Moss Book 2 is as pure a sequel as you can expect, taking inspiration from the critically acclaimed first game, Moss. Developed by Polyarc Games, the sequel picks up right from where the previous game ends. The second iteration stays true to the first game, going for a more evolutionary approach as compared to other sequels that try and reinvent the wheel, an aspect we appreciated throughout our playthrough.
You play as Quill, a cute mouse that you help navigate through intricately detailed worlds, trying your best to defeat minions with the three new weapons available in Quill's arsenal. You still also interact in the game as the Reader, providing you with an isometric point of view just like the original Moss. However, you now do a lot more compared to the first game.
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Moss Book II Review: The VR Sequel You Won't Want To Miss
Literary NFTs: What Are They And Why Writers Should Care
Literary NFTs are a game-changing digital technology for creative people, especially authors. According to statistics, NFT's weekly sales volume has risen from 100 in 2017, going all the way up to around 15 to 50,000 in 2022.
This represents the huge popularity of NFT in recent years. This modern blockchain-based platform has opened a new avenue for book lovers. They can now publish and enjoy reading different literature formats using NFT technology.
Literary NFTs are revolutionizing and bringing a paradigm shift in the traditional ebooks and physical book publishing processes. NFT platforms provide better monetization opportunities and help remove the earning imbalance between the authors and publishing parties.
Hence, opening new doors for a direct reader-writer connection. This blog will cover the fundamentals of literary NFTs and their advantages and disadvantages for writers. So, let's get down and explore more.
## What Is a Literary NFT
Digital literary work is any unique text like novels, poetry collections, scripts, essays, audiobooks, or articles that are written electronically. It can be read and viewed online. When such a digital asset is minted on the blockchain as a non-fungible token, it is called a literary NFT.
Previously, writers had to approach publishers to print and sell their physical books in the traditional publishing industry. However, in the modern world, books are stored digitally using blockchain on a decentralized network of computers.
The immutable feature of this technology makes the writer's work resistant to modifications, censorship, and alterations. Thus, the NFT books cannot be changed without the author's consent. Hence, people can approach an NFT marketplace to buy or sell digital books.
## Benefits of Literary NFTs For Writers
Writers have mostly been underpaid and underappreciated for their written work. In the literary world, commercial publishers tell writers what's sellable and what genre is a flop in the market. Hence, writers often get fleeced and struggle to get paid what their work is worth.
One such example was Blake Butler. In 2008, the publishing industry rejected his work by calling it "dense language and complicated structure" content. But this did not stop Butler.
He transformed the book into a Graphics Interchange Format (GIF) of flashing pages and minted it as literary NFTs. Within a few days, some reading enthusiasts bought the NFT for five Ethereum, equivalent to $7,569.50 then.
This is the reason behind creative people exploring NFT platforms. They use a tech space to sell digital collectibles directly to the readers. Therefore, the NFT market benefits writers as it helps bypass the traditional publishing process. Hence, they can earn a better reward for their time and effort. Some other advantages are that they can:
### Retain Full Creative Control
Some agents do not like publishing books on controversial topics, no matter how good they are. Sometimes traditional publishers reject manuscripts. It is because they believe that this type of content and publication formats are not marketable. Their interests lie only in books written on hot topics that the public wants to read. Hence, this takes away the creative freedom of the author.
That's where literary NFTs work. Independent creators can mint their NFT projects without worrying about censorship. Thus, in this way, they can cut out the role of publishing companies and can express their creative ideas with full freedom.
### Form a Direct Relationship With the Reader
There is no role of commercial publishers in literary NFTs. Hence, you and your fans can directly connect with each other. They can buy NFT editions of their favorite books to show their support. Authors can now engage directly with their followers by commenting, tweeting, and Instagramming in community forums.
Writers can publish limited copies of their literary works as NFTs. Hence, this allows them to create hype and increase the value of their work among the buyers. This blockchain-based technology is helping to remove the intermediary publishing bodies, therefore, the literary NFTs can directly reach the audience. Thus, NFT holders can form direct relationships with the readers.
NFTs have helped build bridges between the author and the consumers. One of the new ways to connect and grab the attention of the fans is to offer limited edition versions of the book. The scarcity of digital books will create curiosity and compel the audience to pay premium prices for the collectible edition.
Moreover, new and independent authors should start a fan base community to become more visible in a sea of NFT writers. They can add variations like creating art or a signature text in every book version. In this way, consumers can get a unique copy of your work, which will arouse their interest in the book.
### Add format versatility to your content offer
Authors can engage with their fans by adding versatility and value to different editions of literary NFTs. Some creators provide their readers with unique features in the NFT books like bonus chapters, glossaries, author's notes, illustrations, etc.
Writers can offer content bonuses to their loyal fans. Moreover, readers can have the first digital NFT editions of the book. They can also enjoy digitally signed copies of their favorite literary NFTs. Hence, this helps writers connect with readers in a new way through content offers.
Support the disruption of royalty structures
Authors have historically been earning less due to the intermediary bodies in the publishing process. They have been given low salaries and receive less percentage of royalties. Retailers, publishers, and distributors capture the maximum profit by selling books.
Thus, NFT has changed the game for authors. As it allows them to immediately earn money (royalties) for their writing projects. Due to smart contracts, the author can even continue earning when the person who bought your book decides to sell it. You'll receive a percentage profit or royalties on secondary sales.
Hence, NFTs empower the authors, who can now dream of making a full-time living with writing. It can serve as a sustainable source of revenue generation every time someone buys or sells NFTs.
### Build a subscription around your content
You can start an NFT community to attract maximum support and subscriptions from a relevant audience. Followers who buy your literary NFTs can post on social media about the quality of your work. This can allow others to interact to develop a better understanding, not just of the work but the literary NFTs overall.
Moreover, fans directly communicate with their writers on NFT platforms so that they can spread hype about their one-to-one experience with the author. Any publicity is good publicity, and if more people know about the writer's thoughts, they will create curiosity among the masses.
Furthermore, NFT memberships allow reading enthusiasts to access exclusive privileges like behind-the-scenes content. This way, you can promote your work in a new form in NFT social clubs.
## Downsides of Using NFTs For Publishing
The book industry has not completely switched to NFT technology due to some of its drawbacks. It is a fact that literary NFTs are much more expensive than physical books, and they are almost inaccessible to the general public. Unlike libraries, not everyone can have equal access to information in the NFT space.
Moreover, there are environmental concerns about NFTs. Until recently, the Ethereum network used a Proof-of-Work protocol, which relied on miners solving complex equations to add their digital collectibles to the blockchain. The calculations become more complicated over time and energy-draining to run the network. Thus, when NFTs were minted, it needed great computational powers causing environmental concerns due to the carbon footprint of such transaction. Thankfully, the Ethereum network recently migrated to a Proof-of-Stake protocol that has a considerably lower impact on the environment.
Other disadvantages of literary NFTs include:
### Incompatible with traditional distribution
In a traditional distribution system, books are printed according to the number of customer orders. They are then dispatched to third-party retailers for sale. However, literary NFTs don't work in this manner. You will not find NFT digital books in local bookstores.
Only writers who understand the tech can put out their books in the online marketplaces of the blockchain. Moreover, NFT marketplaces like OpenSea don't allow the minting of a conventional book or text formats like PDF files.
Unlike the traditional book distribution model, where retailers can sell millions of book copies, literary NFTs have only limited copies, around 100. This makes the NFT structure very different from the typical book distribution system.
### Limited audience: not everyone gets NFTs
It's easy to purchase regular ebooks from online stores like Amazon, Nobel, etc. It's even easier to illegally copy, reproduce and distribute physical books to many people due to piracy.
However, literary NFTs work differently. Many people can read digital files, but only one person can completely own the digital collectible. Only a limited amount of the writer's work is accessible at a given time due to copyright security.
The NFT technology encrypts a proper blockchain-published title and stores data in a decentralized nature on multiple computers. The reader must first decrypt the book to convert it into a readable format. Unlike ebooks, literary NFTs don't exist as free downloadable files that anyone can access. Hence, it has a limited audience.
## How To Publish a Literary NFT
NFTs have revolutionized the way authors can publish their creative work. This platform allows artists and writers to earn greater revenue for their skills. You can upload, store, mint, and sell unique pieces of writings in the NFT marketplaces.
Below is a step-by-step guide on how you can publish literary NFTs.
### 1. Create your masterpiece
The first step is to determine the type of content you want to create. It can be a novel with fictional characters or on-chain poetry. Your unique masterpiece will be regarded as a non-fungible token (NFT); something that cannot be owned elsewhere. These will be your cryptographic assets that cannot be traded or exchanged.
### 2. Load and mint it using a Web3 publishing platform
The second step is to choose the blockchain on which you want to upload your NFT. You have multiple options like Ethereum, Solana, Flow, etc., Then you need to download a digital wallet that is compatible with your selected Blockchain. It will store your cryptocurrencies, and you can use it for transactions in the marketplace. Some of these wallets are MetaMask, Coin-based wallets, etc.,
Lastly, mint your individual NFTs on Web3 publishing platforms. This includes Mirror.xyz, Single.io, Orbis, etc.
### 3. Optional: list your NFTs on a marketplace
The third step is to select the marketplace. You have multiple options like:
OpenSea
What Is An Automated Market Maker (AMM)?
Automated market makers were among the first decentralized finance applications and are still among the most well-known DeFi products today. In 2018, Uniswap became the first decentralized platform to effectively use an automated market maker (AMM) system when it launched.
Before then, traders could only make use of order books, which pair traders based on their bids and offers to facilitate trading on conventional centralized exchanges. This required the exchange operator to keep a price-organized list of all active buy and sell orders for each asset.
Each trading pair handled by the exchange had a corresponding order book (a pair of assets that can be traded for one another). It was an effective and popular strategy except that it required that you rely on and follow the directives of a central authority.
However, the need for more decentralized methods of exchange drove companies to the drawing table and that was the founding of Automated Market Makers.
## What is an Automated Market Maker (AMM)?
Simply put, an Automated Market Makers (AMMs) is a decentralized exchange (DEX) protocol that uses formulas to determine the price of assets. Where centralized exchanges like Binance value assets using an order book, Automated Market Makers use a pricing algorithm. Platforms that use AMMs automated cryptocurrency trading and make it fully decentralized through the use of smart contracts and deft tokenomics.
Users essentially trade against the liquidity locked within smart contracts, not the counterparties, as is traditionally the case. Liquidity pools are a common name for these smart contracts.
It should be noted that in conventional exchanges, the position of a liquidity provider is only open to Businesses or the affluent. However, with AMMs, any entity that satisfies the conditions programmed into the smart contract is eligible to become a liquidity provider. Uniswap, Balancer, SushiSwap, and PancakeSwap are a few examples of AMMs.
## How do Automated Market Makers work?
An AMM's primary goal is to guarantee that users can always trade cryptocurrency, even in the absence of counterparties with matching offers. Because of this, AMMs do not rely on order books to set up trading pairs. Instead, they use smart contracts to manage unique crypto asset pools created especially to supply the liquidity required to support frictionless trading.
For example, when trades take place on a decentralized exchange like Kraken, it is directly between user wallets. In other words, if you sell BTC for ETH or SOL, there is a buyer of ETH using BTC on the other side of the exchange. This transaction can be described as peer-to-peer (P2P).
AMMs, on the other hand, might be thought of as peer-to-contract (P2C). Since trades take place between users and contracts, counterparties in the conventional sense are not required. There is nothing like order types on an AMM because it doesn’t use order books. Instead, a formula determines the price you will receive for an asset you want to buy or sell. Though it's important to note that some potential AMM designs in the future might overcome this restriction.
Furthermore, what are called trading pairs on centralized exchanges exist on DEXes as Individual "liquidity pools". For instance, you would need to locate a BTC/ETH liquidity pool if you wanted to exchange Bitcoin for Ether. AMMs employ predetermined mathematical calculations to ensure that the ratio of assets in liquidity pools is as balanced as possible and to get rid of inconsistencies in the pricing of pooled assets.
For instance, top DeFi exchange protocol, Uniswap, and many other DeFi systems establish the mathematical relationship between the specific assets maintained in the liquidity pools using the straightforward x*y=k equation.
Here, x signifies the value of Asset A, y stands for Asset B, and k remains a constant.
In essence, the liquidity pools of Uniswap always maintain a condition in which the result of multiplying the prices of Assets A and B is always the same. This guarantees that consumers can always get a quoted price for their chosen deal as long as there is enough liquidity in the pool. This leads to the next question:
## What is a Liquidity Pool?
From the above definitions, it is clear that Automated Market Makers rely heavily on Liquidity pools to facilitate trade. Let us now examine what liquidity is and the process through which these DeFi Protocols maintain their liquidity.
A liquidity pool is a reserve where individuals or businesses can deposit crypto assets which will then be used to enable future transactions. Similar to how FX traders buy and sell currency pairs, liquidity pools normally accept two cryptocurrency pairs. For instance, a trader can use an AMM to sell Bitcoin (BTC) and buy Ether (ETH) from a BTC/ETH liquidity pool, and vice versa. Some liquidity pools and AMMs simultaneously manage multiple cryptocurrencies depending on the AMM and decentralized exchange.
## What is a Liquidity Provider?
As mentioned earlier, centralized exchanges usually only accommodate dedicated market makers, companies, and extremely rich individuals. However, Automated Market Makers have made it possible for literally anyone to deposit assets in their liquidity pools. These individuals or business entities are called liquidity providers. With AMMs, anyone who satisfies the necessary criteria can act as a liquidity provider.
Although the specifications differ amongst liquidity pools, most smart contracts demand that you deposit a specific amount of tokens which is usually a substantial amount, typically in the form of cryptos like Ether, Bitcoin, or Binance Coin.
In turn for providing this liquidity, providers get network fees from all trading activity taking place within their liquidity pool. It's one of several ways bitcoin owners can use their holdings to generate passive income.
## Impermanent loss
Notwithstanding although, passive income is always attractive to investors, there are dangers connected to contributing to liquidity pools, one of which is Impermanent loss. This occurs when the same tokens have different values within and outside of a liquidity pool. Since the price-adjusting algorithms of liquidity pools only care about maintaining a balance between the values of the assets within a pool, when the price ratio changes, the pools adjust to fit.
Withdrawing your assets from a liquidity pool when this adjustment is unfavorable can result in a loss. Of course, the loss only occurs if you remove the assets from the pool which is why it is called impermanent.
However, Keeping your assets locked in a liquidity pool for fear of impermanent loss also prevents you from taking advantage of other profitable chances.
Hence stablecoin or wrapped token pairs, as well as other token pairs with similar values, operate well with AMMs as opposed to those with larger ratio gaps. Impermanent loss is usually insignificant if the price ratio between token pairs remains within a modest range.
Just be sure you understand the concept of impermanent loss in relation to your preferred AMM before locking your funds into their liquidity pools.
## Concentrated Liquidity
Concentrated liquidity is one of the newest developments in AMM development. In fact, it is one of the hallmarks of the most recent version of Uniswap, v3. This particular feature is intended to increase the effectiveness of the price-adjusting process, reduce slippage, and enable liquidity providers to charge larger fees.
With concentrated liquidity, Liquidity Providers can allocate assets to particular price points. Liquidity Providers can design unique price curves that suit their preferences by combining various concentrated liquidity positions. Additionally, this enables them to get trading commissions based on the liquidity offered at particular price ranges rather than the liquidity of the entire pool.
## Constant Product Formula
The Constant product formula is a mathematical formula introduced by Ethereum founder Vitalik Buterin which is as follows:
tokenA_balance(p) * tokenB_balance(p) = k
Uniswap later adapted it to the more popular x * y = k formula
In practice it works like this; token price in a liquidity pool is determined by the constant, "k," which denotes a constant asset balance. In an ETH/BTC Liquidity pool, the price of ETH increases every time it is purchased since there is less ETH in the pool than there was before the purchase while the corresponding BTC in the pool decreases in price. In contrast, when more ETH is added to the pool, the price of BTC increases.
