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Relationship between AI and Data
Introduction
Artificial intelligence (AI) successfully imitates human cognition and reasoning processes for use in everyday applications. This is frequently observed in cybersecurity with work automation and threat variant prediction.
But the fuel that is being provided to any AI system, like a car, is what powers it. However, there is a lot more data than fuel. Therefore, the goal of this article is to clarify the crucial role that data plays in AI.
Relationship Between AI and Data
Below are a few Relationships Between AI and Data
It’s Garbage in and Garbages out
An AI system's "output," the solution you're looking for, can only be obtained by providing the right inputs. It takes the form of datasets in this instance. Your output will be distorted if any of these are inaccurate in any manner, and your conclusions will lead you in the incorrect direction.
This is best demonstrated by the trash classification software we created using machine learning (no pun meant with the garbage reference). Data was vitally essential to this project's success.
What are the Characteristics of a Good Data set?
Answering this question can be difficult because it mostly relies on the purpose the AI system is intended to do. But generally speaking, the traits listed below are something to watch out for while sifting through datasets −
It is complete − This ensures that your datasets don't contain any blank spaces or cells. There are no obvious voids in any of the slots; each one contains a bit of data.
It is comprehensive − As thorough as they can be, the datasets are. If your aim is to model a threat vector in cybersecurity, for instance, all of the signature profiles from which it developed must have all of the relevant data.
It is consistent − The variables that have been allocated to the datasets must fit all of them. For instance, the variables you choose (natural, unleaded, premium, etc.) must contain the necessary pricing information to fall into the relevant categories if you are modeling gasoline prices.
It is accurate − This is vital. You must have faith in these data sources since you will be choosing different feeds for your AI system. The result will be distorted and you won't receive the right answer if any pieces are inaccurate.
It must be valid − When using time series datasets, this is essential. When examining recent datasets, you don't want outdated data that can obstruct the AI system's ability to learn. Allow it to gain knowledge from recent data. Your application will determine how far back to go. For instance, with cybersecurity, looking back a year is usually sufficient.
It is unique − Each piece of data must be distinct from the variables it serves, much like consistency. For instance, you don't want the same natural gas price to vary depending on two separate factors.
Not all AI Systems are Built Equally
When we think about actual datasets, we frequently see a lengthy list of numbers or quantitative data. But there are other databases for qualitative data, such as movies, images, and so on.
These datasets are referred to as "Structured" and "Unstructured," respectively, by AI systems. Not even all AI systems can manage each of these sets, it's vital to remember that.
However, there are systems that can utilize both and just little human involvement. Therefore, it's crucial to choose the appropriate dataset for your system; otherwise, your output can provide a result that differs from what you had in mind.
The Issue of Quality Versus Quantity
An AI system must initially consume and learn from a large amount of data in order to learn and generate the required outputs. This can be processed quickly, but the question is: should we prioritize quality over quantity? Always choose the latter.
Shorter datasets will require more processing time from the AI system, but there is some assurance that the results will be reliable and useful. Giving an AI system a lot of data in the vain expectation that it would learn something from it is counterproductive.
Conclusion
Artificial intelligence (AI) successfully imitates human cognition and reasoning processes. The fuel that is being provided to any AI system, like a car, what powers it. Data was vitally essential to this project's success; here are some of the key characteristics of a good data set. For instance, the variables you choose (natural, unleaded, premium, etc.) must contain the necessary pricing information if you are modeling gasoline prices. An AI system must consume and learn from a large amount of data in order to learn and generate the required outputs.
It's crucial to choose the appropriate dataset for your system and to ensure that it produces reliable and useful results. The question is − should we prioritize quality over quantity?
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