Artificial Intelligence Contribution to Computer Science
WRIT 307 Project 2 Assignment Technical Report Point Value 175 Length / Format 1,400 words (max), 1,200 words (min); word count does not include title, abstract, table of contents, appendix items, citations, etc. Use the standard citation format for your major discipline. Required Sections • Title Page / Abstract • Table of Contents (coded) • Introduction • Main Sections / Sub-Sections (coded; 2 levels of headings required; avoid “stacked” headings) • Conclusion / Recommendation • References/Works Cited • Appendix Items (at least 2) Required Resources You will need to reference at least five sources for this project (make sure that they fit in line with “research expectations” discussed in class). Goals / Description (AKA “What you’re getting out of this”) There are some goals for PRO 2: • Develop a strong understanding of how to be more concise in sentence-level writing and editing, and demonstrate an application of this understanding to your writing. Spend some time in the revision process – long enough to understand how to use this time to be more concise, clear, and direct with your writing. It’s the reason why we’re starting with your PRO 1 draft. You don’t get to look at writing this way too often in your academic careers, so embrace the opportunity. • To think about how research works rhetorically as well as literally, and to apply this understanding to your writing. • Understand and correctly use basic components / structures of a technical report. Topic Specifications Cut the word count down from PRO 1, without eliminating any significant chunks of content (i.e. cut back – mostly – at the sentence level through sentence-level revision). Utilize techniques and structures emphasized in class. I am expecting at least a 400-500 word cut with sentencelevel content (other reductions can be more content-related). Commented [M1]: This is a pretty generic term for PRO 2 – technical reports take all forms of shapes and sizes (assessment reports, progress reports, ESA and EIS statements, lab report results, etc.) – but the basic building blocks that we’ll learn in this unit transfer over to various types of reports. Learning this basic report structure will provide a strong foundation for future reports. Commented [Office2]: “Sentence-level” means that you are changing the sentence structure of your writing to be more clear and concise. Content-related editing can also take place in the form of deleting unnecessary sentences/paragraphs, focusing content, etc. 2 Cautionary Note I am looking at change in your sentence-level writing ---- so that it is more concise and active. When grading, I compare progress you’re making from your PRO 1 draft. Some of the changes you make will feel awkward and weird (it’s kind of like changing the way you swing a golf club). If you’re not making sentence-level changes, you’re missing the point of PRO 2. Some examples of the ways in which you will be more concise: • Eliminate informal pronouns (“I”, “you”, “our”) • Eliminate passive voice, make more active (not “the mountains were mapped” but “the survey team mapped the mountains”) • Avoid unnecessary prepositional phrases (not “in order to review” but “to review”) • Use shorter words when appropriate (not “utilize” but “use”) • Eliminate unnecessary adverbs by using stronger verbs(not “the fluid moved quickly” but “the fluid flushed” • Be verb oriented – avoid nominalizations (not “seek an investigation” but “investigate” • Paraphrase instead of quote except when quotes are more appropriate Evaluation Criteria / Learning Outcomes (AKA “What I’m looking for”) Keep in mind criteria from PRO 1 (clear intro / thesis / topic sentences / subordinate development / paragraph structure / quotation and paraphrase / citations, etc.) – those are the building blocks of solid writing, and they’re still in play for PRO 2. Grading Rubric Objective Comment Grade Does your project meet the goals of the assignment (word count, topic development, research, citation style)? Does PRO 2 sufficiently address comments made by me in PRO 1? Does your project provide a sufficient title, abstract, table of contents? Does it contain appropriate headings and subheadings? Does it contain 2 appendix items w/ correct integration? Does your project contain a clear introduction with a quantifiable problem or opportunity? Are your paragraphs structured around main ideas, and are these paragraphs fully developed (i.e. thorough and sufficient research, subordinate rather than coordinate)? Is layering of content (in spots) used for additional paragraph development? Is information paraphrased instead of directly quoted (except for when quotation necessary)? Is the research correctly integrated and layered (i.e. paraphrased, cited)? ***Does PRO 2 show evidence of stylistic revision from PRO 1? (i.e. prepositional phrase / weak verbs / active voice). Is the writing free of errors? Overall 3 Classroom Schedule for PRO 2 9/13 Intro PRO 2 (Formal Technical Report) Cognition and Design / Style (Layout) / Audience Awareness Headings / Subheadings / Table of Contents / Introductions / Appendix 9/16 Cognition and Design / Style (Layout) / Audience Awareness Footnotes and Bookmarks and Cross-References / Titles / Abstracts / Intros PRO 1 Due 11:00 PM 9/18 LIB 218 9/20 Research and Rhetoric 9/23 Conciseness and Word Count / Writer’s Diet / Prepositional Phrases / Weak Verbs 9/25 Conciseness and Word Count / Writer’s Diet / Prepositional Phrases / Weak Verbs 9/27 QUEST 2 9/30 RDW 10/2 Intro PRO 3 (Career / Grad School) 10/4 PRO 2 Due 11:00 PM No Class – Happy Fall Break!
