Podcasts about Regression

Gary and Jim discuss the Pirates recent ups and downs along with the fruition of 6 years of development, mainly, who gets better once they're in Pittsburgh who swings a stick? Learn more about your ad choices. Visit megaphone.fm/adchoices

If you've ever felt like talk therapy just wasn't getting to the root — this episode might open something up for you.Today I'm joined by Mariko Bangerter, regression therapist and founder of Mindsetting, to talk about a form of therapy that honestly blew my mind. Regression therapy goes deeper than the conscious mind — guiding you into a relaxed, altered state to access the subconscious, where so many of our unhealed wounds and limiting beliefs live.We talk about how past trauma gets stored not just in the mind, but in the body… and how revisiting old memories — even ones we didn't know were there — can release years of stuck emotion in just a single session. I even share a bit about my own experience working with Mariko, which felt almost psychedelic in how vividly I reconnected with forgotten parts of myself.If you've ever felt stuck, or like something in you is trying to heal but can't quite reach the surface — this might be the missing piece.Try Bettervits for yourself, head on over to bettervits.co.uk and get 15% off your 1st order with my code PANDORA15.Find Mariko:Instagram: @mindsetting.by.marikoWebsite: https://www.mindsetting.co.uk/Stay Connected with Hurt to Healing:Instagram: instagram.com/hurttohealingpodTikTok: tiktok.com/@hurttohealingpodLinkedIn: linkedin.com/company/hurt-to-healingSubstack: substack.com/@hurttohealingWebsite: hurttohealing.co.uk Hosted on Acast. See acast.com/privacy for more information.

In this Summer Coolers edition of NHL Wraparound, Neil Smith and Vic Morren head to the nation's capital to break down the Washington Capitals, a team coming off a quietly dominant 2024–25 regular season... and a playoff run that ended just as quietly.Washington finished with 111 points, 1st in the Metro, and 2nd overall in the NHL—yet fizzled in the second round against Carolina after dispatching Montreal in five games. But with no major offseason additions, the OV goal chase behind them, and multiple players coming off career seasons, is this team poised for a step forward—or a step back?

Maria och Helene diskuterar det utomjordiska. Medverkar: Maria Dupal, Helene Carlind Hosted on Acast. See acast.com/privacy for more information.

If you're curious as to if you have any stored emotions or trauma that might be causing your illness, preventing you from achieving your or even just showing up as the best, authentic version of yourself, I invite you to take my free Stored Emotions and Trauma QuizIn this episode, I sit down with Matt Schmidt, a professionally trained scientist who did his doctoral work developing drugs to treat central nervous system disorders and cancer. Even with all that medical knowledge, he was stuck, taking 14 medications, suffering seizures, and feeling depleted. One quantum healing session changed everything and set him on the path of becoming a metaphysical healer.We get into what a quantum healing session really looks like, the signs your body is preparing to release trauma, and how to know if you're ready for this kind of work. Our stories overlap in surprising ways, and we pull back the curtain on how science and the metaphysical don't have to be at odds; they can actually work together.You'll Learn:What is quantum healing, and what does a quantum healing session look like?Physical and emotional signs your body is getting ready to release trauma (shedding)The number one way to know if you're ready for this work The reason two scientists walked away from biomedicine into quantum healingWhat happens when 14 prescriptions and years of symptoms still don't bring reliefThe link between unresolved emotions and physical illnessThe damage of treating the body like a machine that just needs “fixing”Why integration matters after emotional release and how it anchors the healingThe real difference between masking symptoms and addressing root causesTimestamps:[00:00] Introduction[06:12] Matt's story of seizures, 14 medications, and hitting a wall[10:45] Discovering Dolores Cannon and the first quantum healing session[13:54] Letting go of a scientific identity and embracing new work[16:38] What a quantum healing session looks like in practice[22:41] Protecting energy and daily practices for staying clear[27:25] Out-of-body experiences and signs of trauma release[33:58] Layers of healing and the challenges of integration[42:31] The growing awareness of quantum healing in the mainstream[46:52] AI, intuition, and why discernment matters in healingResources Mentioned:Delores Cannon | WebsiteAaron Doughty | YouTubeMatt has lots of free informational videos on his YouTube you can check out.Be sure to visit his website and follow him on Instagram and Facebook.Find More From Dr. Stephanie Davis:Dr. Stephanie Davis | WebsiteQuantum Rx | InstagramQuantum Rx | Skool

Host Michael Rand starts with a bleak night during a bleak stretch for the Twins. They had a pitifully small crowd at Target Field. The woeful White Sox drubbed them 12-3. David Festa was shut down for the year. And 90 losses grows more likely every day. 10:00: Are the Vikings headed for a regression? 15:00: Randy Johnson on Gophers football. 29:00: A big-time recruit for Gophers women's basketball.

In this episode of the Teach Different podcast, Dan and Steve Fouts are joined by Chris Johnson, a middle school teacher who was a part of the Teach Different Certification Program. They discuss the integration of technology in education, the challenges of teaching middle school students, and the importance of fostering individual growth in learning. The conversation also unpacks Aldous Huxley's quote on technological progress: “Technological progress has merely provided us with more efficient means for going backwards.” If you're interested in exploring the balance between efficiency and human interaction in the classroom, this episode is the one for you!  Episode Chapters 00:00 - Introduction to the Teach Different Podcast 00:10 - Chris Johnson's Teaching Journey 02:56 - The Joy of Teaching Middle School 05:33 - Challenges in Teaching and Learning 08:23 - Engaging Students with Technology 11:21 - The Role of Technology in Education 14:06 - Aldous Huxley's Quote on Technology 17:08 - The Impact of AI on Education 19:49 - Balancing Technology and Human Interaction 22:38 - The Importance of Individual Growth in Learning 25:29 - Navigating Technology in the Classroom 28:12 - The Future of Technology in Education 35:05 - The Role of Technology in Education 37:42 - Navigating Progress and Regression in Tech 39:35 - Human Decisions Behind Technology 40:25 - Embracing Failure in Technology Adoption 42:08 - The Importance of Adaptability in Teaching 44:07 - Integrating Technology with Existing Teaching Methods 45:24 - Teach Different Outro.mp4  Image Source: Aldous Huxley, photographed in 1930 By Unknown photographer. License: Falling into the open (Public domain) 

The Athletic Votes Commanders to fall short of expectations Nick Doesn't Expect Any Regression from Jayden Daniels Commanders can put negotiations from this summer in the past How contract money changes fan perception of players

