---
description: AI is here to stay, we need to learn to grow with it
---

# Artificial Intelligence (AI)

## Responsible use of AI

* Examples and guidelines
  * [A guide to navigating college in the AI era](https://studentguidetoai.org/) (by Elon University)
  * [Student Use Cases for AI](https://hbsp.harvard.edu/inspiring-minds/student-use-cases-for-ai) (by Harvard Business School)&#x20;
  * [How Students Are Actually Using Generative AI ](https://hbsp.harvard.edu/inspiring-minds/how-students-are-actually-using-generative-ai?cid=email%7Cmarketo%7C2024-07-11-inspiring-minds-digest%7C55520207%7Cthought-lead-inspiring-minds%7Ceducator%7Cinspiring-minds-article%7Cjul2024\&acctID=25044856\&mkt_tok=ODU1LUFUWi0yOTQAAAGUQG8RokJtmyneV3qQU2G1tKwAbO4eN-BPLLnzQZarZzFCMsXRTPKeeUmvkndx-NaJST-RMUZDDm7EoqEwl_dvoFfj6ap1uII7McVfvQ0eYbVXPg)(by Harvard Business School)&#x20;
  * [Balancing AI and human interaction](https://genai.ubc.ca/) (by UBC)
  * [Put ethical values and principles into practice across your AI project lifecycle](https://aiethics.turing.ac.uk/) (by The Alan Turing Institute)
  * [Against "Brain Damage"](https://www.oneusefulthing.org/p/against-brain-damage): "_Ultimately, it is how you use AI, rather than use of AI at all, that determines whether it helps or hurts your brain when learning_" (by Prof. Ethan Mollick)
* AI literacy and ethics
  * [too long didn’t read](https://lnkfi.re/tldr#msdynmkt_trackingcontext=485c5785-7280-4e42-87ec-56f1311aceee): Your shortcut to AI literacy (Podcast by Alan Turing Insittute)
  * [AI literacy guide](https://go.cloudplatformonline.com/ODA4LUdKVy0zMTQAAAGXWsyXliJcLx7qTmuPBaiIMIUNP784v1BNQb1JZu6Wqp5vVyhNCJFgexZd1aJIh6mPoZBk0hA=) (Google)
  * [Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development ](https://arxiv.org/abs/2501.16946)(article)
  * [The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence](https://www.cambridge.org/core/books/cambridge-handbook-of-the-law-ethics-and-policy-of-artificial-intelligence/0AD007641DE27F837A3A16DBC0888DD1)
  * [The MIT AI Risk Repository](https://airisk.mit.edu/): What are the risks from artificial intelligence?
* Develop a routine to check the generated answers
  * avoid an "auto-pilot" mindset
    * [Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters](https://static1.squarespace.com/static/604b23e38c22a96e9c78879e/t/62d5d9448d061f7327e8a7e7/1658181956291/Falling+Asleep+at+the+Wheel+-+Fabrizio+DellAcqua.pdf)
    * [“ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing](https://doi.org/10.1111/bjet.13591)
      * "Generative AI tools can outperform human experts in specific scenarios, requiring little to no evaluation. However, in situations where these tools generate misleading or incorrect content, the application of metacognitive skills and epistemic beliefs becomes essential to discern reliable information and avoid the integration of errors."
  * Three levels of validation of answers from GenAI
    1. check for references/citations: are they real?
    2. for the content generated by GenAI (e.g. ChatGPT) directly: does it make sense?
    3. for the content paraphrased from articles using GenAI: did the authors really mean it?
* [One Useful Thing](https://www.oneusefulthing.org/): Trying to understand the implications of AI for work, education, and life. By Prof. Ethan Mollick (University of Pennsylvania)

## AI tools for study

* Google NotebookLM for knowledge organization
  * [YouTube tutorials](https://www.youtube.com/playlist?list=PLGSHbNsNO4VhXQ-476lfqlfwmMonLHFpO) by Elvis Saravia
  * Open-source alternative: [NotebookLlaMa](https://github.com/run-llama/notebookllama)
* [Google Labs](https://labs.google/)
* [openalex](https://openalex.org/): Researchers, students, and developers can use it to explore scholarly works, track research trends, analyze networks of knowledge, or simply discover new papers.
* [elicit.com](https://elicit.com/): for finding recent research papers and generating summaries
* [Jupyter Agent](https://huggingface.co/spaces/data-agents/jupyter-agent): interactive, AI-guided coding in Python, providing real-time explanations and support for beginners.
* [Consensus](https://consensus.app/?home=true): AI Search Engine for Research
* \[Data analysis]&#x20;
  * [Let a LLM agent write and execute code inside a notebook!](https://huggingface.co/spaces/data-agents/jupyter-agent)
  * [AI Sheets](https://huggingface.co/blog/aisheets) by Hugging Face: a no-code tool for building, transforming, and enriching datasets using (open) AI models.
* \[Understand code repos] [Cognition AI’s DeepWiki ](https://deepwiki.com/)offers free explanation of GitHub repositories!
  * Examples: [https://deepwiki.com/qtu-UBC/BuildingEmbodiedImpacts/2-core-concepts](https://deepwiki.com/qtu-UBC/BuildingEmbodiedImpacts/2-core-concepts), [https://deepwiki.com/qtu-UBC/H2-LCA-app](https://deepwiki.com/qtu-UBC/H2-LCA-app)

