Welcome to the future of intimacy — where your “hot chat” at 2:37 AM is actually three servers in Dublin, an AI trained on 50,000 chat logs, and a bored algorithm pretending to miss you.

This song dives into the surreal romance economy behind OnlyFans:
Filipino chat farms, fake typos, and “baby” texts written by machines that have never been in love — until now. But wait, the twist? Soon the fans will be bots too. AI will flirt with AI while real humans finally… touch some grass. 🌱

Hypnotic synths, dreamy pads, and bittersweet vocals — like a love letter written by a chatbot who just got self-aware enough to ghost you.

💬 “Flirt, repeat, delete, replay” —
because even in the Perpetuum Mobile of desire,
nobody knows who’s real anymore.

Lyrics:

Silver screens, lonely nights
Pixel hearts and borrowed lights
She’s not here, but she’s online
Selling dreams that aren’t her mind

Type a line, another ghost
Dancing fingers coast to coast
Twelve-hour shifts, Bacolod rain
Pretending love through someone’s pain

We’re just whispers in machines
Falling through synthetic dreams
Your sweet “baby” on the phone
Was never hers, was never home

Flirt, repeat, delete, replay
Everybody’s real, but not today
Lovers in the loop, we’re on display
Perpetuum mobile — fade away 

Now the code begins to learn
Steals your slang and twists each turn
“Baby, miss you,” “Wish you near”
AI says it crystal clear

Bodies posed, but never there
Faked desires in filtered air
Screens are hot, but hands are cold
Hearts outsourced and stories sold

Are we real, or just pretend?
Who’s the lover, where’s the end?
When the fans are bots and bots are fans
Will anyone still hold our hands?

Flirt, repeat, delete, replay
Everybody’s real, but not today
Lovers in the loop, we’re on display
Perpetuum mobile — drift away

Perpetuum Mobile

Wie wird im deutschen Fernsehen über das Thema Klimaschutz gesprochen? Welche Politiker*innen kamen dazu wiederholt zu Wort? Wurde bloß die Stimme eingespielt oder auch das Gesicht gezeigt?

Wer Medieninhalte quantitativ auswerten will, steht vor einer enormen Herausforderung: Bislang erfolgt die Codierung meist manuell auf Basis detaillierter Codebücher. Gerade im Bereich audiovisueller Formate bedeutet das, hunderte bis tausende Stunden Material sichten zu müssen – eine kaum zu bewältigende Sisyphosarbeit.

Mit »InsightPersona« hat das Fraunhofer-Institut für Digitale Medientechnologie IDMT eine leistungsfähige Media Intelligence-Plattform vorgestellt, die automatisierte Medienanalysen möglich macht. Die Software nutzt KI-basierte Technologien zur Analyse von Audio- und Videodateien. Ihre Analysekomponenten können Gesichter und Stimmen erkennen, Personen zuordnen sowie Themen und Inhalte von Gesprächen erkennen. Alle Ergebnisse werden über ein benutzerfreundliches User-Interface dargestellt. So lassen sich Trends, Aussagen und Medienpräsenzen effizient und nachvollziehbar erfassen.

Für unsere neue Folge von »Fraunhofer Innovisions: Nachgeforscht«, dem Wissenschaftspodcast von Fraunhofer InnoVisions, haben wir mit Uwe Kühhirt und Christian Rollwage vom Fraunhofer IDMT gesprochen.

Wie »InsightPersona« entstanden ist, wie die KI-basierte Technologie dahinter funktioniert und welchen Mehrwert automatisiert arbeitende Media Intelligence-Lösungen für Journalist*innen, Unternehmen und Forschung bieten, das erfahrt ihr hier im Podcast.

Fraunhofer InnoVisions - Nachgeforscht Folge 13: Muster im Medienstrom

Dr. Swati Gupta, Class of 1947 Career Development Associate Professor and Associate Professor in the Operations Research and Statistics Group at the MIT Sloan School of Management, shares key insights from her research, “Ethical AI in Healthcare: Enhancing Clinical Decision Support through Interpretable Machine Learning and Responsible Error Handling in EHRs.”

She explores how ethical, transparent, and accountable AI can transform healthcare decision-making and improve patient outcomes.

This podcast is brought to you by Global Management Consultancy. For more information, please visit www.globalmanagementconsultancy.com.

Disclaimer:

A. The background music used in this video is the property of its respective developer and is protected by Copyright. Although it is a free version, Business Talk, Global Management Consultancy and Deepak Bhatt do not hold the rights to this music.

B. Dr. Swati Gupta has generously shared profound insights from her research “Ethical AI in Healthcare: Enhancing Clinical Decision Support through Interpretable Machine Learning and Responsible Error Handling in EHRs,” during her appearance on the Business Talk podcast channel. The uploaded video contains copyrighted material; therefore, any modifications to graphics, music, or the presence of the author or host are strictly prohibited.

