Artificial Intelligence (AI) is the most
highly sophisticated technology ever developed. However, it is not nearly as
new as to think. It has undergone several changes from the year 1950.
- The first generation of AI was used as ‘descriptive
- The second generation of AI was used as
- The third and current generation is used as
While predictive analytics can be beneficial
and save time for data scientists, it is still entirely dependent on historical
Data scientists are left helpless when faced
with new, unknown scenarios.
To have accurate “artificial intelligence,” we need machines that can “think” independently, especially when faced with unfamiliar situations.
We need AI that cannot analyze the data shown but expresses a feeling when something does not add.
In short, AI uses that has mimics as
The fourth generation of AI is called
‘artificial intuition,’ which enables the computers to recognize the threats
and opportunities without being told allow to decisions without specifically
being instructed on how to do.
It is similar to a seasoned detective who
enters a crime scene and knows which something does not seem correct. Alternatively,
an experienced investor who can manage a trend before anybody does.
The concept of artificial intuition was
considered impossible five years ago.
Now, organizations like Google, Amazon, and IBM are developing solutions, but a few companies have already managed to operationalize it.
does it work?
How does artificial intuition accurately
analyze anonymous data without any historical context to point it in the right
The answer lies inside itself.
Once presented with a current dataset,
artificial intuition’s complex algorithms can identify any correlations or
anomalies between data points.
It does not happen automatically. However,
instead of developing a quantitative model for processing data, human intuition
applies to the qualitative method.
It analyzes the dataset and develops a
conceptual language that represents the overall configuration as observed.
This language uses various mathematical models
- Euclidean and multidimensional space
- Linear equations and eigenvalues to represent
as an enlarged image.
It envisions this enlarged image as a puzzle;
artificial intuition can glance at the completed puzzle from scratch. And then,
it works backward to fill in the gaps based on the Eigen vector’s
In linear algebra, this eigenvector is a
nonzero vector that changes by a scalar factor when applying the linear
This concept assists in imagining anomalous
identifiers. Eigenvector does not fit into the immense imagination, is undetermined.
to use Artificial Intuition?
Artificial intuition can be applied globally as Artificial Intelligence fourth industrial technology but is currently making considerable headways for financial support.
Large global banks are increasing effect to
detect sophisticated new financial cybercrime schemes, including
- Money laundering
- ATM Hacks
Anonymous financial activity is usually hidden
among thousands and thousands of transactions with their own set of connection
By using a too complex mathematical algorithm,
artificial intuition rapidly recognizes the five most significant limitations
In 99.9% of the cases, when analysts get the
five most important ingredients and interconnections out of tens of hundreds,
they immediately identify the type of crime being presented. Artificial
intuition can produce accurate data, identify this data, and detect it with a
higher accuracy level. Moreover, a lower level of false positives and present
it easy for the perfect analysis.
Hidden, such relationships for innocent
transactions, artificial intuition detects and alerts banks with the
“unknowns.” Not only that, the data is explained that is traced and
logged into it. It enables bank analysts to design enforced anonymous activity
reports to the Financial Crimes Enforcement Network (FCEN).
artificial intuition affects workplaces?
Artificial intuition is not intended to
provide service as a replacement for human instinct. It is an additional tool
that helps people that perform their jobs efficiently.
In the banking example shown above, artificial
intuition not making any final decisions independently. The analyst’s job is to
review the identified transactions and confirm the machine’s suspicions.
AI has undoubtedly come a long way since Alan Turing
first presented the concept back in the early 1950s. Artificial intuition marks the point when AI
indeed became “intelligent.”