Bibliografia

Articoli scientifici, riferimenti a newsletter di specialisti e altre risorse di cui mi sono servito per scrivere gli approfondimenti.
  1. AAAI Association for the Advancement of Artificial Intelligence (2025) Future of AI Research https://aaai.org/wp-content/uploads/2025/03/AAAI-2025-PresPanel-Report-Digital-3.7.25.pdf
  2. Abbott E.A. (2020) Flatlandia, Feltrinelli (Prima pubblicazione 1884)
  3. Affirming the Scientific Consensus on Bias and Discrimination in AI (2025) https://www.aibiasconsensus.org/
  4. Ameisen E. et al (2025) Circuit Tracing: Revealing Computational Graphs in Language Models. Transformer Circuits Thread (Anthropic) https://transformer-circuits.pub/2025/attribution-graphs/methods.html
  5. Balassone S. (2023) Scusi il disturbo — Chiacchiere con personaggi che furono o che sono (podcast) Radio Immagina
  6. Biese P. (2025) https://substack.com/@pascalbiese
  7. Bommasani R. e altri 114 autori (2022) On the opportunities and risks of foundation models arxiv.org:2108.07258
  8. Borji A. (2023) A Categorical Archive of ChatGPT Failures https://arxiv.org/abs/2302.03494
  9. Cameron R.W. (2024) Decoder-only transfomers: the workhorse of generative LLMs Deep (Learning) Foqus
  10. Chen C. (2025) China built hundreds of AI data centers to catch the AI boom. Now many stand unused MIT Technology Review https://www.technologyreview.com/2025/03/26/1113802/china-ai-data-centers-unused/
  11. Cho A. et al (2024) Transformer Explainer: Interactive Learning of Text-Generative Models https://arxiv.org/pdf/2408.04619
  12. Chomsky N., Roberts I. and Watumull J. (2023) The False Promise of ChatGPT The New York Times
  13. Dahl M. et al (2024) Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models https://arxiv.org/abs/2401.01301
  14. Dash S. (2025) https://medium.com/@shaileydash
  15. Deepseek-AI (2024) DeepSeek-V3 Technical Report https://arxiv.org/abs/2412.19437
  16. Deepseek-AI (2025) DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning https://arxiv.org/abs/2501.12948
  17. de Gregorio Ignacio (2025) https://medium.com/@ignacio.de.gregorio.noblejas
  18. Denis O. (2025) https://www.linkedin.com/in/denis-o-b61a379a/
  19. Dumas C. (2025) How do Llamas process multilingual text? A latent exploration through activation patching. Proc. 41st Int. Conf. on Machine Learning. https://openreview.net/forum?id=0ku2hIm4BS
  20. Ferri A. (2025) Claude Code saved us 97
  21. Floridi L. (2025) https://www.linkedin.com/in/luciano-floridi/recent-activity/all/
  22. Funk Jeffrey (2025) https://www.linkedin.com/in/dr-jeffrey-funk-a979435/recent-activity/all/
  23. Jimenez C.E. (2025) SWE-bench: Can Language Models Resolve Real-World GitHub Issues? https://arxiv.org/abs/2310.06770
  24. Kang C, Choi H. (2023) Impact of co-occurrence on factual knowledge of large language models https://arxiv.org/abs/2310.08256
  25. Kauf C., Chersoni E., Lenci A., Fedorenko E., Ivanova A.A. (2024) Comparing plausibility estimates in base and instruction-tuned large language models arXiv:2403.14859
  26. Kim Y. et al (2025) Medical Hallucination in Foundation Models and Their Impact on Healthcare https://arxiv.org/abs/2503.05777
  27. Kurenkov A. (2020) A Brief History of Neural Nets and Deep Learning Skynet Today
  28. Lenci A. (2008) Distributional semantics in linguistic and cognitive research Rivista di linguistica 20: 1-31 https://www.italian-journal-linguistics.com/app/uploads/2021/05/1_Lenci.pdf
  29. Lenci A. (2023) Understanding natural language understanding systems. A critical analysis https://arxiv.org/abs/2303.04229
  30. Lindsay J. (2025) On the Biology of a Large Language Model. Transformer Circuits Thread (Anthropic) https://transformer-circuits.pub/2025/attribution-graphs/biology.html
  31. Lockett W (2025) https://medium.com/@wlockett
  32. Mitchel M. (2022) L’intelligenza artificiale — Una guida per esseri umani pensanti, Einaudi, Ed. originale 2019
  33. Mitchel M. (2025) Artificial Intelligence learns to reason. Science 387, Issue 6740 DOI: 10.1126/science.adw5211
  34. Nezhurina, Marianna & Cipolina-Kun, Lucia & Cherti, Mehdi & Jitsev, Jenia. (2024). Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models. 10.48550/arXiv.2406.02061.
  35. Nielsn M. (2019) Neural networks and deep learning. Dispobile in http://neuralnetworksanddeeplearning.com/
  36. OpenAI (2025) OpenAI o3 and o4-mini Systen Card https://cdn.openai.com/pdf/2221c875-02dc-4789-800b-e7758f3722c1/o3-and-o4-mini-system-card.pdf
  37. Peterson A.J. (2024) AI and the problem of knowledge collapsehttps://arxiv.org/abs/2404.03502
  38. Peterson A.J. (2025) AI and the problem of knowledge collapse. Springer https://link.springer.com/article/10.1007/s00146-024-02173-x
  39. Piad-Morffis A. (2024) Why reliable AI requires a paradigm shift Mostly Harmless Ideas
  40. Piad-Morffis A. (2024) Let’s build our own ChatGPT Mostly Harmless Ideas
  41. Piad-Morffis A. (2025) https://blog.apiad.net/s/mostly-harmless-ai
  42. Kheya A.G. et al (2024) The Pursuit of Fairness in Artificial Intelligence Models: A Survey https://arxiv.org/abs/2403.17333v1
  43. Knight W. (2025) Under Trump, AI Scientists Are Told to Remove ‘Ideological Bias’ From Powerful Models. Wired https://www.wired.com/story/ai-safety-institute-new-directive-america-first/
  44. Ranieri M., Cuomo S. Biagini G. (2024) Scuola e intelligenza artificiale, Carocci
  45. Raschka S. (2024) How good are the latest open LLMs? And is DPO better than PPO? Ahead of AI
  46. Ravichandiran S. (2021) Getting started with BERT Packt Publishing
  47. Shumailov I. et al (2024a) The curse of recursion: training on genereted data makes model forget https://arxiv.org/abs/2305.17493
  48. Shumailov I. et al (2024b) AI models collapse when trained on recursively generated data. Nature https://doi.org/10.1038/s41586-024-07566-y
  49. Sukhareva M. (2025) https://www.linkedin.com/in/msukhareva/
  50. Turness D. (2025) AI Distortion is new threat to trusted information. BBC https://www.bbc.co.uk/mediacentre/2025/articles/how-distortion-is-affecting-ai-assistants/
  51. Vasvani W., Shazeer N., Parmar N., Uskzoreit J., Jones .L, Gomez A.N., Kaiser L., Polosukhin I. (2017) Attention is all you need arXiv: 1706.03762 (ultima revisione 2023)
  52. Wendeler C., Veselovsky V, Monca G., WEst R. (2024) Do Llamas work in English? On the latent language model of multilinguam transformers arXiv:2402.10588
  53. Xu Y. (2024) A Survey on Multilingual Large language Models: Corpora, Alignment, Bias https://arxiv.org/abs/2404.00929

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