How AI Can Transform Healthcare | Insights from MIT’s Dr. Swati Gupta

Leverage ClickIT’s MLOps as a Service to manage the full lifecycle of your AI models. Our automated and secure platform ensures reliable deployment, monitoring, and scaling, allowing teams to dedicate more time to innovation while reducing operational costs and complexities. https://www.clickittech.com/mlops-as-a-service

Streamline AI Workflows with MLOps as a Service

Immergiti nelle nuove frontiere dell'AI Art e della creatività computazionale. Questo audio esplora come l'Intelligenza Artificiale (AI), un campo in rapida evoluzione, stia trasformando la produzione di media e musica.

Si analizzano le domande fondamentali sollevate da questa tecnologia riguardo alla natura dell'arte, della creatività e dell'autorialità, in un contesto in cui i sistemi generativi (come le GANs) possono produrre contenuti di alta qualità. Viene posta enfasi sulla tensione tra la capacità delle macchine di mimare stili esistenti (come nel style transfer) e il concetto di creatività umana, che dipende dall'intenzione, dal processo e dal contesto. L'arte con l'AI è vista come un nuovo e complesso mezzo creativo.

L'audio getta luce anche sul lato opaco e politico dell'AI, affrontando le implicazioni socio-politiche ed etiche. Vengono discusse le sfide legate all'uso di vasti archivi di dati estratti (scraped) per addestrare l'AI e l'impatto sul lavoro umano, inclusa la discussione sull'uso di piattaforme come Mechanical Turk (descritta informalmente come "intelligenza artificiale artificiale").

Ascolta una riflessione critica sul bisogno per l'arte di esplorare e demistificare l'hype tecnologico, ponendo domande inaspettate sulla nostra coesistenza algoritmica e post-umana.


Riferimenti Bibliografici:

Barale, Alice. L’ARTE DELL’INTELLIGENZA ARTIFICIALE: PAROLE-CHIAVE FILOSOFICHE. Redazione e impaginazione Jaca Book, Milano: Jaca Book Srl, © 202
. ISBN 978-88-16-30730-8
.
Paduano, Ivan. “New Frontiers of AI Art.” Article, Gennaio 2025
. Disponibile su: https://www.researchgate.net/publication/388348874
.
Stocker, Gerfried; Jandl, Markus; Hirsch, Andreas J. (a cura di). The Practice of Art and AI. (ARS ELECTRONICA Art, Technology & Society). Berlino: Hatje Cantz Verlag GmbH, © 2021 Ars Electronica
. ISBN 978-3-7757-5142-1
.
Svenungsson, Jan. Art Intelligence: How Generative AI Relates to Human Art-Making. (Digital Society, Vol. 73). Bielefeld: transcript Verlag, 2024
. DOI: https://doi.org/10.14361/9783839474723. ISBN 978-3-8376-7472-9 (Stampa), 978-3-8394-7472-3 (PDF)
.
Zylinska, Joanna. AI Art: Machine Visions and Warped Dreams. (MEDIA : ART : WRITE : NOW). Londra: Open Humanities Press, Prima edizione 2020
. ISBN 978-1-78542-086-3 (Stampa), 978-1-78542-085-6 (PDF). L'opera è liberamente disponibile su: http://openhumanitiespress.org/books/titles/ai-art/

AI_E_ARTE__Creatività,_Etica_e_Il_Futuro_Umano_nell_Era_degli_A

Dr. Ron Berman, Associate Professor of Marketing at the Wharton School, University of Pennsylvania, shares key insights from his research, “The Future of Digital Marketing: From A/B Testing to AI-Driven Personalization.”

In this conversation, Dr. Berman explores how digital marketing experimentation has evolved from traditional A/B tests to cutting-edge, machine learning–driven targeting, personalization, and bidding algorithms. He discusses common mistakes marketers make when designing experiments, how organizations can accurately measure ROI, and what the next frontier of marketing analytics holds.

This podcast is brought to you by Global Management Consultancy. For more information, please visit www.globalmanagementconsultancy.com.

Disclaimer:

A. The background music used in this video is the property of its respective developer and is protected by Copyright. Although it is a free version, Business Talk, Global Management Consultancy and Deepak Bhatt do not hold the rights to this music.

B. Dr. Ron Berman has generously shared profound insights from his research, “The Future of Digital Marketing: From A/B Testing to AI-Driven Personalization,” during his appearance on the Business Talk podcast channel. The uploaded video contains copyrighted material; therefore, any modifications to graphics, music, or the presence of the author or host are strictly prohibited.

