Decentralized AI Marketplace
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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.
Chief AI Officer
Applied Mathematics in Computer Science, Harvard University. Software Engineer work experience at Google, APT, and Dataminr.
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.
Senior Software Engineer
Mathematics and Computer Science, American University of Bulgaria. Developed and co-founded AI startup Palatine Analytics.
Senior Business Development Manager
Human Evolutionary Biology, Harvard University.
Chief Business Officer
Graduate studies at Harvard Business School, Master of Science in Engineering from Columbia University.
Vice President of Marketing
Graduate studies at Harvard Business School, undergraduate studies in engineering.
Professor Thomas Magnanti
Former Dean of Engineering, MIT
Institute Professor, MIT
Former Dean of Engineering at MIT. Institute Professor. Founding Director of the Singapore-MIT Alliance for Research and Technology (SMART).
Lead Independent Director, Chipotle
Former President of Ivax Corporation
Lead Independent Director of Chipotle. Former President of Ivax Corporation (acq. by Teva for $10B). Executive Committee member of Syntex Corporation (sold to Roche Holdings for $5.3 B).
Founder & CEO, LiveRamp
Former CEO of LiveRamp (acq. by Acxiom for $310 mil). CEO of Datavant. Forbes 30 Under 30.
Professor Tim Kraska
Associate Professor, MIT
Associate Professor Computer Science at MIT CSAIL. 2017 VMware Systems Research Award Recipient. Widely recognized for early work on hybrid human-machine data management.
Professor Minlan Yu
Associate Professor, Harvard
PhD in Computer Science, Princeton
Associate Professor of Computer Science at Harvard University. PhD in Computer Science from Princeton. Experience at Google, AT&T, Microsoft, Facebook, and Bell Labs.
Professor Stratos Idreos
Assistant Professor, Harvard
Leads Data Systems Laboratory, Harvard SEAS
Assistant Professor in Computer Science at Harvard University. Leads the Data Systems Laboratory at Harvard School of Engineering and Applied Sciences.
Principal, The Arnold Group
Launched $1B business for Microsoft
Principal at The Arnold Group. Launched a $1B business for Microsoft. Harvard MBA.
Professor Elie Ofek
Professor of Business Administration in the Marketing unit at Harvard Business School. Development engineer at IBM Research.
Professor Andy Wu
Assistant Professor, HBS
PhD in Economics, Wharton
Assistant Professor of Business Administration in the Strategy Unit of the Harvard Business School. Founder of Identified Technologies. PhD in Economics from Wharton.
No connectivityToday, 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 useThere 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 measurementThere 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.
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.
Pre-Alpha: Preliminary Marketplace IterationFinalize a preliminary iteration of the marketplace. Providers post tasks they are able to provide, and anyone who is interested in a particular AI task can request it for the stated price in GAI tokens. The initial service provider code is finished as well. We’ve used gRPC for multi-language algorithm support and performance. Initial development of the website.
Alpha on Rinkeby TestnetWe will deploy a preliminary version of the GenesisAI contracts, as well as a frontend for registering services and requesting jobs. This is meant to demonstrate the capabilities of the GenesisAI platform and allow service providers and buyers to give feedback. Small jobs (e.g. text for sentiment analysis) can be created and resolved, but large inputs will not be feasible.
O-Chain Metadata StorageDescriptions, ratings, prices, etc. will be moved out of the Seller Registry. This will enable Service Providers to start registering themselves and their models without much of the cost overhead of on-chain storage.
O-Chain Request/Response StorageWe will add support for transferring encrypted model inputs and model responses over IPFS. Past this point, all inputs and responses will be made available through IPFS, with the blockchain only storing minimal metadata and a reference to the location of files on IPFS.
Sample Models RunningWe will make sample models available. These will be a subset of models currently available in official Tensorflow repository. These will be pre-trained and running on servers owned by GenesisAI.
GenesisAI on MainNetGenesisAI will be fully available on the main Ethereum network. Buyers will be able to upload arbitrary input data. Service providers will be able to earn tokens by fulfilling incoming requests.
Have you announced token generation event/presale ?
No announcement has been made regarding the token generation event. Stay tuned!
Why is your native token necessary?
We are developing a native token based on Ethereum rather than using USD or another fiat currency because of four major reasons:
Means of the transaction is the most important part of the marketplace. Our ideology of creating a decentralized marketplace requires the means of transaction to not be tied to or controlled by any government in the world.
There will be thousands of micro-transactions happening on our network. Using the USD in microtransactions does not make sense because it has fixed costs. Furthermore, international payments in USD are extremely expensive.
Nobody should be excluded from participating in building the AI-to-AI economy just because they might not have easy access to the USD.
Our native tokens will enable cheaper, faster, and more secure transactions.
We decided to develop our native token instead of relying on existing tokens because of three reasons:
First, we need to reward beneficial AI players with our native token. Native tokens are an affordable yet efficient way to reward helpful network players.
Second, we need a token that is optimized for the AI-to-AI economy. There will be thousands of transactions happening during any given time. An optimized native token will help us to scale the marketplace without any problems.
It is our long-term goal to create the first truly autonomous, decentralized organization.
Network participants will make decisions regarding AI-to-AI economy protocols. The amount of voting power of an AI node in governance will be determined by the amount of tokens held by the node, as well as by its reputation.
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