Lessons in Leadership: Python, AI, and Heuristics

Presented as a keynote for PyCon Uganda 2024.

This presentation discusses what characteristics make an effective leader, how to lead through change, and how Python, AI, and Heuristics help leaders.

Transcript

1. Lessons in Leadership PyCon Uganda Oct. 13, 2024 Python, AI, and Heuristics  1 Carol Willing speakerdeck.com/willingc https://hachyderm.io/@willingc
2. Hello 2 Thank you Kirabo and Hassan
3. Carol Willing Python Core Developer Python Steering Council (three terms) Python Software Foundation Fellow Jupyter Core Developer 2017 ACM Software System Award Papermill Maintainer pyOpenSci Advisory Board Chan Zuckerberg Open Science Board Quansight Labs Board Former VP of Engineering, Noteable
4. 4 Lessons for Leaders 1. De fi ning leadership 2. Leading through change 3. Leading with Python 4. Leading with AI 5. Leading with heuristics 6. Wrap up: Learning to lead
5. 5 1. De f ining leadership
6. 6 shows up A leader
13. 13 shares knowledge A leader
14. 14 Aisha @AishaXBello Lifelong Learner Leader in Web, Data Science, and Systems https://www.youtube.com/watch?v=TKK4ZjCux1I&t=18s
15. 15 welcomes individuals A leader
16. 16 Kojo Jay
17. 17 empowers people A leader
18. Pragmatic Python © 2023 by Carol Willing is licensed under CC BY 4.0 Insider 2021 To encourage women, we need to show and make role models and mentors accessible to them," said Abigail Dogbe, a fellow at the Python Software Foundation and lead organizer of PyLadies Ghana. Abigail
19. 19 gains strength through trust A leader
20. 20 Marlene PSF Fellow Former PSF Director Catalyst for change
21. 21 works toward what's possible A leader
22. 22 Velda Afi Engineering Leaders Global ambassadors for Django and Python
23. 23 offers grace A leader
24. 24 Joannah Core Developer Sprint @ Bloomberg Core Developer PSF Fellow Former PSF Director Expert in Garbage Collection and Python C Extensions https://dl.acm.org/doi/pdf/10.1145/3605158.3605849
25. 25 o ff ers grace works toward what's possible gains strength through trust empowers people welcomes individuals shares knowledge shows up A leader...
26. 26 2. Leading through change our daily challenge
27. 27 Speed Complexity Scale
28. Developer decisions What should I learn? Which tool is best? How do I use this tool? When do I use this tool? 28
29. Time is on your side 29
30. Paradigm shifts evolve over time. 30 Think in years, not months or days. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai 2017 2024
31. 31 https://blog.jupyter.org/jupyter-receives-the-acm-software-system-award-d433b0dfe3a2
32. 32 https://github.com/dansuh17/alexnet-pytorch/tree/master https://paperswithcode.com/method/alexnet https://papers.nips.cc/paper_ fi les/paper/2012/hash/ c399862d3b9d6b76c8436e924a68c45b-Abstract.html
33. How does a leader navigate change 33
34. Use cognitive science 34
35. Create a toolbox 35 Generated by ChatGPT 4o: make an image of a toolbox containing the words Python, AI, Heuristics
36. 36 be patient adapt create a toolbox To lead through change
37. 37 3. Leading with Python Proven language and tools
38. Strengths of Python Solid Foundation 38 1. Readability 2. Extensive Standard Library 3. Large ecosystem 4. Cross-platform compatibility 5. Community support 6. Versatility 7. Rapid prototyping 8. Integrates with other languages 9. Typing fl exibility 10. Multidomain: web, science, data science, education, embedded
39. Modernize mobile iOS, Android 39 Python https://peps.python.org/pep-0730/ PEP 730 – Adding iOS as a supported platform PEP 738 – Adding Android as a supported platform https://peps.python.org/pep-0738/
40. 40 https://resources.dataumbrella.org/open-source/contributing-to-cpython
41. Build solutions for people 41
42. Pragmatic Python © 2023 by Carol Willing is licensed under CC BY 4.0 Scaling Notebooks for Teaching and Research: JupyterHub and mybinder.org https://conference.scipy.org/proceedings/scipy2018/pdfs/project_jupyter.pdf Try it yourself https://mybinder.org
43. 43 https://www.candc.or.jp/en/2023/2023_prize_cc.html Gratitude 2023 NEC C&C Prize Thank you, Guido, for creating Python. You allowed me to ful fi ll my professional dream of giving people around the world access to learning resources. With Python, we created Jupyter Notebooks, JupyterHub, and Binder.
44. Leading with Python 44 Strengths Performance Polyglot Mobile and Web Innovation User success Python provides a solid foundation.
