Podcasts about Parameter

Wir leben in einer Welt, in der Kriege, Bürgerkriege und geopolitische Spannungen großen Einfluss auf die Märkte haben. Es gibt viele Berater, die bereit sind, den Anlegern mit Rat und Tat zur Seite zu stehen, und noch mehr Strategen, die vorgeben, zu wissen, wie man in einer Zeit geopolitischer Spannungen mit den Märkten spielt. Was aber war bisher richtig?   Du hast einen Themen-Wunsch für den Podcast oder interessierst Dich für ein Seminar mit mir? Schreibe mir gerne einfach per E-Mail: krapp@abatus-beratung.com Viel Spaß beim Hören, Dein Matthias Krapp (Transkript dieser Folge weiter unten)   NEU!!! Hier kannst Du Dich kostenlos für meinen Minikurs registrieren und reinschauen. Es lohnt sich: https://portal.abatus-beratung.com/geldanlage-kurs/    

In dieser Folge diskutiert Moritz die aktuelle Debatte um Prostatakrebs-Screening – ausgelöst durch die späte Diagnose beim ehemaligen US-Präsidenten Joe Biden. Ihr erfahrt, warum der klassische PSA-Wert allein nicht reicht, welche zusätzlichen Parameter wichtig sind, was die Leitlinien in D, CH und A empfehlen und wie moderne Medizin eine frühzeitige und präzise Erkennung ermöglichen kann. Du hast Fragen, Anregungen oder Ideen? Melde dich gern unter mail(at)moritzbinder.com Mehr über meine Arbeit erfährst du hier: https://moritzbinder.com/ Wichtiger Hinweis: Bitte beachtet den Disclaimer 👉 https://podcast.moritzbinder.com/disclaimer

What's the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html   Topics we discuss, and timestamps: 0:00:41 APD basics 0:07:57 Faithfulness 0:11:10 Minimality 0:28:44 Simplicity 0:34:50 Concrete-ish examples of APD 0:52:00 Which parts of APD are canonical 0:58:10 Hyperparameter selection 1:06:40 APD in toy models of superposition 1:14:40 APD and compressed computation 1:25:43 Mechanisms vs representations 1:34:41 Future applications of APD? 1:44:19 How costly is APD? 1:49:14 More on minimality training 1:51:49 Follow-up work 2:05:24 APD on giant chain-of-thought models? 2:11:27 APD and "features" 2:14:11 Following Lee's work   Lee links (Leenks): X/Twitter: https://twitter.com/leedsharkey Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey   Research we discuss: Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926 Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476 Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis   Episode art by Hamish Doodles: hamishdoodles.com

Willkommen bei Back 2 Basics – der Reihe für aufstrebende E-Commerce Händler und ihren ersten Kontakt mit Affiliate Marketing - vom Next Level Affiliate Marketing Podcast. Bist du engagierter Merchant und hast bereits deinen Online-Shop bei Shopify, Woocommerce, Magento oder Shopware, und suchst nun nach einer Erweiterung zum typischen Google, Amazon, Facebook und Apple Marketing-Mix? Dein Host Nawid Company erklärt in dieser Serie klar strukturiert die Grundsteine des Affiliate-Marketingbereichs damit du bestens vorbereitet für die ersten Schritte bist. So wirst mit Back 2 Basics und der Interview-Reihe Time for Learning schnell zum Profi. Die heutige Folge behandelt folgende Themen: - Click-ID - First-Party-Cookie - Linkdecoration - UTM Parameter - Redirect - Third-Party-Cookie - UTM-Parameter - Weiterleitung

In dieser Folge JUNKMILES sprechen wir über den vielleicht wichtigsten physiologischen Parameter beim Laufen: die Ökonomie der Bewegung.

Der Titel der heutigen Episode ist »Zeitlos«. Wie komme ich darauf? Die Motivation für diese kurze Episode der Reflexion ist eine Reihe von Tweets. Der erste war von Axel Bojanowski, dem — wie ich meine — führenden Wissenschaftsjournalisten im deutschsprachigen Raum. Er schreibt: »Der mit Abstand beste deutsche Wissenschaftspodcast ist Zukunft Denken« Natürlich freut mich eine solche Empfehlung aus derartig berufenem Munde ganz besonders. Es spornt auch an, weiter hart an diesem Projekt zu arbeiten. Es gab dann aber noch eine Reaktion eines Hörers, der den Aspekt der Zeitlosigkeit der Episoden betont hat. Das hat mich zum Nachdenken angeregt. Der erste Aspekt von Zeit ist ein eher banaler, aber einer, auf den ich gerne kurz eingehen möchte. Ich bekomme immer wieder Zuschriften, wo sich Hörer öfter neue Folgen wünschen. Warum das schwierig ist, erkläre ich in aller Kürze. Dann aber zu weiteren Aspekten der Zeitlosigkeit, die eher inhaltlicher Natur sind, denn dieser Kommentar hat mich zum Nachdenken gebracht zumal es einige Überschneidungen zu vorigen Episoden gibt. Was hat etwas das Zitat von Gerd Gigerenzer aus Episode 122 »Je größer die Unsicherheit ist, desto mehr Informationen muss man ignorieren.«  mit dem Zitat von Stafford Beer aus Episode 121 gemein? »Information and Action are one and the same thing« Zur Dimension der Informationsdichte kommt noch die Dimension der Zeit auf eine sehr interessante Weise hinzu. Je Größer die Unsicherheit, desto wichtiger ist also nicht nur die Auswahl der Parameter, der Daten, sondern auch die richtige Zeitlichkeit im Umgang mit dem Problem. Was bedeutet dies für News? Für den gesellschaftlichen und politischen Umgang mit komplexen Problemen? »Die relevanten Information entstehen Wochen, Monate, bei aktivistischen Großereignissen wie etwa Covid auch Jahre später. Diese geht dann aber im Lärm des nächsten Events unter.« Fortschritt und Entschleunigung haben aber eine durchaus interessante Gemeinsamkeit, wir Herfried Münkler bemerkt: “...Chance des Reflexionsgewinns durch Entschleunigung: Man kann die Bedeutung beim Treffen von Entscheidungen über größere Zeitspannen zu verfügen kaum überschätzen. und diese Zeitspannengewinn hängt nun einmal am Übergang vom mündlichen zum schriftlichen.” […]  »Man konnte nunmehr sehr viel komplexere Fragen zum Gegenstand von Beratungen machen, als das in den direkten partizipatorischen Formen der Antike möglich war. Und man konnte Herausforderungen und Probleme in längerfristigen Perspektiven ins Auge fassen.« Sind wir immer am Puls der Zeit? Oder sind wir eher am Puls des Rauschens? Warum gibt es keine Wissenschafts-News und warum ist es gerade in komplexen Zeiten wichtig, Abstand von schnellen Medien zu halten? Warum sind Bücher gerade in schnellen Zeiten von besonderer Bedeutung? Wie kann man die Welt in Schichten verschiedener Geschwindigkeiten begreifen? Stewart Brand bezeichnet dies als Pace Layering: »Build a thing too fast, and mistakes cascade. Build a thing at the right pace, and mistakes instruct. Build a thing too slow, and mistakes are forgotten, then endlessly repeated in the endless restarts. For instance, with infrastructure: Building a thing at the right pace steadily all the way to completion probably works best with: Continuity of control Protected and guided by continuity of oversight and Guided by continuously monitored undersight—from workers and early customers. Continuity is the key.« Was aber machen wir mit Systemen — um wieder auf Stafford Beer zurückzukommen — deren tatsächlicher Zweck sich vom deklarierten Zweck entfernt hat? Wir enden nochmals mit einem Zitat von Stewart Brand: »Fast learns, slow remembers.  Fast proposes, slow disposes.  Fast is discontinuous, slow is continuous.  Fast and small instructs slow and big by accrued innovation and by occasional revolution.  Slow and big controls small and fast by constraint and constancy.  Fast gets all our attention, slow has all the power.« Was haben Sie mitgenommen? Schreiben Sie mir! Referenzen Podcast Umfrage — Bitte teilnehmen! Andere Episoden Episode 122: Komplexitätsillusion oder Heuristik, ein Gespräch mit Gerd Gigerenzer Episode 121: Künstliche Unintelligenz Episode 119: Spy vs Spy: Über künstlicher Intelligenz und anderen Agenten Episode 104: Aus Quantität wird Qualität Episode 99: Entkopplung, Kopplung, Rückkopplung Episode 92: Wissen und Expertise Teil 2 Episode 84: (Epistemische) Krisen? Ein Gespräch mit Jan David Zimmermann Episode 80: Wissen, Expertise und Prognose, eine Reflexion Episode 49: Wo denke ich? Reflexionen über den »undichten« Geist Episode 47: Große Worte Episode 32: Überleben in der Datenflut – oder: warum das Buch wichtiger ist als je zuvor Fachliche Referenzen Tweet von Axel Bojanowski (2025) Herfried Münkler, Verkleinern und entschleunigen. Die Zukunft der Demokratie? ARD (2022) Stewart Brand, Pace Layering: How Complex Systems Learn and Keep Learning (2018) Stewart Brand, How Buildings Learn: What Happens After They're Built, Penguin (1995)

Schau dir das vollständige Video Interview auf YouTube anYouTube Kanal gleich abonnieren und keine neue Folge mehr verpassenZusammenfassungChristina erklärt, warum Standard-Referenzbereiche für Laborwerte oft irreführend sind, da sie nicht zwingend eine gesunde Population abbilden. Werte sollten immer im Kontext individueller Symptome betrachtet werden, wobei bei Vitaminen/Mineralstoffen oft der obere Normbereich anzustreben ist.Wir besprechen oft übersehene, aber kritische Werte: HOMA-Index (Insulinresistenz-Früherkennung, oft besser als HbA1c!), Homocystein (B-Vitamine, Methylierung) und eine umfassende Schilddrüsen-Diagnostik (mehr als nur TSH: fT3, fT4, Antikörper, rT3). Christina betont die richtige Messmethode: Bei Mineralstoffen (Mg, Zn, B6) ist Vollblut oft aussagekräftiger als Serum, um die zelluläre Versorgung zu prüfen. Auch bei B12 sind HoloTC/MMA dem Serumwert vorzuziehen.Fokus Schilddrüse: Häufige Ursachen (Stress, Nährstoffmängel, Darm, Toxine) und wichtige Parameter für eine gute Diagnostik werden erklärt, inkl. optimaler Wertebereiche (z.B. TSH kleiner 1,5, fT4 größer 1,2, fT3 größer 3,0).Darmgesundheit: Sinnvolle Parameter einer Stuhlanalyse (Flora, pH, sIgA, Calprotectin, Zonulin, Verdauungsrückstände) werden beleuchtet – weniger ist oft mehr als Tests mit hunderten Stämmen.Abschließend empfiehlt Christina ein Basislaborpanel für die jährliche Prävention – eine wichtige Investition in die eigene Gesundheit. Proaktives Gesundheitsmanagement durch gezielte Messungen ist der Schlüssel.Naturecan CBD-Öl: Dein natürlicher Begleiter für Balance.Als Coach empfehle ich dieses Bio-Breitspektrum-CBD-Öl (10%) zur Unterstützung bei Stress, Schlafproblemen und zum Abschalten. Der Entourage-Effekt optimiert die Wirkung. Ich nutze es abends für besseren Schlaf und höre von Kunden positive Erfahrungen bei Verspannungen.Wichtig: CBD ist kein Wundermittel, kann aber natürliche Unterstützung bieten. Naturecan legt Wert auf Qualität (THC-frei, laborgeprüft, TÜV-Süd zertifiziert).Teste es selbst: Finde deine ideale Dosis mit der Pipette.Spare 15%: Nutze den Code TULIPAN15Naturecan CBD-Öl bestellen *Was du in dieser Episode lernst

In dieser Folge schauen wir uns den wichtigsten Parameter im Trainingsplan überhaupt an: die Intensität.Viele Trainingspläne starten mit Übungsauswahl oder Satzzahlen – doch der wahre Fortschritt beginnt woanders:

