https://www.sciencedirect.com/science/article/abs/pii/S0262407921018017
Rorvig, M. (2021). Supersized AI. The New Scientist, 251(3355), 36–40. https://doi.org/10.1016/s0262-4079(21)01801-7
Posts Tagged ‘gpt3’
Supersized AI – ScienceDirect
November 2, 2024Why the Godfather of A.I. Fears What He’s Built | The New Yorker
October 9, 2024https://www.newyorker.com/magazine/2023/11/20/geoffrey-hinton-profile-ai
Geoffrey Hinton has spent a lifetime teaching computers to learn. Now he worries that artificial brains are better than ours.
\November 13, 2023
Holly Herndon’s Infinite Art | The New Yorker
October 9, 2024https://www.newyorker.com/magazine/2023/11/20/holly-herndons-infinite-art
The artist and musician uses machine learning to make strange, playful work. She also advocates for artists’ autonomy in a world shaped by A.I.
A Coder Considers the Waning Days of the Craft | The New Yorker
October 9, 2024Disinformation is on the rise. How does it work?
October 3, 2024I Tried Data Analysis ChatGPT Plugin — Every Analyst’s Dream or a Nightmare in Disguise? | by Livia Ellen | Towards Data Science
September 8, 2024Chatbots in science: What can ChatGPT do for you?
August 15, 2024Detecting hallucinations in large language models using semantic entropy | Nature
August 11, 2024https://www.nature.com/articles/s41586-024-07421-0
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
Researchers are figuring out how large language models work
August 11, 2024https://www.economist.com/science-and-technology/2024/07/11/researchers-are-figuring-out-how-large-language-models-work
QT:{{”
A sparse autoencoder is, essentially, a second, smaller neural network that is trained on the activity of an LLM, looking for distinct patterns in activity when “sparse” (ie, very small) groups of its neurons fire together. Once many such patterns, known as features, have been identified, the researchers can determine which words trigger which features. The Anthropic team found individual features that corresponded to specific cities, people, animals and chemical elements, as well as higher-level concepts such as transport infrastructure, famous female tennis players, or the notion of secrecy. They performed this exercise three times, identifying 1m, 4m and, on the last go, 34m features within the Sonnet LLM.
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