This allows the pool to maintain a steady balance where the combined value of ETH and BTC in the pool is always equal. In the end, whatever the level of price volatility, there will eventually be a return to a condition of equilibrium that reflects a fairly accurate market price. The strategy encourages traders to profit from price discrepancies between the AMM and other crypto exchanges until it is balanced again if the AMM price strays too far from market pricing on other exchanges.
## Automated Market Maker Variations
50/50 token pair pools: This is the type of AMM operated by Uniswap. This ground-breaking technology enables users to construct a liquidity pool using any pair of ERC-20 tokens in a 50/50 ratio. It is, so far, the most reliable and oldest AMM model on Ethereum.
Multiple assets liquidity pools: Instead of having just a trading pair in one liquidity pool, some Protocols find a way to stretch it. For example the Automated Market Maker, Balancer goes one step further than Uniswap. By enabling customers to build dynamic liquidity pools of up to eight different assets in any ratio, Balancer pushes the boundaries of Uniswap and increases the adaptability of AMMs.
Similar Assets Liquidity pools: Another AMM model is the type operated by Curve where digital assets of a similar class such as stablecoins get locked in the same liquidity pool. This enables it to provide effective trades, and offer some of the cheapest deals and rates in the market while addressing the liquidity issue.
## Popular DeFi Platforms Using AMM
### Uniswap
### Uniswap
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Year In Review: Sensorium In 2022
We ended last year with the promise that we would raise the bar in 2023. And that we did.
New beginnings, endings and re-starts, the world has seen it all in 2022, especially in tech. We’re finally talking more about the metaverse, blockchain technology, artificial intelligence and, of course, crypto. The future looks exciting.
And Sensorium is leading the charge with the launch of some long-awaited VR and blockchain projects.
Here are some of the milestones we reached this year:
Wakatta is now Sensorium Chain
Last year, the Sensorium team embarked on a mission to create an independent layer-two blockchain tailored to the needs of our products and the rapidly growing metaverse arena.
Wakatta was designed from scratch to deliver a solid set of technical features, including ultra-low fees, fast transactions, and the possibility to mint traditional and programmable NFTs.
At the time, options for ready-made infrastructure solutions with those characteristics weren’t available. The changing nature of the market gave birth to new solutions, better optimized to serve the needs of Web3 environments.
After long consideration and a broad-market assessment, Sensorium decided to retake control of the Wakatta project as part of an M&A deal. All initial investors in the Wakatta Seed Round were compensated in full in the context of this operation.
Moving forward, we are taking the progress made with Wakatta and leveraging it for the creation of Sensorium Chain — an enterprise network that will provide a seamless experience to all our ecosystem users, turning SENSO into a single-token solution that enables purchases of assets and exclusive content without the need of an additional currency for gas fees.
Sensorium Chain is part of a company-wide Web3 strategy to ensure all products are ready to support the exciting applications of this new paradigm. From DAO services and self-sovereign identity to true ownership and token-based payments, we are building a future where users are always at the forefront of innovation and take a leading role in shaping their digital footprint.
Stay tuned with the development of Sensorium Chain. Follow us on Twitter, Discord, and Telegram (Chat / Channel)
Sensorium Galaxy's Carl Cox Avatar Featured On Mixmag Cover
Sensorium is pleased to announce that its collaboration with legendary electronic music DJ and artist Carl Cox is on full display in the December issue of Mixmag, one of the world's leading electronic music and clubbing publications.
In a first-of-its-kind, Carl Cox graces the digital cover of the famed magazine in the form of his avatar, created by Sensorium for a series of upcoming virtual reality concerts the pioneering artist is slated to perform within Sensorium Galaxy.
SENSO
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Sensorians Unite - The Grand Meme Battle Is Back
New year, new memes. And to celebrate entering what we hope to be a very exciting season for us, we’re holding the second round of our grand meme battle - and giving away a spectacular prize pool. Ready, set, go!
## Timeline:
Selection Round: January 18 - 25
Voting: January 26 - 30 (Until 16:00 UTC)
Winner announcement: January 31 ## Judging: During the selection round, the 10 best memes will be handpicked by our team in a closed vote. Next up, a public poll will be held in our English Telegram chat, where our community of Sensorians will have the chance to choose the best meme out of the 10 finalists. ## Entry requirements: - Fill in this Google form. - Retweet the pinned post from SENSO’s Twitter account. - A meme picture must be posted to the English Telegram chat with the hashtag #SENSO_Memebattle2. - Only one meme can be submitted per account. - English only. We want everyone to be able to understand the memes so that we can all enjoy them the same way. - Image and text must be easy to understand. PNG and JPEG formats are accepted. (No GIF) - Pro tip: To better your odds, it’s worth making sure your memes are directly related to the Sensorium VR worlds’ lore, our AI avatars, our incredible AI DJs or SENSO token. ## No-nos:
- Plagiarism: submitting the same meme more than once.
- Submitting more than one meme per participant.
- Cheating and contributing to a fake vote volume in the open poll.
- Offensive or obscene content, religious themes, nudity, etc. ## Total prize pool:
3400 SENSO
1st place: 1,000 SENSO
2nd place: 800 SENSO
3rd place: 550 SENSO
Remaining 7 finalists: 150 SENSO Looking forward to seeing all the brilliant memes from our community of Sensorians. Good luck!
Voting: January 26 - 30 (Until 16:00 UTC)
Winner announcement: January 31 ## Judging: During the selection round, the 10 best memes will be handpicked by our team in a closed vote. Next up, a public poll will be held in our English Telegram chat, where our community of Sensorians will have the chance to choose the best meme out of the 10 finalists. ## Entry requirements: - Fill in this Google form. - Retweet the pinned post from SENSO’s Twitter account. - A meme picture must be posted to the English Telegram chat with the hashtag #SENSO_Memebattle2. - Only one meme can be submitted per account. - English only. We want everyone to be able to understand the memes so that we can all enjoy them the same way. - Image and text must be easy to understand. PNG and JPEG formats are accepted. (No GIF) - Pro tip: To better your odds, it’s worth making sure your memes are directly related to the Sensorium VR worlds’ lore, our AI avatars, our incredible AI DJs or SENSO token. ## No-nos:
- Plagiarism: submitting the same meme more than once.
- Submitting more than one meme per participant.
- Cheating and contributing to a fake vote volume in the open poll.
- Offensive or obscene content, religious themes, nudity, etc. ## Total prize pool:
3400 SENSO
1st place: 1,000 SENSO
2nd place: 800 SENSO
3rd place: 550 SENSO
Remaining 7 finalists: 150 SENSO Looking forward to seeing all the brilliant memes from our community of Sensorians. Good luck!
Ho Ho Hodl SENSO! Create A Christmas Story with An AI Avatar & Win SENSO Tokens
‘Tis the season for, you’ve guessed it, some holiday virtual goodies. Feeling the Christmas spirit already? Great. We’re challenging you to share your festive experiences with a Sensorium AI avatar and try your luck at winning some of the 5000 SENSO tokens up for grabs. At last, a Christmas gift that needs no wrapping!
## How Do I Participate?
### Step #1 Download the Sensorium mobile App: iOS
Android *If you have our app already installed, make sure you're up to date with the latest version released. For a better experience, we recommend deleting the old version and downloading the newest.
** Discover all the features you can explore with the Sensorium mobile app (AI text chat, video talk, AR mode, dancing and much more). ### Step #2 Register, create your avatar and go to the “Chat” mode. ### Step #3 Kick off a conversation and ask your AI companion about Christmas or New Year, in English, via the text chat. You can use emojis!
### Step #1 Download the Sensorium mobile App: iOS
Android *If you have our app already installed, make sure you're up to date with the latest version released. For a better experience, we recommend deleting the old version and downloading the newest.
** Discover all the features you can explore with the Sensorium mobile app (AI text chat, video talk, AR mode, dancing and much more). ### Step #2 Register, create your avatar and go to the “Chat” mode. ### Step #3 Kick off a conversation and ask your AI companion about Christmas or New Year, in English, via the text chat. You can use emojis!
Sensorium Teams Up With Polygon Studios To Accelerate The Development And Adoption Of Web3 Projects
Sensorium, the company behind the industry-leading Sensorium Galaxy metaverse, is pleased to announce that it is entering into a collaboration agreement with Polygon Studios.
As part of this wide-ranging alliance, Polygon’s blockchain infrastructure will be crucial in underpinning and furthering Sensorium’s Web3 developments, supporting token and NFT-related features within the Sensorium Galaxy metaverse, SENSO dApp, and the recently announced UNDER project.
The first Sensorium product to rely on Polygon’s infrastructure will be SENSO DAPP — a Play-to-Earn tycoon game where players are tasked with scouting NFT artists, organizing metaverse music events and selling tickets in return for SENSO token rewards.
“Polygon is a go-to hub for some of the most important Web3 projects and having the platform as our partner is an important step in raising the ambitions we have for Sensorium's blockchain ecosystem. The move will also help us create better opportunities for our community to engage with cutting-edge technology and enter a revolutionary new era in digital experiences, which is one of the greatest goals at Sensorium”, explains Alexander Firsov, Sensorium’s Chief Web3 Officer.
Tens of thousands of decentralized apps (dApps) having been built on Polygon so far, the platform has become a major force in the push for Web3 development and adoption, with services catering to segments of the industry, ranging from decentralized finance (DeFi) to gaming and metaverses.
Urvit Goel, VP of Global Games and Platform Business Development at Polygon, said: "In collaborating with Polygon, Sensorium will be able to tap into a vast, sustainable, and highly composable ecosystem and offer its users low-cost and efficient transactions backed by Ethereum's robust security model. We're eager to see the Sensorium ecosystem grow and flourish under this alliance."
More specifically, Polygon provides key Web3 properties to its users, including scalability, security and Ethereum-compatibility, which Sensorium will now be leveraging across its range of products.
Sensorium is edging closer to the public release of Sensorium Galaxy, a metaverse dedicated to delivering high-end entertainment events, and developed hand in hand with the world’s top technological and content partners.
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더 보기What Is An Automated Market Maker (AMM)?
Automated market makers were among the first decentralized finance applications and are still among the most well-known DeFi products today. In 2018, Uniswap became the first decentralized platform to effectively use an automated market maker (AMM) system when it launched.
Before then, traders could only make use of order books, which pair traders based on their bids and offers to facilitate trading on conventional centralized exchanges. This required the exchange operator to keep a price-organized list of all active buy and sell orders for each asset.
Each trading pair handled by the exchange had a corresponding order book (a pair of assets that can be traded for one another). It was an effective and popular strategy except that it required that you rely on and follow the directives of a central authority.
However, the need for more decentralized methods of exchange drove companies to the drawing table and that was the founding of Automated Market Makers.
## What is an Automated Market Maker (AMM)?
Simply put, an Automated Market Makers (AMMs) is a decentralized exchange (DEX) protocol that uses formulas to determine the price of assets. Where centralized exchanges like Binance value assets using an order book, Automated Market Makers use a pricing algorithm. Platforms that use AMMs automated cryptocurrency trading and make it fully decentralized through the use of smart contracts and deft tokenomics.
Users essentially trade against the liquidity locked within smart contracts, not the counterparties, as is traditionally the case. Liquidity pools are a common name for these smart contracts.
It should be noted that in conventional exchanges, the position of a liquidity provider is only open to Businesses or the affluent. However, with AMMs, any entity that satisfies the conditions programmed into the smart contract is eligible to become a liquidity provider. Uniswap, Balancer, SushiSwap, and PancakeSwap are a few examples of AMMs.
## How do Automated Market Makers work?
An AMM's primary goal is to guarantee that users can always trade cryptocurrency, even in the absence of counterparties with matching offers. Because of this, AMMs do not rely on order books to set up trading pairs. Instead, they use smart contracts to manage unique crypto asset pools created especially to supply the liquidity required to support frictionless trading.
For example, when trades take place on a decentralized exchange like Kraken, it is directly between user wallets. In other words, if you sell BTC for ETH or SOL, there is a buyer of ETH using BTC on the other side of the exchange. This transaction can be described as peer-to-peer (P2P).
AMMs, on the other hand, might be thought of as peer-to-contract (P2C). Since trades take place between users and contracts, counterparties in the conventional sense are not required. There is nothing like order types on an AMM because it doesn’t use order books. Instead, a formula determines the price you will receive for an asset you want to buy or sell. Though it's important to note that some potential AMM designs in the future might overcome this restriction.
Furthermore, what are called trading pairs on centralized exchanges exist on DEXes as Individual "liquidity pools". For instance, you would need to locate a BTC/ETH liquidity pool if you wanted to exchange Bitcoin for Ether. AMMs employ predetermined mathematical calculations to ensure that the ratio of assets in liquidity pools is as balanced as possible and to get rid of inconsistencies in the pricing of pooled assets.
For instance, top DeFi exchange protocol, Uniswap, and many other DeFi systems establish the mathematical relationship between the specific assets maintained in the liquidity pools using the straightforward x*y=k equation.
Here, x signifies the value of Asset A, y stands for Asset B, and k remains a constant.
In essence, the liquidity pools of Uniswap always maintain a condition in which the result of multiplying the prices of Assets A and B is always the same. This guarantees that consumers can always get a quoted price for their chosen deal as long as there is enough liquidity in the pool. This leads to the next question:
## What is a Liquidity Pool?
From the above definitions, it is clear that Automated Market Makers rely heavily on Liquidity pools to facilitate trade. Let us now examine what liquidity is and the process through which these DeFi Protocols maintain their liquidity.
A liquidity pool is a reserve where individuals or businesses can deposit crypto assets which will then be used to enable future transactions. Similar to how FX traders buy and sell currency pairs, liquidity pools normally accept two cryptocurrency pairs. For instance, a trader can use an AMM to sell Bitcoin (BTC) and buy Ether (ETH) from a BTC/ETH liquidity pool, and vice versa. Some liquidity pools and AMMs simultaneously manage multiple cryptocurrencies depending on the AMM and decentralized exchange.
## What is a Liquidity Provider?
As mentioned earlier, centralized exchanges usually only accommodate dedicated market makers, companies, and extremely rich individuals. However, Automated Market Makers have made it possible for literally anyone to deposit assets in their liquidity pools. These individuals or business entities are called liquidity providers. With AMMs, anyone who satisfies the necessary criteria can act as a liquidity provider.
Although the specifications differ amongst liquidity pools, most smart contracts demand that you deposit a specific amount of tokens which is usually a substantial amount, typically in the form of cryptos like Ether, Bitcoin, or Binance Coin.
In turn for providing this liquidity, providers get network fees from all trading activity taking place within their liquidity pool. It's one of several ways bitcoin owners can use their holdings to generate passive income.
## Impermanent loss
Notwithstanding although, passive income is always attractive to investors, there are dangers connected to contributing to liquidity pools, one of which is Impermanent loss. This occurs when the same tokens have different values within and outside of a liquidity pool. Since the price-adjusting algorithms of liquidity pools only care about maintaining a balance between the values of the assets within a pool, when the price ratio changes, the pools adjust to fit.
Withdrawing your assets from a liquidity pool when this adjustment is unfavorable can result in a loss. Of course, the loss only occurs if you remove the assets from the pool which is why it is called impermanent.
However, Keeping your assets locked in a liquidity pool for fear of impermanent loss also prevents you from taking advantage of other profitable chances.
Hence stablecoin or wrapped token pairs, as well as other token pairs with similar values, operate well with AMMs as opposed to those with larger ratio gaps. Impermanent loss is usually insignificant if the price ratio between token pairs remains within a modest range.
