Agenda

Working with data is always challenging, but working in a highly regulated industry like banking, is even harder. In order to take the right decisions you need the right information and the information comes from data. Without the proper environment and tools, the mission of bringing business value becomes almost unreachable because lack of information creates uncertainty.

What are the challenges of using data in BCR? What are the main data driven achievements and how do we thrive to success?

Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry.

In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.

First I show you how to create a simple “GridWorld” simulation of the MDP, from the ground up, to help demonstrate why and how RL works. Then I derive a simple RL algorithm that’s capable of solving your simulation. Finally I will provide actionable next steps to show you how to take this learning and apply it to industry.

This presentation includes a Jupyter notebook that you can tinker with during the presentation. Full instructions will be provided. Although this presentation is suitable for beginners, you will benefit if you have some exposure to data science and machine learning.

It is no news that data is generated in enormous volumes and some of it takes more time to analyze than the results it brings. Join our keynote to gain skills on how to reduce time and complexity of integrating new data sets by using Data Bricks, Data Lake and Airflow in Microsoft Azure in order to ingest data . We will not only cover data analytics but also machine learning through the implementation of chatbots and the approach taken to structure the information they rely on.

The world is changing due to innovation in Perception, Mixed Reality (MR) and Artificial Intelligence. In recent times, we have seen different companies use immersive realities (Augmented, Virtual or Mixed Reality) to build experiences that enhance their business experiences and better engage their customers.

Solutions exist in entertainment, gaming, architecture, manufacturing, e-commerce and many other industries. As a result of this, we have to build immersive experiences that humans can use. We all talk about how the web is for everyone, I personally believe that all technology experiences should be for everyone and this includes immersive technology. Regardless of race, height, disability, accent or other differences, as developers, we should be able to build immersive experiences for the global market which means building tech that everyone can use.

At the end of the day, we build for customers and we would want to have a satisfied user base. Are you a developer working in Artificial Intelligence, Augmented Reality, Virtual Reality or Mixed Reality and trying to learn how to build immersive experiences? In this talk, I will highlight a few ways these Immersive Realities are changing the way we interact with computers today. I will highlight best practices for inclusive design in immersive experiences. Lastly, this talk will also cover things to do post-design (during the implementation) of the application to help promote accessibility.

As AI becomes more and more present in our everyday lives, we rely on it to bring objective, unprejudiced, fair decisions, enhancing the ability of the human mind. But what is fairness? Could we define a single universal metric that quantifies this and apply it to all Machine Learning problems? In this presentation I will show some of the most common biases, how they can occur in our data or algorithms and how to counter them in order to obtain a better user experience for all AI beneficiaries.

You can control a drone using 20 lines of code. That’s the easy part. Sending the drone telemetry to Azure IoT is a little tricky, however, it requires another 20 lines of code. And when all the information is on Azure IoT, we can perform Anomaly Detection on the drone telemetry using ML.Net on the cloud.

We will use a simple house drone ($100), Python and C#. And, besides the drone telemetry analysis, I’ll share additional enterprise scenarios.

Let’s build this!

1st part of the track is about how to create an SDK to control the drone. 2nd part is how to send the telemetry to Azure IoT. 3rd part is how to analyze this telemetry using ML.Net. 4th part is to share real scenarios on where to apply this. I’m planning to spend +60% of the session on steps 3 and 4.

For each event, I prepare a custom demo for the 2nd part.

Transforming and bringing into the future the 170-year old MassMutual life insurance company, the Data Science team has partnered with internal Marketing, Distribution, and Mobile teams to develop an innovative mobile application which MassMutual’s ~7,500 agents can use to identify customer leads. The app presents agents with various opportunities to identify new prospective customers, as well as intelligent cross-sale/up-sale opportunities with the agent’s existing customer base. Kevin will share an overview of the leads program and ‘LeadsApp,’ as well as some of the data-driven lead sources that the MassMutual Data Science team has developed.

Yet there is much confusion about what data fabric means, its benefits, and use cases.

Why on earth should we dance with robots? What does it teach us? What doesn't it teach us?

What does dancing with robots look like now? What are the limitations? What is the potential? What will it look like in the future? 

What are the implications for human creativity? Is it something to be feared or rejoiced?

How does one dance with a robot steps 1, 2, 3?