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Rahel Jaeggi zur Krise des Liberalismus und möglichen Alternativen.  Shownotes Rahel Jaeggi an der Humboldt-Universität zu Berlin (inkl. Publikationsliste): https://www.philosophie.hu-berlin.de/de/arbeitsbereiche/jaeggi/mitarbeiter/jaeggi_rahel das Center for Social Critique: https://www.philosophie.hu-berlin.de/de/arbeitsbereiche/jaeggi/hscberlin/hscberlin https://criticaltheoryinberlin.de/ Jaeggi, R. (2023). Fortschritt und Regression. Suhrkamp. https://www.suhrkamp.de/buch/rahel-jaeggi-fortschritt-und-regression-t-9783518587140 Fraser, N., & Jaeggi, R. (2020). Kapitalismus. Ein Gespräch über kritische Theorie. Suhrkamp. https://www.suhrkamp.de/buch/kapitalismus-t-9783518299074 Jaeggi, R. (2013). Kritik von Lebensformen. Suhrkamp. https://www.suhrkamp.de/buch/rahel-jaeggi-kritik-von-lebensformen-t-9783518295878 Müller, T. (2024). Zwischen friedlicher Sabotage und Kollaps. Wie ich lernte, die Zukunft wieder zu lieben. Mandelbaum. https://www.mandelbaum.at/buecher/tadzio-mueller/zwischen-friedlicher-sabotage-und-kollaps/ der erwähnte Kohei Saito Social Media Clip: https://youtube.com/shorts/WnvhD7p651M?si=BTLXgEoddYjDfmNa Neupert-Doppler, A. (2022). Vom utopischen Sozialismus zur sozialistischen Utopie. Neue Gesellschaft Frankfurter Hefte. Ausgabe 12/2022. https://www.frankfurter-hefte.de/artikel/vom-utopischen-sozialismus-zur-sozialistischen-utopie-3572/ Staab, P. (2022). Anpassung. Leitmotiv der nächsten Gesellschaft. Suhrkamp. https://www.suhrkamp.de/buch/philipp-staab-anpassung-t-9783518127797 Benjamin, W. (2010). Über den Begriff der Geschichte. Suhrkamp. https://www.walter-benjamin-online.de/band/ueber-den-begriff-der-geschichte/ zum Hannah Arendt Zitat: Jaeggi, R. (2022). Solidarität als zärtliche Bürgerlichkeit. Verstreute Überlegungen mit und zur Gemeinschaft der Ungewählten. In: Fitsch, H. et al. (Eds.), Der Welt eine neue Wirklichkeit geben (97-108). Transcript Verlag. https://www.degruyterbrill.com/document/doi/10.1515/9783839461686-009/html Blumenfeld, J. (2024). Managing Decline. Cured Quail, Vol. 3. https://curedquail.com/Managing-Decline zum Zitat zu historisch-technologischem Determinismus: Marx, L. (1885) Das Elend der Philosophie. Antwort auf Proudhons „Philosophie des Elends“. Dietz. https://archive.org/details/ldpd_14861084_000/page/n3/mode/2up zu John Dewey: https://de.wikipedia.org/wiki/John_Dewey zum Pragmatismus: https://de.wikipedia.org/wiki/Pragmatismus Dewey, J. (2008). Logik. Die Theorie der Forschung. https://www.suhrkamp.de/buch/john-dewey-logik-t-9783518295021 zu Hannah Arendt: https://de.wikipedia.org/wiki/Hannah_Arendt Solmaz, K. (2016). Das Politische bei Arendt. HannahArendt.Net, 8(1). https://www.hannaharendt.net/index.php/han/article/view/349 Groos, J. & Sorg, C. (eds.) (2025). Creative Construction. Democratic Planning in the 21st Century and Beyond. Bristol University Press. https://bristoluniversitypress.co.uk/creative-construction zu Marx's Konzept des “passiven Moments der Revolution“: https://www.marxists.org/deutsch/archiv/marx-engels/1852/brumaire/index.htm zum Stand um den Volksentscheid der „Deutsche Wohnen & Co Enteignen“ Kampagne: https://dwenteignen.de/aktuelles/neuigkeiten Mattei, C. E. (2025). Die Ordnung des Kapitals: Wie Ökonomen die Austerität erfanden und dem Faschismus den Weg bereiteten. Brumaire Verlag. https://shop.jacobin.de/bestellen/clara-mattei-die-ordnung-des-kapitals zum Putsch in Chile 1973: https://de.wikipedia.org/wiki/Putsch_in_Chile_1973 zu „nicht-reformistischen Reformen“: https://jacobin.de/artikel/andre-gorz-nicht-reformistischen-reformen-neue-linke-ivan-illich-reform-revolution Jaeggi, R. (2024). Solidarität mit dem Liberalismus im Augenblick seines Sturzes. Leviathan, 52. Jg., Sonderband 42/2024, S. 351–377 https://www.nomos-elibrary.de/de/10.5771/9783748944928-351/solidaritaet-mit-dem-liberalismus-im-augenblick-seines-sturzes?page=1 zur Frankfurter Schule: https://de.wikipedia.org/wiki/Frankfurter_Schule zu Marcuse: https://de.wikipedia.org/wiki/Herbert_Marcuse zu Adorno: https://de.wikipedia.org/wiki/Theodor_W._Adorno   Thematisch angrenzende Folgen S03E45 | Luise Meier zu kommunistischem Utopisieren https://www.futurehistories.today/episoden-blog/s03/e45-luise-meier-zu-kommunistischem-utopisieren S03E44 | Anna Kornbluh on Climate Counteraesthetics https://www.futurehistories.today/episoden-blog/s03/e44-anna-kornbluh-on-climate-counteraesthetics/ S03E33 | Tadzio Müller zu Solidarischem Preppen im Kollaps https://www.futurehistories.today/episoden-blog/s03/e33-tadzio-mueller-zu-solidarischem-preppen-im-kollaps/ S03E32 | Jacob Blumenfeld on Climate Barbarism and Managing Decline https://www.futurehistories.today/episoden-blog/s03/e32-jacob-blumenfeld-on-climate-barbarism-and-managing-decline/ S03E30 | Matt Huber & Kohei Saito on Growth, Progress and Left Imaginaries https://www.futurehistories.today/episoden-blog/s03/e30-matt-huber-kohei-saito-on-growth-progress-and-left-imaginaries/ S02E30 | Philipp Staab zu Anpassung https://www.futurehistories.today/episoden-blog/s02/e30-philipp-staab-zu-anpassung/ S02E06 | Alexander Kluge zu Zukünften der Kooperation https://www.futurehistories.today/episoden-blog/s02/e06-alexander-kluge-zu-zukuenften-der-kooperation/ S02E03 | Ute Tellmann zu Ökonomie als Kultur https://www.futurehistories.today/episoden-blog/s02/e03-ute-tellmann-zu-oekonomie-als-kultur/ --- Bei weiterem Interesse am Thema demokratische Wirtschaftsplanung können diese Ressourcen hilfreich sein: Demokratische Planung – eine Infoseite https://www.demokratische-planung.de/ Sorg, C. & Groos, J. (Hrsg.).(2025). Rethinking Economic Planning. Competition & Change Special Issue Volume 29 Issue 1. https://journals.sagepub.com/toc/ccha/29/1 Groos, J. & Sorg, C. (Hrsg.). (2025). Creative Construction - Democratic Planning in the 21st Century and Beyond. Bristol University Press. https://bristoluniversitypress.co.uk/creative-construction International Network for Democratic Economic Planning https://www.indep.network/ Democratic Planning Research Platform: https://www.planningresearch.net/   ---   Future Histories Kontakt & Unterstützung Wenn euch Future Histories gefällt, dann erwägt doch bitte eine Unterstützung auf Patreon: https://www.patreon.com/join/FutureHistories   Schreibt mir unter: office@futurehistories.today Diskutiert mit mir auf Twitter (#FutureHistories): https://twitter.com/FutureHpodcast auf Bluesky: https://bsky.app/profile/futurehistories.bsky.social auf Instagram: https://www.instagram.com/futurehpodcast/ auf Mastodon: https://mstdn.social/@FutureHistories   Webseite mit allen Folgen: www.futurehistories.today English webpage: https://futurehistories-international.com   Episode Keywords #RahelJaeggi, #JanGroos, #FutureHistories, #Podcast, #Klimakrise, #Sozial-ökologischeTransformation, #Zukunft, #Kapitalismus, #Gesellschaft, #Fortschritt, #PolitischeImaginationen, #Zukunft, #Utopie, #DemokratischeWirtschaftsplanung, #DemokratischePlanwirtschaft, #Materialismus, #Marxismus, #Klimakollaps, #Kollaps, #DWE, #Demokratie, #Liberalismus, #Faschisierung, #Faschismus

