Meeta Dash

VP of Product, Appen

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VP of Product at Appen

As VP Product at Appen Meeta is building a machine learning data annotation platform focused on Computer Vision, Autonomous Vehicles, Conversational AI and NLP. Prior to Appen, she held several product leadership roles in Cisco Systems, Tokbox/Telefonica, and Computer Associates with a focus on AI, Chatbots, Voice/Video and Data Analytics. She has an MBA Degree from UC Davis and an engineering degree from the National Institute of Technology, India.

Building Ethics and Diversity in AI

Bias in machine learning is a significant concern as technology gets increasingly ubiquitous across many industries. Some types of bias can be attributed to limits in design and tooling; however, the bias in the training data itself is a general phenomenon. Skewed training data propagates into discriminatory AI models that amplify human prejudices.

Building a data labeling framework that uses a diverse set of crowd workers to collect and label the data can help reduce bias.

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Comprehensive Data Pipelines for Automotive AI

It’s no secret that there is an enormous business opportunity with the rise of autonomous vehicles and the connected car. Whether you are building a fully autonomous vehicle, improving driver assistance features, or in-cabin experience, high-quality annotated training data is the key to effective AI systems. This session will help take you from Level 1 to Level 5 autonomy, driving you ahead of the competition.

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What Did We Learn Labeling 13 Billion Units of Training Data?

The AI system you build is as good or bad as the data you have trained it on. Whether you are training a self-driving car, building a customer service chatbot, or diagnosing diseases with AI, a scalable training data strategy is integral to your success.
In this session, we will discuss how much training data is enough, how to effectively manage quality, quantity, and throughput in your data, and more!

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