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The AX VISIO merges high-end optics with AI to spot and name wildlife species instantly, transforming traditional binoculars into smart devices for nature enthusiasts at CES 2024.
At this year's CES, Swarovski Optik and renowned designer Marc Newson have introduced the AX VISIO, billed as the world’s first AI-supported binoculars. This innovative device combines high-performance analog long-range optics with digital intelligence to detect and identify over 9,000 bird and wildlife species at the touch of a button.

Swarovski Optik and Marc Newson invested around five years in developing the AX VISIO. The binoculars are manufactured with a high degree of vertical integration at Swarovski's facility in Absam, Austria, ensuring adherence to strict quality standards.
Marc Newson compared the AX VISIO to a modern camera, highlighting its optical, electronic, and digital components. He noted that the device is designed to be as user-friendly as possible while delivering cutting-edge technology.
Swarovski Optik plans to regularly update and enhance the performance of the digital features through system and function updates. Additionally, an open programming interface (API) will allow external developers to create new functionalities, ensuring the AX VISIO remains a versatile and evolving tool for wildlife enthusiasts.
“The analog components of the device, such as the optics and housing, comply with our sustainable SWAROVSKI OPTIK quality standards. The performance of the digital features is regularly updated and enhanced through regular system and function updates. An open programming interface gives creative external providers the opportunity to expand the AX Visio with new functionalities. Our aim is to promote an innovative system that continuously develops and meets users’ needs for enhanced vision and more information as effectively as possible,”, Swarovski Optik
The AX VISIO represents a significant leap in combining traditional optical excellence with modern AI, making it a valuable tool for both amateur and professional wildlife observers.
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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12 January 2024
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