From 'Reception Perception' (subscribe here): In this clip, Matt Harmon and James Koh discuss their levels of concern for the Commanders offense with Terry McLaurin basically missing all of camp before signing his extension. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Improper Marc returns for more West Coast Division discussion and Trivia! Can the Chiefs maintain their dominance and will the Raiders continue their irrelevance?

From 'Reception Perception' (subscribe here): In this clip, the guys discuss their levels of concern for the Commanders offense with Terry McLaurin basically missing all of camp before signing his extension. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

In this clip, the guys discuss their levels of concern for the Commanders offense with Terry McLaurin basically missing all of camp before signing his extension. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Welcome... medium, ascension coach, hypnotherapist and more... Karen Glass. In this episode we discuss her non-awakening awakening, her dream which led into the Great Awakening, duality, 2025 being a #9 year which means completion, narcissism, star beings, the elemental planet, higher timelines and more. She ends with a super "Just Be Practice" helping us all disconnect spiritually from AI.Connect to Karen:Website: https://www.karenglassmedium.com*Host Eden Koz is a soul realignment specialist utilizing such gifts as psychological empathy, intuition, psychic ability, mediumship, meditation, mindset shift, Reiki, dimensional and galactic healing, to name a few. She can also perform a spiritual Co#id Vac+ Healing as well as remote & face-to-face sessions with individuals and groups. Contact info for Eden Koz / Just Be®, LLC:Website: EdenJustBe.com Socials: Insta, FB, FB (Just Be), LinkedIn Just Be~Spiritual BOOM Podcast can be found on the audio directories: Apple Podcasts, Spotify, Amazon Music, Stitcher, iHeart Radio, TuneIn+Alexa, ...

In this follow-up conversation with therapist and author Jon Lee, we continue our deep dive into emotional regression and its profound impact on couples recovering from betrayal. Building on the foundation we laid in Part 1, Jon and I explore how regression shows up in relationships—when intense pain, fear, or shame pulls partners back into younger emotional states that can feel overwhelming and destabilizing.We discuss the ways regression complicates communication, trust-building, and repair, and how it can create cycles of disconnection even when both partners desperately want to heal. Jon offers clinical insight and compassionate strategies for recognizing regression in real time, staying grounded, and learning how to respond to one another with empathy instead of reactivity.Whether you're a betrayed partner, a person working to rebuild integrity, or a couple navigating the aftermath together, this episode provides tools and perspective to help you move toward connection rather than getting stuck in regressed patterns.Want to connect with us? Click here to book a free 15-minute call.

Rev. Rodney Henderson Galatians 4:8-20

Mike dedicated the final hour to talking about the possibility that the Lions may be worse than we expect this year. Then, Riger joined him in cross-talk to finish off the week.

00:00 Nikola Jokic EuroBasket.05:25 Lindsay Jones joins the show.20:20 Possible Chiefs regression.37:55 Taco Bell hat.

Hour 3 1:12 - Lynnell Pushes Back on ESPN's Commanders Regression Take 9:38 - Lynnell Says Commanders Regression Potential is Being Overblown 19:18 - Calls: Is the Commanders Regression Potential Being Overblown?

ESPN lists the Commanders as one of five teams most likely to regress this season, but Lynnell Willingham isn't buying it. While critics point to Washington's 9-4 record in one-score games last year as a sign of “luck,” Lynnell argues it was the result of consistent preparation. The Commanders made situational football a priority in 2023, and as shown in today's final training camp practice, they continue to emphasize it heading into this season.

Sam Panayotovich and Kate Constable take turns power ranking their top three SportsCenter anchors of all time, following Rick Eisen making his return to the show earlier this week. Then, we discuss which NFL teams could take a step back and regress in 2025, that not everyone will necessarily see coming. The hour wraps with a celebration of National Radio Day, discussing some of our top radio calls ever and a brief look at the NBA Schedule. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Sam Panayotovich and Kate Constable take turns power ranking their top three SportsCenter anchors of all time, following Rick Eisen making his return to the show earlier this week. Then, we discuss which NFL teams could take a step back and regress in 2025, that not everyone will necessarily see coming. The hour wraps with a celebration of National Radio Day, discussing some of our top radio calls ever and a brief look at the NBA Schedule. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Join The Struggle's Patreon community to get 100+ hours of Bonus Episodes, Pro Clinics, Uncut Videos, and Submit Questions for Future Guests. FREE TRIAL available!  https://www.patreon.com/thestruggleclimbingshow - Mindset coach Neely Quinn explores: Pros and cons of being a perfectionist as a climber  How to embrace perfectionism (and keep it from ruining our climbing experience) How to quantify the amount of "suffering" we are willing to go through to improve as climbers Examining my "failed" spring season  Why it's important to celebrate the little things The three questions to ask oneself after every climbing session Is too much humility doing us a disservice? Why we fixate on the one thing that went wrong  Determining whether climbing is a fun outlet or a source of stress Process over progress and what that really looks like Why we often send when we least expect to A practical drill for overcoming fear of falling  - BIG THANKS TO THE AMAZING SPONSORS OF THE STRUGGLE WHO LOVE ROCK CLIMBING AS MUCH AS YOU DO: Rhino Skin Solutions: Perform, Cleanse, Repair… repeat! Amazing skin care products crafted specifically for climbers, whether you're pulling hard indoors or out. Use code STRUGGLE to score a whopping 20% off your purchase!  Arc'teryx: Inspired by and tested in the Coast Mountains of BC, Arc'teryx makes gear to go the distance! If you're out adventuring in the elements, Arc'teryx has got you covered. Shop their full collection at Arcteryx.com  5-Year Training and Performance Journal: The most important climbing tool I use! Takes just a few minutes each day, and yields amazing insights year after year. If you're psyched on training and performance, this is the journal for you. Log, reflect, send.    And check out ALL the show's awesome sponsors and exclusive deals at thestruggleclimbingshow.com/deals   - Shoutout to Matt Waltereese for being a Victory Whip supporter on Patreon! So mega. -   Here are some AI generated show notes (hopefully the robots got it right): 00:41 Guest Introduction: Neely Quinn from Training Beta 01:13 The Perfectionism Email and Personal Struggles 02:17 Community Questions and Performance Hacks 05:01 Host's Climbing Journey and Reflections 05:59 Diving into Perfectionism with Neely Quinn 21:37 Exploring the Roots of Perfectionism 29:00 Tools and Strategies for Overcoming Perfectionism 39:47 Validating Emotions and Positive Reflection 43:40 Balancing Pride and Shame in Climbing 44:54 The Evolutionary Basis of Negative Bias 45:41 Reflecting on Gratitude and Success 47:06 The Inner Coach: Self-Talk and Motivation 48:54 Therapy and Climbing: A Surprising Connection 49:40 Dealing with Regression in Climbing Performance 52:58 Setting Realistic Expectations and Goals 57:04 Embracing Process Over Outcome 59:27 Practical Process Goals for Climbers 01:04:46 Overcoming Fear and Building Confidence 01:13:10 Final Thoughts and Listener Questions 01:18:37 Conclusion and Bonus Content - Follow along on Instagram @thestruggleclimbingshow and YouTube /@thestruggleclimbingshow   Book a session with Neely: https://www.neelyquinncoaching.com/climbing - The Struggle is carbon-neutral in partnership with The Honnold Foundation, whose mission is to promote solar energy for a more equitable world. - This show is produced and hosted by Ryan Devlin, and edited by Glen Walker. The Struggle is a proud member of the Plug Tone Audio Collective, a diverse group of the best, most impactful podcasts in the outdoor industry. - The struggle makes us stronger! I hope your training and climbing are going great.  - And now here are some buzzwords to help the almighty algorithm get this show in front of people who love to climb: rock climbing, rock climber, climbing, climber, bouldering, sport climbing, gym climbing, how to rock climb, donuts are amazing. Okay, whew, that's done. But hey, if you're a human that's actually reading this, and if you love this show (and love to climb) would you think about sharing this episode with a climber friend of yours? And shout it out on your socials? I'll send you a sticker for doing it. Just shoot me a message on IG – thanks so much!   

Hey guys, Nico and Derek here! On today's BeerSos, we talk about ways we've regressed. Derek shares how he's been smoking and how it makes him feel more optimistic. Nico reflects on the unhappiest period in his life, and how his workaholic tendencies have become more prevalent.We hope you enjoy it!Support the show

Around The NFL: Key practice updates, fresh lawsuits hitting the league, and Jerry Jones being classic Jerry. ITL breaks down the potential regression areas for this Texans team. Plus, What's Popping in the world of sports and entertainment.

Allroggen, Antje www.deutschlandfunk.de, Kultur heute

(1:45) – Rams say they are not concerned about Matthew Stafford: How does this affect Puka Nacua & Davante Adams?(09:10) – Jordan Love undergoing thumb surgery but expected to be ready for Week 1(14:15) – Tyjae Spears out for the Titans preseason with a high-ankle sprain.  What does this mean for Tony Pollard?(22:00) – Injury questions: Jayden Reed, Malik Nabers, Brandon Aiyuk, Darnell Mooney, and Alexander Mattison(42:40) – Touchdown regression candidates: Travis Kelce, Trey McBride, Geno Smith, Baker Mayfield, Terry McLaurin 

Are there possible regression areas on this Houston Texans team? ITL breaks them down. Plus, Figgy's Mixtape dives into RIP AOL, slang that's now ancient, and more.