Just be sure you understand the concept of impermanent loss in relation to your preferred AMM before locking your funds into their liquidity pools.
## Concentrated Liquidity
Concentrated liquidity is one of the newest developments in AMM development. In fact, it is one of the hallmarks of the most recent version of Uniswap, v3. This particular feature is intended to increase the effectiveness of the price-adjusting process, reduce slippage, and enable liquidity providers to charge larger fees.
With concentrated liquidity, Liquidity Providers can allocate assets to particular price points. Liquidity Providers can design unique price curves that suit their preferences by combining various concentrated liquidity positions. Additionally, this enables them to get trading commissions based on the liquidity offered at particular price ranges rather than the liquidity of the entire pool.
## Constant Product Formula
The Constant product formula is a mathematical formula introduced by Ethereum founder Vitalik Buterin which is as follows:
tokenA_balance(p) * tokenB_balance(p) = k
Uniswap later adapted it to the more popular x * y = k formula
In practice it works like this; token price in a liquidity pool is determined by the constant, "k," which denotes a constant asset balance. In an ETH/BTC Liquidity pool, the price of ETH increases every time it is purchased since there is less ETH in the pool than there was before the purchase while the corresponding BTC in the pool decreases in price. In contrast, when more ETH is added to the pool, the price of BTC increases.
This allows the pool to maintain a steady balance where the combined value of ETH and BTC in the pool is always equal. In the end, whatever the level of price volatility, there will eventually be a return to a condition of equilibrium that reflects a fairly accurate market price. The strategy encourages traders to profit from price discrepancies between the AMM and other crypto exchanges until it is balanced again if the AMM price strays too far from market pricing on other exchanges.
## Automated Market Maker Variations
50/50 token pair pools: This is the type of AMM operated by Uniswap. This ground-breaking technology enables users to construct a liquidity pool using any pair of ERC-20 tokens in a 50/50 ratio. It is, so far, the most reliable and oldest AMM model on Ethereum.
Multiple assets liquidity pools: Instead of having just a trading pair in one liquidity pool, some Protocols find a way to stretch it. For example the Automated Market Maker, Balancer goes one step further than Uniswap. By enabling customers to build dynamic liquidity pools of up to eight different assets in any ratio, Balancer pushes the boundaries of Uniswap and increases the adaptability of AMMs.
Similar Assets Liquidity pools: Another AMM model is the type operated by Curve where digital assets of a similar class such as stablecoins get locked in the same liquidity pool. This enables it to provide effective trades, and offer some of the cheapest deals and rates in the market while addressing the liquidity issue.
## Popular DeFi Platforms Using AMM
### Uniswap
### Uniswap
What Are Liquidity Pools? DeFi Liquidity Explained
The cryptocurrency market is a very active community that initiates thousands of transactions to be verified daily, but verifying transactions can be pretty slow.
Crypto liquidity pools provide a faster means of turning digital assets into cash, and this option can be helpful for people who make frequent transactions.
This article will inform you about liquidity pools, how they work, and several other things you should know about them.
## What is a liquidity pool?
A liquidity pool is a smart contract containing large portions of cryptocurrency, digital assets, tokens, or virtual coins locked up and ready to provide essential liquidity for networks that facilitate decentralized trading.
A decentralized exchange relies greatly on liquidity because of the regular rate at which transactions are made on their network; for this reason, decentralized exchanges need to be connected to liquidity pools that can help maintain a steady functional network that doesn’t delay transactions made by traders.
In crypto liquidity pools, digital assets are locked and ready for exchange. Liquidity pools serve as a digital asset reserve that can provide liquidity to help speed up transactions for decentralized finance (also known as DeFi) markets such as decentralized exchanges (also known as DEX).
Unlike traditional finance, which pairs buyers and sellers to complete a transaction, liquidity pools do not need to connect users to complete a trade. Instead, they function automatically through automated market makers (amms) that connect you to the smart contract with your requested digital assets locked up in them.
Automated market makers are algorithmic protocols that determine digital asset prices and automate asset trades on liquidity pools.
A liquidity pool gathers its assets through users called liquidity providers (also known as LPs), who contribute to a percentage of the crypto asset in a typical liquidity pool smart contract.
## How do liquidity pools work?
Liquidity pools use automated market makers (AMMs) that connect users aiming to trade pairs with the appropriate smart contracts for them. AMMs are the protocols used to determine the price of digital assets, and it does a great job of providing the most reasonably accurate market price on liquidity pools.
Before liquidity pools can achieve their principal function of providing enough liquidity for crypto markets worldwide, they will require the tokens of liquidity providers.
To become a liquidity provider, you can adapt the steps below to any liquidity pool platform of your choosing:
1. Credit your crypto wallet with the crypto tokens you aim to lock up in a liquidity pool and connect it to the liquidity pool platform of your choosing or sign up on a liquidity pool platform and credit the liquidity pool wallet of your account on that platform’s wallet.
2. Find the trading pairs you’d like to invest in and deposit an equally divided portion of your crypto investment into that trading pair; for example, to invest $1000 into an ETH and USDT trading pair, you would need to deposit $500 each in both ETH and USDT on that liquidity pool.
Once you’ve made your deposit, select the period you want to have it locked up in the pool.
After the period of lockup has elapsed, you, as a liquidity provider, will be rewarded with liquidity pool tokens according to your selected trading pair and liquidity pool platform.
However, during the lockup period, you will also acquire an earning portion of the transaction fees paid to make exchanges with the pool you committed your crypto.
Some crypto liquidity pools also provide the option of staking liquidity pool tokens in exchange for earning the platform's native token.
## Why are liquidity pools important?
Liquidity pools play a significant role in providing liquidity in illiquid markets and boosting the DeFi ecosystem. The low liquidity that peer-to-peer exchanges offer can slow down the speed of transactions in financial markets. Still, with the help of liquidity pools where tokens are locked up in smart contracts, people can make transactions quickly.
The price of assets on crypto liquidity pools is also very fair. They can only be influenced by the current market exchange rate, which offers relatively accurate prices for the assets they supply to liquidity pool intelligent contracts. Unlike traditional exchanges, where buyers and sellers can influence the bidding price of their transactions with other traders, liquidity pools provide a more consistent environment where prices can be more accurate.
## Pros of liquidity pools
### Faster transactions With sufficient liquidity being provided through liquidity pools, you can make faster transactions and turn your tokens into cash within a shorter period. Liquidity pools provide a faster means of making transactions than P2P exchanges, which require traders to release assets, verify trades and spend some time making transfers needed to complete the exchanges. Liquidity pools already have reserves of the crypto pair you wish to exchange, allowing for faster, trustworthy exchange. ### Secured exchange with reduced possibility of scam The process through which users of liquidity pools acquire their crypto pairs is secure compared to that of a P2P transaction. P2P transactions require two users to trust each other to complete their end of the contract. Still, with liquidity pools, automated market makers (amms) automatically connect users with contracts containing their trading pairs. Amms also releases the crypto already locked up in smart contracts. With such a system, people making transactions on a crypto network can quickly receive their assets without the possibility of the other trader refusing to release them. ### Fair price on exchanges Prices offered for exchanges on liquidity pools are not influenced by bias or greed, which P2P exchanges can be affected by because traders determine the trading price of their exchanges. Amms provides the market price for making exchanges on liquidity pools, and the prices amms provide are based on authentic information that users can trust. ## Cons of liquidity pools
### Scam Liquidity Pools The smart contract code of a liquidity pool may be accessible to developers. Developers with such access can breach the smart contract by obtaining all your assets locked in the liquidity pool without your permission. For this reason, users of liquidity pools are advised to do extensive research on the integrity of the liquidity pools they connect to their wallets and read the terms and conditions of the smart contract they join. ### Risky price change Since Automated Market Makers (AMMs) determine prices on liquidity pools, assets locked up in their smart contracts are subject to constant change. Amms constantly update the prices of trading pairs on the list of trading assets they offer on pools. ### Impermanent Loss The change in prices offered by liquidity pools can lead to a significant loss or gain of assets stored in the pool. The crypto market is volatile, and a tremendous price change can lead to losing assets locked up in a pool. Volatile changes can easily affect small asset portions, and lost assets may be unrecoverable for investors who only lock up a small asset portion to a liquidity pool. ## Trending liquidity providers
### Balancer
### Faster transactions With sufficient liquidity being provided through liquidity pools, you can make faster transactions and turn your tokens into cash within a shorter period. Liquidity pools provide a faster means of making transactions than P2P exchanges, which require traders to release assets, verify trades and spend some time making transfers needed to complete the exchanges. Liquidity pools already have reserves of the crypto pair you wish to exchange, allowing for faster, trustworthy exchange. ### Secured exchange with reduced possibility of scam The process through which users of liquidity pools acquire their crypto pairs is secure compared to that of a P2P transaction. P2P transactions require two users to trust each other to complete their end of the contract. Still, with liquidity pools, automated market makers (amms) automatically connect users with contracts containing their trading pairs. Amms also releases the crypto already locked up in smart contracts. With such a system, people making transactions on a crypto network can quickly receive their assets without the possibility of the other trader refusing to release them. ### Fair price on exchanges Prices offered for exchanges on liquidity pools are not influenced by bias or greed, which P2P exchanges can be affected by because traders determine the trading price of their exchanges. Amms provides the market price for making exchanges on liquidity pools, and the prices amms provide are based on authentic information that users can trust. ## Cons of liquidity pools
### Scam Liquidity Pools The smart contract code of a liquidity pool may be accessible to developers. Developers with such access can breach the smart contract by obtaining all your assets locked in the liquidity pool without your permission. For this reason, users of liquidity pools are advised to do extensive research on the integrity of the liquidity pools they connect to their wallets and read the terms and conditions of the smart contract they join. ### Risky price change Since Automated Market Makers (AMMs) determine prices on liquidity pools, assets locked up in their smart contracts are subject to constant change. Amms constantly update the prices of trading pairs on the list of trading assets they offer on pools. ### Impermanent Loss The change in prices offered by liquidity pools can lead to a significant loss or gain of assets stored in the pool. The crypto market is volatile, and a tremendous price change can lead to losing assets locked up in a pool. Volatile changes can easily affect small asset portions, and lost assets may be unrecoverable for investors who only lock up a small asset portion to a liquidity pool. ## Trending liquidity providers
### Balancer
What Is Ethereum 2.0 - And Why Is It So Important?
Back in 2015, Vitalik Buterin launched the Ethereum network.
At that time, he designed the Ethereum network to function through a proof of work consensus model and serve as an Ethereum blockchain platform where anyone could execute many activities at high speed.
The Ethereum network proof of work consensus model has a not-so-complex block reward mining process where complex mathematical puzzles verify transactions on the Ethereum network.
Through proof of work, Ethereum miners use mining to validate Ethereum transactions made on the Ethereum network and earn rewards for participating in the Ethereum ecosystem.
Recently, the Ethereum network went through two upgrades. Initially, the first upgrade was from proof of work Ethereum mainnet to a separate new network – a proof of stake beacon chain, created to serve as a new and improved Ethereum blockchain.
The proof of stake model is a consensus mechanism where Ethereum miners earn more block rewards depending on how much staked Eth they already have on the Ethereum network.
The Ethereum Foundation identifies the proof of stake layer as the "consensus layer or beacon chain," and the proof of work layer as the "execution layer or Ethereum mainnet." However, the Ethereum community identifies "Eth2" & "Eth1," as the "consensus" & "execution" layers, respectively.
After testing the proof of stake consensus layer as a separate layer, the Ethereum foundation has brought about the merge.
The merged Ethereum network is the second upgrade to the Ethereum network and is currently used to validate the blocks of transactions on the Ethereum blockchain.
After this read, you will realize the stages the Ethereum network has gone through and how they function to provide users with a scalable network.
## What is the Ethereum Merge?
The Ethereum Merge is the second phase of the upgrade and the current Ethereum network that combines two different consensus mechanisms.
The Ethereum mainnet and the Beacon chain are the consensus models that make up the Ethereum Merge. On Ethereum's merged block, the Ethereum mainnet and the Ethereum beacon chain function through proof of stake.
The merge signifies the complete change to the proof of stake validation protocol that utilizes staking services.
The Ethereum Merge now serves as a separate blockchain, swapping proof of work with the proof of stake validation process.
## What is Ethereum 2.0?
Ethereum 2.0 (also known as Beacon chain) is the first upgrade that validates transactions through the proof of stake consensus mechanism, unlike Ethereum 1.0, which validates transactions using proof of work.
On the Ethereum 2.0 network, validators of Ethereum transactions can earn more by staking rewards.
Ethereum 2.0 requires fewer gas fees and maintains the decentralized way users love to process transactions on the Ethereum blockchain. The Ethereum 2.0 upgrade also provides a platform where users can process more transactions simultaneously. The only discomfort regular miners might experience through this upgrade is that for anyone to partake in a validation process, they must stake a minimum of around 32 ETH tokens.
But anyone can still join one of the many staking pools to make up the difference. A staking pool combines several stakers aiming to achieve the total staking amount required to become a validator on Ethereum's blockchain.
## Ethereum 1.0 vs. Ethereum 2.0: What’s the difference?
Ethereum 2.0 functions on pos blockchains, while Ethereum 1.0 functions on pow blockchains. With pos blockchains, the Ethereum network's scalability is far higher than when it used just the pow blockchain.
Ethereum 2.0 provides an increased level of transaction speed. It provides a platform where users create secure smart contracts faster than before.
## What is Proof-of-Stake (PoS)?
Proof of Stake (or the PoS blockchain) is a consensus layer that allows users to earn more by staking eth tokens over time. The more you stake, the more block rewards you can earn.
The Proof of Stack model has increased the Ethereum network's scalability by a tremendous amount. The Ethereum network now provides an even more cost-friendly service approach to validating Ethereum transactions and earning a fair share of rewards.
A staking pool is easy to join. To become a validator, all you need to do is join staking pools. Ethereum 2.0’s proof of work verification process has increased the number of people who can participate in validating Ethereum transactions, adding a boost to the Ethereum ecosystem.
## Ethereum 2.0 Roadmap
The upgrade to Ethereum 2.0 has gone on since 2020, and these are the phases which it went through:
### Phase 0
This phase was the launch of Ethereum 2.0's beacon chain. During this phase, the beacon chain could only execute some activities on the execution layer as a live blockchain test.
### Phase 1
In this next phase, Ethereum 2.0 implements shard chains. Shard chains are the separate chains that will make up Ethereum's blockchain after this phase. Shard chains are a huge factor in increasing the scalability of Ethereum's network.
### Phase 1.5
Phase 1.5 is the merge phase that brought the pow and pos chains together to form one pos chain.
This phase did not affect any users of the former pow chain, and it provided a barely noticeable smooth transition.
### Phase 2
This phase needs to be more detailed but believed to be when the beacon chain can fully partake in all Ethereum network functionalities, such as the pow execution layer.
At this phase, Ethereum 2.0 will function at its full capabilities.
### Ethereum 1. x
This final phase also needs to be more detailed. It is described as a developmental phase where Ethereum developers strive to improve Ethereum 1.0 as users continue to enjoy the benefits of Ethereum 2.0.