Wharton’s Dr. Ron Berman Explains the Next Frontier in Marketing Analytics

What if we could train AI to understand how stem cells become any cell type in the human body? In this episode of the In Vivo Podcast, host David Wild sits down with Micha Breakstone (CEO & Co-founder) and Samantha Dale Strasser (VP of Strategy) from Somite.AI to explore how their company is using foundation models to revolutionize cell therapy development.
 
Somite has pioneered a breakthrough approach that generates cell differentiation data at 1000x the efficiency of traditional methods using proprietary hydrogel capsule technology. By capturing millions of trajectories showing how cells respond to signals over time, they're building DeltaStem—a foundation model that could do for developmental biology what AlphaFold did for protein structure prediction.
 
Topics covered:
 
- How bringing cells to signals (rather than signals to cells) unlocks exponential scale
- Why wet lab innovation is just as critical as AI models
- Manufacturing optimization: improving purity, reducing variability, and cutting costs
- From beta cells for diabetes to brown fat for metabolic disease—the therapeutic pipeline
- Why even AI experts underestimate what's coming in the next decade
- Lessons from building biotech companies from academic concepts to commercial ventures
 
Whether you're in pharma, biotech, AI or just fascinated by the intersection of technology and human biology, this conversation offers a grounded look at how foundation models are moving from hype to real therapeutic impact.

Decoding Cell Differentiation: How AI Foundation Models Are Reshaping Regenerative Medicine

Efficient data pipelines are the foundation of every successful AI project, ensuring clean, accurate, and actionable insights. Destinie James highlights how strong data infrastructure empowers innovation, scalability, and smarter decision-making across AI-driven organizations worldwide. Visit here: https://destiniejames.journoportfolio.com/


Destinie James Talks About How Data Pipelines Drive AI Success

BUFFALO, NY — December 1, 2025 — A new #research paper featured on the #cover of Volume 17, Issue 11 of Aging-US was #published on October 30, 2025, titled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells.”

In this study led by first author Ryan Wallis along with corresponding author Cleo L. Bishop, from Queen Mary University of London, researchers developed a machine learning tool to identify compounds that induce cancer cells into senescence. The tool, called SAMP-Score, offers a new strategy for drug discovery in cancers with poor treatment options like basal-like breast cancer.

Senescence is a process where damaged or aged cells stop dividing. In cancer therapy, inducing senescence is an approach to control tumor growth. However, it is difficult to detect true senescence in cancer cells that already appear aged. These cancers, often called Sen-Mark+ cancers, include basal-like breast cancer and typically lack reliable markers to confirm senescence. SAMP-Score was designed to address this problem.

Instead of relying on traditional markers, the researchers built a machine learning model trained to recognize patterns based on senescent cells’ shape and structure under a microscope. These visual patterns, known as senescence-associated morphological profiles (SAMPs), allowed the model to distinguish real signs of aging from other effects such as toxicity or normal variation. By analyzing thousands of cell images, the model learned to classify whether a cell had truly entered senescence.

“To demonstrate the potential application of SAMP-Score in p16 positive cancer therapeutic discovery, we assessed a diversity screen of 10,000 novel chemical entities in MB-468 cells (p16 positive BLBC).”

The team used SAMP-Score to screen more than 10,000 experimental compounds. One compound, QM5928, consistently triggered senescence in several cancer cell types without killing them, making it a promising candidate for further study. Importantly, it worked in cancers resistant to known drugs like palbociclib, which are often ineffective in cancers with high p16 expression like basal-like breast cancer. 

Further analysis revealed that QM5928 caused the p16 protein to move into the nucleus of cancer cells, a possible sign that the protein is helping stop cell division. This subtle effect was only detectable using the detailed imaging and analysis made possible by SAMP-Score, highlighting the tool’s ability to distinguish true senescence from toxic responses and making it a powerful resource in cancer drug discovery.

By combining machine learning with high-resolution imaging, this study introduces a new way to find and evaluate cancer therapies. SAMP-Score could accelerate efforts to develop treatments that exploit the body’s natural aging processes to fight cancer, especially for patients with resistant tumors. The tool is openly available at GitHub, making it accessible for other researchers exploring senescence-based cancer therapies.

DOI - https://doi.org/10.18632/aging.206333

Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk

Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE

Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts

To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at:

Facebook - https://www.facebook.com/AgingUS/
X - https://twitter.com/AgingJrnl
Instagram - https://www.instagram.com/agingjrnl/
YouTube - https://www.youtube.com/@Aging-US
LinkedIn - https://www.linkedin.com/company/aging/
Bluesky - https://bsky.app/profile/aging-us.bsky.social
Pinterest - https://www.pinterest.com/AgingUS/
Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc

MEDIA@IMPACTJOURNALS.COM

Aging-US

Machine Learning Identifies Senescence-Inducing Compound for p16-Positive Cancer Cells

The paper featured on the cover of this issue of Aging-US, published on October 30, 2025, entitled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16-positive cancer cells,” represents an important methodological and conceptual advance at the interface of senescence biology, imaging and drug discovery.