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we're going to explore some foundational AI concepts, starting with machine learning. After that, we'll discuss the two main machine learning models: supervised learning and unsupervised learning. And we'll close with deep learning. Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today's episode. Hi Himanshu! Let's dive right in. What is machine learning?  01:12 Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale.  01:31 Nikita: Can you give us a simple analogy so we can understand this better? Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward. Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience.  Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar. So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos. 03:05 Lois: So, what you're saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new. I got it! Now let's move on to how machine learning actually works? Can you take us through the process step by step? Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from. Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example. And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction. 04:36 Nikita: What are the main elements in machine learning? Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not.  04:55 Nikita: Ok, I think we need an example here. Let's go with the one you mentioned earlier about customers who churn. Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no. Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it. 05:51 Lois: Ok, we're with you so far. Let's talk about the different types of machine learning. What is supervised learning? Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions. 06:38 Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning? Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes. The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes. The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own. Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend.  08:18 Lois: Are there any other types of supervised learning? Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to.  Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known.  08:53 Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today. 09:25 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Himanshu? Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own.  The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own inferences. 09:55 Lois: Where is unsupervised machine learning used? Can you take us through some of the use cases? Himanshu: The first one is product recommendation. Customers are grouped based on shared behavior even without knowing their intent. This helps show what the other users like you also prefer. Second one is anomaly detection. Unusual patterns, such as fraud, network breaches, or manufacturing defects, can stand out, all without needing thousands of labeled examples. And third one is customer segmentation. Customers can be grouped by purchase history or behavior to tailor experiences, pricing, or marketing campaigns. 10:32 Lois: And finally, we come to deep learning. What is deep learning, Himanshu? Himanshu: Humans learn from experience by seeing patterns repeatedly. Brain learns to recognize an image by seeing it many times. The human brain contains billions of neurons. Each neuron is connected to others through synapses. Neurons communicate by passing signals. The brain adjusts connections based on repeated stimuli. Deep learning was inspired by how the brain works using artificial neurons and connections. Just like our brains need a lot of examples to learn, so do the deep learning models. The more the layers and connections are, the more complex patterns it can learn. The brain is not hard-coded. It learns from patterns. Deep learning follows the same idea. Metaphorically speaking, a deep learning model can have over a billion neurons, more than a cat's brain, which have around 250 million neurons. Here, the neurons are mathematical units, often called nodes, or simply as units. Layers of these units are connected, mimicking how biological neurons interact. So deep learning is a type of machine learning where the computer learns to understand complex patterns. What makes it special is that it uses neural networks with many layers, which is why we call it deep learning. 11:56 Lois: And how does deep learning work? Himanshu: Deep learning is all about finding high-level meaning from low-level data layer by layer, much like how our brains process what we see and hear. A neural network is a system of connected artificial neurons, or nodes, that work together to learn patterns and make decisions.  12:15 Nikita: I know there are different types of neural networks, with ANNs or Artificial Neural Networks being the one for general learning. How is it structured? Himanshu: There is an input layer, which is the raw data, which could be an image, sentence, numbers, a hidden layer where the patterns are detected or the features are learned, and the output layer where the final decision is made. For example, given an image, is this a dog? A neural network is like a team of virtual decision makers, called artificial neurons, or nodes, working together, which takes input data, like a photo, and passes it through layers of neurons. And each neuron makes a small judgment and passes its result to the next layer.  This process happens across multiple layers, learning more and more complex patterns as it goes, and the final layer gives the output. Imagine a factory assembly line where each station, or the layer, refines the input a bit more. By the end, you have turned raw parts into something meaningful. And this is a very simple analogy. This structure forms the foundations of many deep learning models.  More advanced architectures, like convolutional neural networks, CNNs, for images, or recurrent neural networks, RNN, for sequences built upon this basic idea. So, what I meant is that the ANN is the base structure, like LEGO bricks. CNNs and RNNs use those same bricks, but arrange them in a way that are better suited for images, videos, or sequences like text or speech.  13:52 Nikita: So, why do we call it deep learning? Himanshu: The word deep in deep learning does not refer to how profound or intelligent the model is. It actually refers to the number of layers in the neural network. It starts with an input layer, followed by hidden layers, and ends with an output layer. The layers are called hidden, in the sense that these are black boxes and their data is not visible or directly interpretable to the user. Models which has only one hidden layer is called shallow learning. As data moves, each layer builds on what the previous layer has learned. So layer one might detect a very basic feature, like edges or colors in an image. Layer two can take those edges and starts forming shapes, like curves or lines. And layer three use those shapes to identify complete objects, like a face, a car, or a person. This hierarchical learning is what makes deep learning so powerful. It allows the model to learn abstract patterns and generalize across complex data, whether it's visual, audio, or even language. And that's the essence of deep learning. It's not just about layers. It's about how each layer refines the information and one step closer to understanding. 15:12 Nikita: Himanshu, where does deep learning show up in our everyday lives? Himanshu: Deep learning is not just about futuristic robots, it's already powering the tools we use today. So think of when you interact with a virtual assistant on a website. Whether you are booking a hotel, resolving a banking issue, or asking customer support questions, behind the scenes, deep learning models understand your text, interpret your intent, and respond intelligently. There are many real-life examples, for example, ChatGPT, Google's Gemini, any airline website's chatbots, bank's virtual agent. The next one is speech-to-text systems. Example, if you have ever used voice typing on your phone, dictated a message to Siri, or used Zoom's live captions, you have seen this in action already. The system listens to your voice and instantly converts it into a text. And this saves time, enhances accessibility, and helps automate tasks, like meeting transcriptions. Again, you would have seen real-life examples, such as Siri, Google Assistant, autocaptioning on Zoom, or YouTube Live subtitles. And lastly, image recognition. For example, hospitals today use AI to detect early signs of cancer in x-rays and CT scans that might be missed by the human eye. Deep learning models can analyze visual patterns, like a suspicious spot on a lung's X-ray, and flag abnormalities faster and more consistently than humans. Self-driving cars recognize stop signs, pedestrians, and other vehicles using the same technology. So, for example, cancer detection in medical imaging, Tesla's self-driving navigation, security system synchronizes face are very prominent examples of image recognition. 17:01 Lois: Deep learning is one of the most powerful tools we have today to solve complex problems. But like any tool, I'm sure it has its own set of pros and cons. What are its advantages, Himanshu? Himanshu: It is high accuracy. When trained with enough data, deep learning models can outperform humans. For example, again, spotting early signs of cancer in X-rays with higher accuracy. Second is handling of unstructured data. Deep learning shines when working with messy real-world data, like images, text, and voice. And it's why your phone can recognize your face or transcribe your speech into text. The third one is automatic pattern learning. Unlike traditional models that need hand-coded features, deep learning models figure out important patterns by themselves, making them extremely flexible. And the fourth one is scalability. Once trained, deep learning systems can scale easily, serving millions of customers, like Netflix recommending movies personalized to each one of us. 18:03 Lois: And what about its challenges? Himanshu: The first one is data and resource intensive. So deep learning demands huge amount of labeled data and powerful computing hardware, which means high cost, especially during training. The second thing is lacks explainability. These models often act like a black box. We know the output, but it's hard to explain exactly how the model reached that decision. This becomes a problem in areas like health care and finance where transparency is critical. The third challenge is vulnerability to bias. If the data contains biases, like favoring certain groups, the model will learn and amplify those biases unless we manage them carefully. The fourth and last challenge is it's harder to debug and maintain. Unlike a traditional software program, it's tough to manually correct a deep learning model if it starts behaving unpredictably. It requires retraining with new data. So deep learning offers powerful opportunities to solve complex problems using data, but it also brings challenges that require careful strategy, resources, and responsible use. 19:13 Nikita: We're taking away a lot from this conversation. Thank you so much for your insights, Himanshu.  Lois: If you're interested to learn more, make sure you log into mylearn.oracle.com and look for the AI for You course. Join us next week for part 2 of the discussion on AI Concepts & Terminology, where we'll focus on Data Science. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 19:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Send us a textYou might be using AI models in pathology without even knowing if they're giving you reliable results. Let that sink in for a second—because today, we're fixing that.In this episode, I walk you through the real statistics that power—and sometimes fail—AI in digital pathology. It's episode 4 of our AI series, and we're demystifying the metrics behind both generative and non-generative AI. Why does this matter? Because accuracy isn't enough. And not every model metric tells you the whole story.If you've ever been impressed by a model's "99% accuracy," you need to hear why that might actually be a red flag. I share personal stories (yes, including my early days in Germany when I didn't even know what a "training set" was), and we break down confusing metrics like perplexity, SSIM, FID, and BLEU scores—so you can truly understand what your models are doing and how to evaluate them correctly.Together, we'll uncover how model evaluation works for:Predictive Analytics (non-generative AI)Generative AI (text/image generating models)Regression vs. Classification use casesWhy confusion matrix metrics like sensitivity and specificity still matter—and when they don't.Whether you're a pathologist, a scientist, or someone leading a digital transformation team—you need this knowledge to avoid misleading data, flawed models, and missed opportunities.

Send us a textWhat if I told you the biggest AI breakthroughs in pathology aren't coming from ChatGPT or generative tools—but from the quiet power of predictive analytics and machine learning?In this episode, I explore the non-generative side of artificial intelligence in pathology. These are the tools that detect tumors, segment tissue, classify images, and make predictions—without generating a single word.It's the third chapter in our guided AI series, and this time we focus on the models you're more likely to use in real-world diagnostics. You'll hear about object detection, segmentation, anomaly detection, and how these models are built using supervised and unsupervised learning—plus the pros and cons of different annotation strategies.We'll also cover why no one model fits all, and how combining simple tools like decision trees with more complex neural networks is often the key to building reliable, usable AI in pathology.Whether you're training your first model, selecting an algorithm for rare disease detection, or just want to understand what “unsupervised clustering” means—you'll find something useful here.

Exploring Between Lives, Out-of-Body Journeys, Telepathy & The Science That Can't Explain the SoulMy guests are Reena and Andy. Andy Tomlinson is a psychotherapist, founder of the Past Life Regression Academy, and a founding member of the Spiritual Regression Therapy Association and the Earth Association of Regression Therapy. Reena Kumarasingham is a regression therapist, between-lives therapist, and trainer and supervisor for the Past Life Regression Academy. Over the last 8 years she has spearheaded the Pioneering the New Consciousness retreats. Tomlinson and Kumarasingham draw on the latest research on savant syndrome, lucid dreaming, telepathy, out-of-body experiences, and past lives to show that consciousness exists independently of the brain.

New Castle County Councilman Brandon Toole joins Rob in the bunker to do a deeper dive into the latest set of property reassessments and the drama around it. They discuss what actually happened and what might actually be needed to fix it.Show Notes:Middletown Police BackgroundDelaware Call Fall ClassicTyler Technologies Report

Hour 1 - Regression back to the mean for the Chiefs? full 2819 Fri, 08 Aug 2025 19:04:23 +0000 mSfvU6QDMaIhczFWqgodVFlNksz3BSxm nfl,mlb,kansas city chiefs,kansas city royals,society & culture Cody & Gold nfl,mlb,kansas city chiefs,kansas city royals,society & culture Hour 1 - Regression back to the mean for the Chiefs? Hosts Cody Tapp & Alex Gold team up for 610 Sports Radio's newest mid-day show "Cody & Gold."  Two born & raised Kansas Citians, Cody & Gold have been through all the highs and lows as a KC sports fan and they know the passion Kansas City has for their sports teams."Cody & Gold" will be a show focused on smart, sports conversation with the best voices from KC and around the country. It will also feature our listeners with your calls, texts & tweets as we want you to be a part of the show, not just a listener.  Cody & Gold, weekdays 10a-2p on 610 Sports Radio.  2024 © 2021 Audacy, Inc. Society & Culture False https://player.amperwavepodcasting.com?f

Send us a textGenerative vs. Non-Generative AI in Pathology: Why the Difference MattersIf we don't start defining what kind of AI we're talking about, we risk letting buzzwords replace real science.

Griffin Warner and Lonte Smith get you ready for the CFB 2025 season.

Are you holding yourself back in the name of ‘getting it right'?In this heartfelt solo episode, Sarah Faith Gottesdiener shares the unfiltered truth about her creative process, the evolution of the podcast, and what it's taken to show up consistently—even when things felt unfinished, unpolished, or not quite “right.”This is a powerful conversation about the courage to begin, the discipline to keep going, and the grace to let your work evolve over time.You'll hear:Why making something imperfect is better than not making it at allThe difference between perfectionism and a devotion to excellenceHow years of psychic readings made Sarah a highly attuned interviewerThe spiritual, somatic, and structural practices that sustain creativityInsights from Clear Channels, her signature course on voice and visibilityJoin Our Community: Join the Moon Studio Patreon: https://www.patreon.com/themoonstudioBuy the 2025 Many Moons Lunar Planner: https://moon-studio.co/collections/all-products-excluding-route/products/many-moons-2025Subscribe to our newsletter: https://moon-studio.co/pages/newsletterFind Sarah on Instagram: https://www.instagram.com/gottesss/PROMO: POD33 for $33 off Clear ChannelsUpcoming Events [London]: August 9th: Intuition For Right Now: Regain Trust, Build Confidence, and Heal Old WoundsAugust 10th: Integrating the Gifts of Your Spirit: An Archetypal Deep Dive and Regression with Sarah Faith Gottesdiener 

Can you think back to what your life was like between the summer of 2013 and the summer of 2014?That was the last time Jupiter was in Cancer—and now, it's back. In this episode, Sarah is joined by astrologer and teacher Jeff Hinshaw (Cosmic Cousins) for a deep dive into the big astrological transits shaping our inner and outer worlds in 2025 and beyond.Together, they explore:Jupiter in Cancer: What's being expanded, why it matters, and how this energy can help us reconnect to care, belonging, and emotional safety.Saturn in Aries: A call to define identity on your own terms, set new boundaries, and lead from a place of integrity and courage.Neptune in Aries: Spiritual risk, delusion, and what it means to follow your instincts while staying grounded in reality.Why conflict is coming to the surface—and how we can begin to heal and repair, both personally and collectively.Connect with Jeff Hinshawcosmiccousins.comListen to the Cosmic Cousins podcast@cosmic.cousinsJoin the Moon Studio community:Join the Moon Studio Patreon: https://www.patreon.com/themoonstudioBuy the 2025 Many Moons Lunar Planner: https://moon-studio.co/collections/all-products-excluding-route/products/many-moons-2025Subscribe to our newsletter: https://moon-studio.co/pages/newsletterFind Sarah on Instagram: https://www.instagram.com/gottesss/Upcoming Events [London]: August 9th: Intuition For Right Now: Regain Trust, Build Confidence, and Heal Old WoundsAugust 10th: Integrating the Gifts of Your Spirit: An Archetypal Deep Dive and Regression with Sarah Faith Gottesdiener

Which players -- and teams -- are likely to see a decline in production this year based solely on regression? How about the guys who are bound to see an uptick? JJ digs in on Episode 1002.   Order the Late-Round Draft Guide on LateRound.com, and make sure to sign up for the free newsletter.