Sich den passenden Monitor auszusuchen, das ist nicht immer so einfach, wie es klingt. Denn es gibt eine ganze Menge Funktionen und Parameter, die einem sehr wichtig oder auch komplett egal sein können: Welches Format möchte ich haben, und welche Auflösung, Pixeldichte und Diagonale? Wann brauche ich eine hohe Bildwiederholrate, eine spezielle Entspiegelung einen großen Farbraum oder HDR-Unterstützung? Wie ist das mit der Ergonomie, Stichwort Helligkeit und Höhenverstellung? Welche Vorteile haben gebogene (curved) Bildschirme – und welche Nachteile? In dieser Folge des c't uplink widmen wir uns nicht nur diesen Fragen, sondern sprechen auch über Dockingstationen, wahlweise direkt im Monitor integriert oder auch extern für USB-C und Thunderbolt. Zu Gast: Ulrike Kuhlmann, Christof Windeck Host: Jan Schüßler Produktion: Gordon Hof Unser Titelthema zu Monitoren, Docks und Halterungen lesen Sie in c't 10/2025: https://www.heise.de/select/ct/2025/10

Sich den passenden Monitor auszusuchen, das ist nicht immer so einfach, wie es klingt. Denn es gibt eine ganze Menge Funktionen und Parameter, die einem sehr wichtig oder auch komplett egal sein können: Welches Format möchte ich haben, und welche Auflösung, Pixeldichte und Diagonale? Wann brauche ich eine hohe Bildwiederholrate, eine spezielle Entspiegelung einen großen Farbraum oder HDR-Unterstützung? Wie ist das mit der Ergonomie, Stichwort Helligkeit und Höhenverstellung? Welche Vorteile haben gebogene (curved) Bildschirme – und welche Nachteile? In dieser Folge des c't uplink widmen wir uns nicht nur diesen Fragen, sondern sprechen auch über Dockingstationen, wahlweise direkt im Monitor integriert oder auch extern für USB-C und Thunderbolt. Unser Titelthema zu Monitoren, Docks und Halterungen lesen Sie in c't 10/2025: https://www.heise.de/select/ct/2025/10

Sich den passenden Monitor auszusuchen, das ist nicht immer so einfach, wie es klingt. Denn es gibt eine ganze Menge Funktionen und Parameter, die einem sehr wichtig oder auch komplett egal sein können: Welches Format möchte ich haben, und welche Auflösung, Pixeldichte und Diagonale? Wann brauche ich eine hohe Bildwiederholrate, eine spezielle Entspiegelung einen großen Farbraum oder HDR-Unterstützung? Wie ist das mit der Ergonomie, Stichwort Helligkeit und Höhenverstellung? Welche Vorteile haben gebogene (curved) Bildschirme – und welche Nachteile? In dieser Folge des c't uplink widmen wir uns nicht nur diesen Fragen, sondern sprechen auch über Dockingstationen, wahlweise direkt im Monitor integriert oder auch extern für USB-C und Thunderbolt. Zu Gast: Ulrike Kuhlmann, Christof Windeck Host: Jan Schüßler Produktion: Gordon Hof Unser Titelthema zu Monitoren, Docks und Halterungen lesen Sie in c't 10/2025: https://www.heise.de/select/ct/2025/10

Matthew 28:18-2016 Then the eleven disciples went to Galilee, to the mountain where Jesus had told them to go. 17 When they saw him, they worshiped him, but some doubted. 18 Then Jesus came to them and said, “All authority in heaven and on earth has been given to me. 19 Therefore, go and make disciples of all nations, baptizing them in the name of the Father and of the Son and of the Holy Spirit, 20 and teaching them to obey everything I have commanded you. And surely I am with you always, to the very end of the age.”                                                                                                                                                                                                                                           Mark 16:15-2015 He said to them, “Go into all the world and preach the gospel to all creation.16 Whoever believes and is baptized will be saved, but whoever does not believe will be condemned.17 And these signs will accompany those who believe: In my name they will drive out demons; they will speak in new tongues;18 they will pick up snakes with their hands; and when they drink deadly poison, it will not hurt them at all; they will place their hands on sick people, and they will get well.” Luke 24:46-4946 He told them, “This is what is written: The Messiah will suffer and rise from the dead on the third day, 47 and repentance for the forgiveness of sins will be preached in his name to all nations, beginning at Jerusalem. 48 You are witnesses of these things. 49 I am going to send you what my Father has promised; but stay in the city until you have been clothed with power from on high.” John 20:21-2321 Again Jesus said, “Peace be with you! As the Father has sent me, I am sending you.” 22 And with that he breathed on them and said, “Receive the Holy Spirit. 23 If you forgive anyone's sins, their sins are forgiven; if you do not forgive them, they are not forgiven.”                  A Sermon Series "Fix Your Eyes Upon Jesus"                  1. The Procession of the Commission – John 20:21 "As the Father has sent Me, I am sending you."                 2. The Preeminence of the Commission – Matthew 28:18 "All authority in heaven and on earth has been given to Me."                 3. The Perimeter of the Commission – Mark 16:15 "Go into all the world and preach the gospel to all creation."                 4. The Proclamation of the Commission – Luke 24:47 "Repentance for the forgiveness of sins will be preached in His name..."                 5. The Purpose of the Commission – Matthew 28:19-20 "Make disciples of all nations... teaching them to obey everything I have commanded you."                 6. The Power for the Commission - John 20:22 "And with that he breathed on them and said, 'Receive the Holy Spirit.'"                   7. The Presence in the Commission – Matthew 28:20 “I am with you always...”                Our Response 

主要参考文献 Vendler, Zeno (1957) Verbs and Times. The Philosophical Review. 66 (2): 143–160. Smith, Carlota S. (1991). The Parameter of Aspect. Studies in Linguistics and Philosophy. Vol. 43. Dordrecht, The Netherlands: Springer Netherlands. X▶︎https://x.com/sigajugo Instagram▶︎https://bit.ly/3oxGTiK LINEオープンチャット▶︎https://bit.ly/3rzB6eJ オリジナルグッズ▶︎https://suzuri.jp/sigajugo note▶︎https://note.com/sigajugo おたより▶︎https://bit.ly/33brsWk BGM・効果音: MusMus▶︎http://musmus.main.jp/ #落ち着きある #ひとり語り #豆知識 #雑学 #教育

In dieser Folge besprechen wir anhand einer Studie aus Göttingen die Relevanz von CA 19-9 in der Nachsorge des Pankreaskarzinoms. In einer Test- und Validierungsgruppe konnte hier eine verlässliche Rezidivvorhersage teils lange vor der Korrelation in der Bildgebung nachgewiesen werden. Moderation: Sophie Heisler Gast: Dr. med. Felix Rühlmann Besprochene Publikation: Azizian A, Rühlmann F, Krause T, Bernhardt M, Jo P, König A, Kleiß M, Leha A, Ghadimi M, Gaedcke J. CA19-9 for detecting recurrence of pancreatic cancer. Sci Rep. 2020 Jan 28;10(1):1332. doi: 10.1038/s41598-020-57930-x. PMID: 31992753; PMCID: PMC6987233.

Deep Dive in Quantenphysik, Rechenpower und Fachkräftemangel: Prof. Dr. Kerstin Borras im DEEPTECH DEEPTALKIn dieser Folge sprechen wir mit Prof. Dr. Kerstin Borras, leitende Wissenschaftlerin am DESY in Hamburg und Professorin für Physik an der RWTH Aachen, über die Rolle von Quantencomputing in der Spitzenforschung – und darüber, wie Deutschland in diesem zukunftsweisenden Feld aufgestellt ist.

Bist du auch frustriert, weil du ständig an deinem LinkedIn-Profil herumschraubst, aber der Erfolg einfach nicht kommt? Oder du hast das Gefühl, dein Buchprojekt wird nie fertig und du scheiterst immer wieder an der Sichtbarkeit?   In dieser Folge geht es um die entscheidenden Parameter, die du beachten musst, um deinen Erfolg endlich planbar zu machen. Statt endloser Experimente, die dir nicht weiterhelfen, teile ich mit dir, warum Klarheit und Strategie der Schlüssel zum langfristigen Erfolg sind – und warum du keine Zeit mehr verschwenden solltest.    Ich teile mit dir meine persönlichen Schritte, um deinen Erfolg in die richtige Richtung zu lenken und deine Ziele endlich zu erreichen.   Highlights der Episode: Warum „Probieren“ nicht der Weg zum Erfolg ist   Welche 3 Parameter du unbedingt beachten musst   Ein Best-Practice-Beispiel für deinen Weg zum Erfolg   Werde endlich sichtbar. Werde vom Teelicht zum Leuchtturm deiner Branche!   Deine Jessi

Fütterungscontrolling ist der wahre Gamechanger in der Tiergesundheit. Nur wenn die Herde gesund gefüttert wird, kann sie auch fit bleiben und gesund sowie nachhaltig Milch geben. Der mit Abstand wichtigste Parameter in der Fütterung ist die Trockenmasseaufnahme (TMA) pro Kuh und Tag.

Willkommen zu einer neuen Folge meines Podcasts! In dieser Episode begrüße ich den renommierten Experten Franz Josef Radermacher vor einem besonderen Gast an der Wand: Albert Einstein. Was hat Einstein mit Energie zu tun? Warum ist Energie die Grundlage menschlichen Wohlstands? Und wie gestalten wir eine nachhaltige Zukunft in einer global vernetzten Welt? Dieses Gespräch nimmt uns mit auf eine Reise durch die Geschichte der Energienutzung, die Herausforderungen der Energiewende und die geopolitischen Dimensionen, die oft übersehen werden. Prof. Radermacher ist Vorstand des Forschungsinstituts für anwendungsorientierte Wissensverarbeitung, stellv. Vorstandsvorsitzender von Global Energy Solutions e. V. (Ulm), emerit. Professor für Informatik, Universität Ulm, 2000 – 2018 Mitglied des Wissenschaftlichen Beirats beim Bundesministerium für Verkehr und digitale Infrastruktur (BMVI); er ist Ehrenpräsident des Ökosozialen Forum Europa, Wien, Mitglied des UN-Council of Engineers for the Energy Transition (CEET) sowie Mitglied des Club of Rome, Winterthur. Was hat Einstein mit diesem Gespräch zu tun? Wir beginnen mit der Frage, welche Rolle Energie in unserer Gesellschaft spielt sowie der Tatsache, dass vielen Menschen, vermutlich den meisten, nicht klar ist, was unsere Gesellschaft antreibt? So waren 2023 mehr als 81 Prozent des gesamten weltweiten Energieverbrauchs durch fossile Quellen gedeckt, und die Menge an fossilen Energieträgern wächst ständig. Wie hat Energie die Menschheit geprägt? Welche Energiequellen hatten wir früher und welchen Einfluss hatte die Veränderung der Energieträger auf unsere Gesellschaft und unseren Lebensstandard? Warum dominieren fossile Brennstoffe heute noch? Kann Energie Armut bekämpfen? Ist es Energie, die Wohlstand schafft? Warum sind zwei oft übersehene Parameter von so großer Bedeutung: Energiedichte und Platzbedarf?  Kernkraftwerke benötigen wenig Fläche im Vergleich zu Windrädern oder Photovoltaik:  »Da ist ja ein Faktor 100 dazwischen.« […] »Weil auch Fläche ein extrem knappes Gut ist, ist es problematisch, wenn man eine Energie mit ziemlich niedriger Dichte hat.« Gleichzeitig sind Energie und Emissionen, besonders Treibhausgase, globale Phänomene, die lokal nicht zu lösen sind. »Von 2004 bis 2023 haben die globalen Investitionen in Wind und Solar rund 4 Billionen Dollar ausgemacht, und trotzdem sind die fossilen Energieträger dreimal schneller gewachsen.“ Zudem: „In den großen Industrienationen […] eine Reduktion der CO2-Emissionen, aber gleichzeitig einen Zuwachs in Indien und China, der diese Reduktionen um das Faktor 5 überschattet.«, Robert Bryce Überrascht uns China? China hat mittlerweile die EU auch in den Pro-Kopf-Emissionen überholt. Was passiert, wenn Schwellenländer folgen? »An China kann man erkennen, was passiert, wenn ein armes Land versucht, Wohlstand aufzubauen. Und das geht bis heute nur mit fossilen Energieträgern.« Sind schnelle Lösungen gefährlich? Großinfrastruktur, Energiesysteme sind immer eine Frage von Jahrzehnten. Wenn wir versuchen, Dinge hier über das Knie zu brechen, ist die Wahrscheinlichkeit, dass Sie große und extrem teure Fehler machen, enorm. Außerdem stellt sich die Frage, welche Relevanz Europa überhaupt noch hat? Welche Maßnahmen gegen den Klimawandel könnten erfolgreich sein? Was wurde etwa in Baku beschlossen? Funktionieren Transferzahlungen? Warum scheitert eine Renewables Only Strategie zwangsläufig? »Die Idee, Renewables Only, ist ja eine von Deutschland immer wieder propagierte Idee.[… Es] ist nur eine Methode, [Entwicklungsländer] arm zu halten« Aber was ist die Alternative? Was ist Carbon Capture? Was ist die Rolle von Kernkraft? Welche Mischung verschiedener Verfahren ist sinnvoll? Zuletzt diskutieren wir über Strom vs. Moleküle und den All-Electric-Irrtum. Damit verbunden ist der Irrglaube, Wasserstoff könnte das Renable-Desaster lösen.  Welche geopolitischen Herausforderungen sind mit diesen Themen verknüpft? Ist Prof. Radermacher optimistisch — für Europa, die Welt? Was könnte man jungen Menschen empfehlen? Referenzen Andere Episoden Episode 109: Was ist Komplexität? Ein Gespräch mit Dr. Marco Wehr Episode 107: How to Organise Complex Societies? A Conversation with Johan Norberg Episode 95: Geopolitik und Militär, ein Gespräch mit Brigadier Prof. Walter Feichtinger Episode 94: Systemisches Denken und gesellschaftliche Verwundbarkeit, ein Gespräch mit Herbert Saurugg Episode 86: Climate Uncertainty and Risk, a conversation with Dr. Judith Curry Episode 81: Energie und Ressourcen, ein Gespräch mit Dr. Lars Schernikau Episode 73: Ökorealismus, ein Gespräch mit Björn Peters Episode 70: Future of Farming, a conversation with Padraic Flood Episode 62: Wirtschaft und Umwelt, ein Gespräch mit Prof. Hans-Werner Sinn Prof. Radermacher Forschungsinstitut für anwendungsorientierte Wissensverarbeitung/n Global Energy Solutions Prof. Radermacher im Vorstand der Global Energy Solutions All In: Energie und Wohlstand für eine wachsende Welt, Murmann (2024) Fachliche Referenzen Vaclav Smil, How the World Really Works, Penguin (2022) Vaclav Smil, Net Zero 2050, Fraser Institute (2024) Robert Bryce, The Energy Transition Isn't (2023) Robert Bryce, Numbers Don't Lie (2024)

