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Today’s data-driven world is full of numbers, figures and facts, so it may not be possible for a human being to interpret all the data easily. As a result, machine learning is playing a vital role in the overall development of advanced analytics models through Microsoft’s Azure, IBM’s Watson, Amazon, and more. The algorithms can assist these systems in learning better insights by enhancing their analysis and data collection over time.
At some point of time, you may have been asked to write a certain phrase or character in subject line of your email so that your message does not end up in the recipient’s spam folder. This exercise will become less common with the passage of time, thanks to machine learning technology, which will tell email programs how to detect and recognize spam. This new technology will not only change the way inbox thinks about the incoming mail, but will also change the perception of data scientists regarding data science and data analysis.
The Data Science Machine
Massachusetts Institute of Technology (MIT) researchers have been able to design a new system known as Data Science Machine. The system tries to search hidden or buried patterns that are necessary in influencing a predicted result or outcome. The patterns apparently need human intuition also but the researchers have developed a big data analysis system that tries to supersede human intuition searching for buried patterns.
The new system has been designed by Max Kanter, along with Kalyan Veeramachaneni, who is a scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The new prototype will get rid of human intuition and would make the searching process faster and efficient, meaning humans might become outdated in big data analysis. The system would not only search for patterns, but would also design the feature set.
Big data searching procedure involves searching hidden patterns and selecting features of data to evaluate. Now, the data science machine would make decisions when it goes searching for patterns.
Testing the Process
In order to test the machine’s ability, MIT scientists registered in 3 data science competitions. They competed against human teams in order to find predictive patterns in unfamiliar data sets.
In two of the three competitions, the machine predictions were actually 94 and 96 percent precise as winning submissions. Moreover, in the third competition, the predictions were 87 percent accurate. It was found that human teams toiled hard for months for their prediction algorithms, while data science machine consumed only 2-12 hours for their entries. 906 teams participated in the 3 competitions, in which the data science machine managed to come ahead of 615.
The researchers also used a different machine in order to learn techniques or solutions to practical problems emerging in big data analysis, such as predicting how students or the number of students who may drop out from their online courses.
For predicting rate of student dropouts from online courses, two most important indicators were how much time each student spends on course website as compared to other learners, and when does a student start work on his assignment in relation to the deadline. The online learning platform of the MIT does not compile these points but other troves of data could hold interactions, which would allow this data or information to be deduced properly.
Furthermore, Veeramachaneni has said that machine learning can be used to provide effective solutions for big data analysis such as finding out how a wind-powered farm generates certain amount of power.
Does Machine Learning Still Require Data Governance?
Many approaches and strategies have been developed for machine learning as data scientists have been able to create more sophisticated algorithms for examining and drawing logical judgements from data. Generally, these strategies can be categorized as either monitored learning or unmonitored learning.
Frequently, monitored learning is used in information retrieval, spam detection, and database marketing. The machines are instructed to discern patterns with the help of labeled training data.
On the other hand, machines are taught to find patterns in unlabeled data in unmonitored learning. This type of learning is used to detect bundles of data present in similar structures, helping the humans in defining the patterns in big sets of data.
The process of abridging the number of random variables, which are under consideration, called dimension reduction, is another significant use of unmonitored learning. The algorithms permit us to better understand advanced quantities of data, which are too vast, for human beings to process.
In today’s big data environment, if businesses, governments or institutions do not incorporate proper data governance practices, it would lead to bigger data problems. Therefore, it is essential to shape your company’s or business’s data governance practices to take optimum advantage of new ways to identify new and profitable business opportunities across the world with the help of machine learning techniques.
However, it must be noted that people crave relevant, timely, and most importantly, personalized interactions because these types of interactions drive engagement. But it would be better if we could manage to extract optimal benefit from both the worlds. Machine learning improving the data constantly, and your team along with your team’s expertise, insights and connection with the community.
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