Performance Enhancement of Information Retrieval via Artificial Intelligence (BRENNOR JACOBS)

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Sharma, L., & Srivastava, V. (2017). Performance Enhancement of Information Retrieval via Artificial Intelligence.. International Journal of Scientific Research in Science Engineering and Technology, 3(1), 187-192. Retrieved from Google Scholar.

There is an ocean of data information available to world, big data is full of useful information that when used correctly can effectively increase the knowledge of an area of study. Because big data is always growing, and unstructured, proper strategies must be used in order to make use of the raw data.

There has been an increasing interest recently in the use of Artificial Intelligence (AI). Just like the human brain learns from its environment and makes decisions based on experience, so too does AI machine learning try and make sense of its environment in order to make decisions through artificial neural networking.

Artificial Intelligence consists of many subdivisions that do not always communicate, some divisions focus on the approaches of a tool and its accomplishment its accomplishment, while others focus on the solution of specific problems. AI is also an interdisciplinary field made up of many sciences and professions including computer science, psychology, mathematics, linguistics, neuroscience and more. The main goal of AI however, is to create a software that thinks much like a human brain.

AI has become an essential part of everyday life, and are now being used by businesses to solve challenging problems in computer science. As mentioned before Big data is a collection of digital information which is constantly growing through channels like social media, email, B2B transactions, etc. By successfully analyzing Big data businesses achieve a better understanding of their customers, employees, and partners, and operations that can be a driving force for an organizations profit. As the study at the University of Texas notes, if a Fortune 1000 business increased the usability of its data, it would earn an extra $56,000 per employee or $2 billion a year in added revenue. Big data analysis works to acquire a better understanding of what is happening within a business and why, but also what else is achievable.

The history of AI began with the creation of the electronic computer by mathematician Alan Turning, who’s theory of computation suggested that by shuffling 0 and 1 a machine could simulate any conceivable act of mathematical deduction. These technological advancements, combined with concurrent discoveries in Neurology Information Theory and Cybernetics inspired the possibility of building an electronic brain. Through continuous efforts and decades of research, AI evolved. Computers began solving word problems in algebra, proving logical theorems, beating humans in checkers and even speaking English.

Although the late 70’s and the later years of the 80’s brought periods of scarce economic funding, AI found its greatest successes in the 90’s and early 21st century. Because AI is used in areas of logistics, data mining, and medical diagnosis. Greater computation power of computers and more emphasis on solving specific problems and the creation of relationships between AI and other fields has pushed AI as a required tool in many of these aspects. AI continued to make strides in 1997 when the AI software Deep Blue became the first computer system to beat a reigning world chess champion. While in 2011, IBM’s question answering system successfully defeated the two greatest Jeopardy quiz show champions by a large margin.

The emergence of the internet also brought a revolution in the digital universe, no longer did one have to be a computer scientist to search for data. Edgar Codd, a IBM mathematician proposed a new Relational Database model which became a hierarchal file system that allowed files to be accessed using a simplified index system. It was now possible for anyone to go online and uploaded their own data, or analyze data uploaded by someone else, and it became much easier to retrieve previous data and store new information using the data management system. This advancement effectively reduced the price of data storage to being more cost effective than paper.

The authors suggest that by increasing the capability of an informed search allows for a heuristic that can be “used as a guide which would lead to better overall performance in getting to the goal state”. Instead of searching blindly through all the information the guide would help determine the “best one” to try first and expand from there. The authors followed a methodology based on intelligent agents which observe and learn through sensors and acts upon an environment using actuators. Two models used were the model based agent to structure a partially observable environment that it can not see, and a goal based agent, that describes a desirable situation that also allows the agent to choose how it reaches the goal. Using these models help the AI search relevant nodes in its search, expanding the search tree and categorizing sub trees.

Researchers are continuously working to handle growth of data as well as to convert it into valuable assets. So where does computing Big data go from here? In 2010, Google Executive Chairman Eric Schmidt noted in a conference saying “As much data is now being created every two days, as was created from the beginning of human civilization to the year 2003”. There will be a great future some day for expert system applications in all aspects of health care, both clinical and administrative, improving patient care and in allocation of financial, social, and other resources. Just like the human brain, AI is always learning from experience, cognition and perception.

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