A multitude of technological innovations have shaped the current digital environment and Industry 4.0, two of which are artificial Intelligence and big data. These two trends share the goal of maximizing value extraction from the massive amount of data generated nowadays.
What is the relationship between Artificial Intelligence and Big Data?
Despite the fact that both ideas centre around data, their functionalities are very different. Artificial Intelligence (AI) and big data are mutually dependent.
Big Data functionalities
Big Data functions as input, receiving vast amounts of data that must be standardized and processed to be helpful.
Artificial Intelligence Functionalities
The end result of this process is artificial intelligence. It is made up of software that uses the output produced by these outcomes to develop a set of algorithms that enable machines and programs to act intelligently and rationally, much like people do. This offers businesses a number of benefits.
For this reason, artificial intelligence runs on big data. The latter takes in nutrients from the processed data and learns from it, creating advanced analytics solutions for various industries and forming and recognizing patterns.
The increase in data and processing speed has made possible the development of Artificial Intelligence, which uses this information to analyze the environment and act with it accordingly. It would be a natural error to compare these two terms since they are two concepts that feed off each other and go hand in hand.
What is the role of Big Data in Artificial Intelligence?
Data is necessary for artificial intelligence to develop its intelligence, both initially and over time. Artificial intelligence systems will be able to learn more and produce more accurate and efficient results if they have access to a larger volume of data.
As AI improves, fewer human intervention will be required to monitor machinery and manage operations. Since artificial intelligence is still in the process of continuous learning, it is always consuming data.
Conversely, big data is necessary for artificial intelligence, which is also true. Such vast amounts of data would not have the value that they do without artificial intelligence models, which can unlock these data stores’ potential and transform them into intelligence.
Deviation detection: AI is capable of examining the information provided by big data to find oddities. Sensors have the ability to, for instance, mark predetermined ranges and recognize any anomaly that lies outside of them.
Probability of future outcome: The AI can calculate the probability of a future outcome by using a known condition that has a probability of X influencing it.
Pattern Recognition: Identifying patterns in massive data structures that are not visible to humans.
Without machine learning, also known as ML in the English language, these advantages would not be achievable. ML is the default trigger for artificial intelligence. This artificial intelligence technique involves feeding machines data so they can accurately mimic human processes and learn on their own.
- Reasoning
- Machine learning
- General Intelligence
- Robotics
- Natural language processing
- Computer vision
- Programming and machine learning
Millions of samples of data need to be broken down into a format that AI systems can understand in order to refine them so that they can generalize behaviour in the same way that a human brain can.
Artificial Intelligence: The Future of Big Data?
While artificial intelligence has been around for centuries, the last ten years have seen a rise in its application due to the growth of big data. Big Data and AI are growing increasingly intertwined, and your organization’s cognitive and AI initiatives are being driven by the greater availability of data.
New manual data analysis models that are less time-consuming are being developed by artificial intelligence. Nowadays, machine learning and artificial intelligence (AI) combine computer science and what were once statistical models like SQL that were directed by engineers. AI is giving this analytical world completely new powers to decide in a semi-automated manner.
Together, the two terms are becoming necessary drivers for organizations to address emerging operational and production complexities. Companies need to combine the power of human intuition with artificial Intelligence to advance in an increasingly competitive environment. Only by learning from data and humans will machines be able to fulfil their role more.
What do I need to apply these advances?
Nexus Integra is already a platform that allows Big Data and AI to be applied in an industrial environment. This Software plays an essential role by connecting machines, sensors, and any data source, making its processing, homogenization, and intelligent exploitation possible to operate efficiently and establish predictive performance analyses, among other things.
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As inconceivable as it may seem today, the Apollo guidance computer took the first spacecraft to the moon with less than 80 kilobytes of memory. Since then, computing technology has grown at an exponential rate, as has data generation. In fact, the world’s technological capacity to store data has doubled roughly every three years since the 1980s. Just over 50 years ago, when Apollo 11 took off, the amount of digital data generated worldwide could have fit in an average laptop. According to Statista, 64.2 ZB of data were created or duplicated in 2020. They also forecasted that “the amount of data created digitally will more than double in the next five years compared to the amount produced before the invention of digital storage.
Unstructured data:This category includes, among other data types, open customer comments, audio files, images, and posts from social media. It isn’t easy to obtain this kind of data in conventional row-column relational databases. This type of data is not easy to capture in standard row-column relational databases. Traditionally, companies that wanted to find, manage, or analyze large amounts of unstructured data had to use laborious manual processes. There was never any doubt about the potential value of studying and understanding such data, but the cost of doing so was often too exorbitant to make it worthwhile. Given the time it took, the results were often out of date before they were even delivered. Unstructured data is usually stored in data lakes, data warehouses, and NoSQL databases rather than spreadsheets or relational databases.