Take it for granted that there is too much information. So many that it is impossible to separate it into false and true. Fakes appear with the aim of deceiving someone, confusing, hiding real data, or just for fun.
Even sources that do not want to lie can rely on fake information because disinformation has been leaked to news agencies, channels with a good reputation, or comes from competent persons. Roughly speaking, some lie not because they want to deceive, but because they themselves are deceived.
Fact-checking is part of the job of a journalist in a serious publication, but everyone makes mistakes. And on the Internet, where everyone writes and delivers information, and not just professionals who have fact-checking in their job description, there are a lot of such deceived speakers.
What to do with it?
Anti-disinformation mechanisms involve the use of NLP artificial intelligence (AI) tools. People, of course, can perform such checks themselves and make decisions about blocking false materials, but to analyze large amounts of data, you cannot do without the help of a neural network.
So, another necessary component in the Exorde project is the NLP artificial intelligence (AI) module, which will work with unstructured text. NLP is not only neuro-linguistic programming that pick-up artists have adopted. This abbreviation also hides another concept, and without it, the development of many modern technologies would be impossible. We are talking about natural language processing (NLP) - a field of artificial intelligence aimed at creating machines that can understand text and spoken words in the same way as people and respond to the received data, that is, respond to them with their own texts or speech .
Where will the AI ??get the data for analysis?
The object of research in NLP is the text, so one of the main issues in the analysis is the choice of relevant sources of textual materials. Depending on the tasks set, data sources can be electronic media databases, social networking data, open language corpora, and others, including open and accessible research resources.
These media will allow you to analyze social, political, informational aspects and moods in society. Data from social networks make it possible to conduct marketing analysis for companies and corporations, to assess the disorganization of various social strata of society. Open language corpora and scientific resources are mainly aimed at researching and testing various phenomena, hypotheses, and scientific facts.
NLP Challenges: Recognize and Eliminate
For such tools to function properly, they must correctly process and understand human language. But it is full of ambiguities, and this makes it difficult to create software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammatical errors and exceptions to rules, variations in sentence structure are just some of the deviations of human language that take people years to learn.
Today, the use of AI is booming due to access to a huge amount of data and increasing computing power of devices. This opens up opportunities for creating useful NLP tools in areas such as healthcare, media, finance, and others. In the corporate world, NLP technologies are also in demand - to optimize business processes and increase labor productivity.
• NLP helps to recognize and predict diseases. For example, Amazon Comprehend Medical extracts information about diagnoses, medications, and treatment outcomes from patient records, clinical trial reports, and other electronic medical records and establishes relationships between, say, drug name and dosage.
• Natural language processing systems allow companies to find out what customers are saying about their service or product on social networks or other sources.
• Thanks to NLP, cognitive assistants appear that work like a personalized search engine. First, they collect information about you, and then they remind you of something that you cannot remember at the right moment - whether it is the name of a song or the name of a distant relative.
• Companies like Yahoo and Google filter and classify your emails using NLP. By parsing the text in messages passing through their servers, they stop spam before it reaches your inbox.
• The NLP group at MIT has developed a new system for identifying fake news. The technology determines whether a source is accurate or politically biased and can be trusted.
• Amazon's Alexa and Apple's Siri are examples of intelligent voice interfaces that use NLP to answer and respond to voice requests, such as finding a specific store, reporting the weather, suggesting the best route to the office, or turn on the lights at home.
• Traders use NLP to track news, company reports, merger comments, all of which can then be incorporated into a trading algorithm for profit.
So, what can we say about the integration of NLP into the Exorde ecosystem? In our project, the artificial intelligence module will allow us to recognize objects in the text, extract sentences, facts, compare them with each other and perform similarity checks. This approach will make the joint work of participants and AI more efficient, will allow tracking relevant and fresh information received by the network, and form a more objective opinion.
And as a conclusion, I would like to note the following: the use of NLP today is a good form. NLP will allow you to better understand the area under study and identify characteristics of the text that may be missed during manual processing. At the same time, it is important to remember: NLP is only an auxiliary tool, and without careful support it is just a toy in the hands of the researcher.
Also, the Exorde Labs team is proud to announce the launch of Testnet. We are now at the point where Testnet is open to anyone who wants to download the open source modules and participate! This phase includes more detailed web panels showing blocks of data being mined on the Exorde network, the number of free and employed workers, protocol details with additional statistics, URLs, and more. A little later, a new version will appear: Testnet v0.2, with improved staking mechanisms, rewards and dynamic protocol settings. Constant improvements, the most advanced web scraping modules! End of summer - September.
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