Data-mining in Environmental Services {0}

By Jeremy K.

Data-mining for environmental services refers to any research or examination of large-scale data in relation to the environment. Some examples of environmental services that use databases are forecasting, ecosystems, and recycling. The benefits of data-mining in an environmental standpoint are to collect data and analyze the areas that directly affect the environment. In doing so, projects can be started in hopes of resolving the issues that negatively affect our economy. Analysis of such environmental factors can also allow us to improve the efficiency of certain aspects of the environment such as how to make traffic flow smoother. Benefits of datamining in environmental services also include efficiently analyzing issues that arise through cluster diagrams and determining the best option through regression models and various software technologies. Data-mining in environmental services can also help influence the health industry through new discoveries of habits that cause certain diseases such as cancer.


Data mining in Entertainment {0}

By Malcolm I.

The entertainment industry is always evolving to keep up with fans, ratings, and sending information to viewers. With today’s technology it makes entertainment easier to access from viewers without any technical difficulties. Big data is there to help make things easier for the industry. Data mining in the entertainment industry is sometimes used to give insights on what the audience really wants. The information they receive can help expand a show already on the air or help develop new ones (Rijamen, M. V.). Big data also helps the entertainment industry understand whether a movie or series will be a hit.


Data Mining in the Engineering Industry {0}

By Lillian H.

Data mining is the act of collecting data, transforming the data into information, and eventually using the information to improve, create, and fulfill goals. Considering the engineering industry and the massive amounts of data generated by each of its sectors (software, electrical, mechanical, etc.), data mining is extremely valuable in this field. This paper will go further in depth on the benefits, tools, and challenges data mining provides specifically in the engineering fields of software, and electrical.


Machine Learning in Environmental Sciences {0}

By Patrick K.

Environmental sciences have utilized machine learning in various ways from tracking weather patterns to predicting animal behaviors, much of this is created using R. Though, niche, environmental sciences utilizes machine learning in what many would see as unconventional. Two examples of machine learning in environmental sciences are The Biodiversity and Climate Change Virtual Laboratory (BCCVL) and DIVA-GIS. DIVA-GIS specializes in studying biodiversity and mapping out/predicting the area pointed out. For example, DIVA-GIS is used to predict climate in a certain area based on data given to the software. With this, researchers and other institutions can determine where certain species can exist or will potentially go to depending on the data given. (Hijmans, 2017)


Machine Learning in Financial Services {0}

By Aaron L.

One of the two big driving forces of leading edge technological advancements is machine learning, while the other force being big data. Machine learning can be thought of as a sub-discipline of AI where people can feed data to a machine, and the machine will be able to analyze the data to make the best possible decision. In this post, I will be discussing machine learning specifically dealing with financial services. There exist several machine learning applications to present day financial services. The current machine learning applications towards financial services are but not limited to automated portfolio management, fraud detection, and risk management.


Machine Learning in Government Services {0}

By Robert L.

Machine learning is a type of “artificial intelligence that provides computers with the ability to learn without being explicitly programmed” (Rouse). With machine learning, the machine should be able to analyze data from a database or from user inputs, adapt to the different sets of data, and make important decisions based on that set of data; this should theoretically help provide the best course of action to take. The government can greatly benefit from machine learning especially when its purpose is to take care of their citizens through provided government services. So making the right decisions for the citizens is critical because their lives may depend on it. The use of machine learning can be found being tested in service sectors such as in police and fire departments, where their main purpose is to make sure citizens are safe.


Machine Learning in the Entertainment Industry {0}

By Roberto H.

Machine learning deals with a computer being able to absorb information on its own in order to solve problems. Computers are able to self-teach themselves by reviewing lots of examples and then putting their knowledge to the test. There are two types of machine learning which are used for the computer to be able to teach itself. Supervised learning involves giving the computer both the problem and the solution to the problem in order to distinguish how to solve certain types of problems. Unsupervised learning involves giving the computer information without any possible outcomes in order for the computer to be able to discover a solution with only inputs (Louridas, 2016).


Machine Learning in Engineering {0}

Hawkins T.

