We have created Artificial Intelligence (AI). The idea is to make the machines able to simulate typically human capabilities such as reasoning, learning, planning, to achieve certain objectives.
The question arises: have we succeeded in our intent?
We reason together thinking about a person who speaks (A) and one who listens (B). The second receives data from the first, but B will be able to catalog, understand and memorize them only if he knows the same language and uses the same register as A. Otherwise, the speaker’s message will not be understood by the listener. Let’s imagine an optimal condition in which the two people understand each other perfectly: the assumption lies in the type of data that passes from A à B.
When it comes to Artificial Intelligence, the element that defines the optimal condition is Data Intelligence.
What is data intelligence
By Data Intelligence we mean that method that starts from the creation of data and arrives at their extraction and interpretation. The ultimate aim is to initiate actions that bring value to the organization. The prerequisite when you want to create a data must necessarily start from some questions:
- Why am I creating it?
- What do I need it for?
- How will it communicate with other data?
In addition, the data we create must be:
- Structured
- Complete
- Related
- Cataloged
- Classified
Only then will we have data that we can turn into useful information to bring value to the organization.
What if we humans act on the basis of data that we don’t understand?
From Machine Learning to Data Intelligence Learning
We continue with the example of A and B that communicate with each other: the listener does not just record data, but connects them to others present in his memory that derive from past experiences. The real value lies in the resulting enrichment of information.
In Artificial Intelligence, we’ve tried to simulate learning through what’s called machine learning. The basic idea is that systems can learn from data, from previous processing and make decisions with reduced human intervention. The challenge is precisely the quality and type of data that passes through the machine. If it is “dirty” data or data that the system does not recognize and does not know how to catalog, it will never return correct information. That’s why we need to start with Data Intelligence.
We could therefore define AI learning as Data Intelligence Learning.
The Artificial Intelligence Challenge
The real challenge lies in the passage between data written in human language and the elaboration of Artificial Intelligence. For example, if in a Bigdata a person instead of “Doctor” wrote “Dr.” The machine won’t know how to interpret that if there’s no data intelligence algorithm at the base.
It is the latter that allows the machine to take the missing information where “Dr.” is connected to “Doctor”.
As long as the hardware components are based on silicon iron, we will need to create a flow man-machine-man that is fluid and that allows the transition of data until their transformation into information. Only in this way will we improve the synergy between people and technology. However, everything must start from data intelligence and heuristics. In such vision the natural evolution of the Artificial Intelligence is in what we can define Transitive Intelligence, that encloses how much said until now.
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It is a short sentence. Data is vital energy. I hope to create a great news.
In conclusion Semantic Clustering is cool. I talk about it t. As a result, it fell over.
Data Intelligence is very important. Today we talk about Semantic Clustering.
I enjoy his company because he always tells interesting stories. For example about Data Cleansing.
Data Cleansing is Data Quality. Infact, they clean data and transform them in quality data.
This article is usefull? Great! In this paragraph, I’m going to discuss a few reasons why practice is important to ICT skills.
Fantastic!
Whats the name of V of Big Data?
Velocity, Value, Vericity, etc. For example, yuppy. Moreover, that number rises to as much as 90% when you put theory to practice. In conclusion, following up explanation with practice is key to mastering a skill.
The passive voice is a monter, moreover. Firstly, the only way to truly learn a skill is by actually doing what you’ll have to do in the real world. Secondly, I think practice can be a fun way of putting in the necessary hours.
Data intelligent is on the table. Are you sure? Yes, I, am. It is fantastic! I’m tired. Therefore, I’m going to bed.
It is a branch of LNP.