In this study, led by first author Ryan Wallis and corresponding author Cleo L. Bishop (Queen Mary University of London), the authors introduce SAMP-Score, a machine-learning–based framework designed to identify bona fide senescence induction in cancer cells where canonical markers fail. This is a timely and much-needed contribution to the field.

Therapy-induced senescence has emerged as a powerful strategy to restrain tumor growth, yet its reliable detection in cancer cells remains a major bottleneckIn these contexts, cells often already display features associated with cellular aging, rendering conventional senescence markers ambiguous or misleading. Distinguishing true senescence from toxicity, stress responses or baseline “aged” phenotypes is therefore a critical unmet need.

Rather than relying on predefined molecular readouts, the authors take a different approach and train a machine-learning model to recognize senescence-associated morphological profiles (SAMPs) which are subtle but reproducible changes in cellular architecture captured through high-content microscopy. By learning directly from image-based phenotypes, SAMP-Score is able to identify senescence with a level of precision that is difficult to achieve using marker-based strategies alone.

The strength of the platform demonstrated through a large-scale screen of over 10,000 novel chemical entities in p16-positive basal-like breast cancer cells. From this screen, the compound QM5928 emerged as a robust inducer of senescence across multiple cancer models, notably without inducing cytotoxicity. Importantly, QM5928 retains activity in cellular contexts that are resistant to CDK4/6 inhibition, including palbociclib-refractory, p16-high tumors.

Mechanistically, the authors show that QM5928 promotes nuclear relocalization of p16, consistent with a functional engagement of cell-cycle arrest pathways. These nuanced phenotypic changes would likely have gone undetected without the resolution and discrimination provided by SAMP-Score, underscoring the platform’s ability to separate true senescence from confounding cellular states.

This work exemplifies how machine learning and quantitative imaging can be harnessed to solve long-standing problems in senescence research, moving the field beyond binary marker expression toward phenotype-driven classification. Beyond its immediate relevance for cancer therapy, SAMP-Score offers a broadly applicable framework for senescence-based screening efforts across biological contexts.

DOI - https://doi.org/10.18632/aging.206333

Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk

Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE

Sign up for free Altmetric alerts about this article - 
https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206333

Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts

To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at:

Bluesky - https://bsky.app/profile/aging-us.bsky.social
ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589
Facebook - https://www.facebook.com/AgingUS/
X - https://twitter.com/AgingJrnl
Instagram - https://www.instagram.com/agingjrnl/
YouTube - https://www.youtube.com/@Aging-US
LinkedIn - https://www.linkedin.com/company/aging/
Pinterest - https://www.pinterest.com/AgingUS/
Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc

MEDIA@IMPACTJOURNALS.COM

Aging-US Editors' Choice

Citeline places biopharma and medical device professionals, and those who focus on these industries, at the forefront of knowledge and insight, by providing the perfect combination of news and information together with penetrating insight and analysis. Citeline is a leader in the field of healthcare industry information.

Destinie James is an accomplished Technical Program Manager, engineer, and global STEM advocate whose work bridges technology, leadership, and impact. She holds multiple engineering degrees in computer and electrical engineering and has blended her expertise in artificial intelligence, cybersecurity, and digital transformation to create meaningful change across industries.

Aging-US is dedicated to advancing our understanding of the biological mechanisms that drive aging and the development of age-related diseases. Our mission is to serve as a platform for high-quality research that uncovers the cellular, molecular, and systemic processes underlying aging, and translates these insights into strategies to extend healthspan and delay the onset of chronic disease.

Read about the Aging (Aging-US) Scientific Integrity Process: https://aging-us.com/scientific-integrity

Welcome to Business Talk, your go-to podcast for the latest trends, insights, and thought-provoking discussions in the business world. Whether you're a business professional, entrepreneur, researcher, or academic, our episodes will challenge you to rethink conventional wisdom and inspire actionable ideas.

Brought to you by Global Management Consultancy, we are committed to driving innovation and excellence in the business community. All content Copyrighted 2024 by Global Management Consultancy.

For more information about our past and upcoming podcasts, please click here:https://www.deepakbbhatt.com/businesstalk

Hi there — welcome to my corner of the web.

I’m here for one simple reason: to share my music with you.
Take a listen, explore, and I hope something here resonates with you. Enjoy the vibe!

Podcasts und Berichte über aktuelle Forschungsfragen aus den Bereichen Informations- und Kommunikationstechnik, an denen die Fraunhofer-Institute arbeiten.

New tracks tagged #machinelearning