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Mitsubishi, Fujitsu, and Other Tech Companies to Establish a ‘Japan Metaverse Economic Zone’

Numerous large Japanese technological, manufacturing, and financial organizations are collaborating to build the infrastructure for an open metaverse in order to advance the country’s Web3 goal. 

In a news statement issued Monday, Fujitsu announced a collaboration with nine other firms, including vehicle maker Mitsubishi and global bank Mizuho, to develop an interoperable metaverse structure named RYUGUKOKU (TBD) that would be utilized to expand the “Japan Metaverse Economic Zone.”

According to the news release, the purpose of the metaverse alliance is to assist establish the infrastructure for firms to tap into Web3 marketing, work reform, and customer experience efforts. RYUGUKOKU (TBD) would function as a virtual environment that connects users to various Web3 services developed by businesses and government organizations.

The platform will also employ “Auto-Learning Avatars,” which will collect user data in order to create a tailored metaverse experience. The “Pegasus World Kit” will assist users in creating gamified metaverse experiences, and its “Multi-Magic Passport” will enable identity and payment options to allow metaverse interoperability.

Japan is attempting to incorporate Web3 technology into its national agenda. Prime Minister Fumio Kishia said in October that the nation will invest in digital transformation services such as non-fungible tokens (NFTs) and the metaverse. In November, the country’s Digital Ministry announced intentions to establish a decentralized autonomous organization (DAO) to assist government institutions in transitioning to Web3.


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Huawei will establish the East Asian metaverse and Web3 Alliance.

Huawei’s cloud division has formed a “heavyweight” alliance with numerous blockchain businesses to establish its metaverse and Web3 Alliance initiative, which seeks to accelerate the acceptance and spread of these technologies in East Asia and beyond.

As WeChat reported on February 25, Huawei Cloud announced collaboration with Polygon (MATIC), Deepbrain Chain (DBC), Morpheus Labs (MITX), and BlockChain Solutions, with others to come in the future, during the 2023 Huawei Cloud Asia Pacific Partnership Leaders Symposium in Bali, Indonesia.

Pei-Han, CEO of Morpheus Labs, commented on the cooperation and launch announcement, emphasizing his company’s engagement in the project as “one of the essential partners in Huawei Metaverse – Web3 Alliance.”

Increasing Horizons

The summit, which took place between February 22 and 24, brought together various IT businesses in the area, including Cloudsec Asia, Sirius, HKT Enterprise Solutions, China Telecom, China Mobility, and China Unicom, to discuss attempts to widen the technology’s reach.

According to the research, Huawei Cloud provides cloud-native, artificial intelligence (AI), and big data solutions to governments and organizations in roughly 30 countries, with the goal of achieving digital transformation and increasing productivity while serving 730 million end-users worldwide.

Additionally, Huawei’s latest blockchain collaboration is only one of its efforts to develop new technologies, as it declared back in November 2021 that it had already set its eyes on bringing out the 6G network by 2030, despite the fact that 5G technology had just recently debuted on the global arena.

Tencent Cloud, the cloud unit of tech corporation Tencent, previously announced a cooperation with decentralized technology projects such as Ankr (ANKR), Avalanche (AVAX), Layer 1 network Sui, and Ethereum (ETH) Layer 2 zkEVM scaling solution Scroll to promote the global Web3 ecosystem growth.


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What Gucci and others discovered in the metaverse


The metaverse economy is expected to be worth billions of dollars
and the world’s largest fashion labels are already trying.

Forever 21 was trialling new products in December last year, including Y2K-style items, flared trousers, strappy crop tops and fluffy accessories. The most popular design was a bubblegum-pink beanie hat emblazoned with the word FOREVER, which cost just 75p.

The beanie was a virtual item available to buy on Roblox, an online gaming platform launched in 2006. It is regarded as one of the most successful early iterations of the metaverse.

F21’s limited-edition pink beanie sold more than one million units, making it one of their most popular items ever. In November, the brand launched a Metaverse Collection featuring a version of the beanie to match consumers’ avatars.

Jacob Hawkins, F21’s chief marketing and digital officer, explains that Roblox and its ilk can act as R&D testing labs where consumers are the guinea pigs. This blending of the physical and digital in fashion and other industries has been coined as “phygital”.

