Artificial Intelligence Study Group
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- Announcements, updates, questions, presentations, etc. as time allows
- Today: YP informally presented his draft AI course outline and welcomes comment. See Appendix 1 below.
- Last Friday: GS passed his final defense: Congratulations!!
- Fri. April 18 at 3:00 p.m. (an hour earlier than our usual meeting time!) GS PhD defense, Optimizing Small AI Models for Biomedical Tasks Through Efficient Knowledge Transfer from Large Domain Models.
- Yesterday. AI TED ("TECH") talks in the EIT Auditorium.
- Comments anyone?
- Next batch of presenters are welcome to rehearse their talks with us ahead of time if they wish!
- TE is in the informal campus faculty AI discussion group. SL: "I've been asked to lead the DCSTEM College AI Ad Hoc Committee. ... We’ll discuss AI’s role in our curriculum, how to integrate AI literacy into courses, and strategies for guiding students on responsible AI use."
- Met last Wednesday, every two weeks.
- TE presented some slides summarizing the most recent meeting of the group.
- MM has a powerpoint presentation on how to become an NVIDIA Deep Learning Institute certified instructor. Will send it to me.
- Any other updates or announcements? None.
- Masters project on using AI to write a book.
- A source of information about the problem: https://spectrum.library.concordia.ca/id/eprint/993284/
- ET: Growing vegetables from seeds.
- Committee: DB, RS, MM.
- Proposal status. Approved!
- Report suggestion: The book
- Defense presentation suggestion: The experience and process, hints for future book authors, observations, etc. Tour of the book
- Gemini is doing better than others for pictures for the book. They are more realistic compared to the others.
- We did the Chapter 6 video, https://www.youtube.com/watch?v=eMlx5fFNoYc, up to time 13:08. We can start there next time we do it.
- Schedule back burner "when possible" items:
- Anyone read an article recently they can tell us about?
- If anyone else has a project they would like to help supervise, let me know.
- (2/14/25) An ad hoc group is forming on campus for people to discuss AI and teaching of diverse subjects by ES. It would be interesting to hear from someone in that group at some point to see what people are thinking and doing regarding AIs and their teaching activities.
- The campus has assigned a group to participate in the AAC&U AI Institute's activity "AI Pedagogy in the Curriculum." IU is on it and may be able to provide updates now and then.
- Here is the latest on future readings and viewings
- https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals
- https://transformer-circuits.pub/2025/attribution-graphs/methods.html
(Biology of Large Language Models) - We can work through chapter 7: https://www.youtube.com/watch?v=9-Jl0dxWQs8
- https://www.forbes.com/sites/robtoews/2024/12/22/10-ai-predictions-for-2025/
- Prompt engineering course:
https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+AI0117EN+v1/home - https://arxiv.org/pdf/2001.08361
- Computer scientists win Nobel prize in physics! Https://www.nobelprize.org/uploads/2024/10/
- popular-physicsprize2024-2.pdf got a evaluation of 5.0 for a detailed reading.
- Neural Networks, Deep Learning: The basics of neural networks, and the math behind how they learn, https://www.3blue1brown.com/topics/neural-networks
- LangChain free tutorial,https://www.youtube.com/@LangChain/videos
- We can evaluate https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10718663 for reading & discussion.
- Chapter 6 recommends material by Andrej Karpathy, https://www.youtube.com/@AndrejKarpathy/videos for learning more.
- Chapter 6 recommends material by Chris Olah, https://www.youtube.com/results?search_query=chris+olah
- Chapter 6 recommended https://www.youtube.com/c/VCubingX for relevant material, in particular https://www.youtube.com/watch?v=1il-s4mgNdI
- Chapter 6 recommended Art of the Problem, in particular https://www.youtube.com/watch?v=OFS90-FX6pg
- LLMs and the singularity: https://philpapers.org/go.pl?id=ISHLLM&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FISHLLM.pdf (summarized at: https://poe.com/s/WuYyhuciNwlFuSR0SVEt).
6/7/24: vote was 4 3/7. We read the abstract. We could start it any
time. We could even spend some time on this and some time on something
else in the same meeting.
Appendix 1: New proposed 4000/5000 level applied AI course
IFSC 4399/5399 ST: Applied AI for Functional Leaders
The Fall 2025 semester runs from August 20, 2025 through December 11, 2025. This class meets on Thursdays from 4:30 p.m. to 7:10 p.m. EIT 218 as well as via a live webcast. All sessions are recorded.
Course Description: In today's AI-driven world, students and professionals across all levels and disciplines—graduate, undergraduate, and PhD students—must develop an understanding of what AI technologies to use, how those technologies can be practically be used by business and functional leaders, and other considerations while using AI practically such as risk, security and governance frameworks to remain competitive. The Applied AI course is designed to bridge the gap between AI technology innovation, and responsible and practical implementation. This course will equip students with both technical or business skills in AI development, strategic business insights, and expertise in governance, compliance, and risk management.