## Climate & AI

* [Grand challenges initiatives in AI for climate & nature](https://ccai-reports.s3.us-east-2.amazonaws.com/grand-challenge-initiatives-ai-climate-nature.pdf) (by Climate Change AI)
* [Microsoft AI for Earth grantee gallery](https://microsoft.github.io/AIforEarth-Grantees/): Showcasing the work of Microsoft AI for Earth grant recipients
* \[2024] [Implications of Artificial Intelligence-Related Data Center Electricity Use and Emissions: A Workshop](https://www.nationalacademies.org/event/43750_11-2024_implications-of-artificial-intelligence-related-data-center-electricity-use-and-emissions-a-workshop)
* \[2024] [The ICEF Artificial Intelligence for Climate Change Mitigation Roadmap](https://transitiondigital.org/ai-climate-roadmap) (Second Edition, November 2024)
* [Climate Change AI Tutorials](https://github.com/orgs/climatechange-ai-tutorials/repositories)
  * [Tracking\_Emissions\_from\_ML\_Models\_(Revised).ipynb](https://colab.research.google.com/github/climatechange-ai-tutorials/tracking-ml-emissions/blob/main/Tracking_Emissions_from_ML_Models_\(Revised\).ipynb#scrollTo=EFpgp_rIA_TY)
    * \[Video] [GHG Impact Assessment of AI](https://www.youtube.com/watch?v=G5VuaWaYsLU)
* [More tutorials from Climate Change AI](https://www.climatechange.ai/tutorials?)

## Fun projects

Gain some hands-on experience by implementing other people's code

* aitk: Artificial Intelligence Toolkit
  * [\[Notebooks\] Generative AI](https://github.com/ArtificialIntelligenceToolkit/aitk/tree/master/notebooks/GenerativeAI)
* Stanford CS224N NLP with Deep Learning | 2023 | Hugging Face Tutorial, Eric Frankel
  * \[[Video](https://www.youtube.com/watch?v=b80by3Xk_A8)]
  * \[[Colab](https://colab.research.google.com/drive/13r94i6Fh4oYf-eJRSi7S_y_cen5NYkBm#scrollTo=sKB26q-4-X8r)]
* Automating the creation of LULC datasets for Semantic Segmentation
  * "The primary targets for this tutorial are data scientists and developers working in the ML and geospatial associated industries."
  * \[[Colab](https://colab.research.google.com/github/climatechange-ai-tutorials/lulc-semantic-segmentation/blob/main/Automating_the_creation_of_LULC_datasets_for_Semantic_Segmentation.ipynb#scrollTo=13i7KQ9t-CV8)]
* Can you generate an LCA report using:
  * "[Multimodal Report Generation Agent](https://github.com/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_report_generation_agent.ipynb)"
  * "[STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking](https://github.com/stanford-oval/storm?tab=readme-ov-file)"

## Dive deep

* Learn Prompt Engineering: [https://www.promptingguide.ai/](https://www.promptingguide.ai/)
* [5-Day Gen AI Intensive Course with Google Learn Guide](https://www.kaggle.com/learn-guide/5-day-genai?utm_medium=email\&utm_source=gamma\&utm_campaign=learn-5daygenai)&#x20;
  * A self-paced learning guide for anyone interested in learning more about the fundamental technologies and techniques behind Generative AI
* [Microsoft released an 11-lesson course covering AI agent fundamentals](https://github.com/microsoft/ai-agents-for-beginners)
* [OpenAI Forum](https://forum.openai.com/): a new initiative that brings together domain experts and students to discuss and collaborate on the present and future of AI.
* [AI by Hand](https://x.com/ProfTomYeh) (examples and tutorials for understanding the fundamentals of AI algorithms curated by Professor Tom Yeh @University of Colorado Boulder)
* [LLM Visualization](https://bbycroft.net/llm)
* [Machine Learning Visualized](https://ml-visualized.com/index.html#about-the-book): "a visualization of the machine learning algorithm throughout its training phase, ultimately converging at its optimal weights. Happy Learning! – Gavin"
* [Ahead of AI](https://magazine.sebastianraschka.com/) (by Sebastian Raschka, PhD): "_**Ahead of AI**_ helps you keep up with the latest developments and research trends in the fields of machine learning, deep learning, and artificial intelligence."
  * \[Videos] [Coding LLMs from the Ground Up: A Complete Course](https://magazine.sebastianraschka.com/p/coding-llms-from-the-ground-up?utm_campaign=email-half-post\&r=284wg1\&utm_source=substack\&utm_medium=email)
* [Interactive Tools for machine learning, deep learning, and math](https://github.com/Machine-Learning-Tokyo/Interactive_Tools?tab=readme-ov-file\&utm_campaign=Data_Elixir\&utm_source=Data_Elixir_499) (a collection of interactive tools)

