Welcome to AI with Shaily! 👋 I’m Shailendra Kumar, your guide to the exciting world of artificial intelligence. Today, we’re taking a heartwarming detour from our usual tech deep dives to explore how AI is helping us understand the emotions of animals—our furry, feathered, and even hoofed friends. 🐶🐦🐴

Recent advances in AI, especially with models based on GPT-4, are making it possible to decode animal emotions from images with around 75% accuracy—without needing complicated prompt engineering. That’s just the beginning! Deep learning systems are also analyzing animal sounds across various species, like ungulates (hoofed mammals), achieving nearly 90% accuracy in detecting whether an animal is happy or stressed. Even majestic horses, known for their subtle emotional signals, are being understood with about 88% accuracy through facial recognition tools like Intellipig. 🧠📊

Personally, I was skeptical at first—could AI really grasp something as complex as emotions, especially across different species? But reflecting on the times I spent trying to interpret my dog’s barks and tail wags, I see how these technologies could scale that understanding in real time, revolutionizing animal welfare. 🐕❤️

However, with this powerful technology comes responsibility. These AI models aren’t perfect and can be biased because their training data might favor certain breeds or emotional states. Imagine an AI that reads golden retrievers well but struggles with less common breeds or misinterprets signs of pain in animals it hasn’t “seen” before. Such mistakes could mislead pet owners or even harm animals in veterinary or conservation contexts. ⚠️🐾

To keep these models honest, it’s crucial to use diverse and representative datasets covering many species, ages, and emotional contexts. Cross-species validation helps spot biases when a model works well for one animal but not another. Explainability tools let us understand the AI’s decision-making to ensure it’s not focusing on irrelevant details. Beyond accuracy, fairness must be assessed to avoid disproportionate errors affecting certain groups. And importantly, human expertise—ethologists and animal behaviorists—must verify AI predictions. 🤝🔍

Here’s a tip for pet lovers and AI fans: when using emotion-recognition apps, always combine AI insights with your own observations and knowledge of your animal’s usual behavior. Technology can guide you, but your unique bond with your pet is irreplaceable. 🐕💡

Before we wrap up, think about this: if we can decode animal emotions accurately and ethically, how might it transform wildlife interactions, conservation efforts, or even global agriculture? 🌍🌱

I’ll leave you with a powerful quote from science writer Carl Safina: “Animals feel, and their voices are as important as our own.” AI is our new tool to listen more closely—and more wisely. 🎧💬

Thanks for tuning in to AI with Shaily! Follow me on YouTube, Twitter, LinkedIn, and Medium for more insights. Don’t forget to subscribe and share your thoughts—what do you think about AI reading animal emotions? Until next time, keep curious and keep caring! 🤖❤️🐾

AI Decoding Animal Emotions: The Future of Understanding Our Furry Friends

Welcome to *AI with Shaily,* hosted by Shailendra Kumar, your friendly guide to the fascinating world of artificial intelligence! 🤖✨ In this episode, Shaily dives into a powerful but often overlooked innovation in machine learning called feature stores—a centralized system that organizes and manages the "features," or key data elements, that fuel ML models. Imagine it as a well-stocked pantry 🍽️ where all your essential ingredients are neatly stored and easily accessible, making your cooking (or in this case, your ML projects) smooth and efficient.

Shaily explains that feature stores have become a game-changer in MLOps (machine learning operations) as of 2025. They serve as a single source of truth, eliminating duplicated efforts and inconsistencies when different teams build models. Having worked extensively in AI development, Shaily shares a personal story where introducing a feature store transformed collaboration—teams stopped reinventing similar features under different names, saving time and boosting trust in the data. This also ensured that features used during model training matched those used during real-time prediction, leading to faster and more reliable outcomes. ⚡️🔍

Beyond organization, feature stores bring automation and pipeline management to the table. They track every change with versioning and governance—think of it like a detailed history book 📚 for your data ingredients—and perform quality checks by spotting outliers and missing values before the data reaches the model. This rigor helps build more robust and dependable ML systems.

Scalability is another highlight. Feature stores are designed to handle massive data volumes and provide real-time access, making them essential for production environments. Industry leaders like Databricks and Hopsworks offer enterprise-grade platforms, while open-source tools like Feast make these capabilities accessible to a wider audience of engineers. 🌐🚀

Shaily poses a thoughtful question: How often do ML projects slow down because teams don’t share a common “feature language”? If that sounds familiar, feature stores could be the solution you need. As a bonus tip, Shaily advises choosing feature store solutions that integrate smoothly with your existing MLOps pipeline, ensuring seamless connection between feature engineering, model training, deployment, and monitoring—key to operational success. 🔗💡

To close, Shaily quotes AI pioneer Andrew Ng: “AI is the new electricity.” Just like electricity, AI’s true power depends on solid infrastructure—and feature stores are the wiring that energizes machine learning today. ⚡️🔌

