<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: trnkinju</title><link>https://news.ycombinator.com/user?id=trnkinju</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Sun, 06 Sep 2026 19:06:01 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=trnkinju" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by trnkinju in "The Emergent Symbolic Structure of Artificial Neural Networks"]]></title><description><![CDATA[
<p>Symbolism has tried to strike back repeatedly ever since statistical learning revived with AlexNet. With all the due  respect one can have for the names Smolensky and Linzen from the perspective of linguistics, the question about the applicability, generalizability and robustness of the method proposed here should be raised. It seems from section 3.5 of the paper that one cannot be so optimistic about it at least as yet. I get it that the method is still in its infancy, but we've already got the kind of Mech Interp as pushed forward by Neel Nanda and co, among other lines of research. Not that we are forced to make a choice between all interpretability works, or this TPR method is inherently inferior to the other ones, but we can be moderately cautious when looking at such progress.</p>
]]></description><pubDate>Wed, 02 Sep 2026 08:46:01 +0000</pubDate><link>https://news.ycombinator.com/item?id=49533568</link><dc:creator>trnkinju</dc:creator><comments>https://news.ycombinator.com/item?id=49533568</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49533568</guid></item></channel></rss>