Jedes Jahr kommen Milliarden chinesische Pakete in Europa an – und die Zahl steigt unablässig. Wie wirkt sich das auf die Transportunternehmen in der EU aus? Eines ist sicher: Grundsätzlich sind Transporteure dankbar für alle Aufträge, die sie ergattern können. Was da in den letzten Jahren aus Fernost kommt, ist allerdings so viel, dass sich jetzt die EU Kommission für schärfere Regeln ausspricht. Denn Temu, Shein oder auch Ali Express liegen mit ihren Sendungen meist unter der Zollfreigrenze von 150 Euro, weshalb auch der Zoll nur in Einzelfällen die Pakete unter die Lupe nimmt. In der Folge geraten viele Produkte in die EU, die hier aus diversen Gründen eigentlich nicht sein dürften; zum Beispiel, weil sie sicherheitsrelevante Parameter nicht erfüllen oder glatte Fälschungen von Markenprodukten sind. Durch die von der EU-Kommission angedachten Regelungen droht aber der Transportwirtschaft eine bürokratische Last, vor der inzwischen etliche Verbände warnen. Alle Hintergründe hören Sie in dieser Ausgabe von VerkehrsRundschau Funk.

Die "durability" ist ein relativ neuer Parameter der (Ausdauer-)Leistungsdiagnostik, der die Ermüdungswiderstandsfähigkeit beschreibt. Obwohl es immernoch sehr verschiedene Testmöglichkeiten für diesen Parameter gibt, deuteten die ersten Studien an, dass Frauen eine bessere "durability" zeigen.Eine brandneue Studie die heute(!) erschienen ist, zeigt hingegen das Gegenteil im professionellen Radsport. Hier scheinen Frauen eine größere Veränderung ihres Power Profiles nach vorangegangener Belastung aufzuweisen. Mehr dazu im heutigen #sciencesnack #034Hier die Studie:Mateo-March et al. (2025). J Sci Med Sport: Sex differences in durability: a field-based study in professional cyclists:https://doi.org/10.1016/j.jsams.2025.02.009Hier Folge #02∞ zur "durability":https://open.spotify.com/episode/3GzTv1fQPuaNHj4xhCMa57?si=mwOfScs0S9-ukTEHW8FtAAHier die infos zum ersten Science Run Cologne (22. März):https://www.instagram.com/science_run_cologne/Hier eine Playlist mit allen #sciencesnackshttps://open.spotify.com/playlist/10RidgFbWxpq2Uo7ieF84V?si=MbLfAgYMR4aD_AyHzR4Cfg&pi=QSKvlxDxSRi1XHier eine Playlist zu allen Gastfolgenhttps://open.spotify.com/playlist/2KAIQpuFMv21jKYqeBA3zy?si=jjmr2JycTE2Q1Vm9Y16LMA&pi=39X3URPuQxemdHier könnt ihr den Podcast unterstützen!

Wie kann ich meine Tage so gestalten, dass ich ein „Gutes Leben“ habe? Was ist das überhaupt, „Das gute Leben“? Ist das für alle gleich? Gibt es da bestimmte Parameter oder gar Vorschriften, die ich erfüllen muss? Wer beurteilt das, und was, wenn mein Leben doch nicht so gut war? Muss ich dann nachsitzen? Spannende Fragen und Anregungen zum Thema in fünf neuen Episoden. Viel Spaß beim Anhören Ihr Hergen von Huchting

Auphonic (click here to comment) 25. Februar 2025, Jochen

Im Marathon sind verschiedene Parameter relevant. VO2max, fraktionelle Ausschöpfung, Laufökonomie, durability,... ABER vielleicht sollte man auch einfach mal "locker" bleiben. Eine neue Studie zeigt, dass die Elastizität der Muskulatur etwa 16% der Marathonzeit aufklärt. Wie sich das auf die Leistung auswirkt und ob wir demnächst nur noch im Vaporfly laufen sollten, erfahrt ihr in #sciencesnack #032.Hier die Studie:Winn et al. (2025) Eur J Appl Physiol: Skeletal muscle elastic modulus in marathon distance runnershttps://pubmed.ncbi.nlm.nih.gov/39847073/Lerchbauer (2022) Sportärztezeitung: Scherwellenelastographie im Bewegungsapparathttps://sportaerztezeitung.com/rubriken/therapie/12921/scherwellenelastographie-im-bewegungsapparat/Behind Science (Snack) "Lauf für die Prinzessin":https://open.spotify.com/episode/3gJfbCTtuANN3pujAfYLml?si=rGBS-GV5TyWy3iJ-yg36DQHier eine Playlist mit allen #sciencesnackshttps://open.spotify.com/playlist/10RidgFbWxpq2Uo7ieF84V?si=MbLfAgYMR4aD_AyHzR4Cfg&pi=QSKvlxDxSRi1XHier eine Playlist zu allen Gastfolgenhttps://open.spotify.com/playlist/2KAIQpuFMv21jKYqeBA3zy?si=jjmr2JycTE2Q1Vm9Y16LMA&pi=39X3URPuQxemdHier könnt ihr den Podcast unterstützen!

Hast du dich jemals gefragt, warum sportlich aktive Menschen seltener krank werden? Oder warum du dich nach einer intensiven Trainingseinheit manchmal ausgelaugt fühlst? Sport und das Immunsystem sind eng miteinander verknüpft – Bewegung kann unsere Abwehrkräfte stärken, aber auch vorübergehend schwächen. Wie viel Sport ist ideal, um gesund zu bleiben? In dieser Podcast Episode erfährst du, welche Auswirkungen Bewegung auf das Immunsystem hat, warum es gerade für ältere Menschen wichtig ist und welche langfristigen Vorteile ein aktiver Lebensstil mit sich bringt. Erkenne den Zusammenhang von Sport und Immunsystem und entdecke, wie du deine Gesundheit gezielt unterstützen kannst! . . . **In dieser Episode erfährst du:** - Wie Sport dein Immunsystem beeinflusst und auf welche Parameter du dabei achten musst. - Was der Open Window Effekt ist und wie du mit ihm so umgehst, dass er dir nichts anhaben kann. - Welchen Einfluss die Intensität deines Traings auf die Infektamfälligkeit hat (J-Modell ​nach Nieman und Wentz). - Warum gerade ältere Menschen an der Qualität ihres Immunsystems arbeiten dürfen. - Welche positiven langfristigen Effekte regelmäßiger Sport mit sich bringt. - Ob Menschen, die körperlich hart arbeiten, Sport als Ausgleich zu empfehlen ist oder ob die Arbeit reicht. - Zu [den Shownotes...](https://wp.me/paMKmo-26L)

Wie »gut« bist du positioniert? Ich stelle dir heute den Positionierungs-Check vor, der entwickelt wurde, um dir die blinden Flecken auf deiner Positionierungs-Landkarte aufzuzeigen. Und du erfährst auch, wie du 6 entscheidende Parameter deiner Positionierung optimierst – mit zwei gerade zu magischen Fragen ;-)

Scott and Wes explore the experimental world of running TypeScript in Node, breaking down the differences between type stripping and compiling. They cover the pros, cons, and quirks of the current implementation, plus explore tools like tsx, ts-node, and even alternatives like Deno. Show Notes 00:00 Welcome to Syntax! 00:41 Brought to you by Sentry.io. 01:29 Running TypeScript in Node. 01:45 Experimental Type Stripping. 03:17 TypeScript refresher. 04:05 TypeScript can be compiled and/or Type Stripped. 05:09 Current Node implementation is only type stripping. 05:40 Limitations of no compiling. 05:57 Enums. 08:30 Other issues. 08:35 Parameter properties. 09:20 Experimental transform types. 10:01 Importing types with type keyword. 11:17 No need for sourcemaps. 11:42 No dependencies. 13:08 Other tools. 13:25 tsx. 14:28 ts-node. 14:44 JSDoc. 16:30 Deno and Bun. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Du willst schneller werden? Dann ist das hier die richtige Folge für dich! Wir knöpfen uns mit Sportwissenschaftler Dr. Oliver Quittmann die Laufparameter vor, die wirklich für unsere Leistung entscheidend sind, und gehen gemeinsam die besten Trainingsmethoden dafür durch. Von den klassischen Parametern wie VO2max oder Fettstoffwechsel bis hin zu Neuentdeckungen wie die sogenannte "Durability" - wir ranken und beleuchten sie alle, und du hast nach dieser geballten Wissensfolge auf jeden Fall den Durchblick. Das war aber noch nicht alles: Oliver stellt uns neben den bekannten Laborwerten auch noch Parameter vor, die sich im "Do it yourself"-Verfahren auch zuhause ermitteln lassen. Viel Spaß beim Hören!(00:01:49) - Intro Ende(00:05:23) - Welche Parameter sind überhaupt relevant?(00:10:41) - Wozu brauchen wir eine Leistungsdiagnostik?(00:16:12) - Welche Parameter werden bei der Leistungsdiagnostik erhoben?(00:18:25) - Laufparameter-Ranking(00:25:24) - Wieso ist die VO2max der wichtigste Parameter?(00:27:29) - Wie können wir die VO2max trainieren?(00:31:24) - Wozu brauchen wir die Laufökonomie und wie lässt sie sich verbessern?(00:42:32) - Was ist die fraktionelle Ausschöpfung? (00:43:44) - Laktat(werte): Nutzen & Mythen (00:50:57) - Laktatwerte: Auswertung und Verbesserung(00:56:58) - Fettstoffwechsel: Welche Werte brauchen wir & wie messen wir sie?(01:02:24) - Was ist die FatMax?(01:06:31) - Nüchtern trainieren für einen besseren Fettstoffwechsel?(01:10:28) - Wie häufig sollten wir zur Leistungsdiagnostik?(01:14:20) - Wie gut funktioniert Do-It-Yourself-Leistungsdiagnostik?(01:17:04) - Time-Trials zum Selbsttesten(01:20:25) - Wie berechne ich meine Werte?(01:25:52) - Der berüchtigte 3-Minuten-All-out-Test(01:31:57) - Herzfrequenz selbst messen(01:38:08) - Durability: Was kann dieser neue Parameter?Hier findet ihr Olivers YouTube-Kanal.Schaut hier vorbei auf seinem Instagram!Oliver hat auch einen eigenen Podcast, schaut doch hier vorbei!Foto: Oliver J. QuittmannMusik: The Artisian Beat - Man of the Century>> ACHILES RUNNING Podcastfolge über VO2max>> ACHILES RUNNING Podcastfolge über Laktat>> ACHILES RUNNING Podcastfolge über Laufökonomie>> ACHILES RUNNING Podcastfolge über LeistungsdiagnostikHier findet ihr alle aktuellen Gewinnspiele & Rabatt-Aktionen! Hosted on Acast. See acast.com/privacy for more information.