With industries increasingly relying on training and hiring people with AI skills. AI is used for decision-making, automation, and innovation. Graduates with AI knowledge and proficiency are in high demand across finance, healthcare, retail, cybersecurity, and beyond. This course will be a project based on hands-on training with real-world AI and Gen AI tools such a Langchain, Copilot, ChatGPT, Claude, Vercel etc. enabling students to use and perhaps develop AI solutions while understanding the ethical and regulatory landscape (NIST AI Risk Framework, EU AI Act). Students will learn how to perform prompt engineering, build Agents, Agentic AI or RAG models.
Tools & Technologies Thay May Be Covered:
· AI Development: Python, LangChain, or GenAI models
· Cloud AI Platforms: Azure AI Studio, AWS AI Services, GCP
· NLP & Generative AI: Open AI, Gemini, Co-pilot, Claude, Vercel v0
· AI Governance & Risk: COSO, NIST, Colorado AI Act, CIPP
Prerequisites: Computer literate and a willingness to learn AI Tools, the process of application of AI for solving business problems or achieve business goals
Course Objectives: By the end of this course, students will be able to demonstrate:
· Future-Proof Career Skills – Gain expertise in AI, ML, and Generative AI to stay relevant in a rapidly evolving job market.
· Business & Strategy Integration – Learn how to apply AI for business growth, decision-making, and competitive advantage.
· Governance & Ethics – Understand AI regulations, ethical AI implementation, and risk management frameworks.
· Hands-on Experience – Work on real-world AI projects using top industry tools (e.g.: Azure AI, ChatGPT, Python, LangChain).
Course Structure: Each week will typically consist of activities such as lecture/discussion based on assigned readings, case studies involving the demonstration of a specific technology/tool, and hands-on lab exercises. Students will be asked to complete lab assignments based on the material presented in class and, in some cases, come prepared to discuss assigned reading. The readings and assignments will come from real life AI industry cases.
Textbook: All materials will be on the course website. We will use a combination of relevant industry videos, papers, technical documentation, and readings from the web.
Other Resources: You will be expected to provide your own programming environment (e.g., a laptop, sign up for free resources as needed.) If you need additional resources, let the instructor know.
Student Assessment:
· 50% of your grade will be based on the points for completing an AI Project, its Documentation, and Presentation.
· 30% of your grade will be based on completing assignment/s.
· 10% will be based on participation / attendance
· 10% involvement and communication such as posting on weaki etc.
Grading Scale: 90% and above: A; 80-90% B; 70-80% C; 60-70% D, 60% and below: F.
Course Format / Instructor Presence:
The course will be conducted as a lecture-based course. Participation counts and may include small group presentations and exercises. A high level of student participation is required. Make sure that all assigned preparation and readings are done in advance and that you are ready to engage in full examination and discussion of topics. The instructor will not hesitate to call on students for questions and comments. It is, therefore, critical that assignments be completed before class in which they are addressed and reviewed.
Attendance Expectations:
For In Person and Synchronous Online Students: Attendance is critical in mastering the course material. If you must miss class, please send an email to the instructor before class begins explaining the reason for your absence. Participation implies making comments, observations, contributions, and asking questions in the classroom.
Communication Expectations for Regular and Substantive Interactions:
In addition to the class sessions, the instructors for this course will have their weekly office hours posted in the Blackboard course shell. Course communications will be done via the Blackboard Announcements and the UALR email system. Students must use their UALR email when contacting their instructors. On weekdays, a student can expect a response to their email or phone message within twenty-four hours, and forty-eight hours on the weekends. Instructors also will initiate contact with students, so it is essential that students monitor their UALR emails daily, review comments and feedback on assignments, and remain actively engaged no less than several times a week in their course(s). Each student is responsible for knowing the syllabus, deadlines on assignments, school policies, and all communication from the instructors to him/her individually and to the class.
Standard Credit Hour Expectations:
This course will adhere to the standard credit hour policy. For a 3-credit course, students can expect 2.5 hours per week of direct faculty instruction including in-class assessment and exams (midterms and final) and 5 hours per week of out-of-class time (e.g., studying, reading, completing assignments, working on projects, etc.).
Instructor’s Late Work/Exam Make-Up Policy:
Unless otherwise specified in class, work will be due on the date given on the class webpage or on the assignment itself. Late work will be accepted by the instructor at the instructor's discretion. Students who miss a scheduled presentation can petition the instructor for a make-up which may be granted at the instructor’s discretion.