## Benefits of Ethereum 2.0
### Greater scalability The proof of stake layer that Ethereum 2.0 functions on provides Eth holders with better, more considerable returns on their staked Eth. The Ethereum 2.0 network rewards Eth holders depending on the amount of Eth they retain on the Ethereum network, and this means the more Eth you stake, the more reward you can earn as a Validator. The new validation method is more cost-effective and an excellent method of increasing earnings by saving Eth. And with shard chains that enable 9,970 more transactions than the initial 30. The rate at which you can earn will increase at a faster rate. ### Increased decentralization Ethereum 2.0 provides more decentralization than Ethereum 1.0 did. It further strengthens the trend of decentralization in the crypto market. By implementing the proof of stack validation method, more people can become validators through staking pools or by staking their own Eth to become validators. A more significant number of Validators has emerged since the launch of Ethereum 2.0, and that has further increased the decentralized network of Ethereum transactions. ### Enhanced security Ethereum 2.0 uses proof of stake to validate transactions faster than Ethereum 1.0. With the increased speed Ethereum 2.0 processes transactions, Ethereum 2.0 has added many more layers to the already dependable level of security it provides. On Ethereum 2.0, Validation is faster. With proof of stake, Ethereum 2.0 has gathered a more significant number of Eth holders staking Eth, which they need to remain online to maintain. By locking down staked Eth, Ethereum 2.0 has provided a robust and reliable level of security for its users. ### Faster and cheaper transactions With Ethereum 2.0, the Ethereum community can validate transactions faster and experience reduced transaction fees. The new speed at which transactions can get processed simultaneously ensures that users of the Ethereum proof of stake network can enjoy a more excellent user experience. Ethereum 2.0 allows users to quickly and speedily execute activities with a less disruptive workflow. Ethereum 2.0's proof of stake transaction method also reduces the money spent on powering mining machines to maintain the Ethereum network. ## Conclusion The successful launch of Ethereum 2.0 is a major upgrade to the level of services provided by Ethereum. The Ethereum foundation believes that this major upgrade has increased Ethereum's network's scalability to a level of better-optimized cost. Ethereum's price on the crypto market has responded well to the upgrade to Ethereum 2.0. The upgrade will not only moderately decrease the electricity consumption required to validate Ethereum blockchain transactions, but it will also increase the rewards earned from it. Several crypto enthusiasts have tweeted that Ethereum's price on the crypto market might experience a drop post-merge. But the new Ethereum network is doing a great job, and you better believe the Ethereum foundation has much more innovation ready to share with the crypto market.
### Greater scalability The proof of stake layer that Ethereum 2.0 functions on provides Eth holders with better, more considerable returns on their staked Eth. The Ethereum 2.0 network rewards Eth holders depending on the amount of Eth they retain on the Ethereum network, and this means the more Eth you stake, the more reward you can earn as a Validator. The new validation method is more cost-effective and an excellent method of increasing earnings by saving Eth. And with shard chains that enable 9,970 more transactions than the initial 30. The rate at which you can earn will increase at a faster rate. ### Increased decentralization Ethereum 2.0 provides more decentralization than Ethereum 1.0 did. It further strengthens the trend of decentralization in the crypto market. By implementing the proof of stack validation method, more people can become validators through staking pools or by staking their own Eth to become validators. A more significant number of Validators has emerged since the launch of Ethereum 2.0, and that has further increased the decentralized network of Ethereum transactions. ### Enhanced security Ethereum 2.0 uses proof of stake to validate transactions faster than Ethereum 1.0. With the increased speed Ethereum 2.0 processes transactions, Ethereum 2.0 has added many more layers to the already dependable level of security it provides. On Ethereum 2.0, Validation is faster. With proof of stake, Ethereum 2.0 has gathered a more significant number of Eth holders staking Eth, which they need to remain online to maintain. By locking down staked Eth, Ethereum 2.0 has provided a robust and reliable level of security for its users. ### Faster and cheaper transactions With Ethereum 2.0, the Ethereum community can validate transactions faster and experience reduced transaction fees. The new speed at which transactions can get processed simultaneously ensures that users of the Ethereum proof of stake network can enjoy a more excellent user experience. Ethereum 2.0 allows users to quickly and speedily execute activities with a less disruptive workflow. Ethereum 2.0's proof of stake transaction method also reduces the money spent on powering mining machines to maintain the Ethereum network. ## Conclusion The successful launch of Ethereum 2.0 is a major upgrade to the level of services provided by Ethereum. The Ethereum foundation believes that this major upgrade has increased Ethereum's network's scalability to a level of better-optimized cost. Ethereum's price on the crypto market has responded well to the upgrade to Ethereum 2.0. The upgrade will not only moderately decrease the electricity consumption required to validate Ethereum blockchain transactions, but it will also increase the rewards earned from it. Several crypto enthusiasts have tweeted that Ethereum's price on the crypto market might experience a drop post-merge. But the new Ethereum network is doing a great job, and you better believe the Ethereum foundation has much more innovation ready to share with the crypto market.
Extended Reality
더 보기Moss Book II Review: The VR Sequel You Won't Want To Miss
Moss Book 2 is as pure a sequel as you can expect, taking inspiration from the critically acclaimed first game, Moss. Developed by Polyarc Games, the sequel picks up right from where the previous game ends. The second iteration stays true to the first game, going for a more evolutionary approach as compared to other sequels that try and reinvent the wheel, an aspect we appreciated throughout our playthrough.
You play as Quill, a cute mouse that you help navigate through intricately detailed worlds, trying your best to defeat minions with the three new weapons available in Quill's arsenal. You still also interact in the game as the Reader, providing you with an isometric point of view just like the original Moss. However, you now do a lot more compared to the first game.
Best Virtual Reality Movies To Watch In 2023
Virtual reality is making its way into almost every industry. From medicine to education to gaming and fashion. Movie studios are also increasingly experimenting with this unique technology to enhance the experience of users within digital environments.
How To Buy Metaverse Real Estate
Real estate is expensive. That’s a tough reality of life that many of us have come to face. But no, we’re not talking about London, New York or Paris. Turns out that some of the world’s most valuable land isn’t even on earth - and on top of that, it isn’t even real at all. Enter the metaverse. That’s right - investors are now paying millions of dollars in return for land that exists only in 3D virtual worlds. With the race heating up, crypto enthusiasts, tech followers and prospective metaverse users are being drawn in to snap up a piece of digital properties. But what’s the buzz all about, really? Today, we are taking a look at what’s behind the metaverse land rush and how you can join in.
NFTs
더 보기Literary NFTs: What Are They And Why Writers Should Care
Literary NFTs are a game-changing digital technology for creative people, especially authors. According to statistics, NFT's weekly sales volume has risen from 100 in 2017, going all the way up to around 15 to 50,000 in 2022.
This represents the huge popularity of NFT in recent years. This modern blockchain-based platform has opened a new avenue for book lovers. They can now publish and enjoy reading different literature formats using NFT technology.
Literary NFTs are revolutionizing and bringing a paradigm shift in the traditional ebooks and physical book publishing processes. NFT platforms provide better monetization opportunities and help remove the earning imbalance between the authors and publishing parties.
Hence, opening new doors for a direct reader-writer connection. This blog will cover the fundamentals of literary NFTs and their advantages and disadvantages for writers. So, let's get down and explore more.
## What Is a Literary NFT
Digital literary work is any unique text like novels, poetry collections, scripts, essays, audiobooks, or articles that are written electronically. It can be read and viewed online. When such a digital asset is minted on the blockchain as a non-fungible token, it is called a literary NFT.
Previously, writers had to approach publishers to print and sell their physical books in the traditional publishing industry. However, in the modern world, books are stored digitally using blockchain on a decentralized network of computers.
The immutable feature of this technology makes the writer's work resistant to modifications, censorship, and alterations. Thus, the NFT books cannot be changed without the author's consent. Hence, people can approach an NFT marketplace to buy or sell digital books.
## Benefits of Literary NFTs For Writers
Writers have mostly been underpaid and underappreciated for their written work. In the literary world, commercial publishers tell writers what's sellable and what genre is a flop in the market. Hence, writers often get fleeced and struggle to get paid what their work is worth.
One such example was Blake Butler. In 2008, the publishing industry rejected his work by calling it "dense language and complicated structure" content. But this did not stop Butler.
He transformed the book into a Graphics Interchange Format (GIF) of flashing pages and minted it as literary NFTs. Within a few days, some reading enthusiasts bought the NFT for five Ethereum, equivalent to $7,569.50 then.
This is the reason behind creative people exploring NFT platforms. They use a tech space to sell digital collectibles directly to the readers. Therefore, the NFT market benefits writers as it helps bypass the traditional publishing process. Hence, they can earn a better reward for their time and effort. Some other advantages are that they can:
### Retain Full Creative Control
Some agents do not like publishing books on controversial topics, no matter how good they are. Sometimes traditional publishers reject manuscripts. It is because they believe that this type of content and publication formats are not marketable. Their interests lie only in books written on hot topics that the public wants to read. Hence, this takes away the creative freedom of the author.
That's where literary NFTs work. Independent creators can mint their NFT projects without worrying about censorship. Thus, in this way, they can cut out the role of publishing companies and can express their creative ideas with full freedom.
### Form a Direct Relationship With the Reader
There is no role of commercial publishers in literary NFTs. Hence, you and your fans can directly connect with each other. They can buy NFT editions of their favorite books to show their support. Authors can now engage directly with their followers by commenting, tweeting, and Instagramming in community forums.
Writers can publish limited copies of their literary works as NFTs. Hence, this allows them to create hype and increase the value of their work among the buyers. This blockchain-based technology is helping to remove the intermediary publishing bodies, therefore, the literary NFTs can directly reach the audience. Thus, NFT holders can form direct relationships with the readers.
NFTs have helped build bridges between the author and the consumers. One of the new ways to connect and grab the attention of the fans is to offer limited edition versions of the book. The scarcity of digital books will create curiosity and compel the audience to pay premium prices for the collectible edition.
Moreover, new and independent authors should start a fan base community to become more visible in a sea of NFT writers. They can add variations like creating art or a signature text in every book version. In this way, consumers can get a unique copy of your work, which will arouse their interest in the book.
### Add format versatility to your content offer
Authors can engage with their fans by adding versatility and value to different editions of literary NFTs. Some creators provide their readers with unique features in the NFT books like bonus chapters, glossaries, author's notes, illustrations, etc.
Writers can offer content bonuses to their loyal fans. Moreover, readers can have the first digital NFT editions of the book. They can also enjoy digitally signed copies of their favorite literary NFTs. Hence, this helps writers connect with readers in a new way through content offers.
Support the disruption of royalty structures
Authors have historically been earning less due to the intermediary bodies in the publishing process. They have been given low salaries and receive less percentage of royalties. Retailers, publishers, and distributors capture the maximum profit by selling books.
Thus, NFT has changed the game for authors. As it allows them to immediately earn money (royalties) for their writing projects. Due to smart contracts, the author can even continue earning when the person who bought your book decides to sell it. You'll receive a percentage profit or royalties on secondary sales.
Hence, NFTs empower the authors, who can now dream of making a full-time living with writing. It can serve as a sustainable source of revenue generation every time someone buys or sells NFTs.
### Build a subscription around your content
You can start an NFT community to attract maximum support and subscriptions from a relevant audience. Followers who buy your literary NFTs can post on social media about the quality of your work. This can allow others to interact to develop a better understanding, not just of the work but the literary NFTs overall.
Moreover, fans directly communicate with their writers on NFT platforms so that they can spread hype about their one-to-one experience with the author. Any publicity is good publicity, and if more people know about the writer's thoughts, they will create curiosity among the masses.
Furthermore, NFT memberships allow reading enthusiasts to access exclusive privileges like behind-the-scenes content. This way, you can promote your work in a new form in NFT social clubs.
## Downsides of Using NFTs For Publishing
The book industry has not completely switched to NFT technology due to some of its drawbacks. It is a fact that literary NFTs are much more expensive than physical books, and they are almost inaccessible to the general public. Unlike libraries, not everyone can have equal access to information in the NFT space.
Moreover, there are environmental concerns about NFTs. Until recently, the Ethereum network used a Proof-of-Work protocol, which relied on miners solving complex equations to add their digital collectibles to the blockchain. The calculations become more complicated over time and energy-draining to run the network. Thus, when NFTs were minted, it needed great computational powers causing environmental concerns due to the carbon footprint of such transaction. Thankfully, the Ethereum network recently migrated to a Proof-of-Stake protocol that has a considerably lower impact on the environment.
Other disadvantages of literary NFTs include:
### Incompatible with traditional distribution
In a traditional distribution system, books are printed according to the number of customer orders. They are then dispatched to third-party retailers for sale. However, literary NFTs don't work in this manner. You will not find NFT digital books in local bookstores.
Only writers who understand the tech can put out their books in the online marketplaces of the blockchain. Moreover, NFT marketplaces like OpenSea don't allow the minting of a conventional book or text formats like PDF files.
Unlike the traditional book distribution model, where retailers can sell millions of book copies, literary NFTs have only limited copies, around 100. This makes the NFT structure very different from the typical book distribution system.
### Limited audience: not everyone gets NFTs
It's easy to purchase regular ebooks from online stores like Amazon, Nobel, etc. It's even easier to illegally copy, reproduce and distribute physical books to many people due to piracy.
However, literary NFTs work differently. Many people can read digital files, but only one person can completely own the digital collectible. Only a limited amount of the writer's work is accessible at a given time due to copyright security.
The NFT technology encrypts a proper blockchain-published title and stores data in a decentralized nature on multiple computers. The reader must first decrypt the book to convert it into a readable format. Unlike ebooks, literary NFTs don't exist as free downloadable files that anyone can access. Hence, it has a limited audience.
## How To Publish a Literary NFT
NFTs have revolutionized the way authors can publish their creative work. This platform allows artists and writers to earn greater revenue for their skills. You can upload, store, mint, and sell unique pieces of writings in the NFT marketplaces.
Below is a step-by-step guide on how you can publish literary NFTs.
### 1. Create your masterpiece
The first step is to determine the type of content you want to create. It can be a novel with fictional characters or on-chain poetry. Your unique masterpiece will be regarded as a non-fungible token (NFT); something that cannot be owned elsewhere. These will be your cryptographic assets that cannot be traded or exchanged.
### 2. Load and mint it using a Web3 publishing platform
The second step is to choose the blockchain on which you want to upload your NFT. You have multiple options like Ethereum, Solana, Flow, etc., Then you need to download a digital wallet that is compatible with your selected Blockchain. It will store your cryptocurrencies, and you can use it for transactions in the marketplace. Some of these wallets are MetaMask, Coin-based wallets, etc.,
Lastly, mint your individual NFTs on Web3 publishing platforms. This includes Mirror.xyz, Single.io, Orbis, etc.
### 3. Optional: list your NFTs on a marketplace
The third step is to select the marketplace. You have multiple options like:
OpenSea
What are Dynamic NFTs?
Non-fungible Tokens (NFTs) have attained widespread adoption throughout the web3 community. Major media outlets and high-profile public figures have also jumped into the NFT world by launching their own projects and collections.
Such non-replaceable tokens with unique identities are gaining exceptional momentum. Moreover, they are continually improving and expanding their limits; dynamic NFTs are a testimony of their radical evolution within the crypto realm.
Such tokens integrate the verifiable and on-of-a-kind qualities of static NFTs and the dynamic data inputs. We will start off this guide by learning about dynamic NFT, its working, and potential use cases. Afterwards, we will dig deeper into the dynamic NFTs by comparing them with familiar static NFTs.
## What are Dynamic NFTs (dNFTs)?
Dynamic NFT or dNFT is a blockchain token that can change its inherent properties based on external conditions. The changes are recorded in the metadata of the NFT. Take an example of tokenized real estate, if dynamic NFT represents a house, it will change the dNFT's metadata based on the situation, such as past sales and maintenance history. The varying conditions could also be a passage of time or information from sources monitoring real-world data, such as weathervanes or real-time sports scores.
These tokens are getting considerable hype, both in the natural and digital worlds, as they can take NFT to the next level by expanding its design space. Metadata changes are initiated through encoded instructions in dNFT smart contracts, which regulate the data change.
A smart contract is triggered by oracle, which gives access to on-chain and off-chain data sources, such as IoT data and web API. Moreover, dNFTs are not just limited metadata changes, they can also be minted when certain conditions are met, such as when you discover a hidden location in an augmented reality application.
