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Using Artificial Intelligence in Big Data

Using Artificial Intelligence in Big Data

Data sets will continue to increase, therefore the level of application and investment will continue to increase over time. Human intervention, as always, will continue to be relevant, although this relevance is projected to continue reducing with time.

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SwissCognitiveSimply put, Artificial Intelligence () is the level of intelligence exhibited by machines, in comparison to natural intelligence exhibited by human beings and animals. Therefore it is sometimes referred to as Machine Intelligence. When taught, a machine can effectively perceive its environment and take certain actions to better its chances of achieving set goals successfully. How can a machine be taught?

The root of Machine learning involves writing codes or commands using a programming language that the machine understands. These codes help lay out the foundation of the machines’ thinking faculty, such that the machine is programmed to perform certain functions defined in the codes. These machines are also programmed to use their basic codes to generate a continuous sequence of related codes in order to increase their thinking, learning, and problem-solving capabilities when the workload is increased. Just as cranes are machines designed to lift heavy loads which humans cannot lift, some machines are programmed to think further and solve analytical problems which are cumbersome to the human brain and some software. This machine assistance for thinking and analysis dates way back to the times of the Abacus. Technology has advanced to the point where there is literally no limit to the amount of information/ data that a machine can work with. This brings us to the topic of Big Data.

Big Data, just as the phrase implies, is simply huge or large or broad or complex or a high amount of a specific set of information which can be understood by, and stored in a computer/ machine. Professionally, Big Data is a field that studies various means of extracting, analysing, or dealing with sets of data that are so complex to be handled by traditional data-processing systems. Such an amount of data requires a system designed to stretch its extraction and analysis capability.

The ideal and most effective means of handling Big Data is with Our world is already steeped in Big Data. There is a massive amount of data online and offline about any topic you can think of, ranging from people, their routine, their preferences, etc to non-living things, their properties, their uses, etc.

This huge stockpile of data, when properly harnessed, can give valuable insights and business analytics to the sector/ industry where the data set belongs. Thus, artificially intelligent algorithms are written for us to benefit from large and complex data.

How Companies Are Applying Artificial Intelligence and Big Data

We have addressed the meaning of these terminologies, we will dedicate this part of our Artificial Intelligence essay reviewing how applications are benefiting from the synergy between algorithm and Big Data analytics, such as:

● Natural language processing, where millions of samples from the human language are recorded and linked to their corresponding computer programming language translations. Thus, computers are programmed and used in helping organizations analyze and process huge amounts of human language data.

● Helping agricultural organisations and corporations broaden their monitoring capability. helps farmers to count and monitor their produce through every growth stage till maturity. can identify weak points or defects long before they spread to other areas of these huge acres of land. In this case, satellite systems or drones are used by the for viewing and extracting the data.

● Banking and securities, for monitoring financial market activities. For instance, the Securities Exchange Commission (SEC) is using network analytics and to foil illegal trading activities in financial markets. Trading data analytics are obtained for high-frequency trading, making decision-based trading, risk analysis, and predictive analysis. They are also used for early fraud warning, card fraud detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc.[…]

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