AI, Data and Information Fusion

- Overview

Artificial intelligence (AI) is related to information fusion (IF). Big data is definitely here to stay, and AI will be in high demand for the foreseeable future. 

AI works by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set.

Data and AI are merging into a synergistic relationship, and AI is useless without data, and data cannot be mastered without AI. By combining these two disciplines, we can begin to see and predict future trends in business, technology, commerce, entertainment, and everything in between. 

Data fusion is the process of combining data from multiple sources to produce more accurate, reliable, and actionable information. Data fusion is a key component of many AI applications, including autonomous vehicles, medical diagnosis, and fraud detection.

The goal of data fusion is to provide a more complete picture of a situation or phenomenon than any single data source could provide on its own. Data fusion is categorized as: Low level, Feature level, Decision level. 

Please refer to the following for more information:

• Wikipedia: Data Fusion

- The Fusion of AI and Big Data

AI and big data can achieve more together. First, feed the data into the AI ​​engine to make the AI ​​smarter. Next, less human intervention is required for the AI ​​to function properly. In the end, the less AI requires humans to function, the closer society is to realizing the full potential of this ongoing AI/big data loop. 

This evolution will require the participation of humans trained in data analysis and programming of AI algorithms. The ultimate goals of AI are as follows: reasoning, automatic learning and scheduling, machine learning, natural language processing, computer vision, robot technology, general intelligence. 

For these AI fields to mature, their AI algorithms need a lot of data. For example, natural language processing would not be possible without millions of samples of human speech, recorded and broken down into formats that AI engines can more easily process. 

Big data will continue to grow as AI becomes a more viable option for automating more tasks - and AI will become a larger field as more data becomes available for learning and analysis.

- Data Science, Big Data, and AI

Data science is the process of extracting raw and unstructured data and transforming it into structured and filtered data by combining scientific methods and mathematical formulas. It uses a variety of tools and techniques to discover business insights and turn them into actionable solutions. Data scientists, engineers, and executives perform steps such as data mining, data cleaning, data aggregation, data manipulation, and data analysis.

Experts define data science as the interdisciplinary field of using scientific methods, processes, algorithms and systems to extract data. At the same time, they define AI as the theory and development of computer systems capable of performing tasks that would normally require human intelligence. 

Artificial Intelligence (AI) is a subset of data science and is often considered a representation of the human brain. It uses intelligence and intelligent systems to provide business process automation, efficiency and productivity. Here are some real-life AI applications: chatbot, voice assistance, automatic recommendation, language translation, Image Identification.

Using data science and AI in companies can help them achieve incredible goals. It can also trigger automation and efficiencies in processes that require more labor and hours. Therefore, many industries have merged data science and artificial intelligence.

- How AI Can Be Used for Big Data

The Internet now provides a level of specific information about consumer habits, likes and dislikes, activities and personal preferences that was not possible a decade ago. Social media accounts and online profiles, social activity, product reviews, flagged interests, "likes" and shared content, loyalty/rewards applications and programs, and CRM (customer relationship management) systems all add potential insights to the big picture. in the data pool.  

Regardless of the industry, one of AI's greatest assets is its ability to learn. Its ability to identify trends in data is only useful if it can adapt to changes and fluctuations in those trends. By identifying outliers in the data, the AI ​​knows which customer feedback is deemed important and can adjust as needed. 

AI's proficiency in using data analytics is the main reason why AI and big data now seem to be inseparable. AI machine learning and deep learning are extracting data from each data input and using those inputs to generate new rules for future business analysis. Problems arise, however, when the data used is not good data.  

- Business Analysis

A recent (Forbes) resrach shows that The combination of AI and big data can automate nearly 80 percent of physical work, 70 percent of data processing work, and 64 percent of data collection tasks. This shows that these two concepts have the potential to have a huge impact on the workplace, in addition to their contributions to marketing and business work. 

For example, both fulfillment and supply chain operations are particularly dependent on data, so they are turning to developments in AI to provide real-time insights into customer feedback. In this way, businesses can shape finances, strategy, and marketing around the flow of new information. 

Essentially, before data can be run through machine learning or deep learning algorithms, there must be an accepted method of data collection (mining) and data structure. That's where professionals with a degree in business data analytics come in. They will be highly rated by companies that are serious about making the most of data analytics.  

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- The Data Fusion Process

Data fusion is the process of integrating multiple data sources to produce information that is more consistent, accurate and useful than that provided by any single source. The concept of data fusion originated from the evolved ability of humans and animals to integrate information from multiple senses to improve their survival. For example, a combination of sight, touch, smell and taste can indicate whether a substance is edible.

The data fusion process is usually classified as low, medium, or high, depending on the processing stage in which fusion occurs. Low-level data fusion combines multiple raw data sources to produce new raw data. The fused data is expected to be more informative and comprehensive than the raw input. For example, sensor fusion, also known as (multi-sensor) data fusion, is a subset of information fusion.

Data fusion is the joint analysis of multiple interrelated datasets that provide complementary views of the same phenomenon. The process of correlating and fusing information from multiple sources is often more accurate than the inferences that can be drawn from analyzing a single data set. Data fusion is a multifaceted concept with clear advantages, but at the same time there are many challenges that need to be carefully addressed.

Data fusion refers to the collection of different kinds of information into a process that produces a single model. Among the different ways to combine data from different sources, the multi-block (or multi-table) approach is a relevant choice. These methods can be used to combine different data matrices obtained using different analysis techniques.

- Multi-source Information Fusion

Multi-source information fusion is a complex estimation process that allows users to more accurately assess complex situations by effectively combining core evidence from massive, diverse and sometimes conflicting data received from multiple sources. It involves integrating information from these multiple sources to produce a specific and comprehensive unified estimate of an entity, activity or event. 

This definition has some key operational words: concrete, comprehensive and substantial. From an information-theoretical perspective, fusion, as an efficient information-processing function, must (at least ideally) increase the specificity and comprehensiveness of our understanding of entities, otherwise perform functions. 

Multi-source information fusion is sometimes implemented as a fully automated process or as a human-in-the-loop/in-the-loop process for analysis and/or decision support.

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