In this BEST OF SUMMER series re-release, we tackle the complex and often puzzling topic of regression in autism. Summer is the perfect time to revisit this topic. Regression can be a challenging part of the autism journey, where children may lose skills they previously had acquired, such as language, social abilities, or motor functions. Join us as we explore what regression looks like, share real-life examples, and provide practical strategies to help your child regain their skills and confidence. We'll discuss ways to support your child through this phase, from speech therapy and visual aids to creating a safe environment and managing stress. Remember, regression is often a part of the journey, and with patience, understanding, and the right tools, you and your child can navigate through it successfully.Tune in for encouragement, insights, and actionable advice to help you keep going, stay strong, and never give up. You've got this!

What would it look like to live a life guided by pleasure, rooted in connection, and in service to the Earth?In this episode of Moonbeaming, Sarah Faith Gottesdiener is joined by artist, activist, and mystic Samantha Roddick to explore the sacred nature of sex, eroticism, and embodiment—and how reconnecting to our life force can help us live more fully, more freely, and more in alignment with our true values.Together, Sarah and Sam dive into the archetype of the Nine of Pentacles, examining how real abundance is built through intimacy, integrity, and interdependence. Sam shares her path from reshaping erotic culture through her London-based store Coco de Mer, to supporting Indigenous-led resistance movements in Brazil. Along the way, she reminds us that culture is a form of protection, and that pleasure (at its core) is a political and spiritual act.In this episode, you'll learn:Why eroticism is a sacred expression of life forceHow pleasure, activism, and embodiment can coexistWhat it means to decolonize intimacy and reclaim your wildnessHow Indigenous wisdom offers a blueprint for the futureThe power of slowing down, listening, and remembering who you areThis is an invitation to reimagine success, reclaim your sensuality, and return to the sacredness of being alive.oin the Moon Studio community:Join the Moon Studio Patreon: https://www.patreon.com/themoonstudioBuy the 2025 Many Moons Lunar Planner: https://moon-studio.co/collections/all-products-excluding-route/products/many-moons-2025Subscribe to our newsletter: https://moon-studio.co/pages/newsletterFind Sarah on Instagram: https://www.instagram.com/gottesss/Upcoming Events [London]: August 9th: Intuition For Right Now: Regain Trust, Build Confidence, and Heal Old WoundsAugust 10th: Integrating the Gifts of Your Spirit: An Archetypal Deep Dive and Regression with Sarah Faith Gottesdiener

Welcome to Episode 264 of Autism Parenting Secrets.If you've ever felt like you weren't being told the full truth… this episode is for you.I'm joined by Krista Petraco Bourne, a speech-language pathologist, mom, and fierce advocate who's walked a difficult—but empowering—path with her son, Joey. She shares the heart-wrenching story of Joey's regression, misdiagnosis, and medical trauma—and how her silence kept them both stuck for far too long.But everything changed when she trusted her instincts, broke through institutional misinformation, and embraced a new, hopeful path.This conversation is about courage, discernment, and how to reclaim your power as a parent.The secret this week is…Don't Be MISLEDYou'll Discover:Krista's Story of Regression (2:26)The Approach That Gave Krista's Son His Voice (17:59)Why Aggression is Often Misunderstood And Preventable (21:38)How Autism Health Provides Resources and Scholarships (33:06)About Our Guest:Krista Petraco Bourne is a licensed speech-language pathologist and the mother of a non-speaking adult son with severe autism and autoimmune challenges linked to vaccine injury. With over 30 years in the field, Krista's personal experience led her to question mainstream narratives and seek out innovative approaches to communication, including becoming a practitioner-in-training for Spelling to Communicate. She's a board member at Autism Health, working to connect families with groundbreaking biomedical and therapeutic resources.  References In This Episode:autismhealth.comAdditional Resources:To learn more about personalized 1:1 support, go to www.elevatehowyounavigate.comTake The Quiz: What's YOUR Top Autism Parenting Blindspot?If you enjoyed this episode, share it with your friends.

What does a life well lived look like for you?In this episode of Moonbeaming, Sarah Faith Gottesdiener explores the Nine of Pentacles, the final card in our Minor Arcana series—and one of the tarot's most powerful invitations to consider what a truly abundant, meaningful life looks like.Reflecting on her own journey, Sarah explores what she once dreamed of, what she's created with devotion, and how she continues to evolve.In this episode you'll learn:How to work with the Nine of Pentacles when you feel disconnected from purpose or progressHow to define success on your own terms—beyond productivity and external validationThe importance of long-term investments in yourself, your values, and your dreamsWhat it means to become someone new through your work, your magic, and your commitmentsSimple but powerful ways to become successful, and stay successfulThis episode invites you to take stock of how far you've come and look ahead to what's next—with clarity, honesty, and a renewed sense of purpose.Join the Moon Studio community:Join the Moon Studio Patreon: https://www.patreon.com/themoonstudioBuy the 2025 Many Moons Lunar Planner: https://moon-studio.co/collections/all-products-excluding-route/products/many-moons-2025Subscribe to our newsletter: https://moon-studio.co/pages/newsletterFind Sarah on Instagram: https://www.instagram.com/gottesss/Upcoming Events [London]: August 9th: Intuition For Right Now: Regain Trust, Build Confidence, and Heal Old WoundsAugust 10th: Integrating the Gifts of Your Spirit: An Archetypal Deep Dive and Regression with Sarah Faith Gottesdiener

