About this episode
Current AI practice is not engineering, even when it aims for practical applications, because it is not based on scientific understanding. Enforcing engineering norms on the field could lead to considerably safer systems.
This episode has a lot of links! Here they are.
Michael Nielsen's "The role of 'explanation' in AI". https://michaelnotebook.com/ongoing/sporadica.html#role_of_explanation_in_AI
Subbarao Kambhampati's "Changing the Nature of AI Research". https://dl.acm.org/doi/pdf/10.1145/3546954
Chris Olah and his collaborators:
"Thread: Circuits". distill.pub/2020/circuits/
"An Overview of Early Vision in InceptionV1". distill.pub/2020/circuits/early-vision/
Dai et al., "Knowledge Neurons in Pretrained Transformers". https://arxiv.org/pdf/2104.08696.pdf
Meng et al.:
"Locating and Editing Factual Associations in GPT." rome.baulab.info
"Mass-Editing Memory in a Transformer," https://arxiv.org/pdf/2210.07229.pdf
François Chollet on image generators putting the wrong number of legs on horses: twitter.com/fchollet/status/1573879858203340800
Neel Nanda's "Longlist of Theories of Impact for Interpretability", https://www.lesswrong.com/posts/uK6sQCNMw8WKzJeCQ/a-longlist-of-theories-of-impact-for-interpretability
Zachary C. Lipton's "The Mythos of Model Interpretability". https://arxiv.org/abs/1606.03490
Meng et al., "Locating and Editing Factual Associations in GPT". https://arxiv.org/pdf/2202.05262.pdf
Belrose et al., "Eliciting Latent Predictions from Transformers with the Tuned Lens". https://arxiv.org/abs/2303.08112
"Progress measures for grokking via mechanistic interpretability". https://arxiv.org/abs/2301.05217
Conmy et al., "Towards Automated Circuit Discovery for Mechanistic Interpretability". https://arxiv.org/abs/2304.14997
Elhage et al., "Softmax Linear Units," transformer-circuits.pub/2022/solu/index.html
Filan et al., "Clusterability in Neural Networks," https://arxiv.org/pdf/2103.03386.pdf
Cammarata et al., "Curve circuits," distill.pub/2020/circuits/curve-circuits/
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