It Looks Like GPT-5.1 Leaked – Polaris Alpha
Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. Good for coding front-end, a bit slow, new release from OpenAI The AI community is trying to piece together a new wave of discoveries that all seem to point straight at the …
Why Missing Data Is Not Missing at Random and Why That Matters
Author(s): ANGELI WICKRAMA ARACHCHI Originally published on Towards AI. The medical study that got it wrong You’re analyzing a clinical trial for a new antidepressant: 1,000 patients enrolled 700 completed the 3-month follow-up 300 patients have missing follow-up scores Your team says: …
From raw text to training gold: How to collect and prepare data for custom LLMs
Author(s): Laura Verghote Originally published on Towards AI. Practical guidance for building clean, domain-relevant datasets for fine-tuning, continued pretraining, or training from scratch If you’ve worked on language models beyond a quick prototype, you already know where the real bottleneck is. It’s …
Data Quality and Filtering at Scale for Training Large Language Models
Author(s): M Originally published on Towards AI. From heuristic filters to AI classifiers: practical techniques for curating trillion-token datasets Training a language model on the raw internet is like trying to learn from every conversation happening in the world simultaneously. Most of …
Sourcing and Collecting Data for Training Large Language Models
Author(s): M Originally published on Towards AI. Real-world insights from FineWeb, DCLM, The Stack v2, and modern LLM training When people talk about training language models, the conversation often jumps straight to architecture choices or training techniques. But here’s the reality: you …
The End of Prompt Engineering? Stanford’s Self-Improving AI Learned Clinical Reasoning on Its Own
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Stanford’s Agentic Context Engineering lets models reflect, learn, and build their own playbook. I tested it on clinical lab data — and watched it teach itself temporal reasoning. As we saw in my …
The Rise of Composable Data Teams
Author(s): Tobi Beck Originally published on Towards AI. Post 6 of the series “Reinventing the Data Team in the Age of AI” About this series: AI is fundamentally reshaping what it means to work in data. From empowering analysts to build pipelines …
AI Has Already Won — We Just Haven’t Admitted It Yet
Author(s): Zain Ahmad Originally published on Towards AI. The war between humans and machines never started with weapons — it started with dependence. It didn’t happen overnight. There was no “AI revolution,” no single moment when humanity handed over the keys. Instead, …
Why 95% of AI Automation Projects Fail
Author(s): Luca Derumier Originally published on Towards AI. Why 95% of AI Automation Projects Fail As CTO at Codika, I spend a lot of time talking to business leaders about automation. Lately, these conversations have followed a predictable pattern. They start with …
Tree-GRPO Cuts AI Agent Training Costs by 50% While Boosting Performance
Author(s): MKWriteshere Originally published on Towards AI. How tree search revolutionizes reinforcement learning for multi-turn language model agents Training AI agents to handle complex, multi-step tasks has always been expensive. Really expensive. Every time an agent interacts with its environment, you’re burning …
The Truth: 70% Healthcare AI Errors from Hidden Distribution Shifts
Author(s): Vikram Lingam Originally published on Towards AI. Discover how the TSSA framework detects time-series shifts to slash machine learning errors in healthcare AI Imagine this: a hospital AI system, trained on pre, pandemic patient data, suddenly starts flagging healthy patients as …
Why Traditional ML Fails at Fraud Detection (And How I Fixed It)
Author(s): Dewank Mahajan Originally published on Towards AI. How data science, domain intuition, and robust feature engineering come together to fight modern financial fraud. Why Fraud Detection Is a Human Story Fraud isn’t just a data problem.It’s a battle of wits between …
E2B AI Sandboxes: Features, Applications & Real-World Impact
Author(s): Moein Moeinnia Originally published on Towards AI. E2B AI SandBox — AI Code Execution Introduction: The AI Code Execution Challenge Imagine building an AI assistant that can analyze data, generate visualizations, or write and execute code on the fly. Sounds powerful, …
What Doctors Get Wrong About Hidden Bias in Treatment Effects
Author(s): Vikram Lingam Originally published on Towards AI. Why the typical approach to analyzing medical data often leads to incorrect conclusions regarding what really works for patients Did you know that in a major oncology trial, doctors hailed a drug as a …
MCP is Taking Over: The Protocol That’s Making AI Agents Smarter, Faster, and Mysteriously Independent
Author(s): Shreyansh Jain Originally published on Towards AI. Unlocking: Why Model Context Protocol and Agent-to-Agent Collaboration Are Transforming Autonomous Systems, APIs, and Real-Time Automation Large Language Models (LLMs) are powerful tools, but they must be capable of acting on that information independently …
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