Du willst schneller werden? Dann ist das hier die richtige Folge für dich! Wir knöpfen uns mit Sportwissenschaftler Dr. Oliver Quittmann die Laufparameter vor, die wirklich für unsere Leistung entscheidend sind, und gehen gemeinsam die besten Trainingsmethoden dafür durch. Von den klassischen Parametern wie VO2max oder Fettstoffwechsel bis hin zu Neuentdeckungen wie die sogenannte "Durability" - wir ranken und beleuchten sie alle, und du hast nach dieser geballten Wissensfolge auf jeden Fall den Durchblick. Das war aber noch nicht alles: Oliver stellt uns neben den bekannten Laborwerten auch noch Parameter vor, die sich im "Do it yourself"-Verfahren auch zuhause ermitteln lassen. ...Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen? Dann schaue auf www.kostenlos-hosten.de und informiere dich. Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.Gern unterstützen wir dich bei deiner Podcast-Produktion.

In dieser Folge habe ich Jonas zu Gast. Er studiert Medizin und eines seiner Ziele ist es, die Arzt-Patienten-Kommunikation zu verbessern. Auf seinem Instagram-Kanal Jonesrulez und seinem Podcast „Blutige Anfänger“ teilt er sein Medizinwissen auf leicht verständlich und auf humorvolle Art und Weise. In unserem Gespräch dreht sich alles um das Thema Blutwerte. Wir sprechen über die Bedeutung des Blutbildes in der Gesundheitsvorsorge. Dabei schauen wir auf Werte, die routinemäßig erhoben werden und analysieren, auf welche Werte du im Kontext verschiedener Fragestellungen wirklich achten solltest. Themen sind unter anderem Klassische Fehlinterpretationen und Missverständnisse bei der Analyse des Blutbildes Wo du genau hinschauen solltest, um Nährstoffmangel zu erkennen Warum Ärzte nicht automatisch alle relevanten Parameter testen (können) Wie du entscheidest, ob du Nahrungsergänzungsmittel benötigst Wie du die Wirksamkeit von Nahrungsergänzungsmitteln überprüfst Konntest du etwas für dich mitnehmen? Dann würde ich mich riesig über deinen Like freuen.

Kein anderes Lebensmittel wird in solchen Mengen täglich benötigt wie Wasser - umso wichtiger ist es, genau hinzuschauen, was unser Leitungswasser tatsächlich enthält. Nach der Trinkwasserverordnung wird unser Leitungswasser zwar regelmäßig auf verschiedene Parameter getestet, doch Experten warnen vor bisher unregulierten Stoffen wie Mikroplastik und Arzneimittelrückständen, für die es noch keine Grenzwerte gibt. In dieser Episode spricht Wasserexperte Ulf Gemeinhardt über die tatsächliche Qualität unseres Leitungswassers, klärt über Zusammenhänge zwischen Wasserqualität und Gesundheit auf und zeigt Wege zur optimalen Trinkwasseraufbereitung. ________ Hier findest du alle Infos und Angebote von Ulf Gemeinhardt:

A 671B parameter Mixture-of-Experts language model. It highlights the model's architecture, including its innovative load balancing and multi-token prediction strategies, and its efficient training process using FP8 precision. Benchmark results demonstrate DeepSeek-V3's strong performance compared to other open-source and some closed-source models, particularly in math and code tasks. The document also provides instructions for running DeepSeek-V3 locally using various frameworks and hardware, including NVIDIA and AMD GPUs and Huawei Ascend NPUs. Finally, licensing and contact information are included.

F*ck it, we're going to do it live! (In our best Bill O'Reilly voice) On this live stream episode of the MJ:FH pod, we break down Midjourney's highly anticipated v7 release, explore Runway's groundbreaking cinematic AI innovations, and dissect the latest developments in video generation. We provide an exclusive first look at Runway's new Frames feature and share candid insights about its potential impact on creative workflows. We also analyze Krea emerging platform, Magnific's editorial portrait capabilities, and the strategic positioning of various AI tools in the market. The episode concludes with an engaging discussion about the future of AI storytelling and community building in the generative AI space. #AIart #Midjourney #GenerativeAI #CreativeTech #AItools #DigitalArt #AIinnovation #CreativeWorkflow #ArtificialIntelligence #GenerativeArtist --- ⏱️ Midjourney Fast Hour [00:00] Welcome & Weekly Updates [02:38] Midjourney Development News [18:54] Runway Frames Preview [29:15] Magnific's Editorial Evolution [36:24] Luma & Kling Updates [45:45] Krea [54:30] New AI Tool Releases [01:02:12] Live Session Format Discussion [01:11:45] Midjourney Style Parameters [01:21:15] Wrap Up & Goodbyes --- Takeaways The week was chaotic for both hosts, highlighting the challenges of balancing work and personal life. Midjourney's V7 release is anticipated, with new features and improvements on the horizon. Users prefer higher quality outputs even if it means longer wait times for video models. Runway is focusing on cinematic quality and user experience in its updates. Facial expressions in AI-generated images remain a challenge that needs addressing. Nonlinear storytelling is an exciting frontier for AI tools, offering new creative possibilities. Magnific has evolved from a simple upscaler to a more comprehensive tool with unique features. The importance of user feedback in shaping the development of AI tools is emphasized. Both hosts express excitement about the future of AI in creative industries. The conversation highlights the competitive landscape of AI tools and the need for continuous innovation. Image upscaling can sometimes distort patterns and features. Macro photography reveals incredible detail and quality. Different image generation tools have unique strengths and weaknesses. Midjourney stands out for its creativity and control. Storytelling in image generation is an untapped feature. User-friendly platforms like Krea are gaining popularity. Community engagement is crucial for the success of AI tools. AI tools are evolving rapidly, making it hard to keep up. The quality of generated images can vary significantly between tools. Training data quality impacts the output of AI-generated images. Community engagement is crucial for podcast growth. Storyboarding tools like Midjourney and LTX offer different functionalities. Consolidating AI tools can enhance workflow efficiency. Live sessions can foster real-time interaction with the audience. Midjourney's mood board feature is anticipated to be useful. Personalization codes can enhance user experience in AI tools. Organizing generated content is essential for effective use. Parameter variations can significantly affect output quality. AI tools are evolving rapidly, requiring constant adaptation. User feedback is vital for improving AI tool functionalities.

Den Begriff "VO2max" (= maximale Sauerstoffaufnahme) kennen die meisten Ausdauersportler:innen - denn je besser die persönliche VO2max ist, desto schneller und länger können wir laufen. Und um diesen Wert zu verbessern, machen viele Läufer:innen brav Intervalltraining. Aber ist das überhaupt der einzige oder beste Weg zum Ziel? Was steckt hinter diesem magischen Parameter und mit welchen Methoden lässt er sich am effektivsten trainieren? In diese Fragen stürzen wir uns mit Sportwissenschaftler Dr. Oliver Jan Quittmann von der Sporthochschule Köln. Er präsentiert uns die neuesten Studien und heißesten Erkenntnisse rund um die VO2max - exklusiv in dieser Podcastfolge. Viel Spaß beim Hören!(00:01:47) - Intro Ende(00:07:01) - Wie wirkt sich eine Sportpause auf die VO2max aus?(00:09:17) - Was ist die VO2max und was sagt sie aus?(00:12:16) - Wie bestimmt man die VO2max?(00:17:25) - Welches Protokoll ist das sinnvollste? (00:21:07) - Länger leben mit guter VO2max?(00:25:28) - Einfluss der VO2max auf die sportliche Leistung(00:28:20) - Für welches Leistungslevel ist die VO2max entscheidend?(00:33:23) - Was ist eigentlich eine gute VO2max?(00:39:14) - Gibt es ein VO2max-Limit?(00:43:05) - Rolle der Genetik bei der VO2max(00:46:34) - Wie lässt sich VO2max trainieren? - Einblick in die Studienlage(00:49:58) - HIT vs. Low Intensity: Was ist besser?(00:53:27) - Polarisiertes Training: Wie können wir den Ansatz für uns nutzen?(00:55:24) - Wie sieht die perfekte HIT-Einheit aus?(01:02:01) - Was sollten wir beim Höhentraining beachten?(01:08:03) - Atemtechnik und ihre Wirkung auf die VO2max(01:12:08) - Die Ernährung für VO2max: Wie viel bringt rote Bete Saft wirklich?(01:18:15) - Tracken der VO2max richtig tracken: Ist die Laufuhr zuverlässig?(01:27:31) - Wann werden die ersten Fortschritte sichtbar?Hier findet ihr Olivers YouTube-Kanal.Schaut hier vorbei auf seinem Instagram!Oliver hat auch einen eigenen Podcast, schaut doch hier vorbei!Foto: Oliver J. QuittmannMusik: The Artisian Beat - Man of the CenturyBlack Friday-Aktion bei SHOKZ: Jetzt 30% sparen!--> 10 EUR Rabatt mit unserem Code "ACHILLES10"Hier findet ihr alle aktuellen Gewinnspiele & Rabatt-Aktionen! Hosted on Acast. See acast.com/privacy for more information.

Inhalt Sie kennen es alle: Im Einkaufsgespräch wie auch im Verkauf kommt es häufig zu Diskussionen darüber, was der jeweils andere Geschäftspartner falsch macht. Heute erfahren Sie, wie Sie Ihre Gespräche mit Lieferanten und Kunden optimieren und Ihre Ziele erreichen können. All das und noch vieles mehr erfahren Sie in dieser Folge von Herrn Prof. Dr. Ulrich Nöhle.   Ihr Experte Prof. Dr. Ulrich Nöhle Öffentlich bestellter und vereidigter Sachverständiger für Lebensmittelsicherheit und Hygiene Kontaktdaten Tel.: 0170 188 1652 E-Mail: ulrich.noehle@web.de    Weitere Informationen zum Thema dieser Folge Jeden Monat in nur 4 Stunden mehr Sicherheit – von der Rohwarenbeschaffung über die Produktion bis zur Auslieferung. Erleben Sie die Online-Seminar-Serie „QM & QS: In kritischen Situationen sicher entscheiden“. Weitere Informationen und Anmeldung finden Sie unter www.behrs.de/7369 oder senden Sie eine E-Mail an service@behrs.de. Wir freuen uns über Ihr Feedback! Teilen Sie uns Ihre Meinung gerne per E-Mail an podcast@behrs.de mit.   Links •    Kostenfreie Informationen zu Hygiene und Recht  •    BEHR'S…SHOP   •    BEHR'S…AKADEMIE  •    BEHR'S…ONLINE •    QM4FOOD   •    HACCP-Portal   Hat Ihnen diese Folge gefallen? Dann freuen wir uns, wenn Sie unseren Podcast abonnieren. Hinterlassen Sie auch gern eine Bewertung, ein Feedback auf iTunes und teilen Sie diesen mit Freunden und Bekannten.  Hinterlassen Sie uns hier Ihre Bewertung, denn Ihre Meinung zählt und hilft uns, den Podcast noch besser auf Ihre Bedürfnisse zuzuschneiden.  

Strecke, Pace, Herzfrequenz - die meisten Läufer:innen tracken gerne und viel. Aber reicht das Tracken über die Laufuhr oder Strava schon aus oder können wir unser Training noch effektiver dokumentieren? Und welche Parameter bringen uns im Training wirklich weiter? Die Antworten bekommen wir in dieser Podcastfolge von Laufcoach und Physiotherapeutin Paula Thomsen aka Laufvernarrt. Sie erklärt, wie ein gut geführtes Trainingstagebuch uns dabei helfen kann, unsere Fortschritte festzuhalten und unsere Ziele systematisch zu verfolgen. Und die richtige Auswertung will ebenfalls gelernt sein: Auch dafür holen wir uns von Paula in dieser Folge Tipps ab. Viel Spaß beim Hören!(00:01:39) - Intro Ende(00:08:47) - Trackt Paula ihr Training?(00:10:17) - Der weibliche Zyklus: Wie beeinflusst er die Leistung?(00:12:13) - Gefahren von (ungesundem) Tracken(00:16:46) - Welche Formen das Trackens gibt es?(00:18:30) - Was sind die Vorteile eines Lauftagebuchs?(00:20:25) - Kann jede:r Läufer:in davon profitieren?(00:22:05) - Sollten wir auch die Ausgleichssportarten dokumentieren?(00:25:04) - Welche Parameter gehören in ein Lauftagebuch?(00:29:47) - Auswertung: Wie gelingt es uns am besten?(00:34:18) - Dokumentation digital oder analog: Was ist besser?(00:38:43) - Ernährungstagebuch: Wie sinnvoll ist das?(00:41:25) - Schmerzen tracken(00:42:25) - Wie tracken wir Wettkämpfe?(00:45:45) - Umgang mit Rückschlägen (00:51:30) - Paulas Learnings aus den Lauftagebüchern(00:54:47) - Trainingsplan mit dem Tagebuch abgleichen?(00:57:36) - Wie oft sollten wir die Daten auswerten?(01:01:17) - Wie können wir Lauftagebücher für die Zielsetzung nutzen?(01:03:15) - Kein Fortschritt: Was tun?(01:06:50) - Abschließende TippsHier findet ihr Paula auf Instagram.Hier geht es zu Paulas Webseite.Foto: Paula ThomsenMusik: The Artisian Beat - Man of the CenturyHier findet ihr unsere aktuellen Gewinnspiele & Rabatt-Aktionen!Ihr wollt mehr von uns hören? Dann klickt hier!Abonniert außerdem unseren Newsletter, um keine Podcastfolgen und Aktionen mehr zu verpassen! Hosted on Acast. See acast.com/privacy for more information.

