Aaron Birnbaum and Matthew Makansi

Introduction: Can AI really distinguish dog breeds like human experts? One day while taking a walk, I saw a fluffy white puppy and wondered, Is that a Bichon Frise or a Maltese? No matter how closely I looked, they seemed almost identical. Huskies and Alaskan Malamutes, Shiba Inus and Akitas, I always found myself second-guessing. How do professional veterinarians and researchers spot the differences at a glance? What are they focusing on? 🤔 This question

Running cool experiments is easily one of my favorite parts of working in data science. Most experiments don’t deliver big wins, so the winners make for fun stories. We’ve had a few of these at IntelyCare, and I’m sharing each story in a way that highlights a concept related to experimentation. And in this post, we’ll share a story about how we avoided doing something stupid by running an experiment first, and using it to

Running experiments is a task that often falls to data scientists. If that’s you, congrats! It can be a rewarding and high-impact area of work, but also requires tools found outside the typical ML-heavy data science curriculum. Even with the best tools, only a small share of experiments deliver meaningful business value. I’ve been lucky to design and execute many experiments. Of those, I have a few winners. From these, I’m sharing some stories to

Originally published on HuggingFace TL;DR We present LettuceDetect, a lightweight hallucination detector for Retrieval-Augmented Generation (RAG) pipelines. It is an encoder-based model built on ModernBERT, released under the MIT license with ready-to-use Python packages and pretrained models. What: LettuceDetect is a token-level detector that flags unsupported segments in LLM answers. 🥬 How: Trained on RAGTruth (18k examples), leveraging ModernBERT for context lengths up to 4k tokens. 🚀 Why: It addresses (1) the context-window limits in

What if you want to write the whole object detection training pipeline from scratch, so you can understand each step and be able to customize it? That’s what I set out to do. I examined several well-known object detection pipelines and designed one that best suits my needs and tasks. Thanks to Ultralytics, YOLOx, DAMO-YOLO, RT-DETR and D-FINE repos, I leveraged them to gain deeper understanding into various design details. I ended up implementing SoTA

When learning Python, many beginners focus solely on the language and its libraries while completely ignoring virtual environments. As a result, managing Python projects can become a mess: dependencies installed for different projects may have conflicting versions, leading to compatibility issues. Even when I studied Python, nobody emphasized the importance of virtual environments, which I now find very strange. They are an extremely useful tool for isolating different projects from each other. In this article,

I’ve always been fascinated by Fashion—collecting unique pieces and trying to blend them in my own way. But let’s just say my closet was more of a work-in-progress avalanche than a curated wonderland. Every time I tried to add something new, I risked toppling my carefully balanced piles. Why this matters:If you’ve ever felt overwhelmed by a closet that seems to grow on its own, you’re not alone. For those interested in style, I’ll show

Introduction In my previous article, I discussed one of the earliest Deep Learning approaches for image captioning. If you’re interested in reading it, you can find the link to that article at the end of this one. Today, I would like to talk about Image Captioning again, but this time with the more advanced neural network architecture. The deep learning I am going to talk about is the one proposed in the paper titled “CPTR:

In Game Theory, the players typically have to make assumptions about the other players’ actions. What will the other player do? Will he use rock, paper or scissors? You never know, but in some cases, you might have an idea of the probability of some actions being higher than others. Adding such a notion of probability or randomness opens up a new chapter in game theory that lets us analyse more complicated scenarios.  This article

Despite the AI hype, many tech companies still rely heavily on machine learning to power critical applications, from personalized recommendations to fraud detection.  I’ve seen firsthand how undetected drifts can result in significant costs — missed fraud detection, lost revenue, and suboptimal business outcomes, just to name a few. So, it’s crucial to have robust monitoring in place if your company has deployed or plans to deploy machine learning models into production. Undetected Model Drift