Most Wanted

The current generation is given the chance to start college early in life.  Then there are some students who decide not to attend college and obtain a certification instead.  Obtaining a college degree and certifications along the way is the best overall solution to success.  As employers are very selective when hiring any college graduates.  Although, as the years progress the percentage of college graduates hired by employers has increased.  A Forbes leadership web journal article posted in April of 2015 by Susan Adams discusses the change in these statistics and more.

As the reader can clearly see, Susan Adams utilizes surveys from the National Association of Colleges and Employers (NACE) to show the percentage rate of employers planning to hire recent college graduates.  Considering this article was written in 2015, the percentages used were from the previous two years 2013 and 2014.  Within a year, the percentage of employers hiring graduates increased by a whopping 1%, but they did not hit their mark of 2.1% as promised.  Although this may seem astonishing, I can understand the issues employers may have had during the hiring process of recent graduates.

There are many groups that can assist recent college graduates with their path towards employment.  Such as the university students attended can provide career services.  These services can help the student with their resume, prepare for interviews, and even help them locate employment opportunities within a given field.  Another place of which recent college graduates can find assistance with job placement is non-profit groups or recruiting agencies.  One specific non-profit group that was linked with NACE matches these placement offices with potential employers.

Between a non-profit location in Bethlehem, PA and NACE, the two companies worked together to combine survey all of their current employer members.  The questionnaire’s main purpose was to find the employers ratings of degrees, that they would target when hiring recent college graduates.  Quite a few members responded and the highest-ranking degree was engineering.  The next in rank was business majors, followed by the computer science degrees, which left all the other degrees at the bottom.

Another portion of the survey rated skills that employers would value the greatest in a college graduate new hire.  Companies stated that these college graduate candidates should be able to think critically and utilize problem solving skills.  Followed in ratings is teamwork, then professionalism while demonstrating a strong work ethic.  Other skills that employers expect within a new hire is, oral and written communication skills, information technology applications, leadership, and career management.  All of the listed skills can be obtained along with a college degree and are extremely important to become a successful employee.

Any recent college graduate that notices they are having trouble finding a job within their field, should ensure they have worked with career services at their university, and start to market themselves.  In order for a recent college graduate to become marketable within their major’s field they should start networking with other students and graduates who already work in the industry.  Depending on the field internships may be available and even extra-curricular activities or volunteer work can be interesting to employers when reviewing candidates. 

Overall, the reason many of the skills discussed in Susan Adams article, are beneficial for any recent college graduate as they would become a well-rounded employee.  An employee without a majority of these skills can become problematic and even cause an entire department to crumble with issues.  The ability to critically think while on the job and problem solve will help them to rectify many obstacles along the way.  Strong work ethic and maintaining a professional demeanor can allow an employee to excel and maintain a reputation.  The expertise gained within a college graduate’s major is important, but life lessons and all of the mentioned skills are extremely useful to succeed in a career.

References

Adams, S. (2015). The college degrees and skills employers most want in 2015. Forbes.

Artificial Intelligence

The thought of machine-based intelligence to learn and decide upon given input, seems almost supernatural.  Artificial intelligence has been a vision and dream of thousands if not millions of people throughout the years.  There are numerous counts of artificial intelligence (AI) being researched, studied, and implemented over the course of generations.  Even though, the start of artificial intelligence was purely imaginary in theater, films, and movies, it gave artificial intelligence a bad impression from the horrible stories portrayed.

Although, the possibilities of utilizing artificial intelligence within robotics is a viable option and it is even being tested in science research labs across the world today.  There are even far more opportunities available when utilizing artificial intelligence separate of robotics, such as the well-known SIRI, newly created self-driving cars, and visual recognition.  As mentioned by Joachim Buhmann (2008) in a book section titled “Visual Recognition: How can we learn complex models?”, within the book Security Informatics and Terrorism: Patrolling the Web: Social and Technical Problems of Detecting and Controlling Terrorists’ Use of the World Wide Web:

Therefore, learning of these image categories should not be hand tailored by humans, but the statistics should speak for themselves and the algorithm should find a representation which actually warrants the label given to that image. (p. 166)

In the given study by Buhmann, there are three structural properties utilized for an artificial intelligence model to make various informed observations and decisions about the given input.  The first of the three is called decoupling, which is the process of breaking down the image into smaller portions.  The second is modularity, which is a method of separating and organizing the group of objects, such as pixels.  Third and final structural property is hierarchies, as the hierarchical dependencies can efficiently be utilized to enable an efficient search procedure.

