porteclefs

5 ms·

1.
LLMs will make scientists do more, less well(arxiv.org)
3 points by porteclefs 2mo ago|0 comments
2.
What Comes After Science?(science.org)
33 points by porteclefs 11mo ago|21 comments
3.
Can AI yield true mathematical knowledge?(cambridge.org)
2 points by porteclefs 11mo ago|0 comments
4.
AI Boom Occurring Across Every Scientific Field(arxiv.org)
4 points by porteclefs 2y ago|0 comments
5.
Mathematical Knowledge from Opaque Models(arxiv.org)
1 points by porteclefs 3y ago|0 comments
6.
by porteclefs 4y ago
The paper aims to clarify the representational status of deep learning models (DLMs) in relation to their targets. It highlights the confusion caused by the interchangeable usage of terms 'representation' and 'model' in
7.
The Representational Status of Deep Learning Models(arxiv.org)
1 points by porteclefs 4y ago|1 comments
8.
Deep Learning Opacity in Scientific Discovery(cambridge.org)
2 points by porteclefs 4y ago|1 comments
9.
by porteclefs 4y ago
Are philosophers too pessimistic about AI in science while scientists are too optimistic? This paper argues that both perspectives miss something for critical about AI-infused science. By analyzing the role of deep learning in scientific di
10.
Deep Learning Is Epistemologically Novel(link.springer.com)
2 points by porteclefs 4y ago|1 comments
11.
by porteclefs 4y ago
Epistemology of deep learning is philosophically novel, cannot be reduced to familiar epistemic categories that justify belief in the reliability of instruments and experts.
12.
by porteclefs 4y ago
This paper makes we wonder whether there are ways to automate the search for the most plausible posits (see Figure 1). If scientists could choose the right combinations of data, then could a lot the discovery process could be automated?