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The Infinite Retina

The Infinite Retina

By : Irena Cronin, Robert Scoble
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The Infinite Retina

The Infinite Retina

By: Irena Cronin, Robert Scoble

Overview of this book

What is Spatial Computing and why is everyone from Tesla, Apple, and Meta investing heavily in it? Irena Cronin and Robert Scoble answer that question to help you understand where Spatial Computing - an augmented reality where humans and machines can interact in a physical space - came from, where it's going, and why it's so fundamentally different from the computers or mobile phones that came before. They present seven visions of the future and the industry verticals in which Spatial Computing has the most influence - Transportation; Technology, Media, and Telecommunications; Manufacturing; Retail; Healthcare; Finance; and Education. The book also shares insights from leading experts, industry veterans and innovators, including Sebastian Thrun, Ken Bretschneider, and Hugo Swart. They dive into what they think will happen in the medium term and what it could mean for humanity in the long term. This new second edition has many substantial updates, including two new chapters on the relevance of Generative AI to Spatial Computing and a new chapter on the impact of the Apple Vision Pro. Each existing chapter has been updated to have the most current information and commentary.
Table of Contents (20 chapters)
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2
Part I: Why Spatial Computing and Why Now?
6
Part II: The Seven Visions
14
Part III: The Spatial Business
19
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20
Index

Challenges and Ethical Considerations

As the fusion of Generative AI and Spatial Computing continues to advance, it presents a host of challenges and considerations that must be navigated to harness its full potential while maintaining ethical standards and real-world applicability.

Data Quality and Bias

Data quality and bias are indeed pivotal in shaping the reliability and fairness of applications that leverage Generative AI and Spatial Computing. These two factors directly influence the performance and outcomes of AI systems, with a significant impact on real-world decisions and perceptions.

Data quality pertains to the suitability of data for making decisions. High-quality data must be accurate, complete, timely, consistent, and collected using robust methodologies. For Generative AI systems, which often learn and make inferences from large datasets, the adage “garbage in, garbage out” is particularly apt.

The use of poor-quality data can lead to...

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