For the given artificial intelligence study, the system is able to generate reasoning and learning with compositionality.  This compositionality can be viewed as a type of strategy, that allows inferring pieces which are exceedingly associated with the entity’s uniqueness and to translate the appearance variables by the relations amongst the pieces.  As described by Buhmann (2008):

Composition systems use a small number of generic parts as their key design concept. These parts are pieces in your data, which occur sufficiently often to code them as separate entities. More complex representations are then assembled by combining these parts to new generic features. This assembly is called a composition. (p. 170)

Thus, the generation procedure of reasoning and learning is a combination of comparing variables and the process of elimination.

Whether the results of the analysis toward the theory for reasoning was Boolean, Bayesian or both, concludes according to the study.  Considering, the basis of the artificial intelligence model consists of the Bayesian network, that couples the compositions, shape, and image categorization.  Thus, this development allows the system to filter out the relevant compositions that are available within the larger set of candidates.  The most logical next step is filtering out all relevant combinations of combinations and to utilize them for the decision-making process.  Training these composition systems is a quite delicate process considering, it is required to have sufficient regularization at each level of learning.

According to the research findings available, there are numerous computations that would not be appropriate for the artificial intelligence system within the reading.  Considering, the artificial intelligence system described within the case study utilizes observations and decision-making on images inputted, then any type of computation that does not provide an image would be inappropriate.  As an example, requesting the artificial intelligence system to decide whether a business should invest in one specific business proposition or if it should decide on another.  As Trapp discusses, “Indeed, the potential of AI to unlock the secrets in the ever-increasing amounts of data being collected is helping to transform a particular and important part of the prediction business - forecasting and planning.” (Trapp, 2018).  Therefore, this specific artificial intelligence system would not be helpful in a forecasting situation.  Although, the artificial intelligence system described is able to recognize patterns, which allows for backpropagation. 

Reasoning backpropagation is an essential component within artificial intelligence systems as it allows the system to quickly calculate the variables to make a decision within a timely manner.  Just as the name implies, a backward pass for an error to each and every internal node within the neural network.  This allows the system to utilize calculated weight gradients for that specific node.  Thus, backpropagation networks are essential to deal with the various types of available data.

An artificial intelligence system may seem to be a scary thought and almost completely imaginary, but they are already in existence and seem less frightening first hand.  Each system is taught how to handle specific observations and decisions, but the likelihood of one handling it all is still a bit out of reach.  Implementing artificial intelligence in an organization is not as farfetched of an idea as it used to be.  The benefits of the implementation within an organization are far greater than the risks.

References

Buhmann, J. M. (2008). Visual Recognition: How can we learn complez models? In B. K. Shapira, Security Informatics and Terrorism: Patrolling the Web: Social and Technical Problems of Detecting and Controlling Terrosits' Use of the World Wide Web (pp. 166-). Zurich: IOS Press.

Trapp, R. (2018, June 14). How AI Can Help Leaders Make Better Decisions. Retrieved from Forbes: https://www.forbes.com/sites/rogertrapp/2018/06/14/how-ai-can-help-leaders-make-better-decisions/#76b260e35e2f

Business Intelligence and Analytics

Between the two given case studies, in my opinion, the business version was far more appealing.  The case study is actually from the book, Bridging the Socio-technical Gap in Decision Support Systems and the section title is A Participatory Case Study of Business Intelligence Systems Development.  The specific system that is being utilized by the chosen case study is an Oracle based Business Intelligence system with a participatory case method, as Arnott determines, “…an exploratory case study concerning the development of a large-scale enterprise-wide BI implementation.” (Arnott, 2010).  Looking further into the method that is utilized during the case study process, which is the participatory research method. 