UA Little Rock Plagiarism/Academic Policies:
Plagiarism on any assignment will at a minimum result in 0 points for the assignment. We reserve the right to pursue further disciplinary action if appropriate (e.g. any student caught cheating on an assignment/assessment will receive an “F” for the course, and we may pursue action with the Committee on Academic Integrity). Plagiarism includes copying someone else’s work and claiming it as your own, or collaborating excessively with another person or persons and claiming the work as solely your own. It is strongly recommended that students maintain a record of the preparation of their major assignments. For more information on academic offenses, please refer to the following websites:
Academic Integrity and Grievance Policy: https://ualr.edu/deanofstudents/section-vii-administration/academic-integrity-grievance-policy/
Academic Offenses: https://ualr.edu/deanofstudents/section-vii-administration/academic-offenses/
Other Complaints: https://ualr.edu/deanofstudents/student-complaints/
Inclement Weather Policy:
During inclement weather, UA Little Rock will make a decision whether or not to close based on all available information. The chancellor will decide whether or not conditions warrant canceling classes and activities and closing the campus or whether classes and activities will be canceled but with specified campus offices open. Online or web-enhanced classes will continue as scheduled at the discretion of the faculty member. The UA Little Rock website, UA Little Rock email, the university’s main telephone number (501.569.3000), and the Rave campus alert notification system are the official means of communicating information concerning weather-related closings. When necessary, the university will announce a separate decision about canceling night classes (those classes starting at 4:20 p.m. or later) by 2 p.m., if possible. For further information, Please review the Inclement Weather Policy available at https://ualr.edu/policy/home/admin/weather/.
UA Little Rock Disability Policy:
If you are a student with a disability, or if you simply want to discuss resources that might help you learn more effectively, you can contact the Disability Resource Center at any time. You are welcome to drop in or call 501-916-3143 to make an appointment. Their staff will work with you to discuss accommodations and identify resources on campus or in the community that might be helpful for you. Accommodations are established through an interactive process. For more information, visit the DRC website or review the steps to request accommodations. We want you to know you are valued, welcome, and wanted at UA Little Rock.
Instruction Contact Information:
Instructor # 1: Mr. Yogesh Pandit, CEO of Hexanika.com
Email: yogesh.pandit@hexanika.com
Office: Google / Zoom or Teams Call
Phone: 646-733-6636
Office Hours: TBD
Instructor # 2 Name: Dr. Edi Tudoreanu, Professor of Information Science
Email: metudoreanu@ualr.edu
Office: EIT 560 / Google / Zoom or Teams Call
Phone: 501-916-5229
Office Hours: TBD
Other Course Participants will be listed below:
· There will be guest or industry speakers
· TBD
· TBD
· TBD
Course Schedule of Topics:
Module 1: Foundations of AI and Business Alignment (Weeks 1-4)
· Unit 1: Introduction to AI for Business and Leadership
o Overview of AI capabilities (ML, DL, Generative AI)
o Business impact: AI-driven innovation in finance, healthcare, and retail
o Introduction to AI governance frameworks (NIST, CIPP, EU AI Act, etc.)
· Unit 2: AI Lifecycle and Implementation Strategy
o AI model development, deployment, and monitoring
o Case study: AI adoption in enterprise settings
o AI governance structures and risk mitigation strategies
· Unit 3: Key AI Technologies and Tools
o Supervised vs. Unsupervised Learning
o AI Infra Set Up: Python, Jupyter Notebooks, and cloud-based AI tools (AWS/ Azure AI Studio/Google); Vercel v0, Claude, etc.
o Governance focus: AI compliance and regulatory challenges
· Unit 4: AI for Business Growth and Market Leadership
o AI-driven automation and decision-making
o Case study: AI-powered business analysis and forecasting
o Compliance focus: Ethical AI and responsible AI adoption
Module 2: Advanced AI and Business Integration(Project initiation) (Weeks 5-6)
· Unit 5: AI in Marketing, Sales & Customer Experience
o Salesforce Agents, Automation and chatbots
o Business case: AI in Marketing, Sales and Customer Success
o Governance focus: Privacy and data security concerns (GDPR, CCPA)
· Unit 6: AI for Operational Efficiency
o Business use cases: AI for fraud detection and prevention or accounting and finance
o Compliance focus: AI security and AI driven adversarial attacks
· Unit 7: AI in HR and Business Optimization
o AI-driven personalization, recommendation engines
o Business case: AI in recruitment, talent management
o Compliance focus: AI bias mitigation and fairness in hiring
· Unit 8: Auto & Reinforcement Learning
o Autonomous systems, Agents and Agentic AI
o Business case: AI-driven self-learning models either in marketing, sales or operations with industry focus
o Resistance focus: Overcoming corporate fear of AI adoption
Submission: Project Plan, Documentation by leveraging Gen AI
Module 3: AI Governance, Compliance & Ethics (Weeks 7-10)
· Unit 9: AI Governance Frameworks & Global Regulations
o NIST AI Risk Management, ISO/IEC 23894, EU AI Act
o Industry-specific regulations (HIPAA for healthcare AI, SEC for AI in finance)
o AI governance tools (audit logs, explainability reports)
· Unit 10: AI Explainability & Bias Management
o Interpretable AI techniques
o Case study: Bias in AI hiring systems and credit risk models
o Business responsibility in AI model transparency
· Unit 11: AI Security, Privacy, and Risk Management
o Secure AI model deployment strategies
o Governance: AI trust frameworks (eg: IBM AI Fairness 360)
o Case study: Managing AI risks in cloud-based solutions
· Unit 12: AI Resistance and Corporate Change Management
o Strategies for AI adoption in enterprises
o Business case: AI integration in legacy systems
o Ethics: Impact of AI on jobs, social responsibility, and legal liabilities
Module 4: AI Strategy, Implementation, and Future Trends (Weeks 11-12)
· Unit 13 Overview of AI Deployment and Scalability
o Deploying AI models on cloud (Azure AI Studio, AWS, GCP)
o Business case: Scaling AI solutions in enterprise environments
o Compliance: AI model monitoring, drift detection
· Unit 14: AI for Competitive Advantage & Industry-Specific Applications
o AI in industry: e.g.: banking, healthcare, etc.