Apologies for lower audio quality; we lost recordings and had to use backup tracks. Our guests today are Anastasios Angelopoulos and Wei-Lin Chiang, leads of Chatbot Arena, fka LMSYS, the crowdsourced AI evaluation platform developed by the LMSys student club at Berkeley, which became the de facto standard for comparing language models. Arena ELO is often more cited than MMLU scores to many folks, and they have attracted >1,000,000 people to cast votes since its launch, leading top model trainers to cite them over their own formal academic benchmarks:The Limits of Static BenchmarksWe've done two benchmarks episodes: Benchmarks 101 and Benchmarks 201. One issue we've always brought up with static benchmarks is that 1) many are getting saturated, with models scoring almost perfectly on them 2) they often don't reflect production use cases, making it hard for developers and users to use them as guidance. The fundamental challenge in AI evaluation isn't technical - it's philosophical. How do you measure something that increasingly resembles human intelligence? Rather than trying to define intelligence upfront, Arena let users interact naturally with models and collect comparative feedback. It's messy and subjective, but that's precisely the point - it captures the full spectrum of what people actually care about when using AI.The Pareto Frontier of Cost vs IntelligenceBecause the Elo scores are remarkably stable over time, we can put all the chat models on a map against their respective cost to gain a view of at least 3 orders of magnitude of model sizes/costs and observe the remarkable shift in intelligence per dollar over the past year:This frontier stood remarkably firm through the recent releases of o1-preview and price cuts of Gemini 1.5:The Statistics of SubjectivityIn our Benchmarks 201 episode, Clémentine Fourrier from HuggingFace thought this design choice was one of shortcomings of arenas: they aren't reproducible. You don't know who ranked what and what exactly the outcome was at the time of ranking. That same person might rank the same pair of outputs differently on a different day, or might ask harder questions to better models compared to smaller ones, making it imbalanced. Another argument that people have brought up is confirmation bias. We know humans prefer longer responses and are swayed by formatting - Rob Mulla from Dreadnode had found some interesting data on this in May:The approach LMArena is taking is to use logistic regression to decompose human preferences into constituent factors. As Anastasios explains: "We can say what components of style contribute to human preference and how they contribute." By adding these style components as parameters, they can mathematically "suck out" their influence and isolate the core model capabilities.This extends beyond just style - they can control for any measurable factor: "What if I want to look at the cost adjusted performance? Parameter count? We can ex post facto measure that." This is one of the most interesting things about Arena: You have a data generation engine which you can clean and turn into leaderboards later. If you wanted to create a leaderboard for poetry writing, you could get existing data from Arena, normalize it by identifying these style components. Whether or not it's possible to really understand WHAT bias the voters have, that's a different question.Private EvalsOne of the most delicate challenges LMSYS faces is maintaining trust while collaborating with AI labs. The concern is that labs could game the system by testing multiple variants privately and only releasing the best performer. This was brought up when 4o-mini released and it ranked as the second best model on the leaderboard:But this fear misunderstands how Arena works. Unlike static benchmarks where selection bias is a major issue, Arena's live nature means any initial bias gets washed out by ongoing evaluation. As Anastasios explains: "In the long run, there's way more fresh data than there is data that was used to compare these five models." The other big question is WHAT model is actually being tested; as people often talk about on X / Discord, the same endpoint will randomly feel “nerfed” like it happened for “Claude European summer” and corresponding conspiracy theories:It's hard to keep track of these performance changes in Arena as these changes (if real…?) are not observable.The Future of EvaluationThe team's latest work on RouteLLM points to an interesting future where evaluation becomes more granular and task-specific. But they maintain that even simple routing strategies can be powerful - like directing complex queries to larger models while handling simple tasks with smaller ones.Arena is now going to expand beyond text into multimodal evaluation and specialized domains like code execution and red teaming. But their core insight remains: the best way to evaluate intelligence isn't to simplify it into metrics, but to embrace its complexity and find rigorous ways to analyze it. To go after this vision, they are spinning out Arena from LMSys, which will stay as an academia-driven group at Berkeley.Full Video PodcastChapters* 00:00:00 - Introductions* 00:01:16 - Origin and development of Chatbot Arena* 00:05:41 - Static benchmarks vs. Arenas* 00:09:03 - Community building* 00:13:32 - Biases in human preference evaluation* 00:18:27 - Style Control and Model Categories* 00:26:06 - Impact of o1* 00:29:15 - Collaborating with AI labs* 00:34:51 - RouteLLM and router models* 00:38:09 - Future of LMSys / ArenaShow Notes* Anastasios Angelopoulos* Anastasios' NeurIPS Paper Conformal Risk Control* Wei-Lin Chiang* Chatbot Arena* LMSys* MTBench* ShareGPT dataset* Stanford's Alpaca project* LLMRouter* E2B* DreadnodeTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, Partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:14]: Hey, and today we're very happy and excited to welcome Anastasios and Wei Lin from LMSys. Welcome guys.Wei Lin [00:00:21]: Hey, how's it going? Nice to see you.Anastasios [00:00:23]: Thanks for having us.Swyx [00:00:24]: Anastasios, I actually saw you, I think at last year's NeurIPS. You were presenting a paper, which I don't really super understand, but it was some theory paper about how your method was very dominating over other sort of search methods. I don't remember what it was, but I remember that you were a very confident speaker.Anastasios [00:00:40]: Oh, I totally remember you. Didn't ever connect that, but yes, that's definitely true. Yeah. Nice to see you again.Swyx [00:00:46]: Yeah. I was frantically looking for the name of your paper and I couldn't find it. Basically I had to cut it because I didn't understand it.Anastasios [00:00:51]: Is this conformal PID control or was this the online control?Wei Lin [00:00:55]: Blast from the past, man.Swyx [00:00:57]: Blast from the past. It's always interesting how NeurIPS and all these academic conferences are sort of six months behind what people are actually doing, but conformal risk control, I would recommend people check it out. I have the recording. I just never published it just because I was like, I don't understand this enough to explain it.Anastasios [00:01:14]: People won't be interested.Wei Lin [00:01:15]: It's all good.Swyx [00:01:16]: But ELO scores, ELO scores are very easy to understand. You guys are responsible for the biggest revolution in language model benchmarking in the last few years. Maybe you guys want to introduce yourselves and maybe tell a little bit of the brief history of LMSysWei Lin [00:01:32]: Hey, I'm Wei Lin. I'm a fifth year PhD student at UC Berkeley, working on Chatbot Arena these days, doing crowdsourcing AI benchmarking.Anastasios [00:01:43]: I'm Anastasios. I'm a sixth year PhD student here at Berkeley. I did most of my PhD on like theoretical statistics and sort of foundations of model evaluation and testing. And now I'm working 150% on this Chatbot Arena stuff. It's great.Alessio [00:02:00]: And what was the origin of it? How did you come up with the idea? How did you get people to buy in? And then maybe what were one or two of the pivotal moments early on that kind of made it the standard for these things?Wei Lin [00:02:12]: Yeah, yeah. Chatbot Arena project was started last year in April, May, around that. Before that, we were basically experimenting in a lab how to fine tune a chatbot open source based on the Llama 1 model that I released. At that time, Lama 1 was like a base model and people didn't really know how to fine tune it. So we were doing some explorations. We were inspired by Stanford's Alpaca project. So we basically, yeah, grow a data set from the internet, which is called ShareGPT data set, which is like a dialogue data set between user and chat GPT conversation. It turns out to be like pretty high quality data, dialogue data. So we fine tune on it and then we train it and release the model called V2. And people were very excited about it because it kind of like demonstrate open way model can reach this conversation capability similar to chat GPT. And then we basically release the model with and also build a demo website for the model. People were very excited about it. But during the development, the biggest challenge to us at the time was like, how do we even evaluate it? How do we even argue this model we trained is better than others? And then what's the gap between this open source model that other proprietary offering? At that time, it was like GPT-4 was just announced and it's like Cloud One. What's the difference between them? And then after that, like every week, there's a new model being fine tuned, released. So even until still now, right? And then we have that demo website for V2 now. And then we thought like, okay, maybe we can add a few more of the model as well, like API model as well. And then we quickly realized that people need a tool to compare between different models. So we have like a side by side UI implemented on the website to that people choose, you know, compare. And we quickly realized that maybe we can do something like, like a battle on top of ECLMs, like just anonymize it, anonymize the identity, and that people vote which one is better. So the community decides which one is better, not us, not us arguing, you know, our model is better or what. And that turns out to be like, people are very excited about this idea. And then we tweet, we launch, and that's, yeah, that's April, May. And then it was like first two, three weeks, like just a few hundred thousand views tweet on our launch tweets. And then we have regularly double update weekly, beginning at a time, adding new model GPT-4 as well. So it was like, that was the, you know, the initial.Anastasios [00:04:58]: Another pivotal moment, just to jump in, would be private models, like the GPT, I'm a little,Wei Lin [00:05:04]: I'm a little chatty. That was this year. That was this year.Anastasios [00:05:07]: Huge.Wei Lin [00:05:08]: That was also huge.Alessio [00:05:09]: In the beginning, I saw the initial release was May 3rd of the beta board. On April 6, we did a benchmarks 101 episode for a podcast, just kind of talking about, you know, how so much of the data is like in the pre-training corpus and blah, blah, blah. And like the benchmarks are really not what we need to evaluate whether or not a model is good. Why did you not make a benchmark? Maybe at the time, you know, it was just like, Hey, let's just put together a whole bunch of data again, run a, make a score that seems much easier than coming out with a whole website where like users need to vote. Any thoughts behind that?Wei Lin [00:05:41]: I think it's more like fundamentally, we don't know how to automate this kind of benchmarks when it's more like, you know, conversational, multi-turn, and more open-ended task that may not come with a ground truth. So let's say if you ask a model to help you write an email for you for whatever purpose, there's no ground truth. How do you score them? Or write a story or a creative story or many other things like how we use ChatterBee these days. It's more open-ended. You know, we need human in the loop to give us feedback, which one is better. And I think nuance here is like, sometimes it's also hard for human to give the absolute rating. So that's why we have this kind of pairwise comparison, easier for people to choose which one is better. So from that, we use these pairwise comparison, those to calculate the leaderboard. Yeah. You can add more about this methodology.Anastasios [00:06:40]: Yeah. I think the point is that, and you guys probably also talked about this at some point, but static benchmarks are intrinsically, to some extent, unable to measure generative model performance. And the reason is because you cannot pre-annotate all the outputs of a generative model. You change the model, it's like the distribution of your data is changing. New labels to deal with that. New labels are great automated labeling, right? Which is why people are pursuing both. And yeah, static benchmarks, they allow you to zoom in to particular types of information like factuality, historical facts. We can build the best benchmark of historical facts, and we will then know that the model is great at historical facts. But ultimately, that's not the only axis, right? And we can build 50 of them, and we can evaluate 50 axes. But it's just so, the problem of generative model evaluation is just so expansive, and it's so subjective, that it's just maybe non-intrinsically impossible, but at least we don't see a way. We didn't see a way of encoding that into a fixed benchmark.Wei Lin [00:07:47]: But on the other hand, I think there's a challenge where this kind of online dynamic benchmark is more expensive than static benchmark, offline benchmark, where people still need it. Like when they build models, they need static benchmark to track where they are.Anastasios [00:08:03]: It's not like our benchmark is uniformly better than all other benchmarks, right? It just measures a different kind of performance that has proved to be useful.Swyx [00:08:14]: You guys also published MTBench as well, which is a static version, let's say, of Chatbot Arena, right? That people can actually use in their development of models.Wei Lin [00:08:25]: Right. I think one of the reasons we still do this static benchmark, we still wanted to explore, experiment whether we can automate this, because people, eventually, model developers need it to fast iterate their model. So that's why we explored LM as a judge, and ArenaHard, trying to filter, select high-quality data we collected from Chatbot Arena, the high-quality subset, and use that as a question and then automate the judge pipeline, so that people can quickly get high-quality signal, benchmark signals, using this online benchmark.Swyx [00:09:03]: As a community builder, I'm curious about just the initial early days. Obviously when you offer effectively free A-B testing inference for people, people will come and use your arena. What do you think were the key unlocks for you? Was it funding for this arena? Was it marketing? When people came in, do you see a noticeable skew in the data? Which obviously now you have enough data sets, you can separate things out, like coding and hard prompts, but in the early days, it was just all sorts of things.Anastasios [00:09:31]: Yeah, maybe one thing to establish at first is that our philosophy has always been to maximize organic use. I think that really does speak to your point, which is, yeah, why do people come? They came to use free LLM inference, right? And also, a lot of users just come to the website to use direct chat, because you can chat with the model for free. And then you could think about it like, hey, let's just be kind of like more on the selfish or conservative or protectionist side and say, no, we're only giving credits for people that battle or so on and so forth. Strategy wouldn't work, right? Because what we're trying to build is like a big funnel, a big funnel that can direct people. And some people are passionate and interested and they battle. And yes, the distribution of the people that do that is different. It's like, as you're pointing out, it's like, that's not as they're enthusiastic.Wei Lin [00:10:24]: They're early adopters of this technology.Anastasios [00:10:27]: Or they like games, you know, people like this. And we've run a couple of surveys that indicate this as well, of our user base.Wei Lin [00:10:36]: We do see a lot of developers come to the site asking polling questions, 20-30%. Yeah, 20-30%.Anastasios [00:10:42]: It's obviously not reflective of the general population, but it's reflective of some corner of the world of people that really care. And to some extent, maybe that's all right, because those are like the power users. And you know, we're not trying to claim that we represent the world, right? We represent the people that come and vote.Swyx [00:11:02]: Did you have to do anything marketing-wise? Was anything effective? Did you struggle at all? Was it success from day one?Wei Lin [00:11:09]: At some point, almost done. Okay. Because as you can imagine, this leaderboard depends on community engagement participation. If no one comes to vote tomorrow, then no leaderboard.Anastasios [00:11:23]: So we had some period of time when the number of users was just, after the initial launch, it went lower. Yeah. And, you know, at some point, it did not look promising. Actually, I joined the project a couple months in to do the statistical aspects, right? As you can imagine, that's how it kind of hooked into my previous work. At that time, it wasn't like, you know, it definitely wasn't clear that this was like going to be the eval or something. It was just like, oh, this is a cool project. Like Wayland seems awesome, you know, and that's it.Wei Lin [00:11:56]: Definitely. There's in the beginning, because people don't know us, people don't know what this is for. So we had a hard time. But I think we were lucky enough that we have some initial momentum. And as well as the competition between model providers just becoming, you know, became very intense. Intense. And then that makes the eval onto us, right? Because always number one is number one.Anastasios [00:12:23]: There's also an element of trust. Our main priority in everything we do is trust. We want to make sure we're doing everything like all the I's are dotted and the T's are crossed and nobody gets unfair treatment and people can see from our profiles and from our previous work and from whatever, you know, we're trustworthy people. We're not like trying to make a buck and we're not trying to become famous off of this or that. It's just, we're trying to provide a great public leaderboard community venture project.Wei Lin [00:12:51]: Yeah.Swyx [00:12:52]: Yes. I mean, you are kind of famous now, you know, that's fine. Just to dive in more into biases and, you know, some of this is like statistical control. The classic one for human preference evaluation is humans demonstrably prefer longer contexts or longer outputs, which is actually something that we don't necessarily want. You guys, I think maybe two months ago put out some length control studies. Apart from that, there are just other documented biases. Like, I'd just be interested in your review of what you've learned about biases and maybe a little bit about how you've controlled for them.Anastasios [00:13:32]: At a very high level, yeah. Humans are biased. Totally agree. Like in various ways. It's not clear whether that's good or bad, you know, we try not to make value judgments about these things. We just try to describe them as they are. And our approach is always as follows. We collect organic data and then we take that data and we mine it to get whatever insights we can get. And, you know, we have many millions of data points that we can now use to extract insights from. Now, one of those insights is to ask the question, what is the effect of style, right? You have a bunch of data, you have votes, people are voting either which way. We have all the conversations. We can say what components of style contribute to human preference and how do they contribute? Now, that's an important question. Why is that an important question? It's important because some people want to see which model would be better if the lengths of the responses were the same, were to be the same, right? People want to see the causal effect of the model's identity controlled for length or controlled for markdown, number of headers, bulleted lists, is the text bold? Some people don't, they just don't care about