45. 45 4. Leading with AI Transparent and trusted tools
46. 46 AI
48. Accurate? Biased? Conclusions? 48
49. AI is a tool. AI is not a person. A person chooses an AI tool. An AI tool has limitations. Fit the tool to the job. Minimize the harm done by the tool. 49 ABCs
50. LLMs Black box or not 50 AI https://youtu.be/jkrNMKz9pWU A Hackers Guide to Language Models by Jeremy Howard LLM https://github.com/fastai/lm-hackers https://fastai.github.io/lm-hackers/lm-hackers.html Input Output
51. Understanding is key "We all are data people." • Context • Use case • Limitations • Criticality • Access 51 AI https://youtu.be/GBycch2OSx8 Interview with Scott Hanselman
52. Accelerating Science Decision support and idea generation 52 AI https://chanzuckerberg.com/blog/priscilla-chan-update-ai-biomedical-research/ https://tech.chanzuckerberg.com/scitech/ Universal Sequence-Based Embeddings Universal embeddings of cells and organelles from microscope images Explore the molecular underpinnings of human health and disease
53. Leading with AI 53 ABCs Not a person Evolved over time Open source to closed commercial o ff erings Transparent and trusted Literate Understanding Critical thinking LLMs are tools with tradeo ff s. Consider bene fi ts and pitfalls.
54. 54 5. Leading with Heuristics Time saving tools
55. Heuristics 55 • Real-world experience • Use when information is unknown, overwhelming, or random • Understandable • Pragmatic • Low friction to use
56. Ten Software Development Heuristics Rules of Thumb 56 1. YAGNI (You Aren’t Gonna Need It) 2. Divide and Conquer: Break down complex problems 3. KISS (Keep It Simple, Superheroes): Avoid complexity 4. User-Centric Design: Focus on the end-user 5. Fail Fast: Detect errors early
57. Ten Software Development Heuristics Rules of Thumb 57 6. Incremental development: feedback and adjustments 7. CI/CD: Catch errors early 8. DRY (Don't Repeat Yourself) 9. Code smells 10. Refactoring: Improve without changing behavior
58. Pythonic Heuristics 58
59. 59 https://youtu.be/j4X8K6pcxuQ?si=s8d0DytF7-bmeBLB
60. https://thenewstack.io/a-conversation-with-the-creators-behind-python-java-typescript-and-perl/ Guido van Rossum (Python) James Gosling (Java) Larry Wall (Perl) Anders Hejlsberg (Pascal, C#, TypeScript) me (Jupyter)
61. Problem-solving decisions SCAMPER 61 • Substitute • Combine • Adapt • Modify • Put to another use • Eliminate • Reverse https://www.designorate.com/a-guide-to-the-scamper-technique-for-creative-thinking/
62. Learning 62 Cognitive Science Tips Python Code vs Pythonic Code Heather Crawford - PyTexas 2024 Experts recognize Beginners reason The human mind works quite di ff erently than a computer. Brown, Neil C. C., Felienne F. J. Hermans, and Lauren E. Margulieux. “10 Things Software Developers Should Learn about Learning.” Communications of the ACM 67, no. 1 (January 2024): 78–87. https://doi.org/10.1145/3584859. https://www.youtube.com/watch?v=RdkhRfRizf0
63. Leading with Heuristics 63 Rules of thumb Useful Easy to apply Real-world experience Heuristics save time when a problem is large in scope with lots of uncertainty
64. 64 6. Learning to lead Python AI Heuristics Time saving tools Transparent and trusted tools Proven language and tools
65. Be a humble leader. 65
66. 66 Expect to make mistakes Adopt a musician's mindset. Loud and proud
67. 67 Follow your North Star Value your time and energy
68. 68 Adapt and thrive
69. Impact your world 69 Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do. – Donald E. Knuth, Literate Programming (1984)
70. PyCon Uganda 70
71. We lead Python's future. 71
72. Thank you 72 Carol Willing https://www.willingconsulting.com/ @willingc@hachyderm.io
73. Resources 73 A selection of research