o Case study: e.g.: AI-driven drug discovery and financial fraud prevention
o Compliance: AI liability and regulatory accountability
· Unit 15: AI Governance and Responsible Innovation
o Innovating with AI : e.g. financial services (algorithmic trading, fraud detection)
o Ethics: Ensuring fairness and avoiding discrimination in AI models
o Risk assessment frameworks for enterprise AI adoption
· Unit 16: The Future of AI: Trends, Risks & Opportunities
o Generative AI landscape – what to chose and when
o Agents and Agentic AI
o AI and Web3, decentralized AI governance
Module 5: Capstone Project & Final Presentations (Initiated in Week 5 - Weeks 12-14)
· Unit 17: AI Capstone Project (Process starts in Week 7/8)
o Showcase an AI-driven business solution with governance compliance
o AI application areas: Business analytics, customer engagement, fraud detection
o Report: Final Plan, Requirements Document, Design, Architecture
· Unit 18: Final Project Presentations & Certification
o Peer review and feedback
o Industry guest panel discussion on AI’s role in future business strategies
o Course completion certification
Appendix 2: Transcript
AI Discussion GroupFri, Apr 25, 2025
0:11 - D. B.
Hi, everyone.
0:12 - Unidentified Speaker
Hello, everyone.
0:13 - M. M.#+#R. S.
Hello.
0:14 - M. M.
Y., would you like to present your syllabus today? Yeah, I will present.
0:21 - D. B.
I just requested an email earlier. I've sent you the attachment.
0:27 - Y. P.
So when I'm presenting, if you can share. And if we can start in 10, 15 minutes, it will give me time settle down where I am.
0:42 - D. B.
Sure, sure. We got some other stuff to talk about in the beginning anyway. Thank you, sir. Yep, okay.
0:54 - Unidentified Speaker
All right. Okay, so thanks to Y. for showing us his syllabus today.
1:01 - D. B.
Last Friday G. passed his final defense. Congratulations. Yep. So I'm hoping to see more, more announces like this as the, as the months go by. Congratulations.
1:15 - D. D.
You were there, D., right? Well, I mean, I didn't see the meeting where he passed, but I did see the, I did see his defense.
1:28 - Unidentified Speaker
Okay.
1:28 - D. B.
Yesterday there were these AI sort of TED-like talks, I think, They didn't want to call them TED Talks because it's a copyrighted term or something like that. But anyway, they were essentially AI TED Talks in the EIT auditorium. And so I wondered if anyone went there and saw them or any of them. I did. I went to a few of them. I didn't go to all of them. Some were better than others. I think some of the presenters would have benefited from rehearsing ahead of time with us here so we could do things like tell them to increase the font size on their slides. So my plan is to send a message to M. J., who's the person who arranged this, and let her know that. Because I know she has more presenters. She couldn't schedule them all for yesterday. So she's got more, and I could let her know that the presenters are welcome to rehearse their talks here. T., any updates or news or interesting outcomes from this faculty AI discussion group?
2:55 - Unidentified Speaker
T.? T. was there.
2:59 - M. M.
I don't know. I can share my part. I actually advertised the developer, everybody to become kind of familiar with the theory of artificial intelligence to know what the models are doing to become developers of NVIDIA or to become certified instructors. I can send you the presentation, D.. At least for our people, I really recommend to E. and everybody, you know, to, and R., everybody. You can become a certified instructor or developer at least. They have a very interesting right now, I'm working on developing the professional certificates for professionals for large language one, and another one we do for agentic AI, for agents. Okay.
4:00 - D. B.
And what should I put it?
4:03 - M. M.
I mean, is this a recording that you have available? No, I have a PowerPoint presentation. But M. can probably send you everything. She promised to send you.
4:17 - D. B.
Who did? M.. Oh, well, I haven't heard from her. I may be behind on my emails at this point. I can send my PowerPoint presentation. Sure. Yeah. Okay.
4:32 - M. M.
And this presentation, again, is on how to become a developer?
4:39 - Unidentified Speaker
Certified.
4:39 - D. B.
What? Certified instructor. How to become a, okay.
4:44 - M. M.
Deep Learning Institute, NVIDIA, Deep Learning Institute, Certified Instructor, how to get a professional certificate from NVIDIA.
4:58 - Unidentified Speaker
Deep learning.
5:01 - D. B.
You mentioned become a, not deep mind, deep learning.
5:11 - M. M.