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Artificial Intelligence Contribution to Computer Science
Contents TOC \o "1-3" \h \z \u Abstract PAGEREF _Toc21456634 \h 1Introduction PAGEREF _Toc21456635 \h 1AI in Machine Learning PAGEREF _Toc21456636 \h 2Machine learning in hospitals PAGEREF _Toc21456637 \h 3AI in Robotics PAGEREF _Toc21456638 \h 4AI in Teaching PAGEREF _Toc21456639 \h 4AI in Security PAGEREF _Toc21456640 \h 5AI in Human-computer Interaction PAGEREF _Toc21456641 \h 5Conclusion PAGEREF _Toc21456642 \h 6Works Cited PAGEREF _Toc21456643 \h 7
Abstract
Artificial Intelligence is comparatively new to the computer field and is mainly concerned with studying intelligent behaviors in automated systems. AI aims at capacitating systems to ensure that they can carry out the activities performed by human beings more effectively. The literature examined the contributions of artificial intelligence to computer science. Focus is made on issues such as machine learning and its application, robotics, teaching, security, and human-computer interaction.
Introduction
Over the past few years, artificial intelligence has emerged and developed as an essential edge of technological innovations with numerous possible influences in various fields. It is keynoting that the advancements which have been made in different artificial intelligence disciplines such as machine learning, computer vision, decision making, and robotics have significantly influenced new experiences, products, and services. Artificial intelligence has been implemented in areas such as navigation systems, machine translation, and web search, among other fields, which form part of the daily lives of millions of individuals across the world. Most importantly, the integration between artificial intelligence and wide computer science has facilitated research and development, which have facilitated opportunities in areas such as industries and health. Artificial intelligence has contributed to computer science by facilitating machine learning, development of robotics, teaching, and promoting security and human-computer interaction.
Computer science involves the study of data, information, and computations through the use of both practical and scientific approaches. It is important to note that artificial intelligence has made it possible for computer scientists to produce develop and implement large scale systems that are implemented in large organizations. Often, AI systems rely on large quantities of data to generate a predictive model and carry out automated inference bases on large knowledge bases (Hager, p.3). Ideally, integration of computer science and artificial intelligence has made it possible for government organizations, industries, and hospitals to use machine learning algorithms as well as predictive models to acquire recommendations. As artificial intelligence continues to develop, the demand for systems applying predictive models has also gone up, and this is attribute to the need for organizations making decisions based on the past and current data and information.
AI in Machine Learning
Ideally, machine learning is an essential area of AI. It is crucial understanding that machine learning has had numerous applications in various day-to-day activities. For example, web search engines make use of learning algorithms that help in ranking web pages so that individuals are able to obtain the information they are searching. Social platforms such as Facebook uses learning algorithms recognizing their friend’s images. Since computer science is mainly focused on human-computer interaction, artificial intelligence has aided in the development of intelligent machines that have the ability to reason like human beings.
The development of machine learning has aided in the development of numerous systems that may be applied in various industries and sectors. For instance, AI has promoted the development of machine learning algorithms that are applied in market segmentation (Das, p.32). Companies with large customer information in their databases may employ unsupervised machine learning algorithms to evaluate and group potential customers into different segments; this makes it possible for firms to market and sell their commodities.
Artificial intelligence also promotes supervised training data, which is used in inferring instructions. For example, some of the places where these functionalities are employed include face and speech recognition. IN face recognition machine learning helps in identifying the different features which make the individuals’ appearances to vary while in speech recognition aids in identifying spoken words and also change spoken language to a machine-readable format. Learning algorithms are also applied in spam filtering where genuine and unsolicited emails are separated, and this makes it possible for individuals not to fall under the trap of hackers.
Machine learning in hospitals
There are numerous concepts of computer scienc...
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