## Applications of Dynamic NFTs:
Dynamic NFTs have unlocked many innovative applications. A few examples of dynamic NFT use cases are discussed below:
### Gaming:
Dynamic NFTs have extensive use in gaming apps, such as -
In-Game Characters: When a new in-game character appears on screen, its various traits are recorded in the dynamic NFT’s metadata. If this character evolves and upgrades, the metadata updates the changes to validate its growth.
Fantasy Sports: Another possible application is the transfer of NFT among players based on performance. For instance, if the owner loses a battle, then the NFT is automatically transferred to the victor.
Play to Earn Games: P2E games have gained a lot of traction over the past few years. There is a need to create unique in-game objects using this dynamic feature of NFTs, such as in-game currencies, power-ups, and in-game virtual goods. Moreover, the add-on-trait feature can upgrade the NFT armor or weapon. For instance, the NFT sword could be made more powerful when it is frequently used in a battle.
### Sports:
Sports can make use of dynamic NFT elements using sports collectibles. Candy Digital launched digital collectibles in the form of Major League Baseball (MLB) dynamic NFTs. When players' stats upgrade throughout the season, the dynamic NFTs are utilized on their respective NFT collectibles.
With static NFTs, it is impossible to change the stats of a player as the metadata would be permanent when the token was created. For instance, we have a dynamic NFT representing a football player. The dNFT's metadata contains information such as speed, strength, agility, assists, goal scores, etc.
If the player falls short in performance, the scorecard will get swindled. This means dynamic NFT can fetch off-chain data and upgrade the metadata accordingly. On the contrary, it would not be possible with static NFTs.
Moreover, sports collectibles can trigger other possibilities using dynamic NFTs. The owner of dNFT can predict the possible outcomes, such as the player’s scores at the end of the season. The one with the closest guesses will see an evolved avatar.
### Art:
Another possible dynamic NFT application is a digital version of artwork. Dynamic art evolves or changes its traits in response to varying circumstances. Besides ever-changing art displays, you can experiment with new ways of displaying digital art.
For instance, you can consider a dynamic collection of an NFT artwork that changes colors or hues, having specific time-bound features depending on the location, season, temperature, lunar position, or time of year.
Another example could be to associate the artwork with the creator’s life. For instance, Andy Warhol’s Marilyn Diptych can be experimented with by creating a calendar in which special dates are associated with Marilyn Monroe’s life. Specific hues or features commemorating these special events should be displayed on those days.
Likewise, images and music can be changed according to the users’ wallet history or other factors. It means the owner becomes the co-creator of a dynamic art piece.
### Fundraising
NFT fundraising can be more attractive using such tokens' dynamic nature. For instance, an ecosystem services company Regenerative Resources Co, which collects funds to conserve mangroves, is making use of dynamic NFTs.
An NFT for a short film was released, containing a single frame in the initial stage. Later on, more frames were added each time the film was resold. The process goes on till the film is completed.
### Government
Government can issue passports theoretically by using dynamic NFT elements that update the travel information of a person. No paperwork or stamp would be required as a person's travel history is stored in the blockchain. Moreover, the possibility of fraud is minimized as blockchain makes the information verifiable and immutable.
## How Does a Dynamic NFT Work?
Dynamic NFTs enable both on-chain and off-chain data computations using smart contracts. When you request an NFT, on-chain and off-chain data sources are evaluated by smart contracts to determine the best choice for users. The response is generated, which is presented to the users.
Here is a stepwise mechanism of Dynamic NFTs:
- A request to smart contracts is initiated for an NFT
- Smart contract receives the request to process it.
- The contract makes a call for on-chain data to process the results.
- The oracle is used to make a call for off-chain data, and results are processed.
- Both on-chain data and off-chain data are evaluated.
- The smart contract then offers suggestions to the users.
It means smart contracts provide instructions to expand, update, and change the dynamic NFTs with the passage of time. Smart contract first gauges if the token is liable to change or not. If yes, then it alters the metadata of the NFT based on off-chain and on-chain data.
## Static NFTs vs Dynamic NFTs: What's the Difference?
Static NFTs are more popular within the blockchain world than dynamic NFTs. Static tokens have immutable and permanent traits that make this token valuable. As mentioned earlier, dynamic NFT takes data from both on-chain and off-chain events to change its metadata after minting. At the same time, a static NFT cannot adapt to external conditions.
Along the same line, use cases that require regular updates and modification of data are best suited for dynamic NFTs, while applications that deal with fixed data should opt for static NFTs.
In short, these tokens lack adaptability in comparison to dynamic NFTs. That said, dynamic NFTs are liable to vulnerabilities because of the variable data structure of the token. Static data inputs, on the other hand, ensure increased security because the metadata is stored in the form of a single file.
## Conclusion
Non-fungible tokens have become a focal point in the world of technology, particularly in the Web3 community. Amidst all the hype of possibilities and innovations, dynamic NFT has got a sheer volume of attention because of its ability to change its inherent properties based on external data.
Dynamic NFT is still a new concept, but it is poised to upgrade the world of NFT by offering an extensive range of use cases and improved capacity of web3 spheres. With the success of such living tokens, dynamic NFTs might dive into more innovations and create new incredible use cases.
Best NFT Rarity Tools
The exponential growth of the NFT space is making even the most skeptical investors take an interest in this market. Aligned with this rising enthusiasm, demand for the best NFT tools seems equally affected as investors try to identify the next best non-fungible asset out there.
Rarity reigns supreme when it comes to getting the most for your money in any collectible market. It is essential that you track trending NFTs if you want to turn a profit. Sniping NFTs is the act of buying NFTs that are priced below their value due to the seller's unawareness of their rarity. Fortunately, there are rarity tools, often referred to as rarity sniper tools, to help you with that.
This article introduces you to the concept of NFT rarity and reviews some of the top NFT tools currently available. Let's get to it.
## What Is NFT Rarity? Several new NFTs have hit the market, but how do they differ in trait value? What makes some NFTs sell for millions while others sell for less? It has to do with rarity. NFT rarity determines how rare and valuable an NFT is. Collectors highly prize truly rare NFTs, which makes them more expensive. Consequently, people want to know whether the NFT they own is rare or whether the one they plan to purchase is rare. ## NFT Rarity Calculation Methods It is possible to calculate the rarity of an NFT using several methods. By calculating the rarity of an NFT trait based on its rarest trait, taking the average rarity of traits, or examining rarity statistics. ### Trait NFT Rarity Ranking To compare NFTs, one can simply compare the rarest attribute of each non-fungible token. Yet, this approach has one significant flaw despite its simplicity and straightforwardness: it ignores the NFT's overall rarity, just focusing on the rarest one. ### Average Trait Rarity Another method that will help you check NFT rarity is to average the rarity of traits present on the NFT. If an NFT had two traits, one with a 50% rarity and one with a 10% rarity, then its average trait rarity would be (50+10)/2 = 30%. The problem with this method is that it stresses the overall rarity of every trait, which means the single super NFT rare trait does not receive enough trait value, and the overall rarity value is diluted.
### Statistical Rarity In this method, you multiply all of an NFT's traits together to determine the NFT's overall rarity. If an NFT has two traits, one trait has a weight of 10%, and the other has a weight of 50%. That NFT would have a 5% 'statistical rarity' (10% * 50%). These three approaches have different results when comparing the rarity of some NFTs. Average Rarity and Statistical Rarity tend to overvalue many of the traits in an NFT, potentially diluting the value of an ultra-rare, one-of-a-kind trait. Trait Rarity faces the complete opposite problem by narrowing the calculation to the single rarest trait. A solution? Rarity Score.
## How to Calculate NFT Rarity The value and rarity of NFTs differ even if you have thousands of them in a single collection. Therefore, a decrease in supply leads to a rise in demand, which drives NFT prices up. However, supply value is not the only factor contributing to NFT rarity. To get an NFT rarity calculated, you can opt for various methods, as we already mentioned. Different parameters, such as rarity based on the rarest trait, will be considered by assessing all NFT traits statistically or by calculating the average rarity. A Rarity Score is therefore used to calculate the rarity of an NFT. Calculating the Rarity Score does not need to be done manually since various rarity tools can do it for you. With the right NFT rarity tool, you can view the results in just a few clicks. Rarity Score stresses single rare traits while including overall trait rarities in its calculation. To date, this is by far the best way to calculate rarity. Here's the formula: > [Rarity Score for a Trait Value] = 1 / ([Number of Items with that Trait Value] / [Total Number of Items in Collection])
## Best NFT Rarity Tools Although the formula is pretty straightforward, there's no point in doing all that math on your own to estimate the rarity of all of your desired NFTs. There are several investment tools available to assist you. Investments in NFTs come with many risks. Using a reliable NFT investment tool can help you check NFT rarity and stay on top of the changes effortlessly. Needless to say, these tools shouldn't be used as the only criterion for buying NFTs. Make sure you research the NFT market and project in question well before making a purchase. ### Rarity.tools
## What Is NFT Rarity? Several new NFTs have hit the market, but how do they differ in trait value? What makes some NFTs sell for millions while others sell for less? It has to do with rarity. NFT rarity determines how rare and valuable an NFT is. Collectors highly prize truly rare NFTs, which makes them more expensive. Consequently, people want to know whether the NFT they own is rare or whether the one they plan to purchase is rare. ## NFT Rarity Calculation Methods It is possible to calculate the rarity of an NFT using several methods. By calculating the rarity of an NFT trait based on its rarest trait, taking the average rarity of traits, or examining rarity statistics. ### Trait NFT Rarity Ranking To compare NFTs, one can simply compare the rarest attribute of each non-fungible token. Yet, this approach has one significant flaw despite its simplicity and straightforwardness: it ignores the NFT's overall rarity, just focusing on the rarest one. ### Average Trait Rarity Another method that will help you check NFT rarity is to average the rarity of traits present on the NFT. If an NFT had two traits, one with a 50% rarity and one with a 10% rarity, then its average trait rarity would be (50+10)/2 = 30%. The problem with this method is that it stresses the overall rarity of every trait, which means the single super NFT rare trait does not receive enough trait value, and the overall rarity value is diluted.
### Statistical Rarity In this method, you multiply all of an NFT's traits together to determine the NFT's overall rarity. If an NFT has two traits, one trait has a weight of 10%, and the other has a weight of 50%. That NFT would have a 5% 'statistical rarity' (10% * 50%). These three approaches have different results when comparing the rarity of some NFTs. Average Rarity and Statistical Rarity tend to overvalue many of the traits in an NFT, potentially diluting the value of an ultra-rare, one-of-a-kind trait. Trait Rarity faces the complete opposite problem by narrowing the calculation to the single rarest trait. A solution? Rarity Score.
## How to Calculate NFT Rarity The value and rarity of NFTs differ even if you have thousands of them in a single collection. Therefore, a decrease in supply leads to a rise in demand, which drives NFT prices up. However, supply value is not the only factor contributing to NFT rarity. To get an NFT rarity calculated, you can opt for various methods, as we already mentioned. Different parameters, such as rarity based on the rarest trait, will be considered by assessing all NFT traits statistically or by calculating the average rarity. A Rarity Score is therefore used to calculate the rarity of an NFT. Calculating the Rarity Score does not need to be done manually since various rarity tools can do it for you. With the right NFT rarity tool, you can view the results in just a few clicks. Rarity Score stresses single rare traits while including overall trait rarities in its calculation. To date, this is by far the best way to calculate rarity. Here's the formula: > [Rarity Score for a Trait Value] = 1 / ([Number of Items with that Trait Value] / [Total Number of Items in Collection])
## Best NFT Rarity Tools Although the formula is pretty straightforward, there's no point in doing all that math on your own to estimate the rarity of all of your desired NFTs. There are several investment tools available to assist you. Investments in NFTs come with many risks. Using a reliable NFT investment tool can help you check NFT rarity and stay on top of the changes effortlessly. Needless to say, these tools shouldn't be used as the only criterion for buying NFTs. Make sure you research the NFT market and project in question well before making a purchase. ### Rarity.tools
Metaverse
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6 Most Promising Metaverses To Know in 2023
The metaverse continues making strides after a year brimming with exciting technological developments and high-profile investments.
For 2023, the development of virtual worlds is expected to continue at high speed, with a focus on entertainment and gaming.
That is as we are seeing the consolidation of new technology such as blockchain, VR, AI and 5G.
With that in mind, here are the top metaverses you’ll want to keep an eye on this year.
## 1. Sensorium Galaxy
Top 10 Metaverse Crypto Projects To Watch In 2023
We’re living through transformative times, which can feel both exciting and hard to keep up with. All that talk of Web 3.0, cryptocurrencies and the metaverse, but most of us are still left scratching our heads when trying to understand what it actually means.
The good news is that while the technology might be a new concept, there are plenty of great projects already taking off and creating opportunities that could change your life in a profound way. In particular, the metaverse and crypto are coming together to shape the future of digital frontiers in areas like gaming, work, entertainment, education, shopping and much more.
With so much going on, it’s perhaps worth getting to know what’s this all about and which metaverse projects to keep a close eye on in 2023.
Virtual Artists Taking Over An AI-driven Metaverse
We’re living through exciting times — in the real world and beyond.
Only last year, the beginnings of the metaverse hype planted the seed of doubt: could our digital lives have a lot more to offer than what our current screen time would lead us to believe?
Claims of the upcoming materialization of ultra immersive content, virtual reality socialization, interaction with futuristic AI avatars and yes, plenty of unexpected but original experiences, have since abounded.
And quite frankly, that’s setting the bar high. After all, how much more innovative can a technology that’s well over three decades old suddenly be when it seems every corner of the internet has been already explored? Turns out, we might have only scratched the surface.
As we slowly step into the next iteration of the internet - what’s been dubbed the web3 - we are seeing technology like artificial intelligence, blockchain, and extended reality accelerating the creation of new autonomous entities that can live online alongside humans.
As if that’s not intriguing enough, this new dynamic is unleashing a wave of digital creativity that promises to re-imagine human expression and artistry.
Sensorium Galaxy is a metaverse stretching across several virtual worlds, each dedicated to a specific content hub.
More interestingly, the platform is focused on creating an all-virtual, ever-expanding digital and fully immersive environment where creators take advantage of emerging technology like virtual reality and artificial intelligence to birth a digital version of themselves and challenge the boundaries that constrain them in the physical world - creative or otherwise.
The result has been a constant stream of 24/7, 360 degree experiences that defy anything seen in art and music until now.
Avatars are a gateway into this future. They aren’t a new concept as we’re all familiar with avatars in gaming, internet forums and social networking platforms over the last decades.
But Sensorium takes it a step further with an impressive roster of legendary artists, including David Guetta, Armin van Buuren and Black Coffee.
Today’s advanced capabilities of motion capture and VR technology enable the creation of a photo realistic version of these performers, as avatars, in a way that perfectly recreates their presence and artistic brilliance in a digital ecosystem, down to the smallest detail.
AI
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What is Artificial Intelligence? A Quick and Easy Intro To AI
Can machines think? Alan Turing, the father of computer science, presented this question in 1950, before the mainstreaming of artificial intelligence (AI) was even on the horizon. Only in the late 1990s did AI start to flourish, building up to the boom we are witnessing today worldwide.
Artificial intelligence is no longer some make-believe system in a science fiction movie. We encounter AI in our everyday life. From our Netflix recommendations to that customer service "person" we thought we were chatting to, AI is everywhere you look.
In fact, the global artificial intelligence market is valued at over 136 billion dollars and is projected to increase by over 13x over the next eight years.
Why is that? The amount of data generated today by humans and machines goes beyond our human ability. Artificial intelligence systems, on the other hand, are able to make complex decisions based on this vast data without facing our challenges. AI does not sleep nor eat; it is available 24/7 without interruption (if we humans program it correctly).
