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BANASD604 Artificial Intelligence
Creation date：2024-06-14 16:06:45
Belonging category：计算机computer science
Artificial Intelligence

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BANASD604 Artificial Intelligence and Its Responsible Use in
Business.
Assessment Task
Development Project.
Individual/Group
Individual.
Length
Completion of application development and recorded presentation.
Learning Outcomes
The Subject Learning Outcomes demonstrated by successful
completion of the task below include:
a) Evaluate and develop Artificial Intelligence technologies and
applications in organisations.
c) Apply Artificial Intelligence to improve business performance
and address social challenges.
Submission
Due by 11:55 pm AEST/AEDT Sunday end of Module 4.2 (Week 8).
Weighting 40%.
Total Marks 100 marks
Assessment Task
This assessment comprises two (2) parts. In Part A, you will create a chatbot to improve business
performance. In Part B, you will produce a presentation explaining your development process.
Please refer to the Instructions section for details on how to complete this task.
Context
User-friendly tools are emerging that facilitate the development of Artificial Intelligence (AI)
technologies without requiring programming. Such tools can help create a chatbot which uses AI
(specifically, natural language processing) to simulate conversation with end-users via a platform
such as a website or messaging device.
In Part A of this assessment, you will gain hands-on experience in developing a chatbot. Specifically,
you will use the freely available Microsoft conversational AI tools to build a fully-functional chatbot
and provide evidence showcasing its development and operationalisation. In Part B, you will
showcase your chatbot (developed in Part A) and demonstrate your knowledge of its benefits and
limitations. Your presentation should focus on the underlying technology, and on comparing
BANASD604_Assessment_2_Brief_Presentation_Module Due4.2 Page 2 of 9
chatbots with other AI solutions. Understanding the underlying AI technology and how well it
supports business and social objectives is important. AI comprises a diverse range of technologies
that are transforming business and social contexts. Thus, each specific AI technology must be
positioned to responsibly solve business and social problems.
Instructions
The assessment task consists of two (2) parts:
1. Part A – Develop a chatbot using Microsoft Power Virtual Agent.
2. Part B – Record a Presentation (5 to 7 minutes) with the chatbot.
To complete this assessment successfully, you must follow the instructions below:
Part A: Develop a chatbot.
You will develop a chatbot using Microsoft Power Virtual Agents.
• Create a Microsoft Power Virtual Agents account.
- Note: the free trial lasts 60 days, and your bot will continue working for up to 90
days after the trial period ends.
• Follow the instructions on the Microsoft website below to develop your chatbot.
Part B: Record an Online Presentation demonstrating your chatbot (5-7 minutes)
You will record a 5 to 7-minute video presentation using PowerPoint slides and a chosen
recording device to summarise and explain the chatbot you have built.
You are required to evaluate the business context for chatbots, technologies used to build
chatbots, and chatbot benefits and limitations. Using this evaluation, you will reflect on your
chatbot and recommend overcoming identified limitations.
The target audience of your presentation is analytics professionals. Thus, technical details
should be avoided, with a preference for relevancy and concision.
Include 6-10 slides (excluding the Appendix) in your PowerPoint presentation, structured as
follows:
a) Title:
- Include course ID and name, student ID, email and name, facilitator name and date
of submission.
b) Demonstration of chatbot:
- Define the business purpose of your chatbot.
- Present a dialogue flow diagram that documents possible chatbot interactions using
a drawing tool such as Microsoft Visio or Word. Consider the elements that will be
included in conversations between your chatbot and users, such as greeting, asking,
informing and checking. When documenting this conversation scenario, consider
users and their journey, along with the business context of the chatbot.
BANASD604_Assessment_2_Brief_Presentation_Module Due4.2 Page 3 of 9
- Demonstrate key design features and development steps of the chatbot, such as
examples of entities, intents, and utterances.
- Justify how your chatbot performs as an Artificial Intelligence application (e.g., the
Turing test).
c) Application of Artificial Intelligence:
- Evaluate the usefulness of Artificial Intelligence technologies in relation to chatbots
and future opportunities for businesses and governments to explore.
▪ In your response, consider the application of machine learning, natural
language processing, and other specific features like large language models
to chatbots.
- Identify limitations and ethical considerations associated with chatbots. Link them to
the chatbot you developed and recommend mitigation strategies.
d) Conclusion
- Summarise your findings. Do not include new information.
e) References
- List references used in APA 7th style guidelines.
f) Appendix:
- Include visual evidence documenting the chatbot you have built and how it functions.
Specifically, you must show video evidence and screenshots of you demonstrating
the chatbot that you have built. In the appendix, include evidence of the training
process by showing how unrecognised intents and/or misclassified entities were
resolved.
In your presentations, you should consider the following points:
• Introduce yourself.
• Ensure that your face and the computer screen are visible in the recording.
• Present your slides in a clear and logical sequence.
• Make sure your slides are professionally developed and referenced using APA style
guidelines where appropriate.
• Use visualisations or diagrams as appropriate.
• Engage with the camera.
• Talk clearly and fluently.
• Use in-text citations on slides where necessary.
Referencing
It is essential that you use current APA style for citing and referencing the sources that you use.
Please see more information on citing and referencing guidelines on the Academic Skills webpage.
Submission Instructions
Submit this task via the Assessment link in the main navigation menu BANASD604: Artificial
Intelligence and its Responsible Use in Business. Your submission will consist of three files:
BANASD604_Assessment_2_Brief_Presentation_Module Due4.2 Page 4 of 9
1. The pre-recorded presentation in a low-resolution video format in a compressed file under
100Mb.
2. A copy of the PowerPoint presentation slides in PDF format.
3. To verify the work you have completed on your bot, it is important that you submit a Zip file
that incorporates the solution. For instructions on exporting a bot to another environment,
review the information and video resource on the Microsoft website.
Please note during the submission process: If you would like to include a second file in your
submission, once your first item has been uploaded, click ‘Browse Your Computer’ to attach your
extra documents as an appendix. Then click Final Submit button. Unless you follow this last step,
your submission will not upload.
The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can
be viewed in My Grades.
Academic Integrity
All students are responsible for ensuring that all work submitted is their own, is academically written
and appropriately referenced following the Academic Writing Guide. Students also need to have
read and be aware of Torrens University Australia Academic integrity policy and procedures and
subsequent penalties for academic misconduct. These are available online.
Students must also keep a copy of all submitted material and any assessment drafts.
Special Consideration
To apply for special consideration for a modification to an assessment task or exam due to
unexpected or extenuating circumstances, please consult the Assessment Policy for Higher
Education Coursework and ELICOS and, if applicable to your circumstance, submit a completed
Application for Assessment Special Consideration Form to your Learning Facilitator.
Case category
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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