In the DSC November 2 editorial, I posed a question I’ve wondered about for a while. Why is it so hard to create a shared-reality world, whether it’s called Virtual Reality, Artificial Reality, Extended Reality, Digital Twins, or any of a number of other terms? And for that matter, what does it matter to data scientists?
It turns out that the answer to the first question is actually highly relevant to data scientists. In order to share a virtual world, you need to have a common conceptual framework about how things are represented in that world.
This is not just a matter of saying that two meshes (3D models) render the same way. Instead, it means that the communication protocols describe the same kind of actions, events, objects, and environments from one system to the next. It means that coordinate systems (where you are on the planet if you’re even on the same planet) must be, well, coordinated. The consistent framework even comes down to how you send messages back and forth, how you signal changes from one environment to the next, and, ensuring that there’s a way of moving from one universe (verse) to the next.
What makes this process more complicated is that you also have to make sure that people who shouldn’t have access to your personal information don’t, for everyone within the respective universe. Over the last twenty years, the read-only web became the read-write web, but even within that context, the enforcement of permissions to permit that capability is still mostly piecemeal and platform-dependent.
To go beyond that, to create the web of access rights and permissions to allow even the simplest of actions, will require either universal adoption of a single commercial platform (unlikely in the extreme) or the rise of a consistent standard that different platforms can employ to perform the same thing independent of implementation. There are currently a number of such standards that have been or are being proposed in this particular area, some exhaustive, others focused on specific aspects of such a shared world. If the history of standardization is a guideline, none of these, by themselves, will survive in their current forms.
However, a standard by itself should increasingly be seen, not as the final word, but rather as a model or prototype for people to examine, explore, implement and test. Just as a machine learning model should always be tested exhaustively with all kinds of scenarios and use cases, so too should a model of the metaverse be seen as an ensemble effort.
My suspicion is that the seeds are being laid down for a project that will take a decade to reach fruition, but that’s okay. The Internet today, arguably far more robust than it was in 1991, still took a decade for even the basic framework to firm up, and close to three to get to the point where most browsers are implementing more or less a single reference standard (the Mozilla model). Good engineering takes time, and the Metaverse is easily one of the largest software projects ever attempted.
Yet to achieve the Metaverse, we have to take the first tentative steps now.
Kurt Cagle Community Editor,Data Science Central
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Data Science Central Editorial Calendar
DSC is looking for editorial content specifically in these areas for November, with these topics having higher priority than other incoming articles.
GANs and Simulations
ML in Weather Forecasting
UI, UX and AI
GNNs and LNNs
DSC Featured Articles
The Biggest Technology Trends In Accounting And Finance
Aileen Scott on 09 Nov 2021
Advanced Secrets Of Quantitative Funds: Utilizing Back Tests
Rumzz Bajwa on 09 Nov 2021
NLP for Business in the Time of BERTera: Seven Misplaced Passions
Anand Ramanathan on 09 Nov 2021
Social Media as the Most Popular Method of Brand Promotion
Yuri Filatov on 08 Nov 2021
Marketing Analytics and the Modern Data Scientist
Akin Babu Joseph on 08 Nov 2021
RPA Testing: Key Benefits and Top Best Practices You Should Know
Ryan Williamson on 08 Nov 2021
Implementing Content Moderation Techniques and Practices
Roger Max on 08 Nov 2021
How to Make a Career in Big Data Engineering
Aileen Scott on 08 Nov 2021
Predictive Analytics: The it Plays in Real Estate Sector
Ryan Williamson on 08 Nov 2021
5 Reasons To Use Power BI For Business Analytics
Imenso Software on 08 Nov 2021
Could an AI ethics audit end up like GDPR?
ajit jaokar on 07 Nov 2021
Data Observability and Its Importance in Determining Intent
Bill Schmarzo on 06 Nov 2021
Fun Mathematical Problem in Stochastic Geometry: Random Triangles
Vincent Granville on 03 Nov 2021
Introduction To Bass Diffusion Model – Predicting Sales For New Product
Kushal Mukherjee on 03 Nov 2021
Artificial Intelligence Could Be About To Replace Your Doctor in Medicine Industry
Dinesh Bhol on 03 Nov 2021
Translating with Google Sheets
Zoran Bogdan on 03 Nov 2021
DSC Weekly Digest 2 November 2021
Kurt A Cagle on 02 Nov 2021
Tips to Manage and Build an Effective Distributed Team
Mitul Makadia on 02 Nov 2021
How IVR Works Well With Modern AI Technologies
Aileen Scott on 02 Nov 2021
Top Blockchain Programming Languages: Choose the Best One for Your Business
Varun Bhagat on 01 Nov 2021
Machine learning and the Search for E.T.
Stephanie Glen on 31 Oct 2021
Spinoza: Building an AI Ethics Framework From First Principles
ajit jaokar on 31 Oct 2021
Use Cases For High Density Region Estimators
Rohan Kotwani on 31 Oct 2021
The Impact of AI on Social Media Marketing
Ryan Williamson on 29 Oct 2021
Prajwal LM on 19 Oct 2021
Picture of the Week
IoT App Development Process