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☮ ♥ ♬ 🧑‍💻Day 19 cont 🙏⛪️🕍🕌⛩️🛕 💽🧑‍💻“The <a href="https://ioc.exchange/tags/LiberalParty" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LiberalParty</a> has accidentally left part of its email provider’s <a href="https://ioc.exchange/tags/subscriber" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#subscriber</a> details exposed, revealing the types of <a href="https://ioc.exchange/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#data</a> harvested by the party during the <a href="https://ioc.exchange/tags/election" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#election</a> campaign.This gives rare <a href="https://ioc.exchange/tags/insight" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#insight</a> into some of the specific kinds of data the party is keeping on voters, including whether they are “predicted Chinese”, “predicted Jewish”, a “strong Liberal” and other <a href="https://ioc.exchange/tags/PersonalInformation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PersonalInformation</a>.”<a href="https://ioc.exchange/tags/AusPol" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AusPol</a> / <a href="https://ioc.exchange/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a> / <a href="https://ioc.exchange/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> / <a href="https://ioc.exchange/tags/voters" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#voters</a> / <a href="https://ioc.exchange/tags/Liberal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Liberal</a> / <a href="https://ioc.exchange/tags/LNP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LNP</a> / <a href="https://ioc.exchange/tags/Nationals" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Nationals</a> <<a href="https://www.crikey.com.au/2025/04/17/victorian-liberals-data-exposed-email-mailchimp-federal-election-crikey/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.crikey.com.au/2025/04/17/victorian-liberals-data-exposed-email-mailchimp-federal-election-crikey/</a>>

☮ ♥ ♬ 🧑‍💻“How ‘inference’ is driving competition to Nvidia’s <a href="https://ioc.exchange/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> chip dominance”<a href="https://ioc.exchange/tags/NVidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#NVidia</a> / <a href="https://ioc.exchange/tags/reasoning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#reasoning</a> / <a href="https://ioc.exchange/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <<a href="https://archive.md/AYHs7" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://archive.md/AYHs7</a>>

Steve Thompson PhDUS authorities can see more than ever, with Big Tech as their eyes <a href="https://proton.me/blog/big-tech-data-requests-surge" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://proton.me/blog/big-tech-data-requests-surge</a><a href="https://mastodon.social/tags/privacy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#privacy</a> <a href="https://mastodon.social/tags/illusion" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#illusion</a> <a href="https://mastodon.social/tags/bigtech" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#bigtech</a> <a href="https://mastodon.social/tags/surveillance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#surveillance</a> <a href="https://mastodon.social/tags/tracking" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#tracking</a> <a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://mastodon.social/tags/press" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#press</a>

Paul GiulanHow to <a href="https://federate.social/tags/backdoor" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#backdoor</a> a <a href="https://federate.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> <a href="https://blog.sshh.io/p/how-to-backdoor-large-language-models" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://blog.sshh.io/p/how-to-backdoor-large-language-models</a><a href="https://federate.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OpenSource</a> <a href="https://federate.social/tags/DeepSeek" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DeepSeek</a> <a href="https://federate.social/tags/Infrastructure" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Infrastructure</a> <a href="https://federate.social/tags/Inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inference</a> <a href="https://federate.social/tags/Embedded" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Embedded</a> <a href="https://federate.social/tags/HuggingFace" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HuggingFace</a> <a href="https://federate.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://federate.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ArtificialIntelligence</a> <a href="https://federate.social/tags/ChatBot" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ChatBot</a>

Jörn (DL2EJF)ENAMS Electrical Noise Area Measurement System (<a href="https://www.enams.de/index.php/en/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.enams.de/index.php/en/</a>) records interference levels in the frequency range from 66 kHz to 31 MHz in Germany and other participating countries. You can view diagrams (<a href="https://www.enams.de/index.php/en/diagrams-evaluation" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.enams.de/index.php/en/diagrams-evaluation</a>) and you can operate yourself a device (<a href="https://darcverlag.de/ENAMS-20" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://darcverlag.de/ENAMS-20</a>)<a href="https://social.darc.de/tags/enams" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#enams</a> <a href="https://social.darc.de/tags/noise" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#noise</a> <a href="https://social.darc.de/tags/hf" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#hf</a> <a href="https://social.darc.de/tags/hamradio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#hamradio</a> <a href="https://social.darc.de/tags/measurement" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#measurement</a> <a href="https://social.darc.de/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://social.darc.de/tags/plc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#plc</a>

Eric Maugendre"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight." Longford (2005) <a href="http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf" rel="nofollow noopener noreferrer" translate="no" target="_blank">http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf</a><a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#modeling</a> <a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nullHypothesis</a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#probability</a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#probabilities</a> <a href="https://hachyderm.io/tags/pValues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pValues</a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#statistics</a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#stats</a> <a href="https://hachyderm.io/tags/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#statisticalLiteracy</a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#bias</a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#modelling</a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#regression</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linearRegression</a>

Eric MaugendreFeature Selection in Python; a script ready to use: <a href="https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/</a><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#interpretability</a> <a href="https://hachyderm.io/tags/featureSelection" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#featureSelection</a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#python</a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#probability</a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#probabilities</a> <a href="https://hachyderm.io/tags/bigData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#bigData</a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#classification</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linearRegression</a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#regression</a> <a href="https://hachyderm.io/tags/Schusterbauer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Schusterbauer</a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AIDev</a>

cob"Cette capacité qu'a le <a href="https://mastodon.social/tags/cerveau" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cerveau</a> d'établir des modèles mentaux et de les affiner en présence d'évènements petit à petit lui confère un <a href="https://mastodon.social/tags/pouvoir" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pouvoir</a> d'anticipation essentiel appelé <a href="https://mastodon.social/tags/inf%C3%A9rence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inférence</a> ."<a href="https://mastodon.social/@cobrate/113545463857222347" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://mastodon.social/@cobrate/113545463857222347</a>"Nos actions de tous les jours sont pour la plupart des <a href="https://mastodon.social/tags/heuristiques" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#heuristiques</a>. Mais la <a href="https://mastodon.social/tags/pens%C3%A9e" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pensée</a> aussi a les siennes [...]."<a href="https://mastodon.social/@cobrate/113584263180304907" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://mastodon.social/@cobrate/113584263180304907</a><a href="https://mastodon.social/tags/mot" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mot</a> <a href="https://mastodon.social/tags/AlbertMoukheiber" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AlbertMoukheiber</a> <a href="https://mastodon.social/tags/science" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#science</a>

Judith van StegerenMany companies are currently scrambling for ML infra engineers. They need people that know how to manage AI infrastructure, and that can seriously speed up training and inference with specialized tooling like vLLM, Triton, TensorRT, Torchtune, etc.<a href="https://fosstodon.org/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://fosstodon.org/tags/training" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#training</a> <a href="https://fosstodon.org/tags/genai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#genai</a> <a href="https://fosstodon.org/tags/triton" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#triton</a> <a href="https://fosstodon.org/tags/vllm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#vllm</a> <a href="https://fosstodon.org/tags/pytorch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pytorch</a> <a href="https://fosstodon.org/tags/torchtune" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#torchtune</a> <a href="https://fosstodon.org/tags/tensorrt" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#tensorrt</a> <a href="https://fosstodon.org/tags/nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nvidia</a>

Jon AwbreyInformation = Comprehension × Extension • Selection 1.1 • <a href="https://inquiryintoinquiry.com/2024/10/05/information-comprehension-x-extension-selection-1-a/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://inquiryintoinquiry.com/2024/10/05/information-comprehension-x-extension-selection-1-a/</a>Our first text comes from Peirce's Lowell Lectures of 1866, titled “The Logic of Science, or, Induction and Hypothesis”. I still remember the first time I read these words and the light that lit up the page and my mind.❝Let us now return to the information. The information of a term is the measure of its superfluous comprehension. That is to say that the proper office of the comprehension is to determine the extension of the term. For instance, you and I are men because we possess those attributes — having two legs, being rational, &c. — which make up the comprehension of “man”. Every addition to the comprehension of a term lessens its extension up to a certain point, after that further additions increase the information instead.❞(Peirce 1866, p. 467)Reference —Peirce, C.S. (1866), “The Logic of Science, or, Induction and Hypothesis”, Lowell Lectures of 1866, pp. 357–504 in Writings of Charles S. Peirce : A Chronological Edition, Volume 1, 1857–1866, Peirce Edition Project, Indiana University Press, Bloomington, IN, 1982.Resources —Inquiry Blog • Survey of Pragmatic Semiotic Information • <a href="https://inquiryintoinquiry.com/2024/03/01/survey-of-pragmatic-semiotic-information-8/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://inquiryintoinquiry.com/2024/03/01/survey-of-pragmatic-semiotic-information-8/</a>OEIS Wiki • Information = Comprehension × Extension • <a href="https://oeis.org/wiki/Information_%3D_Comprehension_%C3%97_Extension" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://oeis.org/wiki/Information_%3D_Comprehension_%C3%97_Extension</a>C.S. Peirce • Upon Logical Comprehension and Extension • <a href="https://peirce.sitehost.iu.edu/writings/v2/w2/w2_06/v2_06.htm" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://peirce.sitehost.iu.edu/writings/v2/w2/w2_06/v2_06.htm</a><a href="https://mathstodon.xyz/tags/Peirce" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Peirce</a> <a href="https://mathstodon.xyz/tags/Logic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Logic</a> <a href="https://mathstodon.xyz/tags/Inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inference</a> <a href="https://mathstodon.xyz/tags/Inquiry" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inquiry</a> <a href="https://mathstodon.xyz/tags/Abduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Abduction</a> <a href="https://mathstodon.xyz/tags/Induction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Induction</a> <a href="https://mathstodon.xyz/tags/Deduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Deduction</a> <a href="https://mathstodon.xyz/tags/LogicOfScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LogicOfScience</a> <a href="https://mathstodon.xyz/tags/Information" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Information</a> <a href="https://mathstodon.xyz/tags/Comprehension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Comprehension</a> <a href="https://mathstodon.xyz/tags/Extension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Extension</a> <a href="https://mathstodon.xyz/tags/InformationEqualsComprehensionTimesExtension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#InformationEqualsComprehensionTimesExtension</a> <a href="https://mathstodon.xyz/tags/Semiotics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Semiotics</a> <a href="https://mathstodon.xyz/tags/SignRelations" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SignRelations</a> <a href="https://mathstodon.xyz/tags/Icon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Icon</a> <a href="https://mathstodon.xyz/tags/Index" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Index</a> <a href="https://mathstodon.xyz/tags/Symbol" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Symbol</a> <a href="https://mathstodon.xyz/tags/PragmaticSemioticInformation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PragmaticSemioticInformation</a>

