Digital intuition


People often resort to intuition. However, if intuition isn't based on experience, it leads to mistakes.


Digital intuition is collective wisdom gained during a long time period. A person can be warned about upcoming events predominantly occurring under similar circumstances. The data analysis method differs from human perception. The analysis results in expanded human cognitive abilities.

Applications (horizon 2 to 10 years):

* News Analysis and the reconstruction of factors affecting a personís opinion.
Detection of conditions for controversy occurrence and disappearance.
Inferring relationships between user data and the targeted advertising content.
Inferring loyalty conditions.
Identifying patterns used for marketing, sophistry, NLP, pick-up, prank, and other habits.


I predict that the software designed for searching patterns in different data arrays will appear in the two years' time. The Analytica.Team domain will feature a cloud version. An offline version will be also available. The data entry methods and feature set will adapt to carry out different tasks. Users will be able to create their own projects or participate in created startups.

The Analytica.Team platform will allow hiring analysts, and analysts will find ways to earn on it.

Visitors to the website Intuition.Digital will be able to evaluate patterns detected and give marks like "Crap", "Obvious", and "Eureka" swiping their fingers across the touchscreen. The greater the number of ratings is, the more interesting the result of artificial intelligence work becomes.

Analytica.Today news rating agency will offer news agencies to place a small banner next to the news items published. If a news item gains a certain number of views, the banner will display the pattern rating for this news item. Clicking on the banner, the user gets access to more detailed data. The analysis data is of concern, and the one who places our free banner also wins new visitors. This way, the project becomes significant.

Technical analysis of news

To make the system function, it is necessary to process a large archive of historical data. First, I collect data for the last 20 years, and Excel is my tool to achieve this objective. Based on the materials covering the first 20 years of the new millennium, it'll be possible to issue a review in "Namedni" style by Leonid Parfenov.

I rate noticeable events from the news feed according to eight criteria which use 124 signs. Therefore, I get the matrix with nine dimensions for technical analysis. Using data obtained, I construct data volume variance graphs and derived function variance graphs with respect to time. It's possible to calculate graph correlations average values and detect local anomalies. To analyze it in detail, the selected message is superimposed on the timeline with similar events, and this is done in each category. Graphs correlation between the merged time intervals around similar events is assessed. The connections detected are enhanced by excluding the parameters weakening the connection from the graphs. We can easily explain many dependencies, but unexplained dependencies can also be found. If the news suspiciously fits into the previously identified sequence patterns, we get predictive power based on mathematics. If the comparison turned out to be interesting, it'll attract attention measured in the number of views. People's attention is the feedback for artificial intelligence which will automatically start searching for heuristic combinations.

The result provides a news forecast. It is similar to a weather forecast which becomes more accurate when more complex algorithms are used.

Message Sequence Statistical Analysis

The invention subject is the technology for identifying statistical links in the sequence of news items, adverts, or other messages. Incoming messages are classified according to several attributes. Selective reclassification is used to account for different trait assessment interpretations. The messages converted into code form an estimator matrix. To detect a pattern in a message sequence on a timescale, it is necessary to compare matrix fragments which follow either before or after messages with the same assessment value according to one or more traits. The correlation dependence with the same data filter on the superimposed time segments is assessed. If the correlation dependence for two or more matrix fragments is high, the data filter becomes narrower. Data on settings and search results are stored in the database as a pattern. The examples discovered are assessed by a person for significance. A new or repeated pattern search starts with settings combining two or more known patterns with similar message codes. The patterns with high significance assessment are more often used to create combined search settings. The data filter is additionally extended using random values. Figuratively speaking, the pattern search criteria evolve by crossing, mutation, and selection. The analysis predictive power is expressed in the assessment of probability with which the new or probable message fits into the previously identified pattern. The past message sequence examples show what typically happens under similar circumstances.

There is no intention to replace a human but to expand cognitive abilities using additional memory and collective experience analysis. It's not a tool for submission but a tool for detecting dishonesty.

Project development:

October 2018. The idea
November 2018. The choice of data source
December 2018. Classifier development
January 2019. Analysis algorithm
February 2019. Business plan and domain registration
March 2019. Data collection start
April 2019. Intellectual property registration
May 2019. Fundraising
* Search for helpers
* Interface to speed up data collection
* Tools for manual analysis and data visualization
* Self-learning analysis, programming
* Collected data analysis
* Presentation materials
* Development plan elaboration
* Additional fundraising


The project development speed and the data processing scale depend on resources. The first stage will cost 100,000 Euros, and by the time artificial intelligence is launched, I would like to attract more than a million.


I'm scared of a short planning time frame. With a short planning time frame, deception and violence provide an advantage. The more deception is practiced, the shorter the planning time frame is. This is a regenerative feedback.

Crowd opinion is based on little news which many people have become aware of. Governance is a technology which is based on experience. If public opinion is manipulated in a repeatable way, the algorithm will indicate anomaly and similarity. Suspicion is still not an accusation. However, when patterns are discovered, manipulators' reputation is destroyed.

The Analytica.Today project is important for conspiracists and anti-conspiracists. If the interrelation between conflicts and natural phenomena is discovered, it means someone has such a blackmail instrument. This is interesting, and people's attention is a liquid commodity nowadays. It's only a matter of time to find interesting connections if you have a large amount of data.

Artificial intelligence for Homo Sapiens

In the course of my work, I plan to publish short reviews. Please subscribe and leave your comments under related video: