Artificial Intelligence Study Group
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- Announcements, updates, questions, presentations, etc. as time allows
- Today: viewing and discussion.
- 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.
- Fri. April 25: YP will informally present his draft AI course outline and welcomes comment. See Appendix 1 below.
- TE is in the informal campus faculty AI discussion group.
- News: SL writes: "I’m excited to share that 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.
- Recall the masters project that some students are doing and need our suggestions about:
- Suppose a generative AI like ChatGPT or Claude.ai was used to write a book or content-focused website about a simply stated task, like "how to scramble an egg," "how to plant and care for a persimmon tree," "how to check and change the oil in your car," or any other question like that. Interact with an AI to collaboratively write a book or an informationally near-equivalent website about it!
- Is this good? https://spectrum.library.concordia.ca/id/eprint/993284/
- ET: Growing vegetables from seeds.
- Proposal drafted.
- Committee: DB, RS, MM.
- Going through prompt engineering course recommended by MM, planning to use it extensively.
- Course is at: https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+AI0117EN+v1/home
- Got 4,000+ word count outputs
- Gemini: writes well compared to ChatGPT
- We did the Chapter 6 video, https://www.youtube.com/watch?v=eMlx5fFNoYc, up to time 13:08. We can start there next time.
- Schedule back burner "when possible" items:
- 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
With industries increasingly relying on AI for decision-making, automation, and innovation, graduates with AI proficiency are in high demand across finance, healthcare, retail, cybersecurity, and beyond. This course offers hands-on training with real-world AI tools (Azure AI, ChatGPT, LangChain, TensorFlow), enabling students to develop AI solutions while understanding the ethical and regulatory landscape (NIST AI Risk Framework, EU AI Act).
Why This Course Matters for Students:
■v Future-Proof Career Skills – Gain expertise in AI, ML, and Generative AI to stay relevant in a rapidly evolving job market.
■v Business & Strategy Integration – Learn how to apply AI for business growth, decision- making, and competitive advantage.
v■ Governance & Ethics – Understand AI regulations, ethical AI implementation, and risk management frameworks.
v■ Hands-on Experience – Work on real-world AI projects using top industry tools (Azure AI, ChatGPT, Python, LangChain).
Why UALR Should Adopt This Course Now:
v■ Industry Demand – AI-skilled professionals are a necessity across sectors, and universities must adapt their curricula.
v■ Cutting-Edge Curriculum – A balanced mix of technology, business strategy, and governance makes this course unique.
v■ Reputation & Enrollment Growth – Offering a governance-focused AI course positions UALR as a leader in AI education.
v■ Cross-Disciplinary Impact – AI knowledge benefits students in business, healthcare, finance, cybersecurity, and STEM fields.
By implementing this course, UALR can produce graduates ready to lead in the AI era, making them highly sought after by top employers while ensuring AI is developed and used responsibly and ethically in business and society.
Applied AI (6 + 8 Weeks Course, 2 Hours/Week)
5-month Applied Artificial Intelligence course outline tailored for techno-functional, functional or technical leaders, integrating technical foundations, business use cases, and governance frameworks.
This can be split in 6 weeks certification plus additional funds for credit course with actual use case.
I have also leveraged insights from leading universities such as Purdue’s Applied Generative AI Specialization and UT Austin’s AI & ML Executive Program.
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Balance: 1/3 Technology | 1/3 Business Use Cases | 1/3 Governance, Compliance & AI Resistance
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Module 1: Foundations of AI and Business Alignment (Weeks 1-4)
v■ Technology: AI fundamentals, Machine Learning, Deep Learning
v■ Business: Industry Use Cases, AI for Competitive Advantage
■v Governance: AI Frameworks, Risk Management, Compliance
· Week 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, EU AI Act)
· Week 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
· Week 3: Key AI Technologies and Tools
o Supervised vs. Unsupervised Learning
o Python, Jupyter Notebooks, and cloud-based AI tools (Azure AI Studio, AWS SageMaker)
o Governance focus: AI compliance and regulatory challenges
· Week 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
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■v Technology: NLP, Computer Vision, Reinforcement Learning
v■ Business: AI in business functions - Marketing, HR, Finance
■v Governance: Bias Mitigation, Explainability, AI Trust
· Week 5: Natural Language Processing (NLP) & AI in Customer Experience
o Sentiment analysis, text classification, and chatbots
o Business case: AI in customer service (chatbots, virtual assistants)
o Governance focus: Privacy and data security concerns (GDPR, CCPA)
· Week 6: AI for Operational Efficiency
o Business use cases: AI for fraud detection, surveillance, manufacturing automation
o Compliance focus: AI security and adversarial attacks
· Week 7: Reinforcement Learning & AI in Decision-Making
o Autonomous systems, robotics, and self-learning models
o Business case: AI-driven investment strategies and risk assessment
o Resistance focus: Overcoming corporate fear of AI adoption
· Week 8: AI in Marketing, 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
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Module 3: AI Governance, Compliance & Ethics (Weeks 7-10)
v■ Technology: Secure AI Systems, Explainability
■v Business: Regulatory Compliance, AI Risk Management
■v Governance: Responsible AI, Transparency, Algorithm Audits
· Week 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)
· Week 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
· Week 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
· Week 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
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Module 4: AI Strategy, Implementation, and Future Trends (Weeks 11-12)
■v Technology: AI Product Development
■v Business: AI Implementation, Enterprise AI Strategy
v■ Governance: AI Regulatory Compliance & Future Legislation
· Week 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
· Week 14: AI for Competitive Advantage & Industry-Specific Applications
o AI in industry : e.g.: supply chain, autonomous vehicles, healthcare diagnostics
o Case study: e.g.: AI-driven drug discovery and logistics optimization
o Compliance: AI liability and regulatory accountability
· Week 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
· Week 16: The Future of AI: Trends, Risks & Opportunities
o Generative AI (DALL-E, ChatGPT, LangChain applications)
o AI and Web3, decentralized AI governance
o Case study: AI-powered governance in blockchain ecosystems
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Module 5: Capstone Project & Final Presentations (Weeks 12-14. Process starts in Week 7/8)
■v Technology: Hands-on AI Application Development
v■ Business: AI Use Case in Industry
■v Governance: Compliance Strategy & Ethical AI
· Weeks 17-19: AI Capstone Project
o Develop an AI-driven business solution with governance compliance
o AI application areas: Business analytics, customer engagement, fraud detection
o Report: Governance strategy and AI risk mitigation plan
· Week 20: 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
Tools & Technologies Covered:
· AI Development: Python, TensorFlow, PyTorch, Scikit-learn, GenAI models
· Cloud AI Platforms: Azure AI Studio, AWS AI Services, GCP Vertex AI
· NLP & Generative AI: ChatGPT, DALL-E, LangChain, BERT, Stable Diffusion
· AI Governance & Risk: SHAP, LIME, AI fairness toolkits
Appendix 2: Transcript
AI Discussion Group