When discussing machine learning in the engineering industry, people have a few misconceptions of the benefits or uses of machine learning or AI in this field. A lot of science fiction imagery is introduced by today’s movies, media, and expansion of technology. This area however, isn’t only concerned with self sufficient robots as we had thought. A lot of machine learning in this industry is concerned with efficiency in the decision making process, and making well informed decisions with the help of machine learning to make these decisions. The application of these machine learning systems can be beneficial, as they can allow you to input lots of data into the system, that could not only help in decision making, but also solution implementation. This can be done, when machine intelligence units are able to learn from the data they are given, and evaluate it through a series of algorithms that could eventually produce a model or framework that an engineer can use as a supplement to his knowledge, to make the most informed decision.


Machine Learning in Education {0}

By Jordan G.

An overwhelmingly large amount of new technologies are being introduced quite frequently. With that in mind, the assimilation of said technologies into our everyday lives is inevitable. One specific technology continually gains momentum each and every year. That technology is machine learning, “a subarea of artificial intelligence that provides computers with the ability to learn without being explicitly programmed” (Rouse, M 2016). Machine learning focuses on computer programs that have the ability to modify themselves when new data is introduced. Due to this industry growing rapidly, private companies have been the frontrunners in investing into machine learning in education. The goal of machine learning in education is to increase efficiency in the classroom, while ensuring the learning process does not diminish.
In society there lies a stereotypical formula for learning in a classroom. A student is given a textbook. The student is then matched with other fellow students to an instructor. The instructor proceeds to teach the students the material in the textbook in a hierarchical fashion, from the beginning less difficult chapters onto the harder chapters. Along the way the student is assessed on how well he or she understood the material through the use of exercises, exams, and projects. As companies continue to invest in education with machine learning, the company’s goals are to change the formula and create a more personal learning environment for students. A main problem from the typical classroom setting is the probability of several students falling behind in the curriculum. Machine learning in education aims to support those students who do tend to fall behind, by creating curriculums based on their personal performances.
The idea is that machine learning algorithms can improve the educational process. In fact Arizona State University decided to experiment with this approach. In order to help guide students through basic general education requirements, the university enrolled 7,600 students into three entry-level math courses while being overseen by 50 instructors. The experimental course, known as Knewton encouraged the Arizona State students to learn at their own pace, leading to 45% of students finishing four weeks early (Fletcher, S 2013). Along with the early completion-rate, the university observed a 17% pass rate increase in the math courses and 56% decrease in the withdrawal rate. Knewton works to identify immediately, each student’s strengths, weaknesses, and learning patterns. Knewton’s adaptive learning works by observing a struggling students weaknesses and delivering content to increase the student’s proficiency on their weakness. Once Knewton has detected a proficiency on a specific subject, Knewton will direct the student to a new activity (
DreamBox is solely a math readiness tool with a target on students at the elementary and middle school levels. It encourages students in kindergarten through eighth grade to play educational games if the games relates to their own personal lesson plan. DreamBox uses machine learning to assess over 48,000 data points in order to help pinpoint the students’ academic needs on certain lessons. DreamBox also focuses on which lessons are essential to be taught in the future to further develop the student’s mathematical abilities. DreamBox’s math lesson plans are also aligned to accommodate to the state regulatory Common Core standards. In order to measure the effectiveness of DreamBox, Harvard University studied the value the program held in the Howard County Public School System. The results showed the students who spent more time on the DreamBox saw larger gains in their overall achievement in math. It also was indicated after a student followed the DreamBox’s adaptive lesson recommendations, the student saw faster knowledge gains. The DreamBox was even successful in aiding students to achieve higher scores on interim tests as well as state tests. But as noted by the study, the impact of products like Knewton and DreamBox are encouraging, but results can sometimes be mixed (
Many combatants of machine learning technologies in the classroom dislike the fact instructors are becoming more and more obsolete. The argument is from some individuals who prefer to learn “the old fashioned way.” A high school social studies teacher named Gerald J. Conti fears machine learning in an educational environment is creating “an educational monoculture,” where a large amount of focus is being placed on STEM subjects (sciences, technology, engineering, and mathematics) (Fletcher, S 2013). Whether or not in the future this becomes entirely true is up to whoever implements these programs and the intensity at which they are encouraged upon the students. From Harvard’s study, it was noted DreamBox’s were sometimes only used in certain situations, such as for low-achieving students and after-school learning. If the common worry from the population is that of Conti’s, then it will be important a balance between the old fashioned way of learning and the new machine learning tools is met. If the technology is used in a similar fashion as the Howard County Public School System; such as for extracurricular help, it can lead to introducing common opponents to the familiarity of the technology and the benefits. But it is certain as the technology evolves, the education industry will continue to want to integrate machine learning tools into the curriculum. It will be entirely up to the education industry to accept the new norm, for the technology has already proven itself as well as being more cost efficient.


