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"content": "<p>How can we understand often uninterpretable <a href=\"https://nerdculture.de/tags/DeepLearning\" class=\"mention hashtag\" rel=\"tag\">#<span>DeepLearning</span></a> systems like artificial neural networks?</p><p><span class=\"h-card\" translate=\"no\"><a href=\"https://mastodon.online/@Neurograce\" class=\"u-url mention\">@<span>Neurograce</span></a></span> (<a href=\"https://nerdculture.de/tags/NYU\" class=\"mention hashtag\" rel=\"tag\">#<span>NYU</span></a>) suggested we use <a href=\"https://nerdculture.de/tags/neuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>neuroscience</span></a> tools!</p><p>- dimensionality reduction<br />- latent factor modeling<br />- representational similarity analyses<br />- representation geometry<br />- network analyses<br />- encoding models<br />- [more]</p><p>Dr. Lindsay proposed guidelines for and side benefits of using these tools to study <a href=\"https://nerdculture.de/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a>.</p><p>Here's the <a href=\"https://nerdculture.de/tags/OpenAccess\" class=\"mention hashtag\" rel=\"tag\">#<span>OpenAccess</span></a> paper that much of the talk was based on: <a href=\"https://doi.org/10.48550/arXiv.2202.07035\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.48550/arXiv.2202.07</span><span class=\"invisible\">035</span></a></p>",
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"name": "Many neuroscientists aim for something like an \"algorithmic\" level of understanding according to Mar's framework.",
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"name": "Summary slide:\n\nIf we want interpretable AI, then maybe we could use the tools neuroscientists use to interpret brains.\n\nWe could even use artificial neural networks to test neuroscientists' analytic tools (e.g., their validity, assumptions, etc.).\n\nThis proposal will require cooperation and new conceptual units.",
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