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Dapps marketplaceWhether you are a data scientist or businessman, you can make your own linguistic applications, sell them and make money.
Artificial IntelligenceWith GraphGrail's AI platform, by using Google Tensorflow and other tools, you can create and teach neural networks for complex classifications.
Smart contractsGraphGrail AI is the "brains" of a smart contract - a cross-blockchain service that uses webAPIs, datasources and analysis to automate contract execution.
Not enough datasetsWhat is the key problem in data-science and text mining? There simply aren't enough datasets with seven figure sample sizes (from thousand to million samples) to teach neural networks in many industries, including banking, telecom, media and government agencies. Even if the business does have such datasets, using machine learning to gather, clean and make training and test sets is extremely difficult and time consuming even for skilled data scientists. Training such as this can take between 5 and 10 months for a normal sized dataset, and even though we use neural networks and Deep Learning frameworks to simplify the task, it is a one time solution - every step must be repeated over and over again to keep your linguistic models up to date. That's why business truly needs GraphGrail AI.
Smart contracts risksSmart contracts, by their nature, are able to run algorithmic calculations and store and retrieve data. Oracles fill this void by watching the blockchain (Ethereum, EOS, Bitshares) for events and responding to them by publishing the results of a query back to the contract. In this way, contracts can interact with the off-chain world. This introduces obvious trust problems. Another complex problem is to prove that smart contract conditions are executed in real world. Smart contracts can easily check the amount of a bank account, but real-world conditions like weather changes, companies' buy-sell events, political or legal decisions, contract details and force majeur events are much more complex. This is where GraphGrail AI comes in.