Since the advent of the internet, information creation has outpaced our consumption. As per an IBM Marketing Cloud report, nearly 2.5 quintillion bytes of information are created daily. Interestingly, 90% of all current data was generated up until 2016. Despite the report's age, it underscores a crucial point.
The rise of AI has further sped up data creation. Today, we can't always tell if tools like GPT were used to create the content we encounter.
When founding Aureal, my vision was simple: to help creators monetize their work in an open market, the ultimate judge, and then we delved into the concepts of reputation and credibility.
The appeal of decentralized apps and communities is that, even as a developer or creator, my opinions are not dictated by commercial interests. We're all building this for humanity's betterment, and we're only as good as the information we consume.
Having been online for several years, it's clear that anyone can be a content creator. The addition of AI makes it even easier to produce content, even if it's generated from a trained model reading user-generated content.
In truth, everyone has an opinion, but not all opinions are worth hearing.
Every day, 2.2 quintillion bytes or about 2.2 billion gigabytes of data are created. To put this into perspective, every minute adds 550 new social media users, 300 hours of YouTube content, 67,444 million Instagram posts, 13 trillion Facebook likes, 510,000 comments, 293,000 status updates, and 136,000 uploaded photos.
Despite the average user spending 146 minutes on social media and a significant amount of time online, it's impossible to consume all the information available. Consuming all the data published in a single day would take several decades.
We've shifted from the age of information to the age of curation. If you know what you're seeking, you can join the 3.5 billion daily Google searches and explore the vast array of curated information from recommendation algorithm overlords and their purported reliable metrics.
Role of recommendations
Recommendation systems play a vital role in helping users navigate this information abundance. They depend on algorithms and metrics to curate and recommend content based on user preferences and interests. However, relying solely on these systems poses inherent challenges.
A significant challenge is the unreliability of the metrics used by recommendation algorithms. Personalization and commercial interests often influence these metrics, resulting in biased recommendations. Additionally, the transparency and accuracy of these metrics are not always guaranteed, making it hard for users to gauge the reliability of the information presented.
Another challenge is the susceptibility of recommendation systems to Sybil attacks. A malicious user may create multiple fake accounts to manipulate the recommendations and promote specific content or misinformation, further compromising the authenticity and trustworthiness of the information consumed.
What we have on Hive and What the World lacks
On Hive, we've implemented key features to revolutionize information curation and consumption. We have a trust-based reputation system that ensures only reliable and high-quality content is shown to users.
We also have credibility measures to assess content authenticity and integrity, enhancing the trustworthiness of the information presented.
We've introduced democratic curation practices, allowing users to influence the recommended content, fostering diversity and inclusivity.
Finally, we've addressed the false promise of one person one vote. Content curation is about the quality, not just the quantity of votes, ensuring deserving content gets recognized.
Rarity of Originality and the problem of information overload
Data replication, lack of originality, and trends exacerbate information overload in various ways.
Content replication across platforms leads to redundancy and clutter in the information ecosystem. Users must sift through multiple versions of the same information, making it hard to find accurate and reliable sources.
Content copying contributes to the overwhelming amount of information available. The ease of online content copying and sharing results in a surge of duplicated articles, blog posts, videos, and other media. This flood of replicated content adds to the noise and makes it harder to identify the original source or verify the information's credibility.
The lack of originality in content production perpetuates information overload. Instead of creating unique and valuable content, many resort to rehashing existing information or following popular trends. This results in a saturation of similar content, making it difficult to find fresh perspectives or insights.
The emphasis on trends further fuels information overload. In the age of social media and viral content, there's pressure to stay current with the latest trends and news. This results in repetitive and often superficial content, making it harder for users to sift through the noise and find meaningful, relevant information.
Hive should be the core of credible, authentic, and original information, and contributors should be generously rewarded. This is where the Epistral protocol comes in.
So here’s is what we’re doing.
We’re using the best pieces of Hive, and putting it on a layer 2, thanks to @disregardfiat for helping us out here.
At Epistral Protocol, we are undertaking an ambitious project to revolutionize the way information is consumed on the internet. We are in the process of creating the world’s first antimimetic network infrastructure. This unique infrastructure does not simply mimic or duplicate existing models. Instead, it introduces innovative systems and processes designed to promote originality and authenticity.
One of the key features of this infrastructure is its connectivity. We are working to connect it to other platforms that are currently not on Hive. This will create a vast, interconnected network, making it easier for users to access a wide variety of content from different sources.
In order to ensure swift and seamless access across this network, we are developing a unified identity system. This system will allow users to navigate the various platforms and access the content they need without any unnecessary hurdles or delays.
At the heart of our infrastructure are LLM models and vector databases. These advanced technologies are being used innovatively, coupled anti-symmetrically to promote the availability and consumption of original content.
Finally, to encourage the creation of original and authentic content, we are introducing a unique reward distribution system. This system is designed to reward originality, ensuring that those who produce unique and valuable content are appropriately compensated.
In conclusion, the antimimetic network infrastructure that we are building will transform the way users access and consume content. By prioritizing originality, authenticity, and credibility, we aim to ensure that the content you consume is not only interesting and relevant but also reliable and trustworthy.
While working on Aureal, we’re enabling creators specifically podcasters to monetise, which is the first part.
We’re transitioning to Epistral Protocol, where we’re ensuring originality and transparency in the recommendation systems so that you discover gems of the internet.
stay tuned for more.
Meanwhile read our whitepaper