Lab Report Analysis Final Draft

Advances in Artificial Intelligence 

Over the past several years there have been many areas of interest in research of Artificial Intelligence or AI, especially using these AI system to solve and manage complex problems that would be time consuming for humans. This analysis will feature three research reports under the common umbrella of Artificial Intelligence, but they study different applications of AI. The first report by Diana Maria, titled, “Telling Computers When And How To Adapt Processes” discusses how to make the computer understand humans based on natural language processing.  The second report by Christian Brandstaetter and Martin Fittner, titled, “Enabling Smart Navigation by Incorporating Human Inspired Evaluation of Route Sections” talks about making a smart navigation system that is inspired by humans and the way we think when choosing a route. Third and final report is A. Raihana, Dr. T.R. Rengasamy, M Jayanthi and S. Hemalatha, titled, “Artificial Intelligence Based Demand Management In Smart Grid” proposes an AI based system to manage the incoming demand for electricity in a grid.

Maria’s research proposes a system that uses natural language processing systems, that takes humans commands either in speech or text like “Turn down the volume” and then takes actions upon it. Using the Natural Language Processing system, she developed her own system known as PASRL. She also intends to use PASRL to determine who performs what, when, where, etc. She writes that her system has two main subsystems: A Predicate Prediction module and an Argument Prediction module. Further, she explains how they developed this system. The results of her research we quite unexpected, as different languages scored differently in their performance test. For example, Czech, German, Chinese had the best performance around 97%-98% compared to English which was only about 86%. She concludes with stating that most immediate application will be interpreting short messages, commands, etc.

The purpose Brandstaetter and Fittner’s report  is to enable smart navigation that is inspired by human behaviors. Their paper introduces a bionic approach to find new ideas to use artificial machines to perform certain task. Furthermore, they use a psychoanalytic approach to develop their system. “Psychoanalysis is a procedure for investigation mental processes using free association, dream interpretation and interpretation of resistance and transference manifestations”, as stated by the Brandstaetter. What this means is they use the data collected to develop a simulated agent that thinks like a human when choosing a route. Also, they discuss what kind of feelings are developed when we are faced with certain situations on road. They concluded with telling the readers that they have shown us a way to make the localization and navigation model, and it could be a primary process which was initially view as secondary process.

Day by day as the demand for electricity increases. The need to develop a system that manages the electricity at a grid increases as well. The third research report proposes a system to solve this. They study and propose a system that uses Artificial Intelligence, to manage the incoming demand of electricity. The proposed system uses an Artificial Neural Network, which works for a day ahead to make a model and forecast the demand. It works very much like predicting the weather, it analyzes the data collected and the patterns a certain area follows, and then it creates a model that predicts the electricity need for the upcoming week. The results shows that the ANN was successfully able to predict the load for three distinct energy sources.

Although all of the three research reports are about Artificial Intelligence, there are many difference in the way they are written. Along with the difference there also comes the similarities. For example, all of the three reports have an Abstract section, in which they introduced their reports and what they hope to achieve. But, Brandstaetter did the best job in hooking its audience. He started with an analogy that “it is challenging for every child not becoming lost in the shopping center.” Analogies like this make it easier for normal people who do not study the field to relate to. Once the reader has something to relate to, it becomes easier to follow the technical description that the author may write.

All three papers also include the Introduction section where they further introduce their research and write more in depth with more technical description of their specific research. In Maria’s paper, she included a paragraph in her introduction section where she listed all of the sections included in her paper with one line summaries on what they include. This acted like a table of contents in a paragraph form. Even though her paper was comparatively shorter at only two pages compared to six and seven pages of the other two paper, that paragraph made it much easier for the reader to navigate her paper. Since the other two papers were much longer, it would have helped the reader if the writers had decided to include a similar paragraph.

There were many structural difference in their papers as well. For example, Maria’s paper had seven sections while Raihana’s paper only had three major sections and the rests were subsections which were not highlighted in bold. Compared to the rests two Brandstaetter’s  paper was divided into five major sections, all of which were highlighted in bold. Furthermore, he numbered all the major section and using used decimal numbers for the subsections, so the reader can easily distinguish which sections are which. As mentioned above Raihana’s paper only has three major sections, Abstract, Introduction, and References. The Overview, Methods, Results, Conclusion are all subsections. Since, these subsections were harder to distinguish, it made it difficult to separate where one section starts and ends. Since the purpose of this publications are to share the knowledge they have acquired, one of their main goals is to make it easy for someone to replicate their research if they want. To do so, they have to explain the information properly. One way to do that is to include graphics. Maria’s paper did not have any graphics which I think was a poor choice, especially in a field that works on inputs and outputs. I think if she had explained how to AI in her system using the input commands, processes it and takes actions upon using a graphical approach, then it would have much easier to understand for a person who may not have much knowledge about the topic being talked about. Overall, Brandstaetter did a better job organizing his paper as he made sure he included all the major key elements of a research paper. He distinctly labelled all his sections, and included significant graphics which served a purpose and then explained those graphics so the reader can relate the writings with the graphics.

Lab Report Analysis Reflection

The genre of my paper was the analysis of lab reports. My lab report analysis mainly compared and contrasted the structural components of the three chosen lab reports. Like many other analysis papers, I started with writing a summary of the texts I was analyzing so the reader has some context. Then I discussed why a certain part of the report was written well or not. Mainly, if the structure of that writing made it easier for the reader to follow through. If I felt that, one of the writers did a better job on the writing, I explained the reasoning behind the stand.

As mentioned above, it was important to me that whoever reads my analysis knows exactly what I am talking. So I wrote a quick summary of what each lab reports were, what they hoped to achieve and what methods did they use to achieve whatever they were seeking. Once I wrote the summary, I included the broader structure of each lab report. For example, how many sections each report had, and what were those section. This helped bridge the gap between the reader’s understanding of the three papers without actually reading all three and then knowing the differences between them. Also, the primary audience I was focusing on while I writing this paper was myself. As the whole purpose of this assignment was to learn how to write a lab report and what to include vs. what not to include.

The need to understand how lab reports are written motivated me to write this paper. So this writing exercise was a good way to learn how to write a lab report. My motivation for writing this assignment also ties in well with the eighth course learning outcomes of this course which is to strengthen the source use practice (including evaluating, integrating, quoting, paraphrasing, summarizing, synthesizing, analyzing, and citing sources). This assignment gave me an opportunity to learn all the skills mentioned in this specific course learning outcome as I used a source to paraphrase the content to provide context and then analyzed it so I exactly know what each part in the lab report means and how to write one. While analyzing the documents, the parts that I found were not as well written gave me an idea that when I write a lab report of my own research, be aware of those mistakes, so I can make my report easier for the general public to follow.

Another course learning outcome I achieved through this assignment is to practice using various library resources, online databases, and the Internet to locate sources appropriate to my writing projects. When I started this assignment, the lab reports I found were not traditional lab reports and one of them was not even a lab report but instead a commentary. After, the Professor instructed me to find other lab reports I learned that the CCNY Libraries’ website has several databases which post research reports from all kinds of field. So, when in future if I do a research I would exactly know where to go look for information.

REFERENCES

Diana Maria, Telling Computers When And How To Adapt Processes. 

Christian Brandstaetter, Martin Fittner, Enabling Smart Navigation by Incorporating Human Inspired Evaluation Of Route Sections. 

Raihana, Dr. T.R. Rengasamy, M. Jayanthi, S. Hemalatha, Artificial Intelligence Based Demand Management In Smart Grid.