Introduction

In the age of artificial intelligence, data has emerged as one of the most valuable assets globally. Particularly for AI applications, the most prized datasets are derived from the everyday digital activities of billions of individuals, encompassing online browsing habits and engagement on social platforms.

The exploitation of user data by major tech corporations has been an ongoing concern, further exacerbated by the proliferation of AI technologies. Contemporary generative AI models heavily rely on extensive datasets for personalized training, prompting AI enterprises to source data from users across open networks and social media platforms through various methods. Essentially, this practice entails the appropriation of individuals’ private data on a global scale.

Simultaneously, numerous tech firms generate immense value, reaching into the billions or trillions of dollars, by leveraging AI models trained on user datasets, all while neglecting to share the financial gains with users. Consequently, users find themselves lacking control, privacy, and equitable compensation for the use of their personal data.

Masa aims to revolutionize the landscape by reshaping how users can benefit from the AI boom. By monetizing users’ digital footprints while safeguarding their privacy, Masa seeks to ensure that users receive fair compensation. This article delves into Masa’s groundbreaking AI pricing model, designed to empower billions of users to actively engage in the AI data economy.

AI Data Pledge: Earning Rewards through AI Training

Masa operates discreetly in the background, capturing users’ digital imprints as they engage in activities like reading emails, browsing social media platforms, visiting websites, and interacting within the digital realm. It guarantees the confidentiality and security of these traces by storing them in the private database zkSBTs (Zero-Knowledge Soulbound Tokens).

To participate, users simply need to access the Masa App, review the data within the Masa network, assess its significance, and opt to stake in different liquidity pools to receive rewards. The step-by-step process is outlined as follows:

Provide data to Masa Network

1. Quest to Earn: Engage in specific activities or tasks to provide data to the network. This includes actions like joining Discord groups, participating in trading competitions and AI model training, and completing data staking within the Masa App to receive rewards.

2. Surf to Earn: Utilize the Masa extension on Chrome to earn rewards while browsing the web. The extension autonomously captures valuable data points and securely stores them in a private database.

3. Nodes-to-Earn: Users and node operators serve as distributed data collection nodes by using the Masa Chrome extension tool and Masa Data Oracle. This allows for the acquisition of public and private data with reduced technical complexity, leading to network incentives.

4. Passive rewards: Masa’s ecosystem partners can leverage the Masa SDK to aggregate data and monitor community activities. Users can earn passive rewards through the aggregated data provided by Masa’s ecosystem partners.

Data Tokenization

Masa employs a point-based system to quantify the worth of users’ data within the Masa Network. Following the launch of the Masa mainnet on April 11, users will have direct access to view the accrued data points through the Masa App.

Subsequently, users can place their data into the staking pool to earn rewards. Within the Masa ecosystem, developers will establish diverse pledge pools to incentivize data contributions from various angles. For instance, developers of AI trading robots may prioritize rewarding contributors who provide data related to wallet interactions.

Enabling Hyper-Personalized AI Applications through Data Contribution

Masa drives AI advancements by incentivizing users to stake their data, paving the way for extensive innovation. At the core of Masa’s philosophy lies the belief that forthcoming AI models will possess a profound understanding of users surpassing self-awareness. This vision hinges on users sharing their personal datasets for AI training and optimization. For instance:

1. AI in Cryptocurrency Market Forecasting: By furnishing developers with real-time datasets encompassing user browsing histories, social sentiments, and on-chain interactions, Masa facilitates AI in identifying cryptocurrency market trends, exploring investment opportunities, and enhancing the accuracy of price predictions.
3. AI for NFT Portfolio Management: Leveraging Masa’s network data oracle, AI receives structured data on current NFT market trends and community sentiments. This enriches AI-driven NFT management tools, enabling the formulation and evaluation of more effective trading strategies.
5. AI in Political Prognostication: Drawing from a plethora of data including social sentiments, engagement metrics, and link interactions, Masa can effectively predict voter sentiment in presidential elections.

summary

Masa upholds the belief that users deserve to reap economic benefits from the flourishing AI landscape. To this end, Masa introduces a novel paradigm enabling users to train AI models using personal data while safeguarding their privacy and autonomy.

By engaging with the Masa ecosystem, users not only secure their digital footprints effectively but also contribute to the data economy in a fair and transparent manner. In the era of rapid AI advancement, the collaboration between human data contributors and AI developers, facilitated by platforms like Masa, is poised to revolutionize the trajectory of the AI data economy. Every click, search, and interaction holds the potential to yield substantial value.

As the dawn of the AI data economy approaches, the launch of the Masa Network mainnet and tokens is scheduled around April 11, urging participants to prepare for this transformative milestone.

Disclaimer:

1. This article is reprinted from [foresightnews], the original title is “AI “Data Pledge”: Masa Network opens a new model of Train AI to Earn”, the copyright belongs to the original author [Time], if you have any objection to the reprint, please contact Gate Learn Team, the team will handle it as soon as possible according to relevant procedures.

2. Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.

3. Other language versions of the article are translated by the Gate Learn team. Without mentioning Gate.io, the translated article may not be reproduced, distributed or plagiarized.