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Featured Webinar


Data Driving Automated Vehicles

Before millions of automated vehicles are deployed in the coming decade, the deep neural networks powering them will require large quantities of carefully annotated and labeled data for training and validation. With the vast and often unpredictable nature of driving, billions of virtual driving miles in simulation using both real world and synthetic datasets will be essential to ensuring that these systems are safe for use. In this webinar, join Sam Abuelsamid from Navigant Research, Danny Shapiro from Nvidia, Ryan Eustice from Toyota Research Institute, and Daryn Nakhuda from Mighty AI as they discuss how the data is captured, curated, prepared, and used to enable autonomous vehicles.



Featured White Paper


The Data Driving Automated Vehicles: 
Where It Comes From and Why Quality Matters


The diversity of environments in which autonomous vehicles must learn to operate is virtually endless: from unknown weather conditions, to pedestrians and cyclists, damaged roads, and other vehicles, machine learning models require vast amounts of data to account for as many scenarios as possible. But data quantity is not the only challenge. Accurately labeling that data is an even more challenging piece of the puzzle that data scientists and engineering teams are trying to solve.


Featured Podcast


TWiML Talk #57 with Daryn Nakhuda, founder and CEO of Mighty AI

Training Data for Autonomous Vehicles

TWiML Talk #57 with Daryn Nakhuda, founder and CEO of Mighty AI

Daryn and show host Sam Charrington chat about the demand for highly specialized and accurate data within the autonomous vehicles market.

In this episode, Daryn shares how our expertise in predictive machine learning across industries helped us evolve to a Top 10 Auto Startup in 2017. Other topics you can look forward to: scale concerns in the race to autonomy, the need for diverse data from around the world, the secretive marketplace, and so much more.


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