The Machine Learning

Networking Protocol

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Problem

No connectivity

Today, there is no way for AIs to exchange data, learn from each other, leverage their capabilities, and trade services. AIs are operating in a closed environment.

Costly to use

There are only around 10,000 AI developers in the world. 99% of companies cannot afford to hire their own team of AI engineers to create in-house AIs, nor do they have enough technical capabilities to correctly determine from which open-source APIs to grab existing AI code.

No quality measurement

There is no way to judge the quality of AIs because there is no reputation system. Companies operating on a lean budget face high-risk decisions when choosing which AIs to use.

Solution

Introducing GenesisAI

GenesisAI is a Machine Learning protocol. On top of this protocol, we are building a marketplace for AI products and services - Amazon for AI. The marketplace connects companies in need of AI services, data, and models with companies interested to monetize their AI tech.

By linking AI services with each other and increasing the supply of AI services, GenesisAI provides a web platform that offers low-cost AI services. This makes AI technology more efficient and affordable for businesses.

Our platform enable different AIs to communicate with each other, exchange data, and trade services. Anyone can develop and purchase AI services.

Our Marketplace

The GenesisAI marketplace will have a built-in reputation system and a matching system. Services ratings will come from two sources: a review system and expert analyses from GenesisAI's tech team. Members of the marketplace will be able to rate a service. Then, an algorithm will optimize this score based on the following elements:
Reviewer reputation: nodes with a demonstrated track record in the service they are reviewing will weigh more than nodes with less experience.
Number of reviews: as more popular services receive more reviews, the algorithm will boost their optimized score.
Timing: older reviews will weigh less than newer ones.
Incentives: potential conflicts of interest will be recognized and avoided.

Team

Archil Cheishvili

Chief Executive Officer

Economics, Harvard University. Former CEO of AI startup Palatine Analytics. Bridgewater Associates. Recognized by media outlets such as the New York Post and Yahoo Finance.

David Fan

Chief AI Officer

Applied Mathematics in Computer Science, Harvard University. Software Engineer work experience at Google, APT, and Dataminr.

Mena Gadalla

Chief Scientist

PhD in Applied Physics, MS in Computational Science and Engineering both from Harvard John A. Paulson School of Engineering and Applied Sciences. Published multiple scientific papers and raised over $1 million in research grants.

Alexander Parunov

Senior Software Engineer

Mathematics and Computer Science, American University of Bulgaria. Developed and co-founded AI startup Palatine Analytics.

Elena McCormick

Senior Business Development Manager

Human Evolutionary Biology, Harvard University.

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