Resources

The following is my selection of resources including articles, courses, podcasts and miscellaneous items related to deep learning, natural language processing, machine learning, technology, economics and geopolitics. I update it monthly with new content.

Research

• Forsberg, Robin and Nelimarkka, Matti (2024) Value conflicts in natural language processing: a literature review. Helsinki Social Computing Group, University of Helsinki. Big Data & Society. Work in progress.

• Forsberg, Robin (2024) Using transformers for supervised text classification of political content: a methodological case study. Helsinki Social Computing Group, University of Helsinki. Work in progress.

• Forsberg, Robin et al (2024) Identifying narratives on YouTube: a case study into NATO. Helsinki Social Computing Group, University of Helsinki & Social Computing Group, University of Zurich. Work in progress.

Policy articles

• Forsberg, R., Kähkönen, A., Moyer, J. (2022) If Finland joins NATO, it needs a new nuclear weapons policy, Bulletin of the Atomic Scientists. Policy article. https://thebulletin.org/2022/12/if-finland-joins-nato-it-needs-a-new-nuclear-weapons-policy/

• Forsberg, R., Kähkönen, A., Öberg, J. (2022) Implications of a Finnish and Swedish NATO Membership for Security in the Baltic Sea Region, Woodrow Wilson International Center for Scholars. Policy article. https://www.wilsoncenter.org/article/implications-finnish-and-swedish-nato-membership-security-baltic-sea-region

• Forsberg, Robin (2018) Credit expansion and housing prices - a comparative study of Sweden and Finland. Master's Thesis. Department of Economics, Hanken School of Economics. https://shorturl.at/cjsV8

• Forsberg, R., Elmgren, P. (2015) SOTE (Healthcare, Social Welfare and Regional Government Reform Package) and Freedom to Choose - Would the Swedish model, where public money follows the patient also to the private healthcare sector, be cost saving for the public sector when implemented in Finland?, Aalto University. Policy article. https://shorturl.at/mtMNV

• Sofia Heikkonen, Ida Koivisto and Riikka Koulu (2023). Regulation and Doctrinal Challenges ofAutomated Decision-Making in Public Administration 

Data + software engineering 

• Satellite image deep learning techniques: https://github.com/satellite-image-deep-learning/techniques

• Use cases for D3.js as a data scientist: https://towardsdatascience.com/why-im-learning-javascript-as-a-data-scientist-e2b87bcdac03

• The 2024 MAD (ML, AI & Data) Tooling Landscape:  https://mad.firstmark.com/

• Data Engineering Design Patterns online book: https://www.dedp.online/about-this-book.html

• Data team handbok. https://github.com/sdg-1/data-team-handbook/tree/main

• Data visualization gallery: https://www.data-to-viz.com/#correlogram

• SFI Complex systems summer school. Santa Fe Institute. https://santafe.edu/engage/learn/programs/sfi-complex-systems-summer-school

• Complexity explorer. Santa Fe Institute. https://www.complexityexplorer.org/

• Sci-Hub. Access academic papers. https://sci-hub.se/

• Anna's Archive. More access to academic papers. https://monoskop.org/Anna%27s_Archive

• Benjamin Rogojan. Guide to data engineering. https://www.linkedin.com/posts/benjaminrogojan_dataengineering-activity-7218751177855782912-WTC4?utm_source=share&utm_medium=member_desktop

YouTube channels

• Internet of Bugs. Great programming-related resources by senior software engineer.

• Decoding Geopolitics. 1hr+ interviews with international relations experts.

• Python Tutorials for Digital Humanities. Great skill building content.

• BERTopic for Topic Modeling - Maarten Grootendorst

Reading list

• Vikram Deshmukh (2018) Modeling Human Migrations Dynamics in NetLogo. https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1621&context=etd_projects

• Hu et al (2021) LoRA: Low-Rank Adaptation of Large Language Models. https://doi.org/10.48550/arXiv.2106.09685

• Khurana, D., Koli, A., Khatter, K. et al. (2023) Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl 82, 3713–3744. https://doi.org/10.1007/s11042-022-13428-4

• Xu, F., et al. A Systematic Evaluation of Large Language Models of Code. (2022) Workshop paper, DL4C ICLR 2022, 1-13. https://arxiv.org/pdf/2202.13169.pdf

• Mialon, G. et al. Augmented Language Models: a Survey. (2023) Meta AI, arXiv.  https://doi.org/10.48550/arXiv.2302.07842

• Schick, T. et al. Toolformer: Language Models Can Teach Themselves to Use Tools. (2023) Meta AI, arXiv. https://arxiv.org/abs/2302.04761

• Ouyang, L. et al. (2022) Training language models to follow instructions with human feedback. OpenAI. https://doi.org/10.48550/arXiv.2203.02155

• Eloundou, T. et al. (2023) GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. OpenAI, OpenResearch, University of Pennsylvania, arXiv. https://doi.org/10.48550/arXiv.2303.10130

• GPT-4 Technical Report. (2023) OpenAI. https://cdn.openai.com/papers/gpt-4.pdf

• Hoffman, Reid & GPT-4. (2023) Impromptu - Amplifying Our Humanity Through AI. https://www.impromptubook.com/wp-content/uploads/2023/03/impromptu-rh.pdf

• Russell, Stuart. (2019) Human Compatible: Artificial Intelligence and the Problem of Control. https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS

• Blank, Steve. (2022) Artificial Intelligence and Machine Learning - Explained. https://steveblank.com/2022/05/17/artificial-intelligence-and-machine-learning-explained/

• Carlson, Matt. (2016) Metajournalistic Discourse and the Meanings of Journalism: Definitional Control, Boundary Work, and Legitimation. https://academic.oup.com/ct/article-abstract/26/4/349/3979552?redirectedFrom=fulltext

• Abend, G. (2008) The Meaning of 'Theory.' Sociological Theory, 26(2), 173-199. https://www.jstor.org/stable/20453103

• Bail, C. et al. (2018) Exposure to opposing views on social media can increase political polarization. PNAS. https://www.pnas.org/doi/abs/10.1073/pnas.1804840115

• Arnoorsson et al. (2023) “Why Am I reading this? Explaining Persnalized New Recommender System.”, Eurographics, EUROVIS.

• Salomé et al. (2022) The Augmented Social Scientist: Using Sequential Transfer Learning to Annotate Millions of Texts with Human-Level Accuracy. Sociological Methods & Research. https://journals.sagepub.com/doi/10.1177/00491241221134526

 www.robinforsberg.com