Goldman Sachs estimates the metaverse’s economy could reach $8tn in 20 years, and fashion brands are experimenting to find a foothold in the new world.

Gucci became the first luxury house to purchase digital real estate in the Sandbox metaverse for a store-cum-event space, creating a virtual gallery displaying NFT artworks and vintage fashion pieces. It also released a pair of $12.99 virtual sneakers that can be worn using augmented reality on a phone.

Burberry partnered with Minecraft in November to create digital “skins” and a real-life collection inspired by the game. Phillip Hennche, the brand’s director of channel innovation, said the partnership generated “huge” interest, and Launchmetrics estimated the project generated a $5.2mn return on investment in advertising.

Launchmetrics’ experiments are key to understanding how luxury might evolve in the metaverse, with brands offering outfits for metaverse avatars for under $10 a go. Brands hope that once consumers own the virtual product, they’ll be more likely to buy the real version when they have more cash. Balenciaga, Prada and Thom Browne are among other designers offering outfits.

Metaverse gaming and NFTs (non-fungible tokens) could account for 10% of the luxury goods market by 2030, representing a €50bn revenue opportunity and a 25% increase in profits. Some companies are taking the plunge, while others remain cautious.

Around half of French luxury brands are experimenting with the metaverse or NFTs, according to a 2022 report by Comité Colbert and consultancy Bain. Kering, the family-controlled group that owns brands including Gucci, Saint Laurent, Alexander McQueen and Bottega Veneta, has created an in-house “lab” to cater to these spaces. Keeping up with developments is crucial as younger consumers have less loyalty to particular brands. Connecting with this group on multiple platforms is becoming more important.

The appeal of virtual sneakers and handbags is clear, but why would consumers want to spend money on them? One answer is the luxury shopping experience, with its security guards, beautiful interiors and terrifying staff. The metaverse is a less intimidating setting, particularly for younger consumers used to interacting and spending money virtually.

Augmented reality collaborations allow consumers to try on 3D versions of clothing or accessories before ordering.

Snapchat users can use their smartphone cameras to create 3D digital versions of products, similar to Snapchat filters. Estée Lauder, Mac, Gucci and Dior have all run AR try-on campaigns for trainers and make-up that have resulted in direct sales. Dior’s digital sneakers were viewed 2.3 million times and resulted in a sixfold return on advertising spending.

Luxury brands have concerns about intellectual property and compliance issues on these new platforms, as they cannot design separate spaces to comply with country standards on data, consent and privacy. Additionally, if Gucci or Balenciaga fashions are appearing in ‘adult’ content, it could pose an image problem. It is unclear how or even if such issues could be resolved.

Hermès won a landmark lawsuit against a digital artist who had sold a collection of “MetaBirkins”, fluffy virtual bags based on the French fashion house’s iconic Birkin bag. Hermès claimed the artist had copied its design to make hundreds of thousands of dollars, and was awarded $133,000 in damages.

L’Oréal worked with industry and major players to ensure brand safety on social media, and Web3 is coming. Tiffany & Co gave owners of a CryptoPunk NFT access to a sale of custom necklaces and pendants for 30 ether each. The collection sold out in under half an hour and was estimated to have made the jeweller more than $12mn. Today, the lowest resale price of an NFTiff is around 9 ether, around $13,000, and the value of the diamond-studded pendant has held up considerably better.

Ian Rogers, chief experience officer at Ledger and former chief digital officer at LVMH, believes that luxury people should understand NFTs and digital ownership better than anyone, as they don’t buy luxury watches to tell the time.

“Buying something for its aesthetics, craft, or resale value gives you status and makes you part of a small group of people who appreciate the same things.”








Polygon’s ZkEVM Mainnet Beta will launch in March.


Polygon, an Ethereum Layer-2 scaling solution provider, has set March 27th, 2023 as the date for the launch of its zero-knowledge Ethereum Virtual Machine (zkEVM) beta.

With the launch date established, Polygon has indicated that it would reveal additional information about the zkEVM beta network in the coming weeks.

The Launch Date has been Set.

Ethereum scaling solution at Layer 2 Polygon has finally revealed the release date for their much-anticipated Ethereum Virtual Machine rollup technology. Polygon has announced the beta launch of their zero-knowledge Ethereum Virtual Machine (zkEVM) on March 27th, 2023. Polygon went to Twitter to herald the launch, noting that after more than three months of “battle testing,” the system is now ready for its mainnet beta launch.