Machine Learning: Consumer Products and Services {0}

By Alexander E.
With the evolution of machine learning, computers have the ability to learn without being programmed by using algorithms to iteratively learn from data. Machine learning is similar to data mining in that they both look for patterns in data. It then uses that data to perform tasks without being explicitly programmed. Machine learning has been developed in consumer products and services, which are items that are purchased by individuals or households for daily consumption. The impact of machine learning has increased the ability to anticipate consumer needs and help consumers make decisions without using their input. When dealing with consumer products and services, machine learning produces many benefits, utilizes tools and software for data to be processed, and faces challenges in the process.
Machine learning can be classified into two different types including supervised learning and unsupervised learning. In supervised learning, there are input variables and output variables in which the input data can predict the output for that data. This algorithm compares the actual and correct output to find errors so that it can modify it. Unsupervised learning is when the system has input variables without output variables and has to figure out what the data is showing. In this algorithm, there are no correct answers.
For machine learning to perform, it requires tools and software that provide functionality. According to the article, one of the open source tools used is called Shogun, which is a machine learning library written in C++ used in: Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. (Yegulalp, 2014). Another tool is called Accord, which is a machine learning and signal processing framework for .Net. Signal processing is “a range of machine learning algorithms for images and audio that stitches together images or performing face detection.” (Yegulalp). H20 is used for business processes, including fraud or trend predictions.
Consumers can benefit from machine learning in many ways. For example, machine learning makes it possible for consumers to personalize their online experiences without having to input any data. In the article, it mentions that “from a consumer perspective, everything that is performed online, every sales process, product interaction… is being tracked by various sources.” (“Use Data to Tell the Future: Understanding Machine Learning”, 2014). Machine learning can be effective when perceptions need to be found from Big Data that’s always changing and diverse. It can outperform traditional methods on accuracy, scale, and speed. These methods are highly superior when analyzing potential consumers across data from many sources including transactional and social media sources. (“Use Data to Tell the Future: Understanding Machine Learning”). There are services like Netflix and Facebook that predict future outcomes for the users. When watching favorite shows on Netflix, it can generate suggestions based on user preferences. Facebook and other social media have the ability to collect data from users and present suggestions of what friends to add or what pages to like.
An application that is using machine learning is The North Face, which is a U.S. outdoor apparel and retail store. The article discusses how North Face created the Expert Personal Shopper that is an online shopping assistant. (Gaudin, 2016). This system asks questions based on what the consumer may be using his/her desired product for. It then offers the best choices based on the criteria selection. Another consumer service that is using machine learning is Amazon. According to article, Amazon is using machine learning to make an improvement in product reviews. (Ghoshal, 2015). The company wants to upgrade the system so that reviews can be more helpful to consumers on products. Amazon also wants to help consumers to learn about the manufacturer changes of existing products that are yet to be announced or mentioned.
Machine learning deals with challenges in the system. It requires a huge amount of data from humans to understand different concepts. In article by James Vincent, he talks about how big firms like Facebook or Google have a copious amount of data to improve machine learning systems. (Vincent, 2016). On the other hand, small tech firms don’t have the luxury in the same amount of data. Another challenge is having artificial intelligence be able to do multiple tasks at once. The current systems are able to be efficient in whatever task they are designed for, but are not able to fully multitask. The third major challenge is trying to understand how artificial intelligence comes to its conclusions. Machines aren’t fully advanced in thinking exactly like humans which limits its capabilities.
In conclusion, machine learning can benefit consumer needs in the future with the expansion of artificial intelligence. It has the potential to analyze what consumers desire and make recommendations or suggestions based on personalization. Consumers will be able to produce less and less input data for their personal needs with the growth of machine learning.