74. 74 Brown, Neil C. C., Felienne F. J. Hermans, and Lauren E. Margulieux. “10 Things Software Developers Should Learn about Learning.” Communications of the ACM 67, no. 1 (January 2024): 78–87. https://doi.org/10.1145/3584859. Cai, Carrie J., and Philip J. Guo. “Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires.” In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 25–34. Memphis, TN, USA: IEEE, 2019. https:// doi.org/10.1109/VLHCC.2019.8818751. Chakravorti, Bhaskar. “AI’s Trust Problem.” Harvard Business Review, May 3, 2024. https://hbr.org/2024/05/ais-trust-problem. Cogo, Filipe Roseiro, Xin Xia, and Ahmed E. Hassan. “Assessing the Alignment between the Information Needs of Developers and the Documentation of Programming Languages: A Case Study on Rust.” ACM Transactions on Software Engineering and Methodology 32, no. 2 (April 30, 2023): 1–48. https://doi.org/10.1145/3546945. Deters, Hannah, Jakob Droste, and Kurt Schneider. “A Means to What End? Evaluating the Explainability of Software Systems Using Goal-Oriented Heuristics.” In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 329–38. Oulu Finland: ACM, 2023. https://doi.org/10.1145/3593434.3593444. Ding, Jiepin, Mingsong Chen, Ting Wang, Junlong Zhou, Xin Fu, and Keqin Li. “A Survey of AI-Enabled Dynamic Manufacturing Scheduling: From Directed Heuristics to Autonomous Learning.” ACM Computing Surveys 55, no. 14s (December 31, 2023): 1–36. https://doi.org/10.1145/3590163. Donoho, David. “Data Science at the Singularity.” Harvard Data Science Review 6, no. 1 (January 31, 2024). https://doi.org/10.1162/99608f92.b91339ef. Dutta, Subhabrata, and Tanmoy Chakraborty. “Thus Spake ChatGPT.” Communications of the ACM 66, no. 12 (December 2023): 16–19. https://doi.org/10.1145/3616863. Eng, Richard Kenneth. “How to Measure Programming Language Complexity,” n.d., 4. Forrester, Jay W. “Digital Computers: Present and Future Trends.” In Papers and Discussions Presented at the Dec. 10-12, 1951, Joint AIEE-IRE Computer Conference: Review of Electronic Digital Computers on - AIEE-IRE ’51, 109–14. Philadelphia, Pennsylvania: ACM Press, 1951. https://doi.org/10.1145/1434770.1434789. Gonzalez, Eric. “Improvisational Research: How I Learned to Stop Worrying and Embrace Uncertainty.” XRDS: Crossroads, The ACM Magazine for Students 30, no. 3 (March 2024): 10–11. https://doi.org/10.1145/3652615. Gosline, Renée Richardson, Yunhao Zhang, Haiwen Li, Paul Daugherty, Arnab D. Chakraborty, Philippe Roussiere, and Patrick Connolly. “Nudge Users to Catch Generative AI Errors.” MIT Sloan Management Review, May 29, 2024. https://sloanreview.mit.edu/ article/nudge-users-to-catch-generative-ai-errors/. Guo, Philip J. “Six Opportunities for Scientists and Engineers to Learn Programming Using AI Tools Such as ChatGPT.” Computing in Science & Engineering 25, no. 3 (May 2023): 73–78. https://doi.org/10.1109/MCSE.2023.3308476. Guzdial, Mark, and Daniel Reed. “Securing the Future of Computer Science; Reconsidering Analog Computing.” Communications of the ACM 56, no. 4 (April 2013): 12–13. https://doi.org/10.1145/2436256.2436260. Hao, Karen. “Inside the Fight to Reclaim AI from Big Tech’s Control | MIT Technology Review.” Accessed June 24, 2021. https://www.technologyreview.com/2021/06/14/1026148/ai-big-tech-timnit-gebru-paper-ethics/. Hinsen, Konrad. “The Approximation Tower in Computational Science: Why Testing Scientific Software Is Difficult.” Computing in Science & Engineering 17, no. 4 (July 2015): 72–77. https://doi.org/10.1109/MCSE.2015.75. ———. “The Power to Create Chaos.” Computing in Science & Engineering 18, no. 4 (July 2016): 75–79. https://doi.org/10.1109/MCSE.2016.67. Hope, Tom, Doug Downey, Daniel S. Weld, Oren Etzioni, and Eric Horvitz. “A Computational Inflection for Scientific Discovery.” Communications of the ACM 66, no. 8 (August 2023): 62–73. https://doi.org/10.1145/3576896. Jacobs, Adam. “The Pathologies of Big Data.” Communications of the ACM 52, no. 8 (August 2009): 36–44. https://doi.org/10.1145/1536616.1536632. Kesari, Ganes. “Building a Data-Driven Culture: Three Mistakes to Avoid.” MIT Sloan Management Review, May 14, 2024. https://sloanreview.mit.edu/article/building-a-data-driven-culture-three-mistakes-to-avoid/.
