Leveraging Big Data: Important Data AI Researcher Should Track
November 26, 2020
Big Data appeared before everyone even realized its existence, and so far, it is the core element of modern technologies. This article will explain the intersection where big data meets AI as well as the key elements of their inseparable connection and the prospects for the further development of AI by using big data.
How AI Is Used in Big Data
There is a well-known phrase that big data is the fuel that powers AI, but what is the basis of this saying? To explain it briefly, AI builds a store of knowledge that will be used for further predictions about consumers’ personal preferences, for instance, what you might be interested in or what things you are going to buy. The same process is used in AI Logo Maker, as the smart system generates a unique label based on the target audience’s fancies. Accumulating this information, AI can easily increase the possibility of offering the exact thing you were keeping in mind, thus, make the everyday routine less complicated.
For this purpose, the data that is gradually collected must be stored somewhere, and that’s the exact thing big data deals with. Forrester Research analyst Brandon Purcell explains that “Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function.”
As an example, natural language processing won’t be possible without billions of samples of human speech having been recorded, structured, and analyzed so AI engines can more easily complete any of the tasks given.
To meet the requirements of AI, nowadays databases are becoming increasingly versatile and powerful. That is mainly because of the principle: the more complicated the process, the bigger the amount of data that is to be processed and restored. To speed up the processing and optimize resource usage, there are lots of efforts put into the improvements of databases. For instance, the result of the machine learning optimization due to big data upgrading may be the following: instead of spending something like 3 months to train a model, it might be sped up to 30 days or even 30 minutes.
And how will that work in ordinary life? Well, imagine yourself being a business owner who wants to track his store’s selling rates every 5 minutes. The AI can be used there to collect the data and provide a forecast for the shop to replenish inventory and process optimization. Simple, isn’t it?
As you see, big data is the inseparable element of AI development. Thanks to the greatest improvements in the database, it is much easier to restore any information and use it when needed. These optimizations are necessary for a variety of AI applications, which have huge prospects of usage in the future.
However, not only high technologies require big data development. For example, it may save the analysis time in the process of calculating the necessary data such as business calculations, preferences predictions based on personal tastes, simple machine learning algorithms, etc. We believe that the necessity of using big data in AI technologies will be even bigger.
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