And it's NVIDIA deep learning. Learning Institute. And actually what they teach and how they teach is much, much better than what Amazon is offering, what Google is offering. I participate in all of these courses and the material that NVIDIA with the theoretical and practical exercise is much, much And they guide you how to take the exams.
5:51 - D. B.
Cool.
5:52 - M. M.
All right. Without I, it's N only. Is, yeah.
6:00 - D. B.
All right. I agree.
6:03 - Unidentified Speaker
OK. Yeah. Thank you.
6:06 - M. M.
Yeah, thanks.
6:08 - D. B.
T., are you there? I am here. OK.
6:12 - T.
I was just wondering if this informal campus faculty discussion group has met recently.
6:20 - D. B.
They have.
6:21 - T.
And I actually put together a little presentation about what was discussed, if you're interested in seeing it. Oh, how long is it?
6:33 - D. B.
It's just a couple of slides.
6:36 - T.
It's just some notes I took from the last meeting. Sure, go for it. Okay.
6:46 - D. B.
I don't know how to share my screen, I guess. Yep.
6:53 - T.
I work computer, so y'all have to ignore everything other than this. Can you see this okay?
7:04 - D. B.
Yeah. All right.
7:05 - T.
Well, you got a lot of slides there.
7:09 - D. B.
You got a whole bunch of them.
7:11 - T.
Well, there's three that has information on it.
7:15 - D. B.#+#T.
Actually, two that has relevant information on it.
7:18 - T.
All right. Go for it. So a quick overview. Of course, this is the Teaching with AI discussion group that meets on Wednesday afternoon, or Wednesday at noon. It's hosted by Dr. E. S.. Psychology department and really their biggest concern discussion right now is about writing basically students doing their capstone projects exclusively using AI and so they're trying to figure out ways to to deal with that. So for this particular meeting, though, that was held on April the 23rd, one thing they've discussed briefly was this Dr. K. from the College of Business, Health, and Human Services. Apparently, he's developing an AI curriculum for the College of Business. And this group is very interested in learning and viewing what he's come up with and what he's doing. And his name has come up of times in the last few meetings. And so, I just thought I would include that. Like I said, I was taking some high-level notes as this was going on. The next thing on the agenda was basically Dr. S. introduced me to the group and asked me about my project and, you know, what I was doing. And I explained to her that the project, whoops, sorry about that. Didn't mean to get off the screen. And one of the things I found out at that meeting is that there are several members of this discussion group that may be interested in assisting with the assessment piece of the project. So I had to definitely keep that in mind for this. And the other thing that I learned was nursing schools apparently like law schools use the Socratic method in teaching. And so that was something I wasn't aware of, which I thought was interesting as well.
9:31 - D. B.
OK.
9:31 - T.
Well, I recommend you write up all this stuff, including the list of the people who are interested in it and growing document for future reference. OK. So really, this was kind of the meat of the discussion was these AI tools. And I've taken, these are actually links. And I can just run through them real quick. If you guys got a minute. If it'll let me, I'm actually connected to our VPN. So hopefully it'll let me get out to these. The website article, I'm not familiar with it. There wasn't really a title for this sheet, but basically it's just a list of AIs that, you know, that are currently being used a lot. G., blah, blah, blah. So that was just one thing that they shared in the discussion. And a lot of us are probably are familiar with these tools. Also, I'm not going to go to the link, but like I mentioned, they're interested in, they're very concerned about capstone projects and students writing, doing their own work versus AI so there's some discussion around using Grammarly to help with some of that and it was kind of interesting to hear some of the tools that Grammarly offers in terms of detecting AI and that kind of stuff so I know that Dr. Ordered a license for her classes courses in the fall to use Grammarly. And so she talked about that a little bit. I haven't really dug that much into Grammarly. I've used it maybe one time quickly. But so that's something to consider. This acknowledging AI tools and techniques is another website or link that they gave. It's just a quick page. I haven't really read through it. Probably be a good idea for me to maybe read through it and summarize it for you. But it's, it was one of the things they sent. Oops, wrong page. Here's a student AIU self-assessment checklist that they came up with. How to use checklist before submitting or go through the checklist, blah, blah, you know, for students to understand, you know, the use of AI and transparency and disclosure and all that kind of stuff. So that's another thing that they came up with, teacher support for communicating the use of AI in assignments. There's another link. It's just another spreadsheet that lists ways and different disclosures, you know, for different tools. So I thought this was kind of interesting to tell you that they're using this kind of stuff or, you know, looking at this kind of stuff. And apparently, Blackboard Ultra has an AI module in it where you can create an AI conversation. And they've talked about this a couple of times. There's a fellow by the name of B. S., I think, was his last name who kind of brought this to the attention of the group and it was kind of an interesting little I didn't know blackboard had this kind of stuff in it but you can create an AI conversation around the topic or scenario to engage your students so and this AI conversation includes the credit questioning and role-play exercises so yeah this is definitely something I'll be looking at Okay, you can review it as part of your related work. Yeah, yeah. So that's really about it. That pretty much took the whole hour and I had to drop off right at the hour to attend another meeting. So that was pretty much the topics of discussion for that meeting. Great. All right. Thank you. Do you want these links and stuff?
