Our businesses, governments and social institutions are all collections of data. The use and control of these digital assets are what make civilization.
The important difference between digital and physical assets is the cost of replication. The value of a digital asset depends on who controls access. Recent developments of (PETS), privacy-enhancing technology now allow us to share digital assets while controlling their use and distribution.
The quantities of data we produce have surpassed our ability to interpret. This has led to the creation of machine learning and artificial intelligence. These algorithms can parse our data, separating signal from noise. The resulting inferences are the most valuable assets, as they allow us to peer into the future.
The market is fragmented. The owners of the data are not experts in its use. The experts in these technologies do not have access to the data. The data is restricted by technology, regulation and fear. If data is shared it can be copied resulting in the loss of value.
What would benefit all involved, is the ability to treat virtual assets like physical assets. You should be able to share your data, while maintaining ownership. Once the data has been used, it returns to the owner without exploitation.
The missing component is a system that allows all members of the value chain to meet, collaborate and monetize their efforts.
ai.market provides that system. The market allows algorithms to train on privacy protected data. AI creators can offer a percentage of the revenue derived from their models, to the providers of the data. The market enables other value contributors to be compensated for improving the quality of the data and data-pipelines.
On the market, project creators negotiate in advance the terms and conditions of their participation. ai.market then enforces this agreement in code. It then provides access to the models via API’s, while managing the billing and distribution of funds. Data providers can monetize their data without fear of losing value and control. The AI model creators gets access to extensive private data to train their models. The end customer receives reliable, high speed access to the models via API’s.
ai.market addresses the cost of training the models through financial participants in the market. Training a data-pipeline is a costly process. Algorithms can require millions of dollars of computing power to achieve desired results. Participants in the market, working to build an effective algorithm against privacy protected data, will require significant financial resources. Conversely, a successful pipeline can generate enormous long term value. The market supports financial contracts. An investor or group of investors take the financial risk of training a pipeline, by backing the team and project, in return for a percentage of the revenue. These contracts are managed and traded on the market, who custodians the models and distributes funds to the contributors.
ai.market allows code, developed on the market, to be registered against data assets. This provides decomposability of the pipelines into their component parts. Decomposability means that components can be mixed and matched, allowing existing deployments to be updated, while accelerating the deployment of new pipelines. Component developers, can directly monetize their contributions. We shift the value from the operators, to the code creators. In the market, the coder receives compensation anytime their contribution is used.
Each data set is listed in a standard format that can be easily identified. We provide this from the ai.market homepage and allow it to be searched using an internal or external search engine.
The second is a standard set of records, that provide the format and all necessary details to create a machine learning algorithm. A comment and reputation system will allow you to view what others experience with this source of data has been and their respective qualifications.acy Enhancing Technologies (PETs).
The final is the mechanism by which payment for access to the data source, can be automated and called upon demand. The automation includes an access token that acts as an API call. Use of the token provides a record of all transactions. This is an immutable record and is available to buyer and seller and regulatory parties. This API token enforces the permissions defined by the data provider and protects the resource from duplication or abuse.
It is expected that predictive models derived from data providers will themselves be monetized through the marketplace. This means that AI organizations, will partner with data providers, to create models that can be sold, with the resulting revenue shared.
The legal frameworks for these transactions are the responsibility of the market.
The marketplace has groups of Vaders, (Value add providers). They can be utilized to provide specific functionality, in the lifecycle of raw data to end user model. The market provides the economic incentive and the tools to engage and compensate the Vaders, but these are autonomous entities. The market has a reputation and trust system that allows users of the market, to evaluate the inclusion of Vaders in delivering market services.