that. The idea is not to impose the judgment that this is not important, but rather to say ex post facto, can we analyze our data in a way that decouples all the different factors that go into human preference? Now, the way we do this is via statistical regression. That is to say the arena score that we show on our leaderboard is a particular type of linear model, right? It's a linear model that takes, it's a logistic regression that takes model identities and fits them against human preference, right? So it regresses human preference against model identity. What you get at the end of that logistic regression is a parameter vector of coefficients. And when the coefficient is large, it tells you that GPT 4.0 or whatever, very large coefficient, that means it's strong. And that's exactly what we report in the table. It's just the predictive effect of the model identity on the vote. The other thing that you can do is you can take that vector, let's say we have M models, that is an M dimensional vector of coefficients. What you can do is you say, hey, I also want to understand what the effect of length is. So I'll add another entry to that vector, which is trying to predict the vote, right? That tells me the difference in length between two model responses. So we have that for all of our data. We can compute it ex post facto. We added it into the regression and we look at that predictive effect. And then the idea, and this is formally true under certain conditions, not always verifiable ones, but the idea is that adding that extra coefficient to this vector will kind of suck out the predictive power of length and put it into that M plus first coefficient and quote, unquote, de-bias the rest so that the effect of length is not included. And that's what we do in style control. Now we don't just do it for M plus one. We have, you know, five, six different style components that have to do with markdown headers and bulleted lists and so on that we add here. Now, where is this going? You guys see the idea. It's a general methodology. If you have something that's sort of like a nuisance parameter, something that exists and provides predictive value, but you really don't want to estimate that. You want to remove its effect. In causal inference, these things are called like confounders often. What you can do is you can model the effect. You can put them into your model and try to adjust for them. So another one of those things might be cost. You know, what if I want to look at the cost adjusted performance of my model, which models are punching above their weight, parameter count, which models are punching above their weight in terms of parameter count, we can ex post facto measure that. We can do it without introducing anything that compromises the organic nature of theWei Lin [00:17:17]: data that we collect.Anastasios [00:17:18]: Hopefully that answers the question.Wei Lin [00:17:20]: It does.Swyx [00:17:21]: So I guess with a background in econometrics, this is super familiar.Anastasios [00:17:25]: You're probably better at this than me for sure.Swyx [00:17:27]: Well, I mean, so I used to be, you know, a quantitative trader and so, you know, controlling for multiple effects on stock price is effectively the job. So it's interesting. Obviously the problem is proving causation, which is hard, but you don't have to do that.Anastasios [00:17:45]: Yes. Yes, that's right. And causal inference is a hard problem and it goes beyond statistics, right? It's like you have to build the right causal model and so on and so forth. But we think that this is a good first step and we're sort of looking forward to learning from more people. You know, there's some good people at Berkeley that work on causal inference for the learning from them on like, what are the really most contemporary techniques that we can use in order to estimate true causal effects if possible.Swyx [00:18:10]: Maybe we could take a step through the other categories. So style control is a category. It is not a default. I have thought that when you wrote that blog post, actually, I thought it would be the new default because it seems like the most obvious thing to control for. But you also have other categories, you have coding, you have hard prompts. We consider that.Anastasios [00:18:27]: We're still actively considering it. It's just, you know, once you make that step, once you take that step, you're introducing your opinion and I'm not, you know, why should our opinion be the one? That's kind of a community choice. We could put it to a vote.Wei Lin [00:18:39]: We could pass.Anastasios [00:18:40]: Yeah, maybe do a poll. Maybe do a poll.Swyx [00:18:42]: I don't know. No opinion is an opinion.Wei Lin [00:18:44]: You know what I mean?Swyx [00:18:45]: Yeah.Wei Lin [00:18:46]: There's no neutral choice here.Swyx [00:18:47]: Yeah. You have all these others. You have instruction following too. What are your favorite categories that you like to talk about? Maybe you tell a little bit of the stories, tell a little bit of like the hard choices that you had to make.Wei Lin [00:18:57]: Yeah. Yeah. Yeah. I think the, uh, initially the reason why we want to add these new categories is essentially to answer some of the questions from our community, which is we won't have a single leaderboard for everything. So these models behave very differently in different domains. Let's say this model is trend for coding, this model trend for more technical questions and so on. On the other hand, to answer people's question about like, okay, what if all these low quality, you know, because we crowdsource data from the internet, there will be noise. So how do we de-noise? How do we filter out these low quality data effectively? So that was like, you know, some questions we want to answer. So basically we spent a few months, like really diving into these questions to understand how do we filter all these data because these are like medias of data points. And then if you want to re-label yourself, it's possible, but we need to kind of like to automate this kind of data classification pipeline for us to effectively categorize them to different categories, say coding, math, structure, and also harder problems. So that was like, the hope is when we slice the data into these meaningful categories to give people more like better signals, more direct signals, and that's also to clarify what we are actually measuring for, because I think that's the core part of the benchmark. That was the initial motivation. Does that make sense?Anastasios [00:20:27]: Yeah. Also, I'll just say, this does like get back to the point that the philosophy is to like mine organic, to take organic data and then mine it x plus factor.Alessio [00:20:35]: Is the data cage-free too, or just organic?Anastasios [00:20:39]: It's cage-free.Wei Lin [00:20:40]: No GMO. Yeah. And all of these efforts are like open source, like we open source all of the data cleaning pipeline, filtering pipeline. Yeah.Swyx [00:20:50]: I love the notebooks you guys publish. Actually really good just for learning statistics.Wei Lin [00:20:54]: Yeah. I'll share this insights with everyone.Alessio [00:20:59]: I agree on the initial premise of, Hey, writing an email, writing a story, there's like no ground truth. But I think as you move into like coding and like red teaming, some of these things, there's like kind of like skill levels. So I'm curious how you think about the distribution of skill of the users. Like maybe the top 1% of red teamers is just not participating in the arena. So how do you guys think about adjusting for it? And like feels like this where there's kind of like big differences between the average and the top. Yeah.Anastasios [00:21:29]: Red teaming, of course, red teaming is quite challenging. So, okay. Moving back. There's definitely like some tasks that are not as subjective that like pairwise human preference feedback is not the only signal that you would want to measure. And to some extent, maybe it's useful, but it may be more useful if you give people better tools. For example, it'd be great if we could execute code with an arena, be fantastic.Wei Lin [00:21:52]: We want to do it.Anastasios [00:21:53]: There's also this idea of constructing a user leaderboard. What does that mean? That means some users are better than others. And how do we measure that? How do we quantify that? Hard in chatbot arena, but where it is easier is in red teaming, because in red teaming, there's an explicit game. You're trying to break the model, you either win or you lose. So what you can do is you can say, Hey, what's really happening here is that the models and humans are playing a game against one another. And then you can use the same sort of Bradley Terry methodology with some, some extensions that we came up with in one of you can read one of our recent blog posts for, for the sort of theoretical extensions. You can attribute like strength back to individual players and jointly attribute strength to like the models that are in this jailbreaking game, along with the target tasks, like what types of jailbreaks you want.Wei Lin [00:22:44]: So yeah.Anastasios [00:22:45]: And I think that this is, this is a hugely important and interesting avenue that we want to continue researching. We have some initial ideas, but you know, all thoughts are welcome.Wei Lin [00:22:54]: Yeah.Alessio [00:22:55]: So first of all, on the code execution, the E2B guys, I'm sure they'll be happy to helpWei Lin [00:22:59]: you.Alessio [00:23:00]: I'll please set that up. They're big fans. We're investors in a company called Dreadnought, which we do a lot in AI red teaming. I think to me, the most interesting thing has been, how do you do sure? Like the model jailbreak is one side. We also had Nicola Scarlini from DeepMind on the podcast, and he was talking about, for example, like, you know, context stealing and like a weight stealing. So there's kind of like a lot more that goes around it. I'm curious just how you think about the model and then maybe like the broader system, even with Red Team Arena, you're just focused on like jailbreaking of the model, right? You're not doing kind of like any testing on the more system level thing of the model where like, maybe you can get the training data back, you're going to exfiltrate some of the layers and the weights and things like that.Wei Lin [00:23:43]: So right now, as you can see, the Red Team Arena is at a very early stage and we are still exploring what could be the potential new games we can introduce to the platform. So the idea is still the same, right? And we build a community driven project platform for people. They can have fun with this website, for sure. That's one thing, and then help everyone to test these models. So one of the aspects you mentioned is stealing secrets, stealing training sets. That could be one, you know, it could be designed as a game. Say, can you still use their credential, you know, we hide, maybe we can hide the credential into system prompts and so on. So there are like a few potential ideas we want to explore for sure. Do you want to add more?Anastasios [00:24:28]: I think that this is great. This idea is a great one. There's a lot of great ideas in the Red Teaming space. You know, I'm not personally like a Red Teamer. I don't like go around and Red Team models, but there are people that do that and they're awesome. They're super skilled. When I think about the Red Team arena, I think those are really the people that we're building it for. Like, we want to make them excited and happy, build tools that they like. And just like chatbot arena, we'll trust that this will end up being useful for the world. And all these people are, you know, I won't say all these people in this community are actually good hearted, right? They're not doing it because they want to like see the world burn. They're doing it because they like, think it's fun and cool. And yeah. Okay. Maybe they want to see, maybe they want a little bit.Wei Lin [00:25:13]: I don't know. Majority.Anastasios [00:25:15]: Yeah.Wei Lin [00:25:16]: You know what I'm saying.Anastasios [00:25:17]: So, you know, trying to figure out how to serve them best, I think, I don't know where that fits. I just, I'm not expressing. And give them credits, right?Wei Lin [00:25:24]: And give them credit.Anastasios [00:25:25]: Yeah. Yeah. So I'm not trying to express any particular value judgment here as to whether that's the right next step. It's just, that's sort of the way that I think we would think about it.Swyx [00:25:35]: Yeah. We also talked to Sander Schulhoff of the HackerPrompt competition, and he's pretty interested in Red Teaming at scale. Let's just call it that. You guys maybe want to talk with him.Wei Lin [00:25:45]: Oh, nice.Swyx [00:25:46]: We wanted to cover a little, a few topical things and then go into the other stuff that your group is doing. You know, you're not just running Chatbot Arena. We can also talk about the new website and your future plans, but I just wanted to briefly focus on O1. It is the hottest, latest model. Obviously, you guys already have it on the leaderboard. What is the impact of O1 on your evals?Wei Lin [00:26:06]: Made our interface slower.Anastasios [00:26:07]: It made it slower.Swyx [00:26:08]: Yeah.Wei Lin [00:26:10]: Because it needs like 30, 60 seconds, sometimes even more to, the latency is like higher. So that's one. Sure. But I think we observe very interesting things from this model as well. Like we observe like significant improvement in certain categories, like more technical or math. Yeah.Anastasios [00:26:32]: I think actually like one takeaway that was encouraging is that I think a lot of people before the O1 release were thinking, oh, like this benchmark is saturated. And why were they thinking that? They were thinking that because there was a bunch of models that were kind of at the same level. They were just kind of like incrementally competing and it sort of wasn't immediately obvious that any of them were any better. Nobody, including any individual person, it's hard to tell. But what O1 did is it was, it's clearly a better model for certain tasks. I mean, I used it for like proving some theorems and you know, there's some theorems that like only I know because I still do a little bit of theory. Right. So it's like, I can go in there and ask like, oh, how would you prove this exact thing? Which I can tell you has never been in the public domain. It'll do it. It's like, what?Wei Lin [00:27:19]: Okay.Anastasios [00:27:20]: So there's this model and it crushed the benchmark. You know, it's just like really like a big gap. And what that's telling us is that it's not saturated yet. It's still measuring some signal. That was encouraging. The point, the takeaway is that the benchmark is comparative. There's no absolute number. There's no maximum ELO. It's just like, if you're better than the rest, then you win. I think that was actually quite helpful to us.Swyx [00:27:46]: I think people were criticizing, I saw some of the academics criticizing it as not apples to apples. Right. Like, because it can take more time to reason, it's basically doing some search, doing some chain of thought that if you actually let the other models do that same thing, they might do better.Wei Lin [00:28:03]: Absolutely.Anastasios [00:28:04]: To be clear, none of the leaderboard currently is apples to apples because you have like Gemini Flash, you have, you know, all sorts of tiny models like Lama 8B, like 8B and 405B are not apples to apples.Wei Lin [00:28:19]: Totally agree. They have different latencies.Anastasios [00:28:21]: Different latencies.Wei Lin [00:28:22]: Control for latency. Yeah.Anastasios [00:28:24]: Latency control. That's another thing. We can do style control, but latency control. You know, things like this are important if you want to understand the trade-offs involved in using AI.Swyx [00:28:34]: O1 is a developing story. We still haven't seen the full model yet, but it's definitely a very exciting new paradigm. I think one community controversy I just wanted to give you guys space to address is the collaboration between you and the large model labs. People have been suspicious, let's just say, about how they choose to A-B test on you. I'll state the argument and let you respond, which is basically they run like five anonymous models and basically argmax their Elo on LMSYS or chatbot arena, and they release the best one. Right? What has been your end of the controversy? How have you decided to clarify your policy going forward?Wei Lin [00:29:15]: On a high level, I think our goal here is to build a fast eval for everyone, and including everyone in the community can see the data board and understand, compare the models. More importantly, I think we want to build the best eval also for model builders, like all these frontier labs building models. They're also internally facing a challenge, which is how do they eval the model? That's the reason why we want to partner with all the frontier lab people, and then to help them testing. That's one of the... We want to solve this technical challenge, which is eval. Yeah.Anastasios [00:29:54]: I mean, ideally, it benefits everyone, right?Wei Lin [00:29:56]: Yeah.Anastasios [00:29:57]: And people also are interested in seeing the leading edge of the models. People in the community seem to like that. Oh, there's a new model up. Is this strawberry? People are excited. People are interested. Yeah. And then there's this question that you bring up of, is it actually causing harm?Wei Lin [00:30:15]: Right?Anastasios [00:30:16]: Is it causing harm to the benchmark that we are allowing this private testing to happen? Maybe stepping back, why do you have that instinct? The reason why you and others in the community have that instinct is because when you look at something like a benchmark, like an image net, a static benchmark, what happens is that if I give you a million different models that are all slightly different, and I pick the best one, there's something called selection bias that plays in, which is that the performance of the winning model is overstated. This is also sometimes called the winner's curse. And that's because statistical fluctuations in the evaluation, they're driving which model gets selected as the top. So this selection bias can be a problem. Now there's a couple of things that make this benchmark slightly different. So first of all, the selection bias that you include when you're only testing five models is normally empirically small.Wei Lin [00:31:12]: And that's why we have these confidence intervals constructed.Anastasios [00:31:16]: That's right. Yeah. Our confidence intervals are actually not multiplicity adjusted. One thing that we could do immediately tomorrow in order to address this concern is if a model provider is testing five models and they want to release one, and we're constructing the models at level one minus alpha, we can just construct the intervals instead at level one minus alpha divided by five. That's called Bonferroni correction. What that'll tell you is that the final performance of the model, the interval that gets constructed, is actually formally correct. We don't do that right now, partially because we know from simulations that the amount of selection bias you incur with these five things is just not huge. It's not huge in comparison to the variability that you get from just regular human voters. So that's one thing. But then the second thing is the benchmark is live, right? So what ends up happening is it'll be a small magnitude, but even if you suffer from the winner's curse after testing these five models, what'll happen is that over time, because we're getting new data, it'll get adjusted down. So if there's any bias that gets introduced at that stage, in the long run, it actually doesn't matter. Because asymptotically, basically in the long run, there's way more fresh data than there is data that was used to compare these five models against these private models.Swyx [00:32:35]: The announcement effect is only just the first phase and it has a long tail.Anastasios [00:32:39]: Yeah, that's right. And it sort of like automatically corrects itself for this selection adjustment.Swyx [00:32:45]: Every month, I do a little chart of Ellim's ELO versus cost, just to track the price per dollar, the amount of like, how much money do I have to pay for one incremental point in ELO? And so I actually observe an interesting stability in most of the ELO numbers, except for some of them. For example, GPT-4-O August has fallen from 12.90