Jon AwbreyInformation = Comprehension × Extension • Preamble • <a href="https://inquiryintoinquiry.com/2024/10/04/information-comprehension-x-extension-preamble/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://inquiryintoinquiry.com/2024/10/04/information-comprehension-x-extension-preamble/</a>Eight summers ago I hit on what struck me as a new insight into one of the most recalcitrant problems in Peirce’s semiotics and logic of science, namely, the relation between “the manner in which different representations stand for their objects” and the way in which different inferences transform states of information. I roughed out a sketch of my epiphany in a series of blog posts then set it aside for the cool of later reflection. Now looks to be a choice moment for taking another look.A first pass through the variations of representation and reasoning detects the axes of iconic, indexical, and symbolic manners of representation on the one hand and the axes of abductive, inductive, and deductive modes of inference on the other. Early and often Peirce suggests a natural correspondence between the main modes of inference and the main manners of representation but his early arguments differ from his later accounts in ways deserving close examination, partly for the extra points in his line of reasoning and partly for his explanation of indices as signs constituted by convening the variant conceptions of sundry interpreters.Resources —Inquiry Blog • Survey of Pragmatic Semiotic Information • <a href="https://inquiryintoinquiry.com/2024/03/01/survey-of-pragmatic-semiotic-information-8/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://inquiryintoinquiry.com/2024/03/01/survey-of-pragmatic-semiotic-information-8/</a>OEIS Wiki • Information = Comprehension × Extension • <a href="https://oeis.org/wiki/Information_%3D_Comprehension_%C3%97_Extension" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://oeis.org/wiki/Information_%3D_Comprehension_%C3%97_Extension</a>C.S. Peirce • Upon Logical Comprehension and Extension • <a href="https://peirce.sitehost.iu.edu/writings/v2/w2/w2_06/v2_06.htm" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://peirce.sitehost.iu.edu/writings/v2/w2/w2_06/v2_06.htm</a><a href="https://mathstodon.xyz/tags/Peirce" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Peirce</a> <a href="https://mathstodon.xyz/tags/Logic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Logic</a> <a href="https://mathstodon.xyz/tags/Inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inference</a> <a href="https://mathstodon.xyz/tags/Inquiry" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inquiry</a> <a href="https://mathstodon.xyz/tags/Abduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Abduction</a> <a href="https://mathstodon.xyz/tags/Induction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Induction</a> <a href="https://mathstodon.xyz/tags/Deduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Deduction</a> <a href="https://mathstodon.xyz/tags/LogicOfScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LogicOfScience</a> <a href="https://mathstodon.xyz/tags/Information" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Information</a> <a href="https://mathstodon.xyz/tags/Comprehension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Comprehension</a> <a href="https://mathstodon.xyz/tags/Extension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Extension</a> <a href="https://mathstodon.xyz/tags/InformationEqualsComprehensionTimesExtension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#InformationEqualsComprehensionTimesExtension</a> <a href="https://mathstodon.xyz/tags/Semiotics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Semiotics</a> <a href="https://mathstodon.xyz/tags/SignRelations" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SignRelations</a> <a href="https://mathstodon.xyz/tags/Icon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Icon</a> <a href="https://mathstodon.xyz/tags/Index" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Index</a> <a href="https://mathstodon.xyz/tags/Symbol" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Symbol</a> <a href="https://mathstodon.xyz/tags/PragmaticSemioticInformation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PragmaticSemioticInformation</a>

ComputoThe summer is coming to an end... let's see what publications it brought to Computo!First, "AdaptiveConformal: An R package for adaptive conformal inference" by Herbert Susmann, Antoine Chambaz and Julie Josse is available with (you guessed it) R code at doi.org/10.57750/edan-5f53The authors put together a detailed review of 5 algorithms for adaptive conformal inference (used to provide prediction intervals for sequentially observed data), complete with theoretical guarantees and experimental results both in simulations and on a real case study of producing prediction intervals for influenza incidence in the United States.The paper highlights the importance of properly chosing tuning parameters to obtain good utility and of having access to good point predictions.As the title implies, the paper comes with an R package, AdaptiveConformal, available from GithHub at <a href="https://github.com/herbps10/AdaptiveConformal" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/herbps10/AdaptiveConformal</a>. It provides implementations for the 5 algorithms, as well as tools for visualization and summarization of prediction intervals.<a href="https://mathstodon.xyz/tags/reproducibility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#reproducibility</a> <a href="https://mathstodon.xyz/tags/openScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openScience</a> <a href="https://mathstodon.xyz/tags/openAccess" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openAccess</a> <a href="https://mathstodon.xyz/tags/openSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openSource</a> <a href="https://mathstodon.xyz/tags/rStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#rStats</a> <a href="https://mathstodon.xyz/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a>