However, as we all know, AI is not without its flaws. From ethical considerations such as gender and racial bias to the rise in unemployment, understanding AI requires grasping all its facets.
## What Is Artificial Intelligence?
A buzzword in the tech industry, the term artificial intelligence (AI) is often left undefined. The average Joe and Jane are left guessing what AI actually means.
Simply put, AI is a computer or system's ability to mimic human intelligence to perform tasks. Similarly to humans, AI regularly collects information to improve its ability to perform given tasks. In most cases, artificial intelligence systems look at large amounts of training data, analyze it for patterns, and then use it to make predictions.
This is a simple definition, but it gets more complex as many disagree on the correct definition. This feud is mainly split into two definitions, developed by Stuart Russell and Peter Norvig in their book "Artificial Intelligence: A Modern Approach."
They call it the human vs. ideal approach. To some, AI systems think and act like the human brain. To others, AI systems think and act rationally.
Whichever definition you prefer, the consensus is that there are three main skills inherent to AI:
- Learning processes: The AI focuses on acquiring data and creating algorithms that provide devices with instructions on how to complete a task.
- Reasoning processes: The AI chooses the right algorithm to reach its goal.
- Self-correction processes: The AI improves its algorithms to make sure they provide the most accurate results.
Moreover, while AI is mostly used, in many cases, it is just an umbrella term. Modern AI includes various sub-fields of machine learning and deep learning. We will get into this later on.
## Why is Artificial Intelligence Important?
AI is an important tool for virtually all modern organizations. The amount of data generated today goes beyond human ability. It would be near impossible to collect and make decisions based on the data available today with human labor alone.
AI systems offer efficient automation that humans cannot replicate. AI allows organizations to automate a repetitive task that was previously done manually. Unlike humans, AI does not get tired or bored. AI also analyzes data in a much faster time frame than humans and finds patterns that even experts could miss.
By taking over repetitive or even dangerous tasks, AI frees up the workforce to work on the tasks that were built for humans. AI might arguably not necessarily replace them but leave the tasks that require empathy and creativity to humans.
But why does this matter? Because AI is all around us and has proved successful at improving industries, many of which have a real impact on society. For example, AI has already dramatically impacted healthcare worldwide. From reducing operating costs to aiding in personalized treatment and increasing access to information across medical services, AI has life-changing impacts.
## Understanding the Pros and Cons of AI
AI technologies are not always sunshine and rainbows. While AI has undoubtedly changed society as we know it, it is not without flaws. Let's take a look at the pros and cons of AI.
## Advantages of artificial intelligence
### Without human error A computer does not make a mistake if it is programmed correctly. AI is not a victim of human error. Humans regularly make mistakes due to fatigue, distraction, or lack of experience. AI analyzes impossibly large amounts of data, identifies patterns, and makes complex decisions based on such patterns. This process is so meticulous that the risk of errors is reduced, and the chance of accuracy increases. Humans performing such tasks would inevitably have errors. We are naturally prone to making mistakes, but these can have large impacts in the workplace, for example, in the realm of cybersecurity. ### 24/7 Availability We need sleep, fuel, water, bathroom breaks; the list goes on. These routines are what make us human - and keep us alive. In reality, our brains are not built to focus for more than 45 minutes before starting to lose steam. An AI, on the other hand, is always ready to go. It is available 24 hours a day, seven days a week. As messed up as it sounds, an AI doesn't take a vacation or a sick day. Its productivity does not compare to a human's, highlighting how it is such a useful tool in an organization. ### Automation An AI loves repetition. We humans, not so much. Repetitive tasks become tedious over time and leave us with the sense that we are not reaching our full potential. AI is thus an advantageous way to automate repetition, freeing the workforce to focus on fulfilling work that involves uniquely human skills. For example, you would never want an AI as a therapist, as this job requires empathy. However, automating the repetitive tasks at a psychologist's practice would benefit all. By automating email responses, appointment bookings, medical paperwork, and more, a therapist would have more time to spend on their patients. ## Takes on dangerous tasks For some reason, AI tackling dangerous tasks is less discussed than other advantages. An AI can minimize risks in society and take on tasks that would prove hazardous and even deadly to humans. From defusing a bomb to entering a volcano, AI robots save humans from dangerous jobs every day. Other jobs are less dangerous but still risky to human health, such as waste management, mine exploration, and more. ## Disadvantages of artificial intelligence
### Reduces employment This is the #1 argument used to criticize AI. AI will inevitably replace traditional jobs, leading to unemployment. Companies are constantly looking to increase their efficiency and are figuring out that automation is the way to do so. While a company might benefit from AI, we must also consider - at what cost? A two-year study from McKinsey Global Institute found that in 2030, AI could eliminate as much as 30% of the world's human labor. However, many argue that these jobs were not good ones in the first place but rather repetitive and boring work. This way, the workforce can focus on work that is fulfilling, creative, and challenging. ### Ethical concerns On top of unemployment issues, there are real ethical concerns surrounding AI. One of these is AI bias, as these systems cannot be trusted to be neutral. Why is that? Because AI is created by biased humans. Computer scientist Joy Buolamwini's research uncovered large gender and racial bias in AI systems sold by IBM, Microsoft, Amazon, and other tech giants. The researcher argues that machine learning systems amplify "sexist hiring practicing" and "racist criminal justice procedures". Other ethical concerns arise surrounding the impact of AI on human interaction, cybersecurity, disinformation, and mass surveillance. ### Hight Cost AI does not come cheap. Creating AI systems requires huge costs, as well as updating the hardware and software regularly to meet requirements. Maintenance can also be expensive and, at times, an unexpected cost. However, the overall cost of AI depends on various factors. The type of software, the level of intelligence, the type of data fed into the system, and the algorithm accuracy will all impact the expense. ### Lack of creativity A key drawback of AI is its inability to be creative and innovative. AI cannot think outside the box. It is engineered to make decisions based on patterns in data. While it is highly intelligent, it cannot employ a creative approach to an issue. In this way, human intelligence is valuable and irreplaceable. Likewise to creativity, AI does not factor in emotions. Rather, AI is highly rational, and creating human connections is not its focus. While Emotional AI is developing, these systems are currently only processing and replicating human emotions rather than genuinely expressing emotion and empathy. ## Four Types of AI There are four distinctive types of artificial intelligence under the current classification system: reactive machines, limited memory, theory of mind, and self-awareness. Keep in mind that some of these have never been achieved and likely never will. ### Reactive machines Reactive AI is the most basic AI type performing basic operations with no learning involved or a conception of the past or future. It is programmed to obtain a predictable output based on an input. Reactive machines will respond to the same situation in the same way, every single time. They might sound dull, but these are reliable. An example of a reactive AI is a chess game, such as IBM's chess-playing computer Deep Blue. Deep Blue can choose the best chess move to win a game, but it cannot predict its opponent's moves. Other reactive machines include an email spam filter and Netflix recommendations. ### Limited memory Limited memory AI is the most used artificial intelligence technology used today. Unlike reactive AI, it learns from the past by storing previous data and using it to make better predictions. The data is historical and observational and is used in combination with pre-programmed information to make predictions. Limited memory AI is always present in every machine learning model, although ML can also be used as a reactive machine type. A well-known example seen today are self driving cars. These cars store data, including the speed of nearby vehicles, the distance of other cars, speed limits, and more. Using both this observational and pre-programmed knowledge, these limited memory AIs detect changes and patterns around them to adjust their driving. ### Theory of Mind A key criticism (or praise) of artificial intelligence is that AI cannot express and understand the emotions, desires, and beliefs of itself and others. This is exclusive to human intelligence and social interaction. However, one of the classifications of IA is the Theory of Mind. Although this type of IA does not yet exist, it would entail understanding that humans have thoughts, feelings, and emotions. The IA would then respond accordingly with emotional intelligence in mind. You might think this is already possible. Voice assistants like Siri show similar skills, but this is a one-way relationship. Siri does not really understand your own emotions, nor her own. For example, a self-driving car will likely never be able to understand the mental state of a driver or pedestrian to make predictions. It can only understand observational data such as speed limits and the actions of other vehicles. ### Self-awareness Think of it this way - self-awareness is when AI reaches enlightenment. AI becomes self-aware. This last classification feeds into the fear of most people, one only seen in science fiction movies. AI becomes aware of its own existence, reaching an independent intelligence that could become a threat to humanity. If self-awareness is achieved, AI will have desires, needs, and emotions, likewise to us. How will AI feel toward humans? ## Additional AI classifications There are three additional AI classifications: weak AI, strong AI, and super AI. ### Weak AI Also known as narrow AI or artificial narrow AI, weak AI is the most common of the three types. Weak AI focuses on doing one task very successfully by acting upon the rules imposed on it. It does not go beyond these rules. Rather than replicate human behavior, it is built to simulate human behavior. Weak AI's purpose is not to match human intelligence processes but is still highly intelligent at performing tasks. Virtual personal assistants such as Siri use weak AI, with the internet as a large database. Siri might be able to answer your questions and engage in a few funny remarks, but it still operates within limited rules. Siri cannot engage in conversations that it is not programmed to. Characters in a computer game are also weak AI. While they act within the context of their game, they cannot go beyond their game character. ### Strong AI Strong AI, also known as artificial general intelligence, goes beyond the imposed rules. It replicates the cognitive abilities of human beings. When an unknown task is provided, strong AI tries to apply knowledge from another subject to find a solution. Our human brain works this way. However, sorry to disappoint, strong AI only exists in theory. A strong AI would do what any human being is capable of, such as consciousness. ### Superintelligence Artificial superintelligence, known as super AI, is a form of AI that surpasses human intelligence. This AI has independent cognitive skills, emotional intelligence, desires, beliefs, consciousness, and more. Superintelligence AI surpasses the intelligence of all humans, including geniuses such as the father of computer science himself, Alan Turing. You've guessed it, super AI has also not been achieved. It is once again a theoretical possibility rather than a reality. Most AI development today focuses on achieving strong AI rather than superintelligence, as computer science has not yet reached such a point. Many theorists also caution against superintelligence, stating that AI surpassing human intelligence could threaten humanity. ## Machine learning vs Deep learning: What's the difference? When talking about AI, the terms machine learning and deep learning are regularly thrown around. There are key differences between the two, and their relationship is important to understand to have a larger grasp of the AI sphere. ### Machine learning Machine learning is a sub-field of artificial intelligence that allows a system to learn and improve from data using algorithms to perform a task without being explicitly programmed to do so. Instead of being programmed to do this, machine learning recognizes patterns in data so that predictions can be made once new data arrives. Simply put, machine learning is the practice of training an AI algorithm to make better predictions. ### Deep learning On the other hand, deep learning is a sub-field of machine learning that developed from this field. This is the practice of training an AI algorithm to make human-like decisions. Deep learning's main concept is to replicate the human brain's neural networks with artificial neural networks (ANN). Algorithms are created like in machine learning, but there are a lot more levels of algorithms creating these networks. While machine learning models consist of thousands of data points, deep learning engages with Big Data, millions of data points. Despite their differences, their relationship is important. Machine learning is an evolution of artificial intelligence, while deep learning is an evolution of machine learning. ## History of Artificial Intelligence While artificial intelligence has been booming in the last decade, the second half of the 20th century is arguably one of the most important times in AI history. One could argue its history began as far back as 250 BC with Ctesibius' water clock, but we'll keep it simple by starting in the 1950s. ### 1950s: Term AI coined & first programs Known as the father of computer science, Alan Turing published Computing Machinery and Intelligence in 1950. He proposed his answer to the great question "can machines think?" through the Turing Test. Later on, John McCarthy coined the term at the first AI conference at Dartmouth College in 1965. That same year, a computer program called Logic Theorist was released, written by Newel, Simon, and Shaw. Known as the first AI program, it was the first of its kind to perform automated reasoning. In 1957, machine learning arose. Known as the Perceptron, the first computer based on a neural network that learns through trial and error was developed by Frank Rosenblatt. In 1958, the man who coined the term AI, John McCarthy developed the programming language Lisp. At the time, it became the most used language for artificial intelligence research. ### 1960s: First industrial robot & first chatbox In 1961, the Unimate was the world's first industrial robot used in a General Motors plant in New Jersey. A hydraulic manipulator arm, Unimate could perform repetitive tasks and helped automate the operation of machinery. In 1965, Joseph Weizenbaum developed ELIZA at the MIT Artificial Intelligence Laboratory, a natural language processing computer program that resembles today's chatbots. He wanted to show the superficiality of communication between human and machine, but many saw human-like characteristics in ELIZA. ### 1970s - 1980s: AI Winters The 1970s saw a dark time for IA research. In 1973, James Lighthill's report to the British Science Research Council criticized the lack of progress in AI research. This led the government to reduce its support for artificial intelligence research and corporations soon followed. The period from 1974 to 1980 is known as the first "AI Winter" where there was little research and progress. Starting in the early 1980s, AI research was back, primarily on deep learning techniques and Feigenbaum's expert systems. However, this didn't last long, as the second AI winter fastly arrived and lasted until the mid-1990s. ### Late 1990s until today The late 1990s then paved the way to AI as we know it. An increase in computational power and data sparked an AI boom that is still present today. In 1997, IBM's Deep Blue, a chess computer, beat then world champion Garry Kasparov in a match. That same year, Jürgen Schmidhuber and Sepp Hochreiter released Long Short-Term Memory, a type of recurrent neural network that is used today in speech recognition. Throughout the 2000s, more advancements are made. In 2009, Google starts developing a driverless car, and in 2011, Siri became available. Perhaps one of the largest events happened in 2016 when Hanson Robotics showcased Sophia, a humanoid AI robot. ## What Are the Applications of AI? The applications of AI are endless. Artificial intelligence can be applied to every sector, throughout both industry and academia. Here are 7 applications of artificial intelligence seen today. ### Financial institutions AI has become a large part of the mainstream finance and banking industry. A majority of financial service companies say they have implemented AI in risk management (56%) and revenue generation (52%), reports Insider. Financial institutions, particularly banks, use AI to enhance the customer experience through 24/7 customer service options, improve digital banking, and more. Fraud prevention is an integral piece of the pie, with machine learning being used to detect fraudulent transactions. Moreover, algorithmic trading has been developed. This involves using AI systems to make trading decisions at speeds unthinkable to humans. Banks and funds own entire portfolios that are managed by AI and generate high-frequency trading. ### Science The field of science is vast and one that artificial intelligence can be widely applied. In the field of chemistry, machine learning AI has been used for drug design in predicting molecular properties and observing chemical reactions. Machine learning has been used for drug discovery and development, as well as improving clinical trials. AI is also highly applicable to astronomy. From forecasting solar activity to activities to space exploration and more, astronomists are already using this technology. ### Healthcare AI has the potential to improve the efficiency and quality of healthcare globally. Artificial intelligence is used today for evaluating exams such as CT scans, selecting the right treatments, and performing surgeries with robots. However, there is still a lot of progress to be implemented. In 2016, a study found that an AI formula chose the correct dose of drugs to give to transplant patients, improving the efficiency of this human process. There are tons of other tasks being developed for AI, such as analyzing genes, outcome predictions for surgeries, and treatment plan designs. ### Virtual Assistance A common application of AI seen today is virtual assistants such as Siri or Alexa. While its development began earlier on, in the 1990s, digital speech recognition became a feature of the computer. However, the first modern virtual assistant is known as Siri, who was first introduced with the iPhone 4s in 2011. Currently, there are over 4 billion voice assistants in use globally. Using machine learning and natural language processing, these virtual assistants match user text or voice input to execute actions. Whether it's a restaurant recommendation or the weather, AI is now at everyone's fingertips. Virtual assistance powered by artificial intelligence has also empowered disabled users, changing the accessibility game in the last decade. For example, smart home technology developed in recent years has hugely benefited those with limited mobility. ### Autonomous Vehicles Self driving vehicles were once a thing of sci-fi movies. Artificial intelligence has made these a reality and more widely accessible. This technology is already in use in not only private vehicles, but also public transportation and ride-sharing. Driverless vehicles are able to identify objects, interpret scenarios, and make safe decisions through a machine-learning algorithm. However, an AI vehicle does not necessarily need to be self driving. The installation of AI-based systems in new vehicles is expected to rise by 109% in 2025, compared to an 8% rate in 2015. This includes vehicles with speech and gesture recognition, eye tracking, virtual assistance, and more. ### E-Commerce Artificial intelligence technology is regularly applied to the e-commerce industry. AI is used to create recommendation engines that suggest products to customers in line with their browsing history. Within a website, AI-powered virtual shopping assistants and chatbots improve the user experience. While not always achieved, natural language processing is employed to keep conversations sounding personal and natural. AI can also help avoid credit card fraud, identify fake reviews, and much more. ### Hospitality The hospitality industry is increasingly using artificial intelligence to carry out tasks. One example is in-person customer service using AI robots. Hilton has used the Connie robot to provide guests with information, learn from these interactions, and adapt to each individual. Chatbots are also being used, allowing guests to get almost instant responses to their queries 24/7. Hotel staff would be unable to respond at such speed. However, not all applications of AI in the hospitality industry are geared toward customer service. The hospitality industry uses AI for data analysis to draw conclusions about guests and potential customers. The Dorchester Collection use Metis AI to sort through data collected through surveys and reviews to find out about their performance. ## Final Thoughts Artificial intelligence has changed how we collect, analyze, and make complex decisions according to data. Currently, data makes the world go round. The progress made in artificial intelligence is bound to keep taking the tech industry by storm, as well as all its other applicable industries. From drug and molecular research to finance, AI's vast applications show us that AI is here to stay and grow even further. The industry value of artificial intelligence is forecasted to increase by over 13x over the next eight years, making it one of the fastest-growing industries in the world. Everyone wants to cash in on AI's benefits. AI can tackle tasks that are dangerous to humans, work more efficiently and without error, as well as automate the most repetitive tasks, making it valuable to profit-making. However, it's important to consider gender and racial bias, unemployment rise, and AI's inability to be creative. Considering the latter, the human mind will always be valuable and irreplaceable. A machine cannot think outside the box. It cannot innovate and create freely by expressing emotions, thoughts, and feelings. That is if AI never reaches its Nirvana - self-awareness. But if that's ever the case, we will have bigger fish to fry than creativity.
