DC Startup Week 2018

In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker.

Lay the foundation for 1:1 marketing and would like to share what personalization means to us from a marketer’s perspective.

With the rise of voice assistants, voice is becoming another surface area for users to interact with your product or service. We can now start to blend this new technology with our existing offerings to improve user experience, engagement, and satisfaction. 

In this session, we'll learn about the Google Actions platform and how it works to provide you all the tools you need to build your own conversational interfaces. We'll also spend time looking at how to design a conversation interface, including thinking through the various phases of dialog and sketching out expected flows. Finally, we'll look at how to review and improve your Action by using the analytics and AI training tools available from Google.

“A young child defines the world purely on the small amount they can see…This is the root of dataset bias: intelligence based on information that’s too small or homogenous.” Advances in AI technologies like machine learning and deep neural networks have potential to save time and boost productivity. But what if we train these technologies using datasets that exclude large portions of the population?
For example, some facial recognition software doesn’t acknowledge dark skin. Why? People of color were excluded from the datasets that were used to train the software. If AI isn’t designed with inclusion upfront, its rewards won’t equally benefit us all. This talk shares the risk of unconscious bias in algorithmic datasets, and how developers can overcome it.

This session covers Google’s products, services and strategies on AI

What does it take to launch a far field voice experience such as an Amazon Alexa skill or a Google Action? Navya walks through the various steps that take the product manager from engaging with field and market researchers, from identifying the initial functional requirements for the skill to engaging with UX designers, developers, voice experiences testers, beta testers, and then marketers through the launch and the post launch of the skill. She provides specific examples and shares learnings from real client deployments of conversational experiences.