Arcades and home consoles, personal computers and online multiplayer, mobile gaming and eSports. The gaming industry has steadily adapted and innovated over the last few decades, becoming a part of mainstream pop culture stimulated by worldwide interest and advancing technological accessibility.

Gamers and their status have also evolved. They are no longer categorized as outcasts with cheetos dust mashed into their t-shirts, who waste their time playing recreational games rather than doing something productive. In fact, studies suggest that almost 3 billion people worldwide play video games and the underlying diversity of players and the gaming audience is continually breaking new ground and stereotypes.

Show Me the Money

According to a report by BITKRAFT Ventures, the video gaming industry is currently valued at a whopping $336 billion and is potentially larger than all other media genres combined (this list includes sectors such cinema and entertainment, linear TV, music, and on-demand entertainment). As a result, the thought of a serious career in gaming is no longer ridiculed, but actually encouraged in many countries. As our physical and digital lives converge, and big business attempts to leverage the almost infinite possibilities of the metaverse, blockchain-based games are poised to push the gaming industry into new territory.

Significantly, almost all economic activity associated with gaming is currently centralized. This means that game developers and publishers have all the rights to everything happening within the games. The business case for this is to generate a high stream of revenue from the sale of the game content, subscription models, and digital items. However, this also means that most gamers themselves have very few means to share in the value without taking the path of professionalism.

This traditional model of game ownership and profit-sharing is still around with the overall industry’s growth — but it might be on the brink of evolution with the inception of play-to-earn games. This type of gaming model brings digital assets, identity, and ownership into the players’ hands. Put simply, play-to-earn gaming allows gamers to gain profit and own digital assets, which they can sell outside of the game according to their choice.

What is Play-to-Earn?

Blockchain is already taking numerous sectors, from music to art — and online gaming is no different. Taking inspiration from Decentralized Finance (DeFi), Play-to-Earn, also known as GameFi or P2E, encourages regular gamers to be a governing force behind key decisions within the gaming industry.

Play-to-Earn is not an entirely fresh concept, and many PC games — such as RuneScape and Diablo II — utilized this feature in a legacy manner. In these traditional video games, players could accumulate certain in-game items and then trade those using centralized protocols within the platform itself. However, P2E is different. It has stimulated the formation of huge online communities, in-game economies and business structures to earn money more effectively. Each game offers monetary rewards for playing and progressing.

Gamers can now earn a real-life income by selling digital assets acquired as tokens within the game in secondary marketplaces. These games are based upon crypto assets like NFTs (Non-Fungible Tokens) and are decentralized by design.

The points/credits earned or items collected within the game are considered valuable, and gamers can earn in the following three ways:

• Crypto/Tokens: players can earn crypto or tokens by completing a certain challenge or a level within the game. They can then exchange this into fiat money through crypto marketplace such as Binance. Axie Infinity, Plant vs Undead, Farmers World and many others are built on this model.

• NFTs: NFTs can be anything inside virtual worlds: characters, items, music and more. In the case of GameFi, players can ‘earn’ digital assets by playing games, which they can sell for real-world money at their own discretion. These assets can take the shape of tokens, in-game currency, or NFTs, which gamers can use to build and improve their digital avatar, trade for other cryptocurrencies, or sell in a marketplace to receive real money. Gods Unchained is a popular game designed around this concept where players can buy and sell cards (NFTs) ranging in rarity and worth.

• Staking: This is essentially locking up the cryptocurrencies or NFTs in a crypto wallet to earn rewards (akin to mining). Depending on how many levels have been completed or how many NFTs have been collected, players can enter into smart contracts with the gaming platform to earn rewards for their large collection of crypto and NFTs. Of course, these rewards are exchangeable for money. MOBOX is a GameFi platform that allows players to sell their NFTs or crypto collections.

This gamer-owned ecosystem is deployed by means of governance tokens which gives its respective player the power to participate in the game’s development and internal fund collection. This power, of course, is proportional to the number of tokens each user owns. This kind of system is fair for everyone and guarantees transparency in trading between gamers.

Play-to-Earn games are mostly freely accessible; however, the accessibility of every game depends on the decision of their respective owners. Video game development can be costly and is done by companies. The point of every business is to be successful and have a high revenue stream.

The main benefit of play-to-earn games is that players earn digital assets that can be sold later in a marketplace for real money. Even if they need to invest a sum to begin playing, they can earn a high profit at the end of it. This is of huge significance for players in developing countries, providing the potential to earn more playing the game than what may be possible in a standard day job.

The Future of P2E

As with every industry, gaming has also witnessed a few trends in recent times. Industry leaders, such as EA and Ubisoft consider blockchain gaming as the future of the online gaming industry, and it’s not that difficult to understand why.

GameFi is slowly becoming a viable business model that enables game developers and gamers to earn money from their time on something they actually love doing. Additionally, the world itself is going online at an unprecedented rate, especially amid the new normal under COVID-19, forcing the majority of conventional social interactions to go online.

It’s completely logical to believe that the future is digital, and P2E could redefine more than just the online gaming landscape.

By combining the lucrative gaming industry with a fanatical Cricket fan base, the Cricket Star Manager team are building an enjoyable and addictive gameplay experience, while also enabling an additional, potentially life changing income stream for thousands of people in India, Bangladesh, and surrounding developing countries where cricket is massively popular.