### Without human error A computer does not make a mistake if it is programmed correctly. AI is not a victim of human error. Humans regularly make mistakes due to fatigue, distraction, or lack of experience. AI analyzes impossibly large amounts of data, identifies patterns, and makes complex decisions based on such patterns. This process is so meticulous that the risk of errors is reduced, and the chance of accuracy increases. Humans performing such tasks would inevitably have errors. We are naturally prone to making mistakes, but these can have large impacts in the workplace, for example, in the realm of cybersecurity. ### 24/7 Availability We need sleep, fuel, water, bathroom breaks; the list goes on. These routines are what make us human - and keep us alive. In reality, our brains are not built to focus for more than 45 minutes before starting to lose steam. An AI, on the other hand, is always ready to go. It is available 24 hours a day, seven days a week. As messed up as it sounds, an AI doesn't take a vacation or a sick day. Its productivity does not compare to a human's, highlighting how it is such a useful tool in an organization. ### Automation An AI loves repetition. We humans, not so much. Repetitive tasks become tedious over time and leave us with the sense that we are not reaching our full potential. AI is thus an advantageous way to automate repetition, freeing the workforce to focus on fulfilling work that involves uniquely human skills. For example, you would never want an AI as a therapist, as this job requires empathy. However, automating the repetitive tasks at a psychologist's practice would benefit all. By automating email responses, appointment bookings, medical paperwork, and more, a therapist would have more time to spend on their patients. ## Takes on dangerous tasks For some reason, AI tackling dangerous tasks is less discussed than other advantages. An AI can minimize risks in society and take on tasks that would prove hazardous and even deadly to humans. From defusing a bomb to entering a volcano, AI robots save humans from dangerous jobs every day. Other jobs are less dangerous but still risky to human health, such as waste management, mine exploration, and more. ## Disadvantages of artificial intelligence
### Reduces employment This is the #1 argument used to criticize AI. AI will inevitably replace traditional jobs, leading to unemployment. Companies are constantly looking to increase their efficiency and are figuring out that automation is the way to do so. While a company might benefit from AI, we must also consider - at what cost? A two-year study from McKinsey Global Institute found that in 2030, AI could eliminate as much as 30% of the world's human labor. However, many argue that these jobs were not good ones in the first place but rather repetitive and boring work. This way, the workforce can focus on work that is fulfilling, creative, and challenging. ### Ethical concerns On top of unemployment issues, there are real ethical concerns surrounding AI. One of these is AI bias, as these systems cannot be trusted to be neutral. Why is that? Because AI is created by biased humans. Computer scientist Joy Buolamwini's research uncovered large gender and racial bias in AI systems sold by IBM, Microsoft, Amazon, and other tech giants. The researcher argues that machine learning systems amplify "sexist hiring practicing" and "racist criminal justice procedures". Other ethical concerns arise surrounding the impact of AI on human interaction, cybersecurity, disinformation, and mass surveillance. ### Hight Cost AI does not come cheap. Creating AI systems requires huge costs, as well as updating the hardware and software regularly to meet requirements. Maintenance can also be expensive and, at times, an unexpected cost. However, the overall cost of AI depends on various factors. The type of software, the level of intelligence, the type of data fed into the system, and the algorithm accuracy will all impact the expense. ### Lack of creativity A key drawback of AI is its inability to be creative and innovative. AI cannot think outside the box. It is engineered to make decisions based on patterns in data. While it is highly intelligent, it cannot employ a creative approach to an issue. In this way, human intelligence is valuable and irreplaceable. Likewise to creativity, AI does not factor in emotions. Rather, AI is highly rational, and creating human connections is not its focus. While Emotional AI is developing, these systems are currently only processing and replicating human emotions rather than genuinely expressing emotion and empathy. ## Four Types of AI There are four distinctive types of artificial intelligence under the current classification system: reactive machines, limited memory, theory of mind, and self-awareness. Keep in mind that some of these have never been achieved and likely never will. ### Reactive machines Reactive AI is the most basic AI type performing basic operations with no learning involved or a conception of the past or future. It is programmed to obtain a predictable output based on an input. Reactive machines will respond to the same situation in the same way, every single time. They might sound dull, but these are reliable. An example of a reactive AI is a chess game, such as IBM's chess-playing computer Deep Blue. Deep Blue can choose the best chess move to win a game, but it cannot predict its opponent's moves. Other reactive machines include an email spam filter and Netflix recommendations. ### Limited memory Limited memory AI is the most used artificial intelligence technology used today. Unlike reactive AI, it learns from the past by storing previous data and using it to make better predictions. The data is historical and observational and is used in combination with pre-programmed information to make predictions. Limited memory AI is always present in every machine learning model, although ML can also be used as a reactive machine type. A well-known example seen today are self driving cars. These cars store data, including the speed of nearby vehicles, the distance of other cars, speed limits, and more. Using both this observational and pre-programmed knowledge, these limited memory AIs detect changes and patterns around them to adjust their driving. ### Theory of Mind A key criticism (or praise) of artificial intelligence is that AI cannot express and understand the emotions, desires, and beliefs of itself and others. This is exclusive to human intelligence and social interaction. However, one of the classifications of IA is the Theory of Mind. Although this type of IA does not yet exist, it would entail understanding that humans have thoughts, feelings, and emotions. The IA would then respond accordingly with emotional intelligence in mind. You might think this is already possible. Voice assistants like Siri show similar skills, but this is a one-way relationship. Siri does not really understand your own emotions, nor her own. For example, a self-driving car will likely never be able to understand the mental state of a driver or pedestrian to make predictions. It can only understand observational data such as speed limits and the actions of other vehicles. ### Self-awareness Think of it this way - self-awareness is when AI reaches enlightenment. AI becomes self-aware. This last classification feeds into the fear of most people, one only seen in science fiction movies. AI becomes aware of its own existence, reaching an independent intelligence that could become a threat to humanity. If self-awareness is achieved, AI will have desires, needs, and emotions, likewise to us. How will AI feel toward humans? ## Additional AI classifications There are three additional AI classifications: weak AI, strong AI, and super AI. ### Weak AI Also known as narrow AI or artificial narrow AI, weak AI is the most common of the three types. Weak AI focuses on doing one task very successfully by acting upon the rules imposed on it. It does not go beyond these rules. Rather than replicate human behavior, it is built to simulate human behavior. Weak AI's purpose is not to match human intelligence processes but is still highly intelligent at performing tasks. Virtual personal assistants such as Siri use weak AI, with the internet as a large database. Siri might be able to answer your questions and engage in a few funny remarks, but it still operates within limited rules. Siri cannot engage in conversations that it is not programmed to. Characters in a computer game are also weak AI. While they act within the context of their game, they cannot go beyond their game character. ### Strong AI Strong AI, also known as artificial general intelligence, goes beyond the imposed rules. It replicates the cognitive abilities of human beings. When an unknown task is provided, strong AI tries to apply knowledge from another subject to find a solution. Our human brain works this way. However, sorry to disappoint, strong AI only exists in theory. A strong AI would do what any human being is capable of, such as consciousness. ### Superintelligence Artificial superintelligence, known as super AI, is a form of AI that surpasses human intelligence. This AI has independent cognitive skills, emotional intelligence, desires, beliefs, consciousness, and more. Superintelligence AI surpasses the intelligence of all humans, including geniuses such as the father of computer science himself, Alan Turing. You've guessed it, super AI has also not been achieved. It is once again a theoretical possibility rather than a reality. Most AI development today focuses on achieving strong AI rather than superintelligence, as computer science has not yet reached such a point. Many theorists also caution against superintelligence, stating that AI surpassing human intelligence could threaten humanity. ## Machine learning vs Deep learning: What's the difference? When talking about AI, the terms machine learning and deep learning are regularly thrown around. There are key differences between the two, and their relationship is important to understand to have a larger grasp of the AI sphere. ### Machine learning Machine learning is a sub-field of artificial intelligence that allows a system to learn and improve from data using algorithms to perform a task without being explicitly programmed to do so. Instead of being programmed to do this, machine learning recognizes patterns in data so that predictions can be made once new data arrives. Simply put, machine learning is the practice of training an AI algorithm to make better predictions. ### Deep learning On the other hand, deep learning is a sub-field of machine learning that developed from this field. This is the practice of training an AI algorithm to make human-like decisions. Deep learning's main concept is to replicate the human brain's neural networks with artificial neural networks (ANN). Algorithms are created like in machine learning, but there are a lot more levels of algorithms creating these networks. While machine learning models consist of thousands of data points, deep learning engages with Big Data, millions of data points. Despite their differences, their relationship is important. Machine learning is an evolution of artificial intelligence, while deep learning is an evolution of machine learning. ## History of Artificial Intelligence While artificial intelligence has been booming in the last decade, the second half of the 20th century is arguably one of the most important times in AI history. One could argue its history began as far back as 250 BC with Ctesibius' water clock, but we'll keep it simple by starting in the 1950s. ### 1950s: Term AI coined & first programs Known as the father of computer science, Alan Turing published Computing Machinery and Intelligence in 1950. He proposed his answer to the great question "can machines think?" through the Turing Test. Later on, John McCarthy coined the term at the first AI conference at Dartmouth College in 1965. That same year, a computer program called Logic Theorist was released, written by Newel, Simon, and Shaw. Known as the first AI program, it was the first of its kind to perform automated reasoning. In 1957, machine learning arose. Known as the Perceptron, the first computer based on a neural network that learns through trial and error was developed by Frank Rosenblatt. In 1958, the man who coined the term AI, John McCarthy developed the programming language Lisp. At the time, it became the most used language for artificial intelligence research. ### 1960s: First industrial robot & first chatbox In 1961, the Unimate was the world's first industrial robot used in a General Motors plant in New Jersey. A hydraulic manipulator arm, Unimate could perform repetitive tasks and helped automate the operation of machinery. In 1965, Joseph Weizenbaum developed ELIZA at the MIT Artificial Intelligence Laboratory, a natural language processing computer program that resembles today's chatbots. He wanted to show the superficiality of communication between human and machine, but many saw human-like characteristics in ELIZA. ### 1970s - 1980s: AI Winters The 1970s saw a dark time for IA research. In 1973, James Lighthill's report to the British Science Research Council criticized the lack of progress in AI research. This led the government to reduce its support for artificial intelligence research and corporations soon followed. The period from 1974 to 1980 is known as the first "AI Winter" where there was little research and progress. Starting in the early 1980s, AI research was back, primarily on deep learning techniques and Feigenbaum's expert systems. However, this didn't last long, as the second AI winter fastly arrived and lasted until the mid-1990s. ### Late 1990s until today The late 1990s then paved the way to AI as we know it. An increase in computational power and data sparked an AI boom that is still present today. In 1997, IBM's Deep Blue, a chess computer, beat then world champion Garry Kasparov in a match. That same year, Jürgen Schmidhuber and Sepp Hochreiter released Long Short-Term Memory, a type of recurrent neural network that is used today in speech recognition. Throughout the 2000s, more advancements are made. In 2009, Google starts developing a driverless car, and in 2011, Siri became available. Perhaps one of the largest events happened in 2016 when Hanson Robotics showcased Sophia, a humanoid AI robot. ## What Are the Applications of AI? The applications of AI are endless. Artificial intelligence can be applied to every sector, throughout both industry and academia. Here are 7 applications of artificial intelligence seen today. ### Financial institutions AI has become a large part of the mainstream finance and banking industry. A majority of financial service companies say they have implemented AI in risk management (56%) and revenue generation (52%), reports Insider. Financial institutions, particularly banks, use AI to enhance the customer experience through 24/7 customer service options, improve digital banking, and more. Fraud prevention is an integral piece of the pie, with machine learning being used to detect fraudulent transactions. Moreover, algorithmic trading has been developed. This involves using AI systems to make trading decisions at speeds unthinkable to humans. Banks and funds own entire portfolios that are managed by AI and generate high-frequency trading. ### Science The field of science is vast and one that artificial intelligence can be widely applied. In the field of chemistry, machine learning AI has been used for drug design in predicting molecular properties and observing chemical reactions. Machine learning has been used for drug discovery and development, as well as improving clinical trials. AI is also highly applicable to astronomy. From forecasting solar activity to activities to space exploration and more, astronomists are already using this technology. ### Healthcare AI has the potential to improve the efficiency and quality of healthcare globally. Artificial intelligence is used today for evaluating exams such as CT scans, selecting the right treatments, and performing surgeries with robots. However, there is still a lot of progress to be implemented. In 2016, a study found that an AI formula chose the correct dose of drugs to give to transplant patients, improving the efficiency of this human process. There are tons of other tasks being developed for AI, such as analyzing genes, outcome predictions for surgeries, and treatment plan designs. ### Virtual Assistance A common application of AI seen today is virtual assistants such as Siri or Alexa. While its development began earlier on, in the 1990s, digital speech recognition became a feature of the computer. However, the first modern virtual assistant is known as Siri, who was first introduced with the iPhone 4s in 2011. Currently, there are over 4 billion voice assistants in use globally. Using machine learning and natural language processing, these virtual assistants match user text or voice input to execute actions. Whether it's a restaurant recommendation or the weather, AI is now at everyone's fingertips. Virtual assistance powered by artificial intelligence has also empowered disabled users, changing the accessibility game in the last decade. For example, smart home technology developed in recent years has hugely benefited those with limited mobility. ### Autonomous Vehicles Self driving vehicles were once a thing of sci-fi movies. Artificial intelligence has made these a reality and more widely accessible. This technology is already in use in not only private vehicles, but also public transportation and ride-sharing. Driverless vehicles are able to identify objects, interpret scenarios, and make safe decisions through a machine-learning algorithm. However, an AI vehicle does not necessarily need to be self driving. The installation of AI-based systems in new vehicles is expected to rise by 109% in 2025, compared to an 8% rate in 2015. This includes vehicles with speech and gesture recognition, eye tracking, virtual assistance, and more. ### E-Commerce Artificial intelligence technology is regularly applied to the e-commerce industry. AI is used to create recommendation engines that suggest products to customers in line with their browsing history. Within a website, AI-powered virtual shopping assistants and chatbots improve the user experience. While not always achieved, natural language processing is employed to keep conversations sounding personal and natural. AI can also help avoid credit card fraud, identify fake reviews, and much more. ### Hospitality The hospitality industry is increasingly using artificial intelligence to carry out tasks. One example is in-person customer service using AI robots. Hilton has used the Connie robot to provide guests with information, learn from these interactions, and adapt to each individual. Chatbots are also being used, allowing guests to get almost instant responses to their queries 24/7. Hotel staff would be unable to respond at such speed. However, not all applications of AI in the hospitality industry are geared toward customer service. The hospitality industry uses AI for data analysis to draw conclusions about guests and potential customers. The Dorchester Collection use Metis AI to sort through data collected through surveys and reviews to find out about their performance. ## Final Thoughts Artificial intelligence has changed how we collect, analyze, and make complex decisions according to data. Currently, data makes the world go round. The progress made in artificial intelligence is bound to keep taking the tech industry by storm, as well as all its other applicable industries. From drug and molecular research to finance, AI's vast applications show us that AI is here to stay and grow even further. The industry value of artificial intelligence is forecasted to increase by over 13x over the next eight years, making it one of the fastest-growing industries in the world. Everyone wants to cash in on AI's benefits. AI can tackle tasks that are dangerous to humans, work more efficiently and without error, as well as automate the most repetitive tasks, making it valuable to profit-making. However, it's important to consider gender and racial bias, unemployment rise, and AI's inability to be creative. Considering the latter, the human mind will always be valuable and irreplaceable. A machine cannot think outside the box. It cannot innovate and create freely by expressing emotions, thoughts, and feelings. That is if AI never reaches its Nirvana - self-awareness. But if that's ever the case, we will have bigger fish to fry than creativity.
