Week 3 Learning Journal Post

 Part 1

This week I focused on improving time management and self-discipline. The key takeaway from the AcademicTips readings is to treat time as precious: make study a top priority, plan deliberately, and act when I know I should. To put that into practice, each night I’ll list tomorrow’s top three tasks, estimate how many minutes each will take, and compare those estimates with what actually happened at day’s end. I’ll work in two 50/10 focus sprints per study block and start with the hardest task first. To keep my motivation up, I’ll keep my goals visible, celebrate small wins, and use quick flash-card reviews whenever I have spare minutes. I’ll measure progress by hitting at least five focused sprints per day, maintaining an estimate-vs-actual log, and giving myself a small reward whenever I complete all three planned tasks.

Part 2

This week’s big idea for me was ethics and integrity in computing. The usual AI worries are real: jobs, fairness, privacy, and democracy. But an even deeper issue is keeping human dignity and authenticity. Things like de-aging actors, bringing back stars after death, AI matchmaking, and “virtual immortality” all raise questions about consent, truth, and respect. I like the view that AIs are becoming digital companions, so we should set clear boundaries, avoid unchecked autonomy, and build safety in from the start. The BBC panel showed real benefits too, like earlier cancer detection and new antibiotics, but also reminded me that incentives, rules, and real-world testing matter, and that people still need to learn instead of handing everything to machines. Andrew Ng’s line stuck with me: move fast and be responsible. Build quick, then test, add guardrails, and evaluate carefully. The codes we read turn this into habits: be honest, avoid harm, protect privacy and intellectual property, treat people fairly, document and test your work, and give credit, all while following our university’s integrity rules. Going forward I will use a simple ethics checklist on every project: name the stakeholders and possible harms, get consent and keep data to a minimum, check for bias, keep a human in the loop for important calls, and write down my assumptions and sources.

Part 3

“What every computer science major should know” helped me set clearer goals for how I will grow in CS. The big takeaway for me is to build a public portfolio that shows real projects instead of only relying on a résumé. I also saw how much core skills matter: comfort with Unix tools, version control, testing, and basic security, plus a solid base in math and CS theory. The piece pushed me to stay language-agnostic by learning across different paradigms and to practice communication so I can explain my work well. From the other materials this week, I’m thinking more about the human side of AI. Michael Sandel’s talk made me question consent and dignity around deepfakes and digital avatars. Mustafa Suleyman’s TED talk framed AI as a powerful companion that still needs careful boundaries. The BBC panel showed how AI can help in medicine yet still struggles to move from lab to clinic, and Andrew Ng’s keynote showed how “agentic” workflows can turn ideas into working tools faster. Putting this together, my plan is to publish a simple portfolio site, ship small but complete projects to it each week, and keep a steady routine of practice with shell, Git, data structures, and testing so I grow both the craft and the judgment to use these tools well.

Part 4

Integrity matters to me because it protects my learning and keeps the class fair. If I submit code that is not mine, I skip the hard work where real understanding happens, and I also treat classmates unfairly. The code is clear that I must not submit solutions that are not my own and I should not share my solution code with others. I can still talk through ideas and ask for hints, but I need to write my own programs. When I get help, including from AI, I should say exactly what I used and how it influenced my work. That level of credit keeps everyone honest and teaches me to document my process. All of this builds a climate of trust where we can learn together, and it protects the reputation of our program for everyone who comes after us. It also lines up with professional expectations in computing, like being honest and respecting the work it takes to create new ideas and artifacts.