“Roses are red, Violets are blue, Poems are hard Mainnet Beta is here ON THE 27TH OF MARCH, Polygon #zkEVM launches the future of Ethereum scaling.”

While the protocol has not detailed what the beta network will feature, the Polygon team has stated that additional information about the launch and the zkEVM beta network would be released in the weeks running up to the launch date. It further said that network security will be of the utmost importance. Polygon’s new zkEVM network has been lauded for enabling “seamless scalability for Ethereum,” and its testnet was launched in December.

The Highest Priority is Security.

According to the Polygon team, security is their first priority, which is why the new Polygon zkEVM has gone through a “gauntlet of testing and audits.” Zero Knowledge technology is widely regarded as a key advancement for blockchains and encryption, significantly boosting transaction speed and lowering transaction costs. ZKevms are a sort of ZK rollup that may process transactions on a separate Layer-2 blockchain before sending the transaction data back to the mainnet blockchain.

Polygon’s zkEVM testnet, which uses the Ethereum Virtual Machine (EVM) for its ZK rollup, became live in October, allowing Ethereum developers to migrate their smart contracts from the main blockchain without requiring any substantial rewriting. Around 75,000 ZK proofs have been created since the testnet’s introduction, with over 5000 smart contracts already implemented. Polygon co-founder Mihailo Bjelic also mentioned that the company is looking into methods to incorporate ZK-technology into the Polygon POS chain. Polygon co-founder Sandeep Nailwal commented,

“Polygon zkEVM Mainnet is set to be the first fully EVM equivalent ZK rollup to reach mainnet. This represents a huge step towards scaling Ethereum and bringing Web3 to the masses.”

They Are Not the Only Ones Working on ZkEVM

Polygon is far from the only group working on developing a viable zkEVM solution. zkSync, a scalability provider, is also collaborating with zkPorter to establish and develop a comparable EVM solution that pulls transaction data off-chain. Scroll, another scaling solution provider, is collaborating with the Ethereum Foundation’s Privacy and Scaling Explorations Group to develop a zkEVM solution.

In addition, the Ethereum Foundation is financing a project called Applied ZKP. Applied ZKP is attempting to create an EVM-compatible zk-rollup. The researchers explained the relevance of the technology by stating that real EVM equivalency implies Ethereum may be scaled properly without using half measures.

“The best way to scale Ethereum is to preserve the existing Ethereum ecosystem: code, tooling, and infrastructure needs to just work. And that’s what Polygon zkEVM is aiming to achieve.”

The method also enables considerable reductions in transaction costs, with proof costs for big batches of transactions reduced to roughly $0.06, and basic transactions costing as little as $0.001. Meanwhile, MATIC, Polygon’s native token, has risen by more than 5.50% in response to the announcement.


Disclaimer:This content is given solely for informative reasons. It is not meant to be used or supplied as legal, tax, investment, financial, or other advice.


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What is Decentralized AI?

What is Decentralized AI? The Relationship Between Blockchain and AI

Artificial intelligence (AI) is becoming increasingly important in business, but decentralization is critical for its success and ethical application. Blockchain and AI have a good relationship, but what is decentralized AI and why do AI and Web3 go together?

Artificial intelligence (AI) is becoming more popular in many industries, but as it becomes more expensive, only a small number of organizations can compete at the highest level. Decentralized artificial intelligence makes use of public blockchains for immutable data storage and economic incentives, promoting collaboration and innovation without relying on trust. This article investigates the various ways in which blockchain and AI interact, the benefits and drawbacks of AI, and how blockchains and cryptocurrencies can help to alleviate many of the concerns surrounding AI and its various subsets.


What is AI?

Artificial intelligence (AI) is an interdisciplinary field of computer science that allows humans to create machines that can complete tasks or learn to perform specific functions more efficiently than humans. AI practitioners frequently model the human brain in order to create novel mechanisms for resolving everyday problems. AI is becoming increasingly popular across industries, with applications including customer service chatbots, self-driving cars, and apps like Alexa and Siri. Because the term “artificial intelligence” means different things depending on the context, there is no single definition that satisfies the entire AI community.


