team - small team
mvp - no
idea - hard to understand
competitors - few
market - #AI
Feb 19th 2018
6 days ago
Apr 15th 2018
in 2 months
GraphGrail Ai is the world's first artificial intelligence platform for blockchain built on the basis of natural language processing technologies with a marketplace of decentralized applications.
Our mission is the creation of a strong Artificial Intelligence (AGI), which would be open to the entire community and controlled and enhanced by the efforts of developers from all over the world.
For the last two years, our team has been conducting research and development (R&D) in natural language analysis (NLP), Information Retrieval and the training of artificial neural networks. The result was the design of language models, which allow any user to simply create a construct and train a neural network for a variety of tasks. The scope of applications is broad and spans from spam model identification to distinguishing text styles and searches for fakes based on language attributes, to testing complex conditions in smart contracts on blockchain.
We are developing a platform for analyzing large amounts of text data, solving problems of extracting knowledge and complex semantic classification on the basis of machine learning, neural networks and Deep Learning Technologies with a priority focus on the banking sector, biotech and medicine, security and law enforcement.
The platform consists of three key platform elements - the universal designer of language applications – the GraphGrail Ai Designer, the ecosystem of crowdsourcing data markup and the enrichment of data-sets – the GraphGrail Ai LabelLance, and the marketplace of ready-made applications – the GraphGrail Ai Marketplace.
All users, even those without special training, can earn money using the platform through creating, improving and voting on language models. The project does this through its associated GAI (Graphgrail + Ai) tokens, which are standard ERC 20 tokens on the Ethereum blockchain. The models may have different attributes: they can be complex learning classifications, prediction models or simply improve the workflow for training the AI.