No data: raw and processed. Currently, none of the full-cycle services in the marketplace provide the possibility of creating linguistic models without programming. As a rule, this task is completed by another company with programmer involvement and takes 5 to 12 months for one subject area. The underlying problem of Artificial Intelligence markets, text data analysis, machine learning, and the practice of using neural networks is the lack of adequate data.
Each company solves the problem of finding a dataset using its own resources. There are no datasets in the public domain. Meanwhile, linguists have access to open text sources, such as Wordnet (https://wordnet.princeton.edu) or NCRP (http://www.ruscorpora.ru), etc. Many areas of human activity have no such dedication resources, which translates into the need to spend hundreds of hours working the programmer for any company that wants to extract knowledge from text data.
Small and medium sized businesses spend money to pay employees who perform simple functions that are easily adjusted to automation using AI. Businesses do not know about the possibility of using AI for routine business process automation, and therefore continue to use human power in information processing, or so-called Big data areas.
There are various kinds of routine operations that can be automated by implementing AI solutions: Answers to frequently asked questions to customer support, sales departments, and call centers; Filling of postal documents, law, accounting, reports, etc.; Analysis and reviews of products, individuals and companies. Nowadays, to utilize technology, it is necessary to recruit and manage highly qualified specialist staff in the area of data analysis, and this, based on a very limited range of business customers, is difficult and costly.
The largest companies in the world receive the greatest benefit from the development of artificial intelligence. First, they already have significant financial resources to buy technology and hire highly talented specialists. Secondly, they already have a large dataset specific to their product and a large number of customers that provide a constant stream of data in the process of its use. Third, they control the full cycle using their products and therefore can quickly test and test new AI solutions to improve their products.
This concentration creates barriers to entry into new and innovative start-ups to the market, which may have solutions that exceed the capabilities of large companies, but can not test them on large data, and this slows the development of the artificial intelligence industry as a whole. . In addition, large companies receive all the benefits of using artificial intelligence in better productivity forms and better products and, as a result, accumulate wealth, potentially killing competition.
Ultimately, cheaper production and competition, even among large companies, will lead to a decrease in solution costs and their quality improvement. PwC estimates these factors as the basis for economic growth. Large corporate strategies for gathering knowledge, talent, and technology are well understood. One AI analyst, Andrew Ng, calls the AI Virtuous Cycle - a closed cycle of AI development. This is a common pattern and the reason why companies continue to improve their products.
All modern text and dialogue and chat-bots systems have significant deficiencies because there are many functions in this service that are detrimental to the effectiveness of solving certain business problems. In an effort to create a universal chat bot, programmers often fail to work on individual components, which reduces the effectiveness of the bots.
The simplest algorithm is based on rules. These rules are difficult and costly to maintain because they must always be updated for every business segment. The imperfect interface is another matter. Communication with chat-bots is usually done via SMS.
Artificial intelligence does not always understand what is being asked and can not react to emotions. Commercial chat-bots are generally unimaginable without programming because any non-standard bot actions require written code. The tasks that the programmer must accomplish are very complex and extensive, some of which include: Make bots understand speech patterns; Create an algorithm to find the best answer (without understanding the context); Integrate with third-party APIs; Writing another algorithm to process and extract data.
GraphGrail AI is the world's first Artificial Intelligence platform for Blockchain, incorporated as a natural language processing technology platform and DApp marketplace. Their mission: the creation of a powerful AI that is open to all, controlled and educated by developers across the planet.
The concept is similar to Elon Musk's OpenAI. They developed a platform for assessing large amounts of textual information, solving the problem of knowledge extraction (IR - data retrieval) and complex semantic classification based on machine learning, neural networks and in-depth learning technology.
Their priorities are the banking sector, biotechnology and medicine, security and law enforcement. The main element of the platform is the international constructor for the complicated classification of text, the AI designer.
Their group has spent the previous 2 years in R & D in the business of natural language analysis (NLP), information retrieval and practice of artificial neural networks. The result is an AI designer, allowing each user to create versions and train neural networks in various tasks. Potential applications include but are not limited to garbage detection, identify text modes, look for predicted forgeries on greeting characteristics, and test the complex conditions in smart contracts.
An important characteristic of this platform is that it supplies the full cycle of working with information, from starting through markup along with the end result. Business executives, novice owners, developers, and data scientists will all have the ability to use rich APIs and the ability to produce specialized software to incorporate AI designers in their own solutions and applications. They allow other companies to use their support and apply it in their own business.
GraphGrail AI designer is an integrated analytics platform that enables businesses and government bureaus to solve the problem of analyzing and classifying large volumes of text data. The designer provides the final product and also includes an easy-to-use API.
Currently, the analytics industry is experiencing a period of rapid growth among customers interested in analyzing texts. These clients include banks, telecommunications companies, retail businesses and government ministries, along with election commissions and government parties.
All of these clients face various problems: unwanted text needs to be classified (into categories like spam, fraud, etc.) and understanding should be disclosed (ie, analysis of goods and services). Developers provide a variety of linguistic models, allowing customers to create ontologies (models) quickly. No programming skills required. Models can be analyzed and refined quickly, complementing and improving the accuracy and completeness.
The main benefit of this service is its ability to guarantee customers a full cycle of information, collection and evaluation: it can collect its own data, neural network AI can learn how to check it, and the final result can be returned via web interface or properly formatted report.
This service is provided by the SaaS business model - software as a service. The subscription model is used with monthly payments for six months or a year. For analytics segments and developers of ready-to-use language solutions, this platform is available with Token payouts and their holds for access to ready-to-use solutions and solution designers with flexible tariff plans, depending on the amount of data processed per month, analytic complexity or number of sources, , API requests are charged separately.
If the user task does not provide for AI deployment, the platform provides the API to use the ability of the platform To work on the API, depending on the complexity and intensity of resource tasks, pascatrisi platform will form the tariff grid. Due to partnerships with cloud computing projects in the areas of cloud computing and cloud data storage, query costs are estimated to be negligible compared to regular rates.
GraphGrail AI comes in the form of a digital currency named GAI. Token GAI will go on sale on February 19, 2018 to May 25, 2018, at a price of 1 GAI equivalent to 0.1 USD. The token purchase itself can be done with foreign currency such as ETH and BTC. Do not forget, get also purchase bonuses at certain time period.
GraphGrail AI itself is the result of teamwork led by Victor Nosko, Higher Education from SFU, winners of various university competitions, winners of Startup-Sabantui, resident and graduate of Southern IT Park (Rostov-on-Don, Russia). Founder startup "GraphGrail" for data analysis. GraphGrail AI team that seeks to provide their best capabilities in this project. In addition, the team also strives to provide the best benefits and services, to anyone interested in joining the project.
The AI GraphGrail team consists of highly experienced professionals in various fields, covering every required competency. The team includes specialists with more than 6 years of experience in data science and natural language processing. We have successfully completed several projects for business and government in the past. Most teams have solid experience in University research and projects, as well as sales and marketing.
So our ICO project review this time, hopefully can increase your knowledge in choosing the best project ICO to you invest. If you are interested in joining this project, or intend to buy a token in the sale. You can visit their official website and page below:
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