🎧 Yapay Zeka Çağında Gözlemlenebilirlik: Geleneksel Yöntemler Neden Yetersiz Kalıyor?

Bu bölümde, AI sistemlerinin karmaşıklığı arttıkça geleneksel izleme araçlarının neden yetersiz kaldığını konuşuyoruz. CPU ve bellek metriklerinden halüsinasyon ve toksisite izlemeye kadar, yapay zeka dünyasının yeni gözlemlenebilirlik ihtiyaçlarını ele alıyoruz.

🔍 Bu bölümde neler var:
• Geleneksel gözlemlenebilirlik vs AI gözlemlenebilirliği
• ActiveFence ve Barclays deneyimleri
• Red teaming ve güvenlik yaklaşımları
• Finansal hizmetlerde AI güvenliği
• Agentic AI'nın geleceği

📖 Detaylı makale: https://medium.com/hobimiz-teknoloji/yapay-zeka-çağında-gözlemlenebilirlik-geleneksel-yöntemler-neden-yetersiz-kalıyor-05b69326f70e

#YapayZeka #AI #Observability #TechPodcast #Gözlemlenebilirlik #MachineLearning #TechTalk

# Yapay Zeka Çağında Gözlemlenebilirlik: Geleneksel Yöntemler Neden Yetersiz Kalıyor?

Let generative outlaw rock ignite your speakers—moonshine‑fueled riffs built by neural nets.
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Keep in touch with latest releases:
https://www.youtube.com/@theartificialhomewreckers
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https://youtu.be/-hJGE_fczQU
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#rock #southernrock #outlawrock #countryrock #americana #airock #generativeai #neuralmusic #machinelearning #algorithmicmusic #newmusic2025 #officialaudio #lyricsvideo #musicvideo #moonshine #guitar #pickuptruck #raspyvocals #malevocals #roadtripmusic #nightdrive #starrysky #moonlight #storysong #indieartist #unsigned #retro #vintagesound #deepvocals #highway 
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[Verse 1]
Black-top fever in a rust-bleed Ford,  
Guitar growlin’, radio roarin’ like a two-stroke chord
Jar full o’ moonfire sloshin’ rocket fuel,  
Slidin’ sideways past the sleepy county school.  
[Chorus]
Sip that starlit venom, let it spark the fuse,  
Copper-kissed thunder we were born to abuse.  
Steals your breath like a pedal-steel cry in the pines—  
Tip it high, graze the sky, drown the dark in shine. 
[Verse 2]
Creek-bed choir where the backwoods still once sang,  
Bottle-neck echoes through the hills with a chain-saw twang.  
Boot-heels muddy with that red-clay stain,  
Trading halos for the howl of an outlaw train.  
[Chorus]
Sip that starlit venom, let it spark the fuse,  
Copper-kissed thunder we were born to abuse.  
Steals your breath like a pedal-steel cry in the pines—  
Tip it high, graze the sky, drown the dark in shine.  
[Verse 3]
Dawn’s sneakin’ up, tryin’ to dim our roar,  
Spokes spit lightning on the blacktop floor.  
Hearts beat double with a gasoline grace,  
Carvin’ legends in the dust of this reckless highway’s face. 
[Chorus]
Sip that starlit venom, let it spark the fuse,  
Copper-kissed thunder we were born to abuse.  
Steals your breath like a pedal-steel cry in the pines—  
Tip it high, graze the sky, drown the dark in shine.

Drown the Dark in Shine

Artificial intelligence is now a part of everyday business work, from managing data to supporting routine tasks. Kirsten Poon, an AI analyst based in Edmonton, works with companies to find ways to apply AI that match their specific needs. Her focus is on making AI tools clear, practical, and useful in real work situations.