A Brief History of AI


Alan Turing is widely regarded as an AI pioneer. His 1950 paper “Computing Machinery and Intelligence” established the field. The Turing test was developed to assess the “intelligence” of computers by comparing their responses to questions to those of humans.


How Does AI Work?

AI models are based on human reasoning and behavior, and they enable machines to quickly learn how to act in a specific way or achieve a specific outcome. Different types of AI aim to achieve different outcomes with varying levels of complexity, and most AI models fall into four major categories.

– Reactive machines: they respond to data inputs but cannot act on previously learned data.

– Limited memory: capable of analyzing data and forecasting future outcomes.

– Theory of mind: the ability to make decisions and adjust behaviors in response to human emotions.

– Self-awareness: the ability to demonstrate human-level intelligence. Despite the various iterations available, AI systems perform tasks that normally require human intelligence.


Classes of AI

AI is classified into three categories: narrow AI, AGI (artificial general intelligence), and superintelligence.

Narrow AI is the most basic type of AI and typically specializes in a single task. Smart assistants, spam filters, traffic reports, and chatbots are all examples of poor AI. Innovations in machine learning (ML) and deep learning are frequently used to facilitate weak AI research (DL).

While superintelligence systems are currently unavailable, AGI, or “strong AI,” can be applied to any complex issue or problem-solving scenario. AI systems are being developed to accurately depict human emotions and solve complex problems on their own.


Machine Learning and Deep Learning

Machine learning (ML) is a popular AI algorithm for mimicking human intelligence. It works by feeding data into an AI machine that learns how to perform specific operations using statistics. ML combines supervised and unsupervised learning elements, allowing users to generate predictable and unpredictable results.

Deep learning (DL) is a subset of ML that employs neural networks that use a deep, layered data structure to mimic those of the human brain. It is used in healthcare, disease prevention, and self-driving vehicle optimization.



AGI is the pinnacle of AI research, a universal algorithm that can learn how to perform any task with human-like cognitive functions. OpenAI’s GPT-3 language model and DeepMind’s MuZerio are bringing us closer to that goal. Superintelligence is a fictitious AGI model that can mimic human behavior and cognition in every possible way.


Blockchain and AI

Blockchains are immutable databases 
that use node-based distributed networksrather than centralized server farms. They are self-governed and have no central authority. Every node in the network must agree on the validity of the corresponding transaction. Blockchains employ a data structure that connects each transaction, necessitating significant computing power.Furthermore, anyone can use a block explorer to view the history of transactions on a blockchain, resulting in a transparent environment for data sharing.

Bad actors are discouraged from adjusting transactional records or validating fraudulent transactions due to financial incentives. The use of cryptocurrencies in decentralized AI systems promotes high-quality contributions in a sustainable, scalable manner.


What is Decentralized AI?

Decentralized AI is a new field of artificial intelligence that incorporates blockchain and other distributed ledger technologies (DLTs). It seeks to ensure equitable and safe AI models through the use of distributed networks of nodes, which prevent power concentration and provide more value to users and society than centralized AI models. It is based on the principles that federated knowledge provides a better learning environment than centralized intelligence and that collective learning offers more opportunities for human advancement than AI models that rely on centralized mechanisms. Blockchains enable AI teams to safely develop intelligence models with the potential to significantly impact society, and decentralized AI promotes transparency and collaboration.


What Problems Does Decentralized AI Address?

Centralization is not inherently bad, but it can lead to open-source standards that protect privacy. Decentralized AI models employ homomorphic encryption to enable trustless collaboration, smart contracts and decentralized applications (dapps) to automate various aspects of the AI modeling process, cryptocurrencies to reward collaborators and promote community governance, and data providers to vet data sets and raise the bar for emerging AI developments. As the use of AI grows in popularity, decentralization serves as a safeguard to ensure that superintelligence does not lead to a dystopian future in which humans are indistinguishable from robots.



The field of AI is rapidly expanding, but its expansion is hampered by the rising computation costs of large data sets. As the industry expands, the number of companies that can afford to compete shrinks, leaving only a few powerful companies to dominate the space. Decentralized AI mechanisms employ distributed ledgers such as blockchains to prevent data manipulation, increase transparency, and avoid the centralization of power that could prevent AI from reaching its full potential. Blockchain and AI collaborate to advance the AI space, accelerate innovation, and create incentives for developing self-driving, intelligent technologies.


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