Build your AI while your Turducken is cooking…

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  1. View point-variation: An object viewed from different angles may look completely different. This is one of the challenges with object detection because most detectors are trained with images only from a particular viewpoint.
  2. Occlusion: The objects of interest can be occluded. Sometimes only a small portion of an object, as little as few pixels could be visible.

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  1. Collect high quality training data with good variation. For example different lighting conditions, different seasons, different day-of-time, different background, etc.
  2. Make sure that the data collected is as close to the scenario where the AI will be used once it is trained.

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$200m bet that smart IoT is the future

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Apple buys edge-based AI startup Xnor.ai for a reported $200M — TechCrunch


Xailient was featured in the Australian Financial Review!

Xailient CEO Lars Oleson (3rd from Right), alongside fellow Antler Alumni David Smyth, Cauzey; Skye Theodorou, Upcover; Kailash Chandrasekaran, Upcover; Michael Li, Lamno; Anindha Parthy, Lamno; Stuart Hunter, Cauzey; Shivani Gopal, Halo Money and Antler Venture Capitalists Bede Moore and Anthony Millet.tuart Hunter, Cauzey; Shivani Gopal, Halo Money.

Xailient

World’s Fastest Computer Vision on the Edge — Fast & Accurate Computer Vision | 1/10th the cost | Privacy by design | Real-time, any device | www.xailient.com

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