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)
Everyday Careem’s platform relies on Machine Learning (ML) in production to enable the movement of millions of its users. In this session, Ahmed Kamal discusses different challenges 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 accelerate the impact of ML everywhere.
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Sequence to Sequence (S2S) modeling using neural networks has been increasingly becoming mainstream in the recent years. In particular, it has been leveraged for applications such as, speech recognition, language translation and question answering. we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting.
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 (Publicis Sapient), Akshay Kulkarni (Publicis Sapient), Pramod Singh (Publicis Sapient)
The key takeaways are: 1. Introduction to NLP and different components such as summarization and generation 2. Introduction to NLP using deep learning 3. End-to-end view of using state-of-the-art recurrent neural network, LSTMs and attention networks for NLP tasks 4. Complete hands-on tutorial using 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. Google has taken years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of tools and libraries that Google uses internally.
Martin Goodson (Evolution AI)
Data leakage occurs when the model gains access to data that it shouldn't have access to. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin will describe the main four manifestations of data leakage and how to recognise the warning signs. Mastering several key scientific principles can mitigate the risk of failure.
Casey Dugan (IBM Research), Zahra Ashktorab (IBM Research)
Can you guess the backdoor of a hacked classifier? We invite you to learn more about novel AI technologies through the design and development of engaging games. And we present our latest research around improving the interactions between humans and AI systems, from empathy-building to feedback design.
Ihab Ilyas (University of Waterloo | Tamr)
Details to come.
Sacha Arnoud (Waymo)
To navigate city streets, self-driving vehicles need a deep semantic understanding of the world around us. We’ll share how Waymo uses deep learning to unlock new capabilities and build safe autonomous vehicles. We’ll also give 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.
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 for using Python and Azure Machine Learning to train your machine learning time series forecasting models both locally and on remote compute resources.
Danielle Deibler (Marvelous, Inc)
In this session, we will describe 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)
Learn about recommended ways to train and deploy Python models on Azure. It ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Code samples for the session will be made available on Github.
Jewel James (Gojek), Maulik Soneji (Gojek)
The story of how we 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.
Marie Smith (Data 360)
Since starting her career at AOL and her current work with the cloud teams and systems in projects with Google and Amazon, Marie helps illuminate some key findings from the rapid prototyping world of Silicon Valley as well as 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.
James Fletcher (GRAKN.AI)
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. KGCNs demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach.
Laurence Moroney (Google)
In this session, Laurence Moroney, Developer Advocate at Google, will show you how to go from wondering what Machine Learning is to building a Convolutional Neural Network to recognize and categorize images. With this you'll have 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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