Kirsten Poon from Edmonton Shares 5 AI Points to Understand Before Getting Starte

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, this show brings you sharp, insightful updates on artificial intelligence and machine learning for curious minds eager to stay ahead. Today’s episode dives deep into the crucial yet often overlooked topic of **data versioning**—the backbone of successful machine learning projects in 2025 and beyond. 📊🤖

Shailendra draws a vivid analogy comparing data versioning to baking a cake 🍰: just like tweaking ingredients or oven temperature needs careful note-taking to replicate the perfect cake, managing datasets with precision is essential for reliable machine learning outcomes. Even tiny changes in data can dramatically impact model performance, making data versioning a superhero cape 🦸‍♂️ for ML teams to track, audit, and improve experiments consistently.

The episode breaks down five best practices for data versioning:

1. **Define what and how granular to version**: Focus on tracking significant changes that affect results, avoiding overload from minor tweaks. This keeps resources optimized while maintaining clarity. 📋✅

2. **Centralize data repositories with clear hierarchies**: Organize raw and processed data in separate, logical spaces—like having distinct shelves for flour and sugar—so data never gets mixed up. 🗂️📁

3. **Leverage modern data version control tools**: Traditional Git struggles with large datasets, but new tools like lakeFS enable seamless tracking of data versions alongside code and models, boosting reproducibility and audit compliance. 🛠️💾

4. **Automate versioning within ML pipelines**: Syncing data snapshots automatically with model experiments reduces manual work and minimizes errors or mismatches. 🤖🔄

5. **Promote collaboration and auditability**: Transparent data change histories improve teamwork across global teams, simplify troubleshooting, and ensure governance policies are met. 🌍🤝

Shailendra shares a personal story about early career challenges when untracked dataset changes caused model performance to vanish mysteriously—an experience that fueled his passion for robust data versioning. Thanks to tools like lakeFS, many ML teams now enjoy harmonious integration of data and model histories. 🎯📈

As a **Bonus Tip**, he recommends integrating data version control with cloud infrastructure ☁️ for scalable, secure, and remote team access—eliminating confusion over which dataset version is the latest.

He leaves listeners with a thought-provoking question: How do you currently track and manage your datasets? Could your approach use a versioning makeover? 🤔

And a memorable closing quote: *“In machine learning, reproducibility is king; data versioning is its crown.”* 👑

If you enjoyed the episode, Shailendra invites you to connect on YouTube, Twitter, LinkedIn, and Medium by searching *Shailendra Kumar*. Subscribe to *AI with Shaily* for more cutting-edge AI news, tips, and discussions. Don’t hesitate to share your thoughts or questions—let’s learn and build better AI together! 🌟💬

Signing off with warmth and encouragement to keep exploring the intelligent path ahead, this is Shailendra Kumar from *AI with Shaily*—where AI meets real life. 🚀🤓

Data Versioning Demystified: The Secret Sauce for Reliable Machine Learning in 2025

Short Stories from the Bleeding Edge of AI

AI Angst - S01E01 {A Silicon Valley AI Short Story}

BUFFALO, NY — July 28, 2025 — A new #research paper was #published in Aging (Aging-US) Volume 17, Issue 6, on June 13, 2025, titled “Development of a novel transcriptomic measure of aging: Transcriptomic Mortality-risk Age (TraMA).”

In this study, led by Eric T. Klopack from the University of Southern California, researchers created a new RNA-based aging measure that predicts health risks and mortality. This measure, called Transcriptomic Mortality-risk Age (TraMA), uses gene expression data to estimate a person’s biological aging. This finding offers a new and potentially more accurate way to track aging and understand health risks, especially for older adults.

Aging is a complex biological process that affects multiple systems in the body and increases the risk of disease and death. Scientists have long looked for reliable ways to measure biological aging. While DNA methylation and blood biomarkers are commonly used, this study focused on RNA—a molecule that reflects gene activity. By analyzing RNA sequencing data from nearly 4,000 U.S. adults aged 50 and older, the team developed TraMA to predict the probability of dying within four years.