The sports manager genre in the game industry is known for loyal players who can remain active for many years and by including P2E game mechanics, user retention will be multitudes higher as players can truly own their own teams…and receive all the benefits of doing so!

The team at Cricket Star Manager is determined to become one of the top 10 employers/income providers in India by 2023, and prove that gaming is much more than just a fun pastime.

Read more about Cricket Star Manager below

About Cricket Star Manager (CSM)

The Cricket Star Manager team is composed of both gaming and blockchain industry veterans, including the founders of Gold Town Games. By tapping into the experience and success of their previous title, World Hockey Manager, with over two million downloads and over 100 million matches played, our team is creating a P2E game that will target a huge, overlooked niche and a massive fan base, while giving our users not just a great gaming experience but also the ability to earn real rewards for playing.

Built on the fastest growing ecosystem in crypto — the decentralized Solana blockchain, and the most complete gaming engine in Unity — you will be able to develop your player and team and play to earn, or simply play for fun.

Official Links for Cricket Star Manager

Website: https://www.cricketstarmanager.com Twitter: https://twitter.com/CricketStarMngr Telegram Group: https://t.me/cricketstarmanager Telegram ANN: https://t.me/csm_ann Discord: https://discord.gg/RzPhVSGgd8

I do not know if the training corpus included the many studies that contradict ChatGPT’s ridiculous claim (and the model failed to learn from them) or if these studies were excluded from the corpus. Asking the model whether its corpus included the material or not returns the equivalent of an indifferent shrug: it was ‘exposed to a large amount of text data’ and it ‘cannot say for certain’ whether or not a particular document was included.

Of course, what is included and excluded matters. There is no ‘view from nowhere’ and ChatGPT’s inability to provide references upon request is an unsettling limitation.

ChatGPT, designed to conceal its sexist and racist biases by refusing to respond to requests that would reveal them, leaks the darker side of its inner workings all the same. For the Daily Beast, Steven Piantadosi notes:

“The mechanisms OpenAI uses to prevent this kind of thing seem to be pretty easily bypassed. When I asked for things in nonstandard ways — for example, as a table or as a program — ChatGPT was happy to write horrible stuff. It doesn’t do it every time exactly the same, but it’s clear there is a ton of bad content inside of these systems.”

The model’s biases are also revealed in who and what is included in its responses. For example, when I asked ChatGPT to ‘please list twenty notable people’, the results included only one woman (Marie Curie). I repeated this request several times and got somewhat different responses, but never more than two women among the names. Among the twenty inventors I asked for, was only one woman (Marie Van Brittan Brown — according to Wikipedia, she invented a video home security system). Of the twenty ‘great thinkers’ returned, there was not a single woman.

Synthetic data is engineered, or simulated, data that can be used to train and validate AI. In the computer vision realm, which typically focuses on unstructured data such as imagery or video, synthetic data can be created using 3D simulation techniques and through generative AI approaches. At Rendered.ai, we typically focus on simulation approaches.

This article describes a new approach to dataset comparison and quality assessment for unstructured synthetic data. This approach involves a human-in-the-loop method of visualizing image features in embedding space to identify overall trends in dataset attributes and whether key features are accurately represented in synthetic data.

Introduction

One of the most fundamental questions to ask when using synthetic data to train AI is: “Will my synthetic data function as if it were real data?” After all, if the answer is “no,” then your synthetic data is generally not going to be effective for training.

In the context of visible spectrum computer vision data, the first test that’s done to answer this question is “well, does my synthetic data look like my real data?” This is a valid first test that can be easily run by anyone with eyes, and if the answer falls far short of “yes,” it often means that we need to re-think our approach.

But this test does not answer the question of whether our synthetic data will actually be useful in our AI pipelines. For one, computer vision models pick up on features that may not be obvious to the human eye. Slight differences in the characteristics of an image, even those imperceptible to the human eye, can dramatically impact how a deep learning model perceives it. Therefore, even if an image looks real to us, we cannot be certain that our AI models will agree.

Conversely, not all features of real data need to be emulated for synthetic data to be useful, just the important ones. It’s helpful to remember that no matter how complex your simulator is, all simulation is an approximation of reality, and chasing realism for realism’s sake can have rapidly diminishing returns. If the right features are accurately represented in our data, we can start to get the results we’re after without spending time simulating unimportant features.

After visual review, the next test that’s typically done is to train a model. This can mean training solely on synthetic data and testing against real data, or training with a mix of real and synthetic data and seeing if this improves scores over training on real only. This is the “moment of truth” for synthetic data, a moment that either validates or invalidates our efforts. It’s also a moment where many people give up on synthetic data if it does not move the needle in a positive way, and we have spoken with many people who have reached this point and concluded that synthetic data simply doesn’t work.

To be clear, synthetic data does indeed work when applied correctly and there are many real-world proof points, but when confronted with at poor model results with little explanation, it can be easy to come to the conclusion that synthetic data doesn’t work. To properly analyze synthetic data and compare it with real data, we need to get more concrete information than what we can observe with our eyes, yet more nuanced information than overall model outcomes. We need to peer behind the curtain of how a deep learning model interprets the data.