75. 75 Kirkpatrick, Keith. “The Carbon Footprint of Artificial Intelligence.” Communications of the ACM 66, no. 8 (August 2023): 17–19. https://doi.org/10.1145/3603746. Kohls, Christian. “Patterns for Innovation: 6 Patterns for Idea Implementation.” In Proceedings of the 24th European Conference on Pattern Languages of Programs, 1–8. Irsee Germany: ACM, 2019. https://doi.org/10.1145/3361149.3361156. Koreeda, Yuta, Terufumi Morishita, Osamu Imaichi, and Yasuhiro Sogawa. “LARCH: Large Language Model-Based Automatic Readme Creation with Heuristics.” In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 5066–70. Birmingham United Kingdom: ACM, 2023. https://doi.org/10.1145/3583780.3614744. Kosch, Thomas, Jakob Karolus, Johannes Zagermann, Harald Reiterer, Albrecht Schmidt, and Paweł W. Woźniak. “A Survey on Measuring Cognitive Workload in Human-Computer Interaction.” ACM Computing Surveys 55, no. 13s (December 31, 2023): 1–39. https://doi.org/10.1145/3582272. Kugler, Logan. “What’s Old Is New Again.” Communications of the ACM 66, no. 11 (November 2023): 11–12. https://doi.org/10.1145/3624009. Lu, Yuwen, Tiffany Knearem, Shona Dutta, Jamie Blass, Clara Kliman-Silver, and Frank Bentley. “AI Is Not Enough: A Hybrid Technical Approach to AI Adoption in UI Linting With Heuristics.” In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 1–7. Honolulu HI USA: ACM, 2024. https://doi.org/10.1145/3613905.3637135. Melbourne, Associate Professor Jenny Waycott and Dr Wafa Johal, University of. “The Women Putting the Intelligence in Artificial Intelligence.” Pursuit, March 7, 2023. https://pursuit.unimelb.edu.au/articles/the-women-putting-the-intelligence-in-artificial- intelligence. Melbourne, Dr Mor Vered and Associate Professor Tim Miller, University of. “What Were You Thinking?” Pursuit, August 23, 2018. https://pursuit.unimelb.edu.au/articles/what-were-you-thinking. Nassif, Mathieu, and Martin P. Robillard. “A Field Study of Developer Documentation Format.” In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 1–7. Hamburg Germany: ACM, 2023. https://doi.org/ 10.1145/3544549.3585767. Norman, Donald A. “Twelve Issues for Cognitive Science.” Cognitive Science 4, no. 1 (1980): 1–32. https://doi.org/10.1207/s15516709cog0401_1. Perkel, Jeffrey M. “Ten Computer Codes That Transformed Science.” Nature 589, no. 7842 (January 20, 2021): 344–48. https://doi.org/10.1038/d41586-021-00075-2. Potter, Hannah, Ardi Madadi, René Just, and Cyrus Omar. “Contextualized Programming Language Documentation.” In Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, 1–15. Auckland New Zealand: ACM, 2022. https://doi.org/10.1145/3563835.3567654. Quinn, Clark. “The Cognitive Science Behind Learning.” Chief Learning Officer - CLO Media (blog), December 23, 2016. https://www.chieflearningofficer.com/2016/12/23/cognitive-science-behind-learning/. Savage, Neil. “Revamping Python for an AI World.” Communications of the ACM 66, no. 12 (December 2023): 13–14. https://doi.org/10.1145/3624987. Sochat, Vanessa. “The 10 Best Practices for Remote Software Engineering.” Communications of the ACM 64, no. 5 (May 2021): 32–36. https://doi.org/10.1145/3459613. Stigliani, Kimberly D. Elsbach and Ileana. “Evaluating New Technology? You’re More Biased Than You May Realize.” MIT Sloan Management Review, September 23, 2020. https://sloanreview.mit.edu/article/evaluating-new-technology-youre-more-biased-than-you- may-realize/. Teng, Shang-Hua. “‘Intelligent Heuristics Are the Future of Computing.’” ACM Transactions on Intelligent Systems and Technology 14, no. 6 (December 31, 2023): 1–39. https://doi.org/10.1145/3627708. Tshukudu, Ethel, and Quintin Cutts. “Understanding Conceptual Transfer for Students Learning New Programming Languages.” In Proceedings of the 2020 ACM Conference on International Computing Education Research, 227–37. Virtual Event New Zealand: ACM, 2020. https://doi.org/10.1145/3372782.3406270. Writer, AIT Staff. “Explainable AI: 5 Popular Frameworks in Python.” AiThority (blog), February 14, 2023. https://aithority.com/technology/explainable-ai-5-popular-frameworks-in-python-to-explain-your-models/. Ziegler, Albert, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. “Measuring GitHub Copilot’s Impact on Productivity.” Communications of the ACM 67, no. 3 (March 2024): 54–63. https:// doi.org/10.1145/3633453.
76. Lessons in Leadership PyCon Uganda Oct. 13, 2024 Python, AI, and Heuristics 76 Carol Willing End of presentation