Darshan H. Brahmbhatt, Podcast Editor of JACC: Advances, discusses a recently published original research paper about a novel echocardiographic parameter to confirm low-gradient aortic stenosis severity.

Persönlichkeitstests sind in Personalabteilungen sehr beliebt. Aber wie verlässlich und sinnvoll sind diese Tests? Antworten gibt Wirtschafts- und Personalpsychologe Rüdiger Hossiep. (00:00:00) Deutschland – ein halbes Leben (00:02:36) Begrüßung Rüdiger Hossiep (00:03:01) Qualität aktuell genutzter Persönlichkeitstests (00:06:22) Warum werden Persönlichkeitstests trotzdem oft benutzt? (00:08:39) Die richtigen Parameter (00:10:28) Myers-Briggs-Typenindikator (00:13:41) Warum die Beliebtheit dieses Tests? (00:14:23) Wie könnten bessere Tests aussehen? (00:18:33) Big Five (00:20:11) Bringt das denn dann überhaupt was? Hier entlang geht's zu den Links unserer Werbepartner: https://detektor.fm/werbepartner/brand-eins-podcast Hier geht's zur aktuellen brand eins: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen Hier findet ihr den Artikel von Yves Bellinghausen im brand eins Magazin 09-2024 „Sie lächeln zu wenig!“: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen/persoenlichkeitstests-sie-laecheln-zu-wenig Hier geht es zum Podcast: „Deutschland – ein halbes Leben. 35 Jahre nach dem Mauerfall“: https://detektor.fm/gesellschaft/podcast-deutschland-ein-halbes-leben-startet Hier geht es zur Podcastfolge über KI in Personalabteilungen mit Niels van Quakebeke: https://detektor.fm/wirtschaft/brand-eins-podcast-niels-van-quaquebeke-fuehrung-ki?highlight=niels%20van%20Quakebeke Und hier findet ihr die Podcastfolgen zum Schwerpunkt „Messen und Vergleichen“: https://detektor.fm/wirtschaft/brand-eins-podcast-ulrich-wagner https://detektor.fm/wirtschaft/brand-eins-podcast-ulrike-scheffer https://detektor.fm/wirtschaft/brand-eins-podcast-niels-rot-bildung https://detektor.fm/wirtschaft/brand-eins-podcast-daniel-memmert >> Artikel zum Nachlesen: https://detektor.fm/wirtschaft/brand-eins-podcast-ruediger-hossiep-persoenlichkeitstests

Persönlichkeitstests sind in Personalabteilungen sehr beliebt. Aber wie verlässlich und sinnvoll sind diese Tests? Antworten gibt Wirtschafts- und Personalpsychologe Rüdiger Hossiep. (00:00:00) Deutschland – ein halbes Leben (00:02:36) Begrüßung Rüdiger Hossiep (00:03:01) Qualität aktuell genutzter Persönlichkeitstests (00:06:22) Warum werden Persönlichkeitstests trotzdem oft benutzt? (00:08:39) Die richtigen Parameter (00:10:28) Myers-Briggs-Typenindikator (00:13:41) Warum die Beliebtheit dieses Tests? (00:14:23) Wie könnten bessere Tests aussehen? (00:18:33) Big Five (00:20:11) Bringt das denn dann überhaupt was? Hier entlang geht's zu den Links unserer Werbepartner: https://detektor.fm/werbepartner/brand-eins-podcast Hier geht's zur aktuellen brand eins: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen Hier findet ihr den Artikel von Yves Bellinghausen im brand eins Magazin 09-2024 „Sie lächeln zu wenig!“: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen/persoenlichkeitstests-sie-laecheln-zu-wenig Hier geht es zum Podcast: „Deutschland – ein halbes Leben. 35 Jahre nach dem Mauerfall“: https://detektor.fm/gesellschaft/podcast-deutschland-ein-halbes-leben-startet Hier geht es zur Podcastfolge über KI in Personalabteilungen mit Niels van Quakebeke: https://detektor.fm/wirtschaft/brand-eins-podcast-niels-van-quaquebeke-fuehrung-ki?highlight=niels%20van%20Quakebeke Und hier findet ihr die Podcastfolgen zum Schwerpunkt „Messen und Vergleichen“: https://detektor.fm/wirtschaft/brand-eins-podcast-ulrich-wagner https://detektor.fm/wirtschaft/brand-eins-podcast-ulrike-scheffer https://detektor.fm/wirtschaft/brand-eins-podcast-niels-rot-bildung https://detektor.fm/wirtschaft/brand-eins-podcast-daniel-memmert >> Artikel zum Nachlesen: https://detektor.fm/wirtschaft/brand-eins-podcast-ruediger-hossiep-persoenlichkeitstests