14:21 - D. B.
or do you just want me to hang on to them for now? I guess I could maybe.
14:28 - T.
Or I could just send you the links themselves, and then you can pass those along or whatever.
14:35 - D. B.#+#T.
Why don't you send me the whole deck of slides, and I'll see if I can. Oh, OK.
14:42 - D. B.
OK. All right.
14:44 - Unidentified Speaker
Thanks.
14:44 - Y. P.
All right. I'm ready if you want me to go next.
14:49 - D. B.#+#T.
Yeah. So Y. is going to present his syllabus.
14:53 - D. B.
I just want to mention quickly that we will get to E., your project, even if we have to run a little late, because you're getting towards the end as well. And with that, let's go to Y.. I've got your syllabus up here. Yeah, go ahead. Thank you, D..
15:19 - Y. P.
So we'll be having this session in the fall. The course is already open for registration. I'm not sure how many people have enrolled as yet, but I've requested that we keep it to the size of 15 or 16, at least in the first round, assuming that we will have those many students. But it is a 14 weeks course. And I think the number suggests what level course it is. The co-instructor is E., his name is there inside. Now, when you look at it, at this document, if D. intends to share with the audience here, please look at it as a draft because I'm getting inputs from people. And when I say people, primarily guest lecturers who are industry people who will come with capstone projects. And unlike the earlier discussion, in this course, we are going to actually request all the students to use GenerativeWare to do their capstone project. So extensively use GenerativeWare, we will be giving them very objective best projects saying that this is what they have to achieve. And in the first four weeks, we will be introducing them to various tools, technologies, infrastructure. So if you go down, D., I'm going to skip the introduction. These are some of the technologies that we may be introducing. So if you go down, it speaks about the course objectives, I'll skip that also. But now, when we go to the course structures, we have listed down the modules. There are 18 modules. So one input I got is to somehow consolidate into 14. So I will be doing that. And we will be primarily touching upon four dimensions. One is the technologies that we have mentioned up top. So D., if you can go down a little bit and start with module one, where it says module one. More down, more down. Yeah, here. So first we'll do just the foundation because we think that this will be a class of people who might be just coming from business background and who may not have any technology coding and other experience. So, giving them just foundation and the business aspect of it. So, that will be in the first module. So, there will be some units covering about just business implementation, how the implementation happens, and also introduction to the tool. And technologies. If you go down, once we have given them the overview, then we go into a little bit of examples, like how is AI used in marketing, AI used in human resources, operations, and other areas. Essentially, these are related to the projects that they will be getting in week four itself, after the first module, we will be introducing them with the capstone project and students will select. And during this, they will start creating their kind of content around the project or plan around the project. So we are not going to wait until the last two weeks. Every week, the team will start presenting the project in the class. So if you see that yellow highlight, that's where the project and some of the documentation and other things will be submitted by the students. Then we are going to touch upon, if you go down, D., the governance, compliance, ethics. Again, these are very highlights. And we'll be talking about also when I say governance, some of the implementation challenges, why, you know, even if there is a great technology, it's not being adopted. The issues, especially in some of the industries like healthcare, financial services, why there are roadblocks. We'll be touching upon that. And the last, we are mainly touching upon where this is going, what everybody should be careful of, the scalability, how you should implement and scale, what are the challenges that are out there. So that is how The course is set up and the last two weeks is essentially very focused on the project that they have built and presentation of the project. So this is how it is. I have three industry guest people who will be involved in it from week four. So they will come a first in the week five to present what their project is. Then they'll be available. Once a week for this audience to, when they present their progress. And at the end, essentially they'll be involved in giving scores and other things for this project. One project is around marketing. There is a lady who heads marketing for one of the banking institutions here. One of them, another one works for ADP and he runs all their AI projects. And there's one more individual who works for W. F.. So these are the three and our guest people who have projects in mind and their team will be working with them. Now, whether they'll get certification, additional recognition, I don't know, but that is how the project is kind of lined up. If you are interested in the projects, what it's about, I'm happy to share that. And if you have any input on the content and if we have to remove, add something, I'm very open to that as well. And I'm restructuring only one piece, where in the first four weeks, there is governance spread across four lectures. Perhaps I'll be combining that into one. And instead of touching upon that every week, it will go into the whole governance model. So that is the course. Based on your expectation? Do I need to add or say something more or less?
22:28 - D. B.
I mean, you gave us an overview. Do you have any questions that you get input from us?
22:36 - A. B.
Or does anybody have any comments? Yeah, I just have kind of a, I guess, question, series of questions, comments. Like, it seems extremely broad for, like, Is this an undergraduate class? It's 4,000 slash 5,000.
22:52 - D. B.
It means it's nominally senior slash graduate.
22:55 - A. B.