TraMA proved to be a strong and independent predictor of early death, multiple chronic diseases, poor cognitive function, and difficulties with daily activities. It was also tested in another large group of long-lived families and in several smaller datasets from patients with conditions like diabetes, sepsis, and cancer. The results confirmed the tool’s usefulness across different populations and health conditions.

“TraMA was also externally validated in the Long Life Family Study and several publicly available datasets.”

Unlike earlier RNA-based aging measures, which were often built using small or non-representative samples, TraMA was developed using modern RNA sequencing technology results and a nationally representative dataset. This increases its reliability and potential for broad public health applications. The tool also demonstrated unique advantages over popular biological aging measures like GrimAge and PhenoAge, capturing distinct aspects of aging and health decline.

Importantly, TraMA tracks biological processes related to inflammation, immune function, and kidney and brain health, systems that play key roles in aging. It was also sensitive to behavioral and socioeconomic factors. For instance, smoking, obesity, and low physical activity were associated with older TraMA scores.

TraMA was also sensitive to changes in biological aging. In one study, researchers measured TraMA at two different time points and found that the more recent scores were better at predicting who would die. This suggests that TraMA can track changes in a person’s aging as their health evolves. It also performed well in both large-scale surveys and small clinical samples, making it a useful tool in many types of research.

By offering a new, accurate, and flexible method for measuring biological aging, TraMA may help researchers better understand how genes, lifestyle, and environment influence aging. This tool opens the door to more precise research on improving health and extending lifespan.

DOI - https://doi.org/10.18632/aging.206272

Corresponding author - Eric T. Klopack - klopack@usc.edu

Video short - https://www.youtube.com/watch?v=Tl0CApUz8cU

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

To learn more about the journal, please visit our website at 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/
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Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc

MEDIA@IMPACTJOURNALS.COM

Aging (Aging-US)

TraMA: New RNA-Based Measure Predicts Mortality Risk and Tracks Aging

This episode of TechUnhinged features Dr. Ammar A. Malik from the Bill & Melinda Gates Foundation, discussing how AI and data analytics are transforming foreign aid. Diving into geospatial tracking and machine learning to sentiment analysis, the conversation explores how technology is driving transparency, exposing inefficiencies in development financing, and reshaping how aid impact is assessed. 


How AI is Disrupting Foreign Aid Forever

plays on loop

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

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

Please subscribe on YouTube :

https://www.youtube.com/@theartificialhomewreckers

Rock n roll has been a fun ride but for now county is calling the soul once again. You can easily navigate genres in the Playlist section.

Strumming their way through heartbreak, hilarity, and highway hijinks, The Artificial Home Wreckers are a country rock band like no other. Born from a chance encounter at a roadside diner with a broken jukebox, these renegades of rhythm specialize in songs that sound like a wild Friday night and feel like a hungover Saturday morning.

Their music is a cocktail of twangy guitars, foot-stomping beats, and lyrics that toe the line between tragic and downright ridiculous. From ballads about exes who "borrowed" their truck (and forgot to return it) to anthems about that one unforgettable karaoke night gone horribly wrong, they’ve got a tune for every lovable misfit out there.

Catch them on tour (if their GPS actually works this time), and remember their motto: “Breaking homes one chorus at a time!”

Where Tech Gets Human

This podcast cuts through the noise, revealing the real human stories behind technology. We expose the raw, unfiltered truth of innovation - the struggles, failures, and breakthroughs that never make the headlines.

Kirsten Poon is an artificial intelligence (AI) analyst from Edmonton with experience in designing, developing, and deploying AI in commercial and industrial environments. Kirsten Poon is passionate about solving complex problems using data-driven methods and has successfully led interdisciplinary teams in enterprise settings. Kirsten builds scalable AI systems, troubleshoots deployment challenges, and works closely with data scientists and AI experts. Her focus is on helping businesses stay current by integrating the latest AI technologies into their workflows for improved performance and innovation.

audio dumping zone. complete stuff goes here: https://laylahope.bandcamp.com/follow_me

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

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