Everyday.Human Derek“Metacognitive <a href="https://ecoevo.social/tags/MindStorm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MindStorm</a>”Can we build a metacognitive mind? Most likely we do in some form perhaps . In its basic sense it’s simply thinking about thinking. Does it go deeper? <a href="https://ecoevo.social/tags/Inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Inference</a> <a href="https://ecoevo.social/tags/Pedagogy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Pedagogy</a> <a href="https://ecoevo.social/tags/Signaling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Signaling</a> <a href="https://ecoevo.social/tags/gametheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#gametheory</a> <a href="https://ecoevo.social/tags/Learning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Learning</a> <a href="https://ecoevo.social/tags/datascience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#datascience</a> <a href="https://ecoevo.social/tags/metacognition" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#metacognition</a> <a href="https://ecoevo.social/tags/cognition" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cognition</a> <a href="https://ecoevo.social/tags/informationtheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#informationtheory</a> <a href="https://ecoevo.social/tags/epigenetic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#epigenetic</a> <a href="https://ecoevo.social/tags/behaviorbiology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#behaviorbiology</a> <a href="https://ecoevo.social/tags/EvolutionaryBiology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EvolutionaryBiology</a> <a href="https://ecoevo.social/tags/Evolutionaryecology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Evolutionaryecology</a> <a href="https://ecoevo.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#computationalneuroscience</a> <a href="https://ecoevo.social/tags/computationalbiology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#computationalbiology</a> <a href="https://ecoevo.social/tags/complexitytheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#complexitytheory</a> <a href="https://ecoevo.social/tags/statisticalphysics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#statisticalphysics</a> <a href="https://ecoevo.social/tags/thermodynamics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#thermodynamics</a> <a href="https://ecoevo.social/tags/Entropy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Entropy</a>

Ian K Tindale<a href="https://mas.to/@carnage4life" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@carnage4life</a> in the near future, what will change this will be when generative AI gains the ability to run cross-discipline inferences across many diverse sources and points of view, which will, unlike this, actually add something useful to the output At the moment generative AI is very much a style formulator, which like the earlier experimental style GANs would be able to generate something asked for in the style of something also asked for – what we have now with LLMs in general is retrieval of stuff it ‘knows’ but in the style of something you want to see it in (or increasingly, something not at all that, but instead a vapid harmless soft-touch safe-option style of answer) In the future it should be more possible to generate a précis or summarised pile of words that not only have something vaguely to do with what was asked for, but preferably also have additional viewpoints from other stances than the ones normally associated with the already written literature, and furthermore, with cross-discipline inferencing, actually invent new knowledge by comparing this scenario with other similarly-patterned or similarly-shaped scenarios in totally different topic areas – an AI would see those far easier than a human could, thus far the only times a human invents something new like that by crossing over the boundaries of topic areas is through flashes of insight, or episodes of divine inspiration or drunkenness or sleep, otherwise it’s actually difficult for us to do that but if there were a system which could see all the options all at once in parallel, inventing the novel would be so easy it’d be just another process instead of something mysterious <a href="https://toot.pikopublish.ing/tags/GenerativeAI" rel="nofollow noopener noreferrer" target="_blank">#GenerativeAI</a> <a href="https://toot.pikopublish.ing/tags/inference" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://toot.pikopublish.ing/tags/inspiration" rel="nofollow noopener noreferrer" target="_blank">#inspiration</a>

Eric Maugendre"Extract Year from a datetime column", by Piyush Raj: <a href="https://datascienceparichay.com/article/pandas-extract-year-from-datetime-column/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://datascienceparichay.com/article/pandas-extract-year-from-datetime-column/</a><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#dataDev</a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Python</a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Pandas</a> <a href="https://hachyderm.io/tags/timeSeries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#timeSeries</a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#data</a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#dataAnalysis</a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#statistics</a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#stats</a>

Eric MaugendreAn easy guide to predict possible future quantities, by Mercy Kibet: <a href="https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0</a><a href="https://hachyderm.io/tags/timeSeries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#timeSeries</a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#data</a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linearRegression</a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#dataScience</a> <a href="https://hachyderm.io/tags/futures" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#futures</a> <a href="https://hachyderm.io/tags/money" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#money</a> <a href="https://hachyderm.io/tags/trends" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#trends</a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Python</a>

Boiling SteamPowerInfer: Fast Large Language Model Serving with a Consumer-Grade GPU [pdf]: <a href="https://ipads.se.sjtu.edu.cn/_media/publications/powerinfer-20231219.pdf" rel="nofollow noopener noreferrer" target="_blank">https://ipads.se.sjtu.edu.cn/_media/publications/powerinfer-20231219.pdf</a> <a href="https://mastodon.cloud/tags/linux" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linux</a> <a href="https://mastodon.cloud/tags/update" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#update</a> <a href="https://mastodon.cloud/tags/foss" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#foss</a> <a href="https://mastodon.cloud/tags/release" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#release</a> <a href="https://mastodon.cloud/tags/powerinfer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#powerinfer</a> <a href="https://mastodon.cloud/tags/faster" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#faster</a> <a href="https://mastodon.cloud/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://mastodon.cloud/tags/cpu" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cpu</a> <a href="https://mastodon.cloud/tags/gpu" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#gpu</a> <a href="https://mastodon.cloud/tags/hardware" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#hardware</a> <a href="https://mastodon.cloud/tags/llm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#llm</a>