Well, I guess that would be my take, especially if it's geared towards like a non-technical, I don't know, like business student or whatnot. It seems extremely broad. So it's like, it seems like it's the intention is to like get them into a hands-on like project. And I see I see parts of it where it's saying, hey, you want to get them familiar with like, you know, Python and Jupyter notebooks. And then I saw some other stuff around like AWS and cloud environments. But then it's also like, you know, like getting into things around like just general data mining, like this is how unsupervised, you know, unsupervised learning works. And then there's this other parts where it's talking about, you know, automation and agentic and data governance and GDPR. And like, it just seems very broad kind of first glance. And like, I'm wondering how is this like for a non-technical student to just jump into this and to pick up, you know, all that. And I guess one scoop would, I guess, be maybe a concern, but maybe not to others here, but that's what kind of jumps out at me.
24:08 - E. G.
A., and I actually had the same argument and you guys should presented it.
24:13 - A. B.#+#E. G.
This is more of a survey, a mile wide, an inch deep.
24:18 - E. G.
Now, I do see this as beneficial in that you're exposed to all of these different things. Now, if you find something that piques your interest, you're able to delve further into it. But what a survey does is it gives you the opportunity you don't know what you don't know. Yeah. And at this point, it gives you an opportunity to be exposed to it. And it's up to you at that point, if you want to take it to the next level.
24:53 - A. B.
Yeah. Does that make?
24:55 - E. G.
Does that align with what you told me you'll get?
24:59 - Y. P.
So thank you, A. and H., for your And maybe what it means is maybe I have to focus on one of the topics and reduce one topic among this so that it becomes more specific. But I hear what you're saying.
25:26 - A. B.
And I'll consider your inputs into the coursework. As an example, right, like, what, like, do you want the student to come out of this, like, understanding, like generative AI and how how that works? And it's okay. Or so you want it to be more specifically around like, just any kind of like, like, just like machine learning, right, which is more of like a traditional kind of like data mining course, because like, if it is around, say, generative AI, then maybe, you know, you spend some, you know, spend some time kind of understanding, well, how the transformer works, right? And things that we've discussed on this call, and to kind of set that, give them that context.
26:08 - A. B.#+#Y. P.
And then that's where you build a product and maybe set up a use case for applying Gen AI or something.
26:17 - D. B.
Sure. Yeah.
26:18 - M. M.
You'll get to give a response. Can I give you a suggestion?
26:23 - Unidentified Speaker
Suggestion?
26:23 - D. B.
Can I? Go ahead.
26:25 - Unidentified Speaker
Yes.
26:25 - M. M.
So like I say, I'm doing workforce development of professional certificates for NVIDIA. So what they ask us to do, to go to job descriptions, what the industry job descriptions are looking for, specific jobs. Like you can probably, they go to Indeed, they go to different resources. So the job description, so you can say that you prepare these students for some particular jobs and see what the industry looking for, not only your three people, but yeah, the job description and you will figure out. And of course, I will say why Nvidia AI is not there. So Azure AI service is okay, but I mean, I've told you the course is compare the courses of IWS Amazon with the courses of NVIDIA. So we have certificates, we do the certificates, professional certificates. But yeah, if you want, put the NVIDIA, if you don't want, it's okay. But like I say, job description speaks what the industry, not only one, but survey of job descriptions and figure out particular jobs, what are the requirements? What we're doing right now for experts in large language models and experts in agenting AI.
28:06 - E. G.
I really think a lot of these units, if you wanted to, could be their own course.
28:17 - A. B.
Oh, absolutely.
28:18 - E. G.
Because I could see deployment scalability, talking about how to set up unmonitored deployments, dependency injection, scalability, serverless architectures, all of these things could actually be a full course.
28:49 - Unidentified Speaker
Correct.
28:50 - Y. P.
So I'll respond to some of the inputs and thought behind this. So first of all, Dr. M., I like your suggestion a lot in terms of what is industry requiring now and giving some examples of jobs and the gaps that are there. So that would be great. I also agree with your last comment on each of this would be a coursework itself. And hopefully we will have all these as course, if not already we have, I know we have some coursework around Python and et cetera, but people who come from marketing human resources other disciplines this will be kind of OK I'm not an expert in it but when I decide to use AI and when I'm talking to people who are technical in nature now I at least know what it means so yes this is kind of introduction to this because many times when it goes into implementation people when they are creating requirements when they are creating functional aspects they don't know what it means and it becomes a roadblock when it comes to application of artificial intelligence. Now what I agree with you and I think A. is that I may and this is the suggestion that I got from others also I may streamline this further into from 18 to 14 units and really think about when it comes to application what really is not required here and I may just remove that content from here when I move from 18 to 14. But the goal is that when people who are non-technical in nature can they start using solutions like Vercel and etc and get comfortable in say building an application or use some prompt engineer and etc for creating marketing solution. So we have projects that will give them experience on using generative AI and automate some of the marketing functions, human resources function with very minimal experience of coding. Now hopefully it will be a class combined with some technical and non-technical people so that's how the project teams would be but if not we will provide the technical support or help if they face any problems during the implementation. But streamlining this I think is an input I got and we'll do that. And Dr. M., I like your solution. I'll align that to some examples or job description that are there right now in the industry. And in NVIDIA also, I'll speak to you offline on that.
