Computerized Data Analyst

Previous post introduce about the big data that is now a big trend. From all of these big data, finally emerge the data scientist or data analysis. With Business Intelligence taking over companies, more and more complex dashboards are design. This leads the companies to hire data analyst to basically explains all the data that is shown, informing and what kind of conclusion that can be draw from those data. It is not a surprise that in when it comes to data analyst, the world is facing shortage of these kind of workers. Besides that, even though there are data analyst that is being hired or people working as data analyst after graduating not many of them can make a proper decisions based on the data that they have collected and tested. So what do companies do to address such problem? They are turning to Artificial Intelligence for help.

For example "Yseop Smart Business Intelligence" is a software that was created by the Yseop enterprise company that focus on Artificial Intelligence. How does Yseop Smart BI works? So with powerful tool and software it is able to analyze and explain data very quickly. Moreover it is able to turn data into narrative, so not only does it explains what the whole data is about but it also informs the user what kind of actions to take and why those actions need to be taken. Not only that, this software is the by far one of a kind in the current market that is able to explain clearly all of its reports and findings in multiple languages in real time. The current languages that the software support are English, Spanish, French and German. It is powered by the Natural Language Generation known as NLG. The image below shows how Yseop Smart BI works. (305 words)

Written by Kaza and Thinesh


References:

Yseop Smart Business Intelligence & Reporting Software. (2014). Yseop Smart Business Intelligence [Online]. Available from http://yseop.com/EN/smart-business-intelligence. [Accessed 15 October 2015].

Bridges, T. (2014), Rude Baguette. [Interview] Yseop's revolutionary approach to turning data into intelligent text. [Online] August 18 2014. Available from https://www.rudebaguette.com/2014/08/18/interview-yseops-john-rauscher-turning-data-intelligent-text/. [Accessed 15 October 2015].

Data Mining

Data mining is a field mostly concerned with extracting information from a vast amount of data. Google Search is the best example of such a system taking an analysis step of knowledge discovery in the database process. It is a computational process which analyses a pattern of searched result involving large set of data also known as "Big Data". Besides, all search applications like question answering systems (SIRI) involve method of interaction between artificial intelligence and machine learning. The main objective of data mining procedure is to extract information from a data set a change it into a meaningful structure for utilization. 

Another example is Association Rule Mining/Pattern Mining being applied in Retail Industry to mine buying behavior of consumers from vast amount of historical buying behavior. (129 words)

Written by Sim Zheng Chi

References:

Pena Fernando, 1988. Artificial Intelligence is Coming. European Management, 6(2), pp. 174-177.

Clark Holloway, C. o. B. A. C., 1983. Strategic Management and Artificial Intelligence. Long Range Planning,, 15(5), p. 89 to 93.

Machine Learning

Machine learning is a sub-field of computer science that advanced from pattern recognition and computational learning theory in AI. Machine learning study and develop calculations or algorithms that can gain from and make prediction on data. In machine learning, there are many algorithms used such as classification, clustering, regression, feature selection, and collaborative filtering/ recommendation systems etc. Many database and decision support systems are developed using these techniques for example finance, retail, and E-commerce. The recommendation system of Amazon.com is a fine example of machine learning because it can identify how the customer behave in buying therefore it give more opportunity for customers to search as similar recommended product will appear. Besides, Amazon developed its own algorithm called item-to-item collaborative filtering. The main function of this algorithm is instead of discovering neighborhood users who have the same interest on items, it finds the items acquired to be purchased together with the selected item. Among these items, customer can find item similarity because Amazon has statistics on what percentage of people will buy. (174 words)

Written by Sim Zheng Chi

Reference:

Greg Linden, B. S. a. J. Y., 2013. Amazon.com Recommendations Item-to-Item Collaborative Filtering, s.l.: IEEE INTERNET COMPUTING.

Business Intelligence

Business intelligence (BI) is additionally the following innovation that were utilized as a part of the business which transform raw information into important data. The activity is other words called data mining, which helps business administration to make precise choice taking into account the data given. As the expanding utilization of business intelligence, organization can distinguish their client behavior and allows them to target business strategies and make precise decision.

Although BI is focuses on business goal and could not compare to data mining and machine learning, however they are powerful to AI to help business to growth because BI can produce valuable result for decision maker. Google Analytics is one prominent example of AI system. Mostly business reports and SWOT analysis comes under Business Intelligence. (128 words)

Written by Sim Zheng Chi

Reference:

Singh, R. K., 2014. What is the difference between artificial intelligence, machine learning, data mining and business intelligence? How they are related?. [Online] Available from https://www.quora.com/ [Accessed 1 October 2015].

Nano Healers

The brain is an intricately designed force of nature. Artificial intelligence technologies attempt to replicate the functions of human brain by mimicking its various neural pathways, essentially, thinking like a human does.

In the medical field, artificial intelligence is able to process vast amounts of information such as the patient’s medical history, immediate family history, environmental data, and even world population data, and identify patterns that were seemingly hidden from human view. Harnessing the power of big data, artificial intelligences can skim through an unprecedented swath of data to obtain and analyse the results.

There are a variety of applications for medical-based artificial intelligences. A technology currently in development involves injecting nano-robotic-particles (nanobots) into the blood stream, and utilizing it to eradicate harmful microparticles that resides within the body, such as bacterium, tapeworms and potentially even viruses. The artificial intelligence communicate within a neural network framework, essentially improvising and evolving its strategies while performing within its target parameters.

The underlying principles of medical nanotechnology is closely related to swarm AI technology, which also has its uses in the military. Like ants in a colony, these nanoparticles strive towards a common goal, akin to predator stalking its prey, all the while minimizing flaws and maximizing its bounty (or in the medical context, health benefits). To sum it up, think A Bug’s Life. (223words)

Written by Thinesh, Kaza and Tan Benwu

References:

Xining,H.(2015).MedTech Boston. Swarm Robotics: The Future of Medicine? [Online] 6 October 2015. Available from https://medtechboston.medstro.com/swarm-robotics-what-you-need-to-know-about-the-future-of-medicine/ [Accessed 2 October 2015]