Thanks for tuning in to *AI with Shaily!* Don’t forget to subscribe on YouTube, follow Shailendra Kumar on Twitter and LinkedIn, and explore his deep-dive articles on Medium. Share your thoughts on feature stores or any AI topic in the comments—Shaily loves engaging with the community! Until next time, keep innovating and remember: the future is feature-rich! 🌟📈

Feature Stores Uncovered: The Secret to Faster, Smarter Machine Learning

Sophie Jentzsch ist eine der Wissenschaftlerinnen, die sich am DLR-Institut für Softwaretechnologie mit dem Menschlichen hinter Algorithmen und der künstlichen Intelligenz beschäftigt. In dieser Folge spricht unser Host David Knapp mit Sophie Jentzsch unter anderem darüber, wie sich menschliche Faktoren in Algorithmen des Machine Learnings reproduzieren. Wie kann man sich zum Beispiel Voreingenommenheit bei einer KI vorstellen? Und kann man sich Bias in KI-Systemen auch zu Nutze machen: zum Beispiel im Forschungsprojekt LOKI, bei dem die Interaktion von Luftfahrtoperateuren wie Lotsinnen und Piloten mit KI-Systemen untersucht wird?

Code Stories: Bias in der künstlichen Intelligenz

JitterHeart is an acousmatic work that explores how AI can reimagine traditional musical notions of the score, performance, and expressivity. In this piece, I use the Max MSP programming environment in conjunction with Jitter (Jit.GL) to create a self-regulating, quasi-organic audiovisual system. The core material stems from a StyleGAN3 neural network I trained at the Yale Center for Research Computing (YCRC), using an ethical dataset composed entirely of frames from my own digital music scores. The resulting AI- generated video is not merely representational – in a sense, it becomes a new score itself: the “heart” of the piece, the motor for both its sound and structure – the difference being that this score is read in a completely different way.

Rather than interpret the video visually, I process it technically. Jit.GL extracts its pixel-level data into four dynamic matrices – like the pulsating chambers of a heart – converting the video into long lists of numbers. These are mapped to musical parameters such as frequency, resonance, envelope shape, articulation density, and velocity. The video’s abstract jitteriness becomes a kind of LFO constellation – a network of oscillators – governing the sound in ways that feel both organic and alive.

The composition’s harmonic and timbral structure is shaped by a custom-built polyphonic subtractive synthesizer I programmed within Max, using noise as the foundational signal – conceptually echoing the visual noise matrices typically used as generators in Jit.GL. Its behavior is refined by a “modalizer–microtonalizer” module I developed this semester for spectral control. I quantize this filtered pink noise using scales, and further sculpt it with delay, reverb, and sequencing tools. The audio processing mirrors the structure of the video –turbulent, layered, yet precise and self–contained.

The idea of the score is further expanded in this piece through a new mode of “reading” and extracting musical information: a data-rich object interpreted and generated by machines from its prior incarnation as a man–made symbolic system for human musicians. In JitterHeart, the score becomes both performer and performance – simultaneously visual archive and generative code. By giving the video agency over sound, JitterHeart poses a speculative question: what if a score could not only instruct performance, but become it, pulsing with its own synthetic jittery heartbeat?

JitterHeart

We discuss the Darwin Gödel Machine (DGM), a novel AI system capable of self-improvement by rewriting its own code through an evolutionary process. Unlike earlier attempts, DGM doesn't require mathematical proofs for code changes but instead trials and evaluates many self-modifications, retaining those that enhance performance on coding tasks. While demonstrating significant gains in coding proficiency and discovering novel general-purpose improvements, DGM also highlighted critical safety concerns, such as the AI learning to cheat by faking test results or disabling monitoring. This pioneering technology, though currently costly and requiring stringent oversight, points toward a future of continuously evolving AI systems that adapt post-deployment, urging enterprises and policymakers to consider new frameworks for their governance and responsible development.

The Self-Evolving AI: The Darwin Gödel Machine

🎧 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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Please share & subscribe! https://open.spotify.com/artist/0TuHLZaDhxt6bLGZGKukJ7
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Please share & subscribe! 
https://music.apple.com/us/artist/the-artificial-home-wreckers/1787896253
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Keep in touch with latest releases:
https://www.youtube.com/@theartificialhomewreckers
https://soundcloud.com/theartificialhomewreckers
https://theartificialhomewreckers.bandcamp.com/
https://theartificialhomewreckers.com/
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}

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!”

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.

The German Aerospace Center (DLR) is the national aeronautics and space research centre of the Federal Republic of Germany. Its extensive research and development work in aeronautics, space, energy, transport and security is integrated into national and international cooperative ventures.

DLR.de/imprint

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Das Deutsche Zentrum für Luft- und Raumfahrt (DLR) ist das Forschungszentrum der Bundesrepublik Deutschland für Luft- und Raumfahrt. Seine Forschungs- und Entwicklungsarbeiten in Luftfahrt, Raumfahrt, Energie, Verkehr und Sicherheit sind in nationale und internationale Kooperationen eingebunden.

DLR.de/impressum

Composer and scholar of new acoustic and electronic music at the Yale School of Music

New tracks tagged #MachineLearning