Tero Keski-Valkama<a href="https://rukii.net/tags/Nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Nvidia</a>'s new <a href="https://rukii.net/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://rukii.net/tags/chip" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#chip</a> claims it will drop the costs of running <a href="https://rukii.net/tags/LLMs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLMs</a>“You can take pretty much any <a href="https://rukii.net/tags/LargeLanguageModel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LargeLanguageModel</a> you want and put it in this and it will inference like crazy. The <a href="https://rukii.net/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> cost of large language models will drop significantly.”<a href="https://www.cnbc.com/2023/08/08/nvidia-reveals-new-ai-chip-says-cost-of-running-large-language-models-will-drop-significantly-.html" rel="nofollow noopener noreferrer" target="_blank">https://www.cnbc.com/2023/08/08/nvidia-reveals-new-ai-chip-says-cost-of-running-large-language-models-will-drop-significantly-.html</a>

Victoria Stuart 🇨🇦 🏳️‍⚧️... Hashtags (chronologically mentioned, cont'd):<a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a> <a href="https://mastodon.social/tags/ActiveInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ActiveInference</a> <a href="https://mastodon.social/tags/sensory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#sensory</a> <a href="https://mastodon.social/tags/prediction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#prediction</a> <a href="https://mastodon.social/tags/PredictionErrors" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PredictionErrors</a> <a href="https://mastodon.social/tags/expectation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#expectation</a> <a href="https://mastodon.social/tags/interpretation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#interpretation</a> <a href="https://mastodon.social/tags/illusion" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#illusion</a> <a href="https://mastodon.social/tags/perception" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#perception</a> <a href="https://mastodon.social/tags/ColorPerception" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ColorPerception</a> <a href="https://mastodon.social/tags/TheDress" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TheDress</a> <a href="https://mastodon.social/tags/PerceptualPrediction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PerceptualPrediction</a> <a href="https://mastodon.social/tags/neurodiversity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#neurodiversity</a> <a href="https://mastodon.social/tags/PerceptionCensus" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PerceptionCensus</a> <a href="https://mastodon.social/tags/ChatGPT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ChatGPT</a> <a href="https://mastodon.social/tags/anthropomorphism" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#anthropomorphism</a> <a href="https://mastodon.social/tags/VegetativeState" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#VegetativeState</a> <a href="https://mastodon.social/tags/AnimalWelfare" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AnimalWelfare</a> <a href="https://mastodon.social/tags/AnimalRights" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AnimalRights</a> <a href="https://mastodon.social/tags/suffering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#suffering</a>

Sónia CabralInference in Difference‐in‐Differences: How Much Should We Trust in Independent Clusters? | Bruno Ferman | Journal of Applied Econometrics <a href="https://onlinelibrary.wiley.com/doi/10.1002/jae.2955#.Y8A1gnkSOis.twitter" rel="nofollow noopener noreferrer" target="_blank">https://onlinelibrary.wiley.com/doi/10.1002/jae.2955#.Y8A1gnkSOis.twitter</a> via @WileyEconomics@twitter.com <a href="https://mastodon.social/tags/spatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spatial</a> <a href="https://mastodon.social/tags/correlation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#correlation</a> <a href="https://mastodon.social/tags/clusters" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#clusters</a> <a href="https://mastodon.social/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#causality</a> <a href="https://mastodon.social/tags/DiD" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DiD</a> <a href="https://mastodon.social/tags/economics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#economics</a> <a href="https://mastodon.social/tags/Econometrics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Econometrics</a> <a href="https://mastodon.social/tags/EconTwitter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EconTwitter</a> <a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#inference</a>

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