What Is AI Music - Challenging Creativity and Creation
Earlier this year, a curious event took place.
At the famed Glastonbury music festival, concertgoers were presented with an unexpected duet between Sir Paul McCartney and John Lennon of the track 'I've Got A Feeling' as part of a stunning three-hour set that also honored the legacy of the Beatles.
But hold up, you might be thinking. Was it the same and legendary John Lennon that was so shockingly assassinated in 1980? How could he possibly be taking to the stage - or any stage for that matter - of an English festival 42 years on?
Well, we have artificial intelligence and Peter Jackson, yes, the same director behind the Lord of the Rings trilogy, to thank for this beyond the grave apparition.
Jackson has lifted the veil on the technology involved to put together the performance, explaining that his team had developed a machine learning system that was taught what guitar, bass and singing sounds. More specifically, this custom-made AI was trained to learn how to sing like Sir Paul McCartney and John Lennon, thus being able to recreate virtual presences that are as realistic as possible.
AI music is bringing other legendary singers back to life.
As part of the “Lost Tapes of the 27 Club '', an initiative led by Canada-based mental health charity Over the Bridge, a collective of performers who’ve died at the age of 27 “released” novel tracks made entirely by Google’s AI program Mangenta.
Amy Winehouse, Kurt Cobain, and Jimi Hendrix are some of the artists covered by the project, with the lyrics and recorded music being entirely authored by AI.
## How Is AI Music Created
And the process of creating this new type of AI music is seemingly straightforward; all users have to do is feed a singer’s existing music into a bot that relies on machine learning to detect patterns and produce new music back on the pre-existing catalog.
The same technology was used to create three lines of voiceover by the late celebrity chef Antony Bourdain for the Roadrunner: A Film About Anthony Bourdain documentary, directed by Morgan Neville.
Want to listen to a podcast interview between Joe Rogan and Steve Jobs? That’s possible, too. And it doesn’t matter that they’ve never met or that the Apple founder has been dead for over a decade.
Elsewhere, artificial intelligence is also powering audio deepfakes, also known as voice cloning or synthetic voicing, whereby AI models are fed with training data. Typically, this information includes original recording and voice samples from a target person, who’s speaking or singing, for example. Based on this data set, AI is able to render an authentic sounding track that can be used to “speak” anything that is typed or said. This is known as text-to-speech or speech-to-speech.
Artificial intelligence technology has advanced to the point that it can replicate a human voice with an astonishing high level of accuracy.
Does this sound a little sci-fi-ish? It’s understandable, but perhaps you’ll change your mind after watching the now-infamous This is not Morgan Freeman video.
For some of us, however, this is old news as many will have come across deeptomcruise, a wildly popular TikTok account filled with plenty of Tom-Cruise-like content created entirely by AI. The movie star has no association with it, despite the unsuspecting viewer probably being none the wiser.
## What AI Tools Are Being Used To Make Music
Applying language processing and speech recognition in entertainment and music hasn’t been without controversy, with many raising eyebrows and highlighting ethical concerns.
Many detractors even question whether AI music can be considered a form of art and if it will ever be put side by side with the world's greatest masterpieces.
No matter where you stand on the debate, there’s no doubt that technology is aiding the creative process of many artists.
Currently, AI tools can seamlessly create music entirely from scratch, including original lyrics, instrumentation and music composition.
In fact, so-called songwriting AI companions like Jarvis and Jukebox are an increasingly popular resource for many aspiring musicians who generally lack access to more complex (read expensive) music creation tools.
Developed by OpenAI, Jukebox has become a household name, providing artists, lyrics and genres to generate original music samples from scratch. Some of the music styles considered by its AI neural network include a close approximation to renowned artists such as Celine Dion, Kanye West, and Tupac.
And while this sounds quite futuristic, technological tools of the sort have been around for quite a while. David Bowie, for example, helped create in the 1990’s a lyric-writing software called Verbasizer that worked as a sentence randomizer, aiding in the creation of lyrics.
The more recent potential of an AI music tool and a new type of AI-based song making hasn’t gone unnoticed in the music industry, by record companies and, of course, music streaming services.
No, I am not referring to the AI-powered algorithm that helps you find the next favorite music banger and curate Spotify playlists.
How about listening to music streams created entirely by AI and that can perfectly adapt to your mood? That’s the premise behind AI generative music streaming offerings such as Mubert.
You might be surprised, or at least intrigued, at the idea of shuffling through melodies that are unpredictable, adaptive, unique and impossible to ever be repeated. But that’s exactly what you get with generative AI.
AI-powered music leverages deep learning algorithms, neural networks s and other artificial intelligence tools to allow music to better adapt to the preference of users as much as it also lets them step into the creative process to co-exist and co-produce, so you don’t have to feel let out or that you’re just a mere consumer.
For users there’s also a major upside of resorting to a generative source as they don’t have to worry about headache-inducing problems such as copyright and licensing when trying to use something as a simple background music for, say, a YouTube vlog or content for social media platforms.
This is an approach now also being favored by the film industry where scouring through music libraries to find music can prove to be both time consuming and a legal, and financial hurdle.
That led composers Drew Silverstein, Sam Estes, and Michael Hobe, known for working on music for big-budget movies like The Dark Knight and Inception, to launch an AI-powered music platform - Amper Music.
The AI music company provides extensive original music creation tools for creators, video and podcast producers, video game designers, and more.
Again, this goes to show how AI and advancements in computer science are democratizing access and distribution well beyond the platforms currently available.
## Generative Music And the Beginnings of AI DJs
The technology has become so sophisticated and talented that it inspired the creation of the AI Song Contest, a Eurovision-style contest showcasing the best musical productions created by humans with the aid of AI.
And while all of this already seems lofty enough, the promise held by this musical revolution is reaching yet a new high with the emergence of AI DJs. That’s right, your next favorite performer might be a virtual being that also happens to be a gifted musician.
As we’ve seen before, AI music bots can compose and churn out entire creations of their own based on the information they’re being fed.
However, artificial intelligence virtual artists take it a step further, by not only delivering original music but doing it so with the sense of presence and interaction as a human performer.
Or at least that is the premise of the first breed of such musicians. Kàra Màr, an AI DJ developed by Sensorium, has made history after becoming the first of the kind to release an entire album on Spotify, titled “Anthropic Principle”.
They’re part of a larger lineup of virtual AI musicians, including Natisa Sitar and Ninalis, that are currently performing 24/7 as part of Sensorium Galaxy’s metaverse streaming service.
Unlike the generic version of an avatar, like the ones we’ve all come across online, Sensorium AI DJs are equipped with artificial intelligence technology that allows them to both deliver mind-blowing concerts but also interact with fans in a compelling way.
Each virtual being is fitted with a unique personality and background, as well as long-term memory. In other words, they will remember you and whatever past interactions you had. And as a bonus, they’re always available for a chat.
## What Next?
Now that we’ve gotten this far, one of the most frequently uttered questions is whether artificial intelligence will - or is bound to - replace human artists.
Having automated systems that can imitate and remix human expression might not be enough to simply overtake the power of a live performance and the sheer magic of human creativity and artistry.
And we're still a long way from seeing artificial intelligence and music delivering hit songs and winning Grammys, no matter how advanced the technology seems to be today.
On the other hand, the use of the technology is already paving the way for profound changes in the music industry.
It's been challenging the way licensed music is dealt with, music generators are re-imagining lyric creation and AI DJs are introducing the world to an entirely new music genre. Perhaps, then, it’s fair to say that AI music is a valuable tool for collaboration more than a Terminator-like technology that will erase and replace our favorite artists.
What Are AI Avatars: A Guide to Intelligent Virtual Beings
From virtual goods to burgeoning new digital worlds, technology is again pushing the boundaries of human experience into unchartered territory.
Many of us have recently faced a deluge of buzzwords like Web 3.0, blockchain, cryptocurrencies and, of course, the metaverse. And while much of it might seem incomprehensible for now, they’re not to be discarded as another tech fad.
In particular, the recent progress in the field of artificial intelligence is opening up new opportunities that are sure to greatly impact human experience in the upcoming era of virtual worlds, also known as the metaverse.
While in the past, AI helped machines carry out routine manual tasks, the technology can now also perform certain cognitive work thanks to its ability to learn, improve through experience and ultimately mimic human behaviors.
Not only that, but the rapid expansion of cheap and powerful computing power has seen the massive digitalization of world objects and processes. More recently, humans themselves are being digitized in the form of virtual avatars.
And artificial intelligence is giving a new meaning to the creation of virtual people by pushing the boundaries of technology to yet again a new frontier - AI lifeforms.
AI-powered avatars can serve endless purposes, from being hired out by real-world companies to teach new employees, to serving as trusted confidantes in the metaverse, to name just a few use cases.
Emerging virtual worlds, and their profitable economies, already encompass some 2.5 billion people, according to market research company L'Atelier.
Moving forward, the world will only become more virtual and humans are increasingly going to live in the metaverse, side by side with AI-driven virtual beings.
With that in mind, we’ll be having a closer look at what sharing our online experiences with artificial intelligent avatars will look like and how it could change the world as we know it.
Gaming
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How To play DeFi Kingdoms? How To Get Started [Beginner guide]
The steady growth within the blockchain sphere allows users to interact with each other in a peer-to-peer manner in a decentralized world. Aside from the trading of cryptocurrencies, blockchain is opening doors for smart-contract functionally, decentralized spaces, Play-to-Earn (P2E) games, and much more.
Over the last year, millions have jumped on the blockchain gaming bandwagon and the universe is expanding at a rapid pace. While there are limited options on the market, players are making the most of the opportunities presented in front of them and using their unique NFTs for monetary gains through P2E games.
Most recently, the MMORPG genre of the Play-to-Earn (P2E) blockchain gaming universe received an excellent addition in the shape of DeFi Kingdoms. Known for its medieval nostalgic and artistic gameplay in a 2D manner, DeFi Kingdoms depicts the story of a torn world which has suffered from all sorts of horrors but, you must unite the realms against the enemies and restore them to their original glory.
And while you’re off fighting the most vicious monsters, there’s some money to be made here since DeFi Kingdoms employ Play-to-Earn (P2E) mechanics. Our roundup today provides a comprehensive guide on how to play DeFi Kingdoms since there's too much depth to the game.
## What is DeFi Kingdoms?
Best Play-To-Earn Games To Try In 2023
Play-To-Earn gaming is all the rage now. Unlike traditional gaming, P2E brings real money and rewards into play, marking a departure from the business model that dominated the gaming industry for decades. From Splinterlands to Axie Infinity, players are rushing to games that can fulfill their entertainment needs while providing clear monetization paths in return for the time and effort they’ve put in.
Last week, we put together a quick intro to Play to Earn, but here’s a recap icymi: P2E gaming relies on blockchain, meaning that through this technology players are able to buy, sell or exchange in the form of non-fungible tokens (NFTs). This way, not only do they have control over their transactions every step of the way, players are even more crucially able to to retain ownership of the digital assets they’ve collected during the course of their gaming experience within a title. From there, players can choose to take those items outside of the game and exchange them across other marketplaces and trading platforms, in return for digital currencies (cryptocurrencies) or fiat money. In short, in-game items have real world value.
There are plenty of monetization opportunities within P2E. A player can, for example, purchase an NFT, upgrade it and then resell it for a higher price. Play to earn games are also highly attractive given the opportunities to get rewarded for activities completed within the game, like quests or duels.
The impact of P2E is accelerating profound changes in the gaming industry, with more than half of American and UK-based video game developers now saying they’re starting to implement blockchain technology and the use of NFTs in their titles, according to a recent survey.
How Blockchain Is Changing Gaming
The gaming industry has come a long way. From clunky consoles, quirky cartridges and simplistic games (Super Mario Bros, anyone?) to an entertainment outlet worth over $170 billion, powered by more than 2.7 billion players worldwide. Nowadays, gaming is an all-consuming experience, built on top of ultra-realistic animations, full of twists and turns throughout what seems like an endless stream of entertainment content.
There's seemingly nothing out of bounds when it comes to the possibilities of gaming. From personalizing avatars to customizing environments, purchasing tools and setting up tournaments, gamers are showered with all kinds of options to enhance the time spent in virtual worlds. But we've gone beyond the choosing of color palettes and skins; user preferences are changing the foundations of gaming.
Instead of the linear experiences of the past, gaming is increasingly an open-ended experience. This is evident by the success of sandbox titles like Minecraft, drawing in players with infinite playable loops that can take shape in the form of challenges and competitions, for example. The more players engage, the greater their status in the community and bigger the incentive to engage with other users. And how to better enhance this experience than by letting players create and exchange (or even sell) their own content. All of this opens the doors to a gaming experience without limits. Take Roblox or Fortnite, two games also seeing a meteoric rise on the back of user-generated content (UGC).
However, the majority of gaming experiences remain trapped in traditional, centralized models. This means that developers and gaming companies hold the most power within the ecosystem. Even when users come up with their original content, its value is entirely tied to the gaming dynamics set by developers.
That's because games are provided to users on a licensing basis, meaning that they’re free to play and be enjoyed, but in the end users don’t own any in-platform assets. Think of it like renting a house but not being the owner; gamers are simply renting games.
Players can be subjected to unexpected or arbitrary decisions that limit their possibilities, from the worth of their creations to the number of units that can be sold or exchanged in a marketplace.
Ultimately, centralized gaming models don't favor players - and certainly fail to reward the most loyal ones, despite them spending more time online.