The participatory research method comprises a vast range of different methodological tactics and techniques, all of which have an objective of granting power from the researcher to the participants, who are normally community members.  During the participatory research, the actual participants have full control over the research agenda, process, and actions.  More importantly, the people are able to analyze and reflect on the data gathered and generated, to acquire the verdicts and decisions of the research process.  Thus, as described within the case study by Arnott (2010):

Participant observation was valuable in this case because the researcher, as a participant observer, was allowed access to research data that would not have otherwise been possible in non-participant observation. There are very few examples of participatory research in BI systems development in the literature, and this case example highlights the advantages of the approach by providing a rich, contextual analysis of the research data. (p. 34)

As discussed within the case study, the various justifications for selecting the Business Intelligence tool, consists of the license agreements, low-cost solutions, product support, user friendly data and style, overall presentation and productivity increase.  To ensure success of the project as a long-term solution, an evaluation was performed on the current BI toolset being utilized.  As they leveraged the current license agreement that Monash had with Oracle, which allowed them to opt for lower costs.  Overall, Arnott states that, “A key requirement of this evaluation was to choose a product that would increase business user’s ability to easily access, query and analyze data in the style that they require.” (Arnott, 2010, p. 208).

The original problem-set that triggered the case study research was the lack of available in-depth evidence pertaining to development research literature of Business Intelligence systems, much less a “large-scale enterprise-wide Business Intelligence implementation” (Arnott, 2010).  Although, there are various research studies available, but none of which contained practical relevance for an industry.  As seen in the case study described by Arnott (2010):

Arnott and Pervan [9] found that only 10.1% of decision support systems (DSS) research was regarded as having a high or very high practical relevance. Worryingly, 49.2% of research was regarded as having little, low or no practical relevance at all. (p. 2)

If Competitive Intelligence (CI) is the collection and analysis of information to get ahead of the competitive activity, view the historical disruptions in the market, and objectively interpret all of the events.  Thus, this process is an essential component to the development of many business strategies.  Considering, the competitive intelligence analysis can provide the necessary insight into the different marketplace dynamics and their challenges within a structured, disciplined, and ethical manner using published and non-published sources.  Therefore, the competitive intelligence gathered consists of the data provided by utilizing the content, context, and process (CCP).  The context portion consists of reviewing the various counterparts, their role, how they would be affected and their background.  Whereas, the content is mainly worried about all of the areas that would undergo some sort of transformation and what exactly would be changing.  Information pertaining to this consists of requirements, functionality, technical and logical architectures.  Lastly, the process concentrates specifically on the end game, such as the final product.  What is to be gained and how will it be executed in the end, are the main concerns.

The business intelligence applications and data processes utilized during the evaluation and measurement phase of the study consisted of a few applications and gathering data from various locations in different ways.  Implementation of a system named TARDIS, would allow the staff members to easily access predefined research-related charts and reports from an intranet site.  These reports were merely based on scripts that are hard coded in SQL but did not grant flexibility and scalable.  As Arnott discussed, “The current BI toolset at Monash uses Oracle BI Standard Edition as the reporting access and presentation layer software.” (Arnott, 2010).  Other various business intelligence applications consist of the operational systems, such as the research systems, educational management, and even the human resource system.  The data warehouse component and the business intelligence presentation tool can also be considered a business intelligence application utilized during this process.  Arnott (2010) describes the framework:

The project has adopted a rigorous extract, transform and load (ETL) framework. This manages the approach in which the data from the source systems is sourced and managed within the BI architecture. It is intended to establish a standard way of developing a robust and scalable ETL process. (p. 206)

The collection of data was done in a couple different ways, such as onsite observation, interviews that were both unstructured and semi-structured, review of project documentation, and informal social interaction with various participants.  Strategizing the participation within the case study research by adopting an unconcealed approach when attaining access, allowed for less ethical issues than a covert approach would have.  An overt approach when conducting a research can provide an appropriate amount of data access and is overall considered a straightforward execution.  Also, the researcher took a participant-observer role within the case study and diligently stepped away from the environment to ensure time for reflection.  This facilitated mitigating any risks of becoming too involved and helped preserve overall objectivity.

References

Arnott, M. G. (2010). A Participatory Case Study of Business Intelligence Systems Development. In A. Respicio, Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade (pp. 199 - 210). Monash, Australia: EBSCO Publishing. doi:10.3233/978-1-60750-577-8-199