32:21 - E. G.
I did not mean to exclude that purposely.
32:25 - Y. P.
We will create some tasks without coding, okay?
32:29 - M. M.
create the modules, JNTIC particularly, multi-agents, without coding much. Sounds good. Okay. Thank you.
32:35 - Y. P.
Thank you all for the input. I have a comment too.
32:39 - D. B.
I guess if it was, you know, if I was experimenting with a course, you know, with this kind of breadth, I wouldn't sort of force myself to keep to the schedule. If some unit feels like it's going well and it should be continued, Well, go an extra class and then cut something off at the end of the semester. And kind of go with the flow a little bit if there seems to be a reason to do that. Just a philosophy.
33:14 - Y. P.
Got it.
33:15 - D. B.
Anyone else have any thoughts or comments? Or Y., do you have any other questions for people?
33:22 - Y. P.
I would like to, if people who have contributed right now. If they have time on their calendar in the next two, three weeks, I would like to have a one-on-one session to speak about it in detail. If there are more inputs, considering that we have one more presenter today. But that is the only thing that I would like to say before I stop.
33:46 - D. B.
OK, so yeah, feel free to reach out. We'll have a, assuming everything goes well, we should have a transcript of it, and you'll be able to look at the transcript and see what people said. And if you want, you can reach out to them based on that, too. Sounds good. Yeah. Thank you. All right. Let me go back to... All right, so E. has been working on her book project, which, congratulations, By the way, your proposal is approved. I think E. is here.
34:29 - E. T.
Yes, I'm here. OK. Yeah, so your proposal is approved.
34:33 - D. B.
This is a one-semester project. So now that your proposal is approved and your project is almost done, you're going to have a defense pretty soon. Do you have any questions or updates or anything you'd like to say?
34:49 - E. T.
Yes. I have an update. I mean, I've finally finished the book. Congratulations. Thank you. I'm working on picture generating parts. So the writing part is done, complete. It's a little below 100,000 words, about 90,000 approximately. The picture generating part, so among the tools that I've tried, the best one I liked was G.. The pictures G. created was a lot more realistic compared to the other Chachapiti. L. AI, I tried. And what other? There was one more, Firefly, and a couple more AI text to picture. Tools that I tried. The best one was G.. And the pictures were very realistic that I have asked G. if it searched web to find those pictures, because they were very, very realistic compared to the others. So next week, I'm planning to give the book to a small group of co-workers for their feedback. And hopefully, I OK.
36:15 - D. B.
My suggestion, my thought for your defense presentation would be to discuss your experience in the process and hints for future book authors and observations and tour the book. If you want to change that around, just let me know. But if you need any input on that, that's my initial suggestion. And then I thought your report could be the actual book.
36:44 - E. T.
Okay, I actually have a question on that. Do I need to prepare a presentation and something to, or just have the book and talk about the experience?
36:56 - D. B.#+#E. T.
The book is a defense. There's an oral defense, so that's your presentation. And then there's a product, which is the report.
37:05 - D. B.
In your case, I'd suggest a book.
37:08 - E. T.
Okay, so I don't need a presentation.
37:11 - D. B.
You do need a presentation, yeah. Oh, I do.
37:16 - E. T.
OK. Just making sure.
37:18 - D. B.
And I suggest scheduling it at 4 o'clock on a Friday afternoon.
37:23 - E. T.
OK. So would it be next week? Or?
37:27 - Unidentified Speaker
OK.
37:27 - D. B.
So I'm your advisor, right? So I normally want to just make sure everything's fine before I approve, before I recommend going ahead with a decision. So I'd like to see a draft of the book and I'd like to communicate offline about your presentation before we go ahead and make a definite schedule.
37:50 - E. T.
Of course, I'll email to the book this weekend. Okay.
37:54 - D. B.
And you know, with all my master's students, I, you know, I give them the opportunity to rehearse the presentation with me if they want. I'll leave that to you. But I do want to So touch base about it before we actually schedule it.
38:13 - E. T.
Sure. I would love that.
38:16 - D. B.
I would love to have the rehearsal before.
38:21 - Unidentified Speaker
OK.
38:22 - E. T.
All right. Well, let's discuss that offline, schedule it and everything.
38:28 - Unidentified Speaker
Yeah. OK.
38:30 - D. B.
What else? We're already 40 minutes in here.
38:35 - Unidentified Speaker
I don't know if we really should get started on that video.
38:44 - D. B.
Does anyone have any other announcements or thoughts or updates or any questions they want to? If we had five minutes left, you like to talk about? If not, I think we'll go ahead and adjourn. And actually, E., why don't you stick around for a couple of minutes, and we'll discuss those things that we were going to talk about.
39:18 - E. T.
Of course. All right, folks.
39:20 - D. B.
Thanks. We'll see you next time.
39:23 - D. B.#+#D. D.
Thanks, guys. Take care.
39:25 - R. R.
Thanks, P.
