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

Digital Decision Making

An analysis of the problem-set found within the case study concludes that a collaboration tool similar to Skype for Business, would be a sufficient solution.  Considering, the ability to communicate and collaborate in a common environment together and for each individual participant is crucial to success.  Including custom development functionality of the tool while keeping the original shell of code.  Thus, the group decision support system methodology and group decision process modeling were applied to the case study to achieve a conclusive solution.

A minimalist design provided by the group decision support system will increase the participation and facilitation of each individual group member.  Therefore, the group collaboration will require specific functions to be successful in finding effective solutions.  The group decision support system grants the numerous group participants, the ability to communicate and work with data input simultaneously.  Considering, this interactive computer-based system allows multiple decision-makers the ability to find the solutions to various issues that are normally unstructured in nature.  The usage of a group decision support system actually improves the overall quality and the effectiveness of each group meeting.  As Prescott (n.d.) defines group decision support systems:

Computer-based GDSS research began in the 1980s as a subtype of a DSS or Decision Support System, an interactive knowledge-based software system that helps business end-users compile raw data, business models, and academic research to help recognize and solve problems individually.(p.2)

During an electronic meeting with the group decision support system, each of the participants are provided with a corresponding computer.  All of the computers are actually connected to one another, including the facilitator’s device and the file server.  At the front of the room a projection screen is provided to allow each of the participants to project information as needed.  The meetings comprise of different stages, from the idea generation, followed by a discussion, then voting and counting of the votes. 

Although the group decision support system may seem like a simple tool, it is quite a bit more complex than most realize.  As described by Mihir Joshl, “Group decision support system (GDSS) is composed of 3 main components, namely hardware, software tools, and people.” (Joshl, 2018).  Imagine a conference room with tables, chairs, projector, and multiple network connected laptops; this would be the main hardware components involved within the group decision support system.  Along with the various software tools available such as, questionnaire and brainstorming tools, organizers, project management, and policy formation tools.  Lastly, all of the people involved which are the participants, group members, facilitator who manages the meetings and tools.  Mihir explains, “The GDSS components together provide a favorable environment for carrying out group meetings.” (Joshl, 2018).  Leading to the modeling of the problem-set solution provided within the case study, group decision process which is also known as group decision making. 

There are many factors that are considered with a group decision process, such as roles, participation, communication, ideation, and finalization.  The terminology specifies that group decision making is utilized whenever there is a decision that needs to be made for a particular problem, with the cooperation of multiple people.  Thus, the group decision process can be improved by implementing the usage of group decision support system.  As such, without the group decision support system the entire group decision process would not be as successful.  Research data shows that the larger groups are less effective than smaller groups, due to the complexity of managing multiple people.  As stated in Forbes by Larson (2017):

A landmark study in the ‘70s found that a “Goldilocks” sized team, one that is not too small and not too big, is 4.6 people...which in the real world rounds up to 5. More recently, researchers at Bain found that after the 7th person in a decision-making group, each extra member reduces decision effectiveness by 10%. (p. 2)

Although, the right sized group is not necessarily the only factor to consider in the overall performance.  Ensuring each team member has the ability and skill available to fill their role to reduce any possible frustration and friction.  Gathering individual input from each team member before the group discussion will help increase the solution choices.  Another great performance booster is communication, describing in detail as to why each decision has been made and the overall reason.

There are a total of three decision support environments, certainty, uncertainty, and risk.  All of which play a key role in any decisions made within a business.  As Chand describes the decision support environment, “The decisions are taken in different types of environment. The type of environment also influences the way the decision is made.” (Chand, n.d.).  Therefore, both the decision and the environment affect one another.  Within a certainty decision support environment, the solution or answer is clear and there is not any other option.  Although, this type of decision support environment is difficult to find in most of the decisions that business make on a daily basis.  Though there are instances where there is complete certainty in the decision-making process and it is normally of little significance to the business.

Whereas, an uncertainty decision support environment contains multiple options and the decision makers do not have a clue as to what the end result will be.  Normally, these situations occur when there are unknown variables at play, such as product demand or natural disasters.  When the decision support environment is risky, there are many various events that can occur.  Although, the decision maker is able to assign a risk probability with the amount of data provided.  The information that is provided comes from previous experiences and numerous variables that are available.

The methods utilized within the case study provided educational data to grant the ability to confirm the recommendations and results.  As such, the group decision process with the usage of the group decision support system would provide the expected facilitation tool.  The overall expectations of the results provided by the case study versus the actual results, are not very different at all.  Considering, the expectations consisted of a system that is participant driven, increases social participation with a collaborative environment, is exactly what a group decision support system provides.  Decision limitations comprise of purchase cost versus development cost, as a group decision support system can be purchased and/or developed.  Although the overall investment cost is quite a bit pricey, it is well worth the funds invested.  Depending on available developers and their overall experience, it can be more expensive to develop custom group decision support software.

Thus, utilizing the group decision process to determine that a group decision support system would be the most beneficial for collaboration between group members.  Whether the decision makers are working on data together simultaneously or holding an e-meeting, the group decision support system tool can provide the necessary resources to increase performance.  Modifying and/or adding additional tools to the group decision support system is as easy as creating a shortcut.  Which will allow for easy modification to the group decision support system if it is ever needed in the future to increase productivity between decision makers.

References

Chand, S. (n.d.). Decisions Making Environments: Certainty, Uncertainty and Risk. Retrieved from Your Article Library: http://www.yourarticlelibrary.com/decision-making/decisions-making-environments-certainty-uncertainty-and-risk/10269

Joshl, M. (2018). Group Decision Support System (GDSS). Retrieved from Management Study HQ: https://www.managementstudyhq.com/features-and-components-of-group-decision-support-system.html

Larson, E. (2017, March 23). 3 Best Practices for High Performance Decision-Making Teams. Forbes. Retrieved from https://www.forbes.com/sites/eriklarson/2017/03/23/3-best-practices-for-high-performance-decision-making-teams/#669caf63f971

Prescott, A. (n.d.). How GDSS Can Enhance Group Decision-Making. Houston Chronicle. Retrieved from https://smallbusiness.chron.com/gdss-can-enhance-group-decisionmaking-36926.html