Persönlichkeitstests sind in Personalabteilungen sehr beliebt. Aber wie verlässlich und sinnvoll sind diese Tests? Antworten gibt Wirtschafts- und Personalpsychologe Rüdiger Hossiep. (00:00:00) Deutschland – ein halbes Leben (00:02:36) Begrüßung Rüdiger Hossiep (00:03:01) Qualität aktuell genutzter Persönlichkeitstests (00:06:22) Warum werden Persönlichkeitstests trotzdem oft benutzt? (00:08:39) Die richtigen Parameter (00:10:28) Myers-Briggs-Typenindikator (00:13:41) Warum die Beliebtheit dieses Tests? (00:14:23) Wie könnten bessere Tests aussehen? (00:18:33) Big Five (00:20:11) Bringt das denn dann überhaupt was? Hier entlang geht's zu den Links unserer Werbepartner: https://detektor.fm/werbepartner/brand-eins-podcast Hier geht's zur aktuellen brand eins: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen Hier findet ihr den Artikel von Yves Bellinghausen im brand eins Magazin 09-2024 „Sie lächeln zu wenig!“: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2024/messen-vergleichen/persoenlichkeitstests-sie-laecheln-zu-wenig Hier geht es zum Podcast: „Deutschland – ein halbes Leben. 35 Jahre nach dem Mauerfall“: https://detektor.fm/gesellschaft/podcast-deutschland-ein-halbes-leben-startet Hier geht es zur Podcastfolge über KI in Personalabteilungen mit Niels van Quakebeke: https://detektor.fm/wirtschaft/brand-eins-podcast-niels-van-quaquebeke-fuehrung-ki?highlight=niels%20van%20Quakebeke Und hier findet ihr die Podcastfolgen zum Schwerpunkt „Messen und Vergleichen“: https://detektor.fm/wirtschaft/brand-eins-podcast-ulrich-wagner https://detektor.fm/wirtschaft/brand-eins-podcast-ulrike-scheffer https://detektor.fm/wirtschaft/brand-eins-podcast-niels-rot-bildung https://detektor.fm/wirtschaft/brand-eins-podcast-daniel-memmert >> Artikel zum Nachlesen: https://detektor.fm/wirtschaft/brand-eins-podcast-ruediger-hossiep-persoenlichkeitstests

8th ESC 2024: OCT Parameter Tracks Atherosclerosis Progression

This week I thought while I was on the road - it was time to revisit my conversation with Ted Kubaitis from 2022 in anticipation of Google sunsetting the parameter tool in Google Webmasters. Its particularly poignant given all the recent parameter issues and well, canonical if I'm being honest. Did everything turn out as Google planned? You can text Confessions - 512-222-3132 Check out my ⁠⁠⁠⁠HCS guide⁠⁠⁠⁠ Ep 22 - ⁠⁠⁠⁠What's Canonical Got To Do With It⁠⁠⁠⁠ You will want to read my write up on what I observed in various sites that were decimated by the Sept HCU. ⁠⁠⁠⁠⁠⁠⁠⁠⁠http://fixthedamncanonical.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ Its set to NAME YOUR OWN PRICE on Gumroad - so if you can't afford even $10, you can get it for $0. Shameless Self Promotion ⁠⁠⁠⁠⁠⁠⁠⁠⁠Confessions of an SEO - On Semrush's Top 10 SEO Podcasts ⁠⁠⁠⁠⁠⁠⁠⁠⁠ Some tools I use and recommend: Cora Software ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/confessionscora⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ On page analysis tool - use it every day. GSC Tool - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/gsctool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ A chrome extension that helps me with repetitive tasks in search console and has a connector to the Google Indexing API so it doesn't go through 3rd parties. Rank Week - ⁠⁠⁠⁠⁠⁠https://bit.ly/conc-s-rankweek⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For those who read these things - please click below - it's a short link to the Confessions of An SEO Knowledge Panel - Thank you for your click!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Confessions of An SEO⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Wherein Frank dreams of Israeli space lasers and Chris ponders flat earth trolls.

Some of you might have heard of the '3 Parameter Weibull distribution.' But what makes this different and (sometimes) helpful? The post 3 Parameter Weibull Analysis appeared first on Accendo Reliability.

Scott and Wes break down the importance of sitemaps for SEO. They dive into the different file formats, essential fields, and common pitfalls to avoid when creating and submitting your sitemap to search engines. Show Notes 00:00 Welcome to Syntax! New Swag Coming Soon. Tolin.ski. 02:09 Brought to you by Sentry.io. 02:39 Sitemaps. 04:36 Why do you need a sitemap? 06:34 Sitemap file formats. 08:31 Choosing a file format. 09:17 Site map file size limits. 10:32 Fully-qualified URLs. 10:50 Fields and metadata. 10:58 What fields matter for SEO? 14:20 What to avoid in your sitemap? 14:23 Dynamic user pages and account pages. 14:43 Parameter-based URLs. 16:01 Redirects, duplicates or disallowed pages. 16:11 Unlisted pages. 17:07 How do you make a sitemap? 20:06 What to do once you have a sitemap. 20:08 Validation. 20:20 Submit to search engine. Bing Webmaster Tools. Google Search Console. 22:11 Cache them! Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Different senses in which two AIs can be "the same", published by Vivek Hebbar on June 24, 2024 on The AI Alignment Forum. Sometimes people talk about two AIs being "the same" or "different" AIs. We think the intuitive binary of "same vs. different" conflates several concepts which are often better to disambiguate. In this post, we spell out some of these distinctions. We don't think anything here is particularly novel; we wrote this post because we think it's probably mildly helpful for people to think this through, and because Buck fairly often wants to reference it. Some example distinctions: In a debate protocol[1], people often refer to the judge and debaters as three "different" AIs because their tokens are subject to different training incentives. But you could also say that the two debaters are "the same AI" because their prompts and context look similar. Or you could say that all three are the "same AI" because they were fine-tuned from the same weights. In a control setting, an untrusted monitor might be fine-tuned from the same base model as the model it's monitoring, leaving it intuitively unclear whether they are "the same AI". We think that it's often important to be able to distinguish different senses of "personal identity" when discussing various issues in AI safety, especially issues related to collusion. We frame each sense of personal identity as telling us whether two actions[2] belong to the "same AI" or "different AIs". Here are the senses we distinguish in this post: 1. Model weights: Were the two actions produced by two different models? 1. Pretrain identity: Models are different if they come from separate pretraining runs 2. Parameter identity: Models are different even if they are fine-tunings of the same pretrained model 2. Shared context: Do the two actions have shared LLM context? 3. Shared activations: Are the two actions downstream of shared activations? 4. Shared memory: In a scaffold, did the processes producing the two actions have access to a shared memory bank? 5. Shared reward: In an RL setting, do the actions share the same rewards? 6. Shared role in training: In protocols like debate, the training incentive can be different in nature for different classes of prompts/contexts. Do the two actions come from contexts in the same class? Model identity Model identity is based on which model parameters were run to produce a given action. "Pretrain identity" conflates all models finetuned from the same pretraining run. Whereas "parameter identity" requires the weights to be identical. Some reasons to care about model identity: The presence of issues such as scheming[3] is more correlated across models with shared training history Models with shared training history might have similar misaligned goals, making them more inclined to coordinate Similarities between their internal ontologies might make it easier to coordinate on Schelling signals or steganographically encode information Shared activations & shared context For "shared activations", we consider two actions to belong to the same AI when they are downstream of the same activations. For a deterministic[4] unidirectional transformer, two actions share activations if they are generated by the same model and their contexts share a common prefix. However, future architectures might break the close correspondence between shared activations and shared context. For example, consider this setup: An LLM sees [prompt 1], generates some [python code], then [prompt 2] is appended instructing the same LLM to critique the code for security flaws, and it generates [critique]. If the activations on the [prompt 1] tokens contain a signal saying that it's time for coordinated defection, the model can see those activations both while generating the code and while generating the critique. If we ins...

As men and fathers we have been given a dominion mandate, yet it can often feel like the territory we gain is lost to the enemy. In this powerful message, Ps. Samuel shows us several ways that we secure the parameter of our lives.

Kathleen Peters, chief innovation officer, leads innovation and business strategy for Experian's Decision Analytics in North America. Kathleen and her team are continuously looking for new ways to define product strategies, road maps and priorities for Experian's identity and fraud portfolio, analyzing industry trends and the latest technologies to bring innovative solutions to our clients. Kathleen joined Experian in 2013 to lead business development and international growth channels for the recently acquired 41st Parameter business in San Jose, California. She went on to lead product management for Experian's fraud and identity group within the global Decision Analytics organization, launching Experian's CrossCore® platform in 2016, a groundbreaking and award-winning offering for the fraud and identity market. The last two years, Kathleen has been named a “Top 100 Influencer in Identity” by One World Identity (OWI), an exclusive list that annually recognizes influencers and leaders from across the globe, showcasing a who's who of people to know in the identity space. -- Critical Mass Business Talk Show is Orange County, CA's longest-running business talk show, focused on offering value and insight to middle-market business leaders in the OC and beyond. Hosted by Ric Franzi, business partner at Renaissance Executive Forums Orange County. Learn more about Ric at www.ricfranzi.com. Catch up on past Critical Mass Business Talk Show interviews... YouTube: https://lnkd.in/gHKT2gmF LinkedIn: https://lnkd.in/g2PzRhjQ Podbean: https://lnkd.in/eWpNVRi Apple Podcasts: https://lnkd.in/gRd_863w Spotify: https://lnkd.in/gruexU6m #orangecountyca #mastermind #ceopeergroups #peergroups #peerlearning

(00:00) Zolak & Bertrand start the third hour by reacting to Sam Kennedy saying Craig Breslow is working within the parameters ownership has set. (11:44) We dive into Alex Cora's message to the team yesterday. (24:00) The crew questions whether the Red Sox could surprise people and the state of the fanbase. (33:16) We finish up the hour with thoughts on Tyron Smith and the Patriots.

KWR0035 – The Kingdom Warrior Kingdom War Room   Hosts:    Dr. Michael K. Lake:  Scholar-in-residence, Strategic Remnant Learning Center – BLA, Host of Biblical Life TV, Co-Host of the Kingdom Intelligence Briefing, and best-selling author.  http://www.kingdomintelligencebriefing.com   Dr. Mike Spaulding:  The teaching pastor of Calvary Chapel of Lima, OH, the author of Upsidedown in America, more than ten other books, and the host of Soaring Eagle Radio and Dr. Mike Live.  https://www.drmikespaulding.com/   Topic:   The Kingdom Warrior book by Dr. Michael K. Lake   In The Kingdom Priesthood, the Remnant discovered the biblical priesthood they had been called to and again entered that sacred service to Almighty God.  Now, it is time for the Remnant to learn how to fight for their King in the last days.   The Kingdom Warrior is a training manual for the Remnant in the art of war from a biblical and Kingdom perspective. Too many books have been written about spiritual warfare that is a patchwork of truth but leaves holes in the defenses of the believer. As the stakes are raised in the final conflict, it is time for the Kingdom warrior to understand a full-spectrum concept of spiritual warfare that is both offensive and defensive.   IN THIS BOOK YOU WILL DISCOVER   ·      The five Aspects of Grace ·      The Binary Nature of the Spirit Realm ·      An Unexpected Weapon of Mass Destruction (WMD)
in the Arsenal of the Believer ·      How to Expel the "ites" and Establish a Parameter of Safety for You and Your Family ·      How to Overcome the Psychological Warfare of the Enemy ·      How Winning the War Within Places You on Solid Footing to Fight the War Without ·      How Each Enemy has a Different Protocol for Engagement ·      And More   In this prophetic hour, God is calling the Remnant to fight with Holy Spirit-anointed precision, and with a passion that has been forged by the fire of God.