Metaverse, digital twins | Alliance

When Neal Stephenson, author of Virtual Samurai, coined the term “metaverse” in his book in 1992, he had no idea that the word would spawn a billion-dollar industry 30 years ago. The metaverse is a hot topic, and many retailers have already created a digital presence for tech-savvy consumers. Analysis by Dr. Biswa Sengupta, Technical Researcher and Head of Machine Learning at Zebra Technologies.

Dr. Biswa Sengupta, Technical Researcher and Machine Learning Manager at Zebra Technologies

Simply put, the metaverse is a virtual space with augmented and mixed virtual realities. Now it is less conceivable that people, towns and countries will exist only digitally. Metaverse combines the power of the simulation technologies we championed in the last century and builds virtual worlds similar to video games where people can have an immersive experience.

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The fashion world has flooded the metaverse. Some stores deepen their online services by allowing users to be able to wear their avatars, purchase products in a virtual store and ship them to their homes in real life. Some organize a Fashion Week in the metaverse, where brands present their latest creations on the catwalks.

The food sector is also looking to move into the metaverse that allows users to walk into virtual grocery stores, fill their shopping carts, pay and ship products to their homes. Plus, if you want to have lunch after your shopping, you can order your favorite hamburger and fries.

Also read: [Chronique] Digital intelligence: some keys to understanding innovation during metaverses

The metaverse has become an additional option in the world of omnichannel commerce.

The metaverse can also be a fusion of machine learning technologies such as computer vision (VA), natural language processing (NLP), and reinforcement learning (AR). More and more companies are seeing the potential of combining voice and vision technologies for decision making. But advances in voice and vision technology alone cannot bring us closer to artificial general intelligence, unless we can use these senses together to make decisions.

This means that the metaverse can be a virtual test, a kind of great multiplayer game where we can build, train, and deploy a rich mix of machine learning technologies to create new retail options and experiences.

Build it, and consumers will come?

Financial institutions and social media giants are among those participating in the race retailers to get their piece of the metaverse pie. But more importantly if consumers want the Metaverse. Let’s not forget that we have to spend time, money and physical energy (not virtual) to interact with the metaverse. At the same time, we can imagine a parallel in a video game where we create a community with our virtual neighbors, but do we have the mental capacity to live a parallel life? Don’t we have too much information?

The answer is yes. Tons of companies can make money showing venture capitalists and users of promised land in the metaverse but it’s important to ask what is the product? The mantra “build it and customers will come a lot” may not work. So what can be done?

The first step in opening up the metaverse is to use it to make decisions but also use it as a synthetic world for generating machine learning data.

You need to gradually build the product market, otherwise customer adoption can be difficult. If industries start opening stores in virtual spaces and using digital currencies to buy/sell digital assets, users will hardly adopt the product.

Also read: [Entretien] Axa’s metaverse is more inspiring to the communities of its employees

The digital twin, first

The progressive step towards building a metaverse and addressing the above points is the digital twin, a subset of the metaverse. Take a small part of the natural world, such as a retail store, and use a simplified metaverse (the digital twin) to create real-time visibility of all properties (objects, employees) in store, supply chain channels, etc.)

Then use technologies like VA to measure store supply and demand in real time. Automatic language processing can sift through thousands of matches and tell you what tasks need to be done. Finally, under digital twin constraints, reinforcement learning (RL) can make decisions about how the future will turn out.

Also read: Digital twin: RTE is changing the management of its industry assets

This will allow store managers to have a real-time view of store operations and rely on digital twins to make actionable decisions. With technology, it makes it possible to combine different vocal and visual qualities to make the best decisions.

Some further steps in the metaverse revolve around the digital twin, but this time for the purpose of creating synthetic data. Other tech companies and start-ups are already championing this line of thinking. The central concept behind all of this is domain randomization.

A digital twin allows us to create synthetic worlds and different subsets of the same world.

For example, most in-depth learning based on the VA algorithm requires a lot of training data. Digital twinning (if done with precision to reduce covariance lag relative to a natural environment) can provide us with annotated synthetic data. Whether it’s millions of miles of driving data for self-driving cars or hundreds of permutations for objects under different viewing statistics (e.g. fruits and vegetables in the store look different when viewed. view left or right, night or day). Using infographics, VA algorithms can check for all possible recurrences of your fruits and vegetables. Similarly, NLPl algorithms struggle to generalize whether the domain is random, i.e. whether materials (texture, color), light direction, lighting conditions, and placement of objects randomly change. The metaverse concept can help us alleviate some of these data efficiency issues.

In summary, incremental steps can lead companies to transition from today’s useful virtual experiences to focused end-product discussions using digital twin, so that enthusiasm for the metaverse can build and reduce input lag.-exit between product and market needs. False, unproven assumptions can weaken businesses, no matter how good the technical solution.

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