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What is Machine Learning and why is it important.

Machine learning is a data analysis method that automates the construction of analytical models. It is a branch of Artificial Intelligence and is based on the idea that systems can learn from data, identify patterns independently and make decisions with minimal human intervention.

Early researchers interested in artificial intelligence wanted to find out whether computers could learn from data. Machine learning, machine learning, stems from the theory that computers can learn to perform specific tasks without being programmed to do so, thanks to pattern recognition among data. Machine learning uses algorithms that learn from data in an iterative way. It allows computers, for example, to locate even unknown information without explicitly telling them where to look for it.

The most important aspect of machine learning is repeatability, because the more models are exposed to data, the more they are able to adapt autonomously. Computers learn from previous processing to produce results and make decisions that are reliable and replicable.

For our discussion, it is important to point out an important factor: Machine Learning needs, at least, (at least) two datasets in order to express its predictive power; a Database set and a Database test. Usually, data from a data history about an application domain.

The answer is relatively simple. While original data is collected in all your interactions with real people (e.g., customers, patients, employees, etc.) and through all your internal processes, synthetic data is generated by a computer algorithm. This computer algorithm generates completely new and artificial datapoints.

What types of synthetic data exist?

There are three types of synthetic data within the synthetic data umbrella. These 3 types of synthetic data are: fictitious data, rule-generated synthetic data, and artificial intelligence (AI)-generated synthetic data. Let us briefly explain what the 3 different types of synthetic data are.

Fictitious data / fictitious data

Dummy data are randomly generated data (e.g., by a dummy data generator).

As a result, the characteristics, relationships and statistical patterns present in the original data are not preserved, captured and reproduced in the generated dummy data. Therefore, the representativeness of the dummy/dummy data is minimal compared to the original data.

• When to use it: to replace direct identifiers (PII) or when you don’t have data (yet) and don’t want to spend time and energy on rule definition.

Rule-based synthetic data generated

Rule-generated synthetic data are synthetic data generated by a predefined set of rules. Examples of these predefined rules might be that you would like to have synthetic data with a certain minimum value, maximum value, or average value. All of the characteristics, relationships, and statistical patterns that you want to be reproduced in rule-generated synthetic data must be predefined.

As a result, data quality will be as good as the predefined set of rules. This results in challenges when high data quality is essential. First, only a limited set of rules can be defined to be captured in the synthetic data. Also, setting multiple rules typically results in overlapping and conflicting rules. Also, you will never completely cover all relevant rules. Also, there may be relevant rules that you are not even aware of. And finally (and don’t forget), this will take you a lot of time and energy resulting in an inefficient solution.

• When to use it: when you have no data (yet)

Synthetic data generated by artificial intelligence (AI)

As expected from the name, artificial intelligence (AI)-generated synthetic data are synthetic data generated by an artificial intelligence (AI) algorithm. The AI model is trained on the original data to learn all the features, relationships and statistical patterns. Then, this AI algorithm is able to generate completely new data points and model them in a way that reproduces the characteristics, relationships, and statistical patterns of the original data set. This is what we call a synthetic data twin.

The AI model mimics the original data to generate synthetic data twins that can be used as if they were original data. This unlocks various use cases where AI-generated synthetic data can be used as an alternative for using original (sensitive) data, such as using AI-generated synthetic data as test data, demo data or for analysis.

Compared to rule-based generated synthetic data: instead of studying and defining relevant rules, the AI algorithm automatically does it for you. Here, not only the features, relationships, and statistical patterns that you are aware of will be addressed, but also the features, relationships, and statistical patterns that you are not even aware of.

• When to use it: when you have (some) data as input to mimic or use as a starting point for smart data generation and augmentation features

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The point of A.I.LoveTourism

How does the Tourism sector “fit” into this discourse?

One example, out of all. In the last three years there is a proliferation of hotel management systems , called P.S.M. ( Property Management System ), which deal with the management and scheduling of the work of accommodation facilities. One of the advertised functions is to predict through machine learning the arrivals in the next tourist season in the facility itself. Many of these programs use attendance data from the previous season.

 What if you don't have this data? Or what if they are not structured in such a way as to intraact with the P.S.M. 

In this case they would come to our aid precisely : Synthetic Data.