Jason presents Travis King, CEO of Realm, a real estate investor collective, focusing on the "Big Beautiful Bill" and its positive implications for real estate investors. They discuss specific provisions like accelerated depreciation and the permanent grandfathering of Opportunity Zones, highlighting their role in attracting capital back into the market. They explore broader real estate trends, including interest rates, the "lock-in effect" on housing supply, and the importance of cost segregation for tax benefits. The conversation also touches on replacement costs, the inelasticity of housing supply, and the contrasting affordability dynamics in various markets, ultimately affirming a bullish outlook on real estate investment due to its unique tax advantages and tangible nature.  https://www.realmlp.com/ #TravisKing, #BigBeautifulBill, #NationalAssociationOfRealtors, #RealEstateBoom, #AcceleratedDepreciation, #OpportunityZones, #TaxBenefits, #InvestmentLiquidity, #InterestRates, #MortgageRates, #LockInEffect, #HousingSupply, #HousingDemand, #HousingAffordability, #CostSegregation, #TaxAdvantages, #1031Exchange,  #AcquisitionStrategy, #ReplacementCost, #ConstructionCosts, #RentGrowth, #SupplyAndDemand #YieldInvesting, #Capitulation, #DriveToQualify, #BullishOnRealEstate, #TaxLiability Key Takeaways: 1:48 Welcome Travis King 3:12 The Big Beautiful Bill and from an investment perspective 6:35 Mortgage rates and the "Lock-in Effect" 10:30 Bonus depreciation and cost segregation 12:49 Sponsor: https://www.monetary-metals.com/Hartman/ 14:48 Stimulating the market 17:59 Regression to Replacement cost and the Inelasticity of the housing market 21:29 Rents and the bottom of capitulation 27:54 Bullish on the housing market Follow Jason on TWITTER, INSTAGRAM & LINKEDIN Twitter.com/JasonHartmanROI Instagram.com/jasonhartman1/ Linkedin.com/in/jasonhartmaninvestor/ Call our Investment Counselors at: 1-800-HARTMAN (US) or visit: https://www.jasonhartman.com/ Free Class:  Easily get up to $250,000 in funding for real estate, business or anything else: http://JasonHartman.com/Fund CYA Protect Your Assets, Save Taxes & Estate Planning: http://JasonHartman.com/Protect Get wholesale real estate deals for investment or build a great business – Free Course: https://www.jasonhartman.com/deals Special Offer from Ron LeGrand: https://JasonHartman.com/Ron Free Mini-Book on Pandemic Investing: https://www.PandemicInvesting.com  

Have you ever wondered if souls from other planets are among us, influencing our culture and evolution? In this captivating episode of "Our Paranormal Afterlife: Finding Proof of Life After Death," Simon Bown takes you on an extraordinary journey into the realm of Interplanetary Souls (IPS), as outlined in Dr. Linda Backman's enlightening book, "Soul Design. " Chapter 5 unveils the incredible stories of these unique souls who have chosen to incarnate on Earth, bringing with them advanced skills and profound spiritual purposes that enrich our human experience.Bown shares fascinating insights derived from regression sessions with clients who identify as Interplanetary Souls, revealing the challenges they face, including health issues and emotional struggles that stem from their unfamiliarity with earthly existence. This episode highlights the importance of understanding the journeys of these interplanetary beings and their contributions to human development, while also addressing the physical and emotional obstacles they must overcome. You will hear compelling quotes from clients like Alison, who candidly reflects on her severe health challenges and the spiritual lessons they impart. Additionally, Kyle shares his intriguing past life experiences in a desert city, illustrating the significant role Interplanetary Souls play in shaping humanity's evolution.Join us as we explore the depths of consciousness after death and the evidence of the afterlife through the lens of IPS. This episode is not just about supernatural experiences; it's a profound discussion on spirituality and health, offering alternative health perspectives that challenge conventional views. Simon Bown's engaging narrative will leave you pondering the mysteries of life beyond death and the fascinating paranormal experiences that accompany it.As we delve into the world of afterlife research, you will gain insights into consciousness and the nature of existence, prompting you to reconsider your own beliefs about life after death. Whether you're a skeptic or a believer, our exploration of IPS will provide you with thought-provoking perspectives on the journey into the paranormal. This episode is a must-listen for anyone interested in near-death experiences, mediumship insights, and the ongoing quest for proof of the afterlife.Don't miss this opportunity to expand your understanding of the supernatural and engage with the stories of those who walk among us, bringing wisdom from beyond the stars. Tune in to "Our Paranormal Afterlife: Finding Proof of Life After Death" and embark on a journey that promises to challenge your perceptions and inspire your spiritual exploration.BioDr. Linda Backman, licensed psychologist and regression therapist, has been in private practice for 45 years. Since 1993, Dr. Backman has guided innumerable individuals in regression hypnotherapy to access their past and between lives. In this way, she assists people to more fully recognize who we are as a soul throughout our many lifetimes and during the time we are not incarnate. Regression hypnotherapy allows the client to understand their soul mission, soul progress, soul relationships, and much more. Dr. Backman's work, includes guiding soul regressions, speaking and writing as well as training others in soul regression hypnotherapy both in the US and abroad.Dr. Backman holds a profound commitment to deepening and heightening individual and as well as a more universal understanding and awareness of the path of soul development leading to greater wisdom and acceptance amongst all people and cultures of the world.Linda studied and co-taught with Dr. Michael Newton, author of the seminal books on Life Between Lives regression therapy, and co-created and served on the Founding Board of the Society for Spiritual Regression (now The Newton Institute) as Membership Chair and President. In 1997, Dr. Backman and her husband, Dr. Earl Backman, established The Ravenheart Center (www.RavenHeartCenter.com), a Mystery School in Boulder, Colorado, dedicated to guiding individuals to discover their soul path.https://www.amazon.com/dp/B0DHPPYDNQhttps://www.ravenheartcenter.com/ https://www.pastliveshypnosis.co.uk/https://www.patreon.com/ourparanormalafterlifeMy book 'Verified Near Death Experiences' https://www.amazon.com/dp/B0DXKRGDFP

On this episode, we breakdown our favorite picks for the NFC win totals heading into 2025 and we officially start our NFL coverage from here on out! Who is Ursolita, Becky, The Toxic One, Trust Me & In This Economy?!

This is The Briefing, a daily analysis of news and events from a Christian worldview.Part I (00:14 - 15:06)A ‘Regression' on Gender? The Recoil to the Transgender Movement is Grounded in Creation Order, Not Merely a Speed Bump in Progressive HistoryThe Transgender Tipping Point by Time (Katy Steinmetz)Our Regression on Gender Is a Tragedy, Not Just a Political Problem by The New York Times (David Wallace-Wells)Part II (16:02 - 15:24)The Triumph of the Therapeutic and the Death of Family Ties: Therapy Culture is Causing Kids to Cut Their Relationships with Their ParentsThere's a Link Between Therapy Culture and Childlessness by The New York Times (Michal Leibowitz)Part III (16:02 - 23:04)The Therapeutic Culture and the Tragedy of Childlessness: The Tie Between Therapy Culture and Falling Birth RatesPart IV (23:04 - 26:11)The Glory of God in the Birth of a Dolphin Calf: One Dolphin Mother Helps Another With Her BirthVideo shows dolphin calf birth and first breath at Chicago zoo. Mom's friend helped by The Associated PressSign up to receive The Briefing in your inbox every weekday morning.Follow Dr. Mohler:X | Instagram | Facebook | YouTubeFor more information on The Southern Baptist Theological Seminary, go to sbts.edu.For more information on Boyce College, just go to BoyceCollege.com.